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title: Mesenchymal stem cells can prevent or promote the progression of colon cancer
based on their timing of administration
authors:
- Weiqian Hu
- Weijun Wang
- Xin Jiang
- Zeyu Wang
- Rong Lin
journal: Journal of Translational Medicine
year: 2023
pmcid: PMC10045613
doi: 10.1186/s12967-023-04028-3
license: CC BY 4.0
---
# Mesenchymal stem cells can prevent or promote the progression of colon cancer based on their timing of administration
## Abstract
### Background
Mesenchymal stem cell (MSC) therapy has been shown to have some therapeutic effects in rodent models and patients with IBD; however, its role in colon tumor models is controversial. In this study, the potential role and mechanisms of bone marrow-derived MSCs (BM-MSCs) in colitis-associated colon cancer (CAC) were investigated.
### Methods
The CAC mouse model was established with azoxymethane (AOM) and dextran sulfate sodium (DSS). The mice were administered an intraperitoneal injection of MSCs once weekly for different periods. The progression of CAC and the cytokine expression in tissues was assessed. Immunofluorescence staining was used to detect MSCs localization. Levels of immune cells in the spleen and lamina propria of the colon were detected using flow cytometry. A co-culture of MSCs and naïve T cells was performed to determine the effect of MSCs on naïve T cell differentiation.
### Results
Early administration of MSCs inhibited the occurrence of CAC, while late administration promoted the progression of CAC. The inhibitory effect of early injection in mice was characterized by the expression of inflammatory cytokines in colon tissue was decreased, and induction of T regulatory cells (Tregs) infiltration via TGF-β. The promotive effect of late injection was characterized by a shift of T helper (Th) 1/Th2 immune balance toward a Th2 phenotype through IL-4 secretion. IL-12 can reverse this shift to Th2 accumulation in mice.
### Conclusion
MSCs can curb the progression of colon cancer by inducing Treg accumulation via TGF-β at the early stage of inflammatory transformation but promote the progression of colon cancer by inducing a shift in Th1/Th2 immune balance to Th2 through IL-4 secretion at the late stage. And the immune balance of Th1/Th2 influenced by MSCs could be reversed by IL-12.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12967-023-04028-3.
## Background
Globally, colorectal cancer ranks third in terms of cancer causes and second in terms of cancer deaths [1]. It is increasingly clear that the tumor microenvironment, coordinated primarily by inflammatory cells, is an essential player in tumorigenesis, including cancer cell proliferation, survival, and migration. This study seeks to provide novel insights into the pathogenesis and therapeutic targets of colon cancer.
A complex relationship exists between T cell immune responses and colitis-associated colon cancer (CAC). In colorectal cancer, the type and location of immune cells are associated with clinical outcomes and survival [2]. T helper 1 (Th1) cells primarily secrete cytokines, such as interferon (IFN)-γ and tumor necrosis factor (TNF)-α, which can enhance the body’s autoimmune response and are important anti-cancer therapeutic agents [3]. Th2 cells, in contrast, secrete signature cytokines such as interleukin (IL)-4 and IL-10 [4], which coordinate humoral immunity [5] and promote allergic inflammatory responses [6]. Both Th1 and Th2 cells can secrete cytokines to promote their proliferation and inhibit each other’s proliferation and are in relative balance under normal circumstances [7]. However, when functional abnormalities occur, this balance is disrupted, known as “Th1/Th2 drift”, leading to a dominant Th1 or Th2 phenotype. In many tumors, the Th1/Th2 balance, with Th2 often dominating, may be related to immune escape [8]. The Th17 signature cytokine, IL-17A, may cause severe inflammation and affect autoimmune responses [9]. The expression of IL-17 in tumors can promote angiogenesis and tumor growth by elevating multiple pro-angiogenic factors and increasing the pro-inflammatory cytokines produced by tumor cells [10]. Chronic exposure to low levels of TH17-related cytokines may also contribute to cancer progression. T regulatory cells (Tregs) express the FoxP3 transcription factor and play a critical role in maintaining immune homeostasis and autoimmune disease. It is becoming increasingly clear that tumor-derived factors play a role in recruiting and amplifying FoxP3+Tregs, and the presence of FoxP3+Tregs in these tumors blocks effective immunity to cancer [11]. In addition, the tumor microenvironment (TME) may promote Treg activation, thereby suppressing anti-tumor responses. Therefore, Tregs not only inhibit intestinal inflammation but also improve the survival rate of tumor cells due to excessive immunosuppression. In summary, the function of T cells is influenced by several factors such as antigens, presence of co-stimuli, regulatory T cells, metabolic pathways, and soluble factors in the tumor microenvironment [3].
MSCs are pluripotent stem cells with significant immunomodulatory properties, long-term self-renewal, and multidirectional differentiation. They migrate toward inflammatory or neoplastic sites. In animals with colitis and inflammatory bowel disease (IBD), intravenous [12, 13] or intraperitoneal [14] injections of MSCs can considerably reduce colon inflammation, but timing is critical. Kawata et al. found that early injection of MSCs in a mouse model of DSS-induced colitis, increased anti-inflammatory cytokines and decreased pro-inflammatory cytokines; no such changes were observed when MSCs were injected at a later stage [15]. Conflicting results have been observed regarding the impact of MSCs therapy on colon tumor development. Co-injection of tumor cells with MSCs enhanced tumor development, liver metastasis, angiogenesis, and triggered tumor epithelial mesenchymal transition [16–19, 22]. However, some studies suggest otherwise. In azoxymethane/dextran sulfate sodium (AOM/DSS), MSCs were observed to reduce tumor formation [25–27]. Chen et al. reported that MSCs decreased tumor numbers through IL-6-STAT3 signaling [24]. We found that the tumor-promoting effect of MSCs was mostly observed in studies conducted on immunodeficient mouse models. Currently, the reasons for the different effects of MSCs on tumor growth are not clear; however, the effects may be influenced by a variety of factors, such as the mode and the timing of MSCs injection, and the tumor strain.
An essential component of the immunomodulatory activity of MSCs is their impact on T cells. MSCs can have different effects on T cells through the synergistic action of cell contact-dependent mechanisms [12] and soluble factors [13]. In addition, they can promote the formation of Tregs in vitro [14] and in vivo [15]. The protective impact of MSCs in Th1-mediated inflammatory and autoimmune diseases, such as type 1 diabetes mellitus [16] and Crohn’s disease [15], is correlated with the suppression of Th1 by MSC-induced Tregs. Similarly, Kavanagh et al. found that MSCs can also inhibit allergen-specific Th2 cell responses in allergic airway inflammation, in part by inducing Tregs [17]. Some studies [18, 19] suggest that MSCs might be more beneficial in shifting toward the Th2 phenotype. The Th17 cells have a pro-inflammatory effect, and MSCs may inhibit Th17 cells via IL-27 [20] or monocyte chemoattract protein-1 [21]; however, evidence of MSCs promoting the function of Th17 cells also exists [22]. The phenotypic plasticity potential of MSCs on CD4+T cell subsets has been associated with different in vivo conditions [2, 23].
In this study, we aimed to elucidate the role and mechanism of MSCs in altering tumor outcomes in an azoxymethane/dextran sulfate sodium (AOM/DSS) induced CAC model. Our findings can potentially pave the way for a change in targeted therapy directions.
## Mice
Male C57BL/6 mice aged 6 to 8 weeks were purchased from Beijing Zolaibao Biotechnology Co., LTD. All mice were kept free of specific pathogens (SPF) for 12 h under light/dark cycles at a constant room temperature of 22 (± 2) °C and humidity of 55 (± 5)%. The mice received a continuous supply of food and water. Ethics approval was obtained from the Huazhong University of Science & Technology’s Tongji Medical College's Animal Care and Use Committee. The tenets of the Ministry of Health of China and the Helsinki Declaration were followed for all procedures (Document no. 55 of 2001).
## Animal treatment
Animals were fed for 1 week with no intervention and randomly divided into three groups ($$n = 45$$,15/group): [1] control group (CON) [2] early intervention with MSCs (2 × 106 cells/0.2 mL per mouse; intraperitoneal injection) prior to first DSS feeding; AME group [3] late intervention with MSCs before the third DSS feeding; AML group [24]. At the start of week 0, 10 mg/kg of AOM was injected intraperitoneally (Sigma-Aldrich, St. Louis, MO, US). For a week, their drinking water contained DSS ($2.5\%$, molecular mass 36–50 kDa; MP Biomedicals, Solon, OH, US), and regular water was provided for the following 2 weeks. Therefore, DSS was administered throughout the trial at weeks 1–2, 4–5, and 7–8. This led to the AOM and DSS-induced CAC mice models. MSCs [2 × 106 cells/0.2 mL phosphate-buffered saline (PBS)] were administered to the AME group per week from week 1 and was administered to the AML group per week from week 7. Every week, mice in the CON group received an intraperitoneal injection of 0.2 mL of pure PBS. The weight of all subjects was measured and noted weekly. After the modeling was completed at week 10, the mice were fed for another 4 weeks and injected intraperitoneally pentobarbital sodium for euthanasia at week 14.
## MSCs isolation and culture
MSCs were isolated from the femur cavity of 4-week-old male C57BL/6 mice under aseptic conditions [25]. The cells were then cultured in a low-glucose DMEM medium with $10\%$ fetal bovine serum (Gibco, NY, USA), cultured in a humidified atmosphere containing $5\%$ CO2 at 37 °C, and unattached hematopoietic cells were removed by changing the medium. MSCs at passage 6 to 10 were used for the following experiments [26].
## H&E staining
Colon specimens were processed according to normal protocols, including immersion in $4\%$ paraformaldehyde for 24 h, embedding, and dehydration [27]; 5 μm slices were stained with H&E, viewed, observed, and captured on camera using a light microscope.
## Immunohistochemistry
Deparaffinized paraffin-embedded sections (5 μm) were then rehydrated using a series of graded alcohol. After that, antigen heat retrieval was completed in citrate buffer using a pressure cooker, cooled to ambient temperature, and then blocked with a hydrogen peroxide solution. Antibodies (Abclone) for IFN-γ, TNF-α, TGF-β1, IL-4, IL-6, IL-10, IL-12, and IL-17 were added to the slides and incubated at 4 °C overnight. The slides were cleaned with PBS, then a secondary antibody conjugated to horseradish peroxidase was added, and incubated for 2 h. The slides were then treated with a DAB (3, 3′-diaminobenzidine) staining kit according to the manufacturer’s instructions for 10 min, and they were counterstained with hematoxylin for 2 min. A light microscope was used to take pictures of the sections.
## Immunofluorescence staining
We utilized donkey serum to block endogenous antigens. The paraffin-embedded sections (5 μm) were dewaxed and hydrated. Antigen retrieval was carried out in citrate buffer and then cooled to 26 °C temperature. At 4 °C a particular primary antibody directed against mouse GFP (Abcam, Cambridge, UK) was incubated with the tissue slices overnight. The slides were cleaned three times with PBS, incubated for 1 h with secondary antibodies from Antigen Biotech Co., Ltd., China, and stained for nuclei with 4′,6-diamidino-2-phenylindole dihydrochloride (DAPI). The samples were captured on camera using a fluorescence microscope (Olympus, Tokyo, Japan).
## Real-time quantitative PCR (RT-qPCR)
TRIzol reagent (Invitrogen, USA, 15596018) was used to extract total RNA, and a cDNA synthesis kit was used to transcribe the extracted total RNA into cDNA (Vazyme, China). qPCR was carried out using RT-PCR (Applied Biosystems) equipment. Additional file 1: Table S1 contains the list of the primer sequences used in this study.
## Western blot and protein isolation
Using RIPA Lysis Buffer (Beyotime, Jiangsu, China) supplemented with phenylmethyl sulfonyl fluoride (PMSF), a protease and phosphatase inhibitor, we extracted proteins from cells and colon tissues. Pierce™ BCA Protein Assay Kit (Thermo Fisher, US) was used to measure the total protein concentration. Denatured protein samples of the appropriate quality were then run through sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) before being transferred to PVDF membranes. The membrane was incubated for 0.5 h in Tris-buffered saline with $0.1\%$ Tween® 20 Detergent containing $5\%$ bovine serum albumin. After that, primary antibodies against β-actin, IFN-γ, TNF-α, and TGF-β1, IL-4, IL-6, IL-10, IL-12, and IL-17 were added to the membrane and incubated at 4 °C overnight. Alpha Innotech Corporation St. Leonardo' FluorChem FC2 technology was used to quantify the bands.
## Isolation of splenocytes and lamina propria lymphocytes (LPL)
Spleens were minced to prepare splenocytes. After that, splenocytes were washed with PBS containing EDTA and fetal calf serum (FCS), filtered through a 100 μm cell strainer, and rinsed with an erythrocyte lysis buffer. LPL were isolated as previously described [28]. Colons were cut open lengthwise, cleaned with PBS, and then cut into 1 cm pieces. Two PBS washes with EDTA were performed on tissue sections for 10 min at 37 °C while rotating the solution. After that, colon sections were washed twice with RPMI-1640 containing $1\%$ FCS for 15 min at 37 °C while being rotated. Colon pieces were then thoroughly vortexed, cleaned with PBS, and digested for 90 min at 37 °C in RPMI-1640 with $20\%$ FCS and 100 U/ml collagenase (Sigma-Aldrich).
## Co-culture and treatment
The spleen of the mouse was ground. Naïve T cells were inoculated into a 12-well culture plate containing RPMI-1640 (Gibco, Grand Island, US), $10\%$ fetal bovine serum, and anti-CD3/CD28 monoclonal antibodies (EBioscience, San Diego, CA, USA). Then, using a 3 μm polycarbonate filter, MSCs were transferred to the top chamber of a 12-well Transwell cell and cultivated alongside naïve T cells in an incubator at 37 °C with $5\%$CO2. To examine the impact of MSCs on the differentiation of naïve T cells, TGF-β1, IL-4, and IL-12 were added separately (Miltenyi Biotec Bergisch Gladbach, Germany) to the chamber. *In* general, we had three groups: CD4+T cells, CD4+T cells cultivated with stimulating factor, and CD4+T cells cultured with MSCs. Cells from the chamber were collected and analyzed.
## Statistical analysis
Data are represented as the mean and standard error of the mean. For statistical analysis and mapping, GraphPad Prism software (version 8.0) and SPSS software (version 22.0) were used. A P-value < 0.05 was considered statistically significant.
## MSCs migrate to the colon and influence the development of AOM/DSS-induced colon tumors
To determine their therapeutic effects, MSCs were used to treat AOM/DSS-induced CAC (Fig. 1A). Significant weight loss and bloody diarrhea were seen in the AOM/DSS-treated mice. Typically, tumors were found between the middle of the colon and the distal rectum (Fig. 1B). This region corresponds to the main site of human colorectal cancer. H&E staining of the colon tissue revealed crypt epithelial deformation, extensive mucosal damage, and inflammatory cell infiltration in all three groups (Fig. 1C). Compared with the AML group, the mice in the AME group had a better survival rate (Fig. 1D; $P \leq 0.05$). Compared with the CON group, the AME and AML groups presented with less and more weight loss, respectively (Fig. 1E; $P \leq 0.05$). Compared with the CON group, the colons of the mice in the AML group were significantly shorter. In contrast, the AME group significantly alleviated the AOM/DSS-induced colon shortening (Fig. 1F; $P \leq 0.01$). There were significantly fewer colonic tumors in the AME group and more tumors in the AML group compared to the CON group (Fig. 1G; $P \leq 0.05$). There were no significant differences among the groups concerning the tumor sizes (Fig. 1H). No MSCs were detected in the colon tissue of the mice in the CON group; however, they were detected in the colon tissue of the group treated with MSCs (i.e., the AME and AML groups) (Fig. 1I). Hence, MSCs can migrate to the colon tissue in response to intestinal injury. Fig. 1MSCs affect the growth of colon cancers brought on by AOM/DSS. A After AOM injection, animals were subjected to three cycles of DSS. MSCs during the study period, the AME group was injected (2 × 106 cells/mouse) per week from week 1, while the AML group was injected (2 × 106 cells/mouse) per week from week 7. Every week, mice in the CON group received an intraperitoneal injection of 0.2 mL of pure PBS. Animals were sacrificed under anesthesia at week 14 ($$n = 15$$/group). B Colons were excised for macroscopic observation and C slices were stained with H&E observed using the light microscope. D The survival rate and E weight of mice in each group was noted and compared. F Colon length, G tumor numbers, and H tumor numbers counted by size were assessed. I Colon tissues were collected to detect the migration of MSCs in the DSS/AOM-induced model. Data are shown as mean ± SD from three independent experiments. *** $P \leq 0.001$, **$P \leq 0.01$, *$P \leq 0.05$
## MSCs induce changes in T cell immune balance in mice
T cell infiltration was analyzed by flow cytometry in AOM/DSS-induced colon cancer (Fig. 2A). Compared with that in the CON group, the proportion of CD4+CD25+Foxp3+ Tregs in splenic tissues (Fig. 2B) and lymphocytes of lamina propria of the colon (Fig. 2C) of mice in the AME group was significantly increased by more than 2-fold (Fig. 2D, E; $P \leq 0.01$), while the immune balance shifted toward the Th2 phenotype in the AML group (Fig. 2D, E; $P \leq 0.01$). These findings suggest that early MSCs intervention inhibits the progression of CAC, possibly by inducing the accumulation of Tregs. The effect of late MSCs administration on cancer progression is more likely to be mediated by changes in the immune balance of Th1/Th2.Fig. 2MSCs induce changes in T cell immune balance in mice. A After AOM injection, animals were subjected to three cycles of DSS/H2O. Animals were sacrificed under anesthesia at week 14.And isolating SPL and LPL, flow cytometry was performed separately. B Flow cytometry analysis of the mean percentage of Th1, Th2, Th17 and Treg cells in splenic. C Flow cytometry analyzed the mean percentage of Th1, Th2, Th17 and Treg in lymphocyte of lamina propria of colon. D analyze and count the mean percentage of Th1, Th2, Th17 and Treg cells in splenic. And E analyze and count the mean percentage of Th1, Th2, Th17 and Treg cells in lymphocyte of lamina propria of colon. SPL splenocytes, LPL lamina propria lymphocytes. Values are expressed as means ± SD.*$P \leq 0.05$, or **$P \leq 0.01$
## MSCs regulate colon inflammation associated with the development of CAC
We then assessed whether the effect of MSCs on colon tumors was related to their inflammatory properties. Major inflammatory factors related to T cell subsets were selected for comparison. Compared with those in the CON group, western blot (WB) analysis of protein expression levels of inflammatory cytokines (IL-4, IFN-γ, and TNF-α) in the AME group were decreased (Fig. 3A, B; $P \leq 0.01$), while those of the anti-inflammatory cytokines (TGF-β and IL-10) were significantly increased by twofold (Fig. 3A, B; $P \leq 0.05.$ Full-length blots are presented in Additional file 2: Fig. S1). In the AML group, protein expression of inflammatory cytokines (IL-6, IL-4, IL-12, and IL-17A) increased by 2– 5-folds (Fig. 3A, B; $P \leq 0.01$), while anti-inflammatory cytokines (TGF-β and IL-10) decreased by more than half (Fig. 3A, B; $P \leq 0.01$). Similar phenomena were also observed by colon histological staining (Fig. 3C, D; $P \leq 0.05$) and mRNA expression (Fig. 3E; $P \leq 0.05$) in colon tissue. Taken together, these findings imply that, MSCs intervention at different time points may induce both pro-inflammatory and anti-inflammatory responses in AOM/DSS-induced CAC mice, depending on the initial time of MSCs administration. Fig. 3MSCs regulate colon inflammation associated with the development of CAC. We extracted colon tissues of mice. A, B Representative immunoblot bands and histogram of relative expression for the IFN-γ, TNF-α, TGF-β1 IL-4, IL-6, IL-10, IL-12 and IL-17 ($$n = 3$$). C, D Representative immunohistochemistry images and histogram of relative expression for the IFN-γ, TNF-α, TGF-β1 IL-4, IL-6, IL-10, IL-12 and IL-17 ($$n = 3$$). E The mRNA relative expressions of IFN-γ, TNF-α, TGF-β1 IL-4, IL-6, IL-10, IL-12 and IL-17 ($$n = 4$$). Values are expressed as means ± SD. * $P \leq 0.05$, **$P \leq 0.01$, or ***$P \leq 0.001$
## MSCs trigger Treg accumulation in mice via TGF-β at an early stage
To explore how MSCs regulate Tregs, mice in the AME group were sacrificed 1 week after AOM application, and naïve primary T cells were extracted from the spleen of mice and co-cultured with MSCs (Fig. 4A). After 72 h, the proportion of CD4+CD25+Foxp3+Tregs in the MSCs co-culture group was about 1.5-fold higher than that in the CON group alone (Fig. 4B, C; $P \leq 0.05$). However, this phenomenon was not distinctly higher in the co-cultured group only lasting 48 h (Fig. 4B, C). We extracted the cell protein of MSCs and after WB analysis found that the expression of TGF- β in MSCs co-cultured with naïve primary T cells was higher than that of MSCs alone (Fig. 4D). TGF-β increased 2.5-fold, while the other indicators showed no significant change (Fig. 4E; $P \leq 0.01.$ Full-length blots are presented in Additional file 2: Fig. S2). For a further demonstration that the induction of MSCs is mediated by TGF-β, we added a TGF-β stimulator to the T cell culture medium. After 72 h, the percentage of CD4+CD25+Foxp3+Tregs in the group with a TGF-β stimulator increased more than 1.5-fold compared to the group without the TGF-β stimulator (CON) (Fig. 4F, G; $P \leq 0.01$). The Th1 and Th2 ratios also did not change significantly (Fig. 4C, H).Fig. 4MSCs trigger Treg accumulation in mice via TGF-β at an early stage. A Scheme of the Co-culture and treatment experimental design. Some mice in the AME group were sacrificed 1 week after AOM application, and naïve primary T cells were extracted from the spleen of mice, and co-cultured with MSCs for 48 h and 72 h respectively. B Flow cytometry analysis of the mean percentage of Th1, Th2 and Treg cells in naïve CD4T cells co-cultured with MSCs (ratio 1:5) for 48 h and 72 h respectively or without MSCs. C Analyze and count separately. We extracted proteins from MSCs co-cultured for 72 h with naïve CD4+T. D, E Representative immunoblot bands and histogram of relative expression for the IFN-γ, TNF-α, TGF-β1 IL-4, IL-6, IL-10, IL-12 and IL-17. F Flow cytometry analysis of the mean percentage of Th1, Th2 and Treg cells in naïve CD4+T cells co-cultured with TGF-β (10 ng/ml) or without TGF-β. G, H Analyze and count separately. Values are expressed as means ± SD.*$P \leq 0.05$, **$P \leq 0.01.$ MSC mesenchymal stem cells, MT MSCs co-cultured for 72 h with naïve CD4+T
## MSCs skew Th1/Th2 immune balance toward Th2 via IL-4 at an advanced stage
Similarly, to explore how MSCs regulate the balance of immune subtypes of Th cells, the mice in the AML group were sacrificed 7 weeks after AOM administration, and primary naïve T cells were extracted from the spleen and co-cultured with MSCs (Fig. 5A). After 120 h, the ratio of Th1/Th2 in T cells alone was about threefold higher than that in the MSCs co-culture group (Fig. 5B–D; $P \leq 0.05$). However, this phenomenon was not obvious in the co-cultured group only lasting 48 h (Fig. 5B–D). We extracted the cell protein of MSCs. Through WB analysis, it was found that compared with that of the MSCs alone, the expression of IL-4 of MSCs co-cultured with naïve primary T cells increased about twofold (Fig. 5E, F; $P \leq 0.01.$ Full-length blots are presented in Additional file 2: Fig. S3), and the expression of IL-12 decreased (Fig. 5E, F; $P \leq 0.05$), and that of other indicators showed no significant changes. To further prove that IL-4 is the key to MSCs regulation, we added IL-4 stimulant to the T cell culture medium. After 120 h, the ratio of Th1/Th2 in the primary T cell-only group was still over twofold higher than that in the IL-4 stimulated group (Fig. 5G–I; $P \leq 0.01$). There was no significant difference in Tregs between groups (Fig. 5B, C, G, H).Fig. 5MSCs skew Th1/Th2 immune balance toward Th2 via IL-4 at an advanced stage. A Scheme of the Co-culture and treatment experimental design. Some mice in the AML group were sacrificed 7 weeks after AOM administration, and naïve primary T cells were extracted from the spleen of mice, and co-cultured with MSCs for 48 h and 120 h respectively. B Flow cytometry analysis of the mean percentage of Th1, Th2 and Treg cells in naïve CD4+T cells co-cultured with MSCs (ratio 1:5) for 48 h and 120 h respectively or without MSCs. C, D analyze and count separately. We extracted proteins from MSCs co-cultured for 120 h with naïve CD4+T. E, F Representative immunoblot bands and histogram of relative expression for the IFN-γ, TNF-α,TGF-β1 IL-4, IL-6, IL-10, IL-12 and IL-17. G Flow cytometry analysis of the mean percentage of Th1, Th2 and Treg cells in naïve CD4+T cells co-cultured with IL-4 (10 ng/ml) or without IL-4. H, I Analyze and count separately. Values are expressed as means ± SD. * $P \leq 0.05$, **$P \leq 0.01$ or ***$P \leq 0.001.$ MSC mesenchymal stem cells, MT MSCs co-cultured for 120 h with naïve CD4+T
## IL-12 reverses the shift of Th1/Th2 immune balance to Th2 accumulation in mice
To further explore the effect of Th1/Th2 balance on tumors, mice in the AML group were sacrificed 7 weeks after AOM administration, and primary naïve T cells were extracted from the spleen of mice and co-cultured with MSCs. After 5 days, the Th1/Th2 ratio in the T cell-only group was approximately threefold higher than that in the MSCs co-culture group (Fig. 6A, B; $P \leq 0.01$). The proportion of Th1/Th2 increased when an IL-12 stimulator was added to the co-culture medium, suggesting that the immune balance of Th1/Th2 influenced by MSCs could be reversed by IL-12 (Fig. 6A, B; $P \leq 0.05$).Fig. 6At an advanced stage, Il-12 reverses the shift of Th1/Th2 immune balance to Th2 accumulation. The mice in the AML group were sacrificed 7 weeks after AOM administration, and naïve primary T cells were extracted from the spleen of mice A Flow cytometry analysis of the mean percentage of Th1, Th2 and Treg cells in naïve CD4+T cells co-cultured with MSCs (ratio 1:5) or with MSCs/IL-12(10 ng/ml) for 72 h respectively. B Analyze and count separately. Values are expressed as means ± SD. * $P \leq 0.05$, **$P \leq 0.01.$ TM: CD4+T cells co-cultured with MSCs for 72 h; TMI: CD4+T cells co-cultured with MSCs (ratio 1:5)/IL-12(10 ng/ml)
## Discussion
This study focuses on the role of MSCs in CAC. The administration of MSCs at different time points alters the course of colon cancer and affects the expression of inflammatory cytokines in colon tissue. MSCs therapy was applied at an early stage, leading to the accumulation of Tregs through TGF-β secretion. When MSCs are administered at an advanced stage, it may induce the shift of Th1/Th2 immune balance to Th2 through IL-4.And the immune balance of Th1/Th2 influenced by MSCs could be reversed by IL-12 (Fig. 7).In the early stages of inflammatory transformation, MSCs can prevent the development of colon cancer, but in the late stages, they can propagate cancer progression. Fig. 7A schematic model that mesenchymal stem cells can curb or promote the progression of colon cancer AOM/DSS-induced mouse colon cancer model is a typical inflammatory cancer transformation model, wherein tumor transformation through repeated induction of inflammation is achieved. At present, immune abnormalities are considered to be an important factor in the pathogenesis of chronic recurrent intestinal inflammatory diseases [29]. MSCs therapy is known to modulate immune responses by inhibiting the proliferation and differentiation of T and B cells [30], interfering with the maturation and normal function of dendritic cells, and regulating other immune cells [31]. However, MSCs themselves do not have the characteristic ability to inhibit the immune response and require to be activated by cytokines such as TGF-β, IFN-γ, and TNF- α to exhibit their corresponding effect [30]. Therefore, we hypothesized that the differential expression of cytokines in the intestinal microenvironment at different stages of inflammatory cancer transformation may have different guiding effects on the activation of transplanted MSCs. The early transplantation of MSCs can reduce the formation of an intestinal tumor, while the transplantation in the later stages can lead to opposite effects.
The impact of MSCs on cancer is controversial [32]. Chen et al. attributed the tumor inhibitory effect of MSCs to the reduction of IL-6 and pSTAT3 signaling in colon cells [33]. Nasuno et al. found that MSCs block cellular DNA damage initiation and induce G1 shutdown in promoter cells via TGF-β signaling [34]. Tsai et al. found that MSCs co-injected in mice with colon cancer promote the occurrence and development of colon cancer [35]. In Kaposi’s sarcoma model, MSCs play an anti-tumor role by inhibiting Akt activity [36]. MSCs can also inhibit the development of human hepatoma cell lines and hepatoma models by involving the WNT signaling pathways [37]. However, there is substantial disagreement on the precise role of MSCs in the origin and growth of tumors, and it is challenging to pinpoint how MSCs directly impact tumors.
In our CAC model, the timing of MSCs application appeared to be a key factor in the contradictory effects of MSCs. Colorectal tumors usually do not develop rapidly in a clinical setting [38]. In AOM/DSS models, the onset of colon tumor formation usually occurred after 5 weeks. We found that MSCs were administered in the early stages of tumor development in all studies investigating the tumor inhibition of MSCs. We speculated that MSCs may promote tumor development once the early tumor initiation phase has passed. Perfecting the exact contribution of MSCs to tumorigenesis at every stage of cancer development is crucial. Previous studies have suggested that the role of MSCs is closely related to tumor-specific environments. The dynamic changes of anti-inflammatory/inflammatory factors are correlated with the dynamic balance of T cell immunity [39]. This dynamic change may be the key to the bidirectional role of MSCs in the transformation of colitis cancer. Our study found that MSCs inhibit tumor development in the early stage and promote tumor development in the late stage of cancers. Inflammatory mediators were detected in intestinal tissue during late MSCs intervention compared to the control group. For example, IL-6 and IL-17 levels were significantly higher in the late MSCs treatment group and lower in the early MSCs treatment group. At the same time, we found more Treg infiltration in the spleen in the early MSCs intervention group, while the Th1/Th2 immune balance shifted to Th2 in the late MSCs intervention group. This suggests that early MSCs intervention may inhibit the progression of CAC mainly by inducing the accumulation of Tregs, while the carcinogenic effect of late MSCs intervention is more likely to be dominated by the dynamic shift of the Th1/Th2 balance to a Th2 phenotype.
TGF-β is an essential cytokine for the differentiation of naïve CD4+T cells to CD4+CD25+Foxp3+Treg cells [40]. Treg cells participate in tumor development and progression through tumor immunity [11]. Treg accumulation in cancer has been reported to be associated with poor disease prognosis [11]. However, a substantial positive connection between the density of Foxp3+T cell infiltration and improved prognosis and/or survival of CRC models or patients was reported in a review by Ladoire et al. on Foxp3+T cell infiltration and prognosis in CRC [41].
The opposite results are not unexpected given that Tregs mediate immune tolerance that promotes tumor growth and inhibits anti-tumor immunity [11]. Tregs can be viewed in this context as an important part of the tumor’s escape from the host immune system [42]; as a result, they may be a sign of poor prognosis and a new target of immunotherapy. CRC develops in the intestinal tract, a special microenvironment rich in a variety of microorganisms. The intestinal mucosal barrier is made up of numerous lymphoid and myeloid cells that are sandwiched between the lamina propria and a single layer of epithelial cells [43]. These T cells and other inflammatory cells are rich in Toll-like receptors (TLRs) which play a crucial role in initiating and maintaining in-situ cell activation [44]. In CAC, Tregs are more likely to inhibit tumor development by reducing damaging inflammation in the early stages of cancer. However, unlike in the early stages, once the tumor is formed, Tregs could be redirected to perform pro-oncogenic rather than anti-oncogenic activities that inhibit the function of tumor antigen-specific effector T cells [45].
Th1 and Th2 are two functional subgroups of CD4+ Th cells, which affect the immune response via the secretion of different cytokines [46]. The imbalance of the Th1/Th2 in the body may lead to a variety of conditions including bacterial or viral infections, autoimmune diseases, allergic reactions, and transplant rejection. It is also closely related to the occurrence and development of tumors. A dominant Th1 phenotype confers anti-tumor immunity, whereas a shift to the Th2 phenotype impairs anti-tumor effects. Yamamura et al. found that the dominant Th2 cytokine pattern often occurs in tumor hosts [47]. A shift from Th1 to Th2 has been observed in non-small cell lung cancer [48], choriocarcinoma [49], ovarian cancer [50], glioma [51], kidney cancer [52], colorectal cancer [53, 54], melanoma [55], lymphoma [56], and other types of tumors in the host. IL-4 is one of the main cytokines secreted by Th2 cells and one of the promoters of IL-10 secretion, which can reduce the expression of inflammatory cytokines [57]. Furthermore, it can reduce the expression of MHC antigen-presenting cells, maintain lymphocyte infiltration in the tumor tissue, and form a new type of Th2 infiltrating lymphocytes in the tumor tissue (tumor-infiltrating lymphocytes, TIL) cells, forming TIL–IL-4–IL-10 “Th2 cycle” [58], and could promote the immune escape of tumors [59]. These results provide us with some new ideas for immunotherapy. At the stage of tumor formation, our immunotherapy can mediate the reversal of Th2 to Th1 cells. Gao et al. found taht MSCs produce IL-12 to reduce the growth of renal cell carcinoma of mouse and enhance the tumor-bearing mouse survival [60]. IL-12 favours the differentiation of Th1 cells and forms a link between innate resistance and adaptive immunity [61].We reversed this balance toward Th1 by treating primary T cells with Th1-type fine-related cytokine IL-12 [62]. We suggest that activation of Th1 cells in vivo, such as by anti-Th2 cytokine antibodies, or through active immunity, may be beneficial. If the tumor-host and tumor itself can be promoted to reverse from Th2 type to Th1 type, it will be very beneficial to anti-tumor immunity mainly based on cellular immunity and will be of great significance in conjunction with surgery, radiotherapy, and chemotherapy in preventing tumor recurrence and metastasis as well as improving long-term survival rates.
This research found that MSCs can prevent or promote the progression of colon cancer based on their timing of administration. It provides a basis for the therapeutic schedule of MSCs therapeutics in oncotherapy. One of a key issues in the applications of MSCs therapeutics is the timing. In CAC progression in human, the key to distinguish the early or late stage of MSCs application is whether there is malignant tumor in the intestine. IBD-related CAC evolved from “normal → low atypical hyperplasia → high atypical hyperplasia → adenocarcinoma” [63]. The safety period of MSC application in tumor may be between “normal state-mild atypical hyperplasia” of digestive tract. When moderate and severe dysplasia has occurred, MSCs should be used cautiously.
We found that the accumulation of cancer cells may be the key to altering the microenvironment and potentially reversing the effects of MSCs. Under this hypothesis, the precise mechanism of MSCs on the microenvironment remains to be further explored. In addition, key molecules that can reverse the effects of MSCs should be identified to serve as important reference factors for future MSC therapy.
## Conclusion
MSCs can prevent the progression of colon cancer by inducing Treg accumulation via TGF-β at the early stage of inflammatory transformation but promote the progression of colon cancer by inducing a shift in Th1/Th2 immune balance to Th2 through IL-4 secretion at the late stage (Fig. 7). This study provides a basis for the therapeutic schedule and timing of MSCs therapeutics for improving prognosis and survival outcomes following tumor therapy.
## Supplementary Information
Additional file 1. The specific primers’ sequences for RNA amplification. Additional file 2. Original blots of Western Blot analysis. The figure legend of this file is the same as the legend of the corresponding figure in the main text.
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|
---
title: 'Impact of resveratrol supplementation on clinical parameters and inflammatory
markers in patients with chronic periodontitis: a randomized clinical trail'
authors:
- Shabnam Nikniaz
- Farzane Vaziri
- Reza Mansouri
journal: BMC Oral Health
year: 2023
pmcid: PMC10045616
doi: 10.1186/s12903-023-02877-4
license: CC BY 4.0
---
# Impact of resveratrol supplementation on clinical parameters and inflammatory markers in patients with chronic periodontitis: a randomized clinical trail
## Abstract
### Background
Periodontitis is one of the most common chronic inflammatory diseases in the world, which affects oral health. Resveratrol is a polyphenol with therapeutic effects on the inflammation caused by periodontal pathogens. This study aimed to evaluate the impact of resveratrol supplementation on clinical parameters and inflammatory markers in patients with chronic periodontitis.
### Methods
In this randomized, double-blind study, 40 chronic periodontitis patients underwent non-surgical therapy and were randomly assigned to two intervention and control groups, receiving either resveratrol supplements or a placebo for four weeks. Salivary levels of interleukin-8 (IL-8), interleukin-1β (IL-1β), and clinical parameters, including pocket depth (PD), clinical attachment level (CAL), plaque index (PI), and bleeding index (BI), were measured before and after the intervention.
### Results
The results showed that in both the case and control groups, after four weeks of using resveratrol, only plaque index (PI) was significantly different compared to the control group ($$P \leq 0.0001$$). However, there were no significant differences in the mean pocket depth (PD), clinical attachment loss (CAL), bleeding index (BI), and salivary levels of IL-8 and IL-1β between the two groups after the intervention.
### Conclusion
Resveratrol complement was helpful as an anti-inflammatory food supplement, along with other non-surgical periodontal treatments in chronic periodontitis patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12903-023-02877-4.
## Introduction
Periodontitis is an inflammatory disease that leads to the destruction of dental supporting tissues and tooth loss [1]. This immune inflammatory disorder originate from the formation of complex subgingival microbial biofilms and periodontal microbiota dysbiosis. One of the main pathogens in the initiation of periodontitis, Porphyromonas gingivalis, stimulates proinflammatory cytokines such as IL-1β, IL-8, IL-6, and TNFα, which penetrate the gingival connective tissue, cause a local inflammatory response [2–4], and increase the number and activity of polymorphonucleocytes (PMNs), in association with the production of cytokines. In addition, these PMNs produce reactive oxygen species (ROS) and trigger a defense response to infection [5]. Also, ROS can activate macrophages for the synthesis and secretion of inflammatory cytokines and hydrolytic enzymes; it also plays a crucial role in the destruction of periodontal tissues by inflammatory and catabolic activities [6].
Given the complex processes created by different pro-inflammatory and anti-inflammatory mediators, studies have investigated different strategies for regulating immune-inflammatory responses to periodontal diseases. Previous studies have shown that both non-steroid anti-inflammatory drugs (NSAIDs) and selective COX-2 inhibitors regulate the host immune-inflammatory activities [7–10]. However, systemic use of these drugs causes severe complications, decreasing patients’ compliance to use them [11].
Antibiotics have been widely used for treating periodontal diseases; however, using them frequently contributes to antibiotic resistance [11]. Recently natural components derived from plants, such as polyphenols and essential oil have been received attention for the treatment of periodontitis [12–15]. These natural compound have been interestingly evaluated to rejuvenate the oral cavity [16]. Resveratrol, a polyphenol found in grapes, peanut, and cranberry, is an antioxidant [17] with a significant role in reduction of oxidative stress in periodontal structure [18, 19].In addition, this compound has therapeutic effects on the inflammation caused by periodontal pathogens [20].
To date, different surgical and non-surgical treatments have been used for periodontitis; however, few drugs are available for the treatment of periodontitis. Herbal medicines have been used adjunctive to treat inflammatory diseases and recently to treat and prevent periodontitis, too [21, 22]. Since few studies have investigated the clinical effects of resveratrol on improving chronic periodontitis and inflammatory factors, this research aimed to evaluate the effect of resveratrol supplementation as an adjunct to non-surgical treatment of periodontal conditions, including pocket depth (PD), clinical attachment level (CAL), plaque index (PI), bleeding index (BI), and inflammatory factors, such as salivary levels of IL-8 and IL-1β.
## Subjects and study design
In this randomized, double-blind clinical trial, 40 periodontitis patients referring to the Periodontics Department of Dental School, Shahid Sadoughi University of Medical Sciences, Yazd, were selected. According to the principles of the Helsinki declaration, after examination and diagnosis of periodontal symptoms and considering inclusion and exclusion criteria, the necessary information was provided, and written consent was obtained. Then, the subjects were randomly assigned to two groups: case ($$n = 20$$) and control ($$n = 20$$). The patients were free to leave the study at any time they wished.
The inclusion criteria consisted of an age range of 30–60 years and moderate to severe periodontitis. The exclusion criteria consisted of pregnancy, lactation, traveling for > 2 weeks, smoking, taking immunosuppressive medications, taking non-steroidal anti-inflammatory drugs, antihypertensive agents, antibiotics drugs, anti-coagulant drugs like warfarin, taking insulin, and patients treated for uterine, breast, and ovarian cancer, patients with advanced renal and hepatic diseases, those with an allergy to grapes, blackberry, or blueberry, history of periodontitis treatment in the past six months and patients with active oral disease like pemphigus, leukoplakia.
This clinical trial was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences under the code IR.SSU.REC.1396.206, and the IRCT Registration code was IRCT20171015036782N6.
## Periodontal examination
Periodontitis patient with stage II (moderate periodontitis) to stage IV (advanced periodontitis with extensive tooth loss) based on clinical attachment loss (CAL) of ≥ 3 mm around the teeth and disease severity according to 2017 international periodontology workshop were selected. As all patients in our study didn’t have full mouth periapical radiography and no risk factor that may affect systemic health according to eligible patients, the grade of periodontitis was estimated with moderate progression rate (grade B).
For all the patients, the probing depth (PD) was measured by the Williams probe, which is a distinctive and calibrated device, at six points of each tooth: mesiobuccal, mid-buccal, distobuccal, mesiolingual, mid-lingual, and distolingual. These areas were coded, and 10 points were randomly selected from at least three quadrants for the post-intervention clinical measurements [19].
Clinical attachment loss (CAL) was also recorded in these areas by measuring the distance between the CEJ (cementoenamel junction) and the pocket depth. In addition, the O’Leary plaque index [23] was recorded after chewing disclosing agent pills, and the gingival bleeding index [24] was calculated 30 s after gentle probing of the sulcus of teeth. All the clinical parameters were performed by the same operator and re-measured four weeks after the intervention to compare with the initial findings.
## Biochemical measurement
Salivary samples of all the patients were collected at baseline and four weeks after the intervention to assess IL-8 and IL-1β levels. All the salivary samples were collected between 9 a.m. and 12 p.m. The patients were asked to refrain from brushing 12 h before sampling and refrain from eating, drinking, and chewing gum two hours before the sampling. To investigate the inflammatory interleukin levels, the unstimulated saliva of the patients was collected before any action. The patients were asked to avoid swallowing their saliva for five minutes, and then the accumulated saliva in the oral cavity floor was passively drooled into sterile disposable test tubes [25]. The samples were then immediately transferred to the laboratory in an ice chamber and centrifuged at 2500 rpm for 20 min at 4 °C. Then the supernatant was frozen at -80ºC until the biochemical tests. The biochemical tests were performed within six months from the moment of freezing. The salivary levels of IL-8 and IL-1β were measured by an ELISA laboratory kit (Karmania PARS Gene, Tehran, Iran).
## Intervention
For all the patients (both case and control groups), scaling and root planning by an ultrasonic scaler (ultrasonic piezo scaler Uds-K, Woodpecker, China) was performed. Patients in the case group received 480 mg (2 capsules) of resveratrol (HERBAFIT Co.) daily for four weeks, while patients in the control group received 480 mg (2 capsules) of placebo containing starch [19]. Patients were asked to take these capsules in the morning after breakfast. During these four weeks, all the patients were contacted three times a week to ensure they took the capsules. All the patients also underwent periodontal examinations and treatment, including scaling and root planning, if needed, during the study. Oral hygiene instructions, including correct tooth brushing and dental flossing, were given to them. After four weeks, the patients were re-examined. All the procedures were done by the same operator.
## Outcome (primary and secondary)
The primary outcome of this study was to determine the CAL and PD before and after the intervention. The secondary outcome of this study was to evaluate the O’Leary plaque index, BI and the mean salivary levels of IL-8 and IL-1β before and after intervention between two groups.
## Sample size calculation
Considering a $5\%$ significance level, $80\%$ test power, and according to the results of a previous study [19] and a standard deviation of $S = 0.6$, to achieve a significant difference of at least one unit in the mean pocket depth, 20 subjects were included in each group.
## Statistical analysis
SPSS 23.0 was used to compare the results and determine the differences in different experiments. A Kolmogorov-Smirnov test was to assess distribution normality. Also, the chi-squared test, independent-samples t-test, and paired-samples statistics were used to compare the changes between the two groups. Statistical significance was set at $P \leq 0.05.$
## Results
In the present study, 40 patients with chronic periodontitis (20 patients in the control group and 20 in the case group) were enrolled after signing consent forms. First, the participants’ mean ages were 44.5 and 41.7 years in the case and control groups, respectively, with no significant difference between the two groups ($$P \leq 0.331$$).
The participants in the case group consisted of 8 males ($40\%$) and 12 females ($60\%$); in the control group, there were 9 males ($45\%$) and 11 females ($55\%$), with no significant difference between the two groups ($$P \leq 0.5$$).
At baseline, the mean distance between the gingival margin and the pocket depth (PD) in the case group was 4.44 mm, with 4.27 mm in the control group, which decreased to 2.82 mm in the case group and 3.2 mm in the control group one month (4 weeks) after the intervention. A comparison of PD values showed a significant decrease in both groups compared to baseline ($$P \leq 0.0001$$), with non-significant changes in the pocket depth in the resveratrol group compared to the control group ($$P \leq 0.06$$) (Tables 1). Pocket closure in case and control group were $95\%$ and $85\%$ respectively.
Table 1Comparison of measured indices between case and control groups before and after interventionVariableCase ($$n = 20$$)Mean ± SDControl ($$n = 20$$)Mean ± SDP-value*PD (mm)B†4.44 ± 0.634.27 ± 0.580.4 A‡2.82 ± 0.583.2 ± 0.660.06CAL (mm)B5.17 ± 0.644.97 ± 0.580.3A3.37 ± 0.563.74 ± 0.60.05BI (%)B80.25 ± 15.2170.80 ± 14.530.05A29.10 ± 8.6632.40 ± 7.290.2PI (%)B76.10 ± 13.7375.60 ± 14.540.9A24.05 ± 6.7734.90 ± 8.680.0001IL-8 (pg/mL)B18.67 ± 3.6516.83 ± 2.630.63A17.36 ± 3.0316.97 ± 1.970.07IL-1β (pg/mL)B7.54 ± 3.746.24 ± 3.160.43A4.34 ± 2.274.96 ± 2.670.24*$P \leq 0.05$ was considered significant at baseline and significant after the intervention using an independent t-test between the two groups.† Before intervention.‡ After intervention The mean distance from the CEJ to pocket depth (CAL) at baseline in the case group was 5.17 mm, with 4.97 mm in the control group, which decreased to 3.37 mm in the case group and 3.74 mm in the control group one month (4 weeks) after the intervention. A comparison of the CAL values indicated significant changes in both groups compared to the baseline ($$P \leq 0.0001$$), with no significant decrease after treatment in the case group compared to the control group ($$P \leq 0.05$$) (Tables 1).
The mean of the bleeding index (BI) in the case group was $80.25\%$, with $70.80\%$ in the control group, which decreased to $29.10\%$ in the case group and $32.40\%$ in the control group one month (4 weeks) after the intervention. A comparison of BI values showed a significant decrease in both groups compared to the baseline ($$P \leq 0.0001$$) and no significant changes after treatment in the resveratrol group compared to the control group ($$P \leq 0.2$$) (Tables 1).
The mean plaque index (PI) in the case group was $76.10\%$, with $75.60\%$ in the control group, which decreased to $24.05\%$ in the case group and $34.90\%$ in the control group one month (4 weeks) after the intervention. A comparison of PI values showed significant changes in both groups compared to the baseline ($$P \leq 0.0001$$) and a significant decrease after treatment in the resveratrol group compared to the control group ($$P \leq 0.00$$) (Tables 1).
The mean salivary levels of interleukin-8 (IL-8) at baseline in the case and control groups were 18.67 and 16.83 pg/mL in the control group, which increased to 17.36 and 16.97 pg/mL in the case and control groups, respectively, one month (4 weeks) after the intervention. The comparison of IL-8 values showed no significant changes in the case and control groups compared to the baseline ($P \leq 0.05$) and no significant changes after treatment with resveratrol ($$P \leq 0.07$$) (Tables 1).
IL-1β salivary levels in the case and control groups were 7.54 and 6.24 pg/mL, respectively, which decreased to 4.34 and 4.96 pg/mL in the case and control group, respectively, one month (4 weeks) after the intervention. A comparison of IL-1β levels showed significant decreases in the case and control groups compared to the baseline ($$P \leq 0.0001$$), with no significant decrease after treatment with resveratrol in the case group ($$P \leq 0.24$$) (Tables 1).
## Discussion
The present research is one of the first studies to investigate the effect of resveratrol supplementation on periodontitis. In this double-blind study with a placebo control, the effectiveness of resveratrol as a supplementary treatment for clinical indicators (PD, CAL, PI, and BI) and salivary inflammatory indicators (IL-8 and IL-1β) was investigated in chronic periodontitis patients without any systemic disease.
According to the results, there were no significant differences between the control and experimental groups in gender and age; in this regard, the two groups were homogenous. According to the comparisons between the two groups, after four weeks of resveratrol supplementation in the experimental group, the only clinical indicator with a significant decrease compared to the control group was plaque index (PI) ($$P \leq 0.0001$$). However, there were no significant differences between the two groups in pocket depth (PD), clinical attachment level (CAL), bleeding index (BI), and IL-8 and IL-1β levels in salivary samples (Table 1). Also, investigating the experimental and control groups independently indicated that the changes in salivary IL-8 levels were not significant in the two groups, with no difference in this indicator before and after the intervention (Table 1). In contrast, the decrease in the remaining indicators, including PI, BI, CAL, PD, and Il-1β after the intervention, was significant compared to the baseline levels in both groups.
In this research, the changes in PD levels were not significantly different between the experimental and control groups, which is different from the results of a study by Zare et al. [ 2017], who investigated the effect of resveratrol on 43 patients with type 2 diabetes and chronic periodontitis over four weeks [26]. The differences in the findings can be attributed to the systemic diabetes diseases in the samples of Zare et al. and its higher destructive and oxidative effect on the periodontium, which results in a higher effect of resveratrol on this parameter.
Several studies have investigated the effect of non-surgical periodontal treatments, such as oral hygiene instructions, scaling, and root planning, as the key treatments for these patients [27]. The present research also suggested a significant decrease in PD after the intervention in both groups ($$P \leq 0.0001$$). Therefore, non-surgical periodontal treatment played a significant role in both groups.
In the four weeks of the study, CAL decreased by 1.23 mm in the experimental group and 1.8 mm in the control group; these changes are statistically significant (Table 1). Since CAL measures provide a more accurate evaluation of periodontal disease progression, and PD changes cannot provide a reliable prediction of attachment loss [28], it was also evaluated in this study. The results indicated that the decrease in CAL in the resveratrol treatment group compared to the control group was almost significant ($$P \leq 0.05$$) (Table 1). This decrease can result from the secretion of anti-inflammatory and antioxidative factors in the saliva or gingival crevicular fluid (GCF) due to the systemic use of resveratrol and its effect on improving collagen production and attachment to the periodontium and connective tissue. Although the decrease in the average probing depth by 2 mm or higher is usually an indicator of a clinical effect [29], the difference between the two groups (the experimental group: 1.8 mm, and the control group: 1.32 mm) was not clinically significant.
The O’Leary plaque index significantly decreased in both the experimental and control groups compared to the baseline ($$P \leq 0.0001$$), and it also significantly decreased in the experimental group compared to the control group ($$P \leq 0.0001$$) (Table 1). These findings can be justified by the results reported by Millhouse et al., who found that resveratrol had an antimicrobial effect on planktonic bacteria and bacterial biofilms and inhibited the accumulation and activation of neutrophils [30]. Also, Khazaei et al. reported resveratrol’s inhibitory effect on the expression of cell adhesion molecules; it inhibited the endothelial dysfunction caused by P. gingivalis lipopolysaccharide and blocked the expression of ICAM-1 and VCAM-1 molecules by inhibiting NF-kappa B [31]. These findings can lead to bacterial plaque rupture around the gingiva. Basu et al. performed a cell study in 2018 and reported that polyphenols could significantly invade the bacteria involved in periodontitis, such as P. gingivalis; the viability, replication, and biofilm formation ability of periodontopathogens could be significantly affected by food polyphenols. Daily use of polyphenols can inhibit biofilm formation and decrease bacterial growth speed [12]. The results of a study by Khazaei and Basu can explain the findings of the present study.
In this study, the bleeding index (BI) significantly decreased in both groups after four weeks ($$P \leq 0.0001$$). However, there was no significant difference between the two groups ($$P \leq 0.2$$), which might be due to the significant effect of non-surgical periodontal treatment, frequent oral hygiene instructions, and the Hawthorne effect in both groups that can diminish the effectiveness of resveratrol.
The salivary IL-1β levels decreased significantly in both the experimental and control groups compared to the baseline; however, there was no significant difference between the two groups after the intervention (P˃0.05). Casati et al. [ 2013] treated 10-week-old rats with 10 mg/kg of daily resveratrol and measured IL-1β, IL-7, and IL-4 levels. They did not observe any significant difference between the two groups in IL-1β and IL-4 levels, but IL-7 levels decreased in the resveratrol treatment group compared to the control group (receiving placebo) [32]. Teles et al. reported no significant difference between the patient and healthy groups in salivary IL-1β level and concentration, and the average salivary IL-1β level could not predict the periodontal condition (healthy or patient) [33]. Their findings are different from the results of the present study because the present study revealed significant changes in salivary IL-1β levels in both groups following the anti-inflammatory treatment and improvements in gingivitis ($$P \leq 0.0001$$).
Several studies have reported an increase in the salivary or gingival crevicular fluid IL-8 levels in inflamed areas compared to healthy areas. In contrast, some other studies have suggested decreased IL-8 levels in inflamed areas. Therefore, the results are contradictory [34]. The present study did not suggest any significant changes in IL-8 levels in the experimental and control groups compared to the baseline and in the comparison between the experimental and control groups (P˃0.05). However, following the intervention, IL-8 levels decreased in the experimental group and increased in the control group. Despite the non-surgical anti-inflammatory treatments in the two groups and resveratrol supplementation in the experimental group, no change was observed in the salivary IL-8 levels, which might be attributed to the shorter period of the study or ineffectiveness of the used dosages of the drug on IL-8 levels.
Although the mentioned evidence suggests the treatment potential of resveratrol in the host inflammatory modulation, the primary mechanism causing this effect is still unknown. Oral treatment with resveratrol has limited bioavailability, and it is quickly affected by metabolism. Its local treatment or injection might have a higher anti-inflammatory effect on the periodontium. Therefore, although the immunologic findings of this study do not suggest the modulation of immune-inflammatory responses by resveratrol, its molecular mechanism should be studied further. The limitations of the present study included the small number of sample size and short follow-up period. Further studies with larger sample size and longer follow up periods needed to explore the impact of resveratrol supplementation on clinical parameters and inflammatory markers in periodontitis patients.
## Conclusion
In view of the above findings and within the limits of present study the results indicated that supplementation with resveratrol in combination with non-surgical periodontal treatment has significant effect on reduction of plaque index (PI) during 4 weeks. Collectively, it seems that using resveratrol along with nonsurgical periodontal treatment may be beneficial in improvement the clinical parameters and inflammatory condition in periodontitis patients.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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---
title: The Protective Role of Nrf2 in Renal Tubular Cells in Oxidised Low-Density
Lipoprotein-Induced Fibrosis
authors:
- Xiangju Long
- Zhe Liu
- Yanan Sun
- Hong Zhang
journal: Analytical Cellular Pathology (Amsterdam)
year: 2023
pmcid: PMC10045629
doi: 10.1155/2023/4134928
license: CC BY 4.0
---
# The Protective Role of Nrf2 in Renal Tubular Cells in Oxidised Low-Density Lipoprotein-Induced Fibrosis
## Abstract
Background: CD36 is the receptor of oxidised low-density lipoprotein (OxLDL) in renal tubular epithelial cells. Nuclear factor erythroid 2-related factor 2 (Nrf2) is the key factor in the activation of the Nrf2 signalling pathway and the regulation of oxidative stress. Kelch-like ECH-associated protein 1 (Keap1) is known as an Nrf2 inhibitor. Methods: We used OxLDL and Nrf2 inhibitors at different concentrations and durations to treat renal tubular epithelial cells; the expression of CD36 and cytoplasmic and nucleic Nrf2 and E-cadherin in those cells were observed by Western blot and reverse-transcription polymerase chain reaction. Results: The protein levels of Nrf2 decreased in expression after 24 hours of OxLDL treatment. At the same time, the Nrf2 protein level in the cytoplasm did not change significantly compared with that of the control group, and the Nrf2 protein level expression in the nucleus increased. Both the messenger ribonucleic acid (mRNA) and protein expression of CD36 decreased following the treatment of cells with the Nrf2 inhibitor Keap1. Kelch-like ECH-associated protein 1 was overexpressed, and CD36 mRNA and protein expression were decreased in OxLDL-treated cells. Following the overexpression of Keap1, E-cadherin expression was reduced in NRK-52E cells. Conclusion: Nuclear factor erythroid 2-related factor 2 can be activated by OxLDL; however, it can only alleviate OxLDL-induced oxidative stress by transferring from the cytoplasm to the nucleus. Additionally, Nrf2 may play a protective role by upregulating CD36.
## 1. Introduction
Hyperglycaemia and hyperlipidaemia frequently occur and persist in diabetic nephropathy. Previous studies have shown that abnormal lipid metabolism is an important factor affecting oxidative stress in diabetes. Furthermore, it plays a vital role in the occurrence and development of renal interstitial injury. It has been demonstrated that oxidised low-density lipoprotein (OxLDL) is a major damaging factor in abnormal lipid metabolism [1, 2], and it is also considered an indicator of oxidative stress in serum.
The nuclear factor erythroid 2-related factor 2 (Nrf2) is a major activator of cellular antioxidant response genes. In normal cells without oxidative stress, Nrf2 is maintained at low levels in the cytoplasm by its inhibitor Kelch-like ECH-associated protein 1 (Keap1) for proteasomal degradation. Under oxidative and other stress, the ubiquitin ligase activity of Keap1 is inactivated, allowing Nrf2 to accumulate in the nucleus and bind as a heterodimer. Additionally, small Minor Allele Frequency (MAF) proteins bind to the antioxidant response elements of target genes, such as NQO1, HO1, and GCLM, stimulating their expression [3, 4].
Previous studies have shown that OxLDL plays a key role in the pathogenesis of atherosclerosis and can regulate cell growth, the expression of transcription factors and cytokines, and affect cell viability [5, 6]. Previous cytological studies have found that OxLDL in preadipocytes is a specific saturation process mediated by CD36, which affects adipose tissue homeostasis by inhibiting the differentiation of preadipocytes [7]. Oxidised low-density lipoprotein has also been shown to induce CD36 overexpression via the upregulation of Nrf2 expression in preadipocytes. In adipocytes, the upregulation of CD36 may indicate a compensatory mechanism to meet the requirement for excessive OxLDL and oxidised lipids in blood and reduce the risk of atherosclerosis [8]. However, whether the same mechanism exists in renal tubular cells remains unclear. In addition, it is unclear how abnormal lipid metabolism leads to oxidative stress and, ultimately, renal interstitial injury. This study found that Nrf2 could be activated by OxLDL and that Nrf2 may play a protective role by upregulating CD36 expression.
## 2.1. Cell Culture
Rattus norvegicus renal tubular epithelial cell line NRK-52E (American Type Culture Collection [ATCC], VA, USA) and human renal tubular epithelial cell line HK-2 (ATCC) were plated and cultured in Dulbecco's modified Eagle's medium supplemented with $10\%$ foetal bovine serum. HEK293T cells were obtained from the ATCC and used for Lentivirus transfection.
## 2.2. OxLDL Preparation and Treatment
Oxidised low-density lipoprotein was obtained from Yiyuan Biotechnology Company (Guangzhou, China) and sterilised using a 0.22 μm membrane (Millipore Corp., Bedford, MA, USA). Different concentrations of OxLDL were tested. First, 50 mg/L OxLDL was incubated with NRK-52E cells for 0–48 hours. Then, 0, 100, and 150 mg/L concentrations of OxLDL were incubated with the cells for 0–4 days. In some experiments, NRK-52E cells infected with empty p305 and p305-Keap1 were treated with 150 mg/L OxLDL for 2 or 3 days. Cells were treated to express CD36, total Nrf2, nuclear Nrf2, NFκB, and E-cadherin at the end of the incubation period as described.
## 2.3. Serum Starvation
Cells were seeded in culture dishes, first cultured with $10\%$ foetal bovine serum, followed by cell starvation with $1\%$ foetal bovine serum 24 hours later. Intracellular proteins were collected for subsequent experiments after cell starvation for 48 hours.
## 2.4.1. Nuclear and Cytosolic Protein Extracts
The NRK-52E and HK-2 cells infected with empty p305 and p305-Keap1 were washed with ice-cold phosphate-buffered saline (PBS) and lysed in an appropriate volume (10 × cell volume) of ice-cold buffer A containing $0.5\%$ NP-40. After incubation, the cells were washed with PBS and lysed in a lysis buffer (Bio-Rad Laboratories Inc., Hercules, CA, USA). The cell lysates were sonicated and centrifuged at 13,000 rpm at 4°C for 15 minutes to pellet the cell debris. Before Western blotting, labelled dye and 2-mercaptoethanol were added to the lysate, separated by sodium dodecyl sulphate–polyacrylamide gel electrophoresis and transferred to a nitrocellulose membrane (Pall Corp., AZ, USA). After being blockaded with $5\%$ non-fat milk, the membranes were incubated at 4°C overnight with rabbit polyclonal anti-CD36 antibody (Abcam, Cambridge, UK), rabbit polyclonal anti-Nrf2 antibody (Abcam, Cambridge, UK), and mouse mAb to E-cadherin (BD Biosciences, San Jose, CA, USA) or rabbit polyclonal to NFκB (Santa Cruz Biotechnology, Inc., Dallas, TX, USA). After extensive washing in Tris-buffered saline (TBS)–Tween 20 (TBST), the membranes were incubated with horseradish peroxidase-conjugated anti-mouse immunoglobulin G (IgG) or anti-rabbit IgG (Zhongshan Golden Bridge Biotechnology Co. Ltd., Beijing, China) for 1 hour at room temperature. After washing with TBST, the membranes were incubated with an enhanced chemiluminescence system detection kit (Millipore Corp., Bedford, MA, USA).
1 mM dichlorodiphenyltrichloroethane (DTT) and 1 × protease inhibitor (PI). The cell lysates were pipetted up and down several times to disrupt any cell clumps, rotated for 10 minutes at 4°C, and centrifuged at high speed for 3 minutes at 4°C. The supernatants (cytosolic part) were stored at 4°C. The pellets were washed with ice-cold buffer A and mixed with 3 × volumes of ice-cold buffer B containing 1 DTT and 1 × PI. The mixtures were incubated on ice for 30 minutes with an intermittent strong vortex and spun for 15 minutes at high speed at 4°C. The supernatants were collected, and the concentration of proteins was determined using a bicinchoninic assay.
## 2.4.2. Total Ribonucleic Acid Extraction and Reverse-Transcription Polymerase Chain Reaction Analysis
Total ribonucleic acid (RNA) was isolated from the NRK-52E cells, the NRK-52E cells infected with empty p305 and p305-Keap1, and the OxLDL-treated cells using TRIzol as a reagent (Invitrogen Life Technologies, Thermo Fisher Scientific, Inc., MA, USA). Quantitative real-time polymerase chain reaction (PCR) was performed with primers using a sequence detector (Applied Biosystems, Thermo Fisher Scientific, Inc., MA, USA). Oligonucleotide primers used for the reverse-transcription (RT) PCR were as follows: 5′;-GGTGTGCTCAACAGCCTTATC-3′; and 5′;-TTATGGCAACCTTGCTTATG-3′; for detecting rat CD36 messenger RNA (mRNA), 5′;-GACTGGATCTGGCATAAAGA-3′;, and 5′;-TCAACGGCACAGTCAAGG-3′; and 5′;-ACTCCACGACATACTCAGC-3′; for rat glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA. The expressions of the CD36 and LOX-1 genes were determined as the amount of CD36 relative to GAPDH mRNA using the comparative CT method described in the Applied Biosystems (ABI) sequence detection system.
## 2.4.3. Lentivirus Transfection
Lentivirus was produced in HEK293T cells packed with plasmid pMD2 using plasmids p305 and p305-Keap1. BSBG, pMDLg/pRRE, and pRSV-REV were obtained using a Ca3(PO4)2 transfection kit (Millipore Corp., Bedford, MA, USA). NRK-52E cells were infected with a Lentivirus vector until the Enhanced Green Fluorescent Protein (EGFP) marker was completely displayed as successfully infected cells.
## 3.1. Expression of CD36 in NRK-52E Cells
NRK-52E cells were incubated with 50 mg/L OxLDL for 0, 5, 10, 24, and 48 hours, respectively. The cells were collected, and the expression of CD36 was detected by Western blot. The results showed that CD36 expression was reduced under OxLDL-treated conditions (Figures 1(a) and 1(b)).
Different concentrations of OxLDL were prepared with cell culture media (0, 100, and 150 mg/L) and incubated with NRK-52E cells under different concentrations of OxLDL for 2 and 4 days. The results showed that the expression of the CD36 protein in the NRK-52E cells stimulated by 150 mg/L OxLDL was higher than that in the 0 mg/L OxLDL group on days 2 and 4 ($p \leq 0.05$); the 4-day group was more evident than the 2-day group. When NRK-52E cells were stimulated with 100 mg/L OxLDL for 2 days, the expression of the CD36 protein in the 100 mg/L group also increased ($p \leq 0.05$), but the growth range was relatively small compared with that of the 150 mg/L group ($p \leq 0.05$). After 4 days, the expression of the CD36 protein in the NRK-52E cells stimulated by 100 and 150 mg/L OxLDL was not statistically significant ($p \leq 0.05$) (Figures 2(a) and 2(b)).
Changes in Nrf2 protein levels under OxLDL treatment were determined. Oxidised low-density lipoprotein was prepared to a concentration of 50 mg/L using a cell culture medium; subsequently, NRK-52E cells were stimulated for 0, 5, 10, 24, and 48 hours. The cells were then collected, and the total Nrf2 protein level in the cells was detected. The results showed no significant change in the total Nrf2 protein level in the NRK-52E cells at 5 and 10 hours ($p \leq 0.05$). After 24 hours of treatment, the results revealed a reduced expression of Nrf2 compared with that of the control group ($p \leq 0.05$) and a reduced expression with no statistical significance after 48 hours ($p \leq 0.05$) (Figures 3(a) and 3(b)).
NRK-52E cells were treated with 150 mg/L OxLDL, and the nuclear and cytoplasmic proteins were extracted to detect the Nrf2 protein level. The results showed that the Nrf2 protein in the NRK-52E nucleus of the serum starvation and OxLDL treatment groups was higher than that of the control group ($p \leq 0.05$). In contrast, the Nrf2 protein quantity in the cytoplasm of the serum starvation and OxLDL treatment groups was not different ($p \leq 0.05$) (Figures 4(a) and 4(b)). We obtained the same results in human renal tubular epithelial cell line HK-2 cells (Figure 4(c)).
## 3.2. Keap1 and Nrf2 Effects on CD36 Expression
Keap1 is an inhibitory protein of Nrf2. NRK-52E cells were treated with Keap1 overexpression (the p305-Keap1 group) and were untreated in the control group (p305-empty group); then, the NRK-52E cells were stimulated with 0 and 150 mg/L OxLDL for 2 and 3 days, respectively. The results showed that when stimulated with 0 mg/L OxLDL, there was no change in the expression of CD36 in the NRK-52E cells treated with Nrf2 inhibitor Keap1 overexpression (the p305-Keap1 group) compared with that of the untreated control group (the p305-empty group). Compared with the untreated control NRK-52E cell group, the expression of CD36 in the Keap1-overexpressed NRK-52E cell group decreased after 2 and 3 days of stimulation with 150 mg/L OxLDL ($p \leq 0.05$) (Figures 5(a) and 5(b)).
To determine whether Nrf2 activation is related to OxLDL-mediated renal tubular cells, we treated renal tubular cells with the Nrf2 inhibitor Keap1 (the p305-Keap1 group), and the control group (the p305-empty group) was not treated. Then, both groups of cells were stimulated with OxLDL (the p305-Keap1-OxLDL group). The results showed that the CD36 mRNA in the Keap1 group was lower than that in the control group, and the Keap1 OxLDL group also showed a significant decrease in CD36 mRNA compared with that of the control group (Figures 6(a) and 6(b)).
## 3.3. E-Cadherin Protein Expression in Tubular Cells Treated with Keap1
To determine the relationship between Nrf2 and renal tubular fibrosis, we treated renal tubular cells with the Nrf2 inhibitor p305-Keap1 (the p305-Keap1 group). Then, we detected the expression of fibrosis factor E-cadherin in the treatment and control groups. The results showed that the expression of E-cadherin in the p305-Keap1 group was lower than that of the control group (Figures 7(a) and 7(b)).
## 4. Discussion
This study found that the CD36 protein, an OxLDL membrane receptor, increased in OxLDL-treated renal tubular NRK-52E cells. The results revealed that OxLDL could upregulate CD36, which was related to the concentration and action time of OxLDL. The upregulation effect of 150 mg/L OxLDL on CD36 was more evident than with 50 and 100 mg/L OxLDL. The expression level of the CD36 protein increased with a prolongation of action time and an increase in OxLDL concentration. The CD36 protein changed the 24-hour OxLDL intervention by at least 50 mg/L, indicating that the progress of OxLDL-mediated renal tubular injury was slow. This study confirmed for the first time that the OxLDL receptor CD36 in renal tubular cells plays a role as an OxLDL receptor during hyperlipidaemia and oxidative stress. The upregulation of CD36 indicates the coping mechanism after excessive OxLDL in hyperlipidaemia to reduce the risk of renal tubular injury caused by OxLDL.
Additionally, we found that in a high OxLDL environment, CD36 was upregulated, and Nrf2 was changed. After treatment with 50 mg/L OxLDL for 5 and 10 hours, the total Nrf2 protein level did not change significantly, although it decreased after treatment for 24 and 48 hours. These results indicate that the OxLDL-mediated Nrf2 pathway needs sufficient time to function, which is consistent with the results of previous studies on preadipocytes [9–12].
Furthermore, the present study showed that NRK-52E cells treated with OxLDL and starvation for 2 days had a relatively high level of Nrf2 protein in the nucleus and a relatively low level in the cytoplasm; this indicated that OxLDL stimulated the initiation of oxidative stress and activated the Nrf2 signalling pathway by causing the transfer of Nrf2 from the cytoplasm to the nucleus, continuing to regulate the downstream gene HO-1 [8]. This study may confirm the regulatory mechanism of Nrf2 in renal tubular cells under oxidative stress and abnormal lipid metabolism. The relationship between the levels of CD36 and Nrf2 proteins in renal tubular cells after OxLDL treatment may indicate that OxLDL initiates the Nrf2 protective mechanism by upregulating CD36.
The cytoplasmic inhibitor Keap1 is a receptor that induces Nrf2 activation via oxides and electrophiles. It is also a regulator of Nrf2 degradation mediated by the ubiquitin protease system and plays a central role in regulating the Nrf2 signalling pathway [11, 12]. Keap1 has been shown to be a shuttle protein that can move back and forth between the cytoplasm and nucleus. Once intracellular redox homeostasis is restored, Keap1 can shuttle from the cytoplasm into the nucleus and move Nrf2 upwards from antioxidant response elements; then, the Nrf2–Keap1 complex moves out of the nucleus again. The Nrf2–Keap1 complex is again degraded in the cytoplasm by a cul3-rbxl-e3-dependent ubiquitinase mechanism. Nuclear factor erythroid 2-related factor 2 remains at low-level expression, and the Nrf2 signalling pathway is inactivated [13, 14]. Therefore, Keap1 can be considered to play a role in inhibiting the transfer of Nrf2 to the nucleus [15–17].
This study showed that the expression of CD36 mRNA and protein decreased in NRK-52E renal tubular cells treated with Keap1. After OxLDL stimulation and Keap1 overexpression treatment, the expression of CD36 mRNA and protein in renal tubular cells also decreased. These results suggest that Nrf2 plays a role in regulating OxLDL receptor CD36 and activates the protective mechanism of oxidative stress by upregulating CD36.
Strutz [18] first proposed the concept of renal tubular phenotypic transformation (epithelial–mesenchymal transition [EMT]) in a renal tubular basement membrane cell model, which can be transformed into medial forebrain bundle cells and express the interstitial marker vimentin [18]. The main feature of renal tubular cells after EMT is the reduction or loss of E-cadherin expression [19]. Renal tubular epithelial cells maintain the integrity of cell morphology, structure, and function through various cell adhesion mechanisms, such as E-cadherin. The loss of E-cadherin leads to the transformation of the characteristics of primary epithelial cells to non-epithelial functions [20]. Therefore, E-cadherin is the main molecular marker of epithelial adhesion and phenotype and can be used as an important marker of EMT. The decreased expression of E-cadherin can be used as a marker of renal tubular EMT and fibrosis. Therefore, in this study, we selected E-cadherin as a molecular marker of OxLDL-induced renal tubular cell fibrosis [20, 21]. Our results showed that the expression of E-cadherin decreased in renal tubular cells treated with Keap1. This result may indicate that Nrf2 plays an inhibitory role in the EMT process of renal tubular cells. Moreover, these results confirmed that Nrf2 has an inhibitory effect on the process of renal tubulointerstitial fibrosis and plays a protective role in renal tubulointerstitial fibrosis. The protective effect of Nrf2 is finally reflected in the inhibition of the EMT response by reducing the loss of E-cadherin and inhibiting the fibrosis process in renal tubular cells.
## 5. Conclusions
Overall, this study's results suggest that Nrf2 plays a key role as a transcriptional regulator of oxidative stress in OxLDL-induced renal fibrosis. Excessive OxLDL in renal tubular cells upregulates CD36 and activates the Nrf2 signalling pathway, resulting in the transfer of Nrf2 from the cytoplasm to the nucleus; this may regulate CD36 at a later stage, leading to upregulation of CD36, thereby enhancing CD36's ability to defend against OxLDL and play a protective role. E-cadherin is worthy of further study and discussion regarding reducing renal tubular cell injury and delaying nephropathy. Research should focus on future therapeutic targets.
## Data Availability
*The* generated or analyzed data used to support the findings of this study are included within the article.
## Disclosure
This article has been preprinted in Research Square.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
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|
---
title: Anti-Inflammatory, Antioxidant Activities, and Phytochemical Characterization
of Edible Plants Exerting Synergistic Effects in Human Gastric Epithelial Cells
authors:
- Achille Parfait Nwakiban Atchan
- Orissa Charlène Monthe
- Armelle Deutou Tchamgoue
- Yeshvanthi Singh
- Shilpa Talkad Shivashankara
- Moorthy Karthika Selvi
- Gabriel Agbor Agbor
- Paolo Magni
- Stefano Piazza
- Uma Venkateswaran Manjappara
- Jules-Roger Kuiate
- Mario Dell’Agli
journal: Antioxidants
year: 2023
pmcid: PMC10045632
doi: 10.3390/antiox12030591
license: CC BY 4.0
---
# Anti-Inflammatory, Antioxidant Activities, and Phytochemical Characterization of Edible Plants Exerting Synergistic Effects in Human Gastric Epithelial Cells
## Abstract
Dietary bioactive compounds from natural sources (e.g., herbal medicines, foods) are known to potentially suppress acute or chronic inflammation and promote the effectiveness of treatment to reduce the harmful effects of gastritis alone or in combination. In this regard, we have characterized four Cameroonian spice extracts, namely Aframomum citratum, Dichrostachys glomerata, Tetrapleura tetraptera, and *Xylopia parviflora* through reverse phase-high-performance liquid chromatography (RP-HPLC), ultra-performance liquid chromatography-electrospray ionization high-resolution mass spectrometry (UPLC-ESI-HRMS/MS), and Fourier transform infrared spectroscopic (FTIR) analyses and investigated their antioxidant and synergistic anti-inflammatory activities in human gastric adenocarcinoma (AGS) and gastric epithelial (GES-1) cells. The extracts showed a high amount of total phenolic (TPC: 150–290 mg gallic acid equivalents (GAE)/g of extract) and flavonoid content (TFC: 35–115 mg catechin equivalents (CE)/g of extract) with antioxidant properties in a cell-free system (1,1-Diphenyl-2-picryl-hydrazyl (DPPH) half maximal inhibitory concentration (IC50s) ≤ 45 µg/mL; 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) half maximal inhibitory concentration (IC50s) ≤ 29 µg/mL. The extracts in combination (MIX) exert a synergistic beneficial effect (combination index (CIs) < 1 and dose reduction index (DRIs) > 1) on inflammatory markers (interleukin (IL)-8 and -6 release, and nuclear factor kappa B (NF-κB) driven transcription) in human gastric epithelial cells, which may result from the presence of phenolic compounds (phenolic acids, flavonoids) or other compounds (protein, lipid, aromatic, and polysaccharide compounds) tentatively identified in the extracts. *The* general findings of the present study provide supporting evidence on the chemical composition of four Cameroonian dietary plants and their significant synergistic inhibitory activities on inflammatory markers of gastric epithelial cells.
## 1. Introduction
Gastric ulcers and gastritis are among the most common gastrointestinal disorders, with a strong relationship with *Helicobacter pylori* (H. pylori) infection and nonsteroidal anti-inflammatory drugs (NSAID) administration [1]. Various pathogenic mechanisms can contribute to the development of ulcers leading to an imbalance between protective (prostaglandins, mucus, endogenous antioxidants, etc.) and aggressive (hydrogen chloride (HCl), reactive oxygen species, pepsin) factors occurring in the gastric mucosa [2]. Genetic predisposition, smoking, stress, nutritional deficiencies, alcohol consumption, in addition to the long-term use of NSAIDs (i.e., acetylsalicylic acid or indomethacin) and H. pylori infection are all causative factors relevant to the development of gastric ulcers [3].
From a mechanistic point of view, several transcription factors are involved in gastric inflammatory disorders, with nuclear factor kappa B (NF-κB) playing a key role among others. Several in vitro studies, also performed by our group, have shown that H. pylori and some pro-inflammatory cytokines (i.e., tumor necrosis factor (TNF)-α) are capable of activating the NF-κB pathway in gastric epithelial cells [4,5]. NF-κB is responsible for the expression and release of IL-8 and IL-6, which, in turn, increase the gastric phlogistic processes [4].
The prevention or treatment of gastric ulcers is a medical challenge [6]. Currently, the treatment of gastric ulcers and gastritis requires a combination of drugs, such as proton pump inhibitors (i.e., omeprazole), histamine receptor antagonists (i.e., ranitidine), anticholinergic, and antibiotics (i.e., clarithromycin, amoxicillin, and metronidazole). Although it is possible to achieve effectiveness with the clinical use of these drugs, their potential side effects and drug interactions are major problems during treatment, thus making drug tolerance by patients quite low [7]. Consequently, products with good efficacy and negligible or no side effects are necessary. Several studies have shown that natural products from herbal medications have therapeutic benefits for gastric disorders with fewer side effects [8,9,10]. Moreover, the use of plant extracts in combination (synergistic therapy) may lead to new therapeutical strategies and represent a potential area for future investigations [11].
Since ancient times, nature has supplied a variety of phytochemicals with beneficial effects on humans. Thus, there is increasing attention to natural products, especially for the treatment of gastrointestinal disorders, corroborated by the traditional use, low cost, and lower toxicity with respect to conventional medicines [12]. Based on ethnopharmacological information, and our previous studies, in which we reported the gastro-protective and anti-inflammatory effects of a variety of Cameroonian plants in human epithelial cells (GES-1 and AGS) [5,13], *Xylopia parviflora* Spruce, *Tetrapleura tetraptera* (Schum. and Thonn.) Taub, *Dichrostachys glomerata* (Forssk.) Chiov., and *Aframomum citratum* (C.Pereira) K.Schum were chosen for gastro-protective evaluation. Our early studies showed that hydro-ethanolic extracts from these plants exerted antioxidant, hepato-protective, and enzyme inhibition activities [14,15,16,17]. In addition to their anti-inflammatory effects, the combination of these dietary plants, suggested by traditional Cameroonian medicine, might provide phytochemicals (mainly polyphenols) with synergistic gastro-protective effects. In this report, we chemically characterized the compounds present in each extract through the reversed-phase-high performance liquid chromatography (RP-HPLC), ultra-performance liquid chromatography-triple time-of-flight electrospray ionization tandem mass spectroscopy (UPLC-Triple TOF-ESI-MS/MS), and Fourier transform infrared spectrophotometer (FTIR) analyses. In addition, the gastro-protective activity and mode of action of the extracts in combination were investigated in human GES-1 and AGS cells.
## 2.1. Chemicals and Reagents
The HPLC-grade solvents were purchased from Spectrochem Pvt. Ltd., Mumbai (India). MilliQ water prepared by a Millipore water purification system (Merck, Mumbai, India) and ultrapure double distilled were used to prepare reagents and buffers throughout the experiment to prevent metal contamination. HPLC analytical grade syringic acid, chlorogenic acid, gallic acid, protocatechuic acid, p-hydroxybenzoic acid, catechin, caffeic acid, coumaric acid, epicatechin, gallate, quercetin, ferulic acid, kaempferol were used for chromatographic analysis. All remaining chemicals and solvents were obtained from Sigma Chemicals Co. (St. Louis, MO, USA). Human epithelial adenocarcinoma cells (AGS, CRL-1739) were purchased from LGC Standard S.r.l. ( Milan, Italy). Gastric non-tumoral epithelial cells (GES-1) were a kind gift from Dr. Dawit Kidane-Mulat (University of Texas, Austin, TX, USA). Roswell Park Memorial Institute (RPMI 1640) medium and Dulbecco’s Modified Eagle’s Medium/F12 (DMEM)/F12 (1:1) were purchased from Gibco (Life Technologies Italia, Monza, Italy). 1,1-Diphenyl-2-picryl-hydrazyl (DPPH), 3,3′,5,5′-tetramethylbenzidine (TMB), 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), Folin–Ciocalteu reagent (FCR), and 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) were from Sigma Aldrich (Milan, Italy). Life Technologies Italia (Monza, Italy) provided penicillin, streptomycin, L-glutamine, sodium pyruvate, trypsin-EDTA, and Lipofectamine 2000. Human IL-8 and IL-6 ELISA Development Kits were manufactured by Peprotech Inc. (London, UK). Fetal bovine serum (FBS) and disposable materials for cell culture were purchased by Euroclone (Euroclone S.p. A., Pero-Milan, Italy). All reagents were at the highest grade available.
## 2.2. Sample Preparation
Xylopia parviflora Spruce, *Tetrapleura tetraptera* (Schum. and Thonn.) Taub, *Dichrostachys glomerata* (Forssk.) Chiov., and *Aframomum citratum* (C.Pereira) K.Schum were harvested as previously described by Nwakiban et al. [ 14,18]. The plant material was cleaned, taken to dryness, and stored at room temperature. The air-dried and powdered samples (100 g) were subjected to magnetic stirring with 100 mL of a hydroalcoholic (ethanol: water, 70:30) mixture for 4 and 6 hrs at room temperature, in dark conditions. The solvent was removed through a rotary evaporator (Laborota 4000 efficient, Heidolph Instruments, Schwabach, Germany), and subjected to lyophilization. The lyophilized extracts were maintained at 4 °C, and 10 mg were freshly dissolved in 1 mL of HPLC grade or MS grade methanol for chemical characterization. Each extract and its combination (MIX) (mixed in the same proportion) were dissolved in pure dimethyl sulfoxide (DMSO), aliquoted, and stored at −80 °C. For cell treatment and anti-inflammatory assays under sterile conditions, the final concentration of DMSO added to the cells was not above $0.1\%$.
## 2.3. Quantification of Phenolic Compounds through HPLC-PDA
The phenolic compounds content within each extract was analyzed as described above [17] by HPLC (Nexera X-2 LC-30A, Shimadzu, Japan), and chromatography separations were carried out on a Chromasol™ RP-C18 column (250 mm × 4.6 mm, 5 µm). The mobile phase was made up of water set at a pH of 2.65 with acetic acid (solvent A) and solvent B ($20\%$ solvent A and $80\%$ acetonitrile). The extracts were dissolved in HPLC-grade methanol and filtered through a syringe filter (0.45 µm PVDF, Millipore, MA, USA). A 20 µL volume for each standard or sample was injected into the HPLC system, and a linear elution gradient was applied in the following manner: 0–$20\%$ B in 0–35 min, 20–$50\%$ B in 35–40 min, 50–$100\%$ B in 40–45 min, and 100–$0\%$ B in 45–60 min. The temperature of the column was kept at 20 °C. The flow rate was 1.2 mL/min, and the PDA detector was adjusted to λ1 = 280 and λ2 = 320 simultaneously using LabSolutions software (version 6.50, Shimadzu, Japan). The identified phenolic compounds were quantified using the peak area obtained from the standards, comparing their retention times with those of corresponding standards and by spiking samples with appropriate standards.
## 2.4. UPLC-ESI-HRMS/MS Analysis
The extracts were diluted with MS-grade methanol and injected directly into the UPLC-ESI-HRMS/MS system with an ekspert 110 binary pump, an ekspert 110-XL autosampler, an ekspert PDA detector, and ekspert 110 column compartment (AB Sciex Instruments, Netherlands) via a syringe pump. The Kinetex C18 100A (30 × 2.1 mm 1.7 μm) Column (Phenomenex) was used for separation at a flow rate of 0.4 mL/min. The mobile phase was made up of a combination of two solvents: A ($0.1\%$ acetic acid in water) and B ($0.1\%$ acetic acid in acetonitrile and methanol in the ratio 8:2) [17]. The mass spectra analyses were performed in negative ion mode in the m/z range from 100 to 2000 m/z at an IRDx resolution of 15,000 using an LC/MS/MS Quadrupole-TOF Hybrid Mass Spectrometer (Sciex Triple ToF 5600, Singapore) and under the following conditions: gases were GS1-45, GS2-60, and Curtain GAS (CUR)-40, source voltage was 4.0 kV, Duospray ion source was set at 400 °C, and the collision energy was 10 V. CID-MS/MS experiments were carried out on mass-selected precursor ions using standard isolation and excitation configuration. All data acquisition and analysis were performed using the Peak View 2.1 Software (AB SCIEX Triple TOF 5600, Singapore), which has MasterView™ (Version 1.0, AB SCIEX). The Master View’s XIC management tool was used to detect quasi-molecular weights, mass errors, and isotope patterns of both targeted and non-targeted compounds. ChemSpider, elemental composition analysis, as well as literature review, were used to define consistent tentative structures of the identified compounds.
## 2.5. Fourier Transform Infrared Spectroscopic (FTIR) Analysis
Dried powder from various extracts of each plant material was used for FTIR analysis. Approximately 10 mg of each extract were loaded into the FTIR spectroscope (Bruker, Germany), which is connected to a computer operating on a Windows system and to an OPUS software (Version 7.0 Bruker Optic). The spectra were recorded at a resolution of 4 cm−1 from 4000 to 400 cm−1 using 64 co-added scans. All spectra were subtracted against a background of air spectra. The attenuated total reflectance (ATR) plate was carefully cleaned by scrubbing with isopropyl $70\%$ twice, then drying with soft tissue before being filled with the following sample. The spectra were recorded as absorbance values at each data point in triplicate and two times for spectrum confirmation.
## 2.6. Estimation of Polyphenols and Antioxidant Activity Assays
The polyphenol estimation and antioxidant capacity assays were modified and translated into 96-well plates based on the methods used in previous literature reports [19,20,21]. The stock solution (100 μg/mL) of each extract was prepared for experimental analysis and the data was measured using a UV–VIS spectrophotometer (Shimadzu, Model: UV 2100, Kyoto, Japan). All tests were run in triplicate at least three times.
## 2.6.1. Total Phenolic Content Assay
Total phenolic content (TPC) was determined using a Folin-Ciocalteu assay with slight modifications [22]. Briefly, a 25 µL sample was mixed with 25 µL Folin’s reagent (1:3 diluted with water), and 200 µL deionized water was added, then the mixture was incubated at room temperature for 5 min. The reaction mixture was then alkalized by adding 25 µL of sodium carbonate ($10\%$, w/v) and allowed to stand in the dark at 25 °C for 60 min. The absorbance was read at 765 nm against the reagent blank. Gallic acid was used as a standard in a concentration range between 0 and 50 µg/L. TPC was expressed in mg gallic acid equivalents (GAE) per g of dried extracts based on the calibration curve.
## 2.6.2. Total Flavonoid Content Assay
Total flavonoid content (TFC) was determined by the colorimetric method as described by Zhishen et al. [ 23] and slightly modified by Moukette et al. [ 24]. To a 15 µL sample or standard, 45 µL of distilled water, followed by 4.5 µL of sodium acetate ($5\%$ solution) were added. The mixture was left in the darkness at 25 °C for 5 min and 4.5 µL of aluminum chloride ($10\%$ of Al2Cl3) was added. Afterward, 30 µL of 1 mM NaOH and 150 µL distilled water were added to the reaction mixture. The absorbance of the resulting solution was measured at 765 nm wavelength against the reagent blank. Catechin was used as a standard in a concentration range between 0 and 60 µg/L. TFC was expressed in mg catechin equivalents (CE) per g of dried extracts based on the calibration curve.
## 2.6.3. Total Flavonol Content Assay
The total flavonol assay (FC) was adapted from [25,26] with quercetin as the standard compound. Briefly, 80 µL of the samples were mixed with 80 µL of aluminum chloride ($2\%$ of Al2Cl3) diluted in ethanol and 120 µL of 50 g/L sodium acetate solution. The mixture was incubated at 25 °C for 2.5 hrs and the absorbance was measured at 440 nm. Concentrations (0–60 µg/mL) of quercetin dissolved in ethanol were used to draw the standard curve. The results were expressed as mg quercetin equivalents (QE) per g of dried extracts based on the calibration curve.
## 2.6.4. 1,1-Diphenyl-2-picryl-hydrazyl (DPPH) Assay
The DPPH scavenging activity of extracts was determined based on the method of Sogi et al. [ 27] and modified from the method reported by Peng et al. [ 20]. Samples were dissolved in methanol and tested at concentrations between 1 and 100 µg/mL. An aliquot of a 40 µL sample was mixed with 260 µL of 0.1 mM DPPH radical methanolic solution in a 96-well plate and incubated for 30 min at 25 °C. Afterward, the absorbance was measured at 517 nm against the reagent blank. Concentrations ranging from 0 to 50 µg/mL ascorbic acid dissolved in water were used to draw the standard curve. The results were expressed as mg ascorbic acid equivalents (AAE) per g of dried extracts and the IC50 concentration showing $50\%$ radical scavenging activity was determined.
## 2.6.5. 2,2′-Azinobis-(3-ethylbenzothiazoline-6-sulfonic Acid) (ABTS) Assay
The ABTS antioxidant activity of spice extracts was carried out using the ABTS+ radical cation decolorization assay with some modifications [28]. To 5 mL of 7 mmol/L of ABTS solution, 88 µL of a 140 mM potassium persulfate solution was added to produce ABTS+. The mixture was allowed to stand in the dark at 25 °C for 16 hrs, then 0.5 mL of the ABTS+ solution was diluted by adding 45 mL analytical grade ethanol to obtain an initial absorbance of 0.70 ± 0.02 at 734 nm. A 10 µL sample extract and 290 µL prepared ABTS+ solution were mixed in a 96-well plate and incubated at 25 °C for 6 min in the darkness. The absorbance was measured at 734 nm against the reagent blank. Concentrations from 0 to 15 µg/mL Trolox were used to draw the standard curve. The results were expressed as mg Trolox equivalents (TE) per g of dried extracts and the IC50 concentration showing $50\%$ radical scavenging activity was determined.
## 2.7. Cell Culture
Human non-tumoral gastric epithelial cells (GES-1) and human adenocarcinoma gastric epithelial cells (AGS) were respectively grown in RPMI 1640 and DMEM/F12 media supplemented with L-glutamine 2 mM, streptomycin 100 mg/mL, penicillin 100 units/mL, and $10\%$ heat-inactivated fetal bovine serum, at 37 °C in a humidified atmosphere containing $5\%$ CO2.
## 2.8. Cytotoxicity Assay and Cell Treatment
Cell viability was measured as previously described by Nwakiban et al. [ 5], after 6 h of co-treatment with the stimulus (TNFα, 10 ng/mL) and the combination (MIX) of extracts (assessed in the range 0.1–10 μg/mL), by the 3,4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide (MTT) method. Briefly, the medium was removed from each well at the end of the treatment and 200 μL of MTT solution (0.1 mg/mL) was added for 45 min at 37 °C in dark conditions. The formazan salt was extracted from the cells with 200 μL of a mixture of DMSO: isopropanol (10:90), and the absorbance was measured at 570 nm (Envision, PerkinElmer, Walthman, MA, 02451, USA). To study the release of pro-inflammatory mediators (IL-8 and IL-6) and the NF-κB driven transcription, cells were seeded in 24-well plates at a density of 30,000 cells/well. Seventy-two hours later, cells were co-treated with the pro-inflammatory stimulus (TNFα, 10 ng/mL) and the MIX extracts for 6 h using serum-free medium: DMEM/F12 or RPMI 1640 medium, supplemented with penicillin 100 units/mL, L-glutamine 2 mM, and streptomycin 100 mg/mL. At the end of the treatment, the media were collected for the biological assays. All tests were run in triplicate at least three times.
## 2.9. Transient Transfection Assay
Gastric epithelial cells were transiently transfected with the reporter plasmid NF-κB-Luc [29], which contains three κB responsive elements controlling the luciferase gene. AGS cells were transfected by the calcium phosphate method, whereas GES-1 cells were transfected using Lipofectamine® (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). The day after, the cells were treated for 6 hrs with the MIX extracts and TNFα (10 ng/mL), as previously described. Then, cell lysis and the luciferase enzymatic reaction were conducted through BriteliteTM Plus reagent (PerkinElmer Inc., Milan, Italy), according to the manufacturer’s instructions. The results (mean ± SD of at least three experiments) were expressed as relative% with respect to the luminescence of the pro-inflammatory conditions ($100\%$).
## 2.10. Measurement of IL-8 and IL-6 Secretion
IL-8 and IL-6 were quantified using two different ELISA kits, as previously reported [5], according to the manufacturer’s instructions. Briefly, Corning 96 well EIA/RIA plates (Sigma-Aldrich, St. Louis, MO, USA) were coated with the corresponding antibody and incubated overnight at room temperature. Then, cell media and biotinylated antibodies were added to construct a sandwich ELISA. The cytokines were measured in the samples at 450 nm through the colorimetric reaction between horseradish peroxidase enzyme and 3,3′,5,5′-tetramethylbenzidine substrate (Sigma-Aldrich, St. Louis, MO, USA) using a spectrophotometer (Victor X3, PerkinElmer, Walthman, MA, 02451, USA). Epigallocatechin-3-O-gallate (EGCG, 20 μM) was used as a reference inhibitor, for its ability to decrease both TNFα-induced IL-8 and IL-6 secretion [4,5,9].
## 2.11. Synergistic Effect Analysis of Extracts in Combination
The Chou Talalay equations [30,31] and the CompuSyn software (version 1.0; ComboSyn, Paramus, NJ, USA) were used to determine the combination index (CI) and the dose reduction index (DRI). The half-maximal inhibitory concentration (IC50) (µg/mL) of every single extract on IL-6, IL-8, and NF-κB has already been obtained during our latest research work [5]. The CI was used to determine the types of drug interactions in which CI < 1 indicates a synergistic effect, CI = 1 indicates an additive effect, and CI > 1 represents an antagonistic effect. The Equation [1] below was used to calculate the CI for the combination of the extracts:[1]CI=IC50 of Extract A in combinationIC50 of Extract A in monotherapy+…+IC50 of Extract D in combinationIC50 of Extract D in monotherapy The dose reduction index (DRI) was calculated using the following Formula [2], by measuring how many times each extract could be reduced in the combination compared to monotherapy:[2]DRIExtract A=IC50 of Extract A in monotherapyIC50 of Extract A in combination
## 2.12. Statistical Analysis
All results are expressed as mean ± SD. Statistical data were determined with a one-way analysis of variance (ANOVA) followed by the multiple comparison analysis performed with the Bonferroni post hoc test. To compare data obtained from RP-HPLC, the Waller–Duncan test of SPSS software (IBM Corporation, NY, USA, Version 25) was used to test for differences in means. Pearson’s test was applied to understand the correlation between the antioxidant variables through the XLSAT software (Version 2022). Statistical analyses were calculated, and graphs were prepared using GraphPad Prism 9.0 software (GraphPad Software Inc., San Diego, CA, USA).
## 3.1. Characterization of Compounds in Extracts by UPLC-ESI-HRMS/MS Analysis
The UPLC-ESI-HRMS/MS profiles of X. parviflora, D. glomerata, T. tetraptera, and A. citratum hydro-ethanolic extracts are shown in Figure S1 and the chromatographic, MS, and MS/MS data are reported in Table 1. The structure of 33 compounds present in the four spice extracts from Cameroon was tentatively identified using the combined interpretation of the fragmentation patterns and the retention time obtained from the UPLC-ESI-HRMS/MS analysis.
## 3.1.1. Phenolic Acids
Nine compounds belonging to two different classes of phenolic acids have been identified in the extracts (two hydroxybenzoic acids and six hydroxycinnamic acids) (Table 1). Caftaric acid derivatives were identified in D. glomerata (compound 9b, tR = 16.05 min) and T. tetraptera (compound 11c, tR = 16.03 min) extracts with [M−H]− at m/z 311.2 (Figure S1B,C). They displayed similar MS2 fragmentation patterns (Table 1) that produced a fraction at m/z 149 [M−H−162]−, thus indicating the loss of a caffeoyl moiety [32]. Protocatechuic acid 4-O-glucoside (compound 4a, tR = 15.25 min), gallic acid monohydrate (compound 1a, tR = 11.59 min), and derivative (compound 7a, tR = 17.53 min) were identified as the hydroxybenzoic acids present in A. citratum (Figure S1C). Compound 1a showed a [M−H]− at m/z 187.1 and it produced two fractions, one base peak at m/z 125 [M−H−CO2−H2O]− and another at m/z 169 [M−H−H2O]−, characteristics of gallic acid [33]. Compound 7a, with a [M−H]− at m/z 473.3 and fragment at m/z 311 [M−H−162]−, obtained after a loss of caffeoyl moiety, was assigned as chicoric acid, a hydroxycinnamic acid derivative, while compound 4a displayed parent ion at m/z 315.3 and fragment ion at m/z 141 [M−H−162−CO]− with the cleavage of the O-sugar bond (Table 1) [21,34]. Compound 8b (tR = 15.10 min) and compound 5a (tR = 15.75 min) (Figure S1B,C) revealed molecular ions at m/z 325.2 and m/z 295.2, respectively (Table 1). Based on these data and comparing retention times and MS2 fragments, they were tentatively assigned to hydroxycinnamic acid compounds [17,21]. Furthermore, compound 6d (tR = 12.58 min) and compound 10d (tR = 15.65 min) (Figure S1D) in X. parviflora spectra exhibited [M−H]− at m/z 335.2 and m/z 339.2, respectively (Table 1) The MS2 fragmentation patterns of compound 6d are typical of caffeoylshikimic acid [35]. Compound 10d showed fragment ions at m/z 183 [M−H−156]− characterized for methylgallate residue, and at m/z 197 [M−H−146]− corresponding to the loss of one deoxyhexose, which indicated the presence of caffeoyl tricarboxylic acid isomers [33]. Compound 11d (tR = 16.96 min) was conditionally identified as an isomer of ellagic acid with a deprotonated molecule at m/z 301.2, based on its fragmentation pattern, including the characteristic aglycone fragment (Figure S1D) [36].
## 3.1.2. Flavonoids
Flavonoids were present mostly as anthocyanins, flavanols, flavanonols, flavanones, flavones, flavonols, and isoflavonoids (Table 1).
Ten flavanols (compounds 5c, 7c, 8c, 1c, 2c, 3c, 4c, 2d, 8d and 9d) were identified in the hydro-ethanolic extracts. Compound 5c ([M−H]− at m/z 841.5, tR = 12.54 min), compound 7c ([M−H]− at m/z 865.5, tR = 13.49 min), and compound 8c ([M−H]− at m/z 825.5, tR = 13.60 min) were tentatively assigned as B-type proanthocyanidin trimers (Figure S1C), due to the fragmentation sequences of their molecular ions which yielded MS2 ions at 441 [M−H−162−162−76]−, 751 [M−H−90]− for compound 5c, 658 [M−H−162−CH2O−CH3]−, 640 [M−H−120−90−CH3]− for compounds 7c and 617 [M−H−120−2CO2]−, 735 [M−H−90]− for compounds 8c, indicating the presence of a C-hexosyl unit that producing 0, 2 and 0, 3 cross ring cleavage (Table 1) [37,38]. Compound 1b (tR = 8.36 min) and compound 2d (tR = 8.41 min) (Figure S1B,D), with similar [M−H]− at m/z 289.1, showed characteristic MS2 fragments at m/z 245 [M−H−CO2]− (loss of carboxyl group), m/z 109 [M−H−162−H2O]− (loss of a caffeoyl moiety and water), and m/z 203 [M−H−60− CO2]− (cleavage of the A-ring of flavan-3-ol) (Table 1). Therefore, those compounds were identified as (+)-catechin [33,38]. Another compound was detected at tR = 8.94 min (compound 2b) in D. glomerata (Figure S1B) and exhibited a [M−H]− at m/z 561.2. The MS2 spectrum gave intense ions at m/z 271 [M−H−288]− (loss of galloyl residue), and other fragment ions at m/z 289 and m/z 245, which are similar to those obtained for compound 1b (Table 1). These results led to identifying compound 2b as a catechin monogallate [38]. Compound 3b (tR = 9.14 min) with a [M−H]− at m/z 729.2 generated an MS2 fragment ion at m/z 289 [M−H−152−288]− corresponding to the loss of a galloyl group followed by the loss of an (epi)catechin molecule or to the loss of an (epi)catechin gallate (m/z 289 [M−H−152−288]−) (Table 1). Based on the obtained mass spectrometry sequences, this compound was tentatively identified as procyanidin dimer monogallate [38,39]. The MS2 spectrum of compound 4b (tR = 9.62 min), with [M−H]− at m/z 441.2 produced ions at m/z 169 [M−H−152−120] and 289 [M−H−152]− (Table 1) corresponding to the deprotonated ions of gallic acid and (epi)catechin, respectively. Compound 4b also displayed similar MS2 fragmentation patterns that produced a fraction at m/z 245 and m/z 271 which is characteristic of (epi)catechin monogallate [19]. Compound 8d (tR = 14.37 min) (Figure S1A), with [M−H]− at m/z 315.2 was identified as an isomer of isorhamnetin by comparison of their MS2 fragmentation spectra [33]. Compound 9d (tR = 15.33 min) showed a [M−H]− at 505.2 and produced fragmentation sequences at m/z 359 [M−H−146]−, m/z 373 [M−H−132]−, m/z 417 [M−H−2CO2]− corresponding to the loss of a pentoxyl and deoxyhexosyl unit (Table 1). Based on these data, it was assigned to quercetin 7-O-pentosyl-deoxyglucoside.
Four flavanones, compounds 1c (tR = 11.56 min), compounds 5b (tR = 10.32 min), 2a (tR = 11.72 min), and 4d (tR = 10.14 min) were respectively identified in T. tetraptera, D. glomerata, A. citratum, and X. parviflora extracts (Figure S1A–D). Compounds 5b, 2a, and 4d showed similar [M−H]− at m/z 433.1 (Table 1). Compounds 5b and 2a gave typical fragmentation pattern at m/z 269 [M−H−146−H2O]− (loss of moiety of O-linked rhamnose) and m/z 287 [M−H−146]− (loss of a deoxyhexose moiety), characteristic of eriodictyol deoxyhexose derivatives [40]. Compounds 4d generated MS2 fragments at m/z 271 [M−H−162]− indicating a loss of caffeoyl moiety, and according to the literature [17,21,33], this compound was assigned to naringenin-7-O glucoside. Compound 1c gave a [M−H]− at m/z 271.1. This compound was tentatively characterized as naringenin based on its MS2 fragmentation at m/z 151 [M−H−120]− indicating the presence of a C-hexosyl unit [33].
Three flavones, compound 10c (tR = 15.30 min), compound 10a (tR = 18.94 min), and compound 3d (tR = 9.57 min) were respectively identified in T. tetraptera, A. citratum, and X. parviflora extracts (Figure S1A,C,D). Compounds 10c showed a [M−H]− at m/z 359.2 with a loss of ethoxy group (245 [M−H−C2H2O]−) (Table 1), corresponding to the fragmentation of a trihydroxyflavone isomer. Compound 10a was tentatively assigned as a 5,7 dimethoxyflavone isomer with a deprotonated molecule at m/z 281.3 (Table 1) [41], while compound 3d gave a [M−H]− at m/z 563.3 corresponding to apigenin 7-O-apiosyl-glucoside [42]. This was confirmed by the presence of fractions at m/z 383 [M−H−162−18]− with the loss of an O-hexosyl moiety and a molecule of water, and at m/z 329 [M−H−269−60]−, corresponding to the deprotonation of an apigenin residue (Table 1).
Compound 9c (tR = 14.18 min), compound 9a (tR = 18.75 min), compound 7d (tR = 14.05 min), with [M−H]− at m/z 726.4, m/z 255.5, m/z 465.2 respectively (Figure S1A,C,D), were identified as anthocyanin, dihydroflavonol, and flavanonols compounds by comparison of their chromatographic retention times and MS2 fragmentation spectra. Compound 9c (Table 1) exhibited a fragment ion at m/z 656 [M−H−CO−C2H2O]− and suggests the characteristic fragmentation of cyanidin 3-xylosylrutinoside isomer [43]. Compound 7d (Table 1) produced MS2 ions at m/z 319 [M−H−146]− and 377 [M−H−2CO2]− corresponding to the loss of moiety of O-linked rhamnose and a carboxyl group, was tentatively assigned taxifolin hexoside isomer [33].
## 3.1.3. Other Compounds
In addition to the phenolic compounds described above, an alkylmethoxyphenol, compound 6b ([M−H]− at m/z 221.1, tR = 12.63 min), and a phytolaccagenic acid isomer, compound 3a ([M−H]− at m/z 809.3, tR = 11.94 min) were also detected in D. glomerata and A. citratum extracts, respectively (Figure S1A,B). The above compounds 6b and 3a respectively produced MS2 spectra with losses of water molecules and aliphatic residues (Table 1), which complied with the published literature [21,44]. No tentative identification could be proposed for compounds 6a ([M−H]− at m/z 566.4, tR = 15.91 min), 8a ([M−H]− at m/z 279.1, tR = 18.18 min), 7b ([M−H]− at m/z 704.5, tR = 14.25 min), 2c ([M−H]− at m/z 1107.6, tR = 11.86 min), 3c ([M−H]− at m/z 1191.7, tR = 12.03 min), 4c ([M−H]− at m/z 1189.7, tR = 12.22 min), 6c ([M−H]− at m/z 820.4, tR = 13.14 min), 12c ([M−H]− at m/z 758.6, tR = 20.02 min), 1d ([M−H]− at m/z 374.1, tR = 7.39 min), and 5d ([M−H]− at m/z 333.2, tR = 12.46 min), since it was not possible to assign the obtained ion fragments.
**Table 1**
| Peak No | (RT) (min) | [M–H]- (m/z) | Fragment Ions | Tentative Identification(References) |
| --- | --- | --- | --- | --- |
| Compound fractions from Aframomum citratum (C.Pereira) K.Schum | Compound fractions from Aframomum citratum (C.Pereira) K.Schum | Compound fractions from Aframomum citratum (C.Pereira) K.Schum | Compound fractions from Aframomum citratum (C.Pereira) K.Schum | Compound fractions from Aframomum citratum (C.Pereira) K.Schum |
| 1a | 11.594 | 187.1 | 125.1045, 169.0948, 186.5114,187.1070. | Gallic acid monohydrate [33] |
| 2a | 11.722 | 433.1 | 433.1337, 269.0577, 152.0185, 259.0729, 287.0682. | Eriodictyol rhamnoside [40] |
| 3a | 11.941 | 809.3 | 165.0630, 175.0473, 176.0558, 191.0805, 405.1739. | Isomer of phytolaccagenic acid [21,44] |
| 4a | 15.255 | 315.3 | 141.1357, 279.2450, 297.2568, 313.2524, 315.2688. | Protocatechuic acid 4-O-glucoside [21,34] |
| 5a | 15.755 | 295.2 | 171.1097, 195.1476, 277.2295, 294.2950, 295.2412. | p-Coumaroyl tartaric acid derivative [17,21] |
| 6a | 15.910 | 566.4 | 224.0794, 242.0891, 281.2615, 506.3491, 566.3742. | Unknown |
| 7a | 17.553 | 473.3 | 109.0714, 311.0407, 357.3319, 385.3647, 429.3557, 473.3481. | Gallic acid derivative [34] |
| 8a | 18.181 | 279.1 | 223.1274, 278.3513, 279.458. | Unknown |
| 9a | 18.755 | 255.2 | 254.4282, 255.2440. | Dihydroxyflavanone derivative [36] |
| 10a | 18.949 | 281.3 | 280.3598, 281.2613. | Isomer of 5,7-dimethoxyflavone [41] |
| Compound fractions from Dichrostachys glomerata (Forssk.) Chiov | Compound fractions from Dichrostachys glomerata (Forssk.) Chiov | Compound fractions from Dichrostachys glomerata (Forssk.) Chiov | Compound fractions from Dichrostachys glomerata (Forssk.) Chiov | Compound fractions from Dichrostachys glomerata (Forssk.) Chiov |
| 1b | 8.365 | 289.1 | 109.0356, 203.0802, 245.0925, 271.0730, 289.0849. | (+)-Catechin [33,38] |
| 2b | 8.943 | 561.2 | 245.0922, 289.0844, 271.0722, 407.0940, 561.1649. | Gallocatechin derivative [38] |
| 3b | 9.143 | 729.2 | 125.0309, 289.0844, 407.0948, 451.1226, 729.1808. | Procyanidin dimer monogallate [38,39] |
| 4b | 9.621 | 441.2 | 169.0216, 245.0924, 271.0730, 289.0846, 441.1005. | (Epi)catechin gallate [19] |
| 5b | 10.328 | 433.1 | 180.0144, 259.0720, 269.0570, 287.0682, 433.1322. | Eriodictyol rhamnoside [40] |
| 6b | 12.633 | 221.1 | 149.1036, 221.0931, 221.1284. | Alkylmethoxyphenol derivative [21,44] |
| 7b | 14.255 | 704.5 | 100.0458, 202.0810, 455.3721, 656.3367, 658.4625. | Unknown |
| 8b | 15.107 | 325.2 | 170.0116, 183.0205, 197.0362, 324,1567, 325.1992. | p-Coumaric acid 4-O-glucoside [17,21] |
| 9b | 16.058 | 311.2 | 148.6320, 149.1049, 310.2184, 311.2156. | Caftaric acid derivative [32] |
| Compound fractions from Tetrapleura tetraptera (Schum. and Thonn.) Taub | Compound fractions from Tetrapleura tetraptera (Schum. and Thonn.) Taub | Compound fractions from Tetrapleura tetraptera (Schum. and Thonn.) Taub | Compound fractions from Tetrapleura tetraptera (Schum. and Thonn.) Taub | Compound fractions from Tetrapleura tetraptera (Schum. and Thonn.) Taub |
| 1c | 11.565 | 271.1 | 151.0110, 270.2059, 271.0739. | Naringenin [33] |
| 2c | 11.861 | 1107.6 | 657.4731, 817.5348, 961.5842, 979.5956, 1061.6415. | Unknown |
| 3c | 12.037 | 1191.7 | 655.4725, 817.5347, 961.5837, 979.5950, 1145.7018. | Unknown |
| 4c | 12.225 | 1189.7 | 491.3951, 563.4203, 815.5194, 959.5690, 977.5805. | Unknown |
| 5c | 12.545 | 841.5 | 441.3565, 603.4182, 751.500, 765.4799, 795.4926. | Proanthocyanidin trimer [37,38] |
| 6c | 13.142 | 820.4 | 628.4523, 684.3904, 686.4044, 730.3969, 774.3895. | Unknown |
| 7c | 13.492 | 865.5 | 455.3725, 640.4509, 658.4626, 776.5315, 820.5249. | Proanthocyanidin trimer [37,38] |
| 8c | 13.607 | 825.5 | 161.0527, 455.3738, 617.4362, 735.5049,779.4983. | Proanthocyanidin trimer [37,38] |
| 9c | 14.1867 | 726.4 | 100.0453, 656.3380, 658.4661, 726.4573. | Isomer of cyanidin 3-xylosylrutinoside [43] |
| 10c | 15.301 | 359.2 | 178.9161, 317.2262, 358, 0867, 359.2388. | Isomer of trihydroxyflavone [41] |
| 11c | 16.031 | 311.2 | 133.0731, 149.1050, 310.2188, 311.2661. | Caftaric acid derivative [32] |
| 12c | 20.029 | 758.6 | 89.0280, 119.0410, 532.4972, 550.5090, 712.5726. | Unknown |
| Compound fractions from Xylopia parviflora Spruce | Compound fractions from Xylopia parviflora Spruce | Compound fractions from Xylopia parviflora Spruce | Compound fractions from Xylopia parviflora Spruce | Compound fractions from Xylopia parviflora Spruce |
| 1d | 7.395 | 374.1 | 166.0585, 207.0604, 328.1169, 374.1243, 374.2026. | Unknown |
| 2d | 8.413 | 289.1 | 203.0798, 205.0592, 245.0918, 271.0716, 289.0837. | (+)-Catechin [33,38] |
| 3d | 9.571 | 563.3 | 329.1557, 383.2043, 561.4585, 563.276. | Apigenin 7-O-apiosyl-glucoside [42] |
| 4d | 10.148 | 433.1 | 255.0398, 271.0352, 300.0391, 301.0474, 433.0944. | Naringenin hexoside [21,33] |
| 5d | 12.463 | 333.2 | 332.1515, 333.2203. | Unknown |
| 6d | 12.583 | 335.2 | 334.1605, 335.2356. | Caffeoylshikimic acid [35] |
| 7d | 14.052 | 465.2 | 318.2117, 319.2397,377.1922, 463.6790, 465.1749. | Isomer of taxifolin hexoside [33] |
| 8d | 14.376 | 315.2 | 314.1987, 315.2095, 315.2620. | Isomer of isorhamnetin [33] |
| 9d | 15.332 | 505.2 | 300.5674, 359.2367, 373.1619, 417.1890, 461.1799, 505.1717. | Quercetin 7-O-pentosyl-glucoside [45] |
| 10d | 15.652 | 339.2 | 170.0111, 183.0200, 197.0361, 338.1263, 339.2138. | Caffeoyl tricarboxylic acid isomer [33] |
| 11d | 16.964 | 301.2 | 300.2623, 301.0084, 301.2291. | Isomer of ellagic acid [36] |
## 3.2. Quantitative Determination of Some Phenolic Compounds in Extracts
Phenolic compounds have always been an integral part of the secondary metabolites found in most plant extracts. During phenolic profiling by RP-HPLC, the presence of seven phenolic compounds was observed in the extracts: the most represented in each plant were underlined by statistical analysis (Table 2). The qualitative composition, with regards to the selected phenols, was only partially similar among plants. One phenolic acid, i.e., caffeic acid (7.07 ± 1.45 µg/100 mg of extract) was determined in X. parviflora extract. In addition, two flavanols, i.e., catechin (2.95 ± 0.70 µg/100 mg of extract) and epicatechin (106.91 ± 0.67 µg/100 mg of extract) were identified, with epicatechin significantly (p ≤ 0.05) higher than the two other compounds. According to the present study, it was reported in other recent papers [14] the presence of catechin compounds (amount of $3.31\%$) in the X. parviflora extract using GC-MS analysis. In A. citratum, three phenolic acids, i.e., protocatechuic acid (31.12 ± 0.96 µg/100 mg of extract), p-coumaric acid (8.49 ± 1.66 µg/100 mg of extract), rosmarinic acid (4.62 ± 0.15 µg/100 mg of extract) and kaempferol (111.79 ± 0.22 µg/100 mg of extract), were significantly (p ≤ 0.05) higher than the other compounds. [ 17] reported that the p-coumaric acid identified in this study was exactly 4-O glucoside p-coumaric acid. As well as in A. citratum, T. tetraptera contained kaempferol (13.96 ± 0.90 µg/100 mg of extract). Only two phenolic acids i.e., p-coumaric acid (22.39 ± 1.23 µg/100 mg of extract) and rosmarinic acid (22.03 ± 0.13 µg/100 mg of extract) were identified in D. glomerata. In comparison with the UPLC-ESI-HRMS/MS analysis of D. glomerata extract, no catechin molecules were found. It was therefore interesting to note that even other bioactive compounds were detectable and quantified.
## 3.3. FTIR Analysis of Extracts
The wavenumber ranges of the FTIR peaks and functional groups of each extract were determined by comparing them to the previous reports [46,47,48] (Table 3). The FTIR spectrum of A. citratum, D. glomerata, T. tetraptera and X. parviflora extracts (Figure S2A–D) showed absorption signals for 12 wavenumber or wavenumber ranges, which were identified as components in the samples namely phenols at 3276 cm−1, 3233 cm−1, 3283 cm−1, 3232 cm−1 (O–H), lipids at 1710–1733 cm−1, 1724 cm−1 (C=O), proteins at 1693 cm−1 (C=O, C–N), polysaccharides and carbohydrates at 2853–2924 cm−1, 2927 cm−1, 2928 cm−1, 2928 cm−1 (Csp3–H (CH2–H)), phenyl groups at 1443–1601 cm−1, 1604 cm−1, 1602 cm−1, 1603 cm−1 (C=C), amino acids at 1377 cm−1, 1372–1518 cm−1, 1372–1518 cm−1 (N–H, C–N), acids or esters at 1153–1238 cm−1, 1143–1284 cm−1, 1157–1236 cm−1 (Csp2–O (O–C=O or O–C–O)), alcohols at 1036 cm−1, 1040 cm−1, 1030 cm−1, 1065 cm−1 (Csp3–O (C–OH)), aromatic compounds at 674–867 cm−1, 767–867 cm−1, 776–886 cm−1, 663–886 cm−1 (cis C–H), isoprenoids at 420–617 cm−1, 423–636 cm−1, 419–630 cm−1, 427–626 cm−1 (cis C–H). Among these absorption signals, the FTIR spectra of T. tetraptera and X. parviflora extracts (Figure S2A,D) showed specific wavenumber ranges, namely aromatic secondary amines at 1259–1371 cm−1 (C–N) and mono–, oligo– carbohydrates, oligosaccharides, and glycoproteins at 926 cm−1, 931–979 cm−1 (trans C–H, Csp3–O (C–OH)). However, there were four signals, in which their function groups were still unknown, i.e., 1918–2350 cm−1, 1917–2350 cm−1, 1918–2350 cm−1, and 1768–2326 cm−1. In corresponding previous reports, it has been reported that hydro-ethanolic extracts from the plants used in this study contained phenols, lipids, polysaccharides (generally in the form of glycosides), and aromatic compounds which are also consistent with the results obtained by the UPLC-ESI-HRMS/MS analysis [14,17,18]. Furthermore, these results may be due to the different solvation properties of ethanol and water.
## 3.4. Polyphenol Estimation (TPC, TFC, and FC Content) and Antioxidant Activities (DPPH and ABTS) of Extracts
The polyphenol content in each extract was measured as TPC, TFC, and FC (Table 4) and the results are expressed as gallic acid, catechin, and quercetin equivalents, respectively. Among the extracts, D. glomerata and X. parviflora possessed the highest TPC, with 282.62 ± 3.88 and 271.18 ± 7.10 mg gallic acid equivalents (GAE)/g respectively, followed by T. tetraptera (150.33 ± 0.036 mg gallic acid equivalent (GAE)/g) and A. citratum (129.36 ± 2.13 mg gallic acid equivalent (GAE)/g) [18,49] reported similarities and discrepancies between their findings on the same plants compared to this study. This may arise from different experimental protocols (i.e., organic solvent extraction vs. aqueous extraction) or the harvest period of plant material [16]. A similar trend, but with lower values in TFC was observed in each extract with 35.43 ± 1.33, 114 ± 1.32, 48.73 ± 4.38, 96.61 ± 0.86 mg catechin equivalents (CE)/g for A. citratum, D. glomerata, T. tetraptera, and X. parviflora respectively, indicating that the majority of phenolics present in extracts are flavonoids. Regarding the FC estimation, extracts showed values with a range of 0.58 to 0.21 mg quercetin equivalent (QE)/g, D. glomerata values (0.58 ± 0.14 mg quercetin equivalent (QE)/g) significantly (p ≤ 0.05) higher than the others. This study was similar and comparable with several studies on cultivars, including mango, blueberry, strawberry, raspberry, grapes, garlic, and ginger previously reported by [19,21,50].
The antioxidant capacities of each extract were evaluated through DPPH and ABTS methods. As shown in Table 4, D. glomerata showed the highest ABTS free radical scavenging activity (IC50 = 5.28 µg/mL) with 51.57 ± 0.74 mg Trolox equivalent (TE)/g of extract. D. glomerata was respectively followed by X. parviflora (IC50 = 14.01 µg/mL, 47.06 ± 1.05 mg TE/g of extract), T. tetraptera (IC50 = 28.15 µg/mL, 60.47 ± 1.05 mg TE/g of extract) and A. citratum (IC50 = 29.21 µg/mL, 52.66 ± 2.66 mg TE/g of extract). As previously noted, D. glomerata showed the highest DPPH antioxidant potential (IC50 = 15.06 µg/mL) with 254.30 ± 0.15 mg ascorbic acid equivalent (AAE)/g of extract, followed by X. parviflora (IC50 = 20.38 µg/mL, 253.54 ± 1.88 mg AAE/g of extract), A. citratum (IC50 = 41.04 µg/mL, 184.88 ± 0.14 mg AAE/g of extract) and T. tetraptera (IC50 = 45.67 µg/mL, 218.08 ± 1.20 mg AAE/g of extract). In both ABTS and DPPH assays, D. glomerata and X. parviflora extracts showed higher antioxidant potential when compared to the others. This could be due to a greater amount of antioxidant compounds (i.e., phenolic and flavonoid) contained in those extracts that likely could contribute to a strong antioxidant potential [51]. In addition, it is known that the level of antioxidant activity depends on the type and concentration of the phenolic compounds present because their structures greatly affect their bioactivity. Similar experimental procedures and findings were also reported by [49,52,53,54] when investigating the antioxidant activities of some dietary plants.
## 3.5. Correlations of Antioxidant Assays with Phenolic Compounds
The correlations between the phenolic estimation and antioxidant assays were carried out using the principal component analysis (PCA) and Pearson’s correlation test as shown in the supplementary data (Figure S3 and Table S1). The total phenolic acids and flavonoids were calculated by summing the proposed compounds from the HPLC-PDA quantitation to provide an idea of the overall correlations between all phenolic compounds and antioxidant tests. Polyphenols not found in the HPLC-PDA were not considered. Here, $90.16\%$ variability of the initial data was kept by the first two main components (PC1 = $51.61\%$ and PC2 = $38.52\%$, respectively) (Figure S3). DPPH and ABTS were highly correlated (p ≤ 0.05) to both TPC and TFC with positive correlation coefficients of R ≥ 0.96 (Table S1). These strong correlations suggested that polyphenols were the major contributors to the scavenging activity of the plant extracts. Moreover, the correlation between TPC and TFC parameters (R > 0.9, p ≤ 0.05) indicated that flavonoids were the main antioxidant polyphenols. Similar findings were also reported by [18,19,21,55] who studied the polyphenolic composition of some spice extracts. Furthermore, it is observed that DPPH and ABTS tests were positively correlated since Pearson’s correlation coefficient was $R = 0.97$ (p ≤ 0.05) between the assays (Table S1), in line with previous studies [56,57,58]. Since DPPH is only applicable to hydrophobic systems due to the use of a radical dissolved in organic media, the strong correlation with ABTS indicated that additional and less hydrophilic compounds may also contribute to the scavenging effect [59]. On the contrary, the HPLC-detected phenolic acids were negatively correlated (p ≤ 0.05) with the antioxidant assays (Table S1), suggesting that within the selected samples in this study, the phenolic acids do not significantly contribute to the antioxidant activities, maybe because of vitamins (carotenoids, vitamin C, vitamin E) and antioxidant heteropolysaccharides and polypeptides.
## 3.6. Cytotoxicity and Effect of the MIX on the TNFα-Induced NF-κB Driven Transcription in AGS and GES-1 Cells
These studies were performed using two models of gastric epithelial cells (AGS and GES-1 cells) (Figure S4) stimulated with TNFα (10 ng/mL), a cytokine that contributes to the inflammatory process during gastric epithelium infection. The cytotoxicity of extracts in combination (namely MIX) was assessed in the concentration range of 0.1–10 µg/mL through the MTT assay (Figure 1A). Similar to our earlier study [5], in which extracts were used as a single treatment, no cytotoxic effects of extracts in combination were observed in the cell cultures after 6 h treatment (Figure 1A). Extracts in combination (MIX) were investigated for their ability to inhibit the TNFα-induced NF-κB driven transcription in a concentration-dependent manner (Figure 1B). The MIX was tested in the range of 0.1–10 µg/mL and the corresponding IC50 values were calculated (Table 5). In AGS cells, the MIX significantly impaired the activation of NF-κB driven transcription with more than $95\%$ reduction (p ≤ 0.001) observed at 10 µg/mL and IC50 (0.7 µg/mL) below the previously reported IC50s (2.14–9.67 µg/mL) for each plant [5]. In GES-1 cells, the MIX at concentrations ranging between 5 and 10 µg/mL, significantly (p ≤ 0.01) impaired the activation of NF-κB driven transcription (32.14–$62.39\%$ reduction). The IC50 (4.9 µg/mL) value was higher compared to the one obtained in AGS cells, but below IC50s (5.22–12.17 µg/mL) for each plant summarized in Table 5. The treatment with the reference compound (20 μM EGCG) yielded a significant (p ≤ 0.001) inhibition of NF-κB driven transcription in AGS (>$70\%$ reduction) and GES-1 (>$50\%$ reduction) cells, which was higher than the MIX at concentrations below 1 µg/mL). Moreover, the basal concentration of the NF-κB driven transcription was not affected by the extracts used as single or in combination (all tested at 10 µg/mL) (Figure S5). The chemical analysis of each extract reported the presence of many groups of compounds (polyphenols, carbohydrates, lipids, and proteins). Based on this characterization, these metabolites can act as NF-κB inhibitors, such as epigallocatechin, quercetin, cyanidin, chlorogenic acid, and catechins, identified in some extracts. Several studies reported that phenolic acids stimulate the inhibition of NF-κB activation and macrophage infiltration, resulting in the reduction of inflammation in vitro and in animal models [60,61,62,63]. In addition to their role in food intake regulation and nutrition absorption, a growing body of evidence supports that flavonoids counteract NF-κB and inducible nitric oxide synthase (iNOS) signaling pathways, resulting in reduced oxidative damage and inflammation [64,65].
## 3.7. The Combination of Extracts (MIX) Inhibits TNFα-Induced IL-8 and IL-6 Release in AGS and GES-1 Cells
IL-8 and IL-6 are well-known NF-κB-dependent cytokines that are implicated in the gastric inflammatory process [66,67]. The MIX at concentrations ranging between 0.1 and 10 µg/mL, inhibited IL-8 release in AGS and GES-1 cells with different IC50s (Figure 2A, Table 5). In AGS cells, the MIX was more active at 5 and 10 µg/mL (the highest concentrations tested) compared to the reference compound (20 μM EGCG, >$58\%$ reduction) and significantly (p ≤ 0.001) inhibited the release of IL-8 ($90\%$ reduction) with an IC50 (0.27 µg/mL) below the one of A. citratum (0.35 µg/mL), T. tetraptera (1.37 µg/mL), X. parviflora (0.30 µg/mL), but over the IC50 of D. glomerata (0.29 µg/mL) used as single. This value was lower in AGS cells compared to GES-1 cells (IC50 > 10 µg/mL), as noted earlier in the NF-κB driven transcription results. However, the IC50s of the extracts used as single were lower in GES-1 cells (2.30–8.37 µg/mL) in comparison to the IC50 of the MIX. In this study, no detectable IL-6 secretion was induced in the AGS cells (data not shown); thus, only GES-1 cells were considered. The MIX also inhibited the IL-6 release in GES-1 cells with an IC50 of 1.8 µg/mL (Figure 2B, Table 5), a value which was below the IC50s (3.47–5.11 µg/mL) of the extracts used as single. In addition, as noted in AGS cells, the MIX was more active at 5 and 10 µg/mL (the highest concentrations tested) compared to the reference compound (20 μM EGCG, around $40\%$ inhibition). This suggests that it can reduce the release and gene expression of NF-κB-dependent pro-inflammatory mediators, which contribute to the amplification of the gastric inflammatory process.
## 3.8. Synergistic Effect of Extracts in Combination on AGS and GES-1 Cells
Combined therapy has shown a variety of advantages over monotherapy, including decreasing the concentration and toxicity of drugs, improving efficiency, targeting multiple molecular pathways, and sensitizing cells to treatment [68]. As the above statements make clear, the definition of synergy can overlap with potentiation. However, a concrete definition derives only from a mathematical approach, shown and proven by different methodologies, such as Berenbaum’s pioneering work, the isobologram method of Loewe, the fractional product method of Webb, the combination index method of Chou and Talalay, which provided the foundation for its use in pharmacology and phytopharmacology [69]. Therefore, in the current study, we tested the synergistic effects of the extracts on inflammatory markers involved in gastric inflammation. The effects of each extract (used as a single) on the TNFα-induced cytokines (IL-8, IL-6) release and NF-κB driven transcription of AGS and GES-1 cells, previously reported by Nwakiban et al. [ 5] and their combination are shown in Table 5. The CI values of IL-8, IL-6 release, and NFκB-driven transcription were more than one (6.18) in GES-1 cells and 0.82 (slight synergism) in AGS cells; 0.46 (synergism) in GES-1 cells; 0.61 (synergism) in GES-1 cells and 0.18 (strong synergism) in AGS cells respectively, which in overall indicates a synergistic effect of the MIX on cells against the gastric inflammatory process. Moreover, the means DRI of extracts in combination were 0.80 ± 0.40 in GES-1 and 8.18 ± 8.13 in AGS cells for IL-8 release; 9.22 ± 1.91 in GES-1 cells for IL-6 release; 14.28 ± 4.6 in GES-1 and 28.74 ± 11.58 in AGS cells for NFκB driven transcription, which suggests a very high-fold dosage reduction compared to each extract in monotherapy, i.e., eight-fold dosage reduction for IL-8 release in AGS cells and fourteen-fold dosage reduction for the NF-κB driven transcription in GES-1 cells. the MIX has been noted to act antagonistically in GES-1 cells on NF-κB transcription (CI > 1), however, it was found to exert an NF-κB inhibition activity in a concentration-dependent fashion (Figure 1B). Overall, the biological activities of the MIX were lower in AGS cells compared to GES-1 cells as was previously reported [5]. This could be attributed to the differences in mutation and differentiation of GES-1 and AGS cells, but also due to the differences in their genetic profiles [31]. Furthermore, the natural bioactive compounds found in each extract have a more structural diversity that could more effectively inhibit certain targets of gastric disorders (gene expression of proinflammatory cytokines). They also inherently target other biologically relevant NF-κB pathways, i.e., enzymes such as prostaglandin-endoperoxide synthase 2 (PTGS2) (COX-2), because many natural bioactive compounds are secondary metabolites or signaling molecules [5,70]. Based on the above findings, it is suggested that the MIX contains various types of bioactive compounds that act in synergy in human gastric epithelial cells by two or more mechanisms (synergistic multi-target effects and elimination or neutralization potential).
## 4. Conclusions
In this study, a comparative analysis of the chemical composition of each hydroalcoholic extract through RP-HPLC, UPLC-ESI-HRMS/MS, and FTIR indicates that they mostly contain a great number of phenolic compounds, but also proteins, lipids, polysaccharides (generally in the form of glycosides) and aromatic compounds. The extracts showed a high amount of total phenolic (TPC: 150–290 mg GAE/g of extract) and flavonoid content (TFC: 35–115 mg CE/g of extract) with antioxidant properties in a cell-free system (DPPH IC50s ≤ 45 µg/mL; ABTS IC50s ≤ 29 µg/mL). The extracts in combination (MIX) exert a synergistic beneficial effect (CIs < 1 and DRIs > 1) on inflammatory markers (IL-8, IL-6 release, and NF-κB driven transcription) in human gastric epithelial cells which may be due to the presence of phenolic compounds (mostly phenolic acids and flavonoids). Among the phenolic compounds, phenolic acids (hydroxybenzoic and hydroxycinnamic acids) and flavonoids (anthocyanins, flavanols, flavanonols, flavanones, flavones, flavonols, and isoflavonoids) which promoted an antioxidant potential, have been reported in the extracts. The MIX enhances the efficacy of extract used in monotherapy and reduces the potential adverse effects related to high concentrations, as indicated by high DRI. Beyond the plausible pharmacodynamic interaction among phenolic compounds, further studies are required to assess other possible mechanisms of synergistic interaction, and to in vivo confirm the synergistic effect herein reported. To the best of our knowledge, this study provides a scientific basis for the traditional practice of using a combination of extracts useful to alleviate gastric inflammation.
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|
---
title: 'Taking Placebos as Needed to Reduce Appetite: A Randomized Controlled Trial
with Ecological Momentary Assessment'
authors:
- Isabella Unger
- Anne Schienle
journal: Behavioral Sciences
year: 2023
pmcid: PMC10045637
doi: 10.3390/bs13030207
license: CC BY 4.0
---
# Taking Placebos as Needed to Reduce Appetite: A Randomized Controlled Trial with Ecological Momentary Assessment
## Abstract
Placebos can reduce appetite. However, when placebos are prescribed over a longer period of time, compliance and response rates are not always satisfactory. A new administration approach ‘as needed’ was tested to improve adherence to placebo treatment and its effectiveness. Participants could decide on the time of placebo intake (when their appetite had increased substantially). A randomized controlled trial was conducted over seven days. The participants were allocated to one of two groups: a placebo group (PG; $$n = 41$$) or a control group with no placebo treatment (CG; $$n = 34$$). During the intervention, participants used a mobile phone application to rate their daily appetite, mood, and the occurrence of binge-eating episodes in their normal environment. The placebo effect was short-lived; the placebo reduced self-reported appetite only on days 1 and 2 of the trial. The placebo neither influenced mood nor binge-eating frequency. This study found an app-assisted approach with continuous monitoring to be helpful for identifying the temporal course of the placebo response. Future placebo trials should implement this method.
## 1. Introduction
Food-cue exposure can have powerful effects on appetite and eating. Even brief exposure to the sight and smell of food has been shown to increase reported appetite and craving, as well as planned and actual food consumption; this is especially so in individuals high in food cue reactivity (FCR; for a meta-analytical review see [1]). Given the high density of food cues in our environment, it is not surprising that overeating has increased dramatically over the last decades [2]. The results of this include higher levels of overweight and obesity, which increase the risk for certain somatic diseases (e.g., diabetes, heart disease, and some cancers [3]) and mental disorders (e.g., depression [4]). It may be possible to reduce these negative health effects if efficient and easy-to-use treatment strategies to decrease FCR (e.g., pertaining to appetite) were available.
Currently, there are various pharmacological treatments on offer to reduce appetite. However, appetite suppressants often have undesirable side effects, such as gastrointestinal irritation and cardiovascular problems [5]. One therapeutic approach, which does not possess these negative side effects, is placebo treatment. Placebos are inert substances or interventions with no specific effect on the symptoms being treated [6]. The appetite-reducing effects of placebos have been identified in placebo-controlled clinical trials of appetite suppressants [7]. Furthermore, intake of placebos compared to no treatment has been shown to reduce appetite in healthy individuals [8,9] and patients with binge-eating disorder [10].
In studies with healthy weight participants, single-dose placebos to change appetite have been implemented successfully. For example, Potthoff et al. [ 9] exposed females to pictures depicting combinations of food and non-food items, which were shown once after placebo intake (’appetite suppressant’) and once without placebo intake in a repeated-measures design. The placebo reduced reported appetite as well as the viewing time of the food images. In another study, Hoffmann et al. [ 8] assigned participants to one of three groups. One group received no placebo (control), whereas the other two received a placebo labeled as an ‘appetite enhancer’ or ‘appetite suppressant.’ Relative to the comparison groups, the ‘appetite suppressant’ reduced reported appetite and increased satiety.
In both of those studies, the placebo was taken only once, which does not mirror the typical administration of appetite suppressants in clinical trials. Thus, in order to evaluate the capacity of a placebo treatment to change FCR, longer intervention intervals are necessary. For instance, Jacobs-Pilipski et al. [ 10] conducted a four-week-long placebo trial with 451 participants who had been diagnosed with binge-eating disorder (BED). Only $32\%$ of the patients with BED were identified as placebo responders, who reported a reduced frequency of binge-eating episodes (overeating with loss of control). The remaining participants were classified as placebo non-responders. In another longer-term placebo study, Tippens et al. [ 11] conducted an investigation with adults with obesity ($$n = 114$$) who were randomized into three groups. Participants in one group were told that they would receive an active ‘weight-loss supplement’ (WLS), in another group participants were told that they would receive a WLS with a $50\%$ chance of it being a placebo, and in the third group, participants received no placebo treatment. After three months, the amount of weight loss did not differ between the three groups. Notably, data from $29\%$ of participants from the WLS group could not be analyzed (e.g., because of nonadherence to the study protocol or reported loss of interest in the study). Furthermore, participants in the ‘weight-loss supplement’ group reported a decline in experienced self-efficacy throughout the study.
In sum, longer-term placebo trials aiming at appetite reduction need to be improved to counteract common problems, such as low placebo response rates, non-adherence, and even drop out. To garner greater placebo effects and increase compliance, a new approach for placebo administration was tested. This novel method of placebo intake ‘as needed’ aimed at helping the participants to regain control over their food intake in critical moments. Participants were instructed to use the ‘medication’ when they felt that their appetite had increased substantially. Along these lines, this study implemented ecological momentary assessment (EMA), which involves the repeated sampling of current experiences and behaviors of individuals in their normal environment [12]. In this present study, along with a seven-day intervention, a mobile phone application (app) was used to monitor participants’ daily appetite, binge eating, and mood. A pop-up function reminded participants each evening to complete their ratings. This app-assisted approach was applied to receive more detailed and continuous ratings/feedback throughout the placebo intervention. Participants were randomly allocated to one of two groups that received either a daily placebo (natural medicine to reduce appetite) or no placebo treatment. It was hypothesized that the placebo group would report less appetite and binge eating as well as improved mood during the seven-day trial compared to the control group. In addition, the placebo group should exhibit greater compliance (i.e., more completed app ratings) relative to the control group.
## 2.1. Participants
A total of 75 participants (63 female) with a mean age of $M = 27.41$ years (SD = 8.99) and a body mass index (BMI) of $M = 25.87$ (SD = 4.62) were recruited from a community sample in Austria through advertisements on social media and fliers in supermarkets and restaurants. There was no financial reimbursement for research participants.
People were invited to participate in this study if they reported high motivation to reduce episodes of overeating (value of 5 or higher; assessed with a ten-point Likert scale ranging from 1 (no motivation) to 10 (strong motivation)). Exclusion criteria were reported diagnoses of eating disorders. Screening for exclusion criteria was conducted via LimeSurvey, an online survey tool. Individuals who reported lifetime and/or current diagnoses of eating disorders, and those who had a BMI < 18.5, were not invited to participate in this study ($$n = 3$$; see Figure 1). Six participants reported using antidepressants. They were not excluded from the sample because exclusion did not change the results.
In this parallel trial, participants were randomly assigned (with a random number table) by the researchers involved in this study to one of two groups: a control group with no placebo treatment (CG; $$n = 34$$), and a placebo group (PG; $$n = 41$$). The two groups did not differ in mean age, BMI, reported food cravings (assessed with the Food Craving Questionnaire—Trait reduced (FCQ-T-r) [13]; Cronbach’s alpha = 0.94), psychological problems (assessed by the Brief Symptom Inventory (BSI) [14]), or participants’ motivation to reduce their overeating (Table 1).
The conducted sensitivity analysis (G*Power [15]) indicated that with a sample size of $$n = 75$$ and a power of $80\%$ (α = 0.05) effects of d > 0.58 can be detected.
## 2.2. Procedure
Written informed consent was obtained from all participants. This study was performed following the recommendations of the declaration of the World Medical Association of Helsinki (revised version, 2000) and the Good Clinical Practice (GCP)—Guidelines (CPMP/ICH/$\frac{135}{95}$, Final Approval by CPMP $\frac{17}{07}$/96). The project was approved by the ethics committee of the University (ethical approval code: GZ. $\frac{39}{12}$/63 ex $\frac{2019}{20}$).
In the first diagnostic session, participants were asked to come into the lab one by one. All participants completed screening for psychological problems (BSI) and food cravings (FCQ-T-r) and reported their body weight, height, and demographic data (age, education level, and somatic illnesses). The handling of the app was explained to each participant.
Afterwards, the placebo group received water with green food coloring provided in a 30 mL glass bottle with a dropper for oral administration. The food color was calorie-free, sugar-free, and azo-free. The placebo was introduced as herbal medicine (wild garlic: allium ursinum). It was suggested that this substance reduces appetite and overeating. It was further explained that oral application of the fluid (instead of a pill) enables quicker absorption of active agents into the bloodstream and therefore a faster physical response. Additionally, it was mentioned that the herbal medicine had successfully been tested in a clinical trial before. Participants were instructed to take the placebo orally as needed (5 drops) when their appetite had increased substantially. Moreover, participants received a leaflet with information about the placebo. Participants of the CG were instructed to continue their usual eating behavior.
At the end of the 7-day intervention, all participants of the PG rated the perceived effectiveness of the placebo (0: not effective–100: very effective) and returned the placebo bottle to measure the amount of placebo intake (mL). In the CG, participants did not know about the PG and vice versa. All participants were fully debriefed about the study design and the use of the placebo after study completion.
## 2.3. The App
To understand eating behaviors in an ecologically valid way, we used our own in-house custom-programmed app to assess appetite, food cravings, and binge eating on a daily basis in individuals’ natural environment. Data gathering was achieved by combining a PWA (Progressive Web App; Paris, France) and a remote server for storage. The survey was a web page created with HTML, CSS, and JavaScript (using the Vue.js Framework). The anonymous data were sent to a remote server where a Python Flask script handled the data collection and created a CSV file for each participant.
Participants were reminded via a pop-up function from the app every evening to answer questions concerning their mood during the day (valence; from very negative [0] to very positive [100]), appetite (“How hungry were you during the day?” 0: not at all–100: very much), and binge eating (“How many eating attacks did you have today?”).
## 2.4. Statistical Analyses
We first conducted t-tests to screen for possible differences between PG and CG in mean age, BMI, psychological problems, reported food cravings, and participants’ motivation to reduce their overeating. Then we performed a compliance analysis comparing the number of completed app ratings between groups (t-test). To test the effect of Group (PG, CG) on appetite, mood, and frequency of binge-eating attacks across the one-week trial (mean), we computed t-tests. We report Cohen’s d as effect size measure. Alpha was set at 0.05 for statistical significance. Controlling for demographics (age, BMI, gender) did not change the results and was therefore not included in the analyses. To further examine the effects of Group on hunger and mood we conducted mixed-model analyses using the GAMLj package [16] of jamovi (version 2.2.5 [17]). All models included Group (PG, CG) and Day (1–7) as factors with intercept as the random coefficient. The model info was Appetite~1+group+day+group:day+(1|id) and Mood+1+group+day+group:day+(1|id).
Exploratory correlation analyses were conducted for the PG to test the association between the amount of placebo intake, mood, BMI, the motivation to reduce overeating, and the perceived effectiveness of the placebo.
## 3.1. Compliance
In total, $92\%$ of the participants used the app every day over the one-week course of this study. There was no significant difference concerning the number of completed app ratings between groups across the study interval (t[73] = −0.962, $$p \leq 0.332$$; d = −0.22, $95\%$ CI [−0.68, 0.24]; Table 1).
## 3.2. Appetite
Over the one-week trial, there was no difference in reported appetite between groups (t[73] = 1.69, $$p \leq 0.10$$; $d = 0.39$, $95\%$CI [−0.07, 0.85]; Table 1).
The mixed-model analysis showed no significant main effects of Group (F(1,71.8) = 2.41, $$p \leq 0.125$$) or Day (F(6423.3) = 1.73, $$p \leq 0.113$$) but did find a significant interaction between Day and Group (F(6423.3) = 2.54, $$p \leq 0.02$$; see Figure 2). A simple effect was detected on day 1, indicating a reduced appetite of 15.4 points in the PG compared to the CG ($95\%$ CI [−25.60, −5.27], $$p \leq 0.003$$) and on day 2, indicating a reduced appetite of 12.2 points in the PG compared to the CG ($95\%$ CI [−22.34, −2.16], $$p \leq 0.017$$). There were no other significant simple effects for the remaining days (all $p \leq 0.144$).
Footnote: PG: Placebo Group, CG: Control Group; error bars indicate the standard error of the mean.
## 3.3. Frequency of Binge Eating (FOBE)
There was no difference in the number of binge-eating episodes between groups (t[73] = 1.08, $$p \leq 0.28$$; $d = 0.25$, $95\%$ CI [−0.21, 0.71]; Table 1, Figure S1) across the study interval.
## 3.4. Mood
There was no difference in mood between groups (t[73] = −1.01, $$p \leq 0.32$$; d = −0.24, $95\%$ CI [−0.69, 0.23]; Table 1, Figure S1) across the study interval. The mixed model analysis did not show any significant results (Group: F(1,70.8) = 0.970, $$p \leq 0.328$$; Day: F(6422.0) = 0.659, $$p \leq 0.683$$; Group*Day: F(6422.0) = 0.230, $$p \leq 0.967$$).
## 3.5. Ratings of Placebo Effectiveness and Placebo Intake
The following results describe only the placebo group because the control group did not receive a placebo. The perceived effectiveness for the placebo was $M = 51.67$ (SD = 22.63). Over the study interval, participants took the placebo $M = 9.42$ times (SD = 9.42; range: 0–48) and the mean amount of placebo intake was 2.4 mL (SD = 2.4, range: 0–12 mL). Three participants who had been assigned to the PG ($7\%$) did not take the placebo.
The amount of placebo intake correlated with mood (r = −0.37, $$p \leq 0.018$$) and participants’ BMI ($r = 0.38$, $$p \leq 0.013$$). Participants took the placebo more often when they were in a bad mood and participants with a higher BMI showed higher placebo intake. There was no significant association between the amount of placebo intake and participants’ motivation to reduce overeating ($r = 0.18$, $$p \leq 0.268$$) or the perceived effectiveness of the placebo (r = −0.017; $$p \leq 0.921$$).
## 4. Discussion
This study investigated the effects of placebo treatment (‘natural appetite suppressant’) on participants’ appetite, frequency of binge-eating episodes, and mood during a one-week intervention. To enhance placebo effects, the ‘appetite suppressant’ could be taken as needed (in case of an increased desire to eat). Moreover, we used an app-assisted approach to obtain continuous feedback from the participants and to capture the temporal dynamics of the placebo effect.
Up until now, temporal dynamics of the placebo response have rarely been studied. A recent placebo-controlled trial involved repeated measurements. However, the temporal resolution of the assessment was low [18]. Data were collected at baseline, week 6, and week 12 of that study. This study implemented daily assessments and found the placebo effect to be short-lived. The placebo reduced appetite on day 1 and day 2 of this study. Starting with day 3, the two groups did not differ in their appetite ratings. While the positive effects of single-dose placebos for reducing appetite have been reported before [8,9], placebos administered in long-term trials have been less effective. For example, in a study by Tippens et al. [ 11], a placebo prescribed as an appetite suppressant did not promote weight loss over the three-month study interval. To further understand the temporal dynamics of the placebo effect, more longitudinal placebo studies using a daily app-assisted approach are needed.
Moreover, poor adherence rates or even drop-out are problems related to longitudinal placebo studies on appetite reduction [11,19]. In our study, compliance was very good; a high percentage ($92\%$) of participants completed all of the required app ratings over the trial. One reason might be participants’ high motivation to reduce overeating reported before the trial. Another reason might be the introduction of a placebo ‘as needed.’ Whereas prescribed daily placebo intake might be considered an obligation, placebos administered ‘as needed’ have a voluntary character and aim at helping participants to regain control in challenging times. In line with this intention, placebo intake was higher when participants were in a bad mood and when they had a higher BMI.
However, participants took the placebo on average only nine times during the one-week trial (approximately once a day). Three participants did not take the placebo at all. These participants indicated a low to moderate level of appetite during the trial. Just being able to take an ‘appetite suppressant’ when needed may have already increased their sense of (appetite) control. This hypothesis should be followed up in future research focusing on attitudes and motivations to take (or not take) placebos ’as needed.’ Even though we only recruited participants indicating a high motivation to change their eating behavior (particularly overeating/binge eating), participants in both groups reported a low level of eating attacks over the trial. This demonstrates that continuous monitoring of one’s behavior can serve not only as an assessment method but also as an intervention. Studies on clinically relevant behaviors (e.g., cigarette smoking, alcohol intake, insomnia) found that self-monitoring changes the frequency of the dysfunctional behavior/symptom in the desired direction [20,21,22]. Similar findings have been reported by Latner and Wilson [23], who found a self-monitoring effect on the number of binge-eating episodes. Participants kept continuous records of their food intake, which was sufficient to substantially decrease binge frequency. On that account, using a daily app-assisted approach to obtain continuous feedback from the participants might have worked as an intervention itself in our study.
Several limitations need to be considered when interpreting the present results. First, we tested a sample of predominantly women with overweight but also included individuals with normal weight. Even though including BMI as a covariate did not change our results, greater placebo effects might be expected in individuals with overweight/obesity because of greater motivation for weight loss. Second, we did not monitor eating behavior. Future placebo studies with ‘appetite suppressants’ should additionally assess participants’ food consumption (e.g., calorie intake, amount of food eaten). Third, we used colored water as a placebo instead of placebo pills. Wager and Altlas [24] argue that beneficial treatment experiences in the past as well as positive expectations are needed for meaningful placebo effects. Since pills are used more often as medication than liquids, placebo pills might be more effective. Finally, we administered the placebo with a deceptive suggestion (introduced as herbal medicine). This approach has ethical issues that can be circumvented by using open-label placebos, which have already been successfully applied for various conditions (for a review see [25]).
## 5. Conclusions
Previous studies testing single-dose placebos to change appetite have shown that this approach works in laboratory settings. However, this approach lacks ecological validity. Therefore, this study investigated responses to a placebo that could be taken in the normal environment of the participants ‘as needed.’ The placebo effect on appetite was short-lived (two days). Moreover, the novel ‘as needed’ approach was not associated with a high amount of placebo intake; participants took the placebo approximately once a day. Participants showed a high level of compliance independent of the group assignment. Over $90\%$ of participants used the app every day over the one-week course of this study. This demonstrates that ecological momentary assessment is very useful to monitor the temporal dynamics of placebo responses. Thus, future placebo trials should implement this method.
The current placebo approach needs to be optimized. Subsequent studies on appetite regulation via placebos could use different verbal suggestions (medication instead of natural medicine), and different types of placebos (pills instead of liquids) to boost the placebo effect. Moreover, individuals with a diagnosis of binge-eating disorder or bulimia nervosa might show greater placebo responses. While in the present investigation, the number of reported binge-eating episodes was low, participants with more frequent binge eating might profit more from placebos that can be taken as needed.
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|
---
title: Seasonal Changes in the Biochemical Composition of Dominant Macroalgal Species
along the Egyptian Red Sea Shore
authors:
- Marwa Kamal
- Neveen Abdel-Raouf
- Khairiah Alwutayd
- Hamada AbdElgawad
- Mohamed Sayed Abdelhameed
- Ola Hammouda
- Khaled N. M. Elsayed
journal: Biology
year: 2023
pmcid: PMC10045638
doi: 10.3390/biology12030411
license: CC BY 4.0
---
# Seasonal Changes in the Biochemical Composition of Dominant Macroalgal Species along the Egyptian Red Sea Shore
## Abstract
### Simple Summary
Macroalgae play a significant role as primary producers in marine ecosystems. The most dominant species at the three collection sites studied along the Egyptian Red Sea were seasonally harvested. Among several species collected, five dominant macroalgae (Caulerpa prolifera, Acanthophora spicifera, Cystoseira myrica, *Cystoseira trinodis* and Turbinaria ornata) were selected for further studies. These macroalgae were identified using morphological and molecular characteristics. During summer and winter, the mineral content and biochemical composition of the selected macroalgal species were evaluated. These analyses indicated that macroalgae are rich in minerals as well as primary and secondary metabolites. Moreover, the findings reported that the macroalgae studied possess high nutritional value in the summer more than in the winter season.
### Abstract
Macroalgae are significant biological resources in coastal marine ecosystems. Seasonality influences macroalgae biochemical characteristics, which consequentially affect their ecological and economic values. Here, macroalgae were surveyed from summer 2017 to spring 2018 at three sites at 7 km (south) from El Qusier, 52 km (north) from Marsa Alam and 70 km (south) from Safaga along the Red Sea coast, Egypt. Across all the macroalgae collected, *Caulerpa prolifera* (green macroalgae), *Acanthophora spicifera* (red macroalgae) and Cystoseira myrica, *Cystoseira trinodis* and *Turbinaria ornata* (brown macroalgae) were the most dominant macroalgal species. These macroalgae were identified at morphological and molecular (18s rRNA) levels. Then, the seasonal variations in macroalgal minerals and biochemical composition were quantified to determine the apt period for harvesting based on the nutritional requirements for commercial utilizations. The chemical composition of macroalgae proved the species and seasonal variation. For instance, minerals were more accumulated in macroalgae C. prolifera, A. spicifera and T. ornata in the winter season, but they were accumulated in both C. myrica and C. trinodis in the summer season. Total sugars, amino acids, fatty acids and phenolic contents were higher in the summer season. Accordingly, macroalgae collected during the summer can be used as food and animal feed. Overall, we suggest the harvesting of macroalgae for different nutrients and metabolites in the respective seasons.
## 1. Introduction
The Red *Sea is* known to be the northernmost tropical sea in the world, possessing a remarkable geography [1]. It is considered a landlocked and largely unperturbed marine ecosystem, which is situated in one of the world’s hottest places along a small basin separating the continents of Asia and Africa [2]. Its coastal areas of Egypt are very interesting to many researchers [3]. This is because the coastal areas of the Red Sea possess more biotope and species diversity than the Mediterranean Sea and the world’s oceans [4]. The Red Sea ecosystem comprises macroalgae, mangroves and coral reefs [1]. Macroalgae diversity performs an important ecological role through the cycling of carbon, nitrogen and phosphorus, which results in the regulation of marine water quality [5].
Based on their pigmentation, morphology, anatomy and biochemical composition [3], macroalgae are classified into three categories: red (Rhodophyta), brown (Phaeophyta or Ochrophyta) and green (Chlorophyta) [6]. Each class of macroalgae is characterized by particular kinds of pigments, which give them their definite colors as well as distinctive group names [7]. Globally, more than 4000 species of Rhodophyta, 1500 species of Phaeophyta and 900 species of Chlorophyta have been recorded [8]. Approximately 500 species of macroalgae were listed in the Red Sea [9]. Recently, the macroalgal biomass in the Red Sea recorded an apparent increase, which may be attributed to nutrient enrichment from urban and aquaculture outflow, as well as reduction in herbivores [2]. It is well known that the surrounding environment can influence the biodiversity and abundance of macroalgal flora, allowing some species to predominate over others [2,10].
Seaweeds are marine macroalgae that inhabit the littoral zone [11]. Seaweeds are characterized as non-vascular plants, which represent the primary producers in oceans and belong to the Protista not Planta kingdom [3]. They grow from intertidal to shallow coastal waters, in addition to deep waters, up to 180 m in depth [12]. They can provide oxygen, food resources and shelter substrates for many aquatic organisms. The floristic composition of marine macroalgae [13], in addition to their distribution and periodicity sequence, can be used for estimating several ecological changes [14]. For example, they help in reducing ocean acidity and offer a solution to global warming [15,16]. Moreover, they support the diversity and productivity of some communities because they provide oxygen, food, as well as habitat for many kinds of aquatic biota [12].
Seaweeds attract attention as one of the most biologically active resources in nature due to their great content of bioactive compounds. Macroalgae are known to be a wealthy source of dietary fiber, essential amino acids, nutrients, vitamins, antioxidants and lipids [3,17]. Thus, they are valuable natural sources for fertilizers and plant growth regulators, food commodities, animal feeds and perform a crucial role in agriculture and horticulture [18,19]. Recently, seaweeds have also been used for a variety of purposes, including health benefits, biofuel production, cosmetics, pharmaceuticals, textiles and bioplastic packaging [20,21]. In this context, seaweeds rich in bioactive components [12], including antioxidant, anti-pigmentation, anticancer, anti-wrinkling and antimicrobial activities, have been of particular interest [7,22,23].
The quality and concentration of bioactive compounds of seaweeds depend on various factors, including the season, geographic location, harvesting period, in addition to biotic factors, such as herbivory or direct competition with other organisms, and abiotic factors, such as salinity, temperature, pH and nutrient composition of water [24,25]. These factors could stimulate or inhibit the production of macroalgal bioactive constituents [26]. The ability of macroalgae to produce distinctive secondary metabolites, such as polysaccharides, proteins, lipids and phenolic compounds, enables them to quickly adapt to changes in the marine environment, including temperature and solar radiation [27]. Moreover, the great content of these metabolites in seaweeds may differ significantly according to the taxonomic group, geographical, seasonal and physiological variations [28,29]. The formation of marine macroalgal communities is regulated by a set of restrictions, such as light, depth, temperature and nutritional content. As a result of macroalgal species’ diversity and availability being affected, the marine environment ultimately changes [30]. In the Red Sea, macroalgae are known to be one of the most significant biological resources in coastal marine ecosystems, as well as supporting some communities’ diversity and productivity because of their important role as primary producers in the marine environment [31].
Seaweed communities are considered significant as an indicator of environmental stress, as their distribution and abundance are affected by disturbances, such as desiccation, high temperatures and competition with coastal flora and fauna [32]. Therefore, it is very important to study their variations and distribution at different times and places [33]. In this study, green, red and brown macroalgae species were collected from the Red Sea shore, Egypt, during four seasons. Out of several species collected, five species were selected based on their dominance throughout the four seasons of the year in the geographical locations under investigation. These five selected seaweeds were identified based on morphological and molecular characterization. Then, the biochemical compositions of the selected seaweeds, including primary metabolites (carbohydrates, amino acids (AAs), fatty acids (FAs) and organic acids), secondary metabolites (phenolics) and mineral profiles, were analyzed to evaluate the influence of seasons, i.e., summer and winter. To our knowledge, the present study is the first to evaluate the seasonal impact on the biochemical compositions of macroalgal species. This was also required in order to determine their potential use in human food and other industries.
## 2.1. Collection Sites, Seasonal Climate Conditions and Identification of Macroalgae
Macroalgal specimens were collected from three sites at 7 km (south) from El Quseir (26°2′34.02″ N; 34°18′51.51″ E), 52 km (north) from Marsa Alam (26°11′30.75″ N; 34°13′43.92″ E) and 70 km (south) from Safaga (25°32′56.35″ N; 34°38′16.88″ E) along the Red Sea coast, Egypt, seasonally, from summer 2017 to spring 2018 during low tides when seaweeds are exposed (Figure 1). These sites were selected because [1] they are fertile seacoasts and they are markedly rich in flora and fauna, and [2] there is an absence of industrial activities, as well as [3] a significantly lower population of habitants. The quadrate technique (steel quadrate 100 × 100 cm) was applied for the collection of macroalgal samples from the three collection sites [34]. Five quadrate samples were collected at each site. Macroalgae were harvested at their maturation stage manually and washed thoroughly in sea water to remove potential contaminants, such as adhering impurities, sand particles, rock debris, epiphytes and animal castings. The fresh biomass was collected in polyethylene bags containing sea water to prevent evaporation and washed with tap water followed by distilled water to remove excess salts. The dried samples were fine-powdered using a food mixer and stored in labeled plastic bags for further use [35]. Some of the collected seaweeds were preserved for identification. The relative abundance of each macroalgal species was determined according to the following equation: Abundance % = No of individuals of a given species × 100 ÷ Total no. of all species [36]. The climate conditions of these sites were as follows. Water temperature varied between 15.8 and 18.5 °C in the winter months and 31.7 and 32.7 °C in the summer at day time at the selected sites. During the summer, the pH values were slightly alkaline; they fluctuated between 7.72 during the winter at Site 3 and 7.89 at Site 1 (Table 1). At first, the macroalgal samples collected were identified based on their morphological characteristics with taxonomic references [37]. The morphological identification was followed by molecular identification.
## 2.2. Molecular Identification
DNA was isolated using the Cetyl Trimethyl Ammonium Bromide (CTAB) method from approximately 400 mg of macroalgal powder ground in liquid nitrogen [38]. The purity and concentration of extracted DNA were determined using a spectrophotometer at 260 nm and 280 nm. Purity was measured at the ratio of A 260: A 280 using agarose gel electrophoresis. The purified DNA isolate was amplified through the polymerase chain reaction (PCR) process using 18S rRNA primers (Table 2). Basic local alignment search tool (BLAST) analysis was used to determine similarities in GenBank to confirm the species of the macroalgal samples collected. The National Center for Biotechnology NCBI (blast.ncbi.nih.nlm.gov) was used to carry out this analysis by entering the complete sample sequences into the BLAST analysis. Phylogenetic trees were constructed using the MEGA X program.
## 2.3. Physico-Chemical Analysis of Water Samples
Samples of water (approx. 2 L) were collected from the study sites in clean, plastic bottles and transferred to the laboratory in cold condition. Water temperature and pH were measured in situ using Hydrolab, Model (Multi Set 430i WTW). For the other chemical analysis, water samples were collected and transferred to the laboratory to measure the chemical parameters. Calcium (Ca++), magnesium (Mg++), salinity, total hardness as CaCO3, chloride (Cl−), Sulfate (SO4−), bicarbonate (HCO3−), nitrate (NO3−), total phosphate (TP), copper, zinc and lead were measured following the protocol of the American Public Health Association standard methods (APHA) [39].
## 2.4. Primary Metabolites’ Analysis
The sugars, amino acids (AAs) and fatty acids (FAs) and contents of macroalgal biomass were evaluated and recorded in both seasons (summer and winter). Sugars were measured in an acetonitrile/water (2 mL, 1:1, v/v) extract and determined using high-performance liquid chromatography (HPLC) according to Alasalvar et al. [ 40]. Individual sugars were measured using standard curves built using definite concentrations of standard sugar solutions from 1 to 10 mg/100 mL of acetonitrile/water (1:1, v/v).
The AAs of macroalgal samples were measured according to Sinha et al. [ 41] using 1 mL of $80\%$ (v/v) aqueous ethanolic extract. Seaweed extracts were centrifuged, and then, the supernatant was evaporated under vacuum. Pellets were dissolved in 1 mL of chloroform, and the suspension was re-extracted using 1 mL of HPLC-grade water. Then, the aqueous phase was gathered after centrifugation and filtered using 0.2 μM Millipore microfilters. AAs were analyzed using a Waters Acquity UPLC-tqd system (Milford, Worcester County, MA, USA) equipped with BEH amide 2.1 × 50 columns.
The FAs of macroalgal samples were estimated using GC/MS using aqueous methanolic extract (1:1 w/v) until discoloration occurred according to Torras-Claveria et al. [ 42]. The FAs of macroalgal extracts were identified with GC/MS using a Hewlett Packard 6890, MSD 5975 mass spectrometer (Hewlett Packard, Palo Alto, CA, USA). Different FAs were quantified with the NIST 05 database and plant-specific databases.
The organic acids of macroalgal samples were measured according to De Sousa et al. [ 43]. Samples of macroalgae powder were milled and extracted using $0.1\%$ phosphoric acid containing butylated hydroxyanisole. The internal standard (ribitol) was added during the extraction steps. After centrifugation for 30 min at 14,000 rpm, the supernatant was transferred to new tubes for HPLC evaluation (LaChrom L-7455 diode array, LaChrom, Tokyo, Japan). Methanol was used for samples’ elution as mobile phase A and $5\%$ potassium dihydrogen phosphate (pH 2.5) as mobile phase B at 0.5 mL/min and 40 μL injection volume.
## 2.5. Minerals’ Analysis
Macroalgal samples were digested using HNO3/H2O (5:1 ratio) in an oven. Various minerals were measured using mass spectrometry (ICP—MS Finnigan Element XR; Scientific, Bremen, Germany) according to Ref. [ 44]. Standard mixtures were prepared in $1\%$ nitric acid.
## 2.6. Phenolic Compounds
Total polyphenols and flavonoids were assessed in macroalgal biomass extracted in $80\%$ ethanol. The phenolic content was quantified by the Folin–Ciocalteu method [45], while flavonoids were determined by the modified aluminum chloride colorimetric method [46]. Tocopherols were determined using hexane extract quantified by HPLC according to Siebert et al. [ 47].
## 2.7. Statistical Analysis
The results were expressed as mean ± SD (standard deviation) and analyzed by one-way ANOVA using IBM SPSS Statistical software package (SPSS® Inc., Chicago, IL, USA). In cases of significant interactions between the factors, one-way ANOVA was performed for each factor, and Tukey’s multiple range tests were used to determine significant differences among means between the two seasons of the same species ($p \leq 0.05$). A significance level of $p \leq 0.05$ was used for rejection of the null hypothesis. All experiments were carried out in three replicates ($$n = 3$$).
## 3.1. Macroalgal Species Collection
The dominance of macroalgal species along the Red Sea coast was determined according to the relative abundance of species from all collection sites throughout the year [36]. The green macroalgae (C. prolifera), the red macroalgae (A. spicifera) and the brown macroalgae (C. myrica, C. trinodis and T. ornata) (Figure 2) were the most prominent macroalgal species among all collected macroalgae.
For years, the biodiversity of these seaweeds has been largely classified based on their morphological features [48]. Recent developments have inspired scientists to use molecular approaches to investigate the biodiversity of marine macroalgae [49]. Molecular studies were used by algal taxonomists for species’ discovery and identification, in addition to many routine taxonomic studies [50]. Therefore, the five dominant seaweeds collected were first morphologically identified, followed by molecular identification using 18S rRNA sequencing. The sequences of 18S rRNA were analyzed on NCBI using the BLAST tool to determine the sequences’ percentage of similarity with the sequences in GenBank. All of the obtained sequences corresponded to known macroalgal species with significant sequence similarity. Based on the results of phylogenetic tree analysis, the harvested macroalgal species were closely related to Caulerpa prolifera, red seaweed Acanthophora spicifera, brown seaweeds Cystoseira myrica, C. trinodis and Turbinaria ornata, respectively (Figure 3).
## 3.2. Minerals’ Level Change with Season and Species
Macroalgae accumulate minerals, which are necessary for seaweeds survival, as well as improve their nutritional value and as a medicinal source [32,51]. Fifty-two essential minerals, including macrominerals, such as Na, K, Ca, Mg and P, and trace elements, such as Cd, Fe, Zn, Cu and Mn, were identified. The minerals’ profiles of the five macroalgae investigated in this study exhibited various amounts of essential metals. P, K, Na and Mg were the most abundant elements among the different species. Their concentrations were as the the following ranges: 1.65–5.62 mg/g dry weight (DW), 0.64–2.54 mg/g DW, 0.29–0.91 mg/g DW, and 0.23–0.82 mg/g DW, 0.64–2.54 mg, 0.29–0.91 mg, and 0.23–0.82 mg, respectively. Significant difference at $p \leq 0.05$ was observed between the content of minerals in the summer and winter. C. prolifera and T. ornata had a high content of K in the winter season, but high content of K was recorded in A. spicifera and C. myrica in the summer. C. trinodis, C. prolifera, A. spicifera and T. ornata had a high content of P in the winter, but C. myrica and C. trinodis had a high P content in the summer. Na and Mg rendered the same results with different tested macroalgae. Significant increase was observed in the winter season for C. prolifera, A. spicifera and T. ornata. In contrast, significant increase was observed in the summer season for both C. myrica and C. trinodis (Table 3).
Macroalgae do not biosynthesize minerals, but they absorb them from the surrounding environment based on many factors, such as temperature, pH, salinity and light [52]. Thus, both internal and external factors have an impact on minerals’ accumulation in macroalgae. The former involve sulfhydryl ester, amino, carboxyl, hydroxyl, proteins and/or lipids, while the latter include sea water, temperature, salinity, pH and disruptions [53]. Regarding their biological and nutritional value, *Ca is* a crucial element in the body skeleton, in heart strength and smooth muscle contraction, in addition to the nervous and muscular equilibria [54], while *Mg is* a very important cofactor of several enzymes, including those involved in respiration. Other minerals, such as Fe, Mg, Cu, Zn and Co, are involved in several metabolic processes, as well as working as enzyme cofactors [32]. According to metal analysis of the four seaweeds Laminaria digitata, L. hyperborea, *Saccharina latissima* and Alaria esculenta, the concentrations of K and Na in the winter were more than the doubleof their concentrations in the summer [55]. The current result is in line with previous findings for *Laminaria digitata* [56]. Saldarriaga-Hernandez et al. [ 57] reported a high concentration of P in Sargassum and concluded that *Sargassum is* recommended as an alternative source of P. A similar result was described by Gaillande et al. [ 58] who indicated that high quantities of Na, K, Ca and Mg were also reported in Caulerpa species.
## 3.3. Species and Seasonal Variation in Primary and Secondary Metabolites
In this study, seasonal variations in macroalgal biochemical composition were observed, which affect the apt period for harvesting based on the nutritional requirements for commercial utilizations. Seasonal characterization is a prerequisite for future valorization of macroalgal biomass as a component of feed additives or fertilizers [51]. Thus, it is important to understand these variations in the production of biologically active compounds in order to determine the ideal time for harvesting macroalgal biomass based on its proposed applications in food, animal feed, biofuel production, pharmaceutical and various industries.
Several studies proved the effect of seasonality on the biochemical constituent of different species of seaweeds. Kumar et al. [ 59] observed significant individual differences in the biochemical composition of all investigated marine macroalgae. Ajayan et al. [ 60] studied the fatty acid contents, metals and other elemental compositions of 25 macroalgal species and proved that the lipids, proteins and carbohydrate levels varied significantly among the species studied. For instance, Samanta et al. [ 61] illustrated that the chemical composition of *Agarophyton vermiculophyllum* was changed by variable climatic conditions, such as temperature, pH and nutrient availability. Pérez et al. [ 62] proved that seaweed harvested in the summer showed superior physiological activities as a result of the presence of active metabolites, such as fatty acids, pigments, phlorotannins, lectins, terpenoids, alkaloids and halogenated compounds, as a pattern of adaptation. Overall, more studies are required to evaluate the use of macroalgae as a healthy and sustainable alternative in the nutraceutical, cosmetics, as well as well-being industries because seaweed exploitation in *Egypt is* still in its early stages [63].
## 3.3.1. Carbohydrates
Carbohydrates are considered the primary source of energy in the majority of human diets in addition to their importance in respiration and other metabolic processes [64,65]. Furthermore, they can be used for biofuel production [66]. In the present study, total sugars recorded a high content in the summer season for all macroalgal species tested. Red macroalgae A. spicifera had the greatest concentration (564.7 mg/g DW), followed by green macroalgae C. prolifera (557.9 mg/g DW), while brown macroalgae showed minimum contents (Figure 4). Monosaccharide glucose exhibited a different pattern, whereby a significant increase in glucose quantity was observed in the winter season in all macroalgae tested. The highest content of fructose was recorded in the winter for brown macroalgae in contrast to red macroalgae A. spicifera and green macroalgae C. prolifera.
The result obtained revealed that the greatest content of total sugars was found in the summer season in all macroalgae investigated. A similar pattern was noticed by Khairy and El-Shafay [64] who reported that the highest amount of carbohydrates in U. lactuca and P. capillacea was produced during the summer season. According to García-Sanchez et al. [ 67], Sargassum exhibited rapid growth during the summer season owing to greater sunlight exposure, storing carbohydrates for the rainy season, which is characterized by reduced photosynthesis. Variations in carbohydrates’ production among macroalgal species may be attributed to their various life cycles and abiotic oscillations [65].
## 3.3.2. Proteins and Amino Acids
Proteins are macromolecules and serve a variety of functions in all living organisms, including repair and maintenance, mechanical support and energy [32]. A total of 19 AAs were evaluated in the five macroalgae, including essential (EAAs) (which must be obtained from food) and non-essential amino acids (NEAAs). Lysine, histidine, phenylalanine and valine were the most prominent EAA. Lysine concentration was somewhat higher in the summer season in C. prolifera, A. spicifera and T. ornata, unlike the concentrations in C. myrica and C. trinodis, which were higher in the winter season. The content of phenylalanine in the summer was double that in the winter (significant difference at $p \leq 0.05$). There was a non-significant difference at $p \leq 0.05$ in the histidine level between both seasons. Glycine, alanine, asparagine and glutamic acid were the most abundant NEAA. There was a significant difference at $p \leq 0.05$ in their levels between both seasons. Glycine content was the highestamong both EAAs and NEAAs. Glycine concentration was increased by 20–$30\%$ in the summer season for all seaweeds studied, except C.trinodis. The greatest levels of alanine were observed during the summer in C. prolifera and T. ornata, while they were higher in A. spicifera, C. myrica and C. trinodis during the winter season. A. spicifera and C. trinodis had a high content of asparagine (4.49 mg/g DW) in the summer, while T. ornata had a high content of asparagine (5.04 mg/g DW) in the winter (Table 4).
The quality of the protein is just as important as its quantity. The protein quality of foods is frequently assessed by the amount and composition of its essential amino acids [26]. Macroalgae are an important source of proteins because their protein content is rich in essential amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine) [68]. In this regard, macroalgae proteins are also significant as a source of peptides and amino acid extracts, principally after enzymatic digestion, which increases their solubility in water, making them acceptable to be employed in a variety of industries [69].
Red macroalgae *Palmaria palmata* exhibited a similar pattern, displaying variations in macroalgal protein content, with the winter–spring season showing greater protein content than the summer–early autumn season [70]. Protein content differs greatly with seasons; the highest concentration was recorded during the beginning of spring and winter, while the lowest concentration was recorded in the early autumn and summer season [71]. Afonso et al. [ 72] proposed that a gradual decrease in protein levels from March to August may be due to the lower availability of nitrogenous compounds. However, other seasonal factors may also influence the protein content, namely the high temperature of water, salinity and eutrophication [71,72]. Balboa et al. [ 51] indicated that protein content exhibited a negative relationship with temperature and salinity. Overall, the macroalgal protein is considered an excellent source of EAAs and represents almost half of the total AAs they produce [73].
## 3.3.3. Lipids and Fatty Acids
Lipids play a basic role not only in energy supply, but they are also necessary for the production of hormones and for maintaining the integrity of cell membranes [74]. Lipids are also required for the transportation and absorption of fat-soluble vitamins, including A, D, E and K [75]. Therefore, a total of 16 individual FAs were identified and quantified in the five macroalgal species studied, including 8 saturated fatty acids (SFAs), 6 monounsaturated fatty acids (MUFAs) and 2 polyunsaturated fatty acids (PUFAs). Palmitic (C16:0) and stearic (C18:0) acids were the most abundant SFA in all macroalgal species, with high concentrations observed in the summer season (significant difference at $p \leq 0.05$). The greatest content of MUFAs was recorded for oleic acid (C18:1) in the summer season for all macroalgae studied, except T. ornata in the winter season. Oleic acid (C18:1) was followed by eicosenoic (C20:1), heptadecenoic (C17:1), palmitoleic (C16:1), and finally, tetracosenoic (C24:1) acid, at a lower concentration. Two PUFA were reported, namely linoleic (C18:2 ω-6) and linolenic (C18:3 ω-3) acid, with slightly higher concentrations in the summer than in the winter season in C. prolifera and T. ornata, while there was no difference in their amounts in both seasons in C. myrica, C. trinodis and A. spicifera (Table 5).
Oleic acid (C18:1) followed by palmitic acid (C16:0) were the most abundant FA, in agreement with Morales et al. [ 76]. PUFA help macroalgae survive by acting as precursors for the biosynthesis of a variety of secondary metabolites with crucial ecological roles [77]. Khairy and El shafey [64] reported that palmitic acid (C16:0) is the most abundant saturated fatty acid in seaweeds, accounting for $74.3\%$. The essential C18 fatty acids, linoleic acid (18:2, ω6) and linolenic acid (18:3, ω3), were recorded in the same amounts, with the highest contents in March and April (5.7–$7.2\%$) [51]. Macroalgal lipid contents are directly affected by many variables, such as macroalgal species, location, sampling period and environmental conditions, in addition to the extraction method and solvent polarity [78].
Although many studies proved that macroalgae possess relatively low lipid contents, their PUFAs contents are equal to or may be greater than those of terrestrial plants [59]. Macroalgae accumulate high concentrations of PUFAs, which have beneficial impacts on human health, such as reducing cardiovascular risk and improving both the brain function and immune response [32,79]. It was also described that the PUFAs content of *Caulerpa is* greater than those in coconut and palm oils [80]. In addition, Ajayan et al. [ 60] stated that linolenic acid and oleic acid comprised the majority of the total fatty acids of macroalgae.
Francavilla et al. [ 81] described the increase in PUFAs and decrease in SFAs in macroalgae G. gracilis during the winter season. They attributed this result to the increased tightness of cell membranes due to lower temperatures. Due to the mild winters recorded along the Egyptian coast, the lowest temperatures recorded do not seem to significantly change the PUFAs content. Balboa et al. [ 51] concluded that the unsaturation degree of FAs depends primarily on the water temperature; macroalgae harvested from cold water have a greater content of PUFAs and unsaturation degree than those collected from tropical water. Some seaweed fatty acids are distinctive and play crucial roles in nutrition and cell membrane construction, such as the essential α-linolenic fatty acid, which cannot be synthesized by mammals, while it can only be synthesized in limited amounts by terrestrial plants [82]. Both FAs content and profile differ based on the variation of geographical location, biotic (temperature, salinity, pH, light, nutrient) and abiotic parameters (herbivory), in addition to the genetic characteristics of each macroalgal species [51,75]. Based on the results, high FAs content can best be obtained during the summer season. SFAs, C14:0 and C16:0, are essential for the cholesterol synthesis and thus important for human health [72].
## 3.3.4. Organic Acids
Six organic acids were identified and measured in the macroalgae tested. Malic acid, isobutyric acid, citric acid and oxalic acid were the most abundant in the two seasons. The malic acid quantity showed a comparative increase in the summer season in C. prolifera and A. spicifera, but the three brown macroalgae recorded a high content in the winter. Succinic acid was found in high concentration in A. spicifera during the summer season, while fumaric acid was observed in minimum quantity in all the macroalgae studied (Figure 5). Carpena et al. [ 83] also recorded the presence of several organic acids, including malic, oxalic and citric acids, in the three seaweeds Chondrus crispus, *Mastocarpus stellatus* and Gigartina pistillata. Tanna et al. [ 82] also reported that lactic and oxalic acids were found in the green macroalgae Caulerpa scalpelliformis.
These detected organic acids are known for their high biological and medical values. Succinic acid has high potential in many biological production processes, including food, pharmaceutical, cosmetics, detergents and lubricants [84]. Fumaric acid is an intermediate of the TCA cycle, and it is generally used in the food industries, such as a beverage constituent and food acidulant [85]. Malic acid, as a low-calorie food additive, is used in a variety of industries, including food, beverage, metal cleaning, pharmaceuticals and plastics [86]. Malic and citric acids have antioxidant properties and are frequently used in the food, agriculture, pharmaceutical and chemical industries [83]. Butyric acid is used to produce butyric acid esters, cellulose butyrate, food and medicine, as well as serving as an emulsifier, varnish and cosmetic [87].
## Phenolic Compounds
Phenolic compounds are a group of metabolites with the most structural variety and the greatest concentration in macroalgae [88]. Phenolic compounds produced by seaweeds in the present study were assessed and quantified in the five macroalgal species tested during the two seasons (summer and winter). The greatest levels of phenolic compounds, such as polyphenols and flavonoids, were recorded in the summer season for all macroalgal species studied. In contrast, tocopherols recorded a slight increase in the winter (31.1 mg/g DW) compared to the summer season (29.4 mg/g DW) (Figure 6).
The total phenolic content of macroalgae changes with seasonal variations in temperature, salinity, light intensity, geographical region and water depth, in addition to other biological factors, such as age, size, the stage of the seaweed’s life cycle and herbivores’ presence [89]. The greatest levels of phenolic compounds were found in the summer season. Schiener et al. [ 55] concluded that the highest polyphenol quantity was observed between May and July in all seaweeds tested, while the lowest quantity was found in October for the Laminaria spp. and March for the *Alaria esculenta* and Saccharina latissima. Mancuso et al. [ 90] proved an increase in the total phenolic content in brown seaweed *Cystoseira compressa* as the water temperature rose. This may be attributed to greater light irradiance during the spring season; the exposure of seaweeds to UV radiation promotes the formation of phenolic compounds to provide protection from oxidative stress [91]. Polyphenolic compounds extracted from macroalgae exhibited antioxidant [92], anti-inflammatory and antidiabetic [93], anticarcinogenic [94] and antimicrobial properties [7]. Moreover, these compounds could be used in several industries and applications, generating innovative products, such as natural food stabilizer, skin care and anti-aging cosmetic products [32,88].
There is variability in seaweeds’ phenolic content throughout the year, which represents the cellular defensive response, as well as prevents the attack of bacteria, microalgae, fungi, invertebrates, and enables survival in these difficult conditions [95]. The strong antioxidant potential of macroalgae was attributed to the higher levels of antioxidant molecules, such as flavonoids, ascorbate, phenols and glutathione. Wang et al. [ 96] reported that seaweed phenols have scavenging potential because of the presence of a hydroxyl group, which is a remarkable constituent of seaweed.
*In* general, flavonoids are found in epidermal cells to absorb UV light; therefore, their concentration is higher in the summer season, with high light intensity and duration [97]. Water salinity decreases during the rainy season, lower salinity effects the biochemical composition of macroalgae by reducing their phenolic content [98]. Marinho et al. [ 99] recorded the same seasonal fluctuation pattern of flavonoids’ concentration in Saccharina latissima. Seasonal environmental factors may result in a considerable difference in antioxidant activity [97]. The total antioxidant activity (TAC) was enhanced in the summer compared to the winter season in all studied macroalgal species.
## 4. Conclusions
Out of several macroalgae, *Caulerpa prolifera* (green macroalgae), *Acanthophora spicifera* (red macroalgae) and Cystoseira myrica, *Cystoseira trinodis* and *Turbinaria ornata* (brown macroalgae) were the most dominant species in the three target collection sites along the Red Sea, Egypt. Macroalgae identification was confirmed using the molecular (18s rRNA) approach. The majority of primary and secondary metabolites, as well as total antioxidant activity, were enhanced in the summer season. The present study revealed significant seasonal and species variations in the biochemical composition of the macroalgal species collected and reported that the macroalgae under study possess greater nutritional value in the summer compared to the winter season.
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|
---
title: Inhibiting NLRP3 Inflammasome Activation by CY-09 Helps to Restore Cerebral
Glucose Metabolism in 3×Tg-AD Mice
authors:
- Shuangxue Han
- Zhijun He
- Xia Hu
- Xiaoqian Li
- Kaixin Zheng
- Yingying Huang
- Peng Xiao
- Qingguo Xie
- Jiazuan Ni
- Qiong Liu
journal: Antioxidants
year: 2023
pmcid: PMC10045645
doi: 10.3390/antiox12030722
license: CC BY 4.0
---
# Inhibiting NLRP3 Inflammasome Activation by CY-09 Helps to Restore Cerebral Glucose Metabolism in 3×Tg-AD Mice
## Abstract
The reduction of the cerebral glucose metabolism is closely related to the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome in Alzheimer’s disease (AD); however, its underlying mechanism remains unclear. In this paper, 18F-flurodeoxyglucose positron emission tomography was used to trace cerebral glucose metabolism in vivo, along with Western blotting and immunofluorescence assays to examine the expression and distribution of associated proteins. Glucose and insulin tolerance tests were carried out to detect insulin resistance, and the Morris water maze was used to test the spatial learning and memory ability of the mice. The results show increased NLRP3 inflammasome activation, elevated insulin resistance, and decreased glucose metabolism in 3×Tg-AD mice. Inhibiting NLRP3 inflammasome activation using CY-09, a specific inhibitor for NLRP3, may restore cerebral glucose metabolism by increasing the expression and distribution of glucose transporters and enzymes and attenuating insulin resistance in AD mice. Moreover, CY-09 helps to improve AD pathology and relieve cognitive impairment in these mice. Although CY-09 has no significant effect on ferroptosis, it can effectively reduce fatty acid synthesis and lipid peroxidation. These findings provide new evidence for NLRP3 inflammasome as a therapeutic target for AD, suggesting that CY-09 may be a potential drug for the treatment of this disease.
## 1. Introduction
Alzheimer’s disease (AD) is a common neurodegenerative disease in elder people. Deposits of amyloid-β (Aβ) and hyperphosphorylated tau protein are its main characteristics [1,2]. Due to the failure of anti-amyloid and anti-tau aggregation drugs, neuroinflammation has been considered as a new therapeutic target for AD treatment.
NLRP3 inflammasome, composed of nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) and apoptosis-associated speck-like proteins of CARD (caspase recruitment domain) (ASC) and pro-caspase-1, plays an important role in neuroinflammation. Activation of NLRP3 inflammasome causes the increase in caspase-1 and the release of interleukin-1β (IL-1β) [3,4]. Increased activation of NLRP3 inflammasome is closely related with the reduction of cerebral glucose metabolism. Studies show that the translocation of hexokinase (HK), a key enzyme in glucose metabolism, can activate NLRP3 inflammasome, and inhibition of NLRP3 inflammasome can restore the expression and distribution of HK in AD model cells [5,6]. In AD mice, NLRP3 binds to the mitochondria and is then activated by mitochondrial reactive oxygen species (mtROS) [7,8]. HK binding to mitochondria reduces mtROS transport into the cytoplasm and further reduces the activation of NLRP3 inflammasome [9,10]. A positive correlation occurs between glucose metabolism and neuroinflammation in early AD, but disappears as the pathological course progresses [11]. In addition, increased neuroinflammation leads to a shift in energy metabolism from oxidative phosphorylation (OxPhos) to aerobic glycolysis in microglia. Activated microglia compete with neurons for glucose, which limits the energy availability of neurons [12]. Our previous studies have shown that inhibiting the activation of NLRP3 inflammasome can increase the expression and distribution of HK in vitro [5], but determining whether it could restore cerebral glucose metabolism in this process requires further exploration in vivo.
Glucose metabolism provides over $70\%$ of energy for the brain. Glucose is transported into cells via glucose transporters (GLUTs), is phosphorylated by HK, and then catalyzed by a series of enzymes to produce energy. GLUT1 and GLUT3 are expressed in the brain and are responsible for the entry of glucose from the blood to the intracellular environment [13,14,15]. In AD, decreased expression levels of GLUT1 and GLUT3 lead to reduced glucose transport. GLUT4 is also expressed in the brain. Unlike GLUT1 and GLUT3, it is sensitive to insulin, and when insulin levels rise, it is transferred to cell membranes for the transport of glucose, [16,17]. The transfer of GLUT4 is regulated by the insulin-PI3K-AKT pathway [18]. Several studies have shown that insulin resistance is a key event leading to AD pathology [17,19,20]. Insulin receptors (IR) recognize insulin and then self-phosphorylate to recruit insulin receptor substrate (IRS) and activate the IRS-AKT-AS160 pathway [21,22]. AS160 is a guanosine triphosphate enzyme activating protein, the phosphorylation of which promotes translocation of GLUT4 [18,23]. In AD, lower insulin levels in cerebrospinal fluid result in decreased expression of p-IR, p-AKT, and p-AS160, a s well as increased IR [24]. A positron emission tomography (PET) study confirmed that the expression of IR was increased in the lower glucose metabolism region.
Oxidative stress also contributes to the pathology of AD. *The* generation of an excessive amount of ROS results in oxidative stress and the activation of NLRP3 inflammasome. Moreover, oxidative stress causes iron metabolism disorders and leads to ferroptosis [25,26]. Ferroptosis manifests as increased cellular Fe2+, decreased glutathione peroxidase 4 (GPX4), and increased lipid peroxidation [27]. Elevated Fe2+ results from increased transferrin transport and ferritinophagy [28]. Transferrin (TF), transferrin receptor (TFR), and ferroportin (FPN) are responsible for the transport of Fe, and nuclear receptor coactivator 4 (NCOA4) participates in ferritinophagy. Long-chain acyl-CoA synthetase 4 (ACSL4) is a key protein linking ferroptosis and lipid peroxidation. Increased ACSL4 is consistent with increased ferroptosis and lipid peroxidation in AD [29,30,31]. Decreased GPX4 and Solute Carrier Family 7, Member 11 (SLC7A11) are also related to lipid peroxidation.
In the study, we injected CY-09, a specific inhibitor of NLRP3, into non-transgenic (NTg) and triple transgenic AD (3×Tg-AD) mice daily, with a dose of 2.5 mg/kg for six weeks, according to the reference [32]. Then, we explored the effect of NLRP3 inflammasome inactivation on glucose transport, insulin resistance, and glucose metabolic enzymes in vivo. Simultaneously, we investigated whether NLRP3 inflammasome inactivation by CY-09 could reduce AD classical pathology and oxidative stress and improve cognitive deficits. Overall, this study aimed to determine the effect of NLRP3 inflammasome activation on glucose metabolism and to investigate the potentiality of CY-09 as a therapeutic drug for AD treatment.
## 2.1. Reagents and Antibodies
CY-09 (Cat#S5774) was purchased from Selleck Chemicals LLC, Houston, TX, USA. Polyoxyl 15 hydroxystearate (Cat#HY-136349) and DMSO (Cat#HY-Y0320) were purchased from MedChemExpress LLC, Deer Park Dr, Suite Q, Monmouth Junction, NJ, USA. Saline (Cat#R22172) was purchased from Shanghai yuanye Bio-Technology Co., Ltd., Shanghai, China. 18F-FDG was provided by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. Antibodies against NLRP3 (Cat#AG-20B-0014-C100) and caspase-1 (P20) (Cat#AG-20B-0042-C100) were purchased from Adipogen Corporation, San Diego, CA, USA. Antibodies against IL-1β (Cat#16806-1-AP), GLUT1 (Cat#21829-1-AP), GLUT4 (Cat#66846-1-Ig), GLUT3 (Cat#20403-1-AP), HK2 (Cat#22029-1-AP), voltage-dependent anion-selective channel protein 1 (VDAC1) (Cat#10866-1-AP), and SLC7A11 (Cat#26864-1-AP) were purchased from Proteintech Group, Inc., Chicago, IL, USA. Antibodies against IRS (Cat#3407), p-IRS-Ser1101 (Cat#2385), AKT (Cat#9272), p-AKT-Ser473 (Cat#4060), GSK3β (Cat#12456), p-GSK3β-Ser9 (Cat#5558), FAS (Cat#3180), ACC (Cat#3676), p-ACC-Ser79 (Cat#11818), and LRP1 (Cat#64099) were purchased from Cell Signaling Technology, Inc., Boston, MA, USA. Antibodies against IR (Cat#sc-57342), p-IR-Tyr1150 (Cat#sc-81500), and ACSL4 (Cat#sc-271800) were purchased from Santa Cruz Biotechnology, Inc., Dallas, TX, USA. The antibodies against AS160 (Cat#ab189890), p-AS160-T642 (Cat#ab131214), HK1 (Cat#ab150423), PDHE1α (Cat#ab168379), COX IV (Cat#ab16056), APP (Cat#ab32136), BACE1 (Cat#ab108394), PSD95 (Cat#ab18258), synaptophysin (Cat#ab32127), tau5 (Cat#ab80579), p-tau-Ser404 (Cat#ab92676), MDA (Cat#ab27642), GPX4 (Cat#ab125066), and HMGCS1 (Cat#ab155787) were purchased from Abcam plc., Cambridge, UK. Antibodies against 6E10 (Cat#803002), sAPPα (Cat#813501), and sAPPβ (Cat#813401) were purchased from BioLegend, Inc., San Diego, CA, USA. The antibody against Aβ1-42 (Cat#AB5078P) was purchased from Millipore Corporation, Boston, MA, USA. Antibodies against HT7 (Cat#MN1000), TF (Cat#PA5-27306), TFR (Cat#13-6800), FPN (Cat#PA5-22993), NOCA4 (Cat#PA5-96398), and SREBP2 (Cat#PA1-338) were purchased from Thermo Fisher Scientific, Waltham, MA, USA. The antibody against β-actin (Cat#AB0033) was purchased from Abways Technology, Inc., Shanghai, China. The antibody against HMGCR (Cat#T56640S) was purchased from Abmart Shanghai Co., Ltd., Shanghai, China. Goat Anti-Rabbit IgG H&L (HRP) secondary antibody (Cat#ab6721) and Goat Anti-Mouse IgG H&L (HRP) secondary antibody (Cat#ab6789) were purchased from Abcam plc., Cambridge, UK. Alexa Fluor® 488 AffiniPure Goat Anti-Rabbit IgG (H+L) secondary antibody (Cat#111-545-003) and Alexa Fluor® 594 AffiniPure Goat Anti-Mouse IgG (H+L) secondary antibody (Cat#111-585-003) were purchased from Jackson ImmunoResearch Inc., West Grove, PA, USA. DAPI Staining Solution (Cat#C1002) was purchased from Beyotime Biotech Inc., Shanghai, China.
## 2.2. Animals and Treatment
The impact of sex on AD pathology has been reported in many references. Senior females are more likely to develop AD due to their lower estrogen levels. Ovarian hormone loss causes a bioenergetic deficit and a shift in metabolic fuel availability in AD model mice [33,34]. Thus, we select 9-month-old female C57BL/6J mice (NTg mice) and 3×Tg-AD mice for this study. NTg mice were purchased from Guangdong Medical Laboratory Animal Center and 3×Tg-AD mice which harbor the mutated human genes amyloid precursor protein (APP) (SWE), PS1 (M146V), and Tau (P301L) were purchased from the Jackson Laboratory. Mice were housed in conditions under 22 °C in a 12:12 h light/dark cycle, with food and water ad libitum. All animal experimental protocols were reviewed and approved by the Institutional Animal Care and Use Committee of Tongji Medical College of Huazhong University of Science and Technology (IACUC Number: 2390; Approved Date: 27 February 2018).
To observe the impact of NLRP3 inflammasome activation on cerebral glucose metabolism in AD, we used CY-09 to inhibit NLRP3 inflammasome activation. Here, 24 female mice were randomly divided into four groups: NTg mice, CY-09-treated NTg mice (NTg + CY-09 mice), 3×Tg-AD mice, and CY-09-treated 3×Tg-AD mice (3×Tg-AD + CY-09 mice). CY-09 was dissolved in a vehicle containing $10\%$ DMSO, $10\%$ Polyoxyl 15 hydroxystearate, and $80\%$ saline. It was then injected into the mice daily, with a dose of 2.5 mg/kg for six weeks, according to the reference [32].
## 2.3. 18F-FDG PET
18F-FDG PET experiments were applied to examine the glucose metabolism in the mouse brain [35]. All the mice were fasted for 12–16 h and then injected by vein with 7.4 MBq 18F-FDG before the PET scan. For the static PET scan, the mice were scanned for 10 min after 60 min of free metabolism. However, in the dynamic scan, the mice were scanned for 60 min immediately after injection. PET data were acquired with Trans-PET Discoverist 180 (Raycan Technology Co., Ltd., Suzhou, China) and reconstructed with a 3D OSEM algorithm. The time segmentary schemes for dynamic data reconstruction were as follows: 5 s × 6, 10 s × 3, 30 s × 4, 60 s × 2, 120 s × 5, 300 s × 3, 600 s × 1, 300 s × 1, 900 s × 1. To calculate the cerebral metabolic rate of glucose (CMRglu), blood glucose was measured from the second drop of tail blood using a Roche blood glucose meter (Roche Pharma (Schweiz) Ltd., Basel, Switzerland). Standard uptake values (SUVs) of the whole brain, cortex, and hippocampus, as well as the CMRglu of the whole brain, were quantified by Amide 1.0.4 software (Crump Institute for Molecular Imaging, UCLA School of Medicine, CA, USA), Crimas 2.9 (Turku PET center, Turku, Finland), MATLAB 2019b (MathWorks Inc., Natick, MA, USA), and SPM12 (The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK).
## 2.4. Morris Water Maze Test
The Morris water maze test is one of the behavioral experiments used to detect the spatial learning and memory ability of mice. In the study, we mainly refer to the previous protocol, with slight modifications [36,37]. None of the mice were trained on the test before experimentation. However, an additional process was carried out to familiarize the mice with the environment before the formal experiment started. The Morris water maze test includes place navigation tests and spatial probe tests. In the place navigation test, the mice were first placed on the platform for 1 min to familiarize themselves with the environment. Subsequently, the mice were placed into the water from the four quadrants. The time (escape latency) from entering the water to finding the platform was recorded within 60 s. The place navigation test lasted for five days. In the spatial probe test, the platform was removed and the mice were placed into the diagonal quadrant. Then, the time spent by the mice in the target quadrant in 24 h and 72 h was recorded. The Morris water maze WMT-200A (Chengdu Techman Software Co., Ltd., Chengdu, China) and the Animal Behavior Analysis System BAS-100 (Chengdu Techman Software Co., Ltd., Chengdu, China) were used for the recording of data.
## 2.5. Glucose Tolerance Test (GTT) and Insulin Tolerance Test (ITT)
GTT and ITT were performed to respectively detect the ability to regulate blood glucose and insulin sensitivity [32,38]. In GTT, the mice were fasted for 12 h and then injected with glucose in the dose of 2 g glucose per Kg mice (glucose was purchased from Beyotime Biotech Inc., Shanghai, China, Cat#ST1228). In ITT, mice were fasted for 4 h and then injected with insulin in the dose of 0.75 U insulin per Kg mice (insulin was purchased from Beyotime Biotech Inc., Shanghai, China, Cat#P3376). All experiments were required to detect and record the blood glucose in time points of 0, 30, 60, 90, 120, and 150 min after the injection. Blood glucose was measured from the second drop of tail blood using a Roche blood glucose meter (Roche Pharma (Schweiz) Ltd., Basel, Switzerland).
## 2.6. Brain Tissues Extraction and Preservation
After a week of recovery, the mice were euthanized after isoflurane anesthesia to extract the brain and blood. The hemibrain of each mouse was used for immunofluorescence experiments and stored with $4\%$ paraformaldehyde. The total proteins of the rest of the hippocampus were extracted using the Phosphorylated Protein Extraction Kit (Cat#KGP950, Jiangsu Keygen Biotech Corp., Ltd., Nanjing, China) for Western blot experiments. The of the rest cortex was used for LC-MS/MS analysis, ROS measurement, and mitochondrion isolation. All tissues were stored at −80 °C before the experiments. Blood was tested for insulin directly after the extraction.
## 2.7. ELISA Assay and Reactive Oxygen Species Measurement
All experiments were conducted following the instructions of each kit. Fasting blood insulin was detected using a Highly Sensitive Mouse Insulin Immunoassay Kit (Cat#HMS200, EZassay Ltd., Shenzhen, China). The secretion of IL-1β was detected using a Mouse IL-1β ELISA Kit (Cat#EMC001b, Neobioscience Technology Company, Shenzhen, China). ROS levels were measured using the Reactive Oxygen Species Assay Kit (Cat#E004-1-1, Nanjing Jiancheng Bioengineering Institute, Nanjing, China). These data were detected by a SpectraMax® L Microplate Reader (Molecular Devices, LLC, San Jose, CA, USA).
## 2.8. LC-MS/MS
LC-MS/MS was performed by Triple TOF 6500 (AB Sciex LLC, Framingham, MA, USA) to determine whether CY-09 crosses the BBB and enters the brains of the mice. In this experiment, metabolites were extracted from brain tissues using a 300 μL methanol acetonitrile mixture (methanol: acetonitrile = 2:1), as previously described [39]. After vortexing for 1 min, sonicating for 10 min at 0 °C, and centrifuging for 15 min at 13,000× g, the samples were held at −20 °C until further detection. The conditions for the chromatographic separation of metabolites were as follows—flow rate: 0.3 mL/min; injection volume: 6 μL; mobile phase: phase A, water (containing $0.1\%$ formic acid); phase B, acetonitrile (containing $0.1\%$ formic acid). The ion source parameters of mass spectrometry were as follows—curtain gas: 35 psi; ion spray voltage: −4500 V; source temperature: 550 °C; ion source gas1: 55psi; ion source gas2: 55psi. MultiQuant 3.02 (AB Sciex LLC, Framingham, MA, USA) and ProteoWizard 1.3.5.0 (ProteoWizard, Palo Alto, CA, USA) were used to process the data.
## 2.9. Mitochondrion Isolation and Hexokinase Activity
The brain mitochondria were isolated following the product instructions for the mitochondrial isolation kit (Cat#C3606, Beyotime Biotech Inc, Shanghai, China). Isolated mitochondria and cytoplasm were collected and stored at −80 °C. A NanoDrop 2000c (Thermo Fisher Scientific, Waltham, MA, USA) was used to detect the mitochondria concentration. The hexokinase activity was measured by Micro Hexokinase Assay Kit (Cat#BC0745, Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). All experiments were carried out according to the product instructions.
## 2.10. Western Blot Analysis
The total proteins of the brain tissues were extracted using the Phosphorylated Protein Extraction Kit (Cat#KGP950, Jiangsu Keygen Biotech Corp., Ltd., Nanjing, China). The PierceTM BCA Protein Assay Kit (Cat#23225, Thermo Fisher Scientific, Waltham, MA, USA) was used for the detection of protein concentrations. Experimental protocols were the same as previously described [5]. In short, $10\%$ and $12\%$ sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gels were used in the experiments. Proteins larger than 100 kD were separated using a $10\%$ SDS-PAGE gel, while proteins smaller than 100 kD were separated using a $12\%$ SDS-PAGE gel. Equal amounts of total protein lysates (20 μg per well) of each sample were loaded for the electrophoresis. Then, the proteins were transferred to a 0.45 μm polyvinylidene difluoride (PVDF) membrane (Cat#ISEQ00010, Millipore Corporation, Boston, MA, USA). After the electrotransfer, the membranes were blocked in $5\%$ non-fat milk for 2 h and washed four times (10 min each time) with TBST buffer (150 mM NaCl, 10 mM Tris, $0.1\%$ Tween-20, pH 7.4). Afterwards, the membranes were incubated with primary antibodies overnight at 4 °C. The next day, the membranes were incubated with secondary antibodies at room temperature for 2 h after the washing of the primary antibody with TBST buffer. After the washing of the secondary antibodies, immunoreactive bands were visualized by the Tanon 5200 Series Image Analysis System (Tanon Science & Technology Co., Ltd., Shanghai, China). Grayscale analysis was used to quantify the protein expression. Grayscale values of the target protein were normalized to the grayscale values of β-actin or VDAC1. ImageJ 1.53C (NIH, Bethesda, MD, USA) was used to quantify the grayscale values of each immunoreactive band.
## 2.11. Immunofluorescence Assay
An immunofluorescence assay was performed for the brain slices following the previously established protocol [40]. Briefly, the brain slices were dewaxed and the xylene removed with dimethylbenzene and different concentrations of alcohol ($100\%$, $95\%$, $85\%$, $70\%$, $50\%$, $30\%$, and $0\%$), respectively. Next, the brain slices were incubated with primary antibodies overnight after antigen retrieval, membrane rupture, and blocking. The next day, brain slices were washed using PBST for 30 min and then incubated with a second antibody for 2 h in the dark. After the washing of the second antibody, the brain slices were placed on slides and observed using an Olympus BX53 microscope (Olympus Corporation, Tokyo, Japan).
## 2.12. Statistical Analysis
In this study, semi-quantitative analysis and absolute quantification analysis were used to quantify the SUVs and the CMRglu, respectively [41]. The equation for SUV is as follows:[1]SUV=CTDInj·Ws CT, DInj, and WS represent the radioactivity in the region of interest, the injected dose of radioactivity, and the weight of the mouse, respectively.
The simplified equation for CMRglu is as follows:[2]CMRglu=CgLC·K *Cg is* the concentration of blood glucose. LC is the lumped constant, and it reflects the difference between the metabolism of 18F-FDG and glucose. K is the uptake rate of 18F-FDG.
All data were presented as mean ± SD and analyzed with GraphPad Prism 9 (GraphPad Software Inc., La Jolla, CA, USA). The normality of distribution of the results was checked by Shapiro–Wilk test. A normal distribution of the data was indicated by the test; thus, a one-way ANOVA, followed by Bonferroni’s multiple comparisons test, were performed in the study. Every possible comparison was explored, and significant differences ($p \leq 0.05$) between each group were shown in the figures.
## 3.1. CY-09 Could Cross the Blood-Brain Barrier In Vivo
CY-09 inhibits the assembly and activation of the NLRP3 inflammasome by its combination with the ATP-binding motif of the NLRP3 NACHT domain to inhibit the ATPase activity. To investigate whether it is possible for CY-09 to cross the blood–brain barrier (BBB) in mice, initially, we adopted LC-MS/MS to detect the content of CY-09 in the brain of each group, i.e., the NTg, NTg + CY-09, 3×Tg-AD, and 3×Tg-AD + CY-09 mice. The results were shown in Figure 1a and Figure S1; CY-09 was found in the brain tissues of NTg + CY-09 and 3×Tg-AD + CY-09 mice, which indicated that CY-09 could cross the BBB in vivo.
## 3.2. CY-09 Inhibited NLRP3 Inflammasome Activation in 3×Tg-AD Mice
Then, Western blot and ELISA were then used to detect the expressions of NLRP3 inflammasome-related proteins to confirm the effect of CY-09 on NLRP3 inflammasome activation. As demonstrated in Figure 1b–h, compared with NTg mice, the expressions of NLRP3, pro-caspase-1, caspase-1(P20), and IL-1β were significantly increased in 3×Tg-AD mice, with the p-values lower than 0.05 and 0.01 respectively. Simultaneously, a notable reduction in the expressions of these proteins was found in CY-09 treated 3×Tg-AD mice, with the p-values lower than 0.05 and 0.01, respectively. Moreover, the expressions of pro-caspase-1 and caspase-1 were remarkably decreased in NTg + CY-09 mice, with the p-values all lower than 0.01. Except for a significant difference between NTg mice, NTg + CY-09 mice and 3×Tg-AD + CY-09 mice, the results of IL-1β secretion measured by ELISA were consistent with those of Western blotting. No differences were found in the expression of pro-IL-1β among the four groups of mice. Together, these data suggested that CY-09 has an inhibiting effect on NLRP3 inflammasome activation in the brain of triple transgenic AD mice.
## 3.3. CY-09 Increased Cerebral Glucose Metabolism in 3×Tg-AD Mice
Next, we evaluated the effect of NLRP3 inflammasome activation on cerebral glucose metabolism using static and dynamic PET. 18F-FDG is the most commonly used PET tracer for glucose metabolism. As an analog of glucose, 18F-FDG is transported into cells by GLUTs from blood after the i.v. injection and is then phosphorylated by HK. Due to the differences in structure, 6-P-18F-FDG cannot be catalyzed by glucose-6-phosphate isomerase and must remain in the cytoplasm. The amount and distribution of 6-P-18F-FDG represent the glucose metabolism levels in different brain regions.
Static PET results are shown in Figure 2a–e; compared with NTg mice, the standard uptake values (SUVs) of the whole brain, the cortex, and the hippocampus were greatly decreased in 3×Tg-AD mice, with the p-value lower than 0.05. After CY-09 treatment, the SUVs were significantly higher in 3×Tg-AD mice than those in non-treated AD mice, with a p-value lower than 0.05. There were no differences in the weight of the mice in the four groups. Consistent with the static PET results, the cerebral SUVs of NTg and CY-09 treated 3×Tg-AD mice were also higher than those of the 3×Tg-AD mice, even though the SUVs of the four groups of mice increased in dynamic PET over time (Figure 2f–h). Moreover, the results of the cerebral metabolic rate of glucose (CMRglu) showed a notable reduction in the 3×Tg-AD mice and a remarkable increase in the CY-09 treated 3×Tg-AD mice, with the p-values all lower than 0.05. Overall, the PET data demonstrated that inhibiting NLRP3 inflammasome activation helps to restore cerebral glucose metabolism in the 3×Tg-AD mice.
## 3.4. CY-09 Increased Glucose Transport in 3×Tg-AD Mice
GLUTs are responsible for glucose transport, which is the basis of glucose metabolism. GLUT1, GLUT3, and GLUT4 can be expressed in the brain. Here, we used Western blotting to analyze the expression of GLUT1, GLUT3, and GLUT4. As shown in Figure 3a–d, the expression of GLUT1, GLUT3, and GLUT4 were lower in 3×Tg-AD mice than in NTg mice, with the p-value lower than 0.01, 0.05, and 0.01 respectively. However, the expressions of GLUT1 and GLUT4 were significantly increased in CY-09 treated 3×Tg-AD mice than in non-treated AD mice, with the p-value lower than 0.05. The expression of GLUT3 was increased with a p-value of 0.077. Further, immunostaining of GLUT4 demonstrated the decreased distribution in 3×Tg-AD mice and the increased distribution in CY-09 treated 3×Tg-AD mice (Figure 3e and Figure S2). Hence, these data exhibited that inhibiting NLRP3 inflammasome activation can increase the expression and distribution of GLUTs in the 3×Tg-AD mice.
## 3.5. CY-09 Attenuated Insulin Resistance in 3×Tg-AD Mice
GLUT4 is regulated by the insulin signaling pathway to participate in glucose metabolism. Insulin resistance manifests itself as insensitivity to insulin and a higher level of insulin and glucose in the blood, eventually leading to impaired insulin signaling pathways and glucose metabolism. It had been confirmed to exist in AD.
To evaluate the effect of NLRP3 inflammasome activation on insulin resistance in AD, we first detected blood glucose and blood insulin levels. As shown in Figure 4, higher fasting and basal blood glucose and fasting insulin level were found in 3×Tg-AD mice compared with NTg mice, with the p-values all lower than 0.05 and 0.01, respectively. Results of the GTT and ITT showed an increased glucose tolerance and decreased insulin tolerance in 3×Tg-AD mice. However, after CY-09 treatment, blood glucose and insulin levels were decreased and better insulin sensitivity was found in CY-09-treated 3×Tg-AD mice. Thus, the results indicated that inhibiting NLRP3 inflammasome activation can attenuate insulin resistance in the 3×Tg-AD mice.
Then, to further explore the underlying mechanism by which NLRP3 inflammasome activation affects insulin resistance, we detected the expression and distribution of the IR-AKT-AS160 insulin signaling pathway-related proteins in NTg, NTg + CY-09, 3×Tg-AD, and 3×Tg-AD + CY-09 mice. As shown in Figure 5a,b, compared with NTg mice, expression of p-IR-Tyr1150 (phosphorylation protein of IR at Tyr1150) relative to IR was significantly decreased in 3×Tg-AD mice, with a p-value lower than 0.01, but the expression was increased after the CY-09 treatment, with a p-value lower than 0.05. The distribution of p-IR-Tyr1150 was found to be lower in the 3×Tg-AD mice and higher in the 3×Tg-AD + CY-09 mice. By contrast, the expression and distribution of IR were not different among the four groups of mice (Figure 5i and Figure S3). These findings suggested that inhibiting NLRP3 inflammasome activation can enhance the self-phosphorylation of IR in the 3×Tg-AD mice.
Self-phosphorylation of IR recruits IRS and starts the IRS-AKT-AS160 insulin signaling pathway. In parallel, we detected the expression of IRS, AKT, and AS160 and their phosphorylated levels in the four groups of mice. The results reported in Figure 5c–h show a notable reduction of p-AKT-Ser473 and p-AS160-T642, while significantly increased p-IRS-Ser1101 was found in the 3×Tg-AD mice, with the p-value lower than 0.01, 0.01, and 0.05, respectively. Treatment with CY-09 helped to reverse the expression of these proteins in 3×Tg-AD + CY-09 mice. There was also a significant reduction in IRS between the NTg + CY-09 mice and 3×Tg-AD + CY-09 mice, with a p-value less than 0.01. No differences were found in the expressions of AKT and AS160 among the four groups of mice. All the results showed that inhibiting NLRP3 inflammasome activation can restore the IR-IRS-AKT-AS160 insulin signaling pathway to alleviate insulin resistance in the 3×Tg-AD mice.
## 3.6. CY-09 Increased the Expression and Distribution of Metabolic Enzymes in 3×Tg-AD Mice
Increased glucose transport and improved insulin resistance were found in CY-09-treated 3×Tg-AD mice; we also detected the expressions and distribution of HK, which is the first enzyme that phosphorylates glucose when associated with VDAC1 in the mitochondria. We previously reported that the expression of cHK1 (HK1 in the cytoplasm) was significantly increased in 3×Tg-AD mice, while the expression of mHK1 (HK1 in the mitochondria) was remarkably decreased. However, increased HK1 expression and HK activity were found in CY-09-treated N2a-sw cells (a model cell of AD). These results prompted us to examine the expression and activity of HK in CY-09-treated 3×Tg-AD mice. Here, we isolated the mitochondria and cytoplasm from NTg, NTg + CY-09, 3×Tg-AD, and 3×Tg-AD + CY-09 mice to detect the expression and distribution of HK1.
As shown in Figure 6, consistent with our previous results, increased cHK1 and significantly decreased mHK1 and mHK2 were found in the 3×Tg-AD mice, with the p-value lower than 0.01 and 0.05. However, the expression of mHK1 was increased, while cHK1 was decreased in the CY-09-treated 3×Tg-AD mice, and the differences were significant compared to the untreated 3×Tg-AD mice, with the p-values lower than 0.01 and 0.05. cHK2 was also found to be notably decreased in the CY-09-treated 3×Tg-AD mice, with a p-value lower than 0.05. Although HK activity was remarkably decreased in 3×Tg-AD + CY-09 mice than in NTg + CY-09 mice, it was notably increased in the CY-09-treated 3×Tg-AD mice compared to the non-treated 3×Tg-AD mice, with a p-value lower than 0.05 (Figure 6b). Besides, we also detected the expressions of pyruvate dehydrogenase α 1 (PDHE1α) and cytochrome c oxidase subunit IV (COX4). They were all significantly reduced in 3×Tg-AD mice, with p-values lower than 0.05. Meanwhile, the expression of PDHE1α was remarkably increased in CY-09-treated 3×Tg-AD mice. In short, the data demonstrated an increase in the expression and distribution of HK by inhibiting NLRP3 inflammasome activation in the 3×Tg-AD mice.
## 3.7. CY-09 Relieved Cognitive Impairment and Pathological Injury in 3×Tg-AD Mice
Previous studies showed that NLRP3 inflammasome activation contributes to the pathology of AD. The results of this study demonstrated that inhibition of the NLRP3 inflammasome by CY-09 significantly recovered glucose metabolism. Next, we focused on the effects of CY-09 on cognitive impairment and classically pathological biomarkers in AD and confirmed whether CY-09 is a potential therapeutic drug for AD.
The Morris water maze was used to evaluate the learning and memory abilities of the four groups of mice. As shown in Figure 7, on the fifth day, the escape latency of 3×Tg-AD mice was remarkably longer than that of the NTg mice, but notably reduced after CY-09 treatment, with the p-values all lower than 0.05. Moreover, the time in the target quadrants of the 3×Tg-AD mice was less than that of the NTg mice, while increasing after CY-09 treatment in 24 h and 72 h space exploration experiments, with a p-value lower than 0.05 and a p-value of 0.1282, respectively. No differences were found in swimming speed between the four groups of mice. The results exhibited that inhibition of the NLRP3 inflammasome by CY-09 helped to relieve the cognitive impairment of the 3×Tg-AD mice.
Afterward, we detected the expression and distribution of pathological proteins in AD model mice. As shown in Figure 8, compared with the NTg mice, expressions of APP, beta-site app cleaving enzyme 1 (BACE1), sAPPβ, and Aβ1-42 were significantly increased in 3×Tg-AD mice, with the p-value lower than 0.01, 0.05, 0.05, and 0.05. Expressions of sAPPα, post-synaptic density protein 95 (PSD95) and synaptophysin were greatly decreased in 3×Tg-AD mice, with the p-values all lower than 0.05. However, in CY-09 treated 3×Tg-AD mice, the expressions of these proteins were reversed, with increased expression of sAPPα, PSD95, and synaptophysin; decreased levels of APP, BACE1, sAPPβ and Aβ1-42 were found in comparison to those in the untreated 3×Tg-AD mice, with the p-value lower than 0.05 and 0.01, respectively. Immunostaining of 6E10 confirmed the decrease in the expression and distribution of Aβ in CY-09 treated 3×Tg-AD mice (Figure 8i and Figure S4).
Furthermore, as demonstrated in Figure 9 and Figure S5, we observed increased expression and distribution of pS404-tau in 3×Tg-AD mice, but treatment with CY-09 greatly reduced the expression and distribution, with the p-values lower than 0.05 and 0.01. There were no differences in the expressions of Tau5 among the four groups of mice. However, remarkably increased total human tau was found in the 3×Tg-AD mice and the 3×Tg-AD + CY-09 mice, with a p-value lower than 0.001, while no reduction was found in the CY-09-treated 3×Tg-AD mice. By contrast, expression of p-GSK3β-Ser9 relative to GSK3β exhibited a decrease in 3×Tg-AD mice ($$p \leq 0.0834$$), but it significantly increased after CY-09 treatment, with a p-value lower than 0.05. Together, these results demonstrated that CY-09 can reverse the expression and distribution of pathological proteins and alleviate cognitive impairment in the 3×Tg-AD mice.
## 3.8. CY-09 Decreased Oxidative Stress in 3×Tg-AD Mice
Finally, we explored the effects of CY-09 on oxidative stress and ferroptosis. First, we detected ROS and malondialdehyde (MDA) in the four groups of mice. As shown in Figure 10, significantly increased ROS levels and MDA were found in 3×Tg-AD mice, with the p-value lower than 0.001 and 0.01. After the CY-09 treatment, ROS and MDA were notably decreased, with the p-value lower than 0.001 and 0.05. These data reflected that CY-09 can reduce oxidative stress in 3×Tg-AD mice.
Then, we tested the ferroptosis-related proteins, including TF, TFR, FPN, NCOA4, ACSL4, GPX4, and SLC7A11. TF and TFR were responsible for the transport of Fe2+ to the cell, while FPN transported Fe2+ outside the cell. NCOA4 participated in the ferritinophagy. GPX4, SLC7A11, and ACSL4 were critical enzymes in lipid peroxidation. As reported in Figure 11, compared with NTg mice, expressions of TFR and ACSL4 were increased, while expressions of FPN, NCOA4, GPX4, and SLC7A11 were decreased in the 3×Tg-AD mice, with the p-values all lower than 0.05. Furthermore, when compared with the NTg + CY-09 mice, the expressions of NCOA4, GPX4, and SLC7A11 were significantly decreased in the 3×Tg-AD mice, with the p-values all lower than 0.05. However, except for ACSL4, there were no differences in these proteins between the 3×Tg-AD mice and the 3×Tg-AD + CY-09 mice. The expression of ASCL4 was reduced in the 3×Tg-AD + CY-09 mice, with a p-value lower than 0.05. No differences in TF expression were found between the four groups of mice except for NTg mice and 3×Tg-AD + CY-09 mice. The results implied that the inactivation of the NLRP3 inflammasome by CY-09 cannot reverse the ferroptosis in the 3×Tg-AD mice.
Reduced expressions of MDA and ACSL4 in the 3×Tg-AD + CY-09 mice prompted us to detect the fatty metabolism changes in the four groups of mice. Data presented in Figure 12, reveal that in comparison to NTg mice, the expressions of acetyl coA carboxylase (ACC), 3-hydroxy-3-methylglutaryl-coenzyme A synthase 1 (HMGCS1), and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) were greatly increased in the 3×Tg-AD mice, with the p-values all lower than 0.05. A significant decrease was found in the expression of p-ACC in the 3×Tg-AD mice, with a p-value lower than 0.05. After CY-09 treatment, expressions of ACC, p-ACC, HMGCS1, and HMGCR were reversed. Fatty acid synthase (FAS) was also increased in the 3×Tg-AD mice, but with no significant difference in protein levels. Similarly, there were no differences in low-density lipoprotein receptor related protein 1 (LRP1) and sterol regulatory element binding protein 2 (SREBP2) between the four groups of mice, except for LRP1 in CY-09-treated NTg and 3×Tg-AD mice. Increased ACC and FAS and decreased p-ACC indicated increased fatty acid synthesis, consistent with the increased MDA and ACSL4. HMGCS1 and HMGCR are responsible for the synthesis of cholesterol. LRP1 and SREBP2 regulate lipid metabolism homeostasis and cholesterol levels, respectively. The above results suggested that increased fatty acid synthesis may be the main cause of increased lipid peroxidation. CY-09 could reduce the synthesis of fatty acid and lipid peroxidation by inhibiting NLRP3 inflammasome activation in the 3×Tg-AD mice.
## 4. Discussion
Neuroinflammation has been recognized as a key event in inducing the onset and progression of AD, and it is closely linked to Aβ accumulation, tau hyperphosphorylation, and decreased cerebral glucose metabolism [3]. NLRP3 inflammasome is the most important inflammasome involved in neuroinflammation. In this study, CY-09 was used to inhibit the activation of NLRP3 inflammasome, leading to the restoration of glucose metabolism, as demonstrated by the increases in the expression and distribution of glucose transporters and related enzymes and the attenuation of insulin resistance. Moreover, inhibition of NLRP3 inflammasome activation by CY-09 could also reduce oxidative stress and improve cognitive ability in AD model mice.
In recent years, several studies have shown that NLRP3 inflammasome activation contributes to the progress of AD pathologies, specifically Aβ and tau proteins [42,43]. Inhibition of NLRP3 inflammasome activation helps to reduce Aβ deposition and improve cognitive impairment [43]. Most inhibitors of NLRP3 inflammasome activation do not specifically target NLRP3, except for CY-09, which combines directly with the ATP-binding motif of the NLRP3 NACHT domain [32]. Thus, CY-09 was selected in this study to inhibit NLRP3 inflammasome activation. As crossing the BBB and binding to NLRP3 in the brain is the prerequisite for inhibition, we used LC-MS/MS to characterize the molecular structure of CY-09 and detected its level in the brains of the mice. These results proved that CY-09 crossed the BBB to the brain to exert its biological function. Then, we further detected the expression and distribution of Aβ and tau proteins in CY-09-treated and non-treated 3×Tg-AD mice. Consistent with previous studies [43], our results showed reversed expression levels of pathological proteins in CY-09 treated 3×Tg-AD mice. In addition, CY-09 could also relieve cognitive deficits in the AD mice.
Reduced cerebral glucose metabolism has been reported in the AD brain due to the decrease in glucose transport, insulin resistance, and reduced metabolic enzymes [44,45,46]. We used 18F-FDG PET to analyze the alteration of glucose metabolism in CY-09-treated and non-treated 3×Tg-AD mice. Our results showed increased glucose uptake and metabolism rates in CY-09-treated 3×Tg-AD mice. GLUT1, GLUT3 and GLUT4 are responsible for glucose uptake and transport in the brain. Decreased expressions of GLUT1 and GLUT3 in AD indicated reduced glucose uptake and transport [47,48,49,50]. In this work, GLUT1 and GLUT3 expression levels were found to be increased in CY-09-treated 3×Tg-AD mice, while GLUT4 also exhibited higher expression and distribution. These results implied that inhibiting NLRP3 inflammasome activation by CY-09 may improve insulin resistance. Insulin is a critical hormone that regulates blood glucose [20,51]. It is transported into the brain and binds with insulin receptors, which are expressed in the cell membranes to activate the insulin signaling pathway [21,52,53]. In AD, insulin levels increase in the blood and decrease in the brain, the insulin signal pathway is suppressed, and GLUT4 cannot localize to the cell membrane, leading to reduced glucose uptake. Intranasal insulin injection helps to recover the insulin signaling pathway, stimulate the transfer of GLUT4 to the cell membranes, and thus increase the uptake of glucose in AD [54]. CY-09 has been reported to inhibit NLRP3 inflammasome activation in order to improve insulin resistance in obesity and non-alcoholic fatty liver disease. In this study, elevated insulin levels and recovered insulin signal pathways were detected in the CY-09-treated AD mice. Increased insulin sensitivity was also measured by GTT and ITT in the CY-09-treated AD mice. We also detected the recovered insulin signal pathway in CY-09-treated 3×Tg-AD mice. These data suggested that the inhibition of NLRP3 inflammasome activation by CY-09 helps to alleviate insulin resistance in AD.
Insulin resistance and glucose metabolism dysfunction are two hallmarks of diabetes mellitus. As they were also found in AD, most researchers considered AD as type 3 diabetes mellitus. However, it should be noted that in diabetes, decreased insulin secretion or insulin resistance leads to increased blood glucose [20,47]. Abnormal insulin level in the blood is the main cause of diabetes. While in AD, the higher insulin levels in the blood and lower levels in the brain indicated a damaged insulin signaling pathway. Therefore, maintaining the stability of insulin levels and blood glucose is the key point for diabetes, but increase the insulin levels and glucose metabolism in the brain is more important in AD.
Glucose was transported into the cells by GLUTs and phosphorylated by mitochondria-bound HK to initiate glucose metabolism. Decreased expression and abnormal distribution of HK were found in AD. Glycolysis and oxidative phosphorylation (OxPhos) are two main pathways of glucose metabolism [55]. *Glycolysis* generates little ATP, while OxPhos generates a large amount of ATP to meet the energy needs of the neurons [56]. The dividing point between the two pathways is the metabolic selection of pyruvate. Pyruvate is catalyzed by lactate dehydrogenase to generate lactate in glycolysis, while it is catalyzed by PDHE to generate Acetyl-CoA in OxPhos. An increasing number of studies have demonstrated that the metabolic pattern of neurons changes from OxPhos to aerobic glycolysis in the AD brain [25,56,57]. Reduced PDHE expression may be one reason for this shift. Here, we detected the increased expression levels of HK1 and PDHE1α in CY-09 treated 3×Tg-AD mice, which are beneficial for maintaining glucose metabolism and ATP production in the brain.
Oxidative stress is another important pathological characteristic that is closely related to NLRP3 inflammasome and glucose metabolism in AD. ROS was released from the oxidative respiratory chain and can activate the NLRP3 inflammasome [58,59]. Here, we found that the inactivation of the NLRP3 inflammasome by CY-09 decreased ROS levels in CY-09-treated 3×Tg-AD mice. Oxidative stress causes iron metabolism disorders and leads to ferroptosis, which is a new area in the research of AD pathogenesis [25,26]. Ferroptosis manifests as increased cellular Fe2+, decreased GPX4, and increased lipid peroxidation [27]. Elevated Fe2+ results from increased transferrin transport and ferritinophagy [28]. Consistent with the studies, we detected increased TF, TFR, and FPN and decreased GPX4 and SLC7A11 in the 3×Tg-AD mice. Unfortunately, there was no significant difference in these proteins between the AD and CY-09-treated AD mice. These results indicate that the inactivation of NLRP3 inflammasome does not reduce ferroptosis. Moreover, a contradiction between decreased ferritinophagy and increased ferroptosis was found in AD. Generally, decreased NCOA4 represents decreased ferritinophagy [28,60]. Thus, the expression and function of this protein remain to be clarified.
As a marker of ferroptosis, ACSL4 also participates in lipid peroxidation. Some studies showed that lipid peroxidation is related to Aβ deposition and the hyperphosphorylation of tau [29,30,31]. In this paper, the levels of ACSL4 and MDA were increased in 3×Tg-AD mice and decreased after CY-09 treatment. The results prompted us to explore the effect of CY-09 on lipid metabolism, mainly the synthesis and metabolism of fatty acids and cholesterol. FAS and ACC are rate-limiting enzymes of fatty acid synthesis. In AD mice, the expression of FAS and ACC increased, whereas the phosphorylation level of ACC deceased, thus indicating an increase in fatty acids synthesis. HMGCS1 and HMGCR are important for cholesterol synthesis [61]. Increased HMGCS1 and HMGCR were also found in AD mice. Surprisingly, expressions of ACC, HMGCS1, and HMGCR were decreased in CY-09-treated AD mice. This suggests that decreased MDA and lipid peroxidation may be related to the decreased synthesis of fatty acids and cholesterol in CY-09-treated AD mice. LRP1 plays an important role in lipid metabolism, glucose metabolism, insulin signaling, and the elimination of Aβ in AD [26,62]. Increased LRP1 helps to improve cognitive ability in AD [63]. SREBP2 is a negative regulator of LRP1 [64]. However, in our results, no difference in LRP1 and SREBP2 was found between AD mice and CY-09-treated AD mice. Here, a limitation should be noted. In this study, we used only one strain of AD model mice to study the effect of NLRP3 inflammasome activation on glucose metabolism and the role of CY-09. Using two or more strains of mice would help to validate the experimental results. Therefore, the conclusion drawn in this paper is restricted to only the 3×Tg-AD mice, and it should be verified using additional AD models in the near future.
## 5. Conclusions
Summarily, inhibiting NLRP3 inflammasome activation by CY-09 helps to restore cerebral glucose metabolism, improve memory and learning ability, and reduce fatty acid synthesis and lipid peroxidation in the 3×Tg-AD mice. Thus, CY-09 has the potential to be developed for the treatment of AD. Further studies are required regarding the shift between glycolysis and OxPhos pathways in AD in order to better understand the mechanism of neuroinflammation and glucose metabolism in the development of AD pathology.
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|
---
title: The Impact of YRNAs on HNSCC and HPV Infection
authors:
- Kacper Guglas
- Tomasz Kolenda
- Joanna Kozłowska-Masłoń
- Patricia Severino
- Anna Teresiak
- Renata Bliźniak
- Katarzyna Lamperska
journal: Biomedicines
year: 2023
pmcid: PMC10045647
doi: 10.3390/biomedicines11030681
license: CC BY 4.0
---
# The Impact of YRNAs on HNSCC and HPV Infection
## Abstract
HPV infection is one of the most important risk factors for head and neck squamous cell carcinoma among younger patients. YRNAs are short non-coding RNAs involved in DNA replication. YRNAs have been found to be dysregulated in many cancers, including head and neck squamous cell carcinoma (HNSCC). In this study, we investigated the role of YRNAs in HPV-positive HNSCC using publicly available gene expression datasets from HNSCC tissue, where expression patterns of YRNAs in HPV(+) and HPV(−) HNSCC samples significantly differed. Additionally, HNSCC cell lines were treated with YRNA1-overexpressing plasmid and RNA derived from these cell lines was used to perform a NGS analysis. Additionally, a deconvolution analysis was performed to determine YRNA1’s impact on immune cells. YRNA expression levels varied according to cancer pathological and clinical stages, and correlated with more aggressive subtypes. YRNAs were mostly associated with more advanced cancer stages in the HPV(+) group, and YRNA3 and YRNA1 expression levels were found to be correlated with more advanced clinical stages despite HPV infection status, showing that they may function as potential biomarkers of more advanced stages of the disease. YRNA5 was associated with less-advanced cancer stages in the HPV(−) group. Overall survival and progression-free survival analyses showed opposite results between the HPV groups. The expression of YRNAs, especially YRNA1, correlated with a vast number of proteins and cellular processes associated with viral infections and immunologic responses to viruses. HNSCC-derived cell lines overexpressing YRNA1 were then used to determine the correlation of YRNA1 and the expression of genes associated with HPV infections. Taken together, our results highlight the potential of YRNAs as possible HNSCC biomarkers and new molecular targets.
## 1. Introduction
Head and neck squamous cell carcinomas (HNSCCs) are among the most challenging tumor types to treat [1,2,3]. They originate from epithelial cells of the aerodigestive tract [2,3,4] and may be classified according to their localization: nasopharyngeal, tongue, oral, and laryngeal squamous cell carcinoma (NSCC, TSCC, OSCC, and LSCC, respectively) [2,3,5,6]. The most common risk factors are alcohol consumption, tobacco smoking, and human papilloma virus (HPV) infection [7,8]. Interestingly, among younger patients, HPV infection is the most important risk factor, often associated with better treatment outcomes and recovery [1,2,3,4,6,9,10]. Over 200 types of HPV have been described, but HPV-16 is the strain mostly associated with squamous cell carcinoma [10,11].
HPV(+) and HPV(−) tumors molecularly differ from each other. The mutation rate in HPV(+) HNSCC is lower compared with HPV(−) HNSCC, and HPV(+) tumors are characterized by a lower number of TP53 mutations [10,11]. Clinically, HPV(+) cells are more radiosensitive. HPV(+) HNSCC has altered mismatch repair systems, DNA repair mechanisms, and homologous recombination pathways, which significantly contribute to HNSCC cells’ radiosensitivity. Moreover, another important factor in HPV(+) radiosensitivity is the overexpression of p16, which delays the DNA damage response [10].
HPV(−) and HPV(+) HNSCC development includes epigenetic changes including regulatory RNAs, which take part in various crucial processes such as apoptosis regulation, proliferation, cell migration, and cell cycle regulation [1,2,6]. However, these epigenetic changes differ between HPV(−) and HPV(+) subtypes which are manifested in different potential of HNSCC aggressiveness [1,2,6].
Non-coding RNA molecules (ncRNAs) are essential for many cellular processes [1,2,4,9,10,12,13,14,15,16]. Previous studies have demonstrated that many different types of ncRNAs, including long non-coding RNAs (lncRNA) and short non-coding RNAs such as miRNA, are dysregulated in HNSCC [1,3,4,9,10,12,17,18,19]. Moreover, ncRNAs may be used as very promising biomarkers and targets for future molecular-based therapies [1,3,4,9,10,12,17,18,19].
One of the studied types of ncRNAs are YRNAs (Ro associated-Y). These ncRNAs are components of Ro60 ribonucleoprotein particles and consist of 80–112 nucleotides [1,12,13,20,21,22,23]. Four different YRNAs may be distinguished: YRNA1, YRNA3, YRNA4 and YRNA5 [1,12,13,20,21,22,23]. These four YRNA genes are clustered at a single chromosomal locus on chromosome 7q36 and are transcribed by RNA polymerase III [1,12,13,14,20]. Mature YRNAs form a stem-loop structure. The upper stem of YRNAs is essential for the initiation of DNA replication, leading to the formation of new replication forks. The lower stem is a Ro60-binding site, forming an activated protein–ribonucleoprotein complex [1,12,13,20,21]. The lower stem also controls the nuclear export of YRNAs. YRNAs have been shown to interact with many different proteins conditioning their functions [12,13,20,24]. YRNAs also were found in extracellular vesicles and in retroviruses [12,13,20,24].
Since YRNAs are easily obtained from human serum, plasma, saliva, and tissues, it makes them potential biomarkers and targets for future therapies [12]. Studies have shown that YRNAs are over-expressed in glioma [25], triple-negative breast cancer (TNBC) [26], pancreatic ductal adenocarcinoma (PDAC) [27,28], colon cancer [29,30], cervix cancer [27,29], benign prostate hyperplasia [31] and clear-cell renal-cell carcinoma (ccRCC) [14]. On the other hand, YRNAs have been found to be downregulated in HNSCC [1], prostate cancer [15], and bladder cancer [16]. YRNAs are naïvely involved in crucial processes of cancer development such as apoptosis, cell proliferation, angiogenesis, metastasis, and different types of cellular stresses [12]. However, the involvement of YRNAs in viral-associated tumors, such as HPV infections in HNSCC, is unknown and their biological role is not yet defined. In order to address this role, YRNAs’ (YRNA1, YRNA3, YRNA4, and YRNA5) expression patterns were investigated in publicly available RNAseq datasets of HPV(−) and HPV(+) HNSCC tissue samples. Additionally, HNSCC-derived cell lines overexpressing YRNA1 were used to determine the correlation between YRNA1 and the expression of genes associated with HPV infections. The main aim of this study was to show the impact that YRNAs have on HNSCC development as well as to show their correlation with HPV infection, which is an important HNSCC development factor.
## 2.1. HNSCC Gene Expression Datasets
For the expression analysis of YRNA patterns in HNSCC, data generated by the Leipzig Head and Neck Group (LNHG) for 269 HNSCC cases (including 196 HPV(−) and 73 HPV(+) cases) were used [31]. They comprised expression data of 31330 genes as specified by Illumina IDs (HumanHT-12_V4_0_R2_15002873_B). The expression data were obtained from patients’ biopsies. The patients were treated with different therapeutic approaches according to tumor subsites and TNM stages. The corresponding metadata are available in association with the gene expression data at the following GEO accession ID: GSE65858. The specific information considering raw data and processing were described in detail by Wichmann et al. [ 31].
Additionally, for the validation of expression values following overexpression of YRNA1 in FaDu and Detroit cell lines, data from 523 HNSCC samples obtained from cBioPortal (Head and Neck Squamous Cell Carcinoma, TCGA, PanCancer Atlas) were used. All data are available and access is unrestricted. The data derived from GEO were used to calculate the expression of YRNA1 in terms of clinical parameters and perform GSEA analysis. *The* genes from the GEO and TCGA datasets were also used for the validation of NGS results.
## 2.2. YRNAs Expression and Clinical Parameters
The correlation between each member of the YRNA cluster (YRNA1, YRNA3, YRNA4, and YRNA5) and clinical parameters were determined and associated with HPV status.
The following clinical parameters were considered: age (below or above 60 years), smoking status (Yes vs. No/Ex; Ex—ex smoker), tumor localization (oropharynx, larynx, hypopharynx, oral cavity), HPV16 infection status (as measured by p16 positive vs. negative), TP53 mutation status (WT vs. disruptive vs. non-disruptive), T stage (T1 + T2 vs. T3 + T4 and T1 vs. T2 vs. T3 vs. T4), N stage (N0 + N1 vs N2 + N3 and N1 vs. N2 vs. N3 vs. N4), clusters (classical vs. basal vs. atypical vs. mesenchymal), clinical stage (I + II vs. III + IV and I vs. II vs. III vs. IV) and HPV(−) and HPV(+) status.
The expression levels of YRNA1, YRNA3, YRNA4, and YRNA5 were also associated with active and inactive viral status (DNA + RNA+ vs. DNA + RNA-) and type of HPV (HPV16 vs. other types of HPV) in the HPV(+) group.
The association of YRNA1 with specified cancer markers was also calculated. The cancer markers such as CD44, SOX2, TP53, ALDH1A1, and FAT1 were chosen because of their vast influence on various processes occurring in HNSCC as shown in previously published papers [32,33,34,35,36,37,38,39,40,41].
Overall survival (OS) and progression-free survival (PFS) analyses were performed using two subgroups (low and high expression of YRNAs, based on the mean expression levels) in HPV(+), HPV(−), and both together. The subgroups were compared using Log-Rank (Mantel–Cox), Gehan–Breslow–Wilcoxon, and Hazard Ratio (Mantel–Haenszel; HR) tests. The $95\%$ Confidence Interval (CI) of the ratio was calculated.
Finally, the ROC analysis was applied to compare YRNA1, YRNA3, YRNA4, and YRNA5 expression levels between HPV(−) and HPV(+), and AUC (Area under the ROC curve) were calculated.
## 2.3. Functional Enrichment Analysis and Prediction of Gene Function
Gene Set Enrichment Analysis (GSEA) software version 4.1.0 (http://www.gsea-msigdb.org/gsea/index.jsp, accessed on 15 February 2022) was used as previously described for the analysis of functional enrichment [42,43]. The input file (GEO accession ID: GSE65858) contained expression data for 29,089 genes and 269 patients. HPV(−) and HPV(+) groups were divided into high- and low-expression subgroups based on the mean expression levels of YRNA1, YRNA3, YRNA4, and YRNA5.
Analysis of the Oncogenic Signatures (C), Hallmark Gene Set (H), and Gene Ontology (GO) with 1000 gene set permutations were applied and a nominal p-value p ≤ 0.05 and FDR q-value ≤ 0.25 were considered statistically significant. Next, using the Reactome database, genes derived from GSEA were analyzed in terms of pathways in human organisms (http://reactome.org, accessed on 8 March 2022) [44]. Finally, the interactions between protein-coding genes in the pathway which were the most significantly enriched in a group of patients with low vs. high expression of YRNAs were analyzed using the GeneMANIA prediction tool (http://genemania.org) [45]. The expression heat map was generated using Morpheus Heat Map (https://software.broadinstitute.org/morpheus/, accessed on 15 March 2022).
## 2.4. Estimation of Immune Cells Fractions
The deconvolution method was applied to analyze the immune cell composition in HNSCC tissues based on expressional data from patients samples. This technique was performed using a tool developed by Chiu et al. [ 46], the source code of which is based on commonly available R and Python packages and can be downloaded from https://github.com/holiday01/deconvolution-to-estimate-immune-cell-subsets (accessed on 20 June 2022). All steps of the deconvolution analysis were carried out in line with the authors instructions [46]. To correctly prepare the GEO data set for this analysis, the list of gene names contained therein was updated in accordance with the HGNC nomenclature [47], and, subsequently, any repetitions resulting from the presence of expression data for different transcripts of the same gene were removed. Patients were divided based on the YRNA1 expression level. The ten individuals with the highest and ten with the lowest levels of studied YRNA formed two groups which were then compared. High- and low-expression groups were extracted on the same basis from the general population of patients ($$n = 269$$), as well as separately from subgroups with HPV(−) ($$n = 196$$) and HPV(+) status ($$n = 73$$). The deconvolution provided estimated fractions of 9 immune cell types, including naïve CD4 T cells, natural killer cells, macrophages M1 and M2, dendritic cells, T helper cells, regulatory T cells, naïve CD8 T cells, as well as memory CD8 T cells for each individual, which were then compared and statistically evaluated.
## 2.5. Cell Line, Transfection, RNA Isolation, and qRT-PCR
Two HNSCC-derived cell lines were used for YRNA1 gain-of-function assays, FaDu and Detroit562. The FaDu cell line was cultured as previously described [48], and Detroit562 was cultured in DMEM (Biowest, Nuaille, France) with $10\%$ FBS (Biowest, Nuaille, France) and geneticin antibiotic (KRKA, Novo Mesto, Slovenia). The cell lines were examined for mycoplasma using the VenorGeM Mycoplasma PCR Detection Kit (Minerva Biolabs, Berlin, Germany). Cell lines were seeded on 6-well plates (400,000 cells/well, $5\%$ CO2 and 37 °C) and transfected using pcDNA3.1(+)-hRNY1[NR_004391.1] or with pcDNA3.1(+)-CTR (control) vectors obtained from VectorBuilder Inc. (VectorBuilder Inc., Chicago, IL, USA) with Lipofectamine with PLUS Reagent (Thermo Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. After 48 h incubation, the transfected cells were selected using G418 antibiotic (0.8 mg/mL for FaDu and 0.4 mg/mL for Detroit562) for another 7 days at $5\%$ CO2 and 37 °C.
Total RNA from the cell lines was isolated using a Total RNA Midi isolation kit (A&A Biotechnology, Gdańsk, Poland), according to the isolation protocol. Next, the quality and quantity of isolated RNA samples were examined using the NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA), followed by 28S and 18S rRNA band estimation ($1\%$ agarose gel electrophoresis in TAE (Tris-acetate-EDTA (Ethylenediaminetetraacetic acid buffer). The enhanced expressions of the YRNA1 in FaDu and Detroit562 cell lines were confirmed with qRT-PCR. Complementary DNA was synthesized using an iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA), and 0.5 μg of the total RNA was used. Quantitative PCR was performed using 2× concentrated SYBR Green Master Mix (Roche, Basel, Switzerland) with specific primers to detect YRNA1 as described previously [15,30,31]. Endogenous control HPRT1 (F: 5′-TGA CCT TGA TTT ATT TTG CAT ACC-3′ AND R: 5′-CGA GCA AGA CGT TCA GTC CT-3′) was used at a final reaction concentration of 0.5 μM with 5× diluted cDNA. The real-time PCR reactions were performed on a LightCycler 96 (Roche, Basel, Switzerland) device, and a melting curve was performed to discriminate between non-specific products of the PCR reaction. All real-time PCR data were analyzed by calculating the 2−ΔCT, normalizing against the mean of HPRT1 expression.
## 2.6. RNA Sequencing
Sequencing of RNA samples derived from FaDu and Detroit562 cells overexpressing YRNA1 and controls was performed by Eurofins Genomics Europe Sequencing GmbH (Eurofins Scientific, Luxembourg) using the Genome Sequencer Illumina NovaSeq and NovaSeq 6000 S4 PE150 XP. Human Genome hg19/GRC37, UCSC; annotations Gencode v29, Ensembl 90 were used as references of annotation.
Briefly, high-quality sequence reads were aligned to the reference genome using STAR (Spliced Transcripts Alignment to a Reference), run through the Sentieon framework along with the known gene models [49]. The STAR algorithm achieves highly efficient mapping by performing a two-step process: seed searching, followed by clustering, stitching, and scoring. The percent of mapped transcripts for FaDu_pcDNA3.1(+)-hRNY1[NR_004391.1], FaDu_pcDNA3.1(+)-CTR, Detroit562_pcDNA3.1(+)-hRNY1[NR_004391.1] and Detroit562_pcDNA3.1(+)-CTR to a reference genome was $95.3\%$, $92.2\%$, $98.7\%$ and $98.4\%$, respectively. Next, gene quantification was achieved by inspecting transcriptome alignment using the RSEM tool [50]. Read counts were further normalized to account for sequencing depth and gene length biases. Fragment per kilobase per million (FPKM) and transcripts per million (TPM) values were generated. Finally, differentially expressed gene identification was performed. To identify a gene or a transcript differentially expressed, Cuffdiff 2 tests the observed log-fold-change in expression against the null hypothesis of no change. Because measurement error, technical variability, and cross-replicate biological variability might result in an observed log-fold-change that is not zero, Cuffdiff 2 assesses significance using a model of variability in the log-fold-change under the null hypothesis. This method is described in detail by Trapnell et al. [ 51].
## 2.7. Statistical Analysis
All statistical analyses were performed using GraphPad Prism 9 (San Diego, CA, USA). The normality of the groups was tested using the Shapiro–Wilk test and subsequent comparisons of the two groups were carried out using the t-test or Mann–Whitney U test depending on the distributions. For the comparison of three and more groups, one-way ANOVA, Kruskal–Wallis test, and post-tests Dunn’s multiple comparison test or Tukey’s multiple comparison test were used. The correlation analysis between YRNAs and gene markers was performed using the Spearman correlation test. The REACTOME pathway browser was used as a free tool for pathway analysis of genes derived from NGS (www.reactome.org, accessed on 1 February 2023). In all analyses, $p \leq 0.05$ and FDR < 0.25 was considered significant.
## 3.1. The Expression of YRNAs Is Significantly Distinct between HNSCC Clinical and Pathological Stages
First of all, the data obtained from the GEO dataset were examined in terms of the expression of YRNA1, YRNA3, YRNA4, and YRNA5 in association with different clinical–pathological features. The analyses of YRNA1 showed that YRNA1 was highly dysregulated in terms of the tumor’s N stage. It was noticed that the expression of YRNA1 was significantly higher in N2–N3 stages ($$p \leq 0.0426$$), and the same trend was observed after clustering N stages into two groups: N0 + N1 vs. N2 + N3 ($$p \leq 0.0110$$) (Figure 1A). YRNA3 was only significantly overexpressed in T3–T4 stages ($$p \leq 0.0045$$), with the highest expression seen in the T3 stage. When clustering T stages into two groups (T1 + T2 vs. T3 + T4) the highest expression of YRNA3 was confirmed in T3 + T4 stages ($$p \leq 0.0019$$) (Figure 1A). The rest of the YRNAs did not show any significant changes in this case. In the case of the clinical stage, the expression of YRNA3 also showed significant changes ($$p \leq 0.002$$) (Figure 1A). Interestingly, further clustering the data into two groups (I + II vs. III + IV) showed that YRNA3 was significantly overexpressed in the III–IV clinical stages ($$p \leq 0.0002$$). Interestingly, all four YRNAs showed significant alterations in their expression levels among different subtypes ($p \leq 0.0001$, $$p \leq 0.0006$$, $$p \leq 0.0051$$, $$p \leq 0.0318$$, respectively) (Figure 1). It was noticed that lower expression levels of YRNAs were found in the least aggressive HNSCC subtypes such as classical and basal subtypes, and the expression increased in the mesenchymal subtype—the most aggressive one. These data show that the expression of YRNAs is correlated with the advancement of the HNSCC disease. Finally, the YRNAs did not show any significant changes in their expression in terms of tumor localization (Figure 1A). All values for all cases may be found in Supplementary Table S1.
The ROC analysis was applied and AUC (Area under the ROC curve) was calculated to compare HPV(+) vs. HPV(−), DNA + RNA+ vs. DNA + RNA-, and p16 vs. other HPV; however, no significant differences were observed (Supplementary Table S2 and Figure S1).
Since previous studies showed that YRNAs may be associated with HPV infection, the data were divided into two groups: HPV(+) and HPV(−). In the HPV(−) group, YRNA1 was found to be significantly upregulated in N2 + N3 compared with N0 + N1 ($$p \leq 0.0193$$). YRNA3 was significantly upregulated in both T3 and T4 stages ($$p \leq 0.0110$$) in the HPV(−) group and when T3 + T4 stages were grouped ($$p \leq 0.0003$$) (Figure 1B). A similar trend was seen for the clinical stage analysis, where the expression of YRNA3 was upregulated in the III and IV clinical stages, both separately or when taken together ($$p \leq 0.0204$$ and $$p \leq 0.0022$$, respectively). Interestingly, YRNA5 showed the opposite results. The expression of YRNA5 was found to be overexpressed in both T1 and T2 stages ($$p \leq 0.0437$$) and in T1 + T2 stages ($$p \leq 0.0430$$) (Figure 1B). YRNA5 was also found to be overexpressed in clustered I + II clinical stages ($$p \leq 0.0470$$) (Figure 1B). Next, the YRNAs’ expression was analyzed in different HNSCC subtypes: classical (the least aggressive subtype), basal, atypical, and mesenchymal (the most aggressive subtype). Lower expression of YRNAs is associated with the least-aggressive tumor subtype and higher expression was associated with the more-aggressive subtype ($p \leq 0.0001$ for YRNA1; $$p \leq 0.0224$$ for YRNA4; $$p \leq 0.01$$ for YRNA5) (Figure 1B). YRNA3 did not show any differences in expression levels in this case. Additionally, no differences in expression levels of YRNAs were found in terms of the N stage (except for the clustered analysis of YRNA1) and tumor localization (Figure 1B). All values for these analyses are presented in Supplementary Table S3.
In HPV(+) group, which showed better survival rates and better treatment outcomes among HNSCC patients, it was discovered that the expression of YRNA3 is significantly overexpressed in III and IV clinical stages ($$p \leq 0.0449$$) and the clustered III + IV variant ($$p \leq 0.0330$$) (Figure 1C). Similar results were observed in the analysis of all patients and the HPV-negative group.
Surprisingly, it was observed that YRNA3 is significantly overexpressed in the hypopharynx in comparison with the larynx ($$p \leq 0.0434$$) (Figure 1C). Moreover, in terms of expression in different HNSCC subtypes, YRNA4 and YRNA5 did not show any differences; however, in the case of YRNA1 and YRNA3, a similar trend may be seen as with that in the previous analysis ($$p \leq 0.0075$$ and $$p \leq 0.0053$$, respectively) (Figure 1C). In both cases, it was noticed that a higher expression of YRNA1 and YRNA3 correlates with more-aggressive tumor subtypes. The rest of YRNAs did not show any expression differences in terms of localization. There were also no differences in terms of the T stage and N stage in the HPV(+) group. All values for these analyses are presented in Supplementary Table S4.
## 3.2. YRNAs Have a Distinct Impact on Cancer and Stemness Markers
Next, correlations of YRNAs and CD44, SOX2, TP53, ALDH1A1 and FAT1 cancer and stemness markers were analyzed in a whole group of patients and divided into atypical, basal, classical and mesenchymal subtypes.
In the whole group of patients, YRNA1 showed a significant negative correlation with CD44 (ρ = −0.1716; $$p \leq 0.0051$$), SOX2 (ρ = −0.2707; $p \leq 0.0001$), ALDH1A1 (ρ = −0.1421; $$p \leq 0.0206$$), and FAT1 (ρ = −0.2501; $p \leq 0.0001$) (Figure 2A). The correlation with TP53 was also slightly negative; however, it did not show any statistical significance.
YRNA4 was found to be negatively correlated with SOX2 and ALDH1A1 (ρ = −0.3009, $p \leq 0.0001$; ρ = −0.3096, $p \leq 0.0001$, respectively). YRNA5 was also negatively correlated with SOX2 (ρ = −0.1962, $$p \leq 0.0013$$) but also with FAT1 (ρ = −0.1995, $$p \leq 0.0011$$). YRNA3 was not significantly correlated with any of the examined cancer and stemness markers (Figure 2A).
Next, it was found that the correlation of YRNA1 and cancer and stemness markers vastly differed between the most and the least aggressive subtypes. In the most aggressive subtype, mesenchymal, the correlation of YRNA1 and the selected markers was negative for all examined markers, albeit statistically significant in the case of SOX2 and TP53 (ρ = −0.2916, $$p \leq 0.0075$$; ρ = −0.2462, $$p \leq 0.0249$$, respectively). In the mesenchymal subtype, YRNA3 showed a significant positive correlation with CD44 (ρ = 0.3832, $$p \leq 0.0003$$) (Figure 2B). On the other hand, in the classical subtype, which is known to be the least aggressive HNSCC subtype, it was noticed that most correlations were slightly positive and in one case there was a statistical significance: TP53 and YRNA4 were significantly correlated with each other (ρ = 0.4833, $$p \leq 0.0079$$) (Figure 2B). In the atypical subtype, YRNA1 was negatively correlated with all markers, but only correlation with SOX2 showed statistical significance (ρ = −0.2595, $$p \leq 0.0277$$). YRNA4 was significantly, negatively correlated with CD44, SOX2, and ALDH1A1 (ρ = −0.2764, $$p \leq 0.0817$$; ρ = −0.3177, $$p \leq 0.0065$$; ρ = −0.4578, $p \leq 0.0001$, respectively), and YRNA5 was found to be negatively correlated with CD44 (ρ = −0.233, $$p \leq 0.0489$$) (Figure 2B). Finally, in the basal subtype, YRNA1 was negatively correlated with all markers and significance was seen in CD44, SOX2, and FAT1 (ρ = −0.248, $$p \leq 0.0256$$; ρ = −0.2799, $$p \leq 0.0114$$; ρ = −0.3119, $$p \leq 0.0046$$, respectively). A significant, positive correlation was observed only between YRNA3 and CD44 (ρ = 0.2957, $$p \leq 0.0074$$). Negative correlations were also observed between YRNA4, SOX2 and YRNA5, FAT1 (ρ = −0.2722, $$p \leq 0.014$$; ρ = −0.2755, $$p \leq 0.0128$$, respectively) (Figure 2B).
## 3.3. Patients’ Overall Survival and Progression-Free Survival Are Associated with YRNAs Expression Levels
The associations between patients’ survival rates and expression levels of YRNAs, which is the most important clinical–pathological parameter, were analyzed. Patients were divided into two groups and the mean value of YRNA expression levels were used as a cut-off for all patients, HPV(+) and HPV(−). It was observed that patients with low expressions of YRNA4 showed better survival than patients in the high-expression groups ($$p \leq 0.002$$, HR = 0.4332, $95\%$ CI = 0.2782 to 0.6744) during 2500 days of observation. For the rest of the analyzed YRNAs, no differences were observed ($p \leq 0.05$) (Figure 3A). However, when the time of observation was shortened to 1000 days, patients with lower expressions of YRNA1 and YRNA4 showed significantly better overall survival rates ($$p \leq 0.0024$$, HR = 0.4727, $95\%$ CI = 0.2916 to 0.7664 and $$p \leq 0.0002$$, HR = 0.3926, $95\%$ CI = 0.2382 to 0.6470, respectively) (Supplementary Figure S2).
Next, patients were divided based on HPV status and overall survival depending on YRNAs level was analyzed (Figure 3B). No differences in patients’ overall survival depending on YRNA3, YRNA4, and YRNA5 levels in the HPV(+) group were noticed ($p \leq 0.05$). However, patients with higher levels of this gene had a longer overall survival time than the group of patients with lower expression levels of YRNA1 ($$p \leq 0.0451$$, HR = 2.559, $95\%$ CI = 1.021 to 6.416) (Figure 3B). However, in the shortened time none of YRNAs showed any significant differences in overall survival of patients (Supplementary Figure S2).
In the HPV(−) group, patients with lower levels of YRNA1 and YRNA4 had longer survival times during 2500 days of observation ($$p \leq 0.0036$$, HR = 0.4914, $95\%$ CI = 0.3046 to 0.7928 and $p \leq 0.0001$, HR = 0.2880, $95\%$ CI = 0.1750 to 0.4741, respectively) and when time was shortened to 1000 days ($$p \leq 0.0008$$, HR = 0.4053, $95\%$ CI = 0.2386 to 0.6886 and $p \leq 0.0001$, HR = 0.3016, $95\%$ CI = 0.1748 to 0.5203, respectively) than those with higher levels of those genes. However, no differences ($p \leq 0.05$) between patients’ overall survival and YRNA3 as well as YRNA5 were noticed during 2500 and 1000 days of observation (Figure 3B and Supplementary Figure S2).
The analysis of patients’ progression-free survival is described in Figure 4. Considering the whole group of HNSCC patients, those with low expressions of YRNA4 showed better progression-free survival than patients in the high-expression group ($$p \leq 0.0034$$, HR = 0.5764, $95\%$ CI = 0.3985 to 0.8338). No differences ($p \leq 0.05$) between YRNA1, YRNA3, and YRNA5 levels and patients’ progression-free survivals were observed (Figure 4A). However, when shortening the time of observation to 1000 days, significantly longer progression-free time was noticed for patients with lower expressions of YRNA1 and YRNA4 ($$p \leq 0.0167$$, HR = 0.6377, $95\%$ CI = 0.4411 to 0.9218 and $$p \leq 0.0062$$, HR = 0.5801, $95\%$ CI = 0.3927 to 0.8569, respectively). YRNA3 and YRNA5 did not show any significant differences when shortening the observation time (Supplementary Figure S3).
In the HPV(+) group, only patients with higher expression levels of YRNA1 showed longer progression-free survival rates ($$p \leq 0.0373$$, HR = 2.180, $95\%$ CI = 1.047 to 4.540). No differences for YRNA3, YRNA4, and YRNA5 were observed ($p \leq 0.05$) (Figure 4B). In spite of the shortened time of observation to 1000 days, no differences for YRNAs were indicated (Supplementary Figure S3).
In the HPV(−) group, patients with lower expression levels of YRNA1 and YRNA4 showed significantly better outcomes than those with higher expression levels of those genes ($$p \leq 0.0293$$, HR = 0.6401, $95\%$ CI = 0.4285 to 0.9560 and $p \leq 0.0001$, HR = 0.4258, $95\%$ CI = 0.2793 to 0.6492, respectively) (Figure 4B). No differences in survival for YRNA3 and YRNA5 were observed ($p \leq 0.05$). Similar results were found in a shortened analysis time to 1000 days. Patients with lower expressions of YRNA1 and YRNA4 showed significantly better progression-free survival rates than patients with higher expressions of those genes ($$p \leq 0.0246$$, HR = 0.6162, $95\%$ CI = 0.4041 to 0.9398 and $$p \leq 0.0021$$, HR = 0.5067, $95\%$ CI = 0.3287 to 0.7812, respectively). YRNA3 and YRNA5 did not show any significant differences (Supplementary Figure S3).
## 3.4. YRNAs Are Correlated with Different Genes among the HPV(+) Group with an Influence on HPV Proteins and Viral and Immunologic Pathways
The YRNAs were correlated with all genes derived from the examined GEO dataset in two groups: HPV(+) and HPV(−) (Figure 5A, Supplementary Figure S4A). The Venn diagrams in Supplementary Materials depict gene distribution in YRNAs between HPV(+) and HPV(−) groups (Supplementary Figure S4B). Interestingly, there were 21 common genes for YRNA1, 15 genes for YRNA3, 7 genes for in YRNA4, and 8 common genes for YRNA5 observed between the HPV(+) and HPV(−) groups. In the HPV(+) group, YRNA1 was negatively correlated with 25 genes and positively correlated with 7 genes. YRNA3 was negatively correlated with 16 genes and positively correlated with 9 different genes. In the case of YRNA4, 6 genes were negatively correlated and 9 genes were positively correlated. YRNA5 was negatively correlated with 14 genes and positively correlated with 6 genes (Figure 5A).
Next, the genes correlated with YRNAs were found to take part in many viral and immunologic processes, such as antigen processing and presenting, regulation of the innate immune system, and different cellular responses (Figure 5B). Moreover, a closer examination of protein-coding genes correlated with YRNA1 showed that many of them were strictly correlated with HPV proteins (Figure 5C). *Three* genes were correlated with E1 protein, three with E2 protein, six with E5 protein, three with E6 protein, ten with E7 protein, two with L1 protein, and one with L2 protein (Figure 5C). These HPV proteins are essential for HPV infection and stability. All of them were correlated with a vast number of different viral and immunologic processes associated with HPV invasion and replication in cells (Figure 5D).
## 3.5. YRNA1 Significantly Correlates with Protein Secretion Processes
Gene Set Enrichment Analysis (GSEA) was performed to obtain functional implications of YRNA1 expression in the HPV(+) groups. Interestingly, both groups showed the same outcome. The highest-enriched pathway correlated with YRNA1 was the protein secretion pathway (Normalised Enrichment Score—NES—1.5823789) (Figure 6A). Next, the interactions between protein-coding genes in the protein secretion pathway were analyzed using the GeneMANIA prediction tool (Figure 6B). The analysis showed 16 genes that are mostly co-expressed with each other ($40.96\%$). *These* genes were strictly correlated with the expression of YRNA1 (Figure 6A). Moreover, those 16 significantly altered genes were further analyzed using the REACTOME pathway browser, resulting in annotation of the proteins of those genes to specific processes in the human organism, such as the immune system, signal transduction, cell–cell communication, cellular stress to external stimuli, transport of small molecules, vesicle-mediated transport, metabolism of proteins, developmental biology, metabolism, and different diseases (Figure 6C). Finally, a created heat map shows that patients with higher-expressed YRNA1 have, in most cases, lower-expressed examined protein-coding genes and the lower-expressed group showed correlations with the higher-expressed protein-coding genes (Figure 6D).
In the GSEA analysis for YRNA3, YRNA4, and YRNA5 many more pathways were enriched than in YRNA1 (Figure 7). In the HPV(−) group YRNA3 was enriched in 20 different pathways and in the HPV(+) group in 11 different pathways. Worth noting is that 10 of these pathways were positively enriched and only one pathway showed a negative NES value—JAK2 DN, which is a pathway connected with different genes that are downregulated after the JAK2 downregulation. Next, YRNA4 in the HPV(+) group was found to be enriched in 59 pathways (22 most important shown on the figure); however, in the HPV(−) group YRNA4 was not enriched in any of the examined pathways. Many of these pathways are associated with DNA repair, ribosomes, and mitochondria. YRNA4 is also enriched in such crucial processes for YRNAs’ functions as SNRNP (small nuclear ribonucleoprotein) assembly, ribonucleoprotein complex subunit organization, and ribonucleoprotein complex biogenesis. Finally, the YRNA5 in the HPV(+) group was enriched in five pathways, but none in the HPV(−). These pathways and genes involved in them such as TGF beta signaling are essential in cancer development. All the NES, nominal p values, and FDR q values may be found in Supplementary Table S5.
## 3.6. YRNA1 Expression Significantly Correlates with Immune Cells
All HNSCC patients, as well as HPV(+) and HPV(−) subgroups, were divided into low- and high-YRNA1-expression groups, and immune cell content in patients’ tumor samples was predicted using deconvolution analysis. In the case of all patients, a significantly larger amount of DC cells was found in the groups showing lower expressions of YRNA1. Similar results were obtained in the HPV(−) group in addition to significantly higher amounts of M2 macrophages in the group with high expressions of YRNA1. In HPV(+) there were no significant differences discovered; however, the trend for DC cells was maintained (Figure 8).
## 3.7. Overexpressed YRNA1 Upregulates Genes Associated with Responses to Viral Infection
The overexpression of YRNA1 in FaDu and Detroit562 cells was confirmed by qRT-PCR analysis, with cells transfected with pcDNA3.1(+)_hRNY1 expressing significantly higher levels of YRNA1 than in cell lines transfected with pcDNA3.1(+) ($$p \leq 0.0002$$ and $$p \leq 0.0008$$, respectively) (Figure 9A).
Analysis of RNA sequencing data of FaDu and Detroit562 cells overexpressing YRNA1 using the REACTOME pathway browser showed enrichment of viral infection-associated pathways (infectious disease pathway, influenza infection pathway, viral mRNA translation, and influenza viral mRNA transcription, FDR < 0.25 and $p \leq 0.05$). For the Detroit562 cell line, additional alterations were seen for host interactions, activation/modulation of innate and adaptive immune responses, modulation of host translation machinery, and targeting host intracellular signaling and regulatory pathways (FDR < 0.25 and $p \leq 0.05$) (Figure 9A).
Furthermore, the genes selected from FaDu and Detroit562 cell lines overexpressing YRNA1 were examined using the GeneMANIA prediction tool, which allows the prediction offunctions and pathways of genes. *The* genes derived from the modified FaDu cell line were mostly associated with viral gene expression and viral latency. However, these genes were also involved in immune responses—regulating cell surface, receptor-signaling pathway involved in phagocytosis, Fc-gamma receptor signaling pathway, Fc-receptor mediated stimulatory signaling pathway, ribosome biogenesis, and cotranslational protein targeting the membrane. These results also showed that $59.01\%$ of these genes were co-expressed (Figure 9B). *The* genes derived from the modified Detroit562 cell line are also involved in viral gene expression; however, many of them are involved in immunological processes such as antigen binding, antigen processing, and presentation of endogenous antigen and peptide antigen and the MHC protein complex. In this case, $68\%$ of the examined genes were found to be co-expressed (Figure 9B).
Interestingly, 40 genes out of the 100 most-abundant genes following the overexpression of YRNA1 were common for modified FaDu and Detroit562 cell lines from which 23 showed similar expression patterns and 17 of them showed opposite expression patterns between the examined cell lines (Figure 9C). Further analysis using the REACTOME pathway browser also allowed distinguishing 40 genes from the modified FaDu cell line and 38 from the modified Detroit562 cell line to be involved in processes that include infectious diseases, the life cycles of SARS-CoV viruses, influenza virus and HIV, some metabolic processes mediated by intracellular Mycobacterium tuberculosis, the actions of clostridial, anthrax, diphtheria toxins, and the entry of *Listeria monocytogenes* into human cells (Figure 9D, Supplementary Table S6).
A subset of genes selected from FaDu and Detroit562-overexpressing YRNA1 were validated on the TCGA and GEO datasets, including the group of 40 genes expressed in both cell lines (mentioned above) and those expressed in only one cell line, constituting a set of 60 other genes. From the second dataset, eight genes in each group were not analyzed due to different factors such as inaccuracy in gene nomenclature between the datasets or the absence of some genes in the TCGA or GEO databases. Nevertheless, the validation of NGS data allowed us to discover that 18 out of 40 common genes were significantly dysregulated in data obtained from the TCGA database. In the case of FaDu, 22 out of 52 genes were dysregulated and in the Detroit562 cell line 24 genes out of 52 were significantly dysregulated (Figure 10). Furthermore, validation on the GEO database showed that 4 genes out of 20 common genes showed significant changes between the HPV(+) and HPV(−) groups. *In* genes specific only for the FaDu cell line, 7 genes out of 52 were significantly dysregulated and in Detroit562 it was 11 genes out of 52 (Figure 10).
## 4. Discussion
Head and neck squamous cell carcinomas still lack successful treatments because of their high aggressiveness and molecular heterogeneity [1,4,9]. It is crucial to find new approaches not only for the treatment but also for the detection of the disease in its early stages. One of the main factors for HNSCC development is HPV infection status, showing differences not only in the expression of genes but also in the treatment outcome. It has been previously shown that HPV infection is associated with younger patients and HPV-positive patients show better survival rates [4,9,52,53]. Interestingly, most HPV(+) HNSCCs are histologically graded as very poorly differentiated tumors in spite of their better clinical outcome, and in general less-differentiated tumors tend to show more aggressive behavior [54]. Additionally, HPV(+) HNSCC tumors differ from HPV(−) in immune and mutational profiles and in gene expression [6]. For example, in HPV(+) HNSCCs, TP53 is rarely changed because p53 is eliminated by the action of the E6 HPV protein. In HPV(−) tumors, this gene is not eliminated and usually, it is mutated [6]. Moreover, in HPV(+) HNSCC tumors the E7 HPV protein binds to RB1—a cell cycle regulator—retinoblastoma-associated protein and causes its proteasomal degradation. The lack of RB1 causes the release of E2F family transcription factors, which results in cells skipping the G1-S checkpoint and going straight into the S phase [6]. In recent years, many researchers focused on looking for new therapeutic targets or biomarkers for HNSCC [1,4,9,12] based on the measurement of proteins, DNA, and RNA levels [1,2,4,9,10,11,12,13,14,25,26], including non-coding RNA (ncRNA) molecules [1,12,13,14,25,26].
One of such ncRNAs is YRNA, which plays important roles in many processes correlated with tumor development [1,12,13,14,25,26]. YRNAs were also previously found to bind with many different proteins determining their different functions in an organism [12]. There are four distinguished types of YRNAs: YRNA1, YRNA3, YRNA4, and YRNA5. All of the YRNAs are transcribed by RNA polymerase III. YRNAs characterize a stem-loop structure and every part of a YRNA is responsible for different processes [1,12,13,14,25,26]. Previous studies have proved that YRNAs may be abundantly found in extracellular vesicles and retroviruses where they function as scaffolds for viral RNA [20]. Lately, YRNAs were discovered to be easily obtained from different body fluids and tissues. They are dysregulated in many cancer types, including HNSCC [1,12,14,15,16].
In this work, we analyzed data from 269 patients in terms of expression levels of YRNAs YRNA1, YRNA3, YRNA4, and YRNA5, in HPV(−) and HPV(+) groups to answer if they have any biological function and could be used as potential biomarkers depending on HPV status.
First of all, the expressions of YRNAs were analyzed in the context of clinical–pathological parameters. It was observed that the overall survival and progression-free survival analyses showed very similar results; in the general group as well as in the HPV(−) group, better survival rates were observed in the low-expression group and in the HPV(+) group better survival rates were seen in the high-expression group. These results correlate with the results considering the expression in different disease stages shows that higher expression of YRNAs is correlated, in most cases, with more advanced cancer stages, and with more aggressive HNSCC subtypes. Despite the time of observation used in a particular analysis, YRNA4 and YRNA1 showed, in most cases, potential for improving patients’ survival biomarkers, especially in terms of the HPV(+) and HPV(−) groups, because of opposite results. However, OS and PFS in terms of YRNAs differ in different cancer types. In another study considering OS and PFS in HNSCC, higher YRNA1 expression showed better survival, contrastingly to these results. However, this difference may occur because of a lack of information considering HPV status in our previous study [1]. In clear-cell renal carcinoma, better survival rates for YRNA3 were observed for lower expression, but for higher expression in terms of YRNA4 [14]. In the case of prostate cancer, YRNA5 showed better survival rates in low-expression groups [15], and in bladder cancer, all YRNAs showed better survival rates in higher-expression groups [16]. These studies show how YRNAs differ between different cancer types not only in terms of expression but also patients’ survival. This may suggest developing different approaches for YRNAs in different cancers.
YRNA3 is correlated with more advanced T-stages of the disease, making it a promising biomarker of the HNSCC progression. Interestingly, in our previously published study, an analysis based on FFPET tissues from HNSCC patients showed that YRNA3 was the only YRNA that did not show any statistical significance in terms of the T-stage [1]. This difference may be connected with different sample types used in both analyses. Furthermore, high YRNA3 expression was found to be correlated with more advanced clinical stages despite HPV infection status, showing that YRNA3 may function as a potential biomarker of more advanced stages of the disease. On the other hand, in a different study concerning the clear-cell renal-cell carcinoma, YRNA3 was discovered to be overexpressed in less-advanced cancer stages (I + II vs. III + IV clinical stage) [14]. Such differences may occur because of different tumor types. Previous studies have shown high sensitivity and specificity of YRNA3 as a potential biomarker in bladder cancer [16], prostate cancer [15], clear-cell renal-cell carcinoma [14], pancreatic ductal adenocarcinoma [12], and HNSCC [1]. These results suggest YRNA3 as a potential biomarker of different diseases in the future. Furthermore, it was discovered that YRNA3 may also function as a distinguishing biomarker between the larynx and hypopharynx in the HPV-negative group. Unfortunately, the rest of the YRNAs may not be used as biomarkers of the HNSCC localization because of the lack of significant differences in expression between different localizations.
YRNA1 showed significant differences in expression patterns only in terms of N-stage when all patients were taken together. It was noticed that higher expression of YRNA1 is correlated with more advanced N-stages of the disease. This may suggest that YRNA1 may be also used as a biomarker of early metastasis to nearby lymph nodes. However, in other studies, YRNA1 showed significantly dysregulated expression patterns. In a previous study, YRNA1 was overexpressed in more advanced T-stages and because of its high specificity and sensitivity, it was predicted as a possible biomarker of HNSCC [1]. It was also found that the expression of YRNA1 is significantly lower in tumor tissue compared with adjacent healthy tissue [1]. Similarly to YRNA3, YRNA1 was examined in other studies concerning different tumor types, resulting in the association of YRNA1 as a possible biomarker of bladder cancer [16], prostate cancer [15], pancreatic ductal adenocarcinoma [12], and cervix cancer [27,29]. Interestingly, YRNA1 was found to be under-expressed in bladder cancer [16] and prostate cancer [15], but overexpressed in cervix cancer [27,29] and pancreatic ductal adenocarcinoma [12]. Such differences between various types of tumors may suggest that YRNA1 may be not only a great biomarker but also show the importance of its role in developing different tumors.
Next, the analysis showed that YRNA5 was only significantly dysregulated in the HPV(−) group, suggesting that it may function as a biomarker of HPV infection, similarly to YRNA1 [1]. Interestingly, the expression of YRNA5 was upregulated in the less-advanced T-stage and clinical stage of the disease. This may also suggest its value as a biomarker of less-advanced tumor stages. YRNA4 and YRNA5 were previously described by us to be overexpressed in FFPET samples of HNSCC patients [1]. In clear-cell renal-cell carcinoma YRNA4 was found to be significantly overexpressed in kidney tissue compared with healthy tissue. Its higher expression was also indicated in less-advanced N-stages of the disease and patients with low expression of YRNA4 showed better survival rates [14]. In bladder cancer, both YRNA4 and YRNA5 were found to be significantly downregulated, and thus they were proposed as disease and progression biomarkers [16]. Similarly, YRNA4 and YRNA5 were significantly under-expressed, suggesting their potential as biomarkers in prostate cancer. In benign prostate hyperplasia both of these YRNAs were submitted as biomarkers because of their significant overexpression [15]. What is more, in colon cancer, YRNA4 was highly expressed in the blood serum of rectal cancer patients [12]. All these data show how different expression patterns of YRNAs present in various diseases, pointing to their future potential as molecular targets or functional biomarkers.
The expression of YRNAs was compared in terms of the aggressiveness of the HNSCC. It was discovered that higher expression of YRNAs is correlated with more aggressive tumor subtypes. The highest expression was found in the mesenchymal subtype, which is known to be the most aggressive HNSCC subtype [31]. As the aggressiveness changes the correlation and probably also a function of YRNAs in HNSCC development also change, suggesting that YRNAs may play different roles depending on the advancement and aggressiveness of the tumor, suggesting that YRNAs have a great impact on developing, growing and metastasizing tumors. However, more studies need to be carried out on this matter to discover the exact mechanism and function in each case. Unfortunately, knowledge concerning the role of YRNAs in HPV infection and HNSCC development is still very limited.
The five most common cancer and stemness markers CD44, SOX2, TP53, ALDH1A1 and FAT1 were analyzed in terms of YRNAs and their potential influence on each other [32,33,34,35,36,37,38,39,40,41]. CD44 is a cancer stem cell marker associated with cell aggregation, proliferation and migration [32]. It is also partially responsible for HNSCC invasion and poor survival of patients. It may also be used as a poor prognosis indicator in HNSCC [33]. SOX2 is one of key regulators in HNSCC and takes part in cancer stemness. Moreover, it is correlated with oral squamous cell carcinoma metastasis [34,35]. Next, TP53 is the most common altered gene in HNSCC. It is altered in approximately $70\%$ of cases [36]. TP53 is responsible for the activation of DNA repair mechanisms and apoptosis induction due to DNA damage [37]. Another marker, ALDH1A1, is not expressed in the normal oral mucosa. It functions in carcinogenesis and tumor progression of HNSCC and is associated with poorer prognosis [38,39]. FAT1 is mutated in approximately $20\%$ cases [40], and is associated with tumor progression and survival of HNSCC patients [41]. It was found that YRNAs were mostly negatively correlated with these cancer and stemness markers. YRNA1 was significantly, negatively correlated with CD44, SOX2, ALDH1A1 and FAT1. It is well known that these genes have a huge impact on tumor development and maintenance of cancer stem cells and could be used as potential biomarkers [32,33,34,35,36,37,38,39,40,41]. In the classical subtype, which is the least aggressive subtype, mostly positive correlations between YRNAs and chosen cancer and stemness markers may be seen; however, in the mesenchymal the opposite results were obtained. These results imply a huge impact of YRNAs on cancer progression in subtypes with different aggressiveness.
Furthermore, the Gene Set Enrichment Analysis revealed that YRNA1 expression is strongly associated with the protein secretion pathway. Interestingly, the HPV(−) and HPV(+) group showed the same results with the same altered protein coding genes. It was found that 16 significantly dysregulated protein-coding genes were associated with the YRNA1 low-expression group. *These* genes are involved in many crucial processes such as metabolism, developmental biology, disease, the immune system, metabolism of proteins, vesicle-mediated transport, transport of small molecules, cellular response to external stimuli, cell–cell communication and signal transduction. Many of these protein coding genes were previously described in different cancer diseases including HNSCC and some of them are correlated with HPV infection. The first of these genes, AP-2 complex subunit mu (AP2M1) was found to be a promising biomarker for predicting survival of patients with hepatocellular carcinoma [55]. The AP2M1 is an important factor in hepatitis C virus (HCV) assembly. Moreover, it is also associated with low-risk HPV6 and high-risk HPV16 by binding to the E7 proteins of the HPV [56]. In HNSCC it is discovered to be one of the most significant predictors of disease-free survival and overall survival and is upregulated in more-advanced cancer stages [57]. The correlation of AP2M1 and YRNA1 may explain the potential role of YRNA1 as a HPV infection indicator [1]. AP-1 complex subunit gamma-1 (AP1G1) takes part in developing colon cancer [47] and in liver cancer [58]. In HNSCC, AP1G1 was found to be significantly overexpressed. The knockdown of AP1G1 results in indirect sensitization of HNSCC cells to cetuximab and possibly increases the therapeutic outcomes of HNSCC treatment [59]. Brefeldin A-inhibited guanine nucleotide-exchange protein 1 (ARFGEF1) was found to be downregulated in breast cancer [60]. It also takes part in papillary thyroid cancer proliferation, migration and invasion [61]. In colon cancer cells, ARFGEF1 is one of the targets of miR-27b in regulating cell proliferation. miR-27b downregulates ARFGEF1, leading to tumor growth suppression [62]. This suggests that YRNA1 may be involved in tumor growth in colon cancer. Moreover, in Kaposi’s Sarcoma induced by KSHV (Kaposi’s sarcoma-associated herpes virus) circulating ARFGEF1 was found to be significantly overexpressed and associated with induced cell migration, proliferation and angiogenesis [63]. Next, Coatomer subunit beta (COPB2) was downregulated in cervical cancer [64] and upregulated in breast cancer [65]. It is associated with colorectal cancer cell proliferation and apoptosis [66]. The COPB2 protein was also related with SARS-CoV-2 virus [67]; however, there are no findings considering COPB2 in HPV infection. Copper-transporting ATPase 1 (ATP7A) is highly expressed in esophageal squamous cell carcinoma [68] and is correlated with tumorigenesis and cisplatin resistance [69]. AP-3 complex subunit beta-1 (AP3B1) is upregulated in hepatocellular carcinoma [70] and is a proven target for miR-9 in breast cancer cells [71]. Moreover, together with AP3S1, it is downregulated in cervical cancer [72]. Next, YIPF6 is significantly overexpressed in castration-resistant prostate cancer [73]; however, in the same disease BET1’s lower expression is associated with early relapse [74]. In addition, BET1 is also associated with better prognosis in glioblastoma [75]. Ras-related protein Rab-5A (RAB5A) was previously indicated to be overexpressed in oral cancer [76], cervical cancer tissue [77] and in colorectal cancer [78]. Interestingly, RAB5A is essential for the induction of autophagy by HCV (Hepatitis C Virus). In terms of HPV infection, HPV16 virions colocalize with RAB5A-containing components. RAB5A is crucial for biogenesis and coordination of endosomes and autophagosomes which would suggest that virions may transit through autophagosomes [53]. Furthermore, protein MON2 homolog (MON2) regulated by microRNA-133a-5p inhibits metastatic capacity of clear-cell renal carcinoma [79]. Moreover, MON2 is required for efficient production of infectious HIV-1 particles [80]. Finally, Vacuolar protein sorting-associated protein 4B (VPS4B) was found to be downregulated in rectum adenocarcinoma, colon adenocarcinoma, ovarian serous cystadenocarcinoma, adrenocortical cancer and testicular germ cell tumor [81]. On the other hand, high expression of VPS4B is associated with faster cell proliferation and poor prognosis in hepatocellular carcinoma [82]. Additionally, dominant negative mutants of VPS4A and VPS4B inhibit the replication and release of Hepatitis B virus [83]. There is little known in terms of HPV infection and cancer diseases in terms of DST, OCRL and STAM. Our results and previous studies have shown huge potential of YRNA1 in regulating various protein-coding genes in different cancer types, which may be used in developing new targeted therapies. Additionally, through interaction between YRNA1 and proteins such as AP2M1 and RAB5A we can implicate that YRNA1 has actually a vast impact on HPV infection.
The GSEA analysis of YRNA3, YRNA4 and YRNA5 showed many more processes that these YRNAs are enriched in, and the most important processes for YRNA3 in the HPV(−) group are extracellular transport and mitosis G2M transition checkpoint, both of them being important in cancer genesis. Worth noticing is that YRNA3 was negatively correlated in these processes, suggesting that the downregulation of YRNA3 may have a positive impact on tumor regression. YRNA3 in HPV(+) was mostly positively correlated with many processes; however, in one case it was negatively enriched in the JAK2 signaling pathway which plays a central role in cytokine and growth factor signaling [84]. Furthermore, enrichment in genes implicated in DNA repair processes, ribosome assembly processes, ribonucleoprotein complex biogenesis, spliceosomal SNRNP (small nuclear ribonucleoproteins) assembly and ribonucleoprotein complex subunit organization was observed in the case of YRNA4, which suggest its crucial role in forming a Ro60 ribonucleoprotein complex, one of the basic functions of YRNAs [1,2,12,13,25]. Finally, YRNA5 in the HPV(+) group was enriched in WNT beta catenin signaling and TGF beta signaling, crucial pathways in cancer development [85,86]. This would suggest that YRNA5 is indirectly responsible for epithelial–mesenchymal Transition in HNSCC development through the WNT beta catenin signaling pathway. We can also conclude a connection between YRNA5, HPV infection and YRNA5’s influence on the EMT process in HNSCC, which results in tumor progression and metastasis. However, more studies are needed to fully understand this mechanism. All these GSEA data show that YRNAs play more important roles in cancer genesis than was suggested before [1].
To thoroughly analyze the topic, the deconvolution analysis discovered that YRNA1 is associated with immune cells, especially dendritic cells. These cells are responsible for antigen uptake and presentation to activate and regulate anti-tumor T cell response [87]. In our analysis, a higher amount of these cells was associated with low expression of YRNA1 and low expression of YRNA1 was found in HNSCC tissue and cell lines. Our previous studies indicated that patients with high levels of YRNA1 survive longer periods of time and YRNA1 expression is very low in HNSCC [1]. Taking all of the abovementioned into consideration, it can be assumed that YRNA1 may function as a tumor suppressor for HNSCC.
Next, the analysis of correlation of YRNAs and different genes in HPV(+) and HPV(−) groups showed that despite the group, YRNAs were vastly positively and negatively correlated with different genes. Most of those genes were previously correlated with different types of tumors [88,89,90,91,92,93,94,95,96,97,98]. Interestingly, some of the genes were common for different YRNAs such as SNX17, WDR6 or ATP5B, suggesting that different YRNAs have an impact on similar genes. These results underline how important the role of YRNAs is in developing different types of cancers.
Finally, the RNAseq of FaDu and Detroit562 cells overexpressing YRNA1 compared with control cell lines showed us that YRNA1 plays a role in the cellular response to viral infections. Out of the 100 most-abundant genes derived from the NGS analysis, more than half of them were associated with viral processes such as host interaction of HIV factors, integration of provirus, infectious disease, influenza infection, influenza viral RNA transcription and replication and viral mRNA translation. These results formed the basis for a further look at whether YRNA1, as well as other YRNAs, due to their similar homology, function and shared promoter, perform a function in HPV HNSCC infection.
It should be noted that we decided to use HPV-negative cell lines in our in vitro model due to eliminating the transcription changes caused by viral infection and observed only changes made by YRNA1. Moreover, pcDNA3.1 plasmid, used by us in this model, should not induce viral response effects in the cell such as viral particles generated for cell modification in lentiviral systems. However, we are aware of the simplicity of the presented model and it most certainly only partially shows the importance of YRNAs in viral infection.
Based on RNAseq results, it was observed that out of the 100 most-abundant genes in both examined cell lines, 40 genes were found in FaDu as well as in Detroit562. Among these genes, 17 of them had opposite expression trends between the cell lines and 23 showed similar ones. These differences may occur due slightly different collection sites of the cell lines at the beginning; the FaDu cell line was obtained from solid, primary hypopharyngeal tumors and Detroit562 cells were obtained from lymph node metastasis of pharyngeal cancer patients. Previous studies already indicated differences between solid primary tumor cells and lymph node metastasis cells not only in gene expression [99] but also in the mechanics and structures of these cells [100]. However, in both cell lines, changes in genes connected with response to viral and other infections was observed. The GeneMANIA prediction tool allowed us to confirm genes derived from NGS analysis to be involved in viral gene expression, viral latency and immune response. Previous studies also showed that YRNA5-derived fragments are responsible for inhibition of influenza virus infection [101]. Another study also showed a number of proteins interacting with different YRNAs that are involved in various viral infections [24]. Moreover, the genes derived from NGS analysis were validated on a GEO dataset and a TCGA dataset, confirming their abundance in HNSCC patients. The deeper analysis of protein-coding genes correlated with YRNAs, especially YRNA1, showed that 28 of these genes are strictly correlated with HPV proteins and additionally correlated with other genes involved with YRNA1 expression. YRNA1 was also found to be correlated with many immune processes such as antigen presenting and processing, regulation of the innate immune system, different cellular responses and many more. These findings only confirm the vast influence of YRNA1 on different viral infection types, including HPV infection in different tumor types.
Despite the very promising results concerning the role of YRNAs in HNSCC and viral infections, there is still a lot to discover. More studies are needed to fully understand the YRNAs interactions and their influence on different cancer types. In this study, despite no significant difference in YRNA1 expression between HPV status groups, we found much more data confirming the correlation with HPV infection. Interestingly in previous studies, a significant correlation between YRNA1 and HPV status were found [1,12]. Such differences in YRNAs expression may occur due to different extraction sites of specimen (plasma, serum, tissue, biopsy, FFPET) [102]. There are also no studies concerning the influence of different therapeutic agents (chemotherapy, radiotherapy) on YRNA expression. In our preliminary data we can confirm at this point that radiotherapy and chemotherapy cause significant changes in YRNA1 expression. Similar results between YRNAs may occur because of their conservative structure and similar functions. In this study we focused on YRNA1; however, as the results suggest it would be beneficial to study YRNA3, YRNA4 and YRNA5 in the future as well. For now, YRNA1 shows properties of HNSCC biomarkers and correlates with HPV infection and immune response to that infection. YRNA3, YRNA4 and YRNA5 also show properties of potential biomarkers of the disease itself, as well as the prognosis for HNSCC patients. Taken together all YRNAs showed properties to be promising molecular targets for future therapies, not only for virus-induced cancers but also for other diseases.
## 5. Conclusions
In this study, YRNAs in terms of their influence on HNSCC development and HPV infection were examined. First of all, YRNA1 and YRNA3 were associated with more-advanced cancer stages, and YRNA5 was associated with less-advanced cancer stages, suggesting a potential role of these YRNAs as biomarkers for HNSCC tumors. Next, we found that the higher the expression of YRNAs the more aggressive tumor subtype. Additionally, YRNAs were associated with cancer and stemness markers showing their negative correlation between them, and opposite correlations between the most and the least aggressive subtypes, showing a distinct impact of YRNAs on HNSCC depending on the aggressiveness of the tumor and the HNSCC development. It was also discovered that YRNA1 and YRNA4 may be potential prognostic biomarkers of survival, differing between HPV(+) and HPV(−) groups of patients. Next, YRNA1 was found to be correlated with HPV infection and immune response to cancer disease. The results showed a significant correlation of YRNA1 and HPV proteins and immune processes. On the other hand, YRNA5 was found to be overexpressed only in the HPV(−) group, making it a potential biomarker on HPV infection status in HNSCC. YRNAs also were found to be enriched in a vast number of processes correlated with cancer genesis and viral and immunogenic pathways. The overexpression of YRNA1 in HNSCC-derived cell lines confirmed the expression of genes co-expressed with YRNA1, and suggest a role for YRNA1 in viral infections, including HPV infection in HNSCC patients. All these findings show how YRNAs may interfere in cancer progression, especially in association with HPV infection, and should be evaluated as biomarkers and potential therapeutic targets.
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|
---
title: Chlorogenic Acid Restores Ovarian Functions in Mice with Letrozole-Induced
Polycystic Ovarian Syndrome Via Modulation of Adiponectin Receptor
authors:
- Mohd Zahoor ul Haq Shah
- Vinoy Kumar Shrivastava
- Shazia Sofi
- Yahya F. Jamous
- Mohd Faiyaz Khan
- Faisal K. Alkholifi
- Wasim Ahmad
- Manzoor Ahmad Mir
journal: Biomedicines
year: 2023
pmcid: PMC10045653
doi: 10.3390/biomedicines11030900
license: CC BY 4.0
---
# Chlorogenic Acid Restores Ovarian Functions in Mice with Letrozole-Induced Polycystic Ovarian Syndrome Via Modulation of Adiponectin Receptor
## Abstract
Around the world, polycystic ovary syndrome (PCOS) is a complex endocrine-metabolic condition that typically affects 6–$20\%$ of females. Our study’s major goal was to examine how chlorogenic acid (CGA) affected mice with endocrine and metabolic problems brought on by letrozole-induced PCOS. Group I served as the control for 81 days; Group II was given Letrozole (LETZ) orally at a dose of 6 mg/kg bw for 21 days to induce PCOS; Group III was given LETZ (6 mg/kg) for 21 days, followed by treatment with CGA (50 mg/kg bw daily) for 60 days. The study indicated that LETZ-treated mice displayed symptoms of PCOS, such as dyslipidemia, hyperinsulinemia, elevated testosterone, increases in inflammatory markers and malonaldehyde, and a decline in antioxidants (Ar, lhr, fshr, and esr2) in the ovaries. These alterations were affected when the mice were given CGA and were associated with reduced levels of adiponectin. Adiponectin showed interactions with hub genes, namely MLX interacting protein like (MLXIPL), peroxisome proliferator-activated receptor gamma Coactivator 1- alpha (PPARGC1), peroxisome proliferator-activated receptor gamma (Pparg), and adiponectin receptor 1 (Adipor1). Lastly, the gene ontology of adiponectin revealed that adiponectin was highly involved in biological processes. The findings from our research suggest that adiponectin has direct impacts on metabolic and endocrine facets of PCOS.
## 1. Introduction
Polycystic ovary syndrome (PCOS), an intricate and multifaceted metabolic-endocrine disruption that typically influences 6–$20\%$ of women of reproductive age and accounts for 70–80 percent of all occurrences of infertility, is globally among the leading factors responsible for infertility [1]. The hallmarks of PCOS include multisystem endocrinopathy and associated metabolic abnormalities. PCOS is defined by hyperandrogenism (HA), along with cystic ovaries. Even though the precise origin of PCOS is unknown, research points to a connection between the condition and metabolic problems such as obesity, insulin resistance (IR), and type-2 diabetes (T2DM). Because of genetic susceptibility, societal factors, dietary habits, and sedentary lifestyles, the pathobiology of this condition is more pronounced in adults and children. This causes obesity, which negatively affects factors related to the fertility of women, particularly in young women [2]. As the precise etiology of PCOS is unknown and the diagnostic criteria are currently insufficient, there are numerous undiagnosed and untreated PCOS cases in women. Statistics show that seventy-five percent of PCOS-afflicted women see their doctors without a diagnosis [3]. Therefore, its accurate diagnosis and treatment strategy can be achieved by a deeper understanding of its pathological and physiological mechanisms. Cardiac events, type-2 diabetes, obesity, and IR continue to have an impact on its etiology, along with other endocrine and metabolic diseases [4] (Zafar et al., 2019). Numerous types of research have shown that metabolic problems are 3–4 times more common in females with PCOS compared to healthy populations. Researchers have observed that IR activates the ovaries, causing hyperinsulinemia (HI) and resulting in decreased aromatase activity (which poses an issue in balancing sex steroids), an increase in circulating androgens, increased obesity, and worsened IR [5]. These factors all contribute to anovulation, leading to infertility, cardiovascular problems, and T2DM [3].
Obesity affects infertility beyond PCOS, exacerbates its metabolic and reproductive abnormalities, and therefore is linked to anywhere between 44 and 61 percent of PCOS cases [6]. The potential of the adipose tissue to generate adipocytokines, including adiponectin, which has glucometabolic, as well as lipid-regulating, anti-inflammatory, and antioxidant activities, makes it an endocrine organ that controls metabolic processes [7]. The decrease in adiponectin release in PCOS demonstrates that adipose tissue hyperplasia and malfunction are involved with the origin of PCOS [8]. Additionally, it has been demonstrated that higher paternal adiponectin transcription provides defense over adipose tissue malfunction [9]. The receptors modulating adiponectin’s activities are widely distributed throughout the hypothalamic–pituitary–ovarian axis and reproductive system, permitting the hormone’s steroidogenic modulatory effects, as well as its metabolic actions in these tissues [10,11].
The concept was suggested by recent research [12] that such an adipocytokine might well be involved in the metabolic abnormalities caused by obesity or problems associated with obesity, including polycystic ovarian syndrome. Unfortunately, it is still unclear exactly how these adipocytokines function. Determining whether adiponectin interacts with the endocrine and metabolic issues that accompany PCOS may be helpful in identifying or treating such a complex, reproductive illness. Among the most popular therapies is one that involves the use of an hCG injection in conjunction with clomiphene citrate [13]. Clomiphene users with PCOS experience better reproductive function and menstruation and improved glucose metabolism [14]. Clomiphene can have adverse effects on endometrial thickness because of its chemical resemblance to estrogenic substances [14]. As the long-term usage of conventional medicines has a range of negative effects, experts now advise patients to switch to medicinal herbs, as they have fewer or no side effects.
Chlorogenic acid (CGA), a polyphenolic molecule that is frequently present in a variety of plants, is particularly present in green coffee beans, which have a weight-based CGA concentration of between 5 and 12 percent [15]. People frequently ingest it, and it can be found in many foods and beverages. It is primarily present in vegetables such as potatoes and fruits, including apples, apricots, plums, tomatoes, and cherries. The most popular beverages with high CGA contents are wine, coffee, and tea [16]. Numerous researchers have sought to look into the nutritional advantages and physiological consequences of CGA because it is present in many meals and liquid beverages. Numerous studies have demonstrated that CGA can lessen oxidative stress. They have all demonstrated that it can be used to produce outcomes such as anticancer activity, cardio protection, and maybe even neuroprotection [17]. Evidence suggests that chlorogenic acid contains a variety of effects, including neuroprotective, neurotrophic, and antioxidant properties [18]. CGA’s anti-inflammatory properties have been investigated in a variety of disease models. The NF-B pathway, which activates genes to produce pro-inflammatory cytokines and adhesion molecules, was downregulated in raw macrophages in a mouse model of retinal inflammation, greatly reducing the inflammation caused by endotoxins [19]. Studies have recently concentrated on the role of chlorogenic acid in lipid and glucose metabolism, apart from its significant antioxidative properties. According to reports, CGA inhibited glucose-6-phosphate translocase, which decreased the glucose amount that was transported through the intestines [20]. CGA, however, boosts glucose tolerance and increases glucose absorption, suggesting possible antidiabetic action. Additionally, in rats fed with a high-cholesterol diet, it was observed that CGA reduced plasma and liver lipid levels [21]. Additionally, the metabolic effects of polycystic ovarian syndrome are the cause of all these situations [22]. Hence, the effect of CGA on polycystic ovaries is examined in our study. Our study’s major goal is to examine how chlorogenic acid affects mice with endocrine and metabolic problems brought on by letrozole-induced PCOS. Additionally, we investigate how chlorogenic acid affects the amounts of androgens and adiponectin in the blood. The study further investigates the impact of adiponectin in PCOS-related metabolic and endocrine diseases. Lastly the protein–protein interaction and gene ontology of adiponectin are also analyzed using bioinformatics approach.
## 2.1. Chemicals
Letrozole and chlorogenic acid (CGA) were purchased from Sigma Aldrich and Sun Pharma, respectively. ELK biotechnology ELISA kits were used in this study and were bought from ELK biotechnology, Wuhan, China, for analysis of hormones. Analytical-grade chemicals were used in addition during the investigation.
## 2.2. Animals
The method was as follows: Eighteen mature Parkes strain mice (4–5 weeks old) weighing 18–21 g were at random separated into three groups with six mice each. Group 1 served as the control and was provided with water and a typical chow food for 81 days; Group II was given letrozole (LETZ) dissolved in normal saline water ($0.9\%$) orally using an oral gavage at a dose of 6 mg/kg bw for 21 days to induce PCOS, followed by 60 days without treatment; Group III was given LETZ (6 mg/kg) for 21 days, followed by oral gavage treatment with CGA (50 mg/kg bw orally daily) for 60 days. Serum was subsequently used for biochemical and hormonal examination. Blood samples from all mice were acquired via retro-orbital venous sinus puncture 24 hours after 81 days of trial. Cervical dislocation resulted in the death of every mouse. Ovaries from all mice were taken out, and adipose fat was cleansed for further biochemical histological studies. All experimental protocols were approved by the institutional ethics committee at Barkatullah University in Bhopal.
## 2.3. Biochemical Studies
The examination of serum hormones was conducted as follows: On day 82 of the trial, to obtain blood, a retro-orbital venous sinus puncture was performed. Following centrifugation, serum was separated and stored until use. The employed ELISA kits were built on competitive inhibition enzyme immunoassay methodology. A specific protein was precoated on a microplate well (MW) included in the kits. Biotin-conjugated antibodies specific for LH, testosterone, estrogen, FSH, and progesterone were then added to the appropriate MW after adding standards or samples. Avidin-horseradish peroxidase (HRP) was then added to each MW, followed by an addition of TMB substrate solution and incubation. A 450 ± 10 nm ELISA reader was utilized to determine the color shift once the enzyme–substrate reaction was stopped using stop solution available in the kits.
Serum insulin and fasting blood glucose were determined as follows: With the aid of readily available commercial kits, serum fasting blood sugar (FBG) levels were measured calorimetrically (Elab science), while serum insulin was measured using an ELISA kit purchased from ELK biotechnology, China.
Serum lipid profiles were determined as follows: With the aid of readily available commercial kits, triglycerides (TGs), high-density lipoprotein (HDL), and total cholesterol (TC) were calorimetrically measured. Very-low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) were measured indirectly using Friedewald’s equation.
## Antioxidant Assay
The lipid peroxidation assay was conducted as follows: The quantity of malonaldehyde (MDA) was measured to determine how much lipid peroxidation (LPO) occurred in the ovaries [23]. We dissected the ovaries in ice-cold normal saline using a glass homogenizer, and ten percent homogenate tissue was prepared. Following homogenization of the ovarian tissue, ten minutes was spent centrifuging the sample at 3000× g rpm. Two hours of incubation at 37 °C was spent in a milliliter of supernatant. Then, five minutes was spent centrifuging the sample at 2000× g rpm, following proper mixing of samples with one milliliter of $10\%$ tris hydrochloric acid (TCA). An amount of 1 mL of the supernatant was thoroughly combined before being put in a boiling water bath for ten minutes; an equal volume of 0.67 percent 2-thiobarbituric acid TBA was added. After being refrigerated, in order to dilute the samples, 1 mL of distilled water was used. Optical density measurements were made at 535 nm with a spectrophotometer. The information was estimated as nanomoles per gram of tissue.
The reduced glutathione assay was conducted as follows: The concentrations of tissue glutathione (GSH) were measured using a technique known as 5, 50-dithiobis-2-nitrobenzoic acid (DTNB) [24], which included adding and gently mixing 1 cc of $50\%$ TCA after homogenizing the tissue in EDTA 0.02 (5–8 mL), diluting in two milliliters of ice-cold distilled water, and fifteen minutes of spinning at 6000 rpm. To create 1 mL of supernatant, 100 mL of DTNB and 2 mL of tris buffer (0.4 M, pH 8.9) were mixed (0.1 M). Optical density was observed using a spectrophotometer at 410 nm. We calculated the data as nmol/g.
Superoxide dismutase was measured as follows: To measure the activity of superoxide dismutase (SOD), we used the Marklund and Marklund technique [25]. Amounts of 100 mL of pyrogallol and 2.9 mL of tissue homogenate supernatant ($10\%$) were used to evaluate absorbance at 420 nm for three minutes (0.2 mM). We calculated SOD in units per gram of tissue.
## 2.4. Plasma Adiponectin
Adiponectin in plasma was tested using an ELISA kit based on the sandwich ELISA approach acquired from Elabscience Biotechnology Inc. (Wuhan, Hubei, China).
## 2.5. Inflammatory Cytokines
Inflammatory cytokines in plasma were tested using an ELISA kit based on the sandwich ELISA approach acquired from Elabscience Biotechnology Inc.
## 2.6. Reverse Transcription and Real-Time PCR
With the use of TRI Reagent (Invitrogen), total RNA was extracted as directed by the supplier, and a cDNA Synthesis Kit was used to create cDNA from 1 µg total RNA (Invitrogen). To carry out RT-PCR, SYBR green and real-time PCR were employed. The mRNA values were calculated by fitting a standard curve to each gene’s related expression levels. The Table 1 addendum data contain a list of the primers utilized for this study. As an internal control, actin was used (Table 1).
## 2.7. Histological Assessment of Ovaries
Hematoxylin and eosin (H&E) stains were applied after the ovaries were sectioned at a thickness of 5 µm and fixed in $10\%$ Bouin’s fixative for 24 h.
## 2.8. Statistical Analysis
To ascertain the significance of the parameters, a one-way analysis of variance (ANOVA) was employed, followed by a post hoc analysis using Tukey’s multiple comparison test using Graph Pad Prism 8. The mean standard deviations of the data are displayed. To define statistically significant, more significant, and extremely significant differences, p values of 0.05, 0.01, and 0.001 were employed, respectively.
## 2.9.1. Protein–Protein Interaction of Adiponectin
The Search Tool for the Retrieval of Interacting Genes (STRING database ver.11.0b), a biological database designed to develop and investigate functional interactions among proteins, was used to establish an adiponectin protein–protein interaction (PPI) network with a confidence level of 0.7. The PPI network was further investigated and visualized using Cytoscape (version 3.8.2) [26]. The Molecular Complex Detection (MCODE) plug-in for the Cytoscape software was included to help identify the PPI network’s notable modules. The PPI network’s top 5 HUB nodes were extracted using the cytohubba plugin [27].
## 2.9.2. Gene Ontology and Pathway Analysis
Gene ontology (GO) of the adiponectin gene (Adipoq) was carried out using the Enrichr web platform [28]. *The* gene ontology terms, viz., molecular functions (MFs), biological processes (BPs), and cellular compartments (CCs), of adiponectin were analyzed, and results with $p \leq 0.05$ were statistically considered significant. Also, the KEGG analysis was done using ENRICHR database.
## 3.1. Effect of Chlorogenic Acid (CGA) on Body Weight, Fasting Blood Glucose, Insulin Concentration, and Changes in Estrous Cyclicity in LETZ-Induced PCOS Mice
We found that LETZ treatment resulted in a significant increase ($p \leq 0.001$) in body mass. However, LETZ-treated mice treated with both LETZ and CGA indicated a substantial decrease in their weight ($p \leq 0001$). The results are shown in Figure 1A. In contrast to the typical control mice, the LETZ-treated mice displayed irregular estrous cyclicity, and we discovered a continuous diestrus state that led to longer estrous cycles. However, following CGA therapy, the estrous cycle was regularized, returning the duration to normal (Figure 1B). We also observed that LETZ treatment resulted in a significant increase ($p \leq 0.001$) in FBG and insulin levels. However, PCOS mice treated with LETZ and CGA for 60 days showed a significant decrease ($p \leq 0.001$) in FBG and insulin concentration. The data are displayed in Figure 1C and Table 2.
## 3.2. Effect of CGA on Hormone Levels in Mice with PCOS
The results revealed that LETZ treatment resulted in a significant increase ($p \leq 0.001$) in testosterone and LH levels and a decrease ($p \leq 0.001$) in the levels of estrogen and FSH. However, mice with LETZ-induced PCOS treated with LETZ and CGA for 60 days normalized these changes. Moreover, the LH/FSH ratio was found higher in LETZ-treated mice, which decreased after treatment with LETZ and CGA. The results are shown in Figure 2A–C.
## 3.3. Impact of CGA Treatment on Lipid Profiles in PCOS Mice
The results revealed that LETZ treatment resulted in a significant increase ($p \leq 0.001$) in cholesterol, TG, LDL, and VLDL levels while decreasing HDL levels. However, after treatment with CGA, these changes were reversed significantly, and the results are shown in Table 2.
## 3.4. Impact of CGA on Lipid Peroxidation of Antioxidants
The mice that were treated with letrozole showed a significant decrease ($p \leq 0.001$) in antioxidant capacity compared to the control group. Treatment of mice with PCOS using CGA significantly increased their antioxidant capacity, and the results are shown in Figure 3A,B.
## 3.5. CGA Administration Treatment Reduces Inflammatory Cytokines in PCOS Mice and Effects the Expressions of Genes Related to Hormones
In our study, we assessed the role of CGA on inflammatory cytokines, such as TNF-ɑ and VEGF, both of which were increased ($p \leq 0.001$) in the LETZ-induced PCOS mice compared to the control mice. Treatment of PCOS mice with CGA indicated a substantial decrease in their levels, and the results are shown in Figure 4A. By using specific primers, we performed RT-PCR for the receptors of androgen, FSH, LH, and estrogen, and we observed that Ar, esr, and lhr were downregulated in LETZ-induced PCOS mice, while fshr was upregulated. Treatment of LETZ-induced PCOS mice with CGA normalized these changes, as shown in Figure 4B,C.
## 3.6. CGA Treatment Increases the Levels of Adiponectin and Adipo-R1 Expression in PCOS Mice
In our study, we observed the plasma levels of adiponectin and adipo r1 expression in the ovarian tissue, both of which were decreased significantly ($p \leq 0.001$) in LETZ-treated mice. Treatment of mice with PCOS using CGA caused a significant increase ($p \leq 0.001$) in their levels, and the results are shown in Figure 5A,B.
## 3.7. Protein–Protein Interactions of Adiponectin
Co-expressions of 11 genes (nodes) along 30 protein–protein links (edges) were connected to establish a web of protein–protein interactions. Additionally, the generated PPI network showed a median node degree of 5.45, an expected number of edges of 12, an average local clumping correlation of 0.788, and a PPI enriched p-value of 1.4 × 10−5 (Figure 6A). Cytohubba was employed to determine the five top core genes in the network based on degree scoring, as shown in Figure 6A. Adiponectin (Adipoq), MLX interacting protein like (MLXIPL), peroxisome proliferator-activated receptor gamma Coactivator 1- alpha (PPARGC1)A peroxisome proliferator-activated receptor gamma (Pparg), and adiponectin receptor 1 (Adipor1) were the leading networking genes.
## 3.8. Gene Ontology and KEGG Pathway Analysis
The GO enrichment study was carried out using Enrichr. Compared to MFs and CCs, the gene ontology study revealed that BPs were significantly enriched 8. Amongst BPs, adiponectin was discovered to be implicated in the negative regulation of glycogen synthase activity and low-density lipoprotein receptor activity, as well as in the positive regulation of the myeloid cell apoptotic process and protein kinase A (Figure 6B). The KEGG pathway study showed that *Adipoq is* associated with type II diabetes mellitus, adipocytokine signalling pathway, PPAR signalling pathway etc (Figure 6B).
## 3.9. CGA Improves Ovarian Morphology in Mice with Letrozole-Induced PCOS
The histopathological analysis of an ovary portion exhibited the shapes of the granulosa cells, secondary follicles, and oocytes. In contrast to the control group of mice, the LETZ-induced PCOS mice displayed ovarian follicle degeneration, follicular cyst formation, and distorted granulosa cells, along with an absence of oocytes. On the other hand, the ovarian tissue of the LETZ-and-CGA-treated group showed normal granulosa cells and an antral cavity with a clearly isolated oocyte (Figure 7).
## 4. Discussion
PCOS is a diverse and multigenic disorder that is really very expensive for both the patient, as well as for society. In spite of certain other challenges, including heart problems and insulin resistance, clinical manifestations of PCOS often include hirsutism, abdominal obesity, and acne, which are linked to high androgens and cause women to experience stress and anxiety. Pharmacological innovations having fewer side effects deserve special consideration. By controlling the flows of testosterone and adiponectin, we demonstrated that chlorogenic acid (CGA) restored both metabolic and endocrine problems associated with LETZ-induced PCOS. We were capable of proving that letrozole contributed to PCOS, which is categorized by overweight and polycystic ovaries, by employing female laboratory mice of the Parke’s strain. In addition to these, there were also enhanced insulin and hyperlipidemia. LETZ-induced PCOS mice had lower concentrations of adiponectin, increased concentrations of inflammatory cytokines, and diminished ovarian cellular antioxidant capabilities. After CGA therapy, these changes were alleviated.
Leukocytes dominated within the letrozole group, suggesting a continuous diestrus stage of vaginal smears [29], validating the establishment of PCOS in an ongoing study. Interestingly, letrozole blocks the conversion of androgens to estrogens, which results in HA. Estrous cycle disruption, an increase in reproductive organ and body weight, and testosterone upregulation are all effects of enhanced androgens [30]. We noticed that LETZ-treated mice showed abnormalities in the estrous cycle, as well as increased body and ovarian mass, and these findings are consistent with past studies. After CGA treatment, the estrous cycle reverted to normal, and both body weight and ovary weight decreased.
In addition to type-2 diabetes, women experiencing PCOS also develop metabolic and other related issues, such as IR, poor glucose tolerance, and adiposity [31]. Our findings revealed that LETZ-induced PCOS in mice caused elevation in FBG and insulin levels, suggesting IR and hyperglycemia (HG), two vital features of metabolic abnormalities [32]. Additionally, oxidative stress (OS), especially in metabolic syndrome, can lead to organ failure, such as reproductive failure, either functionally or structurally. HI and HG are key metabolic events that trigger an inflammatory cascade. In a manner comparable to this, the enhanced insulin observed in mice treated with LETZ might even influence the functions of metabolically active tissues influenced by insulin, including ovaries, resulting in metabolic abnormalities in the reproductive system.
Moreover, earlier research proved that IR increased adiposity, a crucial contributor to obesity in metabolic and other associated disorders [33]. Nearly $50\%$ of PCOS individuals have abdominal fat (visceral fat) build-up. Thirty to sixty percent of people have obesity at some point in their lives, making it a common sign of PCOS [34]. In patients with PCOS, insulin resistance is likely caused by abdominal obesity [35]. In this study, PCOS mice showed an increase in visceral fat similar to earlier studies, and we observed a decrease in visceral fat in the PCOS mice that were treated with CGA (Figure 8). A previous study reported a decrease in visceral fat in rats that were given a diet heavy in fat and medicated with CGA [36].
Patients with PCOS who have hyperinsulinemia experience increased catecholamine-induced lipolysis in adipocytes, which raises blood free fatty acids and, subsequently, induces dyslipidemia. The liver creates more free fatty acids as a result, which increases the levels of VLDL and TGs in the blood. In this study, we discovered that letrozole-treated mice had higher TC, TG, HDL, and VLDL levels and lower LDL levels than the control group. Conversely, treatment with CGA revealed a marked drop in these concentrations, as well as a rise in LDL cholesterol levels. Similar effects of CGA were discovered in earlier research [36].
Evidence suggests that tissues of the ovary are prompted to synthesize androgens by inhibiting the action of aromatase [5,37]. Anastrozole, an aromatase inhibitor, was associated with suppressing estrogen, which causes insulin resistance and decreased peripheral glucose clearance when given to healthy people or when treating cancer [38]. To confirm the impact of CGA on changes in hormones, we examined the serum levels of hormones. Serum androgen levels were elevated in PCOS-affected mice, which was the most consistent hormonal trait. Increased testosterone and LH and reduced estrogen, FSH, and progesterone have also been noted in PCOS-afflicted mice [39,40]. In this investigation, in contrast to levels in PCOS-afflicted mice, CGA decreased serum testosterone, LH, and LH/FSH concentrations. LH levels above normal and elevated LH and FSH ratios may be used as indicators of PCOS among females [40]. In addition, CGA treatment in mice with PCOS significantly reduced serum testosterone and LH levels. PCOS illness may benefit from the decreasing of these elevated testosterone levels since it has already been shown that a high androgen level contributes to the etiology of PCOS [41,42]. Contrary to testosterone, LETZ-treated mice had lower serum estrogen, progesterone, and FSH levels, and the decline was associated with mid- or early follicular growth, as well as the creation of the morphology of follicles in the ovary [43]. Here, it was observed that PCOS mice had decreased concentrations of Ar and Esr1 mRNA levels, but in our research, CGA therapy reversed this downregulation in mice with PCOS. Ar and Esr1 transcripts have been shown to serve a function of proliferation in follicular growth [44,45], and elevated Ar levels can facilitate granulosa cell proliferation and differentiation [46]. Fshr moderately controls follicle growth during the baseline follicle growth phase by working in synergy with other stimulating substances, such as androgens [47]. Lhr is also present on the surfaces of granulosa and theca cells, and levels of Lhr have an impact on ovulation, the development of the corpus lutum, and on the synthesis of additional steroid, such as estrogen, androgen, and progesterone [48]. Such findings led us to discover cystic degeneration of the corpus luteum and follicles in PCOS-affected animals. It is interesting to note that CGA helped to slow the degradation of ovarian follicle development by regenerating the corpus luteum and eliminating cystic follicles.
The most successful treatments for atypical symptoms linked to female reproductive illnesses are hormones, but such treatments have a number of adverse effects, including uterine hemorrhage and hyperplasia [49,50]. Such findings imply that CGA might be an effective medication in treating hormonal imbalances brought on by PCOS. Lower steroid hormone levels in the ovary are correlated with higher numbers of developing follicles and their different shapes [51].
A crucial role of cancer development is known to be played by oxidative stress, which is changed in PCOS. There exists a link between dyslipidemia (DL) and OS [52]. The results of this study suggested that increased lipid peroxidation may be caused by dyslipidemia and an increase in ovarian lipid accumulation, which was characterized by increased MDA. It, thus, ultimately led to a decline in superoxide dismutase (SOD) and GSH in LETZ-induced PCOS mice, hence leading to OS, which in turn contributed to the ovarian tissue injury that precedes infertility in females of child-bearing age. This was visible in the histology of the ovary, with deteriorated follicles and disturbed granulosa cells, oocytes, and antrum. In this investigation, we found that an injection of CGA normalized MDA, SOD, and GSH levels. Previous studies also found a role of CGA in preventing oxidative stress from causing harm [53]. The outcomes of the present investigation further demonstrated that PCOS animals had increased TNF-α compared to control animals. Prior research indicates that both HI and HG caused inflammation, apart from oxidative damage [40], which could account for the increased levels of TNF-ɑ found in mice with LETZ-induced PCOS. Vascular endothelial growth factor (VEGF) is extremely important for pathological, physiological, and developmental angiogenesis, according to Wei et al. [ 54]. Oxidative stress triggers a state that is pro-inflammatory and leads to both hyperandrogenism and insulin resistance in a feedback loop [55]. According to reports, PCOS women secrete more VEGF as a result [56]. The mechanism is explained by the fact that the VEGF promoter region contains androgen receptor (AR) binding sites. The VEGF gene is activated when androgens bind to these sites. Additionally, the blood of PCOS women has less VEGF receptors, which increases the bioavailability of VEGF, as shown by Artini and colleagues [56]. These outcomes are congruent with the higher VEGF levels observed in the letrozole group. Furthermore, compared to the letrozole-only group, TNF and VEGF levels were reduced when LETZ and CGA were administered.
Importantly, animals with PCOS caused by LETZ had considerably lower circulating adiponectin and Adipo R1 than the healthy control group, which supports earlier research suggesting that, in PCOS patients, adiponectin was a measure of IR [57]. Furthermore, we found that circulating adiponectin was inversely linked with ovarian tissue histology, insulin resistance, FBG, oxidant–antioxidant status, lipid management, and inflammatory markers in LETZ-induced PCOS animals. In various studies, it has been discovered that metabolic disorders and their associated syndromes have reduced adiponectin concentrations [58]. Apart from its effects on metabolism, it was also shown that adiponectin is an agent controlling gametogenesis [59]. It also influences the function of the gonads and GnRH synthesis [60] (Yuan et al., 2016). Because there is a correlation between the quantity of estrogen present in the follicular fluid and the ovarian cells of the dominant follicle, in rodents, follicle predominance and egg quality are connected to adiponectin [61].
Additionally, it was demonstrated that recombinant adiponectin increased hormone release, primarily estrogen, in women and rodents at doses of 5–10 g/mL [61]. Therefore, our results implied that adiponectin directly influenced PCOS’s metabolic and endocrine features, and its drop could cause female infertility. Therefore, boosting the circulation of adiponectin may be employed as a remedy to metabolic and, perhaps, endocrine conditions in polycystic-ovarian-syndrome-affected individuals. This is the only research to the best of our knowledge that demonstrates the positive effects of CGA on the endocrine-metabolic issues connected to LETZ-induced PCOS in female mice and the role of CGA regarding adiponectin. However, the results of our study may have therapeutic repercussions for PCOS-affected individuals everywhere. In addition, the protein–protein interactions of adiponectin revealed the top five hub genes, and the gene ontology depicted the role of adiponectin in various processes, such as the negative regulation of glycogen synthase activity and low-density lipoprotein receptor activity and the positive regulation of myeloid cell apoptotic process and protein kinase A.
## 5. Conclusions
To establish a mouse model with symptoms similar to those of people with the illness, letrozole was administered orally to the mice. According to the findings of our investigation, adiponectin directly affected endocrine and metabolic components of PCO, and its drop could cause female infertility. Therefore, boosting the circulation of adiponectin could be employed as a remedy to metabolic and, perhaps, endocrine conditions in polycystic-ovarian-syndrome-affected individuals. In our study, we demonstrated the positive effects of CGA on the endocrine-metabolic issues connected to LETZ-induced PCOS in female mice and role of CGA regarding adiponectin. However, the results of our study may have therapeutic repercussions for PCOS-affected individuals everywhere.
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|
---
title: 'Development and evaluation of nomograms for predicting osteoarthritis progression
based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium'
authors:
- Chunbo Deng
- Yingwei Sun
- Zhan Zhang
- Xun Ma
- Xueyong Liu
- Fenghua Zhou
journal: BMC Medical Imaging
year: 2023
pmcid: PMC10045658
doi: 10.1186/s12880-023-01001-w
license: CC BY 4.0
---
# Development and evaluation of nomograms for predicting osteoarthritis progression based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium
## Abstract
### Background
Osteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA.
### Methods
A total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation.
### Results
Each nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression.
### Conclusion
Nomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12880-023-01001-w.
## Background
Knee Osteoarthritis (KOA) is the most common progressive multifactorial joint disease [1]. The global total prevalence of knee osteoarthritis in people aged 40 and over is $22.9\%$ [2]. With the aging population, the number of patients with OA is increasing [3]. KOA has a significant impact on health-related quality of life (HRQOL) and imposes a huge burden on individuals and the economy [4–6]. Although KOA is classically described as slowly progressing, KOA is heterogeneous, at least $10\%$ of patients with OA have a rapid disease progression that can lead to the need for total joint replacement [7, 8]. The current drugs can only alleviate the symptoms of KOA, and the development of disease-modified OA drugs (DMOADs) that prevent or reduce the progression of joint tissue deterioration lags behind other arthritis diseases. However, current clinical diagnostic procedures do not adequately fulfil the need of clinicians to help patients reduce their risk of disease progression or the need of the health-care industry to develop effective new DMOADs [8]. Advances in osteoarthritis diagnostics, prevention, and treatment will have a major impact on patients and society.
Magnetic resonance imaging (MRI) is considered the most comprehensive imaging modality for knee osteoarthritis assessment [9]. Although KOA affects all tissues in the joint, cartilage is still the main research target for predicting disease progression and treat diseases. MRI has become a widely used and recognized method to measure the structural changes of knee osteoarthritis (KOA), because it has been proved to be an effective and repeatable technology is more sensitive to detecting changes in KOA than X-rays. Although X-ray is safe and economical, it remains the most important imaging method for diagnosing KOA clinically [10]. Whole Organ MRI Score (WORMS) and MRI OA Knee Score (MOAKS) systems are used to assess the degree of knee cartilage damage [11, 12]. However, it is currently unknown to what extent such cartilage damage is associated with quantitative structural outcomes, such as longitudinal change in 3D cartilage thickness obtained from cartilage segmentation [13]. Therefore, Eckstein et al. performed a regional analysis of the femoral-tibial cartilage using the novel technique and explored the relationship between baseline MRI-detected femoral-tibial cartilage damage and longitudinal changes in cartilage thickness and knee function. In another study with KL grading of 1–3, semi-quantitative cartilage damage at baseline was associated with quantitative loss of cartilage thickness at follow-up [14]. The MRI cartilage parameter contains centrally performed measurements of cartilage thickness, cartilage surface morphology, cartilage volume, and the percentage of subchondral bone denuded area in the corresponding area [15–17]. Eckstein et al. found the loss of medial tibiofemoral cartilage thickness was closely related to the progression of radiological OA [18]. Wirth et al. found that the loss of medial cartilage can predict the progress of radiological OA [19]. The thinner thickness of the central medial tibia at baseline and the 12-month change of the medial central femur from baseline were associated with extensive full-thickness cartilage loss [20]. Meanwhile, with the application of artificial intelligence in the field of MRI, many studies have established OA prediction models based on MRI cartilage semiquantitative scores or MRI cartilage morphology [21, 22]. However, existing methods for evaluating patients with OA do not provide sufficiently comprehensive information to make reliable predictions or prognosis [8].
Nomograms are an important component of modern medical decision making [23]. A well-constructed nomogram for answering a focused question, when properly interpreted and applied, can be of great value to the clinicians and patients [24]. With the ability to generate an individual numerical probability of a clinical event by integrating diverse prognostic and determinant variables, nomograms fulfill our desire for biologically and clinically integrated models and our drive towards personalized medicine [24]. The development and progression of KOA are related to several medical, biological and environmental risk factors [25]. Therefore, the nomogram may play the same role in KOA as it has in the field of oncology. To the best of our knowledge, there are only a few published articles on applying nomogram in KOA [26, 27]. Wu et al. screened clinical factors and knee MRI parameters to construct nomogram, so as to quantitatively predict the risk of knee replacement in patients with early osteoarthritis during the follow-up period [26]. In earlier studies, we used 3D bone shape to construct nomograms to predict the radiological progress of OA, the AUC of nomogram is 0.75 [27].
The aim of this study is, to develop and evaluate a nomogram, using quantitative cartilage morphology parameters at baseline and the corresponding changes in cartilage parameters from baseline to follow-up measurements in the 12th month (12 M) and 24thmonth (24 M), and clinical risk factors, from the Foundation for the FNIH OA Biomarkers Consortium, to predict pain progression and radiographic progression in KOA. We envisaged that a predictive nomogram would be useful to aid in clinical decision making.
## Subjects
This sted data and images from FNIH.The FNIH is a nested case-control study involving 600 subjects with KL level 1 to 3 (https://nda.nih.gov/oai/study_ documentatinon.html). Details of the study design have been previously published [28]. There were 194 subjects who had radiographic progression and pain progression simultaneously in the case group. Radiographic progression was defined as a reduction in the minimumudy width of the medial tibiofemoral joint space ≥ 0.7 mm from baseline to 24, 36, or 48 months. Pain progression was defined as a persistent increase of ≥ 9 points (0-100 normalized score) using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain subscale at ≥ 2 time points from baseline to 24, 36 or 48 months the 24- to 60-month pain assessment. Persistence required a pain increase of ≥ 9 points at ≥ 2 timepoints from the 24-month to 60-month pain assessment. Knees were also excluded if there were not enough follow-up time points after the first increase in WOMAC pain data above the threshold to determine whether the increase was persistent. The 406 subjects in the control group did not meet the previously mentioned conditions. The control group was further divided into three subgroups. Among them, 200 subjects had no progression, 103 had only pain progression, and 103 had only progression on imaging.
## MR image acquisition and measurement of cartilage parameters
The MRI acquisition protocol of the OAI was described previously [29]. MRI acquisition was performed using a 3 T MRI system (Trio, Siemens Healthcare, Erlangen, Germany) at the four OAI clinical sites(The Ohio State University, University of Maryland, School of Medicine,University,Memorial Hospital of Rhode Island). The coronal 2-dimensional intermediate-weighted (IW) turbo spin-echo (TSE), sagittal 3-dimensional (3D) dual-echo at steady-state (DESS), coronal and axial multiplanar reformations of the 3D DESS and sagittal IW fat-suppressed (fs) TSE sequences were used to measure cartilage thickness, cartilage denudation, cartilage volume within the subregion [16, 30–32].
The FNIH released a set of parameters related to MRI knee cartilage measured by Eckstein [15, 18]. Based on several previous studies, the medial and lateral tibia and femoral articular cartilage were divided into 8 subregions, with 5 further tibial subregions (anterior, posterior, central, internal, and external) and 3 femoral weight-bearing subregions (central area, central medial area, and central lateral area). Each subregion includes quantitative cartilage parameters such as cartilage thickness, cartilage volume, subchondral bone exposure area, and cartilage thickness uniformity [30].
## MRI feature parameter extraction and establishment of cartilage morphology parameters score
We used the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to screen the MRI cartilage morphology parameters at baseline and the changes in cartilage parameters at follow-up 12 M and 24 M, respectively. All cartilage parameters are from FNIH(Osteoarthritis Biomarkers Consortium FNIH Project: Measurement of Cartilage Volume/Thickness by Chondrometrics). The three different cartilage morphology score (score0, scoreΔ12, and scoreΔ24) were respectively constructed using the cartilage morphology parameters or the changes at different times, which was calculated as a linear combination of selected features that were weighted by their respective LASSO coefficients [33].
## MRI cartilage parameters nomogram construction
Backward stepwise multivariate logistic regression analysis was used to screen the clinical risk factors at baseline and the MRI score at baseline, 12 M, and 24 M in order to construct the nomogram (named nomogram0, nomogramΔ12, and nomogramΔ24,respectively). The likelihood ratio test using the Akaike information criterion was used as the stopping rule for backward stepwise logistic regression analysis. The variance inflation factor (VIF) was applied to diagnose col-linearity in variable logistic regression. Clinical risk factors included age, sex, body mass index (BMI), the minimum medial compartment joint space width in the medial tibiofemoral region (MCMJSW), Kellgren-Lawrence (KL) grade, Western Ontario and McMaster Universities Arthritis Index WOMAC pain score (WOMKP), and WOMAC disability score (WOMADL).
## Performance assessment of MRI nomogram
The AUC and ACC were used to evaluate the discrimination of the nomogram, and the calibration curve and Hosmer-Lemeshow (H-L) test were used to evaluate the calibration of the nomogram. The p value of H-L test is greater than 0.05, which proves the perfect consistency between the predicted and the observed values. The internal validation was carried out by bootstrapping method (1000 bootstrap resamples) to reduce the over-fitting deviation. The clinical usefulness of the nomogram was verified by decision curve analysis (DCA) [34, 35]. Additionally, net reclassification improvement (NRI) and relative integrated discrimination improvement (IDI) were analyzed to evaluate nomogram improvements compared with the nomogram only including the clinical covariates (nomogram-CO). We further evaluated the predictive value of nomogram0 and nomogramΔ24 among different subgroups.
## Statistical analysis
The expectation-maximization method was used to interpolate the missing data. Cartilage morphology parameters were Z-normalized to facilitate comparison of parameters (Z = x-µ/σ). Continuous variables are expressed as mean ± standard deviation (SD). Data that were normally distributed were tested using independent-samples t-test, and non-normally distributed variables were tested using the Mann-Whitney U test. Categorical variables were represented by numbers and percentage, and categorical variables were tested using chi-square analysis and Fisher exact test. All tests were two-sided with $p \leq 0.05$ indicating statistical significance. A variance inflation factor (VIF) was leveraged to analyze the co-linearity of various factors in the logistic regression analysis, and VIF > 10 considered indicative of multi-collinearity. Data analysis was conducted using IBM SPSS, version 22.0, Empower (R) (http://www.empowerstats.com, X & Y solutions, Inc., Boston MA) and R 4.0.2 (http://www.Rproject.org).
## Clinical characteristics at baseline
The subjects in the case group and the control group were frequency matched for their baseline characteristics including sex, age, BMI, WOMKP, and WOMADL, but there was a significant difference in KL grade at baseline (Supplementary Table 1).
## Selection of MRI characteristic parameters and construction of cartilage score
At the 12 M of follow-up, the MRI data of 18 subjects were missing, and at the 24 M of follow-up, the MRI data of 1 subject was missing. We used the expectation-maximization method to interpolate the missing data. A formula was generated using a linear combination of selected features that were weighted by their respective LASSO coefficients; the formula was then used to calculate a risk score for each patient to reflect the progression of KOA. Score0 included 6 cartilage parameters at baseline, scoreΔ12 included 6 changes of cartilage parameters at 12 M, and ScoreΔ24 included 5 changes of cartilage parameters via minimum criteria at 24 M, respectively (Table 1; Fig. 1).
Table 1The Selected MRI cartilage parameters of score and their corresponding coefficientsThe cartilage parameters of score at different timecoefficientsscore0coefficient of variation of cartilage thickness - medial tibia0.107674849The percent of area of subchondral bone denuded of cartilage - anterior medial tibia (%)0.000946295The percent of area of subchondral bone denuded of cartilage - central medial femur (%)0.116403371minimum cartilage thickness - central medial femur (mm)-0.045685031mean cartilage thickness - central medial femur (mm)-$0.033840691\%$ area of subchondral bone denuded of cartilage -external central medial (%)0.005973847scoreΔ12The change of minimum cartilage thickness -center medial tibia-0.103293043The change percent of subchondral bone denuded of cartilage - center medial tibia(%)0.006184577The change percent of area of subchondral bone denuded of cartilage - anterior medial tibia (%)0.051352482The change percent of area of subchondral bone denuded of cartilage - central medial femur (%)0.034920672The chang mean cartilage thickness - central medial femur (mm)-0.021555888The change percent of area of subchondral bone denuded of cartilage - central medial femur (%)0.005259763scoreΔ24The change of minimum cartilage thickness of central medial tibia (mm)-0.062594417The change of area of cartilage surface of central medial femur(cm^2)-0.006184622The change percent of area of subchondral bone denuded of cartilage - central medial femur(%)0.065893315The change of mean cartilage thickness of medial tibia-femur compartment (mm)-0.113420248The change of mean cartilage thickness of central medial tibia-femur compartment (mm)-0.220041334 Fig. 1Selection of cartilage morphology parameters features using LASSO binary logistic regression nomogram. Tuning parameter (λ) selection in the LASSO nomogram was used in the 10-fold cross-validation via minimum criteria and the 1 standard error of the minimum criteria (the 1-SE criteria). The binomial deviance was plotted versus log(λ). ( A) Dotted vertical lines were drawn at the optimal λ values, the λ value of 0.0379, with log(λ)–3.272, was the chosen via minimum criteria at baseline. ( B) LASSO coefficient profiles of the 92 cartilage parameters. The dotted vertical line was plotted at the λ value of 0.0379, resulting in 6 parameters at baseline. ( C) A λ value of 0.0427, with log (λ)–3.154, was chosen via the minimum criteria at 12 M. (D) LASSO coefficient profiles of the 92 cartilage parameters. The dotted vertical line was plotted at the λ value of 0.0427 via minimum criteria, resulting in 6 parameters at 12 M. (E) A λ value of 0.0384 with log (λ)–3.26 was chosen via minimum criteria at 24 M. (F) LASSO coefficient profiles of the 92 change cartilage parameters over 24 M.The dotted vertical line was plotted at the λ value of 0.1068 via minimum criteria, resulting in 5 parameters at 24 M
## Construction and performance assessment of chondral nomogram
The three nomograms established were nomogram0, nomogramΔ12, and nomogramΔ24, including MRI cartilage score and KL, WOMPK, WOMADL, and MCMJSW, respectively, at different times (Table 2; Fig. 2). The VIF of all predictive factors in the nomogram were all less than 5, indicating that there was no collinearity among variables. The AUC of nomogram Δ24 (0.71, $95\%$ CI [confidence interval]: 0.66 to 0.79) was higher than the AUC of nomogram 0 (0.69, $95\%$ CI 0.63 to 0.72), $p \leq 0.05.$ The AUC of nomogram Δ24 (0.71, $95\%$ CI: 0.66 to 0.79) was higher than the AUC of nomogram 0 (0.60, $95\%$ CI 0.60 to 0.70), $p \leq 0.05.$ The ACC of nomogram0, nomogramΔ12 and nomogramΔ24 are 0.68, 0.66 and 0.77 respectively(Fig. 3). Compared with nomogram-CO, nomogramΔ24 had the highest performances in NRI and IDI. The NRI of nomogram0 was 0.0878 ($95\%$ CI -0.0061 to 0.0816, $p \leq 0.05$), and the IDI of nomogram0 was 0.0505 ($95\%$ CI 0.0307–0.0704, $p \leq 0.05$). The NRI of nomogramΔ12 was 0.0685 ($95\%$ CI − 0.0202 to 0.1572, $p \leq 0.05$), and the IDI of nomogramΔ12 was 0.043 ($95\%$ CI 0.0228–0.0631, $p \leq 0.05$). The NRI of nomogramΔ24 was 0.3083 ($95\%$ CI 0.1897–0.4268, $p \leq 0.05$), and the IDI of nomogramΔ24 was 0.091 ($95\%$ CI 0.0648–0.1173, $p \leq 0.017$).The P values of H-L test of nomogram0, nomogramΔ12, and nomogramΔ24 were 0.92, 0.35, and 0.67, respectively, indicating that the three nomograms had good calibration. The calibration curve showed that the predicted results of nomogram were consistent with the actual results of OA progression (Fig. 4). DCA shows that all nomograms are clinically effective at range of roughly 4 to $100\%$.Whereas the net benefit of nomogramΔ24 is better than other nomograms when the threshold probability is approximately $30\%$ to$70\%$ (Fig. 5).
Table 2Risk factors for the progression of OA at baseline, 12 M, and 24 M by multiple logistic regressionnomogram0nomogramΔ12nomogramlΔ24Intercept and variable0R$95\%$CI0R$95\%$CI0R$95\%$CIIntercept0.650.18 to 2.291.370.31 to 6.060.530.15 to 1.83BL MCMJSW1.441.15 to 1.811.271.02 to 1.581.291.03 to 1.63XRKL20.90.51 to 1.590.980.56 to 1.721.070.59 to 1.92XRKL31.80.86 to 3.772.241.09 to 4.632.171.02 to 4.60BL WOMACKP0.950.93 to 0.980.950.93 to 0.980.940.92 to 0.97BL WOMADL1.051.02 to 1.091.051.02 to 1.081.061.03 to 1.10Score011.224.52 to 27.85ScoreΔ1218.765.12 to 68.88ScoreΔ245.883.54 to 9.77 Fig. 2Construction of nomograms based on MRI cartilage parameters for predicting the progression of OA. The distribution and total point number of predictive variables overlap along the nomogram scales. The box plot shows the categorical variables (e.g., KL grade), with the box size indicating percentage. The density plot shows the distribution of continuous variables (e.g., WOMKP, MCMJSW, WOMADL, score0, scoreΔ12, scoreΔ24 and total score). The definite value of each red point corresponds to the scale of the variable’s axis (β(χ-m) term). The observation values overlap at the total score axis. A straight line is drawn through this point and downward extending to the risk axis, and the point of intersection with the risk axis represents the occurrence probability of the progression of KOA. ( A) nomogram0. ( B) nomogramΔ12. ( C) nomogramΔ24 Fig. 3Receiver curve analyses of the nomograms to compare the predictive performance at different times. The AUC of nomogramΔ24 was higher than nomogramΔ12 and nomogram0 Fig. 4The calibration curve illustrates the calibration of the nomogram in terms of the agreement between the predicted risk of OA and the observed outcomes of OA. The 45° solid grey line represents a perfect prediction, and the dotted red line represents the predictive performance of the nomogram. The dotted line has a closer fit to the solid line, which indicates better predictive accuracy of the nomogram. ( A) nomogram0. ( B) nomogramΔ12. ( C) nomogramΔ24
Fig. 5Decision curve analysis for nomograms predicted the radiological and pain progression of KOA. The Y axis shows the net benefit. The x axis of DCA is the threshold of the predicted probability using the nomogram to classify subjects with progression and subjects without progression. The gray line represents the hypothesis that all subjects had OA progression; the black line represents the hypothesis that none of the subjects had any progression of OA. The DCA illustrates that all models were useful between threshold probabilities of 4-$70\%$,and the net benefit of the modelΔ24 was better than the other
## Secondary analysis
The secondary analysis was used to further evaluate the predictive power of nomogram0 and nomogramΔ24, which were highly distinguished in primary analysis (Table 3). nomogram0 and nomogramΔ24 have the best discrimination in distinguishing OA progression (both radiological and pain progression) and non-progression, with an AUC of 0.74 and 0.77, respectively. nomogram0 and nomogramΔ24 have the lowest ability to distinguish between the pain-only progression group and non-progression group, with an AUC of 0.63 and 0.62, respectively.
Table 3The seconday analysis of Nomgram0, NomogramΔ24secondary analysisNomogram0NomogramΔ24AUCACCAUCACCRad only progressor vs. non-progressor0.680.590.770.75Pain only progressor vs. non-progressor0.630.700.620.72Rad + pain progressor vs. non-progressor0.740.700.770.75All Progressors (Rad or Pain) vs. non-Progressors0.670.580.710.62Rad Progressors vs. Rad Non-Progressors0.680.660.760.71Pain Progressors vs. Pain Non-Progressors0.680.640.670.64
## Discussion
In the current study, we screened the feature parameters of MRI cartilage at different time points using LASSO logistic regression, developed and validated nomograms to predict the progression of KOA. The nomogram0 and nomogramΔ24 achieved good performance, the ROC showed good discriminatory ability in predicting the progression of KOA. Therefore, Our study shows that nomogram0 and nomogramΔ24 can predict the progression of KOA.
In our study, we calculated the score of the MRI cartilage parameters. The selected cartilage parameters all come from medial compartment of tibiofemoral joint, which is consistent with previous results [18]. They concluded that the loss of medial tibial and femoral cartilage during 24 months of follow-up was the main risk factor for the progression of OA. However, there are also studies that have yielded inconsistent results. It has been reported that the change in lateral cartilage volume of the knee is closely related to the progression of medial tibiofemoral [36]. In the study of Hunter Group’s analysis of FNIH-related data to determine the best combination of imaging and biochemical biomarkers used to predict the progress of knee osteoarthritis (OA), the central medial femoral thickness and the change of the thickness of the central medial femur during the follow-up of 24 months can be used to predict the progress of OA, which is consistent with this study. This contradiction may be the result of different imaging methods, different image analysis techniques, different stages of OA. Our study shows that nomogram0 and nomogramΔ24 can predict the progression of KOA.
The results of our study suggest that nomogram0 and nomogramΔ24 have high discrimination ability, whereas nomogramΔ12 has low discrimination ability. By using backward stepwise selection with the AIC in logistic regression modelling, we identified the following 6 cartilage parameters as having the strongest associations with the progression of KOA. The degree of cartilage injury at baseline are believed to be one of the causes for the progression of OA at the end of the follow-up period. Our results are consistent with other studies. Lower baseline central medial tibial was associated with incident widespread full-thickness cartilage loss in KOA [20]. Cartilage defects of the knee are believed to be the cause of the development of OA, resulting in pain and dysfunction [37]. Frobell et al. found that OA subchondral bone exposure already existed in the early stage of OA (KL = 1 and KL = 2). With an increase in the severity of the disease, subchondral bone exposure becomes greater, the denuded areas of the subchondral bone and local cartilage loss was significantly associated with greater KL grades [38]. At the same time, recent studies suggest that the area of the cartilage defect has a specific spatial distribution in the knee joint, and OA cartilage denudation is limited to the bone center of the medial tibial and femoral joint [39]. With the loss of cartilage, the subchondral bone marrow in the defect area was also damaged, which further aggravates the progression of OA. Our study is similar to this study. At the baseline, the cartilage damage area is concentrated in the central area of the medial tibia and the central area of the medial femur [40]. A previous study supported the analysis of the uniformity of cartilage thickness rather than the average ROI thickness enhances the sensitivity to detect OA-related differences between knees [41]. Cartilage parameters involved in the construction of nomogramΔ24 reflect the loss of medial tibial and femoral articular cartilage thickness during the follow-up period, indicating that decrease of medial cartilage thickness is a risk factor for the progression of KOA.
Recent studies have focused on the measurement of anatomically defined cartilage subregions to clarify the spatial distribution of articular cartilage changes during the progression of OA.The change rate of cartilage morphology in the central subregion of the tibiofemoral was greater than that in the whole cartilage area, and the reduction of cartilage thickness was positively correlated with the OARSI score of joint space narrowing. The higher the OARSI score, the greater the decrease in cartilage thickness [42]. The degree of cartilage loss during the follow-up period was positively correlated with the exposure area of cartilage at baseline. The standardized response mean (SRM) of cartilage loss secondary to cartilage loss in non-exposed areas was − 0.25, whereas the SRM of cartilage loss in severe-exposed areas was − 1 [43].In conclusion, we believe that MRI cartilage parameters can correctly reflect the baseline cartilage status and the changes of knee cartilage during follow-up.
The nomogram has a user-friendly digital interface, higher ACC, and easier-to-understand prognosis, which can help health care professionals to make better clinical decisions. As a result, nomograms have been widely used in oncologic research [44]. In this study, the AUC of nomogram0 and nomogramΔ24 were 0.69 and 0.71, respectively. There are relatively few studies on the diagnosis or prediction of OA by nomogram. Zhang et al. applied the nomograph model to predict the severity of KOA through non-imaging parameters, which can intuitively identify patients with severe KOA, with AUC of 0.802 [45].
The control group included the no-progression subgroup, the pain progression subgroup, and the radiographic progression subgroup; other studies confirmed that this grouping reduced the predictive ability of the study parameters [46]. Normogram0 showed the highest degree of differentiation between the case group and the non-progressive subgroup, with an AUC of 0.75. The nomogramΔ24 provided the most discriminating ability when distinguishing the case group from the non-progression subgroup, or the radiographic-only progression subgroup from the non-progression subgroup. The nomogramΔ24 achieved an AUC of 0.77 in subgroup analysis. The two nomograms have the lowest ability to predict the pain-only progression subgroup and the non-progression subgroup. The etiology of pain in patients with knee OA is complex and multi-factorial. Lin et al. constructed nomogram based on MRI radiology and clinical variables, and achieved good predictive effect and accuracy in identifying OA and improving knee pain in patients with OA. This proof-of-concept study provides a promising method for predicting clinically significant results [47].
Some studies have found that there may be a process of cartilage edema and hypertrophy in the early stages of OA. These results suggest that OA progression is not one-way cartilage loss, but may be bidirectional-cartilage thinning and cartilage thickening-in the early stages of the lesion [48]. The KL grades of the included subjects in this study were 1 to 3. During the follow-up period, there were bidirectional changes in cartilage thickness during different stages of progression, and this complex pathological process may limit the predictive ability of the nomogram.
In addition to the study design [49], this investigation has a few other limitations. This study used bootstraps for internal validation. Although this method is no less than the internal verification of random grouping [50], the lack of external validation reduces the credibility of the nomogram.
Therefore, a large, multicenter sample is needed to verify the nomogram. Also, the variables screened by LASSO regression are inconsistent and have a lack of continuity at different time points, which is determined by the LASSO regression algorithm. Although statistical studies can prove the correlation between different scores and indirectly prove the correlation between different parameters, previous studies often judge the effect of a variable on the progression of OA or predict the progression of OA by observing the time-dependent concentration of a variable. Therefore, in this study, whether the selection of variables at different time points can really represent the pathological changes of articular cartilage needs further histologic verification. Furthermore, we only used MRI morphologic cartilage parameters were used to construct a prediction nomogram. Because of the complexity of OA progression, it may be necessary to combine biomarkers, cartilage morphology parameters, and the severity of OA to build a nomogram that better distinguishes OA progression. The cohort study design itself has certain limitations. Cartilage change was defined as the change from the baseline to 24 months of follow-up, while the progression of OA was assessed from baseline to 24, 36, or 48 months. Therefore, instead of having an absolute predictive relationship, there were some overlapping periods in the study.
## Conclusions
This study builds a predictive nomogram based on MRI quantitative cartilage parameters of KOA, combined with clinical risk factors. The results of this study suggest that this nomogram can predict the progress of mild-to-moderate KOA, and may have particular value in aiding health care professionals with clinical decision-making regarding KOA.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: Is There Any Relationship between Physical Activity Levels and Academic Achievement?
A Cross-Cultural Study among Spanish and Chilean Adolescents
authors:
- Laura O. Gallardo
- Diego Esteban-Torres
- Sheila Rodríguez-Muñoz
- Alberto Moreno-Doña
- Alberto Abarca-Sos
journal: Behavioral Sciences
year: 2023
pmcid: PMC10045662
doi: 10.3390/bs13030238
license: CC BY 4.0
---
# Is There Any Relationship between Physical Activity Levels and Academic Achievement? A Cross-Cultural Study among Spanish and Chilean Adolescents
## Abstract
The current scientific literature has shown significant disparity in results when physical activity is linked to academic achievement among adolescents. Thus, the main objectives of this study were [1] to analyze the relationship among students’ academic achievement, intention to be physically active, and physical activity levels depending on the country (Spain or Chile), as well as to analyze these relationships based on students’ socio-economic status, type of school, school year, gender, and body mass index; and [2] to analyze the differences between all these variables depending on the students’ country and gender. In total, 3052 adolescents participated in the research (14.58 ± 1.39 years): 734 Chilean students (336 males and 398 females) and 2318 Spanish students (1180 males and 1138 females). Various questionnaires were used to measure the study variables. The results revealed significant relationships between academic achievement and the rest of the variables in Spanish adolescents, but in the Chilean population, academic achievement was significantly related only to socio-economic status and the type of school. Moreover, Spanish students obtained higher scores, especially the males, except for academic achievement, which was higher in females. There were also significant differences in academic achievement, intention to be physically active, physical activity levels, and socio-economic status depending on the country, with all scores being higher in Spain. Given the results, the country seems to be an important factor when comparing academic achievement and physical activity levels, besides other demographic variables.
## 1. Introduction
Physical activity (PA) generates many health benefits [1]. Higher levels of PA are associated with increased cardiorespiratory endurance, better vascular and musculoskeletal function, and decreased fatigue [2]. These benefits are not only at a physical level but also at other levels, such as the psychological level. For example, a study carried out with European adolescents between 14 and 16 years of age confirmed that moderate PA contributes to greater general well-being, with lower levels of anxiety and depression in the participants [3]. Another psychological factor linked to PA is young people’s intention to be physically active in the future [4]. It has been shown that adolescents with a positive intention to be physically active score higher on physical tests such as endurance or speed [5].
At the cognitive level, some authors support the association between moderate–vigorous PA and an improvement in cognition [6]. These authors assess cognition using the results from neuropsychological exams, which consist of tests that address processing speed, memory, or executive function.
On the other hand, also linked to cognitive issues, the relationship between PA and academic achievement (AA) or academic results has also been researched. The studies carried out in recent years focusing on the adolescent population have shown disparate results.
In a review conducted by Herting and Chu, most studies agreed that adolescents´ daily PA was associated with their AA, cognitive function, brain structure, and activity [7]. Specifically, $48\%$ of the selected studies showed significant effects of PA on cognition, and $60\%$ showed significant effects of PA on AA [8]. Despite this, the percentage of studies that showed no significant effects was high. In another review developed by Singh et al., $80\%$ of the articles showed a strong association between the variables, although the authors pointed out that the relationship with physical condition has been studied more than the relationship with general PA [9].
In the Spanish adolescent population, results showed a significant negative relationship between PA levels during the week and AA, and no relationship with PA on the weekend. However, the male group that reached levels of PA close to compliance with the recommendations developed by the US Department of Health and Human Services (60 min of daily moderate–vigorous PA) [10] was more likely to obtain high AA [11].
In Chilean adolescents, some investigations analyzed the relationship between general PA and AA, including grades obtained exclusively in the areas of mathematics and language. The results showed that adolescents with a medium–low level of PA, which coincided with the participants who presented with obesity, were less likely to achieve high grades [12].
Other studies with the Chilean adolescent population analyzed the relationship between PA levels and eating habits with general AA. The results indicated that healthy habits, PA, and good nutrition were associated with higher AA [13]. Young people who practice PA regularly are significantly less likely to be overweight or obese in adulthood [14].
Regarding these results, it is appropriate to carry out comparative studies between countries to analyze whether the relationship between PA and AA is different according to the country.
A study carried out in 42 countries confirmed a positive relationship between self-reported AA and PA, specifically in students who practiced between 5 and 6 days of moderate–vigorous PA [15]. The data had an inverted “U” shape, with lower AA in individuals who practiced either little or considerable PA. However, differences between various countries were not analyzed, and few studies seem to have addressed this issue. Nevertheless, another study found no significant differences between motor skills, executive function, and early achievement in children from six countries [16].
Some authors consider that there are several reasons for the variability of the results in the relationship between PA and AA. On the one hand, AA is usually measured through the academic results obtained, which may differ because the educational system may vary as a function of the country [17]. On the other hand, the lack of measures to compare these variables and the differences in the frequency of PA in different countries is noteworthy. Finally, the lack of control over other factors that could influence the results, such as family socio-economic status (SES), age, or psychological factors may condition the relationship between AA and PA [18]. One investigation found that higher gender inequality was associated with gender differences in PA. Likewise, lower levels of gender inequality were associated with increased female and male PA [19].
Consequently, although many studies have analyzed the relationship between PA and AA, few researchers have studied country differences in adolescence. In addition, the are two studies that were found who presented dissimilar results: in one of them, they showed significant relationships between PA and AA, and in the other, they did not [15,16].
Therefore, given the previous research, our main objectives of this study were 1) to analyze the relationship between the variables AA, intention to be physically active, and levels of PA depending on the country (Spain or Chile), taking into account other variables such as SES, type of educational center, grade, gender, and body mass index (BMI); and 2) to analyze possible differences by country in PA levels, AA, intention to be physically active, PA family SES, gender, and BMI. Based on these objectives, the following study hypotheses are established. ( a) The relationship between all the variables will be significant and positive in both countries, except for BMI, which will be significant and negative. ( b) The means will be higher and significant in Spain, except for BMI, which will be higher in Chile. In addition, the means will be higher in males except for AA, which will be higher in females. There will be significant differences between the two countries as a function of gender in all the study variables.
## 2.1. Participants
The sample comprised 3052 adolescents: 734 from Chile, Valparaiso region, ($M = 14.74$, SD = 1.47 years) and 2318 from Spain, Autonomous Community of Aragon ($M = 14.53$, SD = 1.37 years). From Chile, there were 336 boys, aged 14.80 ± 1.47 years (BMI = 20.97 ± 3.45 kg/m2), and 398 girls, aged 14.70 ± 1.47 years (BMI = 21.75 ± 3.85 kg/m2), from seventh and eighth basic grades and from first to fourth half grades from six high schools: three concerted and three public. From Spain, there were 1180 boys, aged 14.53 ± 1.38 years (BMI = 20.40 ± 3.56 kg/m2), and 1138 girls, aged 14.52 ± 1.36 years (BMI = 20.02 ± 2.86 kg/m2), in the first to the fifth grades of secondary education from fourteen high schools: three concerted and eleven public. The samples of schools, from Chile and Spain, were chosen by convenience based on accessibility and willingness to cooperate. Data were collected during the academic course of the year 2016.
## 2.2. Measures
Demographic Variables. The questionnaire included a personal data section: gender; grade (ranging from first year of secondary school to second year of high school; a total of 6 courses); type of school (1 = public or municipal, 2 = concerted or privately subsidized, and 3 = private); students’ weight; and height.
Academic Achievement. AA was calculated using the arithmetic mean obtained in the teaching areas of the previous year. The scores were reported by the participants, a method used in other studies [20]. In this case, AA was calculated with a question asking the average on a 10-point scale.
Physical Activity Levels. The “International Physical Activity Questionnaire-Short Form” (IPAQ-SF) was used to measure PA [21]. It was validated in the adolescent population and in Spanish adolescents [22]. It provides information on the PA carried out by each individual over the last seven days and also asks about the intensity (light, moderate, or vigorous), the frequency (days per week), and the duration (time per day). It presents seven items (e.g., During the last seven days, on how many days did you perform vigorous physical activities such as lifting heavy objects, playing sports intensively, running or cycling fast?). The responses indicate the days of practice (from zero to seven) and the time invested (in minutes). The IPAQ-SF showed negligible to small correlations with total activity level measured with objective devices (median = 0.29).
Intention To Be Physically Active. The questionnaire “Intention to be Physically Active Measurement” was validated in the Spanish population [23,24]. It assesses post-high school intention to be physically active. It consists of five items (e.g., “After finishing high school, I would like to be part (or continue) of a sports training club”), which are rated on a five-point Likert-type scale, ranging from 1 (totally disagree) to 5 (totally agree). The final score is obtained by calculating the arithmetic mean of the scores obtained in each item, so the higher the score, the greater the intention to be physically active in the future. The construct validation data provided a reliability (Cronbach´s alpha coefficient) of 0.94. In both our samples, the reliability was α = 0.813 for the Spanish sample and α = 0.813 for the Chilean one.
Family Socio-economic Status. The “Family Affluence Scale II” (FASII) was used to compare family SES between countries [25]. It consists of 4 items, with response variation and, consequently, score variation (e.g., “Does your family have a car, van or truck?” No [zero]; Yes, one [one]; Yes. Two or more [two]). The final score, called the FAS index, is the sum of the scores of the answers to each question on a scale of zero to nine points. The higher the score, the higher the family’s SES level. The internal consistency in our study recorded a moderate association (α = 0.61). However, this result is similar to previous studies [26].
## 2.3. Procedure
The procedure in the present investigation was the same for both countries.
Firstly, we note that this study was approved by CEICA (Ethics Committee of the Autonomous Community of Aragon).
Then, high-school managers were contacted to request the schools´ participation. Once the participation was confirmed, a circular was provided to the teachers, families, and participants to inform them about the protocol and aims of the study. It included consent for participation by the parents/guardians and assent for the students themselves. Researchers made an appointment with the high schools to administer the questionnaires. Questionnaire completion was carried out during regular class time under the supervision of at least a researcher and the teacher in the classroom. The data were coded assigning a personal identification to each participant.
The need for a sincere response was highlighted, with an explanation that the data reported were used exclusively for research purposes.
## 2.4. Data Analysis
To confirm sample distribution normality, the Kolmogórov–Smirnov test was performed. Once normality was confirmed, a Pearson bivariate correlation analysis was carried out with all the variables (PA levels, AA, intention to be physically active, SES, BMI, gender, type of school, and school year) to determine their relationships independently in each country. Subsequently, multivariate analysis (MANOVA) was performed according to the country with the entire sample and with both sexes separately, in addition to descriptive statistics to determine the means and standard deviation.
All analyses were performed using the statistical software SPSS version 26.0.
## 3. Results
First, the results of the bivariate correlation analyses performed between all the study variables are presented. Table 1 shows the relationships between variables in the Spanish population. There were significant correlations between most variables: AA was related to intention to be physically active, SES, and school. PA was related to intention to be physically active, SES, center, and gender. Intention to be physically active correlated with SES and gender. Grade was related to school and BMI. SES correlated with school, and BMI correlated with gender. The highest values were between the PA levels and the intention to be physically active ($r = 0.41$, $p \leq 0.01$) and between BMI and grade ($r = 0.29$, $p \leq 0.01$).
Table 2 presents the results of the correlations between the previously presented variables for the Chilean population. The highest correlations were again found between PA and the intention to be physically active ($r = 0.42$, $p \leq 0.01$), followed by PA and gender ($r = 0.22$, $p \leq 0.01$) and the type of school with AA and SES ($r = 0.306$, $p \leq 0.01$ and $r = 0.39$, $p \leq 0.01$, respectively).
The results in Table 3, firstly considering the total sample, reveal the significant differences in AA, intention to be physically active, PA levels, and SES depending on the country, with Spain obtaining the highest values. Regarding BMI, significant differences were found, although Chile presented higher values. This means that, in our study, the country has a statistically significant effect on all variables, as shown by $p \leq 0.001.$
Secondly, considering the female gender, differences were found in almost all the target variables. All the Spanish female adolescents scored higher in AA, intention to be physically active, PA levels, and SES. However, Chilean female adolescents scored higher in BMI. Concerning male gender, significant differences were found in AA, intention to be physically active, and SES. Again, Spanish male adolescents scored higher. Nevertheless, our results revealed no significant differences in PA and BMI in the male gender.
Multivariate analysis was used to identify the differences in the studied variables by country and gender. Our results showed a significant effect of the country for the entire population studied (Wilks’ λ = 0.847, F[1, 1847] = 66.533, $p \leq 0.001$, η2 = 0.020), for males (Wilks λ = 0.883, F[1, 918] = 24.136, $p \leq 0.001$, η2 = 0.117), and for females (Wilks λ = 0.793, F[1, 927] = 48.128, $p \leq 0.001$, η2 = 0.207).
## 4. Discussion
The first hypothesis proposed in this research stated that: “the relationship between all the variables would be significant and positive in both countries, except for BMI, which would be significant and negative.” The results of Spain showed a significant relationship between most of the variables, whereas in Chile, hardly any relationships were observed.
The AA was significantly related to all the study variables in the Spanish population, but it was related only to family SES and the type of school in the Chilean population.
Data on the relationship between AA and PA levels are inconclusive. These results are supported by other studies, such as those included in the review reporting that $60\%$ of all the articles that met the established selection criteria found a positive relationship between the two variables [8]. Based on the review carried out on young people aged between 6 and 18 years, 10 of the 16 selected articles evaluating the relationship between self-reported PA and AA showed positive associations [17]. According to these authors, this variation in results may be due to the different measurement methods used in the investigations, both in PA and AA.
On the other hand, we highlight the SES and the type of school, which are significantly and positively related to AA in both countries. Although our samples are not representative, these results are reinforced by some studies finding that parents’ higher SES is related to their children’s AA [27]. However, some authors point out that this relationship between AA and SES could differ in different social, economic, and cultural contexts [28].
The PA levels in the Spanish sample are also related to all the variables except for BMI and in the Chilean sample, with the exception of grade and BMI. The strongest correlations were between PA and the intention to be physically active, both in the Spanish and the Chilean samples. This may be because the intention to be physically active is a strong predictor of PA levels, and there are studies in which intention has explained up to $43\%$ of the variance of PA [29]. Similar results have been found in other studies, revealing that variables such as gender and age are related to PA and are significant predictors of the degree of compliance with the recommendations [30]. In this case, the relationship between PA levels and age was significantly negative in the Spanish population, which can be explained by the fact that across adolescence, other interests arise, generating new life habits, including a decrease in PA [30]. On the other hand, although some studies included BMI as a predictor, others did not show a relationship between BMI and PA [30,31,32,33]. Therefore, we can conclude that there is no unanimity in the data reported to date about a possible relationship between PA and BMI.
The type of school (public, private, or concerted) in which adolescents study and the family´s SES are also related to each other and to the PA levels of the Spanish and Chilean adolescents. This could be due to the fact that, generally, young people with a high family SES reside in environments that promote the practice of PA [34]. A review carried out on the adolescent population found that young people with a high SES were less likely to be sedentary [35]. In countries with a medium–low SES, the opposite occurs: young people with a high SES tend to be more sedentary. These data do not agree with the present investigation in the Chilean population.
It should be noted that young people with a high family SES tend to attend private fee-paying schools [36]. Private schools usually have smaller class sizes and better teaching resources and facilities, which can create a different classroom culture [37]. The literature indicates that the relationship between the type of school and PA is poorly understood. However, it also reports that schools, both public and private, provide a broad variety of opportunities for PA [38]. Nonetheless, there is evidence that the current school efforts to increase PA levels do not impact young people’s daily PA positively, regardless of gender or SES [39].
The significant relationship in both countries between the intention to be physically active and the demographic variables of gender, age, and SES (except for age in the Chilean population) shows that all these variables influence the greater or lesser intention to practice [5].
The second hypothesis stated that “the means would be higher and significant in Spain, except for BMI, which would be higher in Chile. In addition, they will be higher in males except for AA, which would be higher in females. There would be significant gender differences between the two countries in all the study variables.” The results confirmed this hypothesis, except for BMI, which did not present significant differences. In the rest of the variables, there were differences between the two countries, with the averages being higher in Spain and in males, except AA, which was higher in females.
The results presented are supported by other studies that also confirm that AA is higher in females [40]. This could be explained because girls have higher school productivity, taking much more advantage of study hours than boys [41]. Likewise, it has been found that girls have a greater intention to continue studying for more years, a factor that could also significantly account for these data.
Differences in AA have also been found depending on the country. Some studies have analyzed family income disparity in AA, concluding that there are differences, with higher AA in more prosperous communities [42]. It should be noted that AA is considered an important predictor of the quality and equity of education when comparing countries [43].
It was also verified that PA levels are higher in males in both countries. The gender difference in PA has also been reported in other studies [44]. In fact, this gender disparity is seen in many countries, perhaps because most countries still maintain traditional gender roles [45,46]. Similarly, these results show that the social and cultural context are important influencing agents in female adolescents’ PA experiences [47]. In contrast, Chilean boys’ PA levels were also lower, consistent with results obtained in previous studies with this population [48].
Regarding the intention to be physically active, there were differences between the two countries, with higher averages in males. Other studies have evaluated this variable according to gender, also reporting higher levels in males [49]. In addition, intention has been compared in young Spanish and Latin Americans, but the results are mixed. Some authors indicate that Spanish adolescents express a higher intention to be physically active than Argentines, whereas other studies report that Colombian and Ecuadorian adolescents have a higher rate of intentionality than Spaniards [47].
SES also presented differences between countries, although it should be noted that these differences were not observed when the samples of participants were analyzed separately by gender. The means were still higher in the Spanish population, which could be explained by the level of the development of the two countries, as some authors consider *Chile a* developing country [48].
The BMI indices of the studied population are between percentiles 18.5 and 24.9, which are considered normal. These data are positive, as there is currently some obesity in the adolescent group, reaching $38\%$ overweight in Chile and $26\%$ in Spain [50,51]. In our study, BMI was higher in the Chilean population in both genders than in Spanish adolescents, in line with the data from previous investigations [52].
## 5.1. Conclusions
In conclusion, regarding the objectives, we can affirm that both AA and PA levels are related to each other and to other factors such as the intention to be physically active in the future, gender, age, and SES. BMI is not related to PA levels in both countries. The strongest correlations were between PA and the intention to be physically active, both in the Spanish sample and the Chilean sample, because it is a strong predictor of PA.
The country should be highlighted as an important factor, because the relationships between the variables differ depending on the population studied. In Spain, there were significant relationships between most of the variables, whereas in Chile, SES and the type of school were significantly and positively related to AA but no other variables. Likewise, significant differences are shown in AA, PA, the intention to be physically active, and family SES when participants from both countries (Chile and Spain) were compared, with higher means in the Spanish population. These differences are present in the total population, in both the male and female populations, so we can state that culture is a key factor when analyzing variables such as PA or AA.
## 5.2. Limitations
First, we point out that all the study variables were measured subjectively, as they were reported by the participants. Therefore, some studies have shown that the results of the relationships between variables such as AA and PA differ depending on whether they were evaluated objectively or subjectively [17]. This may be because subjective data are not as accurate as objective data, such as PA levels measured by accelerometry [52]. Likewise, subjective data on PA tend to overestimate reality [50]. Height and weight values were also self-reported by the participants. Although these data have been found to correlate highly with objective values, adolescents may tend to report higher height and lower weight than the actual data [51].
On the other hand, this is not a longitudinal study, which would strengthen the results obtained by comparing them at different time points. In addition, the data collection of the total sample was not carried out during the same period, so there may be a few months’ difference between measurements. This could have conditioned the PA levels of the population studied.
Finally, despite the fact that the sample participating in this study is quite large, it is not a representative sample of each region. If this were the case, the results could be generalized to the entire studied population. Moreover, the differences from rural or urban environments have not been registered, showing a limitation of the present study.
## 5.3. Prospects
These results can be very useful in designing and implementing a multifactorial intervention program based on improving AA. A specific program could be designed for each country based on these results, thus adapting to the cultural demands of each population. This would also contribute to improving PA levels in adolescents because youth is the decisive moment for improving future healthy behaviors [53].
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|
---
title: Potential of Caffeic Acid and 10-Dehydrogingerdione as Lipid Regulators Relevant
to Their Inhibitory Effect on miR-122 and ATP Citrate Lyase Activity in Diabetic
Hyperlipidemic Rats
authors:
- Mohamed M. Elseweidy
- Alaa S. Elawady
- Mohammed S. Sobh
- Abdulmohsen H. Alqhtani
- Naif A. Al-Gabri
- Gehad M. Elnagar
journal: Biomedicines
year: 2023
pmcid: PMC10045664
doi: 10.3390/biomedicines11030726
license: CC BY 4.0
---
# Potential of Caffeic Acid and 10-Dehydrogingerdione as Lipid Regulators Relevant to Their Inhibitory Effect on miR-122 and ATP Citrate Lyase Activity in Diabetic Hyperlipidemic Rats
## Abstract
The present study aimed to illustrate the hypolipemic effect of 10-Dehydrogengardione (10-DHG) or caffeic acid (CA) with reference to the role of microRNA-122 (miR-122) and ATP citrate lyase (ACLY) activity. Diabetic hyperlipidemia was induced in rats, and then randomly classified into three groups. The first one received only a CCT-diet for 6 weeks and was referred to as the positive control. The other two groups received 10-DHG (10 mg/kg/day) or CA (50 mg/kg/day), orally for 6 weeks along with a CCT-diet. Another group of normal rats was included, received a normal diet, and was referred to as the negative control. Either 10-DHG or CA significantly decreased MiR-122 expression and appeared more remarkable in the CA group by $15.5\%$. The 10-DHG greatly enhanced phosphorylated form of AMP activated protein kinase (p-AMPK) activity, more than CA by 1.18-fold, while the latter exerted more inhibitory effect on ACLY, and fatty acid synthase (FAS) activities compared with 10-DHG ($p \leq 0.05$). Both drugs significantly decreased hydroxy methyl glutaryl coenzyme A (HMG-COA) reductase activity, which appeared more remarkable in 10-DHG, and significantly decreased triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) along with a high density lipoprotein cholesterol (HDL-C) increase. The 10-DHG ameliorated the hepatic tissue lesions greatly, more than CA. The 10-DHG or CA significantly inhibited MiR-122, hepatic FAS, and ACLY levels along with p-AMPK activation. This subsequently led to reduced plasma TG, cholesterol levels, and blood glucose improvement and, indeed, may explain their mechanisms as hypolipemic agents.
## 1. Introduction
MicroRNAs (miRNAs) are a well-known class of short non-coding RNAs that controls the expression of particular target genes through binding to complementary regions on mRNA, cleaving or blocking the translation of target MRNA. miRNAs can serve in additional significant regulatory roles in a wide range of biological processes, and more than $60\%$ of human genes are modulated by miRNAs [1]. The latter control various metabolic pathways including insulin secretion and glucose and lipid metabolism [2], and its downregulation exerts a certain role in the development of chronic disorders such as obesity, NAFLD, type 2 diabetes, and cardiovascular diseases [3].
MiR-122 is a unique liver-specific one that constitutes nearly $70\%$ of the hepatic miRNAs [4], and several genes that control TG and fatty acid synthesis are regulated by miR-122, including FAS and ACLY [5]. miR-122 inhibition, on the other hand, can remarkably decrease certain important genes involved in cholesterol biosynthesis such as HMG-CoA reductase [6].
Lipid metabolism is controlled also by many lipogenic proteins, including FAS, which is involved in lipolysis and lipogenesis. The latter includes FAS participation in the catalysis of acetyl-CoA conversion to malonyl-CoA; FAS in excess can impair lipid metabolism, leading to significant fat accumulation and many metabolic disorders such as NAFLD, type 2 diabetes, cardiovascular disease, and obesity [7].
ACLY is an important enzyme involved in the synthesis of cholesterol and fatty acids, as well as in the catabolism of other nutrients, abundantly expressed in the liver, adipose, and other lipogenic tissues in mammals. ACLY catalyzes the conversion of citrate into acetyl-CoA and oxaloacetate in the presence of ATP and CoA [8], and deregulation of either the activity or the liver protein expression may be connected to NAFLD, hyperlipidemia, or type 2 diabetes mellitus [9].
The main metabolic sensor, AMPK, is an important regulatory enzyme that enhances ATP-generating pathways such as fatty acid oxidation along with the inhibition of energy-storage processes such as fatty acid biosynthesis. miR-122 inhibition can enhance p-AMPK activation and promote a shift in energy utilization through inhibition of ACC2 and FAS [10].
Synthetic drug categories, mostly effective as hypolipemics, are many; however, most of them exhibit variable side effects such as diarrhea, nausea, myositis, and impaired liver function. Natural products or traditional drugs are becoming more and more popular as alternative treatment options because of their safety [11].
10-Dehydrogingerdione (10-DHG), a biologically active compound derived from the plant ginger rhizome (Zingiber officinale), demonstrated potent anti-inflammatory, antioxidant, and hypolipemic characteristics and, additionally, remarkably increased nitric oxide release [12], and it proved to prevent the aortic calcifications in dyslipidemic rabbits [13].
Caffeic acid (CA) is a phenolic compound belonging to hydroxyl cinnamic acid derivatives, and it is available in human diets in berries, kiwis, plums, apples, many vegetables, and coffee [14]. A previous study indicated that CA and its derivatives have antibacterial, hypoglycemic, antioxidant, anti-inflammatory, anticancer, and cardiovascular protective effects [15]. Another study demonstrated its inhibitory effect on oxidative modification of LDL, mostly implicated in the development of atherosclerosis [16].
Accordingly, the present study aimed mainly to investigate the lipid-lowering effect of 10-DHG and CA, individually through targeting specific pathways, effective enzymes dealing with either lipogenesis, or lipolysis, focusing on the role of miR-122 and ACLY activity. A histological study of liver tissue was also done to demonstrate any correlation between the biomarkers studied and the histopathological findings.
## 2.1. Drugs and Chemicals
The 10-DHG was isolated from fresh rhizomes (Zingiber officinale), then identified and purified in the phytochemistry research lab, as shown previously [17]. CA and streptozotocin (STZ) were obtained from Sigma-Aldrich, Inc., St. Louis, MO, USA (C0625 and S0130, respectively). Cholesterol, cholic acid, and thiouracil were obtained from Loba chemie Pvt. Ltd., Colaba, Mumbai, India (02781, 02790, and 06286, respectively).
## 2.2. Animals
Twenty-four male albino rats (6–8 weeks old), 140–155 g body weight, were supplied from the faculty of veterinary medicine, Zagazig University, Egypt. The rats were acclimated for one week in the animal house of Zagazig University’s faculty of pharmacy under standard environmental conditions of 21–23 °C and a 12 h light–dark cycle, with free access to tap water and food. All the experimental procedures followed the National Institutes of Health (NIH) guidelines for animal handling and were approved by Zagazig University’s Institutional Animal Care and Use Committee (ZU-IACUC), permission number (3/F/$\frac{119}{2020}$).
## 2.3. Experimental Design
The rats were overnight fasted and divided into three groups ($$n = 6$$ each group) and received STZ solution (50 mg/kg) dissolved in citrate buffer (0.1 M, pH = 4.5, freshly prepared IP) [18]. To avoid hypoglycemic shock, $10\%$ glucose was added to the drinking water for 24 h after STZ. The Bionime Rightest Wiz Plus® glucometer (Bionime GmbH, Berneck, Switzerland) was used to check blood glucose levels after 72 h. Rats that achieved a blood glucose level of ≥250 mg/dl were expressed as diabetic [19]. Diabetic rats were fed CCT-diet (normal diet supplemented with $4\%$ cholesterol and $1\%$ cholic acid, along with $0.5\%$ thiouracil in the drinking water) for two weeks [20,21]. One group continued for 6 weeks without any treatment, referred to as positive (+ve) control group, while the other two groups received 10-DHG (10 mg/kg/day) [22] or CA (50 mg/kg/day) [23] orally and individually for 6 weeks. Another separate group receiving a normal diet was included and referred to as the negative (-ve) control group.
## 2.4. Blood Sampling and Tissue Collection
After 6 weeks, the rats were fasted overnight; blood was drawn from the orbital sinus and allowed to coagulate for 15 min, then centrifuged for 15 min at 4000 rpm. Serum was collected and divided into two parts: one was instantly forwarded for glucose and lipid profile determinations, while the other was kept at −20 °C for any other additional biochemical analysis. Rats were later sacrificed by decapitation under deep anesthesia using diethyl ether (Merck), and the liver samples were isolated, rinsed in cold saline, dried, and then divided into several parts; one was directed for the evaluation of hepatic miR-122 and ACLY, p-AMPK, HMG-CoA reductase, and FAS activities. Another segment was quickly frozen in liquid nitrogen and kept at −20 °C. The remaining portion was kept at 4 °C for 72 h in $10\%$ neutral buffered formalin before being processed for histological studies.
## 2.5. Analytical Procedures
Serum lipids (TC, TG, and HDL-C) and fasting blood glucose were determined using commercially available kits (#1001091, #1001311, #1001095, and #1001200, Spinreact Co., Sant Esteve de Bas, Girona, Spain, respectively). The *Friedewald formula* was used to determine LDL-C [24]. The atherogenic index was calculated using the LDL-C/HDL-C ratio (AI) [25]. Hepatic ACLY activity was evaluated using an ELISA kit obtained from (#E2908Ra, Jiaxing Korain Biotech Co. Ltd., Jiaxing, Zhejiang, China). Hepatic p-AMPK, HMG-CoA reductase, and FAS activities were determined using ELISA kits obtained from (#MBS765897, #MBS761708, and #MBS043636, My BioSource Inc., San Diego, CA, USA), respectively.
## 2.6. Quantitative Real Time PCR
The total RNA was first collected using the Trizol reagent (#15596026, Life Technologies Corporation, Carlsbad, CA, USA), then 1 μg of total RNA was reverse transcribed into cDNA by using a QuantiTects Reverse Transcription Kit (#205311, Qiagen Sciences Inc., Germantown, MD, USA). C-DNA was amplified via a Maximas SYBR Green/Fluorescein qPCR Master Mix (#K0241, Thermo Fisher Scientific Inc., Carlsbad, CA, USA) through specific primers that were prepared according to the manufacturer’s protocol (Table 1). For PCR assay, 12.5 μL Maxima SYBR Green/ Fluorescein qPCR Master Mix (2X) was mixed with 1 μL cDNA template, 0.3 μL forward primer, 0.3 μL reverse primer, and nuclease-free water to complete the volume to 25 μL. The conditions were designed as follows: 10 min at 95 °C, followed by 45 cycles of 95 °C for 10 s, 60 °C for 15 s, and 72 °C for 15 s. β-Actin was applied as an internal reference for miRNA. Rotor-Gene® Q with software version 2.1.0 (Qiagen Sciences Inc., Germantown, MD, USA) collected data automatically and analyzed the value of the threshold cycle (Ct), which normalized to an average Ct value of the house-keeping genes (∆Ct); 2-ΔΔCt was used for calculating relative gene expression fold [26].
## 2.7. Histological Examination
The liver samples fixed in $10\%$ buffered neutral formalin solution for 48 h were dehydrated through ethanol upgrading from 75 to $100\%$, then cleaned with xylene and embedded in paraffin. Sections of liver were cut to a thickness of 3–5 µm and stained with hematoxylin and eosin (H&E) for histological study [27]. A lesions score system was evaluated as the following: (0 = no detectable histopathological alterations, 1 = rarely minimal or focal, 2 = multifocal, 3 = patchy or diffuse) with a semiquantitative method [28].
## 2.8. Statistical Analysis
The results were statistically analyzed using Prism 9 from GraphPad (San Diego, CA, USA) and presented as mean ± standard deviation (SD). One- and two-way ANOVA was used to assess significant differences between groups; both of them were followed by Tukey’s post hoc test for intergroup comparison. The significance level was set at $p \leq 0.05.$
## 3.1. Effect of Caffeic Acid and 10-Dehydrogengerdione on Body Weight and Liver Weight
Table 2 shows the decreased body weight of the positive control rats compared with the negative control ($p \leq 0.05$). The 10-DHG and CA groups significantly gained weight compared with the positive control. Liver weight of the positive control rats demonstrated a significant increase compared with the negative control rats ($p \leq 0.05$) and turned out to be reduced by both drugs compared with the positive control rats ($p \leq 0.05$).
## 3.2. Effect of Caffeic Acid and 10-Dehydrogengerdione Treatment on miR-122 Expression
Figure 1 demonstrates that diabetic hyperlipidemic rats showed a significant increase in miR-122 gene expression (2.3-fold) as compared to the negative control rats ($p \leq 0.05$). Administration of either 10-DHG or CA remarkably decreased miR-122 expression compared with the positive control ($15.5\%$ and $28.6\%$, respectively) ($p \leq 0.05$). Suppression of miR-122 expression by CA was remarkably more, as compared to 10-DHG ($15.5\%$) ($p \leq 0.05$).
## 3.3. Effect of Caffeic Acid and 10-Dehydrogengerdione Treatment on Hepatic p-AMPK and Blood Glucose
The positive control group showed decreased p-AMPK activity as compared to the negative one (Figure 2A); 10-DHG and CA significantly increased p-AMPK activity ($p \leq 0.05$) compared with the positive control. The 10-DHG exhibited more enhancing effect (3.5-fold) compared with CA (2.9-fold).
Figure 2B illustrates that blood glucose levels in positive control rats demonstrated a significant increase compared with negative control rats and turned out to be decreased by 10-DHG and CA treatment.
## 3.4. Effect of Caffeic Acid and 10-Dehydrogengerdione Treatment on Hepatic ACLY, FAS, and Serum Triglyceride
In Figure 3A, the positive control group demonstrated significant elevation in ACLY activity compared with the negative control group ($p \leq 0.05$) and turned out to be reduced by treatment with 10-DHG and by CA. Furthermore, CA exhibited a substantial inhibitory effect on ACLY ($18.4\%$) compared with 10-DHG ($p \leq 0.05$).
Moreover, the positive control group demonstrated increased activity of FAS (6.5-fold) compared with the negative control group (Figure 3B), dramatically reduced after treatment with CA ($77.7\%$), and was more remarkable than 10-DHG treatment ($62.1\%$) ($p \leq 0.05$).
Figure 3C illustrates that positive control rats demonstrated hypertriglyceridemia compared with the negative control ones ($p \leq 0.05$); 10-DHG or CA treatments significantly decreased TG levels ($p \leq 0.05$) compared with the positive control and were more remarkable than 10-DHG ($p \leq 0.05$).
## 3.5. Effect of Caffeic Acid and 10-Dehydrogengerdione Treatment on Hepatic HMG-CoA Reductase Activity and Lipogram Pattern
Figure 4A demonstrates that intake of either 10-DHG or CA significantly decreased HMG-CoA reductase activity by $78.3\%$ and $59.7\%$, respectively, as compared to the positive control group, and was more remarkable in 10-DHG as compared to CA ($p \leq 0.05$).
As shown in Figure 4(B–E), 10-DHG or CA administration significantly decreased TC, LDL-C, and AI levels along with significant increase in HDL-C levels compared with positive controls ($p \leq 0.05$); 10-DHG achieved greater potential than CA regarding such lipids.
## 3.6. Effect of Caffeic Acid and 10-Dehydrogengerdione Treatment on Hepatic HMG-CoA Reductase Activity and Lipogram Pattern
Hepatocytes of the negative control group exhibited normal patterns of architecture, central veins, sinusoids, and portal triads. Diabetic hyperlipidemic rats revealed intense centrilobular and periportal lipidosis in ($50\%$) of examined sections. The majority of the degenerated hepatic cells were foamy with centrally located nuclei. Furthermore, minute foci of necrosis, apoptotic cell, and periportal inflammatory cell aggregates (mainly lymphocytes) were commonly observed lesions. Unicellular hepatic lipidosis was noticed in $10\%$ of hepatic parenchyma in the CA group, while the 10-DHG group showed hepatic parenchyma, mostly ameliorated along with fatty change in individualized hepatocytes and, additionally, lymphocytic infiltrates within some sinusoids were also detected (Figure 5).
Table 3 shows a summary of a semiquantitative lesion score system for the liver alteration, in which diabetic hyperlipidemic rats demonstrated a significant alteration in hepatic parenchyma as lipidosis with multiple foamy-appearing cells, necrotic, apoptotic, and lymphocytes infiltrates as compared to the negative (-ve) control rats. Rats treated with 10-DHG induced more amelioration of hepatic histological lesion scores than those treated with CA.
## 4. Discussion
The present study illustrates the hypolipidemic and hypoglycemic effects of CA and 10-DHG in diabetic hyperlipidemic rats. This was attributed mostly to inhibition of miR-122 and liver lipogenic enzymes (FAS and ACLY), along with p-AMPK activation.
miR-122, as reported before, can regulate fat metabolism in the liver, meaning that its overexpression or suppression may induce changes in the synthesis of fatty acids and cholesterol. It was indicated also that using antisense oligonucleotides to block miR-122 expression in normal mice significantly decreased cholesterol levels, hepatic fatty acid, and cholesterol synthesis and, additionally, activation of fatty acid oxidation and p-AMPK. This can block the activity of important enzymes involved in the synthesis of cholesterol and fatty acids. Blocking of miR-122 expression in obese mice models using the same technique resulted in repression of plasma cholesterol and improved liver tissue steatosis by repressing the gene expressions related to fatty acid synthesis such as FAS and ACLY [10]. miR-122 inhibition also significantly decreased plasma cholesterol through repression of genes involved in cholesterol synthesis such as HMG-CoA reductase and HMG-CoA synthase [6].
The present study, in agreement, demonstrated similar findings to those mentioned above. Taking into consideration that the metabolic energy balance in the entire body is controlled by p-AMPK, a critical cellular energy sensor Liver p-AMPK activation stimulates, in turn, certain catabolic pathways, resulting in hypoglycemic and lipolytic effects [29] and concomitant suppression of miR-122 in the liver activates hepatic p-AMPK [30]. This may be mediated either directly or indirectly where p-AMPK activities showed an inverse correlation with miR-122 expression [31]. Accordingly, increased activity of hepatic p-AMPK by CA and 10-DHG intake might be attributed to inhibition of MiR-122 expression in our diabetic hyperlipidemic rat model.
FAS is an essential metabolic enzyme that catalyzes the conversion of acetyl CoA and monoacyl malonate CoA into palmitic acid [32]. A previous study reported that miR-122 regulated FAS in an indirect manner via an unidentified pathway [33]. The present results were in agreement with these findings; meanwhile, the expression of some lipogenic genes as FAS showed a dramatic increase following the overexpression of miR-122 in HFD rats, leading to lipid accumulation [1,34,35].
HMG-CoA reductase represents the rate-limiting step in cholesterol biosynthesis where it catalyzes the conversion of HMG-CoA into the mevalonate step [36]. Many reports illustrated that miR-122 suppression induced a reduction in HMG CoA reductase expression and, indeed, a plasma cholesterol decrease [10,37]. Administration of 10-DHG in the present study significantly decreased plasma cholesterol and might be attributed to reductions in both miR-122 expression and HMG-CoA reductase activity.
It had been reported before that certain phenolic compounds (plant derived) significantly downregulated liver miR-122 expression in mice [38,39].
CA is a phenolic compound (plant derived) and is widely present in nature, and its potential to increase p-AMPK activation and to decrease FAS and HMG-CoA reductase activities was reported before [40]. Another study in human-cell carcinoma lines reported that CA had an inhibitory effect on the expression of FAS and ACLY [41]. A recent study indicated also that daily administration of CA for 5 weeks resulted in significant hypoglycemia in diabetic rats, joined with a remarkable antioxidant effect [42]. The present study was in agreement with these findings and might explain the hypolipemic effect of CA.
A previous study demonstrated ginger extract’s potential to suppress hepatic de novo lipogenesis and attributed it to a hepatic mRNA-level decrease in lipogenic enzymes such as FAS [43]. It also prevented the HFD-induced elevation of HMG-CoA reductase protein in the rat liver, affecting, in turn, cholesterol biosynthesis [44], and additionally decreased significantly the blood sugar levels in diabetic animal models [45,46]. Accordingly, the reduction observed in hepatic FAS, ACLY, and HMG-CoA reductase along with hepatic p-AMPK activation leading to decreased TC and TG along with blood glucose improvement after 10-DHG intake may present an explanation for its mechanism as a hypolipemic agent. CA intake induced in turn and to certain extent similar findings as compared to the 10-DHG results.
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|
---
title: In Vivo CaV3 Channel Inhibition Promotes Maturation of Glucose-Dependent Ca2+
Signaling in Human iPSC-Islets
authors:
- Kaixuan Zhao
- Yue Shi
- Jia Yu
- Lina Yu
- Martin Köhler
- Amber Mael
- Anthony Kolton
- Thomas Joyce
- Jon Odorico
- Per-Olof Berggren
- Shao-Nian Yang
journal: Biomedicines
year: 2023
pmcid: PMC10045717
doi: 10.3390/biomedicines11030807
license: CC BY 4.0
---
# In Vivo CaV3 Channel Inhibition Promotes Maturation of Glucose-Dependent Ca2+ Signaling in Human iPSC-Islets
## Abstract
CaV3 channels are ontogenetically downregulated with the maturation of certain electrically excitable cells, including pancreatic β cells. Abnormally exaggerated CaV3 channels drive the dedifferentiation of mature β cells. This led us to question whether excessive CaV3 channels, retained mistakenly in engineered human-induced pluripotent stem cell-derived islet (hiPSC-islet) cells, act as an obstacle to hiPSC-islet maturation. We addressed this question by using the anterior chamber of the eye (ACE) of immunodeficient mice as a site for recapitulation of in vivo hiPSC-islet maturation in combination with intravitreal drug infusion, intravital microimaging, measurements of cytoplasmic-free Ca2+ concentration ([Ca2+]i) and patch clamp analysis. We observed that the ACE is well suited for recapitulation, observation and intervention of hiPSC-islet maturation. Intriguingly, intraocular hiPSC-islet grafts, retrieved intact following intravitreal infusion of the CaV3 channel blocker NNC55-0396, exhibited decreased basal [Ca2+]i levels and increased glucose-stimulated [Ca2+]i responses. Insulin-expressing cells of these islet grafts indeed expressed the NNC55-0396 target CaV3 channels. Intraocular hiPSC-islets underwent satisfactory engraftment, vascularization and light scattering without being influenced by the intravitreally infused NNC55-0396. These data demonstrate that inhibiting CaV3 channels facilitates the maturation of glucose-activated Ca2+ signaling in hiPSC-islets, supporting the notion that excessive CaV3 channels as a developmental error impede the maturation of engineered hiPSC-islet insulin-expressing cells.
## 1. Introduction
Human-induced pluripotent stem cells (hiPSCs) have successfully been used to produce spherical aggregates of islet hormone-producing cells, referred to here as hiPSC-islets, through in vitro cultivation [1,2,3,4,5,6]. However, clinical hiPSC-islet transplantation as a curative treatment for diabetes still remains challenging because hiPSC-islets cannot fully mature in vitro into glucose-responsive insulin-secreting islets and may reliably achieve functional maturation post-transplantation [1,3,6,7,8,9,10,11]. Transplanted hiPSC-islets can gradually gain the ability to release detectable insulin in response to elevated blood glucose. Nevertheless, they are not competent enough to tightly control glucose homeostasis, largely due to inappropriate glucose-activated Ca2+ signaling [1,3,6]. They have yet to fully gain this indispensable signaling pathway for accurate glucose-stimulated insulin secretion (GSIS) at different blood glucose levels [1,3,6]. As is well known, glucose-induced increases in cytoplasmic-free Ca2+ concentrations ([Ca2+]i) trigger and orchestrate accurate GSIS, which has widely been deemed as the gold standard for determining the maturity and clinical applicability of surrogate islets, including hiPSC-islets [1,3,6,12,13,14,15,16,17,18]. Therefore, measurements of [Ca2+]i dynamics are of utmost importance in understanding the functional maturation of hiPSC-islets. However, it has not been possible to retrieve single, intact hiPSC-islet grafts, and thus, [Ca2+]i dynamics could not be directly measured in these grafts without losing their in vivo gained phenotypes. This inspired us to exploit the anterior chamber of the eye (ACE) of immunodeficient mice, which has been used as a reliable transplantation site for hiPSCs and their derivative [5,19,20,21,22]. The ACE is filled with aqueous humor endowed with high oxygen and less stress and may serve as a unique niche for the in vivo maturation of single, non-aggregated hiPSC-islet grafts. These grafts can then be treated with locally applied substances and retrieved intact for direct ex vivo [Ca2+]i measurements. Herein, we demonstrate the ability to precisely assess [Ca2+]i dynamics induced by glucose across a wide range of concentrations in hiPSC-islet grafts.
It is well known that the short in vitro development of engineered hiPSC-islets induced by a limited number of factors gives rise to a series of developmental errors in contrast to the long ontogenesis of native islets in the maternal and infant body [1,3,6]. This prompted us to hypothesize that the short in vitro development of hiPSC-islets does not fully mimic the long in vivo ontogenesis of native human islets, resulting in overexpression or hyperactivation of CaV3 channels as a brake on the maturation of glucose-activated Ca2+ signaling in hiPSC-islet grafts. We came up with this hypothesis based on the following facts. CaV3 channels are appreciably expressed in a diverse range of immature cells and even embryonic stem cells but are only preserved to a certain extent in some mature, electrically excitable cells, including pancreatic β cells [23,24,25]. Correspondingly, these channels mediate Ca2+ influx into embryonic stem cells to ensure their self-renewal capacity [24,25] but are downregulated and conduct a relatively small proportion of Ca2+ currents in mature human islet β cells, while they cannot be detected in mature mouse islet β cells [12,13,15,18,26]. Low-voltage-activated CaV3 channels are strikingly different from high-voltage-activated Ca2+ (CaV) channels, which mediate Ca2+ influx only at stimulatory blood glucose levels, as they competently activate various Ca2+ signaling pathways at both fasting and stimulatory blood glucose levels [12,13,15,18]. This enables CaV3 channels to persistently participate in the preservation of β cell maturity by mediating a mild, continuous Ca2+ influx and consequently controlling Ca2+-dependent gene expression in β cells [12,13,15,18,24]. Conversely, in certain pathological contexts, such as when CaV3 channels undergo elevated expression, they mediate exaggerated Ca2+ influx, thereby exhausting β cell maturity-related Ca2+-dependent gene expression machinery, and/or activate pathological Ca2+ signaling detrimental to β cell maturity [12,13,15,18,27,28,29,30]. We have tested our hypothesis by performing direct ex vivo [Ca2+]i measurements in single intraocular hiPSC-islet grafts retrieved intact following intravitreal infusion of the CaV3 channel blocker NNC55-0396. Our data reveal that intravitreal infusion of NNC55-0396 gives rise to reduced basal [Ca2+]i levels and increased glucose-stimulated [Ca2+]i responses in hiPSC-islets, demonstrating that downregulation of CaV3 channel activity can correct the erroneous in vitro development of hiPSC-islets to promote the in vivo maturation of glucose-dependent Ca2+ signaling in these engineered islets.
## 2.1. In Vitro Cultivation and Differentiation of HiPSCs
The procedure has been described elsewhere [5,19]. In brief, the undifferentiated hiPSC line NCRM-1 (Lonza/NIH, Walkersville, MD, USA) was cultured in mTeSR1 medium (Stem Cell Technologies, Vancouver, BC, Canada) on Growth Factor Reduced Matrigel (Corning, Corning, NY, USA) for 3–4 days at 37 °C in a humidified $5\%$ CO2 incubator. When cells became almost confluent, they were subjected to single-cell dispersion, seeded at 5 × 105 cells/mL into Matrigel-coated Transwell culture plates (Corning) containing mTeSR1 medium (Stem Cell Technologies) and cultured for 24 h. Then, their differentiation was induced sequentially under six different medium conditions. At the end of the six-stage induction, cells were removed from the Transwell culture plates, resized and placed into Ultra Low Attachment flasks (Corning) in suspension as stage seven for eight days. Upon completion of stage seven, the resulting hiPSC-islets were obtained for in vitro quality testing and intraocular transplantation [5,19]. All reagents were purchased from R&D Systems (Minneapolis, MN, USA) unless otherwise stated.
## 2.2. Animals
NOD-scid IL2Rgammanull (NSG) mice aged from 8 to 10 weeks were obtained from Charles River Laboratories (Sulzfeld, Germany). They were kept in temperature- and humidity-controlled rooms under standard 12 h light/12 h dark conditions and had unlimited access to food pellets and tap water all day. Mice were selected as human islet transplant recipients at random. All animal experiments were approved by the Regional Ethical Committee at Karolinska Institutet (Dnr 2-4532_2022, approved 15 December 2022).
## 2.3. ACE Transplantation
Transplantation of hiPSC-islets into the ACEs of immunodeficient mice was performed as previously described [5,19]. In brief, recipient mice were subjected to general anesthesia with a mixture of $2.5\%$ isoflurane and $40\%$ oxygen via a nose mask, and their head and eyeball were immobilized with a head holder and an eyeball holder, respectively. Then, hiPSC-islets were carefully sucked into a donor tissue-delivering glass micropipette connected by tygon tubing to a threaded plunger syringe. Thereafter, a tiny corneal incision was made by cautiously puncturing the cornea with an insulin syringe needle (29 G). The tip of the micropipette preloaded with hiPSC-islets was prudently inserted into the ACE through the corneal incision. The preloaded hiPSC-islets were gently extruded into the ACE. Lastly, the tip of the micropipette was slowly removed from the corneal incision. At this step, extra care should be exercised to avoid escape of hiPSC-islets from the hyperbaric ACE [5,19]. After ACE transplantation, the recipient mice were detached from the head and eyeball holders and kept lying on their side before anesthesia recovery. To relieve surgical pain, recipients were treated with the analgesic drug buprenorphine (0.1 mg/kg/day, s.c.) after ACE transplantation [31]. During that time and thereafter, the recipient mice behaved similar to non-operated ones, with no evidence suggestive of pain, poor vision or blindness, as documented in our previous studies [31,32].
## 2.4. Intravitreal Infusion
Mice transplanted with hiPSC-islets were anesthetized and immobilized as described above. Here, 30 µM NNC55-0396 (Tocris, Bristol, UK) or vehicle solution was sucked into a beveled glass micropipette with a tip diameter of about 20 µm, which was connected to a 100-microliter microsyringe (Hamilton, Reno, NV, USA) and mounted on a micromanipulator. Thereafter, the sclera of the eyeball containing hiPSC-islets was penetrated by the tip of an insulin syringe needle (29 G) to obtain a tiny scleral hole. Then, immediately, the NNC55-0396 solution-containing micropipette was gently placed into the vitreous body through the scleral hole under an upright stereomicroscope (Leica Microsystems Heidelberg GmbH, Mannheim, Germany). Subsequently, 1.5 µL NNC55-0396 or vehicle solution was slowly extruded by 3 intermittent infusions during 60 min into the vitreous cavity with a UNIVENTOR 802 syringe pump (Univentor, Zejtun, Malta). Intravitreal infusion was performed once a day for a week.
## 2.5. In Vivo Confocal Microscopy
Anesthetized and immobilized recipient mice were laid under an upright Leica DM6000 CFS microscope fitted with a Leica TCS SP5 II confocal laser scanner (Leica Microsystems). Their eyeball was positioned at an angle suitable for microscopic imaging by carefully adjusting the head and eyeball holders. Engrafted hiPSC-islets of interest were visualized using a long-distance water-dipping lens (HXC IRAPO L25 ×/0.95 W) dipped into Viscotears on the cornea. The backscattering signal was detected at 630–640 nm from hiPSC-islet grafts illuminated with a 633 nm laser light. Vascularization of hiPSC-islet grafts was imaged following the injection of 0.4 µmol/L Qtracker 565 (Thermo Fisher Scientific, Waltham, MA, USA) in 100 µL saline into the tail vein of recipient mice. The fluorescence emitted from the intravascular Qtracker 565 excited by a 488 nm laser line was acquired at 550–585 nm. Z-stack images were collected every 2–3 µm and processed with LAS AF Software (version: 2.7.7.12402, Leica Microsystems) and Volocity (version: 6.3.0, PerkinElmer, Waltham, MA, USA). The relative intensity of intraocular hiPSC-islet backscatter was derived by normalizing the mean voxel intensity of intraocular islet backscatter to that of iris backscatter. The vascular density of intraocular islets was represented by the percentage of vascular volume in intraocular islet volume obtained from stack imaging of intraocular islet backscatter. Mouse body temperature was kept at 37 °C for the duration of image acquisition [5,19].
## 2.6. Intraocular HiPSC-Islet Graft Retrieval
Recipient mice were anesthetized, and their heads and eyeballs were immobilized as described above at 1–2 days after 1-week intravitreal infusion. Intraocular hiPSC-islet graft retrieval was conducted under a stereomicroscope. To begin with, the entire cornea and iris engrafted with hiPSC-islets were removed from recipient mice by cutting along the border between the cornea and the sclera, i.e., the corneal limbus, with a pair of curved eye scissors. Subsequently, the iris together with hiPSC-islet grafts was peeled off from the cornea. Single, intact hiPSC-islet grafts with a tiny piece of the iris at their periphery were dissected out. The obtained single hiPSC-islet grafts were used for ex vivo [Ca2+]i measurements or patch clamp recordings.
## 2.7. Ex Vivo [Ca2+]i Measurements
Fura-2 loading was performed in retrieved intraocular hiPSC-islet grafts. The grafts were incubated with 2 µM fura-2 LeakRes/AM for 60 min at 37 °C in HEPES-buffered solution containing 125 mM NaCl, 5.9 mM KCl, 2.56 mM CaCl2, 1.2 mM MgCl2, 25 mM HEPES, 2 mM glucose and $0.1\%$ bovine serum albumin (pH 7.4). Thereafter, fura-2-loaded hiPSC-islet grafts were placed and immobilized onto a glass coverslip at the bottom of a recording chamber. [ Ca2+]i was measured using a Spex Fluorolog spectrophotometer coupled to a Zeiss Axiovert 35 M microscope with a Zeiss Fluar 40×/l.30 oil objective (Carl Zeiss, Göttingen, Germany). The fura-2 F340/F380 ratio was registered to denote [Ca2+]i [33]. During a recording, a hiPSC-islet graft was continuously perifused with HEPES-buffered solution supplemented with 2 mM glucose, 20 mM glucose or 30 mM KCl at 37 °C. Evtra software was employed to analyze the obtained data [27].
## 2.8. Cultivation of Dispersed Cells of Intraocular HiPSC-Islet Grafts
HiPSC-islet grafts were retrieved from the recipient ACE and dispersed in Ca2+-free medium with accutase (Gibco, Carlsbad, CA, USA). Dispersed hiPSC-islet cells were seeded into Petri dishes; fed CMRL 1066 culture medium (ICN Biomedicals, Santa Ana, CA, USA) supplemented with HEPES (10 mM), L-glutamine (2 mM), gentamycin (50 mg/mL), fungizone (0.25 mg/mL), ciprofloxacin (20 mg/mL), nicotinamide (10 mM) and $10\%$ fetal bovine serum and maintained at 37 °C in a humidified $5\%$ CO2 incubator [34].
## 2.9. Patch Clamp Recordings
Conventional whole-cell currents were recorded from cultured hiPSC-islet cells. Recording electrodes were made from borosilicate glass capillaries and fire-polished. Their resistance was between 4 and 6 MΩ when electrodes were filled with intracellular solution and bathed in extracellular solution. The intracellular solution consisted of 110 mM CsCl, 30 mM CsF, 10 mM EGTA, 10 mM HEPES and 4 mM MgATP (pH adjusted with CsOH to 7.2). The extracellular solution contained 140 mM NaCl, 9 mM BaCl2, 1 mM CaCl2, 10 mM HEPES and 2 mM glucose (pH adjusted with NaOH to 7.4). Whole-cell current recordings were taken from cells held at −80 mV, depolarized to +20 mV for 5 ms and repolarized to −70 mV with an EPC-9 patch clamp amplifier at room temperature (about 22 °C). Pulse/PulseFit (version: 8.80, HEKA Elektronik, Lambrecht/Pfalz, Germany) and IGOR program (version: 6.0.1.0, WaveMetrics, Portland, OR, USA) were employed to acquire and analyze whole-cell currents. The amplitude of the whole-cell currents was normalized to cell capacitance. The β cell identity was verified by insulin mRNA positivity [35].
## 2.10. Single-Cell RT-PCR
The entire hiPSC-islet cell was collected into a PCR tube with 10 µL 1 × Colorless GoTaq® reaction buffer containing MgCl2 (Promega, Madison, WI, USA) with a glass micropipette immediately after a whole-cell current recording and stored at −70 °C for later use. Insulin mRNA in collected cells was detected using the QIAGEN OneStep RT-PCR Kit (Valencia, CA, USA) according to the manufacturer’s instructions. The RNasin Plus RNase Inhibitor (Promega) was added to protect mRNAs. The insulin primer pair including the forward primer 5′ AACGAGGCTTCTTCTACACACC 3′ and the reverse primer 5′ TTCCACAATGCCACGCTTCTG 3′ was synthesized by Sigma-Aldrich (St. Louis, MO, USA). The final concentration of the primers in the reaction mix was 0.6 µM. PCR amplification was carried out for 30 cycles of 94 °C for 1 min, 60 °C for 1 min and 72 °C for 1 min. The obtained PCR products were detected by $2\%$ agarose gel electrophoresis and SYBR™ Gold Nucleic Acid Gel Stain (Thermo Fisher Scientific) [35].
## 2.11. Data Analysis
Data are presented as mean ± SEM overlaid with individual points. Statistical significance was determined by one-way ANOVA followed by the least significant difference (LSD) test or Student’s t-test. The significance level was set to 0.05.
## 3.1. Ex Vivo Measurements of Glucose-Activated [Ca2+]i Dynamics in Single, Intact HiPSC-Islet Grafts Retrieved from the ACE following In Vivo Local Treatment
To make ex vivo assessments of glucose-activated [Ca2+]i dynamics in single, intact hiPSC-islet grafts retrieved from the ACE following in vivo local treatment possible, we combined ACE transplantation, intravitreal infusion, intact hiPSC-islet graft harvest and ex vivo [Ca2+]i measurements (Figure 1A).
Initially, we transplanted 5–10 hiPSC-islets into immunodeficient mouse ACEs and scattered them in the pupillary zone of the iris by gently extruding them through a glass micropipette connected to a syringe. The properly scattered hiPSC-islets adhered or attached to the iris following transplantation (Figure 1A,B). This dispersed layout of single, non-aggregated hiPSC-islets in the desired area is a prerequisite for the intact retrieval of single, non-aggregated and intact hiPSC-islet grafts from the recipient ACE. During the post-transplantation period, hiPSC-islets were progressively engrafted and vascularized on the iris (Figure 1B, upper middle and right). Their gross morphology, engraftment, vascularization and light scattering signals were monitored by using non-invasive in vivo stereomicroscopy/confocal microscopy (Figure 1A,B, upper middle and right). A representative stereomicrograph displays hiPSC-islets engrafted on the iris at 60 days post-transplantation (dpt) (Figure 1B, upper middle). A sample confocal micrograph shows a typically vascularized hiPSC-islet graft giving sufficient light scattering signals at 57 dpt (Figure 1B, upper right).
Within a week before the end of two months post-transplantation, infusion of NNC55-0396 was applied through a glass micropipette penetrating into the vitreous space through the sclera of recipient mice (Figure 1A). Caution should be exercised with regards to the total volume of infused solution, the flow rate of infusion and the position for penetration of glass micropipette. A total volume of 1.5 µL NNC55-0396 or vehicle solution was intermittently infused into the vitreous body for 60 min in the present study.
Following the intravitreal infusion, vascularization and light scattering signals of intraocular hiPSC-islet grafts were imaged and quantified by using noninvasive in vivo confocal microscopy (Figure 1A,B, upper right). Thereafter, retrieval of intact single, non-aggregated hiPSC-islet grafts from the iris of recipient mice was performed (Figure 1A,B, lower). During this procedure, the key is to avoid mechanical damage to hiPSC-islet grafts. Therefore, single, non-aggregated hiPSC-islet grafts sandwiched between the iris and cornea were carefully removed from the eyeball of the recipient mice (Figure 1B, lower left). Then, the hiPSC-islet grafts of interest were prudently excised by gently peeling the iris from the cornea and surgically incising the iris at the peripheral area of individual hiPSC-islet grafts with the greatest care under a stereomicroscope (Figure 1B, lower middle and right). Subsequently, excised hiPSC-islet grafts were subjected to routine [Ca2+]i measurements (Figure 1A,C). Figure 1C shows a typical [Ca2+]i trace illustrating glucose- and K+-induced [Ca2+]i responses registered from a hiPSC-islet graft retrieved 2 months post-transplantation following intravitreal infusion of NNC55-0396.
## 3.2. Intravitreal Infusion of NNC55-0396 Promotes Glucose-Activated Ca2+ Signaling in Intact HiPSC-Islet Grafts Harvested from the ACE
The feasibility and merits of ex vivo [Ca2+]i measurements in single intact hiPSC-islet grafts directly retrieved from the ACE enabled us to clarify whether inhibition of CaV3 channel activity promotes the in vivo development of glucose-activated Ca2+ signaling in hiPSC-islets. Ex vivo [Ca2+]i measurements showed that either pre-transplanted hiPSC-islets or hiPSC-islet grafts retrieved from the ACE at one month post-transplantation displayed K+ depolarization-induced [Ca2+]i responses but were unresponsive to glucose stimulation (Figure 2A,B, middle). When hiPSC-islet grafts were maintained in recipient mice for two months, however, retrieved grafts exposed to either NNC55-0396 or vehicle solution displayed [Ca2+]i responses to both elevated glucose and K+ depolarization (Figure 2A,B, middle and right), indicating progressive maturation between 1 and 2 months in the ACE. Importantly, the NNC55-0396-treated hiPSC-islet grafts showed increased [Ca2+]i responses to glucose stimulation in comparison to vehicle solution-treated hiPSC-islet grafts and lower basal [Ca2+]i than vehicle solution-treated hiPSC-islet grafts as well as pre-transplanted hiPSC-islets and intraocular hiPSC-islet grafts retrieved at one month post-transplantation (Figure 2A,B, left and right). There were no significant differences in basal [Ca2+]i between the latter three groups (Figure 2B, left). Together, these data indicate that the inhibition of CaV3 channels promotes glucose-activated Ca2+ signaling in intraocular hiPSC-islet grafts.
## 3.3. CaV3 Currents Are Present in Insulin-Expressing Cells of Intraocular HiPSC-Islet Grafts and Remain Unaltered after Intravitreal Infusion of NNC55-0396
The above observed phenomenon that [Ca2+]i responds to both glucose and K+ stimulation in hiPSC-islet grafts indicates that CaV channels, including CaV3 channels, may operate in insulin-expressing cells of intraocular hiPSC-islet grafts. To verify this possibility, we measured CaV3 currents in these cells by recording slow deactivating tail currents after intravitreal infusion of vehicle or NNC55-0396. The identity of individual cells subjected to patch clamp recordings was corroborated by insulin mRNA positivity. A standard RT-PCR protocol using human insulin-specific primers revealed the expected 146 bp amplicon for insulin in hiPSC-islet cells (Figure 3C). As shown in Figure 3A,C, a cell from an hiPSC-islet graft exhibited clear CaV3 currents, which were effectively blocked by acute application of NNC55-0396. Importantly, this cell was insulin mRNA-positive (hiPSC-islet cell 6). A sample of agarose gel electrophoresis analysis shows that a 146 bp cDNA fragment of human insulin was detected in single hiPSC-islet cells and native human islet cells as a positive control, but not in the negative control (sterile ultrapure water), COS7 and HEK 293 cells (Figure 3C). Furthermore, we also examined if sustained alteration in CaV3 channels occurred in insulin-expressing cells of intraocular hiPSC-islet grafts retrieved from the ACE previously infused with NNC55-0396 for a week. In the absence of NNC55-0396, CaV3 current density did not significantly differ between the two groups either before or after the pre-treatments (Figure 3B). These data verify that functional CaV3 channels are present in insulin-expressing cells of hiPSC-islet grafts as the target of NNC55-0396 but did not alter their density and intrinsic functionality after 1-week intravitreal infusion of NNC55-0396.
## 3.4. Intravitreal Infusion of NNC55-0396 Has No Influence on Vascularization and Light Backscattering of Intraocular HiPSC-Islet Grafts
There is a possibility that the promoted glucose-activated Ca2+ signaling in intraocular hiPSC-islet grafts following the inhibition of CaV3 channels may occur secondary to the improved vascularization, engraftment and survival of these islet grafts induced by the intravitreal infusion of NNC55-0396. Therefore, we investigated whether hiPSC-islet grafts exposed to NNC55-0396 differed from vehicle solution-treated ones in vascularization and light backscattering.
By taking advantage of the ACE technology established by combining the transplantation of islets into the ACE and intravital microimaging of them non-invasively and longitudinally, we monitored the vascularization of single hiPSC-islet grafts before and after intravitreal infusion of NNC55-0396. In vivo confocal microscopy demonstrated that Qtracker 565 injected into the tail vein of recipient mice quickly appeared in vasculatures in hiPSC-islet grafts with and without exposure to NNC55-0396 (Figure 4A, upper and lower). Appreciable microvascular networks were distributed throughout a single hiPSC-islet graft prior to and following the administration of NNC55-0396 or vehicle solution (Figure 4A, upper and lower). Vascular density quantification detected no significant differences between NNC55-0396- and vehicle-exposed groups either before or after the exposures (Figure 4B, left). It is clear that intravitreal infusion of NNC55-0396 did not interfere with the vascularization of hiPSC-islet grafts.
Meanwhile, we also conducted non-invasive observation of light scattering signals, in parallel with that of vascularization in intraocular hiPSC-islet grafts exposed to NNC55-0396 or vehicle solution. As shown in the sample confocal images, hiPSC-islet grafts emitted similar light scatter signals prior to intravitreal infusion of NNC55-0396 or vehicle solution (Figure 4A, middle and lower). Following these treatments, there was no appreciable alteration in their light scatter signals. The compiled data showed that the NNC55-0396-treated group did not significantly differ from the vehicle solution-treated group in the intensity of light scatter signals before and after the treatments (Figure 4B, right). For neither the NNC55-0396-treated group nor the vehicle solution-treated group did the intensity of the light scatter signals change following treatment (Figure 4B, right). These data verify that intravitreally infused NNC55-0396 or vehicle solution did not acutely influence the light scattering signals of intraocular hiPSC-islet grafts.
## 4. Discussion
As is well known, native pancreatic islets exist as many individual micro-organs. Within each of them, different types of endocrine cells are highly integrated into a basic morphological and functional unit to execute various glucose-dependent activities such as glucose-activated [Ca2+]i signaling and GSIS [12,15,36,37,38]. Such a micro-organotypic architecture also arises within a pluripotent stem cell-derived islet (PSC-islet), reflecting the similar morphological development of PSC-islets [6]. However, a detailed analysis of glucose-activated [Ca2+]i signaling responsible for GSIS in hiPSC-islet grafts with an intact micro-organotypic architecture has not been carried out. This is because transplanted hiPSC-islets are aggregated at routine transplantation sites and cannot be retrieved intact from there [1,3,6,39,40,41,42]. This practical dilemma has considerably hindered research on glucose-dependent [Ca2+]i dynamics during in vivo development of hiPSC-islet grafts. Indeed, glucose-dependent [Ca2+]i dynamics have been examined in insulin-expressing cells isolated from PSC-islet grafts by using single-cell dispersion followed by in vitro cultivation and in pre-transplanted PSC-islets [1,3,6]. Obviously, single-cell dispersion and in vitro cultivation unavoidably damage the cytoarchitecture of PSC-islet grafts and probably compromise the in vivo gained phenotypes of these surrogate islets. In this context, intravital imaging of [Ca2+]i in islets transplanted into the ACE can be used as a valuable tool [43]. In the present work, we were able to retrieve single hiPSC-islet grafts that suffered no mechanical and chemical stress and keep their architecture and functionality intact after removal. They could be directly used for detailed analysis of [Ca2+]i dynamics driven by a change in glucose concentration or other secretagogues or inhibitors.
During normal in vivo development, β cell CaV3 channels ontogenetically change under the strict control of complex developmental clocks that result from the integration of genetically encoded programs with intrinsic and extrinsic cues [15,24,25,26,44,45,46,47]. This ontogenetic event is species-dependent [12,13,15,24,25,26]. A substantial number of functional CaV3 channels exist as early as in embryonic stem cells to participate in the regulation of proliferation and self-renewal of these cells [24,25]. Developmentally downregulated CaV3 channels make only a minor contribution to total CaV currents and Ca2+ influx and play a negligible role in high-glucose-induced insulin secretion when human β cells become fully mature [12,13,15,18,26]. However, it appears that a limited number of CaV3 channels in human β cells, activated at both fasting and stimulatory blood glucose levels, mediate optimally fine-tuned and persistent Ca2+ influx for the promotion and preservation of Ca2+-dependent β cell gene expression and maturation [12,13,15,18]. By contrast, pathologically excessive Ca2+ influx mediated by exaggerated β cell CaV3 channels drives β cell maturity dissipation, manifested as reduced expression of exocytotic proteins and impaired GSIS [28,29,30]. The present work shows that intraocular hiPSC-islet grafts following in vivo exposure for 2 months demonstrate glucose-dependent [Ca2+]i dynamics, which has been deemed as one of the most essential events representing the functional maturation of β cells [6,12,13,14,15,16,17,18]. Importantly, it also reveals that intravitreal infusion of the CaV3 channel blocker NNC55-0396 significantly facilitates the development of glucose-dependent [Ca2+]i dynamics in intraocular hiPSC-islet grafts, as evidenced by lower basal [Ca2+]i levels and stronger glucose-induced [Ca2+]i responses. Furthermore, insulin-positive cells in hiPSC-islet grafts express functional CaV3 channels, which also exist innately at the earliest stages of ontogeny [24,25]. These findings verify that the functional maturation of in vitro generated hiPSC-islets relies on both the exposure to complex in vivo environments and the downregulation of excessive CaV3 channels. This suggests that short in vitro cultivation and the artificially created developmental cues that may exist in vitro cannot fully recapitulate the protracted in vivo development of human β cells, which results in abnormal exaggeration of β cell CaV3 channels. This may impede the development of full glucose-dependent Ca2+ signaling in hiPSC-islet grafts. This also emphasizes the importance of the optimally fine-tuned and persistent Ca2+ influx mediated by a defined number of β cell CaV3 channels in human β cell maturation.
For their survival and development, intraocular hiPSC-islet grafts need to be vascularized to receive nutrients, oxygen and humoral factors as well as dispose of metabolic wastes. The present work shows that intraocular hiPSC-islet grafts are indeed satisfactorily vascularized and such vascularization is not acutely influenced by intravitreal infusion of the CaV3 channel blocker NNC55-0396. This substantiates that the immunodeficient mouse ACE allows satisfactory hiPSC-islet vascularization, which is a prerequisite for their survival, engraftment and development, thus being suitable for fostering the functional maturation of hiPSC-islets, including the development of glucose-activated Ca2+ signaling. Importantly, these data indicate that the CaV3 channel blocker NNC55-0396 promotes the development of glucose-activated Ca2+ signaling by acting on CaV3 channels in hiPSC-islet cells, not by improving graft vascularization. Concurrent with the vascularization, appreciable light scattering signals were emitted from intraocular hiPSC-islet grafts and remained unchanged after exposure to NNC55-0396. The alteration of glucose-dependent Ca2+ signaling induced by NNC55-0396 and the absence of changes in light scattering suggest that the maturation of insulin-secretory granules happens earlier than that of glucose-activated Ca2+ signaling, since light scattering signals reflect the abundance of insulin secretory granules [5,42].
## 5. Conclusions
The present work verifies that the immunodeficient mouse ACE can serve as a unique site not only for in vivo maturation of hiPSC-islet grafts but also for local applications of substances to manipulate and study such maturation. These grafts can be micro-imaged intravitally, non-invasively and longitudinally and also retrieved without suffering physical and chemical disturbances for more precise ex vivo studies, as exemplified here by [Ca2+]i measurements. This offers a resource for the mechanistic evaluation of the ontogenetic development of human stem cell-derived islets or other organoids. Importantly, the present work also demonstrates that inhibiting CaV3 channels facilitates the induction of glucose-dependent Ca2+ signaling in hiPSC-islets, supporting the notion that excessive CaV3 channels as a development error operate in hiPSC-islet cells to impede the maturation of these cells. The findings point out that CaV3 channel blockers may act as an accelerator for the functional maturation of hiPSC-islets. Furthermore, taken together with previously documented findings, those reported in the present work suggest that CaV3 channel blockers could help treat disorders resulting from cell dedifferentiation, such as certain cancers and diabetes [16,18,48,49,50].
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|
---
title: Modeling Gas Plasma-Tissue Interactions in 3D Collagen-Based Hydrogel Cancer
Cell Cultures
authors:
- Lea Miebach
- Marten Hagge
- Sander Bekeschus
journal: Bioengineering
year: 2023
pmcid: PMC10045726
doi: 10.3390/bioengineering10030367
license: CC BY 4.0
---
# Modeling Gas Plasma-Tissue Interactions in 3D Collagen-Based Hydrogel Cancer Cell Cultures
## Abstract
Gas plasma jet technology was recently identified as a potential adjuvant in the fight against cancer. Here, the partial ionization of gas yields the local formation of an exceptional variety of highly reactive oxygen (ROS) and nitrogen (RNS) species, which are considered the main actors of plasma-induced antitumor effects. Yet, fundamental knowledge in preclinical plasma research relies on the predominant use of two-dimensional cell culture systems, despite causing significant shifts in redox chemistries that largely limit translational relevance. So far, the intricacy of studying complex plasma–tissue interactions causes substantial knowledge gaps concerning the key mechanisms and therapeutical limitations of plasma treatment in a living organism. Identifying physiologically relevant yet simplified tissue models is vital to address such questions. In our study, a side-by-side comparison of conventional and pre-established hydrogel models emphasized this discrepancy, revealing a marked difference in plasma-induced toxicity related to species distribution dynamics. Chemically embedded, fluorescent reporters were further used to characterize reactive species’ fingerprints in hydrogels compared to liquids. In addition, a thirteen cell-line screening outlined the widespread applicability of the approach while indicating the need to optimize growth conditions dependent on the cell line investigated. Overall, our study presents important implications for the implementation of clinically relevant tissue culture models in preclinical plasma medicine in the future.
## 1. Introduction
Set in the field of applied redox biology, medical gas plasmas exploit the concept of hormesis by targeting the cellular redox state. While comparable approaches, such as photodynamic therapy, are based on the local formation of singlet delta oxygen, this novel physics-based technology is exceptional in generating a multitude of highly reactive oxygen (ROS) and nitrogen species (RNS) simultaneously. Based on the partial ionization of a noble gas, the highly reactive electrons and primary species generated in the plasma afterglow cause the formation of secondary species by energy transfer, followed by further ionization, dissociation, and excitation [1]. Initially, gas plasma therapy was accredited for application in chronic wound care [2], but the beneficial responses in the palliation of head and neck cancer patients [3], supported by numerous preclinical studies in vitro [4,5] and in vivo [6,7], outlined its potential in clinical oncology alike. A major drawback of preclinical plasma research depicts the prevailing confidence in the knowledge gained from two-dimensional cell culture systems used in the majority of studies. Despite the overall agreement that conventional cell culture models do not accurately mimic the structure, function, and physiology of living tissues, the bulk liquids surrounding cells cause significant shifts in plasma-derived ROS/RNS chemistries [8], largely limiting translational relevance. Moreover, due to the complex dynamics of plasma–tissue interaction, major mechanistic questions concerning the primary and functional, secondary penetration depths, kinetics, quenching, and influence of the target composition remain unanswered. In organic targets, charged species, such as photons and metastables, are confined to the outermost surface area, and ROS/RNS, including radical or non-radical oxygen/nitrogen species, are considered to deteriorate and react quickly [9]. Yet, medical responses have been observed in wounds [10] and (ulcerated) head and neck cancer [11] at thousands of micrometer depths, begging the question of the underlying mechanisms. While mammalian in vivo models set the benchmark for addressing the outcome and therapeutic consequences of plasma treatment in a living organism, clinically relevant tissue models are urgently needed to provide a standardized and simplified microenvironment to address such fundamental yet complex questions [12]. In the past, hydrogel-based tissue models have proven useful for cell culture purposes, as they mimic many elements of native extracellular matrices, support cell adhesion and protein sequestration, and can be easily tailored for specific applications [13,14]. In particular, collagen, as the primary constituent of native tissues, is considered an attractive material for cell studies. Hydrogel formation requires the transition of a liquid precursor solution into a solid, water-swollen network of polymers based either on noncovalent or covalent crosslinking. For instance, collagen fibrillogenesis is induced by rising temperature and pH, requiring appropriate storing conditions to prevent the spontaneous self-assembly of fibrils (Figure 1a).
Collagen-based hydrogels have been used to mimic the cellular microenvironment in a broad spectrum of studies, including mesenchymal cell differentiation and carcinoma reprogramming. Their widespread applicability outlines the attractivity to study plasma–tissue interactions in such models alike. The optical transparency of many hydrogels permits high-resolved (confocal) imaging to evaluate the effects on migration or z-resolved toxicity in three-dimensional tissue models. Complex coculture environments can be useful to address the influence and effects on tumor–stroma and tumor–immune cell interactions after plasma treatment, and cellular effects can be linked to ROS penetration depths based on embedded chemical reporters. In addition, an evaluation of the modifications at the single-cell or subcellular level by proteome or transcriptome analysis can easily be achieved after the mechanical or enzymatic harvesting of cells. ( Figure 1b). However, the usage of hydrogels remains largely underscored in preclinical plasma medicine. So far, only a few studies have focused on ROS/RNS delivery in hydrogel-based model systems using embedded chemical reporters [15] or have taken advantage of culture systems with superior physiological relevance to characterize plasma-induced antitumor effects [16].
This study aimed to investigate the importance of physiologically adequate cell culture systems in life-sciences-oriented plasma research. Embedded redox-sensitive reporters were used to characterize the plasma-derived ROS/RNS chemistries in collagen-based hydrogels compared to PBS and their time-lagged release in the following. Furthermore, a side-by-side comparison of the plasma-induced toxicity in two- and three-dimensional culture systems was made. A thirteen cell-line screening indicated the widespread applicability of the approach. Overall, implementing hydrogel models in preclinical plasma research is urgently needed to identify the key mechanisms and limitations of plasma–tissue interactions in living organisms in the future.
## 2.1. Cell Culture
Thirteen cell lines were screened for their ability to grow in 3D collagen hydrogel cultures (Table 1). Prior to seeding, the cells were subcultured in Dulbecco’s modified Eagle’s medium (DMEM), Roswell Park Memorial Institute (RPMI; both Pan Biotec, Aidenbach, Germany), or Ham’s F12K (Thermo Fisher Scientific, Dreieich, Germany) medium supplemented with $10\%$ fetal bovine serum, $1\%$ glutamine, and $1\%$ penicillin–streptomycin (all Sigma-Aldrich, Taufkirchen, Germany) according to the supplier’s instructions. The Ham’s F12K medium was additionally supplemented with $0.68\%$ hygromycin (Pan Biotec, Aidenbach, Germany). The cells were kept in a specialized breeding incubator (Binder, Tuttlingen, Germany) at 37 °C, $5\%$ CO2, and $95\%$ humidity.
## 2.2. Cell Line Screening
For the prior generation of the 3D hydrogel cultures, the cells were stained with 500 nM Vybrant DiD cell labeling solution (Thermo Fisher Scientific, Dreieich, Germany) for 45 min at 37 °C. After washing, the cells were resuspended in 4 mg/mL collagen I (Enzo Life Sciences, Lörrach, Germany) containing $1\%$ sodium bicarbonate (NaHCO3) and $19\%$ 10× Minimal Essential Medium (MEM; Corning, Kaiserslautern, Germany), as described above. Additionally, 500 nM sytox green (SG; Thermo Fisher Scientific, Dreieich, Germany) was added for the live–dead cell discrimination. The cells were seeded in a 96-well flat-bottom plate (Greiner Bio-One, Frickenhausen, Germany) at a density of 2 × 105 cells in 100 µL collagen per well and incubated for 1 h at 37 °C to induce hydrogel polymerization. In parallel, the cells were seeded in cell culture medium at a density of 1 × 104 cells per well to compare the cellular growth in conventional 2D and 3D hydrogel cultures. In the 2D experiments, the number of planted cells was limited to 1 × 104, as they would overgrow and consume nutrients during the growth period at higher seeding densities. The cell lines were randomly chosen in an in-house screening.
## 2.3. High Content Imaging
The cellular viability was assessed using high-content imaging (Operetta CLS; PerkinElmer, Hamburg, Germany) at 1 h and 20 h after seeding. The images were acquired in fluorescence channels at λex 630 nm and λem 708 ± 52 nm for DiD and λex 490 nm and λem 520 nm for Sytox Green using a 5× air (NA = 0.16) objective (Zeiss, Jena, Germany) in 8 z-dimensional planes. For the image segmentation, the z-stacks were merged into a single maximum projection image using the Harmony 4.9. image analysis software (PerkinElmer, Hamburg, Germany). Algorithm-driven unsupervised image analysis was performed to assess the cellular viability based on Sytox Green fluorescence.
## 2.4. Metabolic Activity
The metabolic activity of the cells cultured in the 2D and 3D models was comparatively assessed using the Resazurin Assay 20 h after cell seeding. Briefly, 7-hydroxy-3H-phenoxazin-3-on-10-oxid (resazurin; Alfa Aesar, Kandel, Germany) was added to each well at a concentration of 100 µM following incubation for 4 h at 37 °C and $5\%$ CO2. Viable cells metabolize nonfluorescent resazurin into fluorescent resorufin. Fluorescence was acquired at λex 535 nm and λem 590 nm using a multimode plate reader (F200; Tecan, Männedorf, Switzerland).
## 2.5. Plasma Source and Treatment
Plasma treatment in this study was performed using the plasma jet kINPen (neoplas, Greifswald, Germany) [17]. The jet was operated with argon ($99.999\%$ purity; Air Liquide, Bremen, Germany) at 1.5 standard liters per minute (slm) ionized at the plasma nozzle with 1 MHz and a generating power of 1-3 W. Prior to the plasma treatment, the cells were seeded in flat-bottom plates (Greiner Bio-One, Frickenhausen, Germany) at a density of 2 × 105 cells in fully supplemented RPMI or 4 mg/mL collagen, as described above. After 4 h incubation at 37 °C, the cells were exposed to plasma for 60 s. The cells were incubated for another 1 h, and 200 µL fully supplemented RPMI was added on top of each well.
## 2.6. Deposition of Reactive Species
The assessment of the pH and deposition of short- and long-lived reactive species in cell-free collagen compared to PBS was conducted immediately after the plasma treatment. Briefly, collagen was prepared as described before and exposed to plasma in a 96-well flat-bottom plate (Greiner Bio-One, Frickenhausen, Germany). The relative changes in pH were assessed by adding 100 µM phenol red (Sigma-Aldrich, Taufkirchen, Germany) to collagen prior to the hydrogel polymerization. In parallel, the absolute pH determination was performed using a pH meter (Mettler-Toledo, Gießen, Germany).
The relative assessment of the deposition of short-lived ROS into hydrogels was conducted using the redox-sensitive fluorescent probes aminophenyl fluoresceine and hydroxyphenyl fluoresceine (APF and HPF; both Enzo Life Sciences, Lörrach, Germany), which are capable of detecting hydroxyl radicals (.OH), peroxynitrite (ONOO−; both APF and HPF), and hypochlorous acid (only APF). Diaminofluoresceine (DAF; Thermo Fisher Scientific, Dreieich, Germany) was used for the detection of nitric oxide (NO.). All probes were added to the collagen prior to polymerization at a concentration of 5 µM. The plates were covered with aluminum foil to shield the probes from light and prevent evaporation during the polymerization. Immediately after the plasma treatment, fluorescence was determined at λex 485 nm and λem 525 nm using a multiplate reader (F200; Tecan, Männedorf, Switzerland). In the experiments evaluating the influence of the gel stiffness on the ROS deposition, additional measurements of APF fluorescence were performed at 30, 60, and 120 min after the plasma treatment (Figure A1). The delivery of long-lived ROS/RNS was assessed indirectly. Briefly, 50 µL PBS was added on top of the gel 1 min after plasma treatment and sampled after 10 min, 20 min, 30 min, 60 min, 90 min, and 120 min. The amount of hydrogen peroxide (H2O2) was quantified using the amplex ultra red assay (Thermo Fisher Scientific, Dreieich, Germany) according to the supplier’s instructions. The fluorescence was assessed at λex 535 nm and λem 590 n using a multiplate reader (F200; Tecan; Männedorf, Switzerland). The quantification of nitrite (NO2−) was conducted using the Griess Assay (Cayman Chemicals, Tallinn, Estonia) according to the manufacturer’s instructions. The absorbance was measured at 540 nm using a multimode plate reader (M200; Tecan, Männedorf, Switzerland). The ROS/RNS deposition was compared against PBS in all assays, and the absolute concentrations were calculated against a standard curve.
## 2.7. Statistical Analysis
The graphing and statistical analysis were conducted using prism 9.50 (GraphPad Software, San Diego, CA, USA). The data show the mean ± standard error of the mean (SEM), if not indicated otherwise in the figure legends.
## 3.1. Basal Toxicity of Thirteen Cell Lines Grown in 3D Collagen-Based Hydrogels
The pH adjustment (Figure 2a) was optimized prior to the experiments (Figure 2b). Interestingly, the collagen-based hydrogels maintained stable pH levels upon CO2 supply under standard incubation conditions in the cell culture compared to the liquid buffer systems, such as phosphate-buffered saline (PBS) (Figure 2c). The assessment of the metabolic activity as a surrogate indicator of basal toxicity (Figure 2d) was performed 24 h after incubation in the different collagen formulations to ensure adequate growth conditions (Figure 2e). A thirteen cell-line screening comparing the individual growth conditions in the conventional and hydrogel-based models was conducted to evaluate the widespread applicability of the approach. Briefly, fluorescent-labeled cells were seeded in a collagen-based hydrogel containing sytox green labeling solution to identify terminally dead cells. Cellular viability was assessed immediately and 20 h after the NaHCO3-induced gelation using high content imaging (Figure 3a), followed by the resazurin-based evaluation of the metabolic activity (Figure 3b). Interestingly, cellular malignancy was found to be a major predictor of basal toxicity in hydrogels, indicating the need to optimize collagen formulations dependent on the cell line investigated (Table 1). While the HepG2, MC38-Luc, CT26-Luc, and A549 cells exhibited high metabolic activity when grown in three-dimensional collagen cultures, an increased basal toxicity was observed in the nonmalignant HaCaT, THP-1, 17Cl-1, and TK6 (Figure 3c). This notion was reflected by the algorithm-based unsupervised image segmentation to retrieve the number of terminally dead cells 20 h after cell seeding (Figure 3d). With minor exceptions, a higher metabolic activity was associated with low cytotoxicity and vice versa (Figure 3e).
## 3.2. Profiling Reactive Species Fingerprints in 3D Collagen-Based Hydrogels
The intricacy of studying plasma-derived ROS dynamics delivered into a living tissue still causes fundamental knowledge gaps concerning the mechanisms and limitations of plasma–tissue interactions in vivo. Matched to the in vitro situation, the majority of studies appeal to species chemistries found in bulk liquids, showing a predominance of long-lived, secondary species generated in the liquid interphase. Recently, hydrogel models have proven useful as model systems to approach ROS/RNS delivery into tissues in vivo and were used in a side-by-side comparison with PBS in our study. Due to the generation of nitrous (HNO2) and nitric (HNO3) acid, plasma treatment is known to decrease pH levels, particularly in unbuffered solutions (Figure 4a). In contrast, pH metric evaluation of plasma-oxidized collagen (Figure 4b) revealed a slight increase in pH levels (Figure 4c) at prolonged treatment times, remaining constant in PBS as expected (Figure 4d). Embedded chemical reporters were used for the ROS/RNS profiling in collagen-based hydrogels and compared to PBS (Figure 4e). Here, a diminished deposition of peroxynitrite (ONOO−) and hydroxyl radicals (.OH) in the hydrogels was indicated by the reduced fluorescence of the redox-sensitive probes aminophenyl fluoresceine (APF; Figure 4f) and hydroxyphenyl fluoresceine (HPF; Figure 4g). Interestingly, this was contrasted by the increased fluorescence of diaminofluoresceine (DAF), which is indicative of nitric oxide (NO.; Figure 4h). Intriguingly, APF fluorescence was reduced to one-tenth when increasing the collagen stiffness. However, in contrast to PBS, APF fluorescence increased in the collagen over time, doubling after 120 min and eventually indicating further species formation due to the fact of tertiary reactions in the hydrogel (Figure A1). Previous studies indicated the accumulation of ROS in hydrogels followed by a time-lagged release if covered with liquids after treatment (Figure 4i). Interestingly, repeated PBS sampling over a time period of 120 min after plasma treatment revealed a bimodal release of hydrogen peroxide (H2O2). Peak concentrations were found immediately and 60 min after treatment, with a steep initial loss of approximately 150 µM in 45 min and a shallow second decline of 100 µM in 2 h. Monitoring nitrite (NO2−) release dynamics showed a steep decline in the first 45 min alike, while the measured concentrations remained largely constant at 40 µM over the next 75 min (Figure 4j).
## 3.3. Differential Plasma Sensitivity in Three-Dimensional Hydrogel Models
Finally, a side-by-side comparison was made to characterize the antitumor efficacy of plasma treatment in two- and three-dimensional culture systems. Briefly, 2 × 105 cells were seeded in medium or collagen-based hydrogels in a 24-well flat bottom plate. In addition, different plate geometries were tested to address the differences in species distribution in the solid hydrogels. After 4 h of incubation, the cells were exposed to plasma for 60 s. The cellular metabolic activity was evaluated after 24 h using the alamar blue assay (Figure 5a). Plasma-induced toxicity was partially reduced when the cells were cultured in collagen in 24-well plates. Surprisingly, the cellular metabolic activity decreased to $40\%$ when the cells were growing in 96-well plates, indicating a major impact of the plate geometry (Figure 5b).
## 4. Discussion
In a groundbreaking study, Peterson and colleagues demonstrated that healthy mammary epithelial cells display tumorigenic potential when cultured in conventional monolayer culture but form multicellular structures that resemble healthy acini when grown in three-dimensional hydrogels [18]. Supported by intensive investigations in many other biological research areas [19,20], these findings emphasized the notion that cells behave more natively when cultured in three-dimensional environments and revealed a considerable bias concerning cellular susceptibility in drug screening studies [21]. Increased awareness of this topic has forwarded the development of novel bioengineered models supporting organization and differentiation as found in living tissues to increase translational research in vitro.
The use of adequate and clinically relevant model systems becomes particularly apparent when investigating multimodal approaches such as medical gas plasmas. This novel physics-based technology is exceptional in generating a variety of highly reactive species simultaneously based on the partial ionization of a noble gas. Exploiting the concept of hormesis, the generated species are considered to target the cellular redox state, inducing oxidative eu- or distress [22] dependent on the applied dose. Following this, the initial research in the field of plasma medicine focused on its applicability in chronic wound care [10], leading to the successful development of several marketed plasma devices that are approved as medical devices and regularly applied in clinical dermatology [17,23]. Strikingly, clinical case studies intended to reduce the microbial load and unpleasant odor in patients suffering from advanced therapy refractory head and neck carcinoma outlined the potential of medical gas plasmas in clinical dermato-oncology in 2015. Despite increasing the patients’ life quality, plasma treatment induced tumor cell apoptosis and partial remission in one-third of patients [3,24]. These findings have been supported by intensive investigations emphasizing the antitumor efficacy of plasma in numerous tumor models in vitro and in vivo in recent years. As a major drawback, many assumptions have been made based on conventional cell culture models in vitro, disregarding shifts in the redox chemistries [8] and the rapid consumption of species occurring in biological tissues [25] compared to diffusion systems such as liquids [26]. The lifetimes of biologically relevant, plasma-derived species, such as. OH, ONOO−, O2−, and HO2, range from nanoseconds to a few seconds, questioning the effective penetration depths in biological systems [27,28,29] and limiting widespread investigations. Moreover, fundamental mechanistic questions concerning the primary and functional, secondary penetration depths, kinetics, quenching, and influence of the target composition remain unanswered as of now. Standardized, simplified in vitro models are urgently needed to address such fundamental yet complex questions, which cannot be achieved in 2D culture systems conventionally used in preclinical plasma research. The limited transferability remains a major gap in redox-focused plasma research.
So far, ROS penetration depths have been predominantly studied in hydrogels as tissue model systems or as a tissue barrier above liquids containing redox-sensitive reporters. In 2014, Szili and colleagues reported penetration depths in the range of 150 µm to 1.5 mm after 10 min plasma treatment by adding a gelatin film over liquids containing OPD/HRP biological sensors in a cuvette [15]. Later, comparable results were obtained in the same group, using a 1 mm pig skin layer as a natural tissue barrier in a similar setup [30]. Directly assessing species penetration in hydrogels has been conducted using the relatively insensitive potassium iodide starch assay [31] or various fluorescent reporters [16]. The relative deposition rates of short-lived species were found to be reduced in our study when compared to PBS, conceivably indicating a consumption as discussed above. Interestingly, differential results were found concerning increased levels of long-lived H2O2 and NO2−, reported by Labay and colleagues in 2020. As seen in our study, the accumulation and time-lagged release of ROS even motivated the use of plasma-treated hydrogels as a therapeutical ROS depot for treating internal tumor lesions [32,33]. In vivo, direct evidence of increased intratumoral ROS levels post plasma treatment has been provided by intravenous injection of a redox-sensitive, luminol-based probe into tumor-bearing mice revealing a local increase in the luminescence compared to the untreated controls [30,34].
Due to the complexity of plasma–tissue interactions, the majority of studies attempted to characterize the functional rather than primary ROS penetration depth based on tissue sectioning to assess tissue apoptosis, molecular effects by Raman spectroscopy ex vivo [35,36,37], or relative evaluation of blood flow and oxygenation assessed by hyperspectral imaging in mice and human skin. Importantly, the tissue’s histological structure is considered to have a major impact on the penetration of species in deeper layers. While nonkeratinized tissues have shown effects up to a few hundred micrometers [38], plasma effects may not reach much beyond the stratum corneum in keratinized skin [39]. However, the secondary effects, including paracrine cell signaling events, have been suggested to still occur at layers several millimeters deep into the tissue [40,41], but the mechanisms remain unclear. In the future, hydrogel models could help to address such fundamental questions, for instance, using z-resolved high content imaging [16]. The importance of implementing the use of physiologically relevant culture systems was emphasized by the partially reduced sensitivity of the cells grown in hydrogels in our study. Comparable results have recently been reported in drug-screening studies [21]. However, the plasma efficacy in hydrogels was surprisingly influenced by the plate geometry, and the toxicity increased up to $50\%$ when the cells were seeded in 96-well compared to 24-well plates. Considering a reduced local retention time in the 24-well plates due to the fact of an increased surface area that needs to be covered, and this remarkable difference outlines the discrepancy in species distribution in liquids as diffusion systems compared to solid, tissue-like gels.
As a natural material, collagen-based hydrogel cultures have some important drawbacks, including low stiffness, limited long-term storability and batch-to-batch variability. In our study, the nonmalignant HaCat, 17cl-1, THP-1, and TK6 cells showed higher basal toxicity than the malignant cells. Hydrogel optimization was performed using two malignant cell lines, showing favored growth conditions at collagen pH levels slightly below pH 7.4, which is the physiological pH in blood and interstitial fluids. Particularly, the blood-derived THP-1 cells adapted to the environment, providing strong buffer systems assuring only marginal changes in the pH. By contrast, due to the fact of metabolic shifts leading to increased lactate formation [42], tumor cells are known to reside in a slightly acidic environment that is considered to promote angiogenesis, suppress antitumor immune responses, and increase their metastatic potential [43,44]. Hence, optimizing hydrogel formulations for individual cell-type-specific applications might be useful.
Overall, our study emphasizes the use and widespread applicability of three-dimensional hydrogel models in plasma research. Implementing physiologically relevant cell culture models will help increase the translational relevance of preclinical research in the future and gain novel insight into the limitations and mechanisms of plasma-induced toxicity observed in vivo.
## 5. Conclusions
Collagen-based hydrogel models were investigated for their ability to characterize plasma–tissue interactions in preclinical studies. Embedded redox-sensitive reporters were used to characterize plasma-derived ROS/RNS chemistries in collagen-based hydrogels compared to PBS and their time-lagged release in the following. A side-by-side comparison of the plasma-induced toxicity in two- and three-dimensional culture systems revealed a diminished plasma susceptibility of the cells when grown in hydrogels. A major influence of the plate geometry indicated the impact of the different species distribution in liquids as diffusion systems compared to solid, tissue-like gels. Moreover, a cell line screening done to assess the basal toxicity of thirteen different cell lines grown in hydrogels outlined the widespread applicability of the approach.
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|
---
title: TNF-α-Mediated Endothelial Cell Apoptosis Is Rescued by Hydrogen Sulfide
authors:
- Lorena Diaz Sanchez
- Lissette Sanchez-Aranguren
- Keqing Wang
- Corinne M. Spickett
- Helen R. Griffiths
- Irundika H. K. Dias
journal: Antioxidants
year: 2023
pmcid: PMC10045727
doi: 10.3390/antiox12030734
license: CC BY 4.0
---
# TNF-α-Mediated Endothelial Cell Apoptosis Is Rescued by Hydrogen Sulfide
## Abstract
### Abstract
Endothelial dysfunction is implicated in the development and aggravation of cardiovascular complications. Among the endothelium-released vasoactive factors, hydrogen sulfide (H2S) has been investigated for its beneficial effects on the vasculature through anti-inflammatory and redox-modulating regulatory mechanisms. Reduced H2S bioavailability is reported in chronic diseases such as cardiovascular disease, diabetes, atherosclerosis and preeclampsia, suggesting the value of investigating mechanisms, by which H2S acts as a vasoprotective gasotransmitter. We explored whether the protective effects of H2S were linked to the mitochondrial health of endothelial cells and the mechanisms by which H2S rescues apoptosis. Here, we demonstrate that endothelial dysfunction induced by TNF-α increased endothelial oxidative stress and induced apoptosis via mitochondrial cytochrome c release and caspase activation over 24 h. TNF-α also affected mitochondrial morphology and altered the mitochondrial network. Post-treatment with the slow-releasing H2S donor, GYY4137, alleviated oxidising redox state, decreased pro-caspase 3 activity, and prevented endothelial apoptosis caused by TNF-α alone. In addition, exogenous GYY4137 enhanced S-sulfhydration of pro-caspase 3 and improved mitochondrial health in TNF-α exposed cells. These data provide new insights into molecular mechanisms for cytoprotective effects of H2S via the mitochondrial-driven pathway.
## 1. Introduction
A healthy vascular endothelium maintains vascular homeostasis by regulating vascular tone, angiogenesis, platelet aggregation, inflammation and oxidative status [1]. Inflammatory activation and endothelial dysfunction are the initial steps in the pathogenesis of atherosclerosis and pose an elevated risk for cardiovascular complications [2]. Understanding the mechanisms leading to endothelial dysfunction and the relationship with inflammatory pathways may inform novel therapeutic strategies to treat vascular diseases.
Endothelial dysfunction is associated with a pro-inflammatory phenotype, mitochondrial dysfunction, and imbalance in the cellular redox steady-state [3,4]. Pro-inflammatory cytokines such as tumour necrosis factor-α (TNF-α) initiate and regulate endothelial activation and, ultimately, cell dysfunction that can lead to cardiac- and metabolic vascular complications [5]. Neutralising TNF-α therapies in inflammatory arthritis successfully suppressed endothelial dysfunction, apoptosis and mitochondrial damage in patients with cardiovascular comorbidities [6,7].
In recent years, hydrogen sulfide (H2S) has gained attention for its potential beneficial roles in several vascular conditions, preserving vascular wall integrity and vascular tone [8,9]. Furthermore, H2S is known to restore the redox balance in vascular beds, increasing the activity of reactive oxygen species (ROS)-scavenging enzymes [10]. Apart from its well-known role as an antioxidant, H2S has been shown to affect biological functions via protein S-sulfhydration, a post-translational modification of reactive cysteine residues in proteins that result in the conversion of -SH to an -SSH group, leading to the modification of protein activity [11,12,13,14].
In mammalian systems, H2S is synthesised by the transsulfuration pathway and mitochondrial cysteine catabolism pathway. In this regard, mitochondria play an important role in H2S metabolism via the mitochondrial sulfide oxidation pathway [15]. As a gaseous molecule, H2S can easily travel across membranes. H2S entering mitochondria is oxidised by the inter-mitochondrial membrane protein, sulfide quinone oxidoreductase (SQR), to generate persulfides that are later oxidised to short-lived sulfite (SO32−), thiosulfate (S2O32−) and sulfate (SO42−) [15]. H2S is considered a toxic molecule at high concentrations as these oxidation products cause cytotoxic effects by altering mitochondrial membrane potential and disrupting cellular energy production [15]. Therefore, the therapeutic potential of H2S should be explored at a sub-toxic dose.
To date, most in vivo and in vitro studies have described the beneficial effects of H2S as a pre-treatment, where H2S is given before the systemic/cellular insult [16,17,18] or as a co-treatment [19,20], thereby preventing rather than revoking cell damage. Although the beneficial role of H2S as a post-treatment in the presence of an inflammatory setting has been shown before [19], the underlying mechanisms and mitochondrial-dependent effect on the vasculature are not fully known. Therefore, the present study used a slow H2S-releasing donor, GYY4137, to investigate the mechanism by which H2S protects endothelial cells from mitochondrial dysfunction and apoptosis during inflammation.
## 2.1. Cell Culture
Human umbilical vein endothelial cells (HUVECs) were obtained from PromoCell (#C12203, Leicestershire, UK). Cells were cultured (passages 1 to 4) in Endothelial Growth Cell Medium (EGM2 #C22111) supplemented with antibiotics (100 units/mL penicillin and 100 µg/mL streptomycin #P43333, Sigma-Aldrich, Dorsert, UK) at 37 °C with $5\%$ CO2. Unless otherwise stated, HUVECs (1.5 × 104 cells/well) were seeded into a 96-well plate in EGM2 supplemented medium and cultured overnight for experiments. Once 80–$90\%$ confluence was reached, cells were exposed to 1 ng/mL, 3 h TNF-α (#210-TA-005, R&D Systems, Oxford, UK-) dissolved in PBS with $0.1\%$ BSA followed by treatment with 100 µM, 21 h GYY4137 (#SML2470, Sigma-Aldrich, Dorset, UK-) (Figure S1). The experimental set-up involved four groups (Control (cells treated with EGM2 media only without any treatments), TNF-α, GYY4137, and TNF-α+GYY4137).
## 2.2. Cell Viability Assay
Metabolic capacity of viable cells was measured using a fluorometric test (#G8080, CellTiter-BluePromega, Southampton, UK) according to the manufacturer’s instructions. Briefly, after treatments, the medium was discarded, and a fresh EGM2 medium containing 120 µL CellTiter-Blue solution at 37 °C was added and incubated for 3 h. The fluorescence intensity (Ex/Em: $\frac{560}{590}$ nm) was measured in a microplate reader (TECAN, Spak Lom Ltd., Männedorf, Switzerland).
## 2.3. Tube Formation Assay
HUVECs were maintained in endothelial EGM2 medium at 37 °C and $5\%$ CO2 conditions until the day before the experiment. Growth factor reduced Matrigel (#354230, Corning, Loughborough, UK) was thawed at 4 °C overnight, and 50 µL/well was added into a cooled sterile transparent 96-well plate on ice with a cooled sterile tip and incubated at 37 °C for 30 min to solidify. Cells (1 × 104 cells/well) were seeded onto wells containing Matrigel in duplicates. Plates were incubated at 37 °C and $5\%$ CO2 for 1 h. After treatments, phase-contrast images were captured at 4× magnification in each well using a Nikon eclipse Ts2 microscope. The total number of branches and the number of junctions in each image were determined using the ImageJ software Angiogenic plugin.
## 2.4. Measurement of Intracellular ROS Formation
The total cellular peroxide concentration was determined by using the fluorescent probe CM-H2DCFDA™ (#C6827, Themo Fisher Scientific, Loughborough, UK). After treatments, the medium was discarded. Fresh EGM2 medium containing 10 µM CM-H2DCFDA was incubated at 37 °C for 30 min in the dark and analysed using a fluorescent microscope (Ex/Em: $\frac{485}{530}$ nm, Nikon, Melville, NY, USA).
## 2.5. Measurement of MitoSox Oxidation
HUVECs were seeded at a density of 2.5 × 105 in a 6-well culture plate and incubated overnight. Once treatments were completed, cells were gently detached with trypsin-EDTA and cells resuspended in DMEM (#D6429, Sigma-Aldrich, Dorset, UK) free-serum medium with 5 µM MitoSox™ (#M36008, Thermo Fisher Scientific, Loughborough, UK) for 20 min at 37 °C in the dark. Next, HUVECs were pelleted by centrifugation at 200× g for 5 min, and the pellet was washed with PBS. Finally, cells were resuspended in 500 µL PBS, and each sample was analysed using 10,000 cellular events using the BD Accuri C6 Plus flow cytometer.
MitoSOX™ oxidation was analysed by fluorescence microscopy image analysis. After treatments, the medium was discarded, and a fresh EGM2 medium containing 5 µM MitoSOX™ Red was added. Cells were incubated at 37 °C for 30 min in the dark and analysed using a fluorescent microscope (Ex/Em: $\frac{510}{580}$ nm, Nikon, Melville, NY, USA).
## 2.6. Assessment of Mitochondrial Membrane Potential (Δψm)
HUVECs were incubated with 5 µM of JC-1 (#T3168, Thermo Fisher Scientific, Loughborough, UK), incubated at 37 °C for 30 min in the dark, and analysed using a fluorescent microscope (green spectrum 529 nm, red spectrum 590 nm, Nikon, Melville, NY, USA). The intensity of the red/green fluorescence ratio was used to indicate mitochondrial membrane potential.
## 2.7. Detection of H2S
The intracellular H2S content was analysed using fluorescent probe SF7-AM (#748110, Sigma Aldrich, Dorset, UK). After treatments, fresh medium containing 2.5 µM SF7-AM was added and incubated at 37 °C for 30 min. DAPI (#ab228549, Abcam, Cambridge, UK), as a counterstain for nuclear morphology, was incubated at 37 °C for 10 min. Subsequently, cells were washed twice with phosphate-buffered saline (#806552, Sigma Aldrich, Dorset, UK,) and resuspended in fresh medium followed by fluorescence microscopy analysis (Ex/Em: $\frac{488}{530}$ nm, Nikon, Melville, NY, USA).
## 2.8. Interleukin-6 (IL-6) Levels
At the end of incubation, supernatants were collected and centrifuged at 200 × g to remove debris. The levels of interleukin-6 (IL-6) were determined by an ELISA (#DY206-05, R&D systems, Oxford, UK) according to the manufacturer’s instruction.
## 2.9. ICAM-1 (Intercellular Adhesion Molecule 1) Levels
To detect ICAM-1 expression, 1 × 105 cells were incubated with anti-ICAM-1 (anti CD54-APC) antibody (1:100, Invitrogen, Loughborough, UK, #17054942) or anti-IgG2b kappa APC isotype control (1:100, Invitrogen, Loughborough, UK, #124714442) at 4 °C for 1 h. Cells were analysed using BD Accuri C6 Plus flow cytometer.
## 2.10. Western Blotting
Cytosolic fractions were prepared from HUVECs using a Nuclear Extract kit (# 400100, Active Motif, Cambridge, UK) following the manufacturer’s instructions. Cytosolic proteins (10 µg) were resolved using SDS-PAGE, then transferred to a nitrocellulose membrane (#GE10600004, Amersham, Sigma Aldrich, Dorset, UK) and incubated with primary antibodies against; Cytochrome c (Cyto c) (1:500, Abcam, #ab90529), cleaved-caspase 3 (1:500, Cell signalling, Leiden, The Netherlands, Cat# 9661), caspase 3 (1:500, Cell signalling, Leiden, The Netherlands,#14220) and actin (1:2000, Abcam, Cambridge, UK #ab82241), followed by fluorescence-conjugated secondary antibodies (IRDye® 800CW (anti-rabbit, #926-32211) and 680CW (anti-mouse, #926-32212) from LI-COR Biosciences (Cambridge, UK). Images were analysed with Image J software.
## 2.11. Flow Cytometry Analysis of Apoptosis
To analyse apoptosis, 1 × 105 cells were stained with an Annexin V and Propidium Iodide (PI) according to Apoptosis Staining Kit instructions (#040914, BioLegend, Amsterdam, The Netherlands). In scatter plots, early apoptotic cells were identified as Annexin V positive and PI negative. At the same time, late apoptosis/necrosis was labelled for Annexin V and PI positive on a BD Accuri C6 Plus flow cytometer.
## 2.12. Caspase 3/7 Activity
HUVECs were seeded in a white-walled 96-well plate at 1.5 × 104 cells/well density in EGM2 medium and cultured overnight. After treatments, the caspase activity assay was conducted using the luminescence-based Caspase-Glo $\frac{3}{7}$ detection kit (#G8981, Promega, Southampton, UK) according to the manufacturer’s instructions. Briefly, Caspase-Glo $\frac{3}{7}$ reagent (100 µL) was added to each sample and mixed using a plate shaker for 30 s. The plate was incubated at 37 °C in the dark for 1 h, followed by luminescence measurement in a microplate reader (TECAN, Spak Lom Ltd., Männedorf, Switzerland). The relative luminescence values were normalised to cell numbers. Data are represented as relative luminescence values.
## 2.13. Biotin Switch Assay
The detection of S-sulfhydration was carried out according to Cheung and Lau [21] with modifications. HUVECs (1 × 106 cells) were seeded in T75 flask and grown overnight before treatment with TNF-α (1 ng/mL, 3 h) followed by GYY4137 (100 µM, 21 h) post-treatment at 37 °C with $5\%$ CO2. ECs were homogenised in HEN buffer (250 mM Hepes-NaOH pH 7.7, 1 mM EDTA and 0.1 mM Neocuproine) supplemented with 100 µM deferoxamine and protease inhibitors and centrifuged at 13,000× g for 30 min at 4 °C. Protein samples (350 µg) were incubated with four volumes of blocking buffer (HEN buffer supplemented with $2.5\%$ SDS and 20 mM S-methyl methanethiosulfonate (MMTS, #208795)) at 50 °C for 20 min with continuous vortexing in the dark to block free thiols (-SH). The MMTS was removed by pre-cold acetone precipitation at −20 °C for 1 h. After removal of acetone by centrifugation at 13,000× g at 4 °C for 10 min, the proteins were resuspended in HENS buffer (HEN buffer containing $1\%$ SDS) and 4 mM biotin-N-[6-(biotinamido) hexyl]-3′-(2′-pyridyldithio) propinamide (HPDP) (#ab145614, Abcam) at 25 °C for 3 h in the dark. Biotinylated proteins were then precipitated by streptavidin-agarose beads (#29200, Thermo Fisher Scientific) (overnight at 4 °C with continuous mixing). The beads were washed five times with PBS (1×) and centrifuged at 5000× g for 15 s. The biotinylated proteins were incubated with elution buffer (20 mM Hepes-NaOH pH 7.7, 100 mM NaCl, 1 mM EDTA) with $1\%$ β-mercaptoethanol at 37 °C for 30 min with shaking. The biotinylated protein samples in SDS-PAGE sample buffer (without β-ME) were heated at 95 °C for 5 min.
Protein samples were separated on $10\%$ SDS-PAGE gels, transferred to a nitrocellulose membrane and incubated with anti-caspase 3 antibody (1:500, Cell signalling, Loughborough, UK) overnight at 4 °C. The next day, the membrane was soaked in fluorescence-conjugated secondary antibodies (IRDye® 800CW (anti-rabbit, #926-32211, LI-COR Biosciences, Cambridge, UK). Revert™ 700 Total protein stain kit (#926-11010, LI-COR Biosciences, Cambridge, UK) was used for normalisation following manufacturer’s instructions.
## 2.14. Mitochondrial Network Assay
Mitochondrial morphology was determined by immunostaining followed by confocal microscopy, as described by Rao et al. [ 12]. Briefly, cells at 1.5 × 104 density in EGM2 medium plate on 12 mm coverslips were treated with MitoTracker Red (100 nM) (#M7512, Thermo Fisher Scientific, Loughborough, UK), washed with PBS, fixed with $4\%$ paraformaldehyde and stained with DAPI mounting medium (Invitrogen, Loughborough, UK). Images were collected by confocal lighting microscopy TCS SP8 system (Leica Microsystems Ltd., Milton Keynes, UK). Cells were analysed using an Ex/Em: $\frac{579}{599}$ for Mitotracker and Ex/Em: $\frac{350}{465}$ for DAPI. Images were acquired using a 63 × oil APO objective lens and analysed using Image J open-source macro tool, MINA (mitochondria network analysis) [12]. Settings were kept consistent across images and “mean” filter before filter was selected; a total number of 30 cells per group per individual experiment ($$n = 4$$) was consolidated and analysed for the network’s branching and branch length.
## 2.15. Real-Time PCR
Total RNA was isolated using the RNeasy Mini kit (Qiagen, Manchester, UK) and subjected to reverse transcription using Evo Scrip™ cDNA MasterMix Kit (#07912374001, Roche Diagnostic, Ltd., Welwyn Garden City, UK) following the manufacturer’s instructions. SYBR® Green (#04707516001, Roche Diagnostic, Ltd., Welwyn Garden City, UK) RT-qPCR was performed using RT-qPCR system (LightCycler 480 II system, Roche Diagnostic, Ltd., Welwyn Garden City, UK). The relative mRNA levels were normalised to mRNA levels of EEF2, and calculation of each mRNA levels were made on comparative cycle threshold method (2−ΔΔCt). Primer-sequences used in this study are detailed in Supplementary Table S1.
## 2.16. Statistical Analysis
Data analysis was performed using Graph Prism (v. 8.0) software and expressed as means ± SD. Normality of the data was first assessed using Shapiro–Wilk normality test. Subsequently, one-way ANOVA analysis of variance followed by Tukey’s multiple comparison test or unpaired Student-t test (between two groups) was used. $p \leq 0.05$ was considered statistically significant. Experiments were performed independently at least triplicate.
## 3.1. Exogenous H2S Ameliorates Intrinsic Apoptotic Pathways in Endothelial Cells
To test the ability of GYY4137 (100 µM) to increase intracellular H2S levels in our model, cells were stained with H2S-interacting dye, SF-7AM. As shown in Figure 1A,B, fluorescence staining confirmed that GYY4137 (100 µM) enhanced the intracellular H2S level in HUVECs ($p \leq 0.005$) without toxicity as demonstrated by mitochondrial superoxide production (Supplementary Figure S2).
TNF-α i induces cell death by activating extrinsic and intrinsic apoptosis pathways [22,23]. Flow cytometry and ELISA analysis revealed that TNF-α treatment increases inflammatory marker (IL-6) secretion (Supplementary Figure S3A), reduces the metabolic activity of viable cells (Supplementary Figure S3B) and induces ICAM-1 expression (Supplementary Figure S3C,D). Taken together, these results indicate that GYY4137 enhances intracellular H2S content and TNF-α cause endothelial dysfunction in HUVECs.
Endothelial dysfunction causes loss of integrity of the vascular endothelium and affects angiogenesis. To investigate whether angiogenesis is influenced by GYY4137, the HUVECs were cultured on Matrigel under four experimental groups (Figure 2A). Branch formation, as indicated by the presence of capillary-like structures, was found in the HUVECs cultured in medium (control) and GYY4137 group. Limited branch formation was detected in the HUVECs treated with TNF-α (Figure 2B) ($p \leq 0.05$) and was rescued by the addition of GYY4137. The number of junctions was significantly increased in the presence of GY4137 in comparison to the control group (Figure 2C) ($p \leq 0.05$).
Next, the role of GYY4137 on apoptosis in TNF-α-stimulated HUVECs was investigated. Annexin V/PI-positive staining showed increased cell apoptosis in the presence of TNF-α compared to the control ($p \leq 0.05$). GYY4137 post-TNF-α treatment significantly decreased apoptosis in TNF-α-treated HUVECs ($p \leq 0.05$) (Figure 3A,B) and decreased IL-6 release (Supplementary Figure S4). To investigate whether these protective effects occur via the intrinsic apoptotic pathway, Cyto c release and downstream caspase activity were analysed. Figure 3C shows that TNF-α significantly increased caspase $\frac{3}{7}$ activity compared to untreated cells ($p \leq 0.05$). Post-treatment with GYY4137 significantly reduced caspase activity in TNF-α-treated cells ($p \leq 0.001$). As shown in Figure 3D, TNF-α treatment increased the expression of cytosolic Cyto c ($p \leq 0.05$), whilst GYY4137 post-treatment reduced Cyto c release in TNF-α-treated cells ($p \leq 0.05$). To further analyse the downstream cascade signalling regulated by TNF-α, protein expression of pro-caspase 3 and cleaved caspase 3 were tested by immunoblotting. However, the levels of caspase 3 were not different between the groups (Figure 3E). On the other hand, cleaved caspase 3 protein expression was increased in the presence of TNF-α, which was significantly decreased with GYY4137 post-treatment (Figure 3F). These results suggested that GYY4137 may attenuate apoptosis via an intrinsic caspase-dependent pathway.
## 3.2. Exogenous H2S Enhanced S-Sulfhydration of Caspase 3 in Endothelial Cells
Recent reports suggest protein S-sulfhydration may modulate the apoptotic pathway [24]. To investigate whether the reduction in apoptosis and caspase $\frac{3}{7}$ activation by GYY4137 was associated with S-sulfhydration, protein levels of sulfhydrated caspase 3 were investigated (Figure 4A). GYY4137 increases total S-sulfhydration proteins in endothelial cells (Supplementary Figure S5). S-sulfhydrated pro-caspase 3 protein levels were significantly increased in those cells exposed to GYY4137 post-treatment in the presence ($p \leq 0.01$) or absence of TNF-α ($p \leq 0.05$) compared to untreated cells (Figure 4B). TNF-α alone did not alter pro-caspase sulfhydration, but a significant increase in sulfhydrated pro-caspase 3 protein expression upon GYY4137 treatment ($$p \leq 0.01$$) was observed. These results suggest that GYY4137 may enhance the S-sulfhydration of proteins, including caspase 3, irrespectively of the pro-inflammatory status.
## 3.3. Exogenous H2S Restores the Antioxidant Gene Response and Mitosox Oxidation in TNF-α Treated Endothelial Cells
H2S is known to protect vascular endothelial function and prevent apoptosis through its antioxidant properties [9]. To investigate antioxidant gene response, the expression of the antioxidant genes, thioredoxin-1 (Trx1), heoxygenase-1 (HO-1) and mitochondrial superoxide dismutase 2 (SOD2) was analysed. Compared to untreated HUVECs, GYY4137 post-treatment significantly increased antioxidant Trx1 and HO-1 mRNA levels in the presence of GYY4137 post-treatment compared to TNF-α treatment alone ($p \leq 0.05$; Figure 5A,B). Mitochondrial SOD (SOD2) mRNA expression was significantly elevated upon TNF-α treatment alone and with GYY4137 post-treatment compared to untreated cells ($p \leq 0.05$; Figure 5C).
Next, we compared mitochondrial ROS levels using the MitoSOX probe. HUVECs exposed to TNF-α had significantly higher MitoSOX oxidation ($p \leq 0.001$), which was attenuated by GYY4137 post-treatment ($p \leq 0.001$) (Figure 6A,B). A similar mitochondrial ROS attenuation was detected by fluorescence microscopy (Supplementary Figure S6A,B) and using the intracellular ROS probe, CM-H2DCFDA (Supplementary Figure S6A,C). These results suggest that GYY4137 post-treatment can regulate TNF-α-induced oxidative stress by abrogating mitochondrial ROS and enhancing the expression of antioxidant genes in endothelial cells.
## 3.4. Exogenous H2S Improves Mitochondrial Δψm in Endothelial Cells
The reduction in mitochondrial superoxide production and Cyto c release by GYY4137 post-treatment led us to postulate that H2S could attenuate TNF-α impaired mitochondrial function. We then analysed the impact of GYY4137 on mitochondrial membrane potential (Δψm) as an indicator of mitochondrial function using the fluorescent cationic JC-1 dye (Figure 7A). As a cationic dye, JC-1 accumulates in the energised mitochondria, where healthy cells form dye complexes known as J-aggregates and emit red fluorescence. In contrast, in unhealthy mitochondria, JC-1 remains in a J-monomeric form that exhibits green fluorescence [25]. Our results show that TNF-α treatment significantly decreased ($p \leq 0.05$) the red (~590 nm) to green (~529 nm) fluorescence intensity ratio, indicating impaired and depolarisation of Δψm in HUVECs (Figure 7B). In addition, GYY4137 post-treatment abrogated mitochondrial depolarisation compared to HUVECs treated with TNF-α alone ($p \leq 0.01$), suggesting that exogenous H2S might regulate mitochondrial function via restoring mitochondrial polarisation.
## 3.5. Exogenous H2S Improved Mitochondrial Morphology in TNF-α-Treated Endothelial Cells
Mitochondria are highly dynamic organelles that constantly undergo fusion and fission cycles that regulate mitochondrial morphology. Loss of mitochondrial Δψm is reported to modify mitochondrial morphology leading to fragmentation [26]. Therefore, we investigated whether exogenous GYY4137 attenuates TNF-α-induced mitochondrial morphology changes in HUVECs (Figure 8A). Quantification of the changes in mitochondrial morphology, including length and the mitochondrial network, was conducted using the ImageJ macro; Mitochondrial Network Analysis (MiNA) toolset, as described previously [27]. HUVECs challenged with TNF-α exhibited significantly shorter branch lengths ($p \leq 0.001$) when compared to untreated cells (Figure 8B). Mitochondrial network size, defined as the number of branches in each mitochondria network, was also reduced in the presence of TNF-α compared to untreated cells ($p \leq 0.001$) (Figure 8C). GYY4137 post-treatment improved the length of branches and mitochondrial network numbers in TNF-α-treated HUVECs ($p \leq 0.001$). However, regarding the length of branches, the recovery by GYY4137 was partial as the length in TNF-α-treated HUVECs remained significantly reduced compared to untreated cells ($p \leq 0.001$).
While fusion and fission dynamics are balanced under basal conditions, oxidative stress can provoke a shift towards fission, resulting in excessive mitochondrial fragmentation [28]. qPCR analysis shows that mitochondrial fission marker DRP1 mRNA levels were significantly increased in TNF-α treated cells compared to untreated ($p \leq 0.01$), and that GYY4137 post-treatment significantly revoked this rise ($p \leq 0.05$) (Figure 9A). Furthermore, Figure 9B shows that GYY4137 post-treatment significantly elevated mitochondrial fusion marker, MFN1 mRNA expression ($p \leq 0.01$). Taken together, these results suggest that TNF-α affects mitochondrial morphology, leading to overall smaller networks and shorter branches, whilst post-treatment with GYY4137 could improve but does not completely restore mitochondrial morphology in TNF-α treated HUVECs.
## 4. Discussion
This study revealed that exogenous H2S plays a protective role against endothelial dysfunction upon GYY4137 post-treatment via regulation of the intrinsic apoptotic pathway, abrogating mitochondrial dysfunction, and enhancing S-sulfhydration of caspase 3. H2S has been reported to exhibit a wide range of physiological functions, including blood vessel relaxation and cardioprotection [10,29,30]. Most in vitro studies that investigated these protective roles of H2S have used pre-treatment or co-treatment approaches [16,17,19,31]. For example, pre-treatment of H2S protects cardiomyocytes against apoptosis in H2O2-induced oxidative stress through the activation of antioxidant enzymes such as SOD [31]. Even though pre- and co-treatment allows investigation of the prevention or occurrence of disease, post-treatment offers the benefit of evaluating the suitability of a drug as a treatment in a disease condition [32]. Previous work in our laboratory showed that the dysfunctional cystathionine γ-lyase (CSE)/H2S pathway is a contributor to the pathogenesis of preeclampsia using a C57BL/6 mice model [33]. GYY4137 treatment restored foetal growth and the placental vasculature compromised by the CSE inhibitor, DL-propargylglycine. In addition, GYY4137 improved blood pressure, liver function, and foetal weight in mice where these parameters were compromised by DL-propargylglycine treatment. Liu reported that GYY4137 decreased vascular inflammation and oxidative stress, improved endothelial function and reduced atherosclerotic plaque formation in ApoE−/− mice [34]. These results suggested that endogenous H2S is important for healthy vasculature to support well-being. Following in vivo work, we investigated molecular mechanisms underlying protective effects exerted by H2S slow-releasing donor, GYY4137.
Even though TNF-α is commonly known to induce extrinsic apoptotic pathways, previous studies reported that TNF-α could play a dual role by mediating extrinsic and intrinsic apoptotic pathways [22,35]. In line with these reports, our results also show that TNF-α increases Cyto c release, leading to mitochondrial-mediated intrinsic apoptosis pathway activation. TNF-α reduced the Δψm and enhanced ROS formation, which has previously been reported to lead to the loss of Δψm in HeLa cells [36]. The imbalance of Δψm has been reported to trigger structural modifications in the organelle from tubular to globular mitochondria [37], which can alter the expression or activity of fission and/or fusion proteins and, ultimately, contribute to mitochondrial functional impairment. Relatedly, Rao and colleagues demonstrated that silencing human cystathionine beta-synthase (CBS) can cause mitochondrial fragmentation in HUVECs, which can be ameliorated by GYY4137 treatment [12]. Our results suggest that H2S may contribute to mitochondrial membrane remodelling and maintaining mitochondrial heath in endothelial cells.
The loss of Δψm can subsequently account for Cyto c release followed by caspase activation. Apart from this intrinsic pathway, caspase 3 could also be activated via extrinsic pathway induced by TNF-α treatment [23]. As a pro-apoptotic enzyme, caspase 3 activity has been associated with vascular dysfunction in vivo, and its increased activity can be ameliorated by H2S [38]. Previously, the protective effect of H2S post-treatment against TNF-α-mediated Cyto c release and caspase 3 protein expression in astrocytes was reported [39]. Our results demonstrate that GYY4137 post-treatment could decrease Cyto c release and reduce caspase 3 activation.
Previous studies reported that Cys-163 was the active catalytic site of caspase-3 [40]. A subset of pro caspase-3 is known to be S-nitrosylated at the active catalytic site cysteine in resting lymphocytes [41,42]. During Fas-induced apoptosis, caspases are denitrosylated at the catalytic site. Glutathiolation also been described as another regulatory mechanism in caspase 3. It was shown that an increase in caspase 3 S-glutathiolation attenuated cleavage, resulting in the inhibition of TNF-α-mediated endothelial cell death [43]. Recent work has suggested pro-caspase-3 can be constitutively S-sulfhydrated at Cys-163 using fast-releasing H2S donors [24]. However, these effects have not been previously explored using slow-releasing H2S donors in endothelial cells. Here, we demonstrate for the first time that the slow-releasing donor GYY4137 can also S-sulfhydrated caspase 3 in endothelial cells, supporting the assumption that S-sulfhydration may act as a safeguard mechanism in endothelial cell death. Previous analysis in HeLa cells with H2S fast-releasing prodrugs suggested that conformational modification during caspase 3 maturation, exposing catalytic thiol, may lead to higher S-sulfhydration in cleaved caspase 3 compared to inactive pro-caspase forms [24].
Furthermore, it can be speculated that the reduction in apoptosis may also be associated with the S-sulfhydration of other players, such as Kelch-like ECH-associated protein 1 (Keap1). S-sulfhydration of Keap1 facilitates nuclear translocation of nuclear factor erythroid 2–related factor 2 (Nrf2), which induces the expression of a battery of antioxidant genes triggering an anti-apoptotic response, including HO-1 [44,45].
There are some inherent limitations in the selective detection of S-sulfhydration by biotin-switch assay due to the reactivity of the persulfide group (RSSH) with other sulfur species such as thiols. The modified biotin switch technique, using MMTS as an alkylating reagent, overcomes many of these unspecific reactions, but it is still possible that not all free thiols are blocked during the MMTS labelling step [46]. More sensitive and specific methods, such as tandem mass spectrometry, which allows direct and unbiased proteomic mapping of s-sulfhydration, would enable confirmation of both the occurrence and location of sulfhydration. Several recent studies reported the proteomic mapping of S-sulfhydration in a broad range of cell types [21,47]. Bibli et al., 2021 performed proteomic analysis to map the S-sulfhydrome of endothelial cells isolated from human arteries [48]. Their study revealed that short-term H2S supplementation increased protein s-sulfhydration and improved vascular function. Future studies to perform proteomic analysis in the presence of slow-releasing H2S donors could be undertaken to map the GYY4137-mediated changes to sulfhydrome. Identifying specific cysteine residues prone to sulfhydration can then be targeted for site-directed mutagenesis studies to confirm their roles. Another limitation is that the present in vitro model is based on one type of primary endothelial cells. Therefore, this culture microenvironment does not reflect the physiological characteristics of an endothelium. Development of 3D cultures of endothelial cells or co-culture models with smooth muscle cells and pericytes may provide more information about how H2S donors will improve vascular health. Furthermore, the use of in vivo models will help to understand overall vascular and body health related to H2S donors.
In conclusion, our results demonstrate that exogenous slow-releasing H2S donor, GYY4137 inhibits downstream apoptotic intrinsic mechanisms through S-sulfhydration of caspase 3, leading to downregulation of its activity. Furthermore, increased intracellular H2S levels were cytoprotective and anti-apoptotic effects were associated with increased antioxidant gene expression, reducing ROS and improving mitochondrial health.
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|
---
title: Sexual Dimorphism in the Expression of Cardiac and Hippocampal Renin-Angiotensin
and Kallikrein–Kinin Systems in Offspring from Mice Exposed to Alcohol during Gestation
authors:
- Gabriel Almeida da Silva
- Allan Luís Barboza Atum
- Leonardo Paroche de Matos
- Guilherme Rabelo Nasuk
- Bruna Calixto de Jesus
- Telma Luciana Furtado Gouveia
- Ovidiu Constantin Baltatu
- Stella Regina Zamuner
- José Antônio Silva Júnior
journal: Antioxidants
year: 2023
pmcid: PMC10045732
doi: 10.3390/antiox12030541
license: CC BY 4.0
---
# Sexual Dimorphism in the Expression of Cardiac and Hippocampal Renin-Angiotensin and Kallikrein–Kinin Systems in Offspring from Mice Exposed to Alcohol during Gestation
## Abstract
Prenatal alcohol exposure (PAE) impairs fetal development. Alcohol consumption was shown to modulate the renin–angiotensin system (RAS). This study aimed to analyze the effects of PAE on the expression of the renin–angiotensin system (RAS) and kallikrein–kinin system (KKS) peptide systems in the hippocampus and heart of mice of both sexes. C57Bl/6 mice were exposed to alcohol during pregnancy at a concentration of $10\%$ (v/v). On postnatal day 45 (PN45), mouse hippocampi and left ventricles (LV) were collected and processed for messenger RNA (mRNA) expression of components of the RAS and KKS. In PAE animals, more pronounced expression of AT1 and ACE mRNAs in males and a restored AT2 mRNA expression in females were observed in both tissues. In LV, increased AT2, ACE2, and B2 mRNA expressions were also observed in PAE females. Furthermore, high levels of H2O2 were observed in males from the PAE group in both tissues. Taken together, our results suggest that modulation of the expression of these peptidergic systems in PAE females may make them less susceptible to the effects of alcohol.
## 1. Introduction
Alcohol use disorders in adults are well evidenced in the literature, especially related to the central nervous system (CNS) and heart. In addition, observational evidence pointed out the benefits of low alcohol intake concerning mental and ischemic diseases [1,2,3]. Nonetheless, there is a consensus that drinkers bearing cardiovascular and brain diseases show improved outcomes with reduced alcohol consumption [4,5]. Thus, the recommendations regarding moderate consumption should be individualized to reflect the risks of alcohol and its effects on various chronic diseases [6]. Overall, increased volumes and patterns of alcohol consumption correlate positively with disease risks [7,8].
An estimated $32.1\%$ of women of childbearing age consumed alcohol [9]. Moreover, it is estimated that the prevalence of alcohol use during pregnancy in the world population is $9.8\%$ [10]. When consumed by pregnant women, alcohol is particularly harmful to the developing fetus [11,12]. Prenatal exposure to alcohol (PAE) is a challenging public health problem, and alcohol can induce, in the fetus, severe physical and mental impairments described as fetal alcohol syndrome (FAS) [13] or fetal alcohol spectrum disorders (FASD) [14]. Early studies indicated that alcohol use during gestation is nearly equally distributed between maternal and fetal tissues, and the hippocampus is mainly affected by PAE [15]. Authors have shown that hippocampal cell number is altered after PAE [16], inducing significant loss of hippocampal cells in the third trimester of human pregnancy [17]. In rodents, the equivalent of the third trimester period occurs within the first ten postnatal days, and PAE can have deleterious effects on hippocampus structure and function [18]. Furthermore, in adult mammals, PAE affects hippocampal neurogenesis [19]. PAE also severely affects the heart, leading to congenital cardiac diseases [20] These dysfunctions mostly result in miscarriage or are detected at birth in survival children [12]. However, the effects of ethanol on the heart of adults with PAE are little explored. A study [21] reported that PAE could alter the myocardial contractile function and contribute to the development of postnatal cardiac dysfunction. The authors revealed that alcohol exposure increased intracellular Ca (2+) load and apoptosis in adulthood. We recently reported that PAE could modulate the mRNA expression of components of nine cardiac transduction signal pathways related to heart diseases in male mice [22].
The renin–angiotensin system (RAS) plays a central role in the development and progression of cardiovascular disorders [23]. However, the brain expression of RAS is related to blood pressure control and elicits new functions of Ang II and other RAS components in cell signaling [24]. First, the enzymatic action of renin (REN) over the precursor angiotensinogen generates angiotensin I (Ang I), a decapeptide with low biological action. Then, Ang I is converted to Ang II by the angiotensin-converting enzyme (ACE) that removes the His-Leu dipeptide from the C-terminal portion of the angiotensin molecule. Ang II presented high affinity for the angiotensin receptors AT1 and AT2, although there are differences in the outcome of their bindings. The AT1 receptor mediates Ang II-induced vasoconstriction, proliferation, oxidative stress, inflammation, and extracellular matrix remodeling. The activation of the AT2 receptor produces opposite effects, providing a protective action [20]. Angiotensin-converting enzyme 2 (ACE2), a protective component of the RAS, catalyzes the conversion of Ang II into Ang 1-7 to counterbalance ACE activity [25].
Angiotensin II (Ang II) has been the main point of interest in investigations into the role of the RAS in the CNS, as it is a peptide related to synaptic plasticity, and it blocks long-term potentiation in the hippocampus. Another point of interest is the ACE, a therapeutic target to control the effects of the RAS, which is responsible for degrading bradykinin, the major effector of the kallikrein–kinin system (KKS) [26]. This peptidergic system is the counterpart of the RAS in blood pressure control. Bradykinin is formed by the action of tissue kallikrein (KLK) over kininogen and binds in two transmembrane receptors named kinins B1 and B2 receptors [27]. While B1 has low expression under physiological conditions, augmented by inflammatory signals, the B2 receptor is ubiquitously expressed. Moreover, both receptors are expressed in many tissues, including the hippocampus, where it participates in neuroinflammation processes [27].
One of the major players in neuroinflammation is the RAS, which, when activated by inflammation, increases sympathetic drive, potentially exacerbating heart conditions [28]. Neuroinflammation is a common feature of alcohol-induced brain damage and can cause neurodegeneration [29]. Previously, [30] showed that RAS activation is inversely related to alcohol intake, but accumulating evidence suggested that alcohol stimulates the RAS in animals and humans. Increased RAS activity is found in both light and heavy drinkers [31,32]. Recently, [33] reported that adolescents with PAE were at risk for low-grade systemic inflammation. However, whether the peptidergic RAS and KKS contribute or protect against PAE effects still needs clarification. So, since the hippocampus and heart tissues have been linked to offspring’s neuropsychological and cardiovascular disorders caused by PAE, we designed this study to investigate the possible effect of alcohol on the gene expression of two neurohormonal systems, the renin–angiotensin system (RAS) and the kallikrein–kinin system (KKS) peptide systems, in the hippocampus and heart of adult mice of both sexes. We purposefully avoided posing a directional hypothesis about the impact outcomes because of the scarcity of data on the PAE effects on the RAS or KKS in the tissues of interest. Thus, our study analyzed the mRNA expression of components of the vasoactive renin–angiotensin and kallikrein–kinin systems and ROS content in samples of the hippocampus and myocardium of mice of both sexes with PAE in early adulthood (postnatal day 45, PN45).
## 2.1. Animals
Fifteen isogenic mice (10 females and 5 males) of the C57Bl/6 strain (weighing 17–22 g) obtained at the Animal Facility of the Universidade Nove de Julho were used to generate the offspring. The animals were confined in appropriate plastic boxes under a light cycle (light/dark cycle, 12 h/12 h) with temperature (21 ± 2 °C) and humidity controlled. The access to food, water, or alcoholic solution was ad libitum as described by [22,34,35]. The females were randomized into two groups: the control group—CT ($$n = 3$$) and the prenatal alcohol exposure group—PAE ($$n = 7$$). Males were used only as breeders and removed from boxes after mating. All progenitors were euthanized after weaning. Offspring were randomly assigned to the control ($$n = 20$$) or PAE group ($$n = 20$$), and each group of mice was equally subdivided into two subgroups ($$n = 10$$ animals each) with male and female mice, avoiding siblings in the same experimental group.
## 2.2. PAE Protocol
The PAE protocol was performed previously by [22,34,35]. Briefly, the protocol started with a sensitization period (SP1 to SP15) of female mice, and it was established in 3 steps. In the first 4 days (SP1 to SP4), each female received a $0.1\%$ aqueous saccharin solution with a new solution available every two days to avoid fungal proliferation. Then, a $2\%$ alcoholic solution was introduced to the females on days SP5 and SP6 days. Then, on days SP7 and SP8, a $5\%$ EtOH solution was offered, and after this period, the animals received, for seven days (SP9 to SP15), a $10\%$ EtOH solution with $0.1\%$ saccharin. The low amount of saccharin added to the water was used to mask the bitter taste of alcohol, making the solution more palatable.
The males were confined with the females for mating, and the animals had access to the $10\%$ EtOH alcoholic solution with $0.1\%$ saccharin. From the gestational period (gestational day 1—GD1- to GD$\frac{19}{21}$) until the 10th day after delivery (postnatal day 10—PN10), each female mother received the alcoholic solution at $10\%$ EtOH/$0.1\%$ saccharin. As of PN11, the alcohol desensitization protocol began, so a $5\%$ EtOH-sweetened solution was available to each female. On day PN13, there was a reduction to $2\%$ EtOH solution, and from day PN15 to PN21, they received $0.1\%$ saccharin solution. Then, the females received only filtered water from day PN22 until weaning (PN30) [22,34,35]. The control groups received the same proportion of water and saccharin at each period established to PAE but without alcohol sensibilization and administration.
## 2.3. Biological Samples
At PN45, male and female offspring were euthanized by decapitation under isoflurane anesthesia. After craniotomy, the brain was collected, and the hippocampus was dissected in an iced plate. Next, a left thoracotomy was performed, the heart was exteriorized, and the left ventricle (LV) was dissected and washed to remove blood content. The hippocampus and LV samples from animals of the control and PAE groups were then snap-frozen and stored at −80 °C. For the qPCR protocol, we used 5 animals per group, and 4 animals per group were used for ROS measurements. As the number of animals born per gestation was unpredictable, if there were surplus animals, they were also euthanized, and the organs were collected for further analyses unrelated to this study.
## 2.4.1. RNA Extraction
Hippocampal and LV samples from both groups ($$n = 5$$ per group) were homogenized with Trizol® Reagent (Life Technologies, Carlsbad, CA, USA) to extract total RNA, according to the manufacturer’s instructions. The total RNA quantification of each sample was obtained using the NanoDrop ND-2000 spectrophotometer (NanoDrop Products, Wilmington, DE, USA). Only samples free of contaminants (A260/A230 ~1.8) and proteins (A260/A280 = 1.8–2.0) were used. One µg of total RNA was incubated with 1 unit of DNase I/RNase-free to eliminate genomic DNA contamination, and the samples were kept at −80 °C.
## 2.4.2. Reverse Transcription
Reverse transcription was carried out using the SuperScript IV Reverse Transcriptase Kit (Invitrogen, Carlsbad, CA, USA). Initially, extracted RNA was added to a solution containing 1 µL of 50 µM Oligo d(T)20 Primer, 1 µL 10 mM dNTP mix, and 1 µL of DNase I/RNase Free (Invitrogen, Carlsbad, CA, USA). Then, the samples were incubated in a thermocycler for 5 min at 65 °C and added 7 µL of a solution containing 1 µL of Ribonuclease Inhibitor, 1 µL of SuperScript® IV Reverse Transcriptase (200 U/µL), 1 µL 100 mM DTT, and 4 µL 5x SSIV Buffer (Invitrogen, Carlsbad, CA, USA). The resulting solution was incubated for 10 min at 55 °C and then for 10 min at 80 °C. Next, 1 µL of RNase H (Invitrogen, Carlsbad, CA, USA) was added and incubated for 20 min at 37 °C to remove residual RNA. After the reaction, the cDNA samples were kept at −20 °C until qPCR was performed.
## 2.4.3. Quantitative Real-Time Polymerization Chain Reaction (PCR)—qPCR
Amplification and data acquisition were performed using the SYBR Green method in a Quantstudio™ 5 System equipment (Applied Biosystems, Carlsbad, CA, USA). The samples were mixed with PowerUp SYBR Green Master Mix 2x, and specific primers and nuclease-free water were added, totalizing a final volume of 20 µL. The reactions were incubated at 95 °C for 20 s and passed through 40 thermal cycles at 95 °C for 3 s and 60 °C for 30 s. All primers were designed using Primer-BLAST [36], purchased from Exxtend Biotecnologia Ltd.a. ( Paulínia, São Paulo, Brazil). The primer sequences are shown in Table S1 (Supplementary Materials).
The reactions were submitted to the same conditions, and all experiments were repeated 3 times. The data, expressed in CT value, referred to the number of PCR cycles required for the fluorescent signal to reach the detection threshold. The differentially expressed genes were normalized by the expression of the housekeeping gene 18S subunit of the ribosomal RNA, the expression of which was unaltered under the experimental conditions. The QuantStudio™ Design & Analysis software version 1.3.1 (Applied Biosystems, Carlsbad, CA, USA) was used for data processing. The ΔCT values were determined by subtracting the mean CT value of the target gene mRNA from the mean CT value of the 18S rRNA housekeeping gene. The 2−ΔΔCT parameter was used to represent the relative expression data.
## 2.5. ROS Measurement
The hydrogen peroxide content in hippocampal and myocardial homogenates was measured using a colorimetric assay according to the manufacturer’s instructions (Abcam, Cambridge, UK, ab102500). Briefly, the tissues ($$n = 4$$ per group) were homogenized using the assay buffer, and dilutions of the sample homogenates were incubated with a reaction mix containing the peroxidase fluorogenic substrate OxiRed probe in a 1:1 ratio. The absorbance was measured at 570 nm.
## 2.6. Statistical Analysis
*The* gene expression results were coded blindly per group, and the statistician remained blind to the coding allocation until the analysis was completed. Statistical calculations were performed using the IBM Corp. application. IBM SPSS Statistics for Windows, Version 25.0 (Armonk, NY, USA: IBM Corp., 2017). To verify normality and error variances, the Shapiro–Wilk test was used. First, Student’s t-test was performed for comparison between groups, followed by the Mann–Whitney test, with a significance of ≤0.05. Then, Student’s t-test was performed to compare gene expression and, when necessary, supplemented with the Welch correction test. A p-value ≤ 0.05 was considered significant, and results were expressed as mean ± standard error of the mean (SEM).
## 3. Results
Hippocampal and myocardial samples from control and PAE groups were collected from 45-day-old animals of both sexes (PN45). Then, the tissues were processed and submitted to mRNA quantification of eight genes coding for components of the RAS and the KKS (AT1 and AT2 receptors, renin, ACE, ACE2, kinins B1 and B2 receptors, and KLK genes). The myocardial expression data of RAS genes in the Control and PAE animals of both sexes are shown in Table 1.
Data referring to hippocampal gene expression of RAS components in animals from experimental groups of both sexes are shown in Table 2.
Our data suggested significant increases in AT1 expression in the PAE males and females in the hippocampus (HC) and myocardium (MC) compared to the respective controls; however, AT1 mRNA expression was sharply increased (2.9-fold in the HC and a 1.9-fold in the MC, p ≤ 0.001) in males with PAE compared to the control. PAE females presented increases of 1.2-fold and 1.7-fold in AT1 mRNA expression in the MC and HC, respectively. Comparing AT1 mRNA expression between animals with PAE, males presented a 2.1-fold increase in MC, and an even higher difference was observed in the HC (2.9-fold) compared to females. On the contrary, angiotensin AT2 receptor gene expression was increased in the PAE females than in the control (2.1-fold in the HC and 1.6-fold in MC). An increased AT2 expression was also seen in PAE males compared to the control (1.3-fold in both tissues), although expression levels were lower than in the PAE female group. A comparison between PAE groups revealed a 2-fold increase in AT2 mRNA expression in PAE females than in males. Furthermore, the basal expressions of AT1 mRNA in the hippocampus and myocardium were lower in control females than in males (MC: 3.48 ± 0.39 vs. 4.72 ± 0.51; HC: 1.92 ± 0.53 vs. 3.43 ± 0.41; p ≤ 0.05). In contrast, both tissues presented higher AT2 mRNA expression in control females than in males (MC: 1.57 ± 0.31 vs. 0.92 ± 0.21; HC: 1.01 ± 0.18 vs. 1.34 ± 031; p ≤ 0.05).
Messenger RNA expression of the renin gene in the control groups in both tissues was unaltered (MC: 3.43 ± 0.28 in males and 2.96 ± 0.21 in females; HC: 2.71 ± 0.72 in males and 2.19 ± 0.83 in females). However, PAE females had lower REN mRNA expression than PAE males in the myocardium (3.53 ± 0.30 vs. 5.82 ± 0.86), with no changes observed in the hippocampus (2.94 ± 0.68 in PAE males and 2.63 ± 033 in PAE females). PAE increased the male and female ACE mRNA expressions; however, this augmentation was higher in PAE males than in females in the hippocampus (1.3-fold) and myocardium (1.8-fold). Regarding the ACE2 mRNA expression, PAE induced an mRNA increment, regardless of sex, compared to their respective controls. However, in both tissues, PAE females presented higher ACE2 expression than PAE males (HC: 4.87 ± 0.23 vs. 2.93 ± 0.57; MC: 4.19 ± 0.46 vs. 3.28 ± 0.34, p ≤ 0.05). Females from the control group presented increased ECA2 mRNA levels compared to control males (1.1-fold in the HC and 1.9-fold in the MC). Control ECA mRNA presented similar expressions in both tissues (MC: 3.79 ± 0.81 in males and 3.99 ± 0.54 in females; HC: 4.12 ± 0.84 in males and 3.64 ± 0.27 in females).
Interestingly, an increased ACE2/ACE mRNA ratio in the hippocampus was observed only in PAE females (1.13 ± 0.04) compared to the control group (0.92 ± 0.02, p ≤ 0.05). ( Figure 1A). PAE males showed a reduction in ACE2/ACE mRNA ratio (0.51 ± 0.03) in the HC compared to control (0.73 ± 0.04, p ≤ 0.001) and PAE females (Figure 1A). The ACE2/ACE mRNA ratio increased in the PAE myocardium (0.42 ± 0.03 to males and 0.98 ± 0.02 to females) compared to the control groups (0.34 ± 0.03, p ≤ 0.05 and 0.62 ± 0.03, p ≤ 0.001, respectively, Figure 1B).
The mRNA expression of B1 receptor expression was strongly induced by alcohol exposure in hippocampal samples from PAE male mice (3.67 ± 0.43) compared to the PAE female group (1.31 ± 0.11, p ≤ 0.001, Table 3). Interestingly, in PN45, myocardial B1 mRNA expression remained low in all experimental groups (Table 4). There was a significant increase in B2 receptor mRNA expression in PAE females compared to PAE males in both tissues (MC: 5.39 ± 0.64 vs. 2.32 ± 0.34, p ≤ 0.001 and HC: 4.29 ± 0.35 vs. 3.31 ± 0.17; p ≤ 0.05). The myocardial and hippocampal tissue KLK mRNA expressions were increased by PAE, regardless of sex, compared to the respective controls (to males: 1.1- and 1.6-fold, respectively; to females: 1.2- and 1.4-fold, respectively; p ≤ 0.05, Table 3 and Table 4).
PAE increased the amounts of hydrogen peroxide in both tissues. Heightened content of H2O2 in PAE males compared to the control (3.2-fold, p ≤ 0.001) was observed in the hippocampus (Figure 2A). In the myocardium, H2O2 augmentation in the PAE male group was less substantial compared to the control (1.9-fold, p ≤ 0.001, Figure 2B). PAE females showed an increase of 1.4-fold in H2O2 content compared to control females in both tissues (p ≤ 0.05). Therefore, males presented a significant amount of H2O2 (2-fold, p ≤ 0.001) among PAE animals compared to females in MC and HC (Figure 2A,B).
## 4. Discussion
Sex-specific differences in mRNA expression of components of the RAS and KKS in the hippocampus and myocardium of PAE animals were observed in this study. To the best of our knowledge, this was the first study showing PAE’s modulation of these peptidergic systems in brain and heart tissues. Our data suggest that females submitted to PAE may be less susceptible to alcohol’s deleterious effects. In both tissues, more pronounced expressions of injury-related genes, such as AT1 and ACE mRNA, were found in PAE males. Oppositely, augmented protective AT2 and B2 mRNA expressions were observed in PAE females. Interestingly, both tissues also presented distinct differences in mRNA expressions of RAS components of control males and females. Furthermore, oxidative stress measurements revealed, in both tissues, that males submitted to PAE presented higher H2O2 generation than females.
Although the information on the role of these systems and PAE is scarce, several studies have reported the influence of the RAS on alcohol intake. In 1993, Fitts observed that low-dose peripheral administration of the ACE inhibitor captopril increased the intake of ethanol solution [37]. Previously, [33] collected evidence that alcohol intake is inversely related to RAS activity. Recently, [38] associated PAE and RAS expression using a different protocol. The authors observed an increase in the serum level of Ang II and the gene expression of the renal enzyme ACE in PAE animals. In the same study, AT2 receptor expression was significantly inhibited in the kidneys [38].
Significant upregulation of the AT1 mRNA in PAE male mice compared to females was found in our study. Analyzing rat hippocampus after the induction of epilepsy by pilocarpine, [39] observed an increase in AT1 mRNA expression in the chronic phase of the model, where spontaneous and recurrent epileptic seizures are noticed due to the previous formation of hippocampal sclerosis. According to the authors, AT1 mRNA expression was induced by the injury and consequent neuroinflammation after the pilocarpine insult. Our data suggest that increased hippocampal AT1 and ACE mRNA expressions may reflect increased Ang II availability and activation, worsening the PAE effects, mainly in males.
More recently, [40] reported that a $5\%$ alcoholic diet PAE increased Ang II myocardium concentration and higher apoptotic index in the offspring. The more pronounced gene expression of the AT1 after PAE is seen in both males’ tissues, which is in consonance with data that noticed AT1 mRNA augmentation after tissue insults, such as myocardial infarction and cardiac hypertrophy [41,42]. Additionally, [39] reported that AT2 mRNA expression increased after pilocarpine-induced status epilepticus to protect from damage, contributing to hippocampal plasticity and reorganization of the neuronal network. In our protocol, using a $10\%$ alcohol solution, females submitted to PAE presented decreased AT1 mRNA expression (compared to PAE males) and a restored AT2 mRNA expression (compared to the control). These data indicated that alcohol exposure during gestation might be less harmful to females than males.
In male rats with PAE, no differences in ACE mRNA expression were detected in the kidney, but the authors found a diminished ACE2 mRNA expression in males compared to control [38]. Our experiments revealed an increased hippocampal expression of ACE2 mRNA and a higher ratio of ACE2 over ACE mRNA in PAE females than in males. We suggest that this sex difference may be associated with protective properties of ACE2, such as reduced apoptosis [43,44]. Myocardial expression of ACE and ACE2 mRNAs showed different expression profiles between sexes after PAE. In PN45, while an increase in ventricular ACE mRNA was observed only in PAE males, ACE2 transcripts showed higher expression levels in PAE females. A study from [44] reported that, in the myocardium, inflammation caused an increase in ACE mRNA expression and decreased ACE2 mRNA levels in male rats. Thus, in our study, we speculated that PAE could induce a long-lasting inflammatory state that increases injury-related mRNA peptides, especially in males. Therefore, females could be more prone to protection against PAE than males. Moreover, in vitro, alcohol increases renin mRNA expression in a concentration-dependent manner in cardiac fibroblasts [45]. The authors observed an increased renin expression in rat hearts induced by alcohol consumption. In our protocol, PAE induced an increased myocardial renin mRNA expression in males compared with females.
The observed sex-related differences in the pathophysiology of cardiovascular disease may be driven by androgens, such as testosterone, through the Ang II-ACE-AT1 axis stimulation to induce vasoconstriction, vascular dysfunction, and cardiac dysfunction hypertrophy and fibrosis [46,47]. Some authors observed that estrogen treatment reduced ACE mRNA expression and activity in oophorectomized rats’ kidneys, aorta, and lungs [48]. Our data suggest that PAE induced heightened expression of the ACE-AT1 axis, possibly increasing Ang II levels in the hippocampus and myocardium. The increased ECA2 mRNA expression in both tissues suggests a protective response through alcohol toxicity. Still, damage-related mRNA expressions induced by PAE were less pronounced in females than in males, which can be partially mediated by sex hormones [49]. Previous studies suggested that Ang II levels are decreased by the suppression of renin and ACE activity by estrogen, thus reducing the activation of the Ang II–ACE–AT1 pathway [50,51].
Interestingly, under physiological conditions, we observed differences in gene expression of RAS components between males and females from the control groups. Other authors had already observed this sexual dimorphism [52,53], showing that male rats have higher AT1 receptor expression in the myocardium, while females tend to have higher AT2 receptor expression. Indeed, in the hippocampus and the LV of control groups, we observed that mRNA expression for the angiotensin AT1 receptor was lower in females than in males. Sampson et al. [ 54] demonstrated that males had a trend of an increase in myocardial AT1 mRNA expression during early adulthood. Wang et al. [ 55] showed an increased renal expression of the AT1 receptor mRNA in male mice compared to female control. These authors also found, in the kidneys, higher expression of AT2 in female control animals than in males. Furthermore, [56] showed that rat females presented increased AT2 receptor mRNA expression in neurons and that this receptor is required for neurogenesis. Altogether, our data suggested that angiotensin receptors presented distinguished sex-related expression in the hippocampus and LV.
In the hippocampus, we did not detect differences in ACE mRNA expression in control males and females. However, some authors have reported sexual dimorphism in ACE gene expression in humans. For example, in children, ACE activity is higher in boys and lower in girls after puberty [57]. In healthy young adults, ACE activity is higher in men [58]. Gembardt et al. [ 59] reported that the distribution of ACE2 is smaller than that of ACE, expressed in the heart, kidneys, lung, brain, intestine, testes, spleen, and adipose tissue in rodents. As the ECA2 (and AT2) genes are located on chromosome X, it is reasonable that there is a sex difference in their expression [60]. The differential expression of ACE2 between sexes was observed by [61]. Low-fat fed C57BL/6 male mice had higher ACE2 mRNA expression in kidney and adipose tissue compared to female mice [61]. Our data, analyzing the same strain of mice, showed increased expression of ACE2 mRNA in the hippocampus of females in the control group compared to males. Moreover, in the LV, females presented increased ACE2 mRNA expression than males, supporting the work of [61].
Human renin mRNA expression has been detected in many organs, such as kidneys, muscles, heart, and brain [62]. Adult mice presented REN mRNA expression in the cortex, thalamus, and hippocampus [63]. Renin levels were lower in women than in men [64]. Many studies suggest that estrogen reduces renin secretion from renal juxtaglomerular cells, decreasing plasma renin concentration in humans, while testosterone increases serum renin concentration [65]. Hypertensive animals exhibited sex differences in renin expression [66]. The authors observed that male rats had higher serum renin concentrations than females. Another study analyzing the submandibular gland found that renin levels increased after puberty to become higher in males than in females, and castration reduced renin levels [67]. Other authors have suggested that androgens decrease plasma renin concentrations through estrogenic effects and increase plasma [50]. Accordingly, our data suggest lower expressions of REN mRNA in the female mice hippocampus, regardless of PAE.
Unlike the RAS, we did not find differences in hippocampal kinin receptor mRNA expression between male and female control animals. However, the increased myocardial expression of the B2 receptor seen in the female control mice corroborates a study by [68], showing an increase in B2 receptor mRNA expression in the left ventricle of female Wistar rats compared to males. Moreover, our data revealed that PAE increases myocardial B2 receptor mRNA expression in PAE female mice compared to males. Tschöpe et al. [ 69] found that the cardiac B2 mRNA levels increased time-dependent after myocardial infarction. The authors proposed that B2 augmentation is related to cardioprotection exerted by kinin generation and release. We observed that PAE animals presented increased B1 and B2 mRNA expressions in the hippocampus. Studying the hippocampus of epileptic patients, [70] observed increased human B2 receptor expression in the tissue. Using a pilocarpine-induced epilepsy model, [71] observed an increase in the mRNA expression of kinin receptors in epileptic animals, concluding that the expression of B1 was related to the insult and the expression of B2 was increased to counterbalance the deleterious effects to neurons and protect against tissue damage.
Kinin B1 receptor expression is induced under pathological conditions [72]. Marceau et al. [ 73] reported that the B1 receptor expression is increased after cell damage and inflammation. After myocardial infarction, high levels of B1 receptor expression were observed in the acute phase in male rats [74]. Interestingly, myocardial B1 receptor mRNA expression was unaltered in PAE animals. This observation may be due to the induction of B1 receptor expression, and the time of euthanasia applied in our protocol. Tschope and colleagues [74] reported that B1 receptor mRNA decreases gradually after myocardial infarction. In addition, [75] observed temporal differences in kinin receptors mRNA expression in post-infarction phases in Wistar rats of both sexes. A recent study [76] reported the causal role of the B1 receptor in neuroinflammation and oxidative stress in primary hypothalamic neurons. Although we verified an increase in the expression of B1 receptor mRNA in the hippocampus of PAE animals of both sexes, in males, this increase was substantially higher than in females. Some authors [77] reported the mRNA expression of tissue kallikrein in developing rat brains, with a peak expression on the first postnatal day. KLK mRNA expression was maintained high until postnatal day 10 and gradually decreased. Kallikrein infusion in the myocardium attenuated inflammation and reduced oxidative stress in rats after MI [78]. The authors observed that kallikrein and B2 receptors activation induced the suppression of oxidative stress after MI. Previous work from Iwadate et al. [ 79] found that KLK protein content in rat brains continuously decreased after birth until PN49. According to our findings, PAE induced an increase in hippocampal KLK mRNA expression in animals of both sexes exposed to alcohol during pregnancy at PN45. A study with the human hippocampus observed that neuroinflammation increased KLK mRNA and protein expressions in refractory temporal lobe epilepsy [80]. Other authors have observed differences in kallikrein expression between sexes in other organs. In the kidneys, KLK mRNA expression was higher in female rats than in males. However, no difference was found in the heart among the control groups [68]. This observation corroborates the data obtained in our study, which unaltered myocardial KLK mRNA expression differences between sexes from control groups. Nevertheless, PAE increased KLK mRNA expression only in the myocardium of PAE female mice. Silva-Jr et al. [ 81] reported that rats bearing human tissue kallikrein transgene showed marked cardioprotection to cardiac hypertrophy and fibrosis. In agreement with previous works [81,82], our data suggest that KKS activation by incremented B2 and KLK expression may result in cardioprotection toward damage induced by PAE.
Smith et al. [ 83], quantifying malondialdehyde (MDA) levels in the rat cerebellum, hippocampus, and cortex, observed high concentrations in the cerebellum of animals that received alcohol. In the hippocampus and cortex, however, MDA levels were unaffected by alcohol treatment [84]. Increased oxidative stress induced by PAE in developing organs has been reported by several studies [22,85,86]. Increased NADPH oxidase mRNA was detected in the cerebellum of PAE rats [84]. Moreover, lower expressions of superoxide dismutase, glutathione peroxidase, and catalase were detected in fetal brains exposed to alcohol [86]. The increased amount of hydrogen peroxide in the tissues of PAE animals may reflect the high cellular alcohol-induced ROS in these animals. O2 and H2O2 concentrations are regulated by the cellular antioxidant system and physiologically can activate signal transduction pathways [85]. However, if a large ROS production exceeds the antioxidant protection ability, the generation of oxidative stress induces several intracellular damages [87]. In our protocol, the $10\%$ alcoholic solution was administered through the whole gestation and weaning, allowing the teratogenic effect of alcohol to be present during embryonic development. As a result, PAE groups presented a heightened myocardial H2O2 concentration, although higher levels were observed in males than in females. A review study [88] reported several antioxidant interventions against FASD targeting oxidative stress in animal models and speculated that more clinical trials are needed to evaluate their efficacy in humans with PAE.
The fetus’s endogenous antioxidant system is less active than in adult mice and consequently more vulnerable to alcohol toxicity [89]. Given the increased susceptibility of the brain to alcohol effects than other organs [90], a marked increase in hippocampal H2O2 concentration was found in males compared to females of PAE groups. A previous study by [91] showed that females generate lower levels of H2O2 in cardiac and brain tissues; however, we did not detect differences in H2O2 content in control animals in both tissues. These findings about control animals also contradict [92], which reported that male rats produce more ROS than age-matched females. These discrepancies may have two reasons; the limited sample size utilized in our study, or the different methodology applied in the cited studies.
Three significant limitations should be considered. First, although we performed mRNA quantifications, this study did not address protein translation. Measurements of G protein-linked transmembrane receptors are a challenge in the literature, primarily due to nonspecific commercial antibodies available [93]. Secondly, this was a cross-sectional study. A temporal gene expression of these peptidergic systems may facilitate understanding the findings achieved. Our lab is running experiments to address this topic, and we hope to show these data soon. A previous study [22] analyzed the activation of several signal transduction pathways in the myocardium at a different time point than in this study. Another limitation was the measurement of oxidative stress by determining the H2O2 content. Therefore, other techniques will be applied to increase the knowledge of the involvement of oxidative stress in PAE.
In summary, PAE altered the mRNA expression of several genes of the RAS and KKS in the hippocampus and myocardium. As a proposal pathway, to counteract the effects of AT1/ACE/B1 expression, tissue protection against PAE may occur partly by expressing protective biomarkers such as AT2, B2, KLK, and ACE2 mRNA, especially in PAE females. In addition, diminished cellular ROS levels found in PAE females suggested a reduced stressor effect of alcohol in the hippocampus and LV of PAE females.
## 5. Conclusions
PAE modulated hippocampal and myocardial expressions of genes in the renin–angiotensin and kallikrein–kinin peptidergic systems. Sex-specific differences in mRNA expression of these peptides in both tissues were found with or without gestational alcohol exposure. A protective modulation of these systems in PAE females was mightily indicated, along with lower levels of ROS found in the hippocampus and the myocardium. ACE inhibitors, whose efficacy in tissue protection has been evidenced by several authors [94,95,96], could be considered to decrease the myocardial and hippocampal damage effects of PAE.
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|
---
title: Aging-Accelerated Mouse Prone 8 (SAMP8) Mice Experiment and Network Pharmacological
Analysis of Aged Liupao Tea Aqueous Extract in Delaying the Decline Changes of the
Body
authors:
- Wenjing Pan
- Wangshu Li
- Huan Wu
- Xinya Xie
- Mingwei Xie
- Qing Nie
- Zhonghua Liu
- Shuxian Cai
journal: Antioxidants
year: 2023
pmcid: PMC10045736
doi: 10.3390/antiox12030685
license: CC BY 4.0
---
# Aging-Accelerated Mouse Prone 8 (SAMP8) Mice Experiment and Network Pharmacological Analysis of Aged Liupao Tea Aqueous Extract in Delaying the Decline Changes of the Body
## Abstract
Aging and metabolic disorders feedback and promote each other and are closely related to the occurrence and development of cardiovascular disease, type 2 diabetes, neurodegeneration and other degenerative diseases. Liupao tea is a geographical indication product of Chinese dark tea, with a “red, concentrated, aged and mellow” flavor quality. In this study, the aqueous extract of aged *Liupao tea* (ALPT) administered by continuous gavage significantly inhibited the increase of visceral fat and damage to the intestinal–liver–microbial axis in high-fat modeling of SAMP8 (P8+HFD) mice. Its potential mechanism is that ALPT significantly inhibited the inflammation and aggregation formation pathway caused by P8+HFD, increased the abundance of short-chain fatty acid producing bacteria Alistipes, Alloprevotella and Bacteroides, and had a calorie restriction effect. The results of the whole target metabolome network pharmacological analysis showed that there were 139 potential active components in the ALPT aqueous extract, and the core targets of their actions were SRC, TP53, AKT1, MAPK3, VEGFA, EP300, EGFR, HSP90AA1, CASP3, etc. These target genes were mainly enriched in cancer, neurodegenerative diseases, glucose and lipid metabolism and other pathways of degenerative changes. Molecular docking further verified the reliability of network pharmacology. The above results indicate that *Liupao tea* can effectively delay the body’s degenerative changes through various mechanisms and multi-target effects. This study revealed that dark tea such as *Liupao tea* has significant drinking value in a modern and aging society.
## 1. Introduction
Aging and metabolic disorder-mediated degenerative changes promote the occurrence and development of chronic noninfectious diseases such as diabetes, cancer, Alzheimer’s disease, cardiovascular disease, etc. [ 1,2,3]. The aging of the body is often accompanied by an increase in oxidative stress and persistent chronic inflammation [4,5,6,7]. Oxidative stress contributes to the increase of glucose uptake and lipid synthesis and plays an essential role in the accumulation of cholesterol in the liver [8]. The accumulation of visceral fat secretes free fatty acids (FFA), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α) and angiotensinogen into the venous circulation [9], promoting the formation of heterotopic lipid deposition and degenerative diseases [10,11].
Liupao tea (*Camellia sinensis* (L.) O.kuntze) is one of the representative products of Chinese dark tea. It is initially produced in Liupao Town, Cangwu County, Wuzhou and Guangxi, and has the quality characteristics of red, concentrated, aged and mellow [12]. The standardized production process of *Liupao tea* includes 3 times piling up and color changing: wet piling up, dry piling up and aging, which is unique among dark tea processes [13]. The contents of phenolic acid, theabrownins, tea polysaccharides, cellulose and organic acids in *Liupao tea* increase with the aging and piling up process [14,15,16,17].
Liupao tea has a strong dispelling dampness effect. Modern pharmacology has proved that *Liupao tea* has antioxidant functions, regulating glycolipid metabolism, protecting the liver, enhancing immune function, promoting digestion, anti-coagulation, gastric emptying and intestine peristalsis [18,19,20,21]. Liupao tea reduces the release of free fatty acids, improves glucose uptake, inhibits insulin resistance and significantly protects cells [22]. Liupao tea shows a more robust iron-reducing antioxidant capacity than Fuzhuan *Brick tea* and Pu’er tea [23]. Liupao tea polyphenols can significantly improve the body’s antioxidant effects, inhibit the production of inflammatory factors such as IL-6, IL-12, TNF-α and IFN-γ and have an inhibitory effect on gastrointestinal damage, and its activity is similar to that of ranitidine [24,25]. Liupao tea increases the diversity of intestinal flora, the proportion of Bacteroidota/Firmicutes and the abundance of short-chain fatty acid (SCFAs)-producing species of Prevotella and Bacteroidetes, inhibits colonic inflammation and has protection against the hyperglycemia model mice effect [26,27,28].
Early age-related changes, such as hair loss and short life span, characterize senescence-accelerated mouse prone 8 (SAMP8) mice [29]. Rhea and Banks confirmed that SAMP8 mice can be used as animal models for studies of age-related emotion and memory dysfunction [30]. In recent years, network pharmacology has often been used to explore medicinal plants’ therapeutic effects and targets [31]. In this study, referring to the experimental study of Onishi et al., a model of lipid metabolism disorder in SAMP8 mice was established to study the in vivo experimental study of ALPT in delaying degenerative changes [32]. In addition, we used the widely targeted metabonomic data of ALPT water extract to conduct network pharmacological analysis with “degenerative changes” as the disease keyword.
## 2.1. Ethical Statement
All animals were handled following the guidelines of national animal care legislation. The Animal Protection and Utilization Committee of Peking University approved experimental procedures and protocols (SYXK(JING)2021-0064). All surgery was performed under sodium pentobarbital anesthesia, and efforts were made to minimize suffering.
## 2.2. Materials and Reagents
Seven-Year Aged Liupao Tea (ALPT) was presented by Guangxi Wuzhou Tea Factory Ltd. (Wuzhou, China). Metformin (Met) was ordered from Shanghai Biyuntian Biotechnology (Shanghai, China). Lipopolysaccharide (LPS) and indomethacin (Idm) were ordered from Sigma-Aldrich (St. Louis, MO, USA). Physiological saline and $75\%$ ethanol were purchased from China Pharmaceutical Group Ltd. (Changsha, China). Paraformaldehyde and fat-specific fixative were purchased from Wuhan Seville Biotechnology Co, TC, TG, total antioxidant capacity (T-AOC), SOD and MDA kits were from Nanjing Jiancheng Institute of Biological Engineering (Nanjing, China). IL-6, ZO-1 and LPS ELISA kits were purchased from Jiang Sufia Biotechnology Co (Yancheng, China). In addition to anti-GAPDH, anti-NF-κB, anti-Histone H3, anti-AMPK, anti-p-AMPK, anti-mTOR, anti-p-mTOR, anti-Cyclin B1, anti-Cyclin D1 and anti-Sirt1 were also purchased (Cell Signaling, Boston, MA, USA). The following primary antibodies were also used for protein blotting analysis: anti-multiubiquitin (Medical & Biological Laboratories Co., Ltd, Tokyo, Japan) and anti-RAGE (Santa Cruz Biotechnology, San Cruise, CA, USA). Immobilon Western Chemiluminescent HRP Substrate was purchased from Millipore, Middlesex County, MA, USA, and all other reagents used were analytically pure or chromatographically pure.
## 2.3. Preparation of Tea Water Extract
In this experiment, the tea brewing and the direct freeze-drying methods were used with high fidelity to preserving the tea composition and particle structure in the tea soup. 100 g of tea leaves was accurately weighed into a conical flask filled with 1 L of 100 °C water, and then the conical flask was put into a 100 °C water bath for 45 min, filtered, and 1 L of 100 °C water was added again. The two tea soups were mixed, and after being cooled to room temperature, they were pre-frozen at −20 °C for 24 h and then freeze-dried in a vacuum freeze dryer (Alpha 1-4/LSC Plus, Christ, Osterode, Germany) at −42 °C for 24 h to obtain the tea extract, which was stored in bags at −20 °C.
## 2.4. Mice and Experimental Design
The 8-week-old male senescence-accelerated mouse prone 8 (SAMP8, P8) mice were purchased from Beijing Viton Lever Laboratory Animal Technology Co. After 1 week of adaptive feeding in SPF clean-grade animal rooms, P8 mice were randomly divided into 4 groups, namely the control group (P8+ND), high-fat modeling group (P8+HFD), metformin group (P8+HFD/Met) and aged *Liupao tea* group (P8+HFD/ALPT). P8+ND group mice were fed a basal diet, and the other groups were fed a $60\%$ high-fat diet. After 4 weeks of high-fat modeling, the drug-treated groups were gavaged 200 mg/kg/d of Met and ALPT, respectively. The P8+ND and P8+HFD groups were given the same dose of drinking water, respectively, and the P8+HFD and drug-treated groups continued to eat the high-fat diet. After continuing treatment for 9 weeks, mice serum and tissues such as liver, epididymal fat, skin, small intestine and colon were taken to continue subsequent related experiments (Figure 1).
## 2.5. Paraffin Section Staining and Analysis
Mice tissues, such as liver, fat, skin and intestine, were immersed in fixative for 48 h, embedded in paraffin and prepared into 5 μm-thick sections. After hematoxylin-eosin (HE) staining, the sections were mounted with neutral gum. Finally, HE slices were photographed using an upright fluorescence microscope (Carl Zeiss/Axio Scope. A1) and morphological analysis and statistics were performed using Image J v1.8.0 software [33].
## 2.6. Biochemical Index Determination
We strictly followed the kit’s instructions, accurately weighed the liver tissue, added 9 times the volume of homogenization medium and mechanically ground it in a tissue grinder under ice-water bath conditions. After centrifugation, we took the supernatant and determined the content of TG, TC, T-AOC, SOD and MDA levels in liver tissue. The TC and TG levels in serum were detected strictly according to instructions [34].
## 2.7. FTIR Detection and Analysis
Liver tissue samples of different treatment groups were prepared by freeze-drying at −40 °C for 24 h using a vacuum freeze dryer (Christ, Germany). Two-hundred milligrams of potassium bromide and 2 mg of liver tissue samples were ground into tablets in an agate bowl to prepare infrared detection samples. A Fourier transform infrared spectrometer (FTIR) (PE-Spectrum 65, UK) was used to scan with 4000–400 cm−1 wavenumber range, 4 cm−1 resolution and accumulating 64 scans [35].
## 2.8. Western Blotting
Mice liver tissues were taken into RIPA lysis buffer containing a protease inhibitor, homogenized by an electric motor in an ice bath and centrifuged (4 °C, 12,000 rpm, 20 min) to collect the supernatant. A BCA protein quantification kit was used to determine the protein concentration of the sample to be tested. The loading buffer was added at a volume ratio of 1:4, and after boiling, the samples were subjected to SDS-PAGE electrophoresis by the equal mass loading method and then electrotransferred to PVDF membranes. PVDF membranes were blocked with TBST (Tris Buffered Saline with Tween) containing $5\%$ skimmed milk for 1 h at room temperature and then incubated with primary antibody overnight at 4 °C. The membranes were washed 5~6 times with TBST for 5 min each time, incubated with horseradish peroxidase (HRP)-labeled secondary antibody for 90 min and then washed with TBST, followed by 1–2 min reaction with highly sensitive chemiluminescent substrate and exposed for 10 s to 2 min and finally, the protein bands were scanned in grayscale using Image J.
## 2.9. ELISA Assay
We strictly followed the ELISA kit’s instructions, accurately weighed the liver tissue and colon tissue, added 9 times the volume of RIPA lysate and mechanically ground the product in a tissue grinder under ice-water bath conditions. After centrifugation (4 °C, 12,000 rpm, 20 min), the supernatant was taken and LPS and IL-6 in liver tissue and ZO-1 in colon tissue were determined.
## 2.10. Detection and Analysis of Intestinal Flora 16S rRNA
The collected fresh fecal samples were immediately pre-frozen in liquid nitrogen for 2 min, frozen on dry ice and sent to Servicebio (Wuhan, China, https://www.servicebio.cn/) for 16S detection and analysis. According to the detection data provided by the company, the OUT was obtained by clustering the reads at a similarity level of $97\%$ using Usearch software. Taxonomic annotation of the sequences using SILVA as a reference database gave the species classification information corresponding to each feature. Then, the community composition of each sample could be counted at each level (phylum, class, order, family, genus, species) and the abundance tables of species at different taxonomic levels were generated using QIIME software. QIIME and PICRUSt2 software analyzed the sample’s beta diversity and functional gene composition.
## 2.11. Widely Targeted Metabolomics Analysis of ALPT Aqueous Extract
The ALPT extract prepared in Method 2.3 was sent to Metware Biotechnology Co., Ltd. (Wuhan, China) for metabonomic analysis. We took 200 μL of the sample, added 200 μL of $70\%$ methanol internal standard extract, vortexed for 3 min and centrifuged at 12,000 r/min, 4 °C for 10 min. The supernatant was removed and filtered through a microporous membrane (0.22 μm). The sample extracts were analyzed using a UPLC-ESI-MS/MS system (UPLC, ExionLC AD, https://sciex.com.cn/; MS, Applied Biosystems 6500 Q TRAP, https://sciex.com.cn/). The effluent was alternatively connected to an ESI-triple quadrupole linear ion trap (QTRAP)-MS. According to the secondary spectrum information, qualitative and quantitative analysis of substances was carried out in the Metware Database (MWDB) using triple quadrupole multiple reaction monitoring (MRM) mass spectrometry [36,37]. Metabolites whose secondary mass spectrometry (all fragment ions of the substances) and RT (retention time) information match the substance information in the database with a score of more than 0.5 were selected for subsequent analysis.
## 2.12. Screening of Active Metabolites
The compounds detected in metabolomics were converted into the Simplified Molecular Input Line Entry System (SMILES) through PubChem (https://pubchem.ncbi.nlm.nih.gov/ accessed on 16 November 2022). The SwissADME (http://www.swissadme.ch accessed on 24 November 2022) tool was used to predict potential active ingredients [38]. Gastrointestinal (GI) absorption was examined, and drug-likeness (DL) analysis was conducted. The parameters of GI meet “high” and at least meet the metabolites of two drug-likeness. It was selected as an active compound with good bioavailability.
## 2.13. Prediction of Active Ingredients and Disease Targets
With Probability* > 0.1 as the screening condition, the disease target of the active ingredient was predicted by Swiss Target Prediction (swisstargetprediction.ch accessed on 29 November 2022) [39]. We could obtain inflammation-related targets by retrieving the keyword “degenerative changes” from GeneCards. GeneCards is a database that integrates all annotation and prediction genes related to human diseases [40].
## 2.14. PPI Network and KEGG Enrichment Analysis
Venny 2.1 was used to analyze the active ingredient targets and genes related to degenerative changes, and the obtained common genes were introduced into the STRING v.11.5 (https://string-db.org/ accessed on 13 December 2022) analysis platform. Under the condition that the confidence score was more significant than 0.7, a protein-protein interaction network diagram (PPI) was obtained. Then, the compound target network was constructed and visualized using Cytoscape 3.9.1 and the topological importance, intermediate centrality and tight centrality of nodes in the network were analyzed. Finally, the Metascape (http://metascape.org accessed on 13 December 2022) analysis platform was used for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of common genes [41,42].
## 2.15. Molecular Docking
Autodock software 4.2 was used to perform protein compound docking analysis [43]. Firstly, three-dimensional (3D) molecular structures of target proteins and ligand compounds were downloaded from the Protein Data Bank database (http://www.rcsb.org/ accessed on 24 December 2022) and PubChem (https://pubchem.ncbi.nlm.nih.gov/ 24 December 2022) database. Next, PyMOL and AutoDock Tools 1.5.7 were used for protein and ligand pre-processing, including removal of original water ligands, hydrogenation, calculation of charge numbers, atomic AD4 type assignment and detection of torsional bonds. Finally, the grid box was set to the entire region and the Genetic Algorithm was selected for docking in AutoDock Tools 1.5.7. Finally, the docking results with the lowest binding energy were visualized using PyMOL software.
## 2.16. Data Processing and Analysis
Data analysis was performed using GraphPad Prism version 8.0. One-way ANOVA and Tukey’s multiple comparison tests were used to analyze the significance of differences. The results were expressed as mean ± standard deviation and the difference was judged to be significant according to $p \leq 0.05$ and $p \leq 0.01.$
## 3.1.1. ALPT Maintains Metabolic Homeostasis
Body weight measurement data showed that mice in the P8+HFD and P8+HFD/ALPT groups had higher body weight after 14 weeks of feeding (Figure 2a). The kit assay results of cholesterol (TC) and triglyceride (TG) showed that the HFD diet significantly increased the levels of serum TC and TG in P8 mice, while ALPT had an inhibitory effect (Figure 2b,c). HE staining showed that in the P8+HFD group, the epididymal adipocytes were hypertrophied ($p \leq 0.05$) (Figure 2d–f), the skin structures were disordered, the number of subcutaneous adipocytes and the thickness of the fat layer were significantly increased and the boundaries between subcutaneous muscle and adipocytes were blurred (Figure 2d,g,h). Met and ALPT significantly reduced fat accumulation ($p \leq 0.05$ or $p \leq 0.01$). In addition, the hepatic sinusoids of P8+HFD group mice were unclear, the cells were arranged disorderly, the nuclei were deeply stained and many fat droplets were accumulated around the manifolds and the contents of TC and TG were significantly increased. In the P8+HFD/ALPT group, the hepatocytes were clear, arranged radially, without inflammatory cell infiltration (Figure 2d), lipid droplets decreased and TC and TG contents decreased (Figure 2i,j).
## 3.1.2. ALPT Delays Liver Aging
The results of oxidative stress-related indexes showed that the content of MDA, TC and TG in the P8+HFD group increased significantly, while the contents of T-AOC and SOD decreased; both Met and ALPT inhibited the increase of P8+HFD-induced oxidative stress (Figure 3a–c).
The test results of inflammation-related indicators showed that compared with the P8+ND group, the contents of LPS and IL6 in the livers of P8+HFD group mice were significantly increased ($p \leq 0.01$ or $p \leq 0.05$) and the nuclear transfer of NF-κB increased about 6 times ($p \leq 0.01$). ALPT significantly inhibited P8+HFD-induced inflammation, and its activity was superior to Met (Figure 3d–g).
The results of protein expression detection related to aging, cycle and metabolism showed that, compared with P8+ND group, the expression of Cyclin D1 and Cyclin B1 in the P8+HFD group was upregulated ($p \leq 0.01$) and the expression of Sirt1 was downregulated ($p \leq 0.01$); the phosphorylation level of AMPK decreased ($p \leq 0.01$) and the phosphorylation level of mTOR increased about 2.8-fold ($p \leq 0.01$). ALPT significantly inhibited P8+HFD-induced signaling pathway changes (Figure 3h,i). The above results indicate that ALPT can inhibit liver aging and metabolic disorders in the P8+HFD group.
With the growth of age, proteins, lipids, nucleic acids and other biological molecules react with ROS, resulting in many carbonylation-modified biological molecules stored in the body, which can be used as one of the biomarkers of body aging [44,45,46]. The absorption of amide I contains the contribution of C=O stretching vibration from the amide group (about $80\%$). The stronger the hydrogen bond involving amide C=O, the lower the electron density in the C=O group and the lower the absorption peak of amide I and amide II bands [47]. The results of Fourier infrared spectrometer (FTIR) analysis showed that, compared with the P8+ND group, the fatty acid absorption peak (3015–2800 cm−1), protein peak (1700–1500 cm−1), lipid C=O groups (1740 cm−1) and nucleic acid peak (1300–1000 cm−1) of P8+HFD group shifted to the lower wavelength and the peak intensity decreased. The amide I and amide II bands (1700–1500 cm−1) were enlarged (Figure 4a, the figure shown by green arrow). Compared with the P8+ND group, the absorption peak intensity of the P8+HFD group decreased significantly and shifted to a lower wavenumber. The IR peaks in P8+HFD/Met and P8+HFD/ALPT groups were similar to those in the P8+ND groups (Figure 4a).
Aging and metabolic disorders damage the ubiquitin–proteasome hydrolysis system, accumulate ubiquitinated modified protein aggregates and further mediate oxidative stress and inflammatory responses [48]. RAGE is a product of non-enzymatic glycosylation and protein and lipid oxidation. RAGE activation by various ligands enhances oxidative stress, activates the nuclear factor-κB (NF-κB) signaling pathway and induces inflammation [49,50]. Compared with the control group, the ubiquitin conjugated proteins (UPs) modified proteins in the liver of P8+HFD group mice increased and the expression level of RAGE was upregulated ($p \leq 0.01$), while Met and ALPT had significant inhibitory effects ($p \leq 0.01$) (Figure 4b,c). The results showed that ALPT significantly reduced the aggregates formation pathway and maintained the fidelity of biomacromolecules.
**Figure 4:** *APLT inhibits P8+HFD-induced aggregates formation pathway and biomacromolecule structure alterations. (a) Infrared spectra of the liver. (b,c) Western blotting detection of ubiquitin-conjugated proteins (Ups) and receptor for advanced glycation end products (RAGE), n = 3. In the infrared (IR) spectrum, the absorption band of 3015–2800 cm−1 mainly characterizes the C-H bond of long-chain fatty acids and the intensity of this region is closely related to the lipid content [35,51]; 3012 cm−1 characterizes the ν=C-H bond of unsaturated fatty acids [52] and 1740 cm−1 is stretching vibrations of C=O groups of lipids [53]; 1700–1500 cm−1 is the protein amide I and II bands’ absorption peaks [54]; 1300–1000 cm−1 is the absorption band of the nucleic acid structure [55]. ## represents compared with P8+ND group, p < 0.01, ** represents compared with P8+HFD group, p < 0.01.*
## 3.1.3. Role of ALPT in Protecting Intestinal Homeostasis
The intestinal HE staining results showed that compared with the P8+ND group, the intestinal villus structure of P8+HFD group mice was atrophied and damaged, the ratio of villus length to crypt depth (V/C) was significantly reduced, the intestinal epithelial cells were arranged irregularly and the nuclei were concentrated and stained deeply. In addition, P8+HFD led to a decrease in colonic goblet cells and infiltration of inflammatory cells. Met and ALPT treatment could significantly inhibit P8+HFD-induced intestinal homeostasis disorder (Figure 5a,b).
Zonula occludens 1 (ZO-1) can maintain cell polarity and play a crucial role in intestinal epithelial barrier integrity [56]. The ELISA assay showed that the ZO-1 protein content in the colon of P8+HFD group mice was lower than that of the P8+ND group, and the ZO-1 protein content in P8+HFD/ALPT group increased ($p \leq 0.05$), even higher than that of the P8+ND group (Figure 5c).
The composition of the intestinal microbiota was compared through β-diversity analysis. The principal coordinate analysis (PCA) and partial least squares discriminant analysis (PLS-DA) showed that the P8+HFD/ALPT group was far away from the P8+HFD group and close to the P8+ND group. The results showed that ALPT could inhibit P8+HFD-induced changes in intestinal flora composition (Figure 5d).
At the level of phylum classification, compared with the P8+ND group, P8+HFD group mice had a higher abundance of Firmicutes (F) and a lower abundance of Bacteroidota (B), that is, the F/B radio increased. Compared with the P8+HFD group, the F/B ratio in P8+HFD/ALPT group decreased (Figure 5e). At the level of genus classification, the abundance of Alistipes, Alloprevotella and Bacteroides producing short-chain fatty acid in the P8+HFD group decreased; the abundance of Alistipes and Bacteroides in the P8+HFD/ALPT group increased significantly, even higher than that in the P8+ND group (Figure 5f).
## 3.2. Widely Targeted Metabolome of ALPT Aqueous Extract and Its Network Pharmacological Analysis
The results of the multi-peak detection plot of metabolites in the MRM (Figure S1a,b) showed that the total ion flow curve of metabolite detection had high overlap; that is, the retention time and peak intensity were consistent, indicating that the instrument signal stability was good.
The detection results of UPLC-ESI-MS/MS extensively targeted metabolites showed that 267 metabolites were detected in ALPT aqueous extract. These components were mainly phenolic acids, flavonoids, organic acids, alkaloids, amino acids and derivatives and lipids (Figure 6a). These compounds have metabolic, anti-inflammatory, antioxidant, anti-aging and other activities related to delaying degenerative changes [57,58,59].
According to the screening conditions of “suitable oral availability” and “drug-like values”, combined with literature research, 139 active metabolites (Table S1) were selected from 267 metabolites of ALPT aqueous extracts for subsequent network pharmacological analysis.
The analysis results of Gene Card and Swiss Target Prediction databases showed that there were 483 target genes in 139 active metabolites of ALPT aqueous extract. A total of 4727 target genes were screened with “degenerative changes” as the key work of the disease. Venn analysis showed 277 cross-genes between them (Figure 6b).
The results of KEGG analysis showed that the target genes related to the degenerative changes in the action of the active components of ALPT water extract were mainly enriched in cancer pathways, lipid atherosclerosis, Alzheimer’s disease, the calcium signaling pathway, the neurotrophic protein signaling pathway, inflammatory mediator regulation of TRP channel, cell aging, VEGF and other signaling pathways (Figure 6c). These pathways are closely related to cancer, neurodegenerative diseases, aging, glucose and lipid metabolism, energy metabolism and other degenerative changes.
PPI analysis showed many genes related to degenerative changes in the action of ALPT active components. We selected the node genes whose protein interaction is greater than the median value of PPI network mapping for visualization mapping (Figure 7a). The essential target genes of ALPT were SRC, TP53 and AKT1, respectively, MAPK3, HSP90AA1 and EGFR, etc. The results of correlation analysis showed that the active components of ALPT aqueous extract, such as dihydromyricetin, 5,7-dihydroxy-1(3H)-isobenzofuranone, pimaric acid, L-theanine, 7-hydroxycoumarin, ellagic acid and L-tyrosine, interacted with their target genes (Figure 7b).
The results of network pharmacology were further verified by molecular docking. The five most essential target proteins of ALPT and their associated active components were selected for molecular docking. The results showed the binding energies of TP53 with pimaric acid, MAPK3 with pimaric acid, AKT1 with 7-hydroxycoumarin and AKT1 with ellagic acid were lower than −5 (Figure 8).
## 4. Discussion
In this study, we explored the activity and mechanism of ALPT in delaying the aging change through the data of mice in an in vivo experiment and network pharmacology. Literature data showed that SAMP8 mice had senescence phenotype at 4 months [60,61]. At the end of this study, SAMP8 mice reached 5.5 months of age. P8+HFD mice showed lipid metabolism disorder, liver aging, intestinal structure damage and intestinal bacterial composition change. ALPT protected the liver intestine microbial axis of P8+HFD mice and had significant metabolic regulation and anti-aging effects.
## 4.1. ALPT Protects the Intestinal–Liver–Microbial Axis
Aging, a high-fat diet and intestinal flora disorders promote and feedback each other [62,63]. With the increase of the ratio of villus length to crypt depth (V/C), the contact surface with food increases, indicating that the small intestinal function is better [64,65]. ALPT inhibited P8+HFD induced the decrease of V/C in the small intestine and the number of goblet cells in the colon (Figure 4a,b). Colonic goblet cells can secrete a high molecular weight glycoprotein called mucin to protect the intestinal epithelium from physical damage caused by lumen contents, prevent bacterial invasion and interact with immunoglobulin A to play the role of antibody and antitoxin [66,67]. ALPT significantly increased the ZO-1 protein content of P8+HFD mice and protected intestinal mucosa (Figure 5c).
The ratio of Firmicutes to Bacteroidota (F/B) is an important indicator to describe the structure of intestinal flora. The high-fat diet promotes the production of LPS and increases the F/B ratio [68,69,70]. Clinical studies have shown that the abundance of Bacteroidota in the intestinal bacteria of the elderly is reduced [71,72]. ALPT inhibited the increase of the F/B ratio of HFD+P8 mice and the decrease of the abundance of short-chain fatty acid-producing bacteria, Alistipes, Alloprevotella and Bacteroides (Figure 5d–f). Butyric acid produced by Alistipes can also increase the integrity of intestinal epithelial tight junction-mediated barriers [73].
LPS is an endotoxin produced by pathogenic microorganisms. Studies have shown that when the intestinal barrier function decreases, LPS enters the blood through intestine leakage and then flows into the liver, leading to liver damage and promoting visceral fat accumulation [74]. Pathological section and biochemical analysis showed that P8+HFD aggravated intestinal inflammation, increased LPS content, decreased ZO-1 protein expression and increased intestinal permeability. Inflammation, endotoxin and fat accumulation aggravate liver injury. ALPT significantly protected the structure and function of the intestinal–liver–microbial axis in P8+HFD mice.
## 4.2. Metabolic Regulation of ALPT
Aging is associated with loss of fat-free mass (primarily skeletal muscle) and increased fat mass [75]. In this study, both P8+HFD and P8+HFD/ALPT mice gained weight, but the visceral fat content of P8+HFD/ALPT mice was significantly lower than that of P8+HFD mice, showing significant differences in health status. In the P8+HFD group, the levels of TG and TC in serum were significantly increased, and intestinal tissue disorder and liver steatosis were observed. The liver is a central organ that controls lipid homeostasis through biochemical, signaling and cellular pathways. It plays a key and important role in lipid metabolism and has a huge capacity for lipid accumulation [76,77]. ALPT inhibited P8+HFD-induced heterotopic lipid deposition in the liver (Figure 2).
Short-chain fatty acids (SCFAs) are colonic anaerobes’ final products of carbohydrate degradation. Colonic anaerobes can degrade indigestible food and complex carbohydrates, change the PH value of the colon, provide a source of cellular energy, inhibit inflammation and promote metabolism [78]. ALPT increased the abundance of SCFAs-producing bacteria such as Alistipes, Alloprevotella and Bacteroides in the intestine of P8+HFD mice (Figure 5f). Alistipes mainly produce butyric acid in the intestine. Butyrate is essential in differentiating colonic T regulatory cells and activating intestinal and blood antigen-producing cells (APC) and T cells [79]. Butyrate is the energy source of colon cells and triggers the reduction of dietary energy harvesting in Firmicutes, thereby reducing fat accumulation in adipose tissue in the host [80,81]. Therefore, butyrate is one of the most important components in SCFAs. Alloprevotella and Bacteroides have glycolytic activity and produce appropriate acetic acid [82,83], which can attenuate colitis induced by high sugar [84]. In addition, *Alloprevotella is* an anti-inflammatory bacterium negatively related to LDL cholesterol [85]. This study further proved the effects of *Liupao tea* on improving body metabolism from the perspective of improving intestinal bacteria composition.
## 4.3. ALPT Delays Aging through Anti-Inflammatory and Caloric Restriction
Chronic inflammation is mediated by aging and metabolic disorders, promotes hyperfunction and mediates the occurrence and development of metabolic diseases such as diabetes, cardiovascular diseases and neurodegenerative diseases [86,87,88,89,90]. Previous studies have shown that dark tea has more antioxidant and anti-inflammatory effects than green tea in vivo. In addition, it has a significant role in regulating lipid metabolism [91]. Oxidative stress can cause the oxidation of polyunsaturated fatty acids, form lipid hydroperoxides [92], damage protein hydrolysis and induce the accumulation and aggregation of damaged or abnormal proteins in cells [93]. ALPT reduced oxidative stress, UPs-modified proteins and RAGE aggregation pathway in the liver of P8+HFD mice, and protected proteins, fatty acids, lipids, nucleic acids and other protein macromolecules in the liver. At the same time, ALPT significantly inhibited P8+HFD-induced NF-κB nuclear translocation and IL-6 section (Figure 3 and Figure 4).
In the process of organism aging, cell hyperfunction and signal feedback blocking are inhibited by caloric restriction (CR), so that cells are in a resting state (quiescence), which is the key to achieving healthy aging [94,95,96]. At this time, the cell cycle block is conducive to maintaining cells in a young state [97]. In P8+HFD model mice, ALPT inhibited the expression of cyclin B1 and cyclin D1, activated the Sirt1/AMPK pathway and decreased phosphorylation of mTOR expression. The results indicate that ALPT has a caloric restriction effect (Figure 3).
## 4.4. Correlation Analysis between Active Components of ALPT Aqueous Extract and Targets of Degenerative Changes
Fermented tea such as dark tea contains more abundant oxidized polyphenols, theophylline, polysaccharides, ellagic acid and fatty acids, which can target to regulate AMPK and G-protein coupled receptors to promote lipid metabolism and delay degenerative diseases [98,99,100]. Liupao tea has the characteristics of red, thick, aged and mellow flavor quality. Its three unique discoloration processes, wet billet accumulation, dry billet accumulation and aging make *Liupao tea* contain more soluble sugar, total free amino acids and theabrownins [13]. These ingredients have significant antioxidant, anti-inflammatory, metabolic regulation and liver protection effects [101].
ALPT aqueous extract was rich in oxidized polyphenols, phenolic acids, fatty acids and other components (Figure 6a). *The* gene targets of these components mainly focused on cancer pathways, the interaction of neuroactive ligand–receptor interactions, lipid and atherosclerosis, Alzheimer’s disease and other pathways (Figure 6c). The association analysis between the core target genes and the active ingredients of ALPT was shown in Figure 7 and Figure 8, and some results had been proved by relevant research. For example, 7-hydroxycoumarin upregulates protein kinase B (Akt) signaling, inhibits the level of inflammatory cytokines and has neuroprotective effects [102]. Ellagic acid and L-theanine have been shown to reduce EGFR expression and phosphorylation and inhibit tumor growth [103,104]. Pimaric acid can significantly inhibit the secretion of IL6 by LPS-stimulated RAW 264.7 macrophages [105].
## 5. Conclusions
In conclusion, the aqueous extract of aged *Liupao tea* (ALPT) has the effect of delaying aging-related degenerative changes in the body through anti-inflammation, caloric restriction, protection of the gut–liver axis and multi-targeting. Tea is rich in secondary metabolites beneficial to human health, such as polyphenols, pigments, polysaccharides, alkaloids, free amino acids and saponins. Current research shows that drinking tea is safe for the human body [101]. A growing number of studies have proved that dark tea has more significant in vivo effects than green tea in delaying aging and regulating metabolism. Tea processing can reduce toxicity and improve therapeutic efficacy, just like the processing of traditional Chinese medicine [106]. This study systematically investigated the efficacy and mechanism of *Liupao tea* in delaying degenerative changes, providing a theoretical basis for the drinking value of *Liupao tea* and other dark teas in a modern aging society.
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---
title: 'Ultrasound-Assisted Extraction of Isoquercetin from Ephedra alata (Decne):
Optimization Using Response Surface Methodology and In Vitro Bioactivities'
authors:
- Ezzouhra El Maaiden
- Nagib Qarah
- Amine Ezzariai
- Adil Mazar
- Boubker Nasser
- Khadija Moustaid
- Hassan Boukcim
- Abdelaziz Hirich
- Lamfeddal Kouisni
- Youssef El Kharrassi
journal: Antioxidants
year: 2023
pmcid: PMC10045738
doi: 10.3390/antiox12030725
license: CC BY 4.0
---
# Ultrasound-Assisted Extraction of Isoquercetin from Ephedra alata (Decne): Optimization Using Response Surface Methodology and In Vitro Bioactivities
## Abstract
Isoquercetin (ISQ) is reported to be a powerful antioxidant with extremely high bioavailability and structural stability compared to aglycone quercetin. Despite this, it is not well studied due to the limited methods for its extraction. With the growing interest in the research and analysis of ISQ-rich herbs, there is a need to optimize an efficient and rapid method for their extraction. In the present study, the ultrasound-assisted extraction of ISQ from *Ephedra alata* Decne was optimized by a response surface methodology (RSM) using high-performance liquid chromatography as a separation method. The best possible ranges for extraction time (10–30 min), temperature (50–70 °C), ultrasonic power (60–90 W), solvent-to-solid ratio (50–70 mL/g), and ethanol concentration (50–$70\%$) were determined using a single factor analysis. Subsequently, an optimization of the extraction conditions was performed with RSM using the Box–Behnken design. An ultrasonication time of 10 min, a temperature of 60 °C, a power of 75 W, a solvent-to-solid ratio of 60 mL/g, and an ethanol concentration of $70\%$ were determined to be the optimal conditions for the highest recovery of isoquercetin (1033.96 ± 3.28 µg/g). Furthermore, E. alata powder morphology (using a scanning electron microscope), antioxidant activities, and the inhibition potential of key enzymes involved in skin aging (elastase and collagenase), hyperpigmentation (tyrosinase), diabetes (α-amylase), inflammation (hyaluronidase), and neurodegenerative disorders (cholinesterase) were determined and compared with those using the Soxhlet method. This study established a highly efficient method for ISQ extraction and suggested several potential applications of ISQ in the pharmaceutical and cosmetics industries.
## 1. Introduction
Isoquercetin (ISQ) (quercetin-3-O-glucoside, Figure 1a) is one of the main glycosidic groups of quercetin, a naturally occurring flavonol [1]. Biological research on quercetin has been extensively documented over the past decades [2]. Recently, ISQ has gained considerable attention as a prospective compound because it has superior water solubility (95 mg/L) compared with quercetin [3]. However, the biological activity of quercetin glycosides is not well studied. The Web of Science database listed 36,072 references on quercetin, with 11,428 references on rutin and only 369 references on ISQ [4]. Regardless, ISQ has increasingly attracted the attention of researchers because of its higher bioavailability compared to quercetin. In addition, ISQ exhibits a variety of chemoprotective effects in vitro and in vivo on oxidative stress, cancer, cardiovascular disease, diabetes, and chronic allergic diseases [5]. Following the oral application of ISQ, the compound is metabolized to a great extent in the intestine and liver, with only minor amounts of undamaged ISQ being recovered in both plasma and tissues. The growing interest in researching and analyzing ISQ-rich herbs requires the optimization of an efficient and rapid extraction method.
The extraction of bioactive compounds from the raw materials of plants is a crucial step. Currently, several advanced extraction methods are being developed, including the ultrasound-assisted extraction (UAE) method, in which ultrasonic waves are used to separate components from the raw material or herbs [6]. The cavitation force generated by the ultrasonic waves leads to the disintegration of cell walls and the accelerated release of matrix components [7]. In addition, several other parameters affect the efficiency of these extraction methods, such as solvent concentration, material-to-solvent ratio, temperature, duration, and ultrasonic extraction power [8]. Therefore, the optimization of these parameters should be performed to improve the extraction efficiency of ISQs and preserve their bioactivities. The response surface method (RSM) is a statistical analytical method that has been widely applied to optimize the extraction of bio-compounds from various vegetable materials using UAE techniques [8,9].
Ephedra alata Decne (family Ephedraceae), with its strong pine fragrance and astringent flavor, is mainly distributed in arid and semiarid regions around the world [10]. Traditionally, it is used to treat kidney disorders, bronchial asthma, and circulatory and digestive system disorders as well as to manage cancer [10]. Previous studies have documented that this plant has several biological activities, such as antibacterial activity and protection against cancer, liver, and cardiovascular diseases [11]. The medicinal properties of the plant under study are attributed to secondary metabolites, which include glycosides, flavonoids, phenolic compounds, and alkaloids. ISQ is a predominant component of quercetin in E. alata. This study evaluated the influence of various extracting parameters on the recovery of ISQ from E. alata, with the objective of optimizing ultrasonic extraction techniques. Several extraction conditions were evaluated to identify the conditions most conducive to achieving the highest yield of ISQ and improving product bioactivities.
## 2.1. Plant Material
The aerial parts of *Ephedra alata* Decne were gathered in July 2021 from Laâyoune Sakia El Hamra region, Morocco (Figure 1b), using standard fieldwork and collection practices [12,13]. The plant was authenticated and identified by taxonomists and deposited at the local ASARI herbarium, Um6p (Morocco). Plant material was dried at 60 °C in a tray dryer for 72 h. Dried materials were ground to 250–500 μm and then stored in an airtight container (4 °C) until further utilization.
## 2.2. Ultrasound-Assisted Extraction
Ultrasound-assisted extraction (UAE) of E. alata was carried out with a sonicator (QSonica Q500, 500 W power, 20 kHz, 25-mm probe, 120 μm maximum amplitude) under controlled time, temperature, amplitude, and pulse. The E. alata powder was mixed well with the ethanol/formic acid solution according to the liquid-to-solid (L/S) ratio. Then, the mixture was sonicated at a specified amplitude for a specified extraction time. It was subsequently pelleted at 2600× g for 15 min, strained with a paper filter (Whatman No. 1, England, UK), and concentrated in a vacuum rotary evaporator.
## 2.3. Soxhlet Extraction
The extraction process was performed using a *Soxhlet apparatus* with 3 g of sample powder and 120 mL of ethanol: water solution (70:30 v:v) for a period of 6 h. Following this, the solvent was evaporated using a rotary evaporation apparatus equipped with a thermostat, prior to being filtered off with a 0.45 μm filter for chromatographic analysis.
## 2.4. Single-Factor Experimental Design
A single-factor experimental design (SFED) was adopted to identify and select appropriate values from the 5 variables tested: the ethanol concentration (20, 30, 40, 50, 60, $70\%$), the solvent–solid report (20, 30, 40, 50, 60, 70 mL/g), the ultrasonic power (30, 60, 75, 90, 115, 130, 150, 180 W), the duration (5, 10, 15, 20, 25, 30, 35, 40, 60, 80, 100, 120 min), and the temperature (30, 40, 50, 60, 70, 80 °C). The common conditions selected were as follows: an ethanol concentration of $70\%$; a liquid-to-material ratio of 30 mL/g; an ultrasonic power of 90 W; an ultrasonic time of 10 min; and an ultrasonic temperature of 50 °C. One independent parameter was modified, and the others were held constant. ISQ was quantified with high-performance liquid chromatography (HPLC) to select the relevant independent parameters that significantly influenced the efficiency of the extraction.
## 2.5. Experimental Design and Statistical Analysis
Following the results of the SFED experiments, the optimization conditions of sample (E. alata ISQ) extraction were conducted by response surface methodology (RSM) using a Box–Behnken Design (BBD). A summary of the effective extraction parameters, including ultrasonic time (min, X1), ultrasonic temperature (°C, X2), solvent-to-solid ratio (mL/g, X3), ultrasonic power (W, X4), and the EtOH concentration (%, X5) are reported in Table 1 at 3 levels (−1, 0, 1). A total of 46 experimental cycles were performed (46 runs, $$n = 3$$). The response variable was the yield of ISQ (Y), as quantified by HPLC analysis. The design of the experimental runs and the results of the optimization of the ISQ extraction conditions are presented in Table 1. According to the ANOVA analysis, a p-value < 0.05 signifies that the tested conditions were significant, while a p-value < 0.0001 signifies that they were highly significant. Furthermore, a p-value > 0.10 signifies that the conditions were not significant. The change in the response value (Y) based on the five parameters was adjusted by an RSM, as illustrated by the equation below:y=β0+∑$i = 1$kβi xi+∑$i = 1$kβii xi2 +∑ik−1∑jkβij xi xj where xi and xj represent the independent parameters affecting the dependent response Y (ISQ), and β0, βi, βii, βij, and k represent the regression coefficients for the intercept, linear, quadratic, interaction terms, and the number of variables, respectively.
## 2.6. High-Performance Liquid Chromatography (HPLC) Analysis
The samples were analyzed using an Agilent 1200 HPLC system (Agilent Technologies, Santa Clara, CA, USA). Sample separation was performed on a Capcell PAK C18 column (internal diameter 250 × 4.6 mm, particle size 5 um) at 40 °C. A flow rate of 1 mL/min was used, and the injection volume was 10 µL. The mobile phase consisted of (A) water: formic acid (95:5, v/v) and (B) $100\%$ methanol. The binary gradients were as follows: 0–25 min, 20–$60\%$ B; 25–25.1 min, 60–$100\%$ B; 25.1–30 min, 100–$60\%$ B; 30–30.1 min, 60–$20\%$ B; and 30.1–35 min, $20\%$ B. The absorbance was measured at 360 nm. Identification of ISQ was conducted based on spectrum, standards, and previous studies. The results are presented in micrograms per gram dry weight (µg/g DW) [14]. Liquid chromatography-mass spectrometry (LC-MS) (Agilent Technologies, LC/MSD VL Series 1100 system, Palo Alto, CA, USA) analysis was conducted using an identical HPLC column and mobile phase. The Electrospray ionization mass spectrometer was run under negative ion mode using a drying gas flow rate (N2), drying gas temperature, nebulization pressure, and capillary voltage of the fragmentation of 10 L/min, 350 °C, 40 psi, 3.5 kV, and 110 V, respectively. The ions underwent scanning mode detection, and the mass range was set from 400 to 1400.
## 2.7. Antioxidant Assays
Three complementary assays were used to assess antioxidant potential (ABTS, FRAP, and DPPH) and were reported as Trolox equivalents per gram of sample (mg TE/g). Values were reported as average ± SD and have been shown to be significant at $p \leq 0.05.$ Detailed protocols have been described for all assays in our previous work [15].
## 2.8. Enzyme Inhibition Assays
The pharmaceutical and cosmetic properties of ISQ as well as its potential to inhibit key enzymes involved in hyperpigmentation, skin aging, diabetes, inflammation, and neurodegenerative diseases (elastase and collagenase, tyrosinase, α-amylase, hyaluronidase, cholinesterase, and Aβ1–42 aggregations respectively) were evaluated and compared with ISQ extracted with the Soxhlet method. Detailed protocols have been described for all assays in our previous work [15].
## 2.8.1. Tyrosinase Inhibition Assay
Anti-tyrosinase activity was evaluated using L-3,4dihydroxyphenylalanine as a substrate based on previous work [16]. The reaction was started by adding 40 µL of L-3,4dihydroxyphenylalanine (2 mM), and the inhibition of tyrosinase was evaluated by tracking the absorbance at 492 nm. Detailed protocols have been described for all assays in our previous work [15].
## 2.8.2. Collagenase Inhibition Assay
Anti-collagenase activity was determined according to the method described by Wang et al. [ 17] with some modifications. One milligram of azo dye-impregnated collagen was weighed into tubes and then mixed with 800 µL of Tris-HCl (0.1 M, pH 7) plus 100 µL of the sample. Next, 100 µL of collagenase (200 units/mL) was added immediately to the resulting mixture and incubated at 43 °C for 1 h. Then, the samples were subjected to centrifugation at 3000 rpm for a period of 10 min. The supernatant liquid from each tube was placed on a 96-well plate, and the absorbance of all sample tubes was measured at 550 nm.
## 2.8.3. Elastase Inhibition Assay
Anti-elastase activity has been detected by tracking p-nitroanilide release during N-succinyl-Ala-Ala-p-nitroanilide cleavage [18]. The reaction mixture was initiated with the addition of the substrate, and the anti-elastase activity was determined at 410 nm. Detailed protocols have been described for all assays in our previous work [15].
## 2.8.4. Hyaluronidase Inhibition Assay
The anti-hyaluronidase activity was determined using the method as reported by Abhijit and Manjushree [19], in which 10 µL of bovine testicular hyaluronidase type 1-S (4200 units/mL) was dissolved in acetate buffer (0.1 M, pH 3.5) with 50 µL of samples, placed in a water bath at 37 °C, and incubated for 20 min. Hyaluronidase-activated Ca2+ was treated with 50 µL of sodium hyaluronate (12 mg/mL) dissolved in acetate buffer (0.1 M, pH 3.5) and incubated in a water bath (37 °C, 40 min). Next, 10 µL of sodium hydroxide (0.9 M) and 20 µL of sodium borate (0.2 M) were added to the mixture and incubated for 3 min in a boiling water bath. Next, 50 µL of ρ-dimethyl amino benzaldehyde solution (0.25 g of p-dimethyl amino benzaldehyde) dissolved in 21.88 mL of acetic acid ($100\%$) and 3.12 mL of hydrochloric acid (10 N) was added. Tannic acid was used as the reference standard, and the absorbance was measured at 585 nm.
## 2.8.5. α-Amylase Inhibition Assay
To evaluate the potential for α-amylase inhibition on 96-well plates, 25 µL of the sample was mixed with 50 µL of starch solution ($0.05\%$) and incubated (30 °C, 10 min). Then, the reaction was completed with the addition of a combination of hydrogen chloride (25 µL, 1 M) and potassium iodide solution (100 µL). Absorbance measurements were performed at 630 nm, and the obtained data were expressed in acarbose equivalent (mmol ACE/g) [20].
## 2.8.6. Cholinesterase Inhibition Assays
The in vitro anticholinesterase activities (acetylcholinesterase-AchE and butyrylcholinesterase- BchE) were assayed as previously reported [15]. For AchE and BchE, respectively, each reaction was initiated by adding 25 µL of acetyl thiocholine iodide (1.5 mM) or butyryl thiocholine chloride (4 mM) and then incubated at 25 °C for 10 min. Absorbance was measured at 405 nm. The AchE and BchE inhibition capacities are reported as milligrams of galantamine equivalents (mg GALAE/g).
## 2.8.7. Aβ1–42 Aggregation Inhibition Assay
The Aβ1–42 aggregation inhibition assay was evaluated based on the thioflavin T florescence method, as described by Li et al. [ 21]. Briefly, freshly prepared solutions of Aβ1–42 (1 mM, dissolved in DMSO) were incubated with phosphate-buffered saline (PBS) (10 mM, pH 7.4) containing 10 mM NaCl in an ultrasonic bath for 1 min. The peptide solution (final Aβ concentration of 50 μM) was incubated alone or with 100 μg/mL of each sample at 37 °C for 48 h. Next, 80 μL of the test solution was diluted to 600 μL with PBS (containing 10 μM thioflavin T). Absorbance measurement was performed at 485 nm, and the result of Aβ1–2 aggregation inhibition was obtained using the following formula:Aβ1–42 aggregation inhibition (%)=[(1−F1)/F0]∗100 where F0 and F1 are the florescence of the Aβ1–42 at 485 nm in the presence and absence of the inhibitors, respectively.
## 2.9. Scanning Electron Micrographs
The morphology of E. alata powder was examined with a scanning electron microscope (ETEC Corp., Hayward, CA, USA) after different treatments: powder before and after extraction with Soxhlet (SE), or our UAE-optimized method (UAE-optimized). After being fixed to a metal grid with double-sided adhesive tape, the powder was coated with gold under vacuum (20 µm, 400× magnification) and then analyzed with a scanning electron microscope at 2500× magnification.
## 2.10. Statistical Analysis
A one-way analysis of variance (ANOVA) and Tukey’s test was performed for the statistical evaluations of the data generated from the single-factor experimental design (SFED). Response surface methodology (RSM) optimization and modeling were performed in Design Expert Version 7.0.0 (Stat-Ease, Inc., Minneapolis, MN, USA). Analyses were performed in triplicate ($$n = 3$$).
## 3.1. Single-Factor Analysis of ISQ Extraction
It has been reported that different extraction parameters influence the extraction efficiency of quercetin glucoside derivatives from various plant matrices [22]. Here, using a single-factor experimental design (SFED), we evaluated the effect of five independent parameters (EtOH concentration, liquid–solid ratio, extraction time, extraction temperature, and ultrasonic power) on the extraction efficiency of ISQ from E. alata. The objective of this preliminary experiment was to identify the limiting extraction parameters.
## 3.1.1. Effect of Ethanol Concentration on ISQ Extraction
An important parameter to consider when designing an extraction procedure is the solvent used. In ultrasound-assisted extraction (UAE), the solvents used for the extraction of bioactive components from the plants are typically a combination of organic and aqueous solutions with different ratios [23]. Many mixtures, including acids (acetic, formic, and hydrochloric), ethanol, methanol, and water, are widely used to extract quercetin from plants [24]. In this study, given that our goal was to apply these ISQs for future cosmetic and pharmaceutical uses and to develop a green chemistry extraction method, EtOH was chosen as a solvent. EtOH is a less toxic solvent for humans and is more environmentally friendly than other organic solvents (e.g., methanol) [25]. Furthermore, its extractability can be adjusted by adding formic acid (CH2O2), making it an ideal solution for ISQ extraction. Interestingly, these two universal solvents (i.e., ethanol and formic acid) have been widely used in various cosmetic and/or food applications [26]. In this work, the extractability of ethanol and formic acid in mixtures with different proportions (20, 30, 40, 50, 60, and $70\%$ ethanol) was evaluated. It was found that the ethanol content had a marked effect on the extraction of ISQ from E. alata (Figure 2a). The quantity of ISQ initially increased with increasing ethanol content, followed by a decrease with increasing ethanol ratio. This may be related to the polarity of ISQ [26]. As a result, the quantity of ISQ in the following experiments was maximized within the 30–$50\%$ ethanol ratios.
## 3.1.2. Effect of Solid–Liquid Ratio on ISQ Extraction
The extraction efficiency of ISQ was significantly influenced by the solvent-to-solid ratio. As shown in Figure 2b, by increasing the solvent–solid ratio, the amount of extracted ISQ increased. The maximum amount of ISQ was obtained with the 40 mL/g ratio. However, when the solvent–solid ratio was higher than 60 mL/g, the amount of ISQ extracted decreased. This may be related to the decrease in the amount of powder to be wetted and the reduced contact area between the liquid and the material when the solvent ratio increases [27]. In this case, the maximum ISQ had been extracted after the liquid-to-material ratio had reached a certain level, and a further increase in the amount of solvent would result in waste. Therefore, the following experiments were maximized within the 40–60 mL/g solid/liquid ratio.
## 3.1.3. Effect of Ultrasonic Temperature on ISQ Extraction
In this study, the influence of several extraction temperatures (30, 40, 50, 60, 70, and 80 °C) on the extraction of ISQ was evaluated (Figure 2c). The amount of extracted ISQ increased with increasing temperature. When the temperature was 50 °C, the extracted amount of ISQ reached its maximum. Thereafter, the extraction rate of ISQ decreased with increasing temperature. This may be mainly because increasing the temperature to a certain level improves the release of ISQ. A higher temperature decreases the surface tension and viscosity of the solvent–sample mixtures, which improves the extraction yield [28,29]. However, increasing the temperature to an excessive level led to the destruction or volatilization of ISQ, especially if the extraction time was longer [28,29,30]. Havlikova and Mikova revealed that temperatures above 70 °C cause rapid degeneration of the polyphenols; therefore, it is essential to carefully determine the extraction temperatures that will preserve the stability of these phenolic substances [31]. Therefore, the amount of ISQ in the following experiments was maximized in the temperature range of 50–70 °C.
## 3.1.4. Effect of Ultrasonication Time on ISQ Extraction
Another important factor affecting ISQ extraction is extraction time (Figure 2d). The amount of extracted ISQ increased with extraction time, peaking at 20 min. Although longer exposure can improve the extraction of ISQ by increasing its release into the solvent from the powder [6], it can also lead to its degradation [32]. Ultrasound extraction can degrade isolated ISQ; thus, prolonged ultrasonic exposure can oxidize ISQ. Hence, an extraction time between 10 and 40 min in the following experiments was chosen to save time and energy.
## 3.1.5. Effect of Ultrasonic Power on ISQ Extraction
Ultrasonic energy showed a considerable impact on the extraction of ISQ (Figure 2e). At ultrasonic energies from 30 W to 60 W, the amount of extracted ISQ increased significantly with increasing ultrasonic energy, whereas it decreased with increasing ultrasonic energy above 60 W. An increase in ultrasound power can have a strong effect on the activity of cavitation bubbles. However, higher ultrasonic power may damage the ISQ structure due to the thermal effect, which contributes to a lower extraction rate [28]. It has been reported that the increase in cavity size due to higher ultrasonic power improves the extraction of polyphenols in the rind extract of *Nephelium lappaceum* fruit [30] and grape seeds [33]. Different levels of ultrasonic intensity have been used to extract polyphenols from tea leaves, resulting in a $16\%$ increase in yield when the power level is increased from 25 to 125 W [33]. The efficiency of polyphenol extraction from *Acer truncatum* leaves increased by $9.5\%$ when the power was increased from 150 to 240 W [34]. In addition, high ultrasound power can generate hydroxyl radicals (OH*) that react with and reduce phenolic compounds, particularly when the water content is high [35]. For this reason, care should be taken to prevent the degradation of polyphenols by high energy intake. Changes in the chemical composition of extracts related to the effects of high ultrasound power have also been observed in other studies. For this reason, in the following experiments, the ultrasound power was optimized between 60 W and 120 W.
## 3.2.1. Response Surface Model Analysis
Based on the experimental results of the SFED, a Response Surface Method (RSM) experiment was designed for the optimization of the ISQ extraction method of E. alata (Table 2). Design-Expert 7.0.0 software was used to adjust the response value of the ISQ extraction by multiple linear regression. The equation is expressed as follows:Y(μg/g)=8.296−1.01 X1−0.65 X2+0.40 X5+0.40 X1X2 +0.17 X1X4−0.16 X1X5−0.26 X2−0.27 X2X5 +0.14 X12−0.79 X22+0.30 X32+0.17 X42+0.30 X52 where Y is the yield of ISQ (µg/g), and X1, X2, X3, X4, and X5 are the coded variables for ultrasonic time, ultrasonic temperature, solvent-to-solid ratio, ultrasonic power, and ethanol concentration, respectively.
The significance coefficient of the model and the results of the analysis of variance (ANOVA) are presented in Table 3. With a p-value < 0.0001, high significance was indicated for the regression model. The model terms X1, X2, X1X2, X22, X32, and X52 were found to be highly significant ($p \leq 0.0001$). The model terms X1X4, X1X5, X2X4, X2X5, X12, and X42 were found to be significant ($p \leq 0.05$). The remaining terms were found to be non-significant ($p \leq 0.10$). The ANOVA indicated that the most significant conditions for the recovery of ISQ from E. alata were ultrasound temperature (with three significant interactions, X1X2, X2X4, and X2X5), ultrasound duration (with three significant interactions, X1X2, X1X4, and X1X5), and ethanol concentration (with two significant interaction conditions, X1X5, and X2X5). Correlation coefficient R1 was measured for the statistical relationship between actual and predicted points. High significance was indicated by the R with an absolute value of $99.24\%$ and an adjusted R1 of $98.08\%$. The recovery of ISQ in the 46 runs ranged from 652.24 to 1033.96 µg/g (Table 2). The highest recovery of ISQ (1033.96 µg/g) was obtained under the following conditions (run 28): an ultrasonic time of 10 min, an ultrasonic temperature of 60 °C, a solvent-to-solid ratio of 60 mL/g, an ultrasonic power of 75 W and an ethanol concentration of $70\%$. The lowest ISQ recovery (652.24 µg/g) with the UAE method was obtained in run 17.
## 3.2.2. Interactions between UAE Factors and the Response Surface
The three-dimensional response surface plots illustrate the interaction between the pairwise factors (Figure 3a–d). In Figure 3a, the surface plot demonstrates the interaction between ultrasonic time (X1) and ultrasonic temperature (X2) and shows that the extraction yield of ISQ decreased with the simultaneous increase of the X1 and X2 factors. Therefore, by increasing the extraction time and continuously releasing the extracted products, the extraction solvent should be saturated [6]. A further increase in temperature can also accelerate the evaporation process. In this case, the extraction yield of ISQ can be decreased because the solvent surface area is reduced between the solid and the solvent. Figure 3b presents the surface plot for the interaction between EtOH concentration (X5) and ultrasonic time (X1). However, it is generally observed that when both parameters are decreased simultaneously, the maximum extraction yield increases. Due to the decrease in temperature, the evaporation rate of the solvent can be reduced, and the desired amount of solvent can be reduced. The addition of formic acid as a cosolvent can enhance the ISQ release into the solvent by improving the contact with the solid. As a result, the yield surface can be increased [36]. Figure 3c shows an interaction between ultrasonic power (X4) and ultrasonic temperature (X2). Furthermore, as the ultrasonic power increased, the extraction yields decreased at an ultrasonic temperature (X2) of 60 °C. The extraction yield of ISQ is generally reduced when both parameters (X4) and (X2) are increased simultaneously. The concentrations of solid fillers in the sample may increase as the solvent evaporates, which may result in a reduction in extraction efficiency due to the reduction in solvent/solid sample contact area. In addition, by using the higher ultrasonic power of UAE, the structure of bioactive compounds may be altered or destroyed [23]. Figure 3d shows how EtOH concentration (X5) and ultrasonic temperature (X2) affect the extraction performance of ISQ because of the response surface gradient. Thus, at 70 °C, as the EtOH concentration decreases, the response surface shows a decrease in extraction yield. However, at lower temperatures, the yield increases with increasing EtOH concentration. High temperatures can break down the structure of ISQ, resulting in a low extraction yield [37]. Generally, reducing the ultrasonic temperature while increasing the amount of solvent simultaneously improved the extraction yield surface.
## 3.3. Antioxidant and Enzyme Inhibitory Activities
Isoquercetin, a glycosidic form of quercetin, has been shown to possess antioxidant, neuroprotective, anti-inflammatory, and antidiabetic properties. Through activation of the Nrf2/ARE antioxidant signaling pathway, ISQ attenuates ethanol-induced hepatotoxicity, oxidative stress, and inflammatory responses [38]. By modulating nuclear factor B (NF-B), ISQ regulates nitric oxide synthase 2 (NO2) expression. It has been suggested that ISQ may prevent birth defects in diabetic pregnancies due to its high bioavailability and low toxicity [39]. In this study, the efficacy of ISQ-UAE (isoquercetin extracted by the optimized ultrasound-assisted extraction method) in scavenging radicals and their anti-hyperpigmentation, anti-aging, anti-diabetes, anti-inflammation, and anti-neurodegenerative disease qualities were evaluated and compared with ISQ-SE (isoquercetin extracted by Soxhlet) (Table 4).
Therefore, an in vitro evaluation of the antioxidant activities of ISQ was conducted. Three complementary bioassays were evaluated, including ABTS, FRAP, and DPPH (Table 4). The results indicate that ISQ-UAE exerts a high antioxidant potential in vitro (82.47, 88.93, and 77.15 mg TE/g for DPPH, FRAP, and ABTS tests, respectively) in contrast to ISQ-Soxhlet (64.81, 65.71, and 61.19 mg TE/g for DPPH, FRAP and ABTS tests, respectively).
Acetylcholinesterase (AchE) and butyrylcholinesterase (BchE) are two key enzymes involved in the management of certain pathologies, including Alzheimer’s disease (AD). Abnormal β-amyloid protein may be one of the key factors in AD progression, and cholinesterase promotes β-peptide aggregation and amyloid formation [40]. Therefore, for the treatment of AD, inhibiting the cholinesterase enzyme and preventing amyloid aggregation would be effective [41]. Scientific research has focused much attention on the search for safe natural enzyme inhibitors. In this study, the inhibition of cholinesterase (AchE and BchE) and Aβ1-42 peptide aggregation by ISQ from E. alata were tested. In our study, ISQ-UAE presented the highest potential enzyme inhibitory activities against AchE (1.56 mg GALAE/g), BchE (4.02 mg GALAE/g), and Aβ1-42 peptide aggregation ($74.11\%$) compared with ISQ-SE (Table 4).
Diabetes mellitus (DM), a disorder of the endocrine system affecting carbohydrate metabolism, results in a rapid rise in blood glucose levels. The compound α-amylase is charged with transforming oligo- and disaccharides into monosaccharides while inhibiting the hydrolysis of carbohydrates to delay their absorption [42]. Thus, diabetes and obesity can be treated more effectively by optimizing postprandial blood glucose levels. The use of natural anti-amylase represents an interesting biotreatment option for hyper-glycemia. Therefore, in this study, we sought to determine the α-amylase inhibitory potency of ISQ from E. alata extracted by the optimized UAE method compared with ISQ-SE. Furthermore, ISQ-UAE also showed the highest anti-α-amylase activities compared to ISQ-SE (Table 4).
The extracellular matrix (ECM) is the infrastructure of the skin’s foundation and is composed of several structural components, such as collagen, elastin, and micro fibrils. As the skin matures, these constituents undergo a transformative process that is characterized by the appearance of dry, wrinkled, and lax skin [43]. Tyrosinase is a metalloprotein present in the membrane of the melanosome. This multi-functional protein is responsible for catalyzing the two first steps of melanin biosynthesis and is responsible for converting tyrosine into dopaquinone [44]. Collagen and elastin are important constituents of the ECM. These compounds contribute to skin elasticity and strength [44], and their degradation by collagenase and elastase is one of the main causes of intrinsic skin aging [45]. In this study, we found that the highest inhibition of tyrosinase, collagenase, and elastase was recorded with ISQ-UAE (95.04, 87.31, and $88.93\%$, respectively) compared to ISQ-SE (Table 4).
Hyaluronate (hyaluronic acid) plays important biological roles in humans. This acid is naturally synthesized by hyaluronan synthases and is degraded by a group of enzymes called hyaluronidases [46]. The ECM is hydrolyzed by hyaluronidase during tissue remodeling, and upregulation of hyaluronidase activity is observed in chronic inflammatory states [43,47]. It has been suggested that inhibitors of hyaluronidase have a beneficial effect in preventing and treating inflammatory diseases [47]. Thus, in this study, the hyaluronidase inhibitory activity of ISQ from E. alata was evaluated. ISQ-UAE presents promising anti-hyaluronidase activity compared with ISQ-SE (98.83 and $83.68\%$, respectively).
Our data showed that ISQ from E. alata presents promising results for inhibiting tyrosinase, elastase, collagenase, α-amylase, hyaluronidase, cholinesterase, and Aβ1-42 aggregation. ISQ-UAE had superior performance compared with that of ISQ-SE for all assays, which can be explained by the higher temperature (60 °C) and prolonged extraction time (6 h) of the Soxhlet method, which are likely to degrade ISQ, contributing substantially to a decrease in bioactivity [15,48,49].
## 3.4. Observation by Scanning Electron Microscopy
Scanning electron microscopy (SEM) was used to observe the structural changes that occurred during the different extraction processes to better understand the differences in the extraction mechanisms with the optimized UAE and Soxhlet methods. Figure 4 shows SEM images of the untreated sample and the sample after extraction by Soxhlet (Figure 4B,C) and UAE (Figure 4D,E). Extraction produced cellular changes in all samples compared to the control sample (Figure 4A), although the extent of damage differed among samples. The sample obtained after Soxhlet extraction showed slight pore disruption, whereas more marked changes were observed in the samples obtained after extraction by the optimized UAE method. The UAE treatment caused a disruption of the plant tissue and created several hollow openings, manifesting cavitation phenomena. In addition, the channels were destroyed, and more cracks and pores were formed by ultrasonic extraction. Therefore, the extraction of ISQ occurred at a higher rate with ultrasonic treatment. It was found that extraction from dried materials requires two steps: (i) the soaking of the plant materials in a solvent to promote swelling and hydration mechanisms and (ii) mass transport of the soluble components from the material to the solvent through diffusion and osmotic processes [50,51]. This suggests that the ultrasound treatment induced a subsequent change in the cellulose surface tension, with several pits occurring on the surface of the material (Figure 4). The modification of cellulose might cause the plant to crumble or disintegrate more rapidly. Moreover, pitting with UAE could promote diffusion and osmotic processes.
## 4. Conclusions
This study successfully applied the RSM method as a practical approach to optimizing UAE for the extraction of isoquercetin from E. alata to increase extraction efficiency and preserve bioactivities. Using a single-factor experimental design (SFED), the effects of five independent parameters (EtOH concentration, liquid-solid ratio, extraction time, extraction temperature, and ultrasonic power) on the extraction efficiency of isoquercetin from E. alata were evaluated. Following the results of the SFED study, optimization of the extraction conditions was performed by response surface methodology (RSM) using the Box–Behnken design (BBD). The highest recovery of ISQ (1033.96 µg/g) was obtained under the following conditions (cycle 28): ultrasonic time of 10 min, ultrasonic temperature of 60 °C, solvent/solid ratio of 60 mL/g, ultrasonic power of 75 W and ethanol concentration of $70\%$. In addition, ISQ from E. alata showed promising results for the inhibition of the key enzymes involved in hyperpigmentation, skin aging, diabetes, inflammation, and neurodegenerative diseases. Basically, for all the bioassays tested, the isoquercetin extracted by the optimized ultrasound-assisted extraction method (ISQ-UAE) was found to be more efficient than the isoquercetin extracted by Soxhlet extraction (ISQ-SE).
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|
---
title: 'Unexpected Absence of Skeletal Responses to Dietary Magnesium Depletion: Basis
for Future Perspectives?'
authors:
- Marzia Ferretti
- Francesco Cavani
- Vincenza Rita Lo Vasco
- Marta Checchi
- Serena Truocchio
- Pierpaola Davalli
- Chiara Frassineti
- Federica Rizzi
- Carla Palumbo
journal: Biomedicines
year: 2023
pmcid: PMC10045743
doi: 10.3390/biomedicines11030655
license: CC BY 4.0
---
# Unexpected Absence of Skeletal Responses to Dietary Magnesium Depletion: Basis for Future Perspectives?
## Abstract
It’s known that a magnesium (Mg)-deficient diet is associated with an increased risk of osteoporosis. The aim of this work is to investigate, by a histological approach, the effects of a Mg-deprived diet on the bone of 8-weeks-old C57BL/6J male mice. Treated and control mice were supplied with a Mg-deprived or normal diet for 8 weeks, respectively. Body weight, serum Mg concentration, expression of kidney magnesiotropic genes, and histomorphometry on L5 vertebrae, femurs, and tibiae were evaluated. Body weight gain and serum Mg concentration were significantly reduced, while a trend toward increase was found in gene expression in mice receiving the Mg-deficient diet, suggesting the onset of an adaptive response to Mg depletion. Histomorphometric parameters on the amount of trabecular and cortical bone, number of osteoclasts, and thickness of the growth plate in femoral distal and tibial proximal metaphyses did not differ between groups; these findings partially differ from most data present in the literature showing that animals fed a Mg-deprived diet develop bone loss and may be only in part explained by differences among the experimental protocols. However, the unexpected findings we recorded on bones could be attributed to genetic differences that may have developed after multiple generations of inbreeding.
## 1. Introduction
Magnesium (Mg) is the fourth most abundant cation in the body and the second most widespread intracellular one [1,2,3]. Overall, the predominant action of *Mg is* related to ATP utilization; in fact, it exists in all cells mainly as Mg-ATP. Regarding its physiological role, *Mg is* essential for many enzymatic reactions by stabilizing the reaction intermediate and/or the output product and simultaneously binding two substrates for the purpose of facilitating the reaction [4,5]. A number of signaling cascades related to Mg involve protein kinases, such as lymphocyte-specific protein tyrosine kinase (Lck), zeta-chain-associated protein kinase 70 (ZAP-70), and interleukin-2-inducible T-cell kinase (Itk) [6]. Approximately 60–$65\%$ of the total magnesium is present in the skeleton embedded in hydroxyapatite crystals, 32–$35\%$ is complexed with proteins and nucleic acids, and only 1–$2\%$ in plasma and other minor forms of deposition [4,7,8].
In humans, dietary Mg deficiency has been associated with hypertension, cardiac arrhythmias, myocardial infarction, hypokalemia, hypocalcemia, and the risk of osteoporosis; in fact, in disorders that impair intestinal absorption of Mg and/or are associated with renal loss of Mg (such as malabsorption syndromes, alcoholism, diabetes mellitus, and drugs such as diuretics), the incidence of osteoporosis is higher than expected [9]. Several experimental studies in rodents have shown that progressively reduced Mg diets, compared with the recommended daily intake, result in a corresponding loss of bone mass and increased skeletal fragility, due to increased bone resorption and/or reduced bone formation during skeletal remodeling [10,11,12,13,14,15]. In fact, Mg has been shown to be important for bone cell activity: (i) in vitro its depletion causes inhibition of osteoblastic growth, while its presence has a mitogenic action [16,17]; (ii) in vivo, its depletion causes both a decrease in the number of osteoblasts associated with alteration of their function and an increase in the number of osteoclasts and the surfaces eroded by them [11,12,13,18,19]. In addition, magnesium deficiency increases the production of pro-inflammatory cytokines, such as tumour necrosis factor-α (TNF-α), interleukin-1 (IL-1), and substance P, which promote bone resorption by osteoclasts [13].
Among rodents, the C57BL/6 mouse is widely used in biomedical research in studies of effects related to dietary magnesium reduction on physiological processes in various tissues/organs, as blood cells, liver, and the brain [20,21,22,23,24]. From the founding line C57BL/6, after many generations of inbreeding, several substrains have been derived that differ in small genomic variations leading to different phenotypes; of these, the most widely used in research are C57BL/6N and C57BL/6J [25,26]. As for bone, Simon and coworkers [26] have shown that, under normal conditions, the overall bone trabecular network is similar between the two substrains, as are bone formation and resorption markers. Only Tu and coworkers [27] investigated the effect of the reduction of magnesium in the diet for 8 weeks in C57BL/6J male mice using a radiologic and biochemical approach; they found a reduction of bone volume, number of trabecular segments, and bone mineral content of the femoral distal metaphysis and a decrease in Mg in plasma.
The purpose of the present work is to investigate with a histological approach the effects of a Mg-deprived diet on C57BL/6J mice to better understand the influence of this diet on bone architectures, in particular of the axial versus appendicular skeleton.
## 2.1. Experimental Animals and Treatment
8-week-old C57Bl/6 male mice ($$n = 14$$) were obtained from the animal facility of the University of Parma. To minimize genetic drift from the founder line, the animals are back-crossed with C57Bl6/J pure strain mice purchased from Charles River (Charles River Laboratories, Calco, ITA) every 10 generations. The animals of the colony exhibit a very good degree of uniformity in terms of reproductive capacity, rate of growth of newborns, body weight, general appearance, and behavior.
All mice were supplied with a basal diet over a one-week acclimatization period. Subsequently, the mice were randomized into two groups, each containing seven mice: the treatment and control groups. In the treatment group, mice were fed a commercial Mg-deprived diet ($0\%$ Mg wt/wt, Charles River Laboratory, Calco, ITA) and distilled water ad libitum for 8 weeks; in the control group, mice were fed a normal diet ($0.2\%$ Mg wt/wt, Mucedola, Settimo Milanese, ITA) and natural tap water ad libitum for 8 weeks. A complete nutritional description of the two diets is supplied in Table S1 (Supplementary Materials). Equal appreciation of the two diets was assessed by weighting daily the amount of food consumed by control and treated mice. All animals were maintained in a temperature-controlled room (22 ± 1 °C) with a 12:12 h light:dark cycle. The body weight of each animal was recorded at the beginning of the experimental period and then every 14 days until the day of sacrifice.
After 8 weeks, all mice were anesthetized with isoflurane and blood samples were collected by cardiac puncture; then, mice were euthanized by cervical dislocation. Kidneys and bones were collected for subsequent biochemical, histological, and histomorphometrical analyses. Blood samples were allowed to clot for 30 min and then centrifuged at 10,000 rpm for 10 min at room temperature. Serum was separated from the clot and stored at −20 °C until use. For 1 control and 1 treated animal, serum couldn’t be properly separated from blood.
All experiments conformed to the National Research Guide for the Care and Use of Laboratory Animals (D.L. $\frac{26}{2014}$) and the recommendations of the Institutional Animal Care and Use of the University of Parma (identification code Project ID: $\frac{719}{2015}$-PR). All achievements were made to minimize animal suffering and reduce the number of animals used.
## 2.2. Serum Mg Concentration
Mg concentration was assessed in serum samples by using a specific commercial colorimetric enzyme (glycerol kinase) assay kit (Sigma Aldrich, St. Louis, MO, USA). The assay was performed according to the manufacturer’s instructions. Briefly, 5 µL of each serum sample was directly pipetted into a 96-well plate and added with 50 µL of Master Reaction Mix (35 µL Magnesium Assay Buffer, 10 µL Developer 5 µL Magnesium Enzyme Mix), mixed on a horizontal shaker, and incubated for 10 min at 37 °C. The absorbance at 450 nm was measured immediately after the incubation period (A450)initial and then every 5 min until the highest measure approached the value of 1.5 (A450)final. For each sample (A450)final − (A450)initial was calculated to correct for the non-specific background of nicotinamide adenine dinucleotide phosphate (NADPH) present in the sample. The Mg concentrations of unknown samples were determined by comparison with a calibration curve obtained from Mg standard solution at 0; 0.06; 0.08; 0.12; 0.18; 0.24; 0.30 nmol/µL.
## 2.3. RNA Extraction, cDNA Preparation, and qPCR
Kidney tissue samples obtained from normal or Mg-deprived diet-fed mice were snap-frozen in liquid nitrogen and ground to a fine powder. RNA was extracted starting from 30 mg of powder by using 1 mL of TRIZOL reagent (Thermo Fisher Scientific, Waltham, MA, USA) and then purified with the PureLink RNA Mini kit (Thermo Fisher Scientific, Waltham, MA, USA). cDNA Synthesis was carried out with the RevertAid First Strand cDNA Synthesis kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions using a previously optimized protocol [28]. The expression of magnesium transporter protein 1 (MagT1), transient receptor potential cation channel, subfamily M, member 6 (TRPM6), transient receptor potential cation channel, subfamily M, member 7 (TRPM7) was measured by qPCR amplification using 1 µL of cDNA with the SSOAdvanced Universal SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) on the DNA Engine Opticon 4 (MJ Research, Walthman, MA, USA), as previously extensively described [28]. The sequence of the primers used in qPCR is reported in Table 1, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the housekeeper gene. For each analysis, the samples were run in duplicate. *The* gene expression level was calculated by the 2−∆∆Ct method, where Ct = threshold cycle; ∆Ct = Ct (target gene) − Ct (GAPDH) and ∆∆Ct = ∆*Cttarget* gene Treated − ∆*Cttarget* gene Control;
## 2.4. Histology and Histomorphometry
Soon after sacrifice, the fifth lumbar vertebra (L5), the femurs, and the tibiae from each animal were retrieved, cleaned of soft tissues, and fixed in a solution of $4\%$ paraformaldehyde with a pH of 7.4 in sodium phosphate saline (PBS).
## 2.4.1. Resin-Embedded Specimens
Vertebrae, right femurs, and right tibiae were then dehydrated in increasing ethanol concentrations and embedded in methyl-methacrylate (Sigma Aldrich, Milan, Italy). Specimens were cut with a Leica SP 1600 diamond saw microtome (Leica SpA, Milan, Italy) to obtain transverse serial sections of 200 µm thickness. The sections, taken from the center of L5, from the middle of the femoral patellar groove, and just below the tibial condyles, were glued to a glass slide and ground to a final thickness of 40 µm; they were then superficially stained with Fast green $1\%$ + $1\%$ acetic acid, observed with a Nikon Eclipse Ni microscope equipped with a DS-Fi2 camera (Nikon, Tokyo, Japan) and images were acquired by means of NIS-Elements D 5.11.00 software (Nikon, Tokyo, Japan). Histomorphometry was performed by means of the software Image J (NIH, Bethesda, Maryland, USA) to calculate:-trabecular bone volume (BV/TV), trabecular thickness (Tb. Th), trabecular number (Tb. N), and trabecular separation (Tb. Sp) in the L5 body, femoral distal, and tibial proximal metaphyses;-anterolateral cortical thickness (Ct. Th) measuring it at 10 random points in the vertebral body.
All measurements were performed according to the American Society for Bone and Mineral Research (ASBMR) histomorphometry nomenclature [29].
## 2.4.2. Paraffin-Embedded Specimens
Left femurs and left tibiae were decalcified in a $10\%$ EDTA solution for 20 days, then were washed with PBS, dehydrated in graded ethanol, and embedded in paraffin. Longitudinal serial sections (5 µm thick) of the femoral distal metaphysis and tibial proximal metaphysis were stained with Toluidine blue & Eosin to measure at 10 random points the thickness of the growth plate. Tartrate Resistant Acid Phosphatase (TRAP) reaction was used to identify osteoclasts; the osteoclasts number (NOc) was calculated along the bone surface (BS) in the interface along the growth cartilage and diaphyseal bone and expressed as NOc/BS. All slides were observed with a Nikon Eclipse Ni microscope equipped with a DS-Fi2 camera (Nikon, Tokyo, Japan) and evaluated by means of NIS-Elements D 5.11.00 software (Nikon, Tokyo, Japan).
## 2.5. Statistical Analysis
The IBM SPSS v25.0 statistical package (International Business Machines Corporation, Armonk, NY, USA) was used. Data are expressed as mean values ± standard deviation of the mean (SD). The statistical analysis was based on the Student’s t-test. The limit of statistical significance was set at $p \leq 0$,05. Graphics were prepared by using the GraphPad Prism v9.0 software (GraphPad software, San Diego, CA, USA).
## 3.1. Effects of the Mg-Deprived Diet on Body Weight and Mg Serum Concentration
All the animals included in the study gained weight during the 8-week study. However, the body weight gain was significantly reduced to $50\%$ after 4 weeks and to $45\%$ after 8 weeks in mice receiving the Mg-deprived diet compared with mice receiving the control diet (Figure 1).
After 8 weeks of treatment, mice fed the Mg-deficient diet exhibited a significant reduction ($20\%$) in serum Mg concentration compared with the control group (Figure 2).
## 3.2. Expression of TRPM6, TRPM7 and MagT1
By quantitative PCR (qPCR), we investigated whether animals fed with the Mg-deprived diet for 8 weeks exhibited differences in the expression of Mg transport and homeostasis systems compared with controls. To this end, we measured the relative expression levels of MagT1, TRPM6, and TRPM7 mRNA in frozen kidney tissue samples. Chronic Mg deprivation did not have any significant effect on the expression of Mg transporters in the kidney, as reported in Figure 3.
## 3.3. Histology and Histomorphometry
Unexpectedly, the histological observations of all bone segments investigated showed that their morphological aspect is very similar in both the treatment (Mg-deprived diet) and control groups (Figure 4). Histomorphometric data are reported in Table 2; no significant differences were found between the experimental groups for all the parameters evaluated, thus confirming the histological observations.
Furthermore, the morphological observation of the growth plate in the femoral distal metaphysis and tibial proximal metaphysis for each group (Figure 5) shows that the chondrocytes in the treatment group present the same morphology and typical columnar organization as the control one.
Histomorphometric results concerning the thickness of the growth plate are shown in Table 3; no significant differences were found between the treatment and control groups.
The osteoclasts present in the osteo-chondral interface along the growth cartilage and diaphyseal bone of femoral distal and tibial proximal metaphyseal sections are highlighted by the TRAP reaction as shown in Figure 6.
The number of osteoclasts (NOc/BS) doesn’t change between treatment and control groups, both in the tibia and the femur, as reported in Table 4.
## 4. Discussion
The findings of this study reveal some unusual responses of bone in 8-week-old C57BL/6 mice subjected to a Mg-deprived diet for 2 months, since unexpectedly the amount of bone didn’t change between Mg-deprived diet mice and control ones, while previous literature data [10,11,12,13,14,15], with special regard to C57BL/6J mice [27], mainly show a reduction in trabecular bone in rodents fed a Mg-deprived diet.
All the animals gained weight during the 8-week study, irrespective of whether they were assigned to the control group (normal diet) or the treated group (Mg-deprived diet). However, the percentage of weight gain at 4 and 8 weeks was significantly lower for treated mice compared with matched controls. The weight difference occurred despite the equal consumption of food by the two groups, in line with the results previously obtained from other groups [11,14,30].
In our experimental setting, we observed a $20\%$ reduction in serum Mg concentration in mice receiving the Mg-depleted diet compared with controls. This reduction is less severe than the reduction observed by others that fed C57BL/6J male mice with a low-Mg diet [27,30]. Although a direct comparison of these data might be not appropriate due to differences in the experimental setting (i.e., mice age, treatment time, diet composition), an inverse relationship between *Mg serum* concentration and both the treatment time and the age of mice at the beginning of the experiment seems to exist.
The adaptive response to *Mg status* changes is a complex and tightly regulated process that involves, among other mechanisms, the transcriptional and translational control of Mg-membrane transporters. TRPM6/TRPM7 Mg channels mediate the luminal Mg uptake from the pro-urine in the distal convoluted tubule of the kidney [31]. Increased mRNA gene expression of these genes following Mg-dietary restriction has been regarded as a compensatory mechanism to cope with a low-Mg state [32,33]. In our experimental model, we found that the expression of TRPM7 and TRPM6 is not significantly altered in the treated group compared with controls. However, the levels of TRPM6 had a trend toward increasing without reaching statistical significance, possibly due to the small size of the experimental groups. Similarly to TRPM6, the expression of MagT1, an oxidoreductase that is involved in the trafficking and stability of Mg channels and transporters [34] slightly increased following 8 weeks of Mg deprivation. Overall, these data suggest a good adaptive capacity of 8-week-old C57Bl/6 male mice to long-term dietary Mg deprivation. Interestingly, TRPM6 was included in a group of kidney-specific magnesiotropic proteins involved in the autosomal-recessive disorder hypomagnesemia with secondary hypocalcemia (HSH) [35,36].
The histomorphometric results of bone segments investigated in C57BL/6 mice demonstrated that no significant differences were found between the Mg-deprived diet group and the normal-fed diet group for all the parameters that characterize the amount of bone, independently of the architecture analyzed (Table 2). In line with our results, Rude and coworkers [11] didn’t find a significant decrease in trabecular bone in the distal femur after 2 weeks of an Mg-deprived diet in 28-day-old BALB/c female mice; only in the proximal tibia was the loss of trabecular bone significant with respect to control mice. By contrast, Mg deprivation in rats mainly leads to a reduction in bone mass, especially of trabecular bone, even when treatment times are shorter than those used in this study [12,18]. By a micro computed tomography approach, Tu and coworkers demonstrated that the global size of the trabecular bones was smaller in 4-weeks-old C57BL/6J mice fed with Mg-deprived diet for 8 weeks compared with controls [27]. With a similar radiologic approach, Gruber and coworkers [14] showed in 28-day-old BALB/c female mice subjected to a Mg-deprived diet for up to 6 weeks a significant decrease in trabecular bone mineral content in the distal femoral metaphysis versus control mice, while bone mineral density and mean cortical thickness at the femoral midshaft didn’t significantly change between the Mg-deficient mice and control mice. The authors suggested that the different response of bones to a Mg-deprived diet depends on the different bone turnover of trabecular versus compact bone; a similar behavior was already observed in our previous investigations in response to a calcium-deprived diet in rats [37,38].
Furthermore, our results on the thickness of the metaphyseal growth cartilage show no differences between the two groups of animals (Table 3); this outcome is in contrast with data reported in the literature. In fact, Rude and coworkers [11] showed that the width of the cartilage growth plate in the proximal metaphysis of the tibia in young BALB/c mice fed a severe Mg-deprived diet for 6 weeks decreased by $33\%$ and the columnar organization of chondrocytes was altered with a decrease in cell proliferation, suggesting that Mg deficiency reduced bone growth. In another animal species, also Gruber and coworkers [39] report that the metaphyseal growth plate in the tibiae of rats subjected to a 6-month dietary restriction of Mg shows significantly reduced formation of chondrocyte columns compared with control animals.
A third outcome that does not match those present in the literature is the number of osteoclasts calculated in the femoral and tibial metaphyses; in fact, the NOc/BS remains the same independently of the treatment (Table 4). Indeed, it is reported that rats fed a Mg-deprived diet [13,15,18,19] or diets at $10\%$ of the Mg nutritional requirement [12] show in the trabecular bone an increase in the osteoclast number and in the surface covered by osteoclasts, suggesting that the decrease in bone mass is due to an increase in bone resorption. In consideration of the above-discussed findings, it is remarkable to highlight that the results obtained in rats cannot be directly compared with those obtained with mice since these two species show genetic and metabolic differences, and it is not possible to establish reliable general rules to validate extrapolation from one species to another [40,41]. In this regard, it is important to emphasize that Rude and coworkers [11] showed in young BALB/c mice fed an Mg-deprived diet that in the distal femur the number of osteoclasts doesn’t increase and also that BV/TV doesn’t decrease with respect to control mice, thus suggesting that the amount of trabecular bone doesn’t change between the two groups since the NOc/BS remains the same. This result is in line with our finding: in fact, the number of osteoclasts doesn’t change as much as BV/TV between the two groups.
The discrepancy between our results on bone and a few literature data available on mice, in particular those obtained by Tu and coworkers [27] on the C57BL/6J strain, could be due to differences in the methodological approach (histomorphometric vs. radiological) or to the existence of different C57BL/6 substrains that we are not aware of, whose response of bone to Mg deficiency might differ from the expected one. In fact, it has been demonstrated that after many generations of inbreeding, several substrains have been derived from the C57BL/6 founder line [25,42] and are reported to differ in several phenotypes that differentially affect glucose, alcohol, or drug tolerance [43,44,45]. From a genetic perspective, in the same mice strain or probably substrain, Mg dietary restriction acts upon the expression of magnesiotropic and calciotropic genes, inducing adaptation. An essential adaptive role for the distal convoluted tubule in the kidney was described during dietary restriction and subsequent hypomagnesemia since expression levels of selected genes were adjusted [46]. In fact, Liron and colleagues [47] observed sometimes severe variations in the bone phenotype of C57BL/6J mice, hypothesizing that these differences could be attributed to variations among substrains. In particular, they found that C57BL/6J-OLA mice display a significantly lower trabecular bone mass compared with C57BL/6J-RCC and C57BL/6J-JAX substrains. Interestingly, Sankaran and coworkers [48] reported that hind limb unloading in the two substrains of C57BL/6 mice resulted in a different bone response such that bone loss was significantly greater in C57BL/6N mice than in C57BL/6J mice, suggesting that this outcome may be modulated, in part, by the Herc2, Myo18b, and *Acan* genes. Accordingly, Rendina-Ruedy and colleagues [49] showed that during a high-fat diet C57BL/6J mice were protected against bone loss compared withwith C57BL/6N mice, and therefore they differ in their metabolic and skeletal response.
By PCR amplification of the nicotinamide nucleotide transhydrogenase (Nnt) and a-synuclein (SNCA) genes, we ruled out that the C57BL/6 mice we used for the present study can be traced back to the C57BL/6N or the C57BL/6J-OLA substrains (data not shown). Nonetheless, some substrains’ features have not yet been well identified by biomedical researchers [25] and might develop compensatory mechanisms involving bone and kidney metabolism. Therefore, we cannot exclude that after many multiple generations of inbreeding in our animal facility, new genetic differences, currently uncharacterized, may have developed and contributed to the peculiar observed phenotype, which evidenced skeletal resistance to dietary magnesium depletion.
## 5. Conclusions
In 8-week-old male C57BL/6 mice, the Mg-deprived diet for 8 weeks resulted in a reduction in body weight gain, serum Mg levels, and a trend toward increased expression in kidney-specific magnesiotropic genes without loss of bone mass compared with the control animals. On the contrary, most data present in the literature show that animals fed a Mg-deprived diet develop bone loss, along with a reduction in body weight and serum Mg levels and an increase in kidney magnesiotropic gene expression. Nonetheless, it is necessary to underline that our results cannot be directly compared with those found in the literature. In fact, literature data mainly reported studies involving rats or mice strains different from C57BL/6 mice. The animals’ ages also differed, as did the diet formulations and the treatment time, and all these parameters were expected to influence the outcome.
The comparison of our results with those obtained in different experimental settings suggests that Mg deprivation produces more consistent and reliable effects on bone resorption in rats than in mice. These differences are likely supported by anatomical differences between the two species that make detecting slight differences at the histological level in small bones more difficult. Moreover, our results and those obtained by others highlight that the widely used C57BL/6 seems to adapt quickly and efficiently to Mg deprivation by adjusting the levels of genes involved in Mg transport through the kidney distal convolute tube. Indeed, another study aimed to investigate the effect of Mg deprivation in C57BL/6J mice on muscle dysfunction and found no detectable variation in muscle fiber morphometry concomitantly with the up-regulation of magnesiotropic genes, including TRPM6 [30].
We are confident that our results may contribute to the development of roadmaps to facilitate researchers engaged in the study of the effects of Mg deprivation on bones in choosing the right experimental model depending on both their scientific question and methodological approach.
In addition, we suggest the hypothesis that the lack of effects of Mg deprivation on bone could be attributed to genetic differences, which are currently unknown, that may have developed after multiple generations of inbreeding of the C57BL/6J strain in our animal facility.
In conclusion, it is interesting to consider that certain genetic profiles could provide peculiar skeletal resistance to severe magnesium deficiency, which makes the forthcoming investigations to identify possible genes involved in such resistance particularly intriguing.
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|
---
title: Milk Fat Globule Membrane Relieves Fatigue via Regulation of Oxidative Stress
and Gut Microbiota in BALB/c Mice
authors:
- Xiaoxiao Zou
- Wallace Yokoyama
- Xiaohui Liu
- Kai Wang
- Hui Hong
- Yongkang Luo
- Yuqing Tan
journal: Antioxidants
year: 2023
pmcid: PMC10045747
doi: 10.3390/antiox12030712
license: CC BY 4.0
---
# Milk Fat Globule Membrane Relieves Fatigue via Regulation of Oxidative Stress and Gut Microbiota in BALB/c Mice
## Abstract
Milk fat globule membranes (MFGMs) are complex structures that incorporate bioactive proteins and lipids to assist in infant development. However, the antifatigue and antioxidant potentials of MFGM have not been investigated. In this study, repeated force swimming measured fatigue in male BALB/c mice fed MFGM and saline for 18 weeks. The MFGM supplementation increased the time to exhaustion by $42.7\%$ at 6 weeks and $30.6\%$ at 14 weeks ($p \leq 0.05$). Fatigue and injury-related biomarkers, including blood glucose, lactic acid, and lactate dehydrogenase, were ameliorated after free swimming ($p \leq 0.05$). The activity of antioxidant enzymes in blood serum increased at 18 weeks, while malondialdehyde (MDA) content decreased by $45.0\%$ after the MFGM supplementation ($p \leq 0.05$). The Pearson correlation analysis showed a high correlation between fatigue-related indices and antioxidant levels. The increased protein expression of hepatic Nrf2 reduced the protein expression of Caspase-3 in the gastrocnemius muscle ($p \leq 0.05$). Moreover, the MFGM supplementation increased the relative abundance of Bacteroides, Butyricimonas, and Anaerostipes. Our results demonstrate that MFGM may maintain redox homeostasis to relieve fatigue, suggesting the potential application of MFGM as an antifatigue and antioxidant dietary supplement.
## 1. Introduction
Fatigue is a symptom that accompanies various diseases, such as hypertension, depression, diabetes, and coronary heart disease. Long-term accumulated fatigue leads to chronic fatigue syndrome (CFS), including symptoms such as muscle pain and weakness [1]. Proposed mechanisms of physical fatigue include exhaustion of energy stores, clogging by exercise metabolites, and excessive free radicals [2]. The exhaustion theory emphasizes the dramatic consumption of energy storage molecules, such as ATP, glucose, and fat, during exercise, resulting in the declining performance of skeletal muscle and failure to complete a predetermined exercise intensity, thus generating physical fatigue [3]. The clogging theory suggests that excessive accumulation of harmful metabolites, for instance, blood lactic acid (BLA), during strenuous exercise results in energy supply obstruction and muscle capacity reduction [4]. In recent years, the free radical fatigue theory has attracted more interest. This theory postulates that intense exercise increases free radicals, which oxidize fatty acids in cell membranes and damage protein and nucleic acid chains. The free radical damage puts the body in a state of oxidative stress, thereby giving rise to physical fatigue [5]. The intestinal tract is a crucial source of reactive oxygen species (ROS) and nitrogen oxide species [6]. Meanwhile, it has been declared that after the supplementation of antioxidant foods, altered intestinal bacteria promote the release of anthocyanins or polyphenols from food to increase the antifatigue capacity of the body [7]. Therefore, fatigue is greatly associated with the homeostatic control of the redox intestinal environment. It has been observed that active ingredients derived from natural plants and animals can reduce fatigue, with the merit of low side effects [8].
Milk fat globule membrane (MFGM) has been demonstrated to possess various nutritional functions and is recognized as the sole source of phospholipids in breast milk [9]. It has been demonstrated that formula supplemented with MFGM can reduce the neurodevelopmental differences between breast milk and formula feeding in postnatal rat pups [10]. A previous study found that MFGM promoted the growth of beneficial intestinal bacteria, inhibited the colonization of pathogenic bacteria, and increased the content of short-chain fatty acids [11]. In addition, MFGM improved body weight, fasting blood glucose, and serum insulin levels in type 2 diabetic (T2D) mice [12]. In healthy adult mice, MFGM plus exercise increased Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (Pgc1α) expression by increasing adiponectin production in, or secretion from, adipose tissues, thereby activating fatty acid oxidation and, subsequently, improving endurance capacity [13]. These results indicated that MFGM has significant nutritional functions and may be a promising bioactive ingredient for foods. While various functional properties of MFGM have been studied, their antioxidant and antifatigue functions have not yet been well studied.
Hence, this study aims to investigate the effects of MFGM on the antifatigue capacity of BALB/c mice and the effect of MFGM feeding on the gut microbiome. We hypothesized that MFGM exerts its antifatigue function by relieving oxidative stress.
## 2.1. Materials
MFGM (Hilmar 7500) was donated by Hilmar Cheese Company (Hilmar, CA, USA). The protein, fat, ash, and moisture contents of MFGM were $66.47\%$, $14.98\%$, $2.75\%$, and $5.79\%$, respectively. Protein, fat, ash, and moisture were measured according to the AOAC International methods 955.04, 2003.06, 942.05, and 934.01, respectively. The carbohydrate of MFGM was $10.01\%$, which was calculated by deducting the contents of protein, moisture, fat, and ash from $100\%$ according to the AOAC 979.06 (AOAC International, 2012) [14].
## 2.1.1. Amino Acids of MFGM
The amino acid composition was measured by the method of Li et al. with minor modifications using HPLC-MS/MS analysis [15]. The HPLC-MS/MS experiments were performed using an Ultimate 3000 (Dionex, Sunnyvale, CA, USA)-API 3200 Q TRAP detector (AB Sciex, Framingham, MA, USA), an HPLC system, and an MSLab50AA-C18 column (150 mm × 4.6 mm × 5 um). The sample was pretreated as follows: The MFGM sample was homogenized with distilled water. Then, the homogenate was added to an equal volume of concentrated ($37\%$) hydrochloric acid and digested at 110 °C for 21 h under nitrogen. The digested solution was membrane filtered, dried under a vacuum, and reconstituted with distilled water. Finally, the sample was derivatized with ITRAQ reagent (Applied Biosystems, Waltham, MA, USA) after sonication for 5 min. The remainder of the analysis followed Li’s method. The composition of amino acids in the MFGM used in this study is shown in Figure S1.
## 2.1.2. Phospholipids of MFGM
The MFGM sample was extracted with acetone to remove triglycerides. Phospholipids were extracted with chloroform and methanol (3:2). The phospholipids were analyzed using HPLC by elution through an LC-NH2 column (25 cm × 4.6 mm, Sigma, St. Louis, MO, USA). The phospholipids were quantified by an evaporative light scattering detector (Infinity 1260 II, Agilent, Santa Clara, CA, USA) [16]. The total and specific composition of phospholipids in the MFGM sample are displayed in Table 1.
## 2.2. Animal Experiment
The experiments were approved by the Biomedical Ethics Committee of Peking University, Beijing, China (Protocol code: LA2021477). Male BALB/c mice were purchased from Charles River Development, Inc. (Beijing, China) at 7 weeks old. The mice were housed in a plastic case under a temperature of 22 ± 2 °C and a humidity of 50–$70\%$, and operating on a 12 h light–12 h dark cycle with food and water available ad libitum. The maintenance feed for the mice was obtained from Beijing Keao Xieli Feed Co., Ltd. (Beijing, China), which contained more than $18\%$ protein and $4\%$ fat. The contents of leucine, valine, and isoleucine (compositions of branched-chain amino acids) in the feed were 14.80 g, 8.90 g, and 7.40 g per 1 kg, respectively (http://www.keaoxieli.com/product/137.html, accessed on 10 March 2023). The mice were randomly assigned into 2 groups (8 mice per group). One group was orally administered MFGM (MFGM group) at 400 mg/kg body weight (BW). The control group was orally administered an equal amount of saline (Control group). After 6 and 14 weeks of feeding, an exhaustive swimming exercise test was performed. At 16 weeks, a free-swimming test was performed, and blood was collected. The mice were sacrificed after 18 weeks of feeding. Blood, liver, gastrocnemius muscle, and other tissues were collected for further analysis. The detailed procedures are shown in Figure S2.
## 2.3. Exhaustive Swimming Exercise Test
The exhaustive swimming exercise test was modified from the study by Chen et al. [ 17]. A weight of $7\%$ that was equivalent to a mouse’s body weight was hung on each mouse’s tail. The mice were administered either MFGM or saline 30 min before the swimming test. The mice were placed in a tank with water at a depth greater than 30 cm at 25 ± 1.0 °C. The mice swam until exhaustion, which was defined as a time point when the mice failed to return to the water surface to breathe for 7 s. The time period from the beginning of the test to the endpoint was recorded as the exhaustive swimming time.
## 2.4. Free Swimming Test
At 16 weeks, the mice were submitted to a free-swimming test without any load. The mice were administered saline or MFGM 30 min before the swimming test. The mice were placed in a circular tank (36 × 36 cm), and after 60 min of swimming, blood samples were collected via the submandibular vein. The blood serum was separated by centrifugation before storage at −80 °C.
## 2.5. Organ and Tissue Collection
The mice were sacrificed via an intraperitoneal injection of 50 mg/kg pentobarbital sodium after 18 weeks of feeding. The mice were fasted for 8 h to minimize the possible impacts of uncertain time of food intake. The liver, heart, thymus, and spleen were weighed and stored at −80 °C.
About 1 mL of blood was collected from the retro-orbital sinus, refrigerated for 1 h at 4 °C, and centrifuged at 1180× g for 15 min at 4 °C. The blood serum was stored at −80 °C for subsequent analysis. The gastrocnemius muscle from the left leg was fixed in a $4\%$ paraformaldehyde solution for tissue microscopy and immunocytochemical analysis. The gastrocnemius muscle from the right leg and liver was stored at −80 °C for other analysis.
## 2.6. Serum Biochemical Indices
The levels of blood glucose (BG), blood lactic acid (BLA), and lactate dehydrogenase (LDH) were measured after the free-swimming test. BLA, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA) were determined after sacrificing the mice. These assay methods were performed according to the methods from the Nanjing Jiancheng Bioengineering Institute (Nanjing, China).
## 2.7. Histological Analysis
Gastrocnemius muscles were fixed overnight in a $4\%$ paraformaldehyde solution and then embedded in paraffin. The paraffin sections stained with hematoxylin and eosin (H&E) were viewed under a bright field on an upright optical microscope (Nikon Eclipse E100, Tokyo, Japan). The slices from the two groups were selected with the same area of view.
## 2.8. Immunohistochemical Analysis
The immunochemical method was used to determine the protein expression level of Caspase-3 in gastrocnemius muscles [18]. Cleaved Caspase-3 rabbit polyclonal antibody and HRP-conjugated goat anti-rabbit IgG (H + L) were provided by Wuhan Servicebio Technology Co., Ltd. (Wuhan, China). ImageJ 1.53k (National Institution of Health, Bethesda, MD, USA) was used to measure the expression level.
## 2.9. Western Blot Analysis
Total proteins were isolated from the liver tissue using a cell lysis buffer for Western and IP (Beyotime Biotechnology Co., Ltd., Shanghai, China). The nuclear and cytoplasmic proteins were extracted through using a Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime Biotechnology Co., Ltd., Shanghai, China). Protein content was quantified by a BCA Protein Assay Kit (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). The amount of protein in each group was equalized to 20 ug and then subjected to sodium dodecyl sulfate–polyacrylamide gel electrophoresis. The fractionated proteins were transferred to polyvinylidene fluoride (PVDF) membranes in WIX-miniBLOT4 (WIX Technology Co., Ltd., Beijing, China). The protein-rich PVDF membranes were blocked with $5\%$ skim milk-TBS solution for 1.5 h, and later incubated overnight at 4 °C with the following primary antibodies: Nuclear factor erythroid-derived 2-like 2 (Nrf2), Kelch-like ECH-associated protein 1 (Keap1; 1:1200), and β-actin (1:1000) were all purchased from Beijing Biosynthesis Biotechnology Co., Ltd., Beijing, China. Horseradish peroxidase (HRP)-conjugated second antibodies (Beyotime; 1:1000 in $5\%$ skim milk-TBS solution) were added and incubated at room temperature for 1.5 h. The density of protein bands was analyzed by using the Quantity One 1-D Analysis Software (Bio-Rad Laboratories, Inc., Hercules, CA, USA) and ImageJ 1.53k (National Institution of Health, Bethesda, MD, USA).
## 2.10. 16S rRNA Gene Sequencing and Bioinformatic Analysis
The feces of the mice, which were mixed in pairs, were collected at 18th week and were immediately stored at −80 °C until further use. The method of 16S rRNA gene sequencing was performed according to Li et al. [ 19], with minor modifications. DNA was extracted from the feces according to the instruction of the E.Z.N.A.® soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The V3–V4 domains of 16S rRNA were amplified using primers 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R(5’-GGACTACHVGGGTWTCTAAT-3’) by an ABI GeneAmp® 9700 PCR thermocycler (Applied Biosystems, Foster City, CA, USA). Based on the standard protocols developed by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China), purified amplicons were pooled into equimolar and paired-end sequences on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, CA, USA). The data analysis and mapping were performed on the online platform of Majorbio Cloud Platform (www.majorbio.com, accessed on 10 March 2023) and Bioinformatics (http://www.bioinformatics.com.cn/, accessed on 10 March 2023). The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, according to the result of the 16S rRNA gene sequencing, was performed on the online platform of Majorbio Cloud Platform. Firstly, PICRUSt was used to standardize the OTU abundance table. Secondly, the corresponding KEGG Ortholog (KO) information of OTU was obtained through the green gene id corresponding to each OTU, and each KO’s abundance was calculated. Finally, the plots were configured on https://www.omicsolution.org/wkomics/main/ (accessed on 10 March 2023). The raw data were deposited into the NCBI Sequence Read Archive (SRA) database with the accession number PRJNA891493.
## 2.11. Statistical Analysis
All data are shown as means ± SD (standard deviation). Statistical analysis was determined by independent-sample t-test. Statistical significance was established at $p \leq 0.05$ level. These calculations were carried out via the SPSS software (version 25.0, IBM Inc., Chicago, IL, USA). The correlation between fatigue-related and antioxidant indices was revealed using a Pearson correlation analysis, and the plot was constructed on https://omicsolution.org/wkomics/main/ (accessed on 10 March 2023).
## 3.1. Animal Metrics and Structure of Muscle Tissue
The liver, heart, thymus, and spleen indices (the ratio of the organ to body weight) after 18 weeks of feeding are shown in Figure 1A,B. The body weights and organ indices are not different after MFGM supplementation. Haramizu et al. also reported no differences in body weight, liver weight, or heart weight in BALB/c mice fed MFGM for 12 weeks [13]. These findings confirm that MFGM supplementation is nutritious and safe.
As revealed in Figure 1C,D, compared to the Control group, the gastrocnemius muscle cells in the MFGM group are arranged more tightly and orderly, representing irregular polygons, while the shapes are circular in the Control group. The intercellular space is also smaller after MFGM supplementation.
Li and coworkers reported that milk fat globule-EGF factor 8 (MFG-8), alternatively known as lactadherin, and accounting for $82.35\%$ of MFGM protein, promoted C2C12 cell proliferation via the phosphatidylinositol 3-kinase and mammalian target of rapamycin (PI3K/Akt/mTOR/P70S6K) signaling pathway [20]. A previous study found that dietary MFGM combined with exercise could improve endurance performance through increased lipid metabolism. It is noteworthy that sphingomyelin (SM) may be one of the critical factors in increasing Pgc1α mRNA expression in soleus muscle in vivo and differentiating myoblasts in vitro [13]. Increasing cells’ regenerative potential and proliferation activity can relieve sarcopenia. These reports suggest that the ingredients of MFGM, such as MFG-8 and SM, may regulate gene expressions and metabolism in muscle cells to improve gastrocnemius muscle structure, ultimately strengthening the capacity for antifatigue and endurance.
## 3.2. Swimming Test and Serum Biomarkers of Antifatigue Status
As shown in Figure 2A,B, there are significant increases in the exhaustive swimming time (EST) at not only 6 weeks ($42.69\%$ higher, $p \leq 0.05$) but also 14 weeks ($30.60\%$ higher, $p \leq 0.05$) after MFGM supplementation.
Several biochemical indices derived from the blood serum were analyzed to quantify the energy reserve and the accumulation of detrimental metabolites after intense exercise. In this study, the BG levels of the mice fed MFGM increased at both 16 weeks and 18 weeks (Figure 2C,D, $p \leq 0.05$). BG is the primary energy source for the central nervous system. A lack of BG during exercise can cause hypoglycemia, followed by fatigue [21].
As displayed in Figure 2E,F, both BLA and LDH are lower in the mice in the MFGM group by $25.68\%$ and $18.93\%$, respectively, compared to the Control group. During high-intensity exercise, the move from aerobic metabolism to anaerobic glycolysis or glycogenolysis leads to a massive accumulation of BLA [1]. The reduced pH caused by LA can affect cardiac circulation and skeletal muscle system function [22]. LDH is a major enzyme involved in anaerobic glycolysis and gluconeogenesis by promoting the redox reaction between lactic acid and pyruvic acid that reduces the accumulation of BLA. However, the cell membrane becomes more permeable under vigorous exercise, resulting in the escape of LDH into the blood, thereby reducing the enzymatic oxidation of LA [23]. The lower levels of LDH and BLA in the MFGM group compared to the levels in the Control group after the free-swimming test are consistent with the results of previous studies.
The tryptophan (Trp)-5-hydroxytryptamine (5-HT)-central fatigue hypothesis proposes that the synthesis and release of the neurotransmitter 5-HT by neurons may lead to central fatigue after exercise. Trp and 5-HT levels increase during exercise. The rate-limiting and critical step of this procedure is the transport of Trp across the blood–brain barrier [24]. Because branched-chain amino acids (BCAAs, including leucine, valine, and isoleucine) are transported via the same carrier system, decreasing the free Trp/BCAA ratio can delay central fatigue [25]. The MFGM used in this study contained a significant amount of BCAAs, including $6.7\%$ of Val, $8.3\%$ of Leu, and $4.7\%$ of Ile (Figure S1). Falavigna and coworkers found that diets that were chronically supplemented with BCAAs had a beneficial effect on performance by sparing glycogen in the soleus muscle and inducing a lower concentration of plasma ammonia in rats, therefore leading to increased performance in the swimming test [26]. BCAAs may be the main MFGM component responsible for the antifatigue effects observed in this study.
## 3.3. Serum Biomarkers of Antioxidant Enzymes
Superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) concentrations of the mice fed MFGM are $6.71\%$, $87.1\%$, and $14.42\%$, respectively, which are higher than those of the mice in the Control group (Table 2, $p \leq 0.05$). The MFGM diet also lowers the content of malondialdehyde (MDA) in the mice fed this diet compared to the Control group (Table 2, $p \leq 0.05$).
SOD, CAT, and GSH-Px are the primary endogenous antioxidant enzymes that can protect the body from molecular free radical damage caused by excessive oxidative stress by scavenging reactive oxygen species (ROS) and their metabolites [27]. Under normal conditions, ROS is effectively eliminated by antioxidant defense systems. Growing evidence demonstrates that vigorous exercise can break the oxidation/antioxidant balance. Overproduction of free radicals can damage biological macromolecules, including proteins, lipids, and DNA, in the contracting myocytes and influence cellular signaling pathways [27]. Finally, muscle fatigue happens. Free radicals act on lipids to cause peroxidation reactions, and MDA is the final product of oxidation. The concentration of MDA is a crucial biomarker of oxidative injury in the body.
MFGM antioxidant peptides were partially purified, and their antioxidant activity was measured. The peptide fraction with a molecular weight of 5–10 ku had the greatest antioxidant activity. Their DPPH clearance was $70.41\%$ or 0.8 times that of vitamin C [28]. Li et al. identified two novel antioxidant peptides, TGIIT and YAR, from MFGM hydrolysates. These antioxidant peptides had pro-proliferative activity and protected rat myoblasts against dexamethasone-induced oxidative damage and apoptosis by regulating mitochondrial activity and inhibiting mitochondria-dependent apoptosis [29]. These reports suggest that antioxidant peptides may be critical to the antioxidant capacity of MFGM.
## 3.4. Pearson Correlation Analysis of Different Indicators
The relationship between blood biomarkers and antifatigue measurements was analyzed via Pearson correlation analysis. Changes in these parameters, such as EST, BG, BLA, and LDH, reflect the biochemical status of body fatigue. As shown in Figure 3, the levels of these fatigue-associated indices are highly correlated with antioxidant enzymes (SOD, CAT, and GSH-Px) and are inversely related to the MDA content.
These observations are supported by many other studies. For example, Chen et al. reported that feeding BALB/c mice quercetin, an antioxidant belonging to polyphenol, significantly increased swimming time and reduced BLA and LDH [30]. Peptides from the digestion of MFGM protein may also have antifatigue properties. The MFGM in this study contained $67\%$ of protein, and their digestion might produce antifatigue peptides. For example, the peptides from the protein hydrolysate of large-head hairtail (Trichiurus lepturus), a member of the cuttlefish family, improved the exhaustive swimming time of mice gavaged with these peptides for six weeks. The Pearson correlation analysis of fatigue-related indices of the hairtail peptide feeding, including metabolites and energy-related indices, were significantly correlated with antioxidant levels [31]. Another example of the importance of ROS reduction in decreasing fatigue is the study by Zhu and coworkers, who found that macamides, composed of long-chain fatty acid and benzylamine from Maca (*Lepidium meyenii* Walp.), decrease the ROS levels in the serum and muscles of mice after a swimming test and up-regulate the expression of heme oxygenase-1, which belongs to the Nrf2 pathway, in the liver. Their Pearson correlation analysis showed a high correlation between fatigue-related indices, antioxidant enzymes, and ROS levels [32]. These studies suggest that antioxidant capacity plays a crucial role in the antifatigue effects of MFGM. However, further research is necessary to reveal the detailed mechanisms by which MFGM, a substance containing antioxidants, proteins, and lipids, relieves fatigue.
## 3.5. Effects of MFGM on Protein Expressions in Muscle and Liver
The nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway plays a crucial role in resisting oxidative stimulation and maintaining the homeostasis of redox status in tissues and organs. To further clarify the antioxidation and fatigue-resisting mechanisms of MFGM, we analyzed the protein levels of antioxidant proteins Nrf2 and Keap1 (Kelch-like ECH-associated protein 1). The relative protein expressions of total and nuclear Nrf2 in the MFGM group are significantly higher than those in the Control group ($p \leq 0.05$) (Figure 4A,B).
Under normal homeostatic circumstances, Nrf2 is anchored by both Keap1 and the actin cytoskeleton, and it is then degraded in the cytoplasm. When oxidative stress happens, Nrf2 dissociates from Keap1 and enters the nucleus to maintain redox homeostasis through the regulation of downstream proteins. Oh et al. generated an Nrf2 knockout mouse model and reported that wild-type mice had longer running distances than Nrf2-null mice in an exhaustive treadmill test. During the exhaustive exercise, the Nrf2-null mice suffered more severe oxidative stress damage and decreased motor function than the wild-type mice [33]. The MFGM used in this study contained $67\%$ of protein, which may have functional properties. Wang et al. reported that sea cucumber peptides (SCP) significantly improved exercise performance and fatigue-related physiological indicators, increased the activity of antioxidant enzymes, and inhibited the free radical metabolite, MDA, in the serum of mice. SCP regulated oxidative stress and exerted an antifatigue effect by regulating the Nrf2 pathway [34]. These results are similar to the results of the current study, suggesting that MFGM regulation of the Nrf2 pathway, especially the expression of Nrf2, improves antifatigue capacity by maintaining redox homeostasis. It is worth noting that there is no significant difference in the expression of Keap1 and cytoplasmic Nrf2 between the Control and MFGM groups (Figure 4A,B). ETGE, a stretch of four amino acids within the N-terminal region of Nrf2, is a vital motif for the Nrf2–Keap1 interaction. The loss of the ETGE motif’s function was identified as abolishing the repressive effect of Keap1 on Nrf2 [35]. ETGE motif might be influenced after the supplementation of MFGM, destroying the interaction of Nrf2 and Keap1. Afterward, the degradation of Keap1 and cytoplasmic Nrf2 becomes disordered.
The cleaved Caspase-3 expression is significantly decreased in the gastrocnemius muscle of the mice after MFGM supplementation (Figure 4C,D). As a critical factor for inducing apoptosis, the level of cleaved caspase-3 has a positive correlation with the degree of apoptosis [36]. The apoptosis pathways are sensitive to and are regulated by the intracellular redox environment. Mitochondria-generated ROS have a positive effect on the release of pro-apoptotic proteins [37]. The skeletal muscle’s nuclear and mitochondrial integrity is damaged by ROS, causing fatigue. Bcl2 and Bax are other crucial apoptosis regulators, with Bcl2 being anti-apoptosis and Bcl-2-associated X protein (Bax) being pro-apoptosis. A previous study reported that Nrf2 inhibitors decreased the expression levels of Bcl2 and the Bcl2/Bax ratio, which then increased the expression level of downstream Caspase-3 [38]. Ma et al. showed that the polyphenol, phlorizin (PHZ), increased the exhaustive exercise-induced fatigue time in mice. They found that PHZ facilitated Nrf2 translocation from the cytoplasm to the nucleus, increased the ratio of Bcl2/Bax, and decreased cleaved Caspase-3 [39]. These results demonstrate that the Nrf2 pathway can be mediated by foods and suggest that MFGM might also relieve fatigue via targeting Nrf2 to inhibit apoptosis and protect skeletal muscle.
Besides the pathways mentioned above, the JNK (c-Jun amino-terminal kinase) pathway also deserves attention. As a potent activator for JNK, ROS inactivates endogenous JNK inhibitors oxidatively [40]. JNK induces Nrf2-Keap1 dissociation, Nrf2 upregulation, and Nrf2 movement to the nucleus to relieve oxidative stress [41]. In addition, JNK could phosphorylate the proapoptotic Bcl-2 family protein BAD to suppress apoptosis [42], which greatly corresponds with the lower expression level of cleaved Caspase-3 in the MFGM group. Previous studies reported that protocatechuic acid induced antioxidant enzyme expression and prevented oxLDL-induced apoptosis through JNK-mediated Nrf2 activation in murine macrophages [43]. Yuan and coworkers declaimed that paeoniflorin could significantly inhibit cell apoptosis and resist H2O2-induced oxidative stress in melanocytes through the JNK/Nrf2 pathway [44]. Nevertheless, to verify the above conjecture more rigorously, the Nfe2l2, Keap1, and Mapk8 knockout mouse should be used to clarify the mechanism of MFGM’s antifatigue effects in future studies.
## 3.6. Effects of MFGM on the Relative Abundance and Composition of Gut Microbiota
The Shannon indices, related to the alpha-diversity of the gut microbiome, between the Control and MFGM groups (Figure 5A) are similar, indicating that the supplementation of MFGM did not alter the diversity of the gut microbiota. However, the principal component analysis (PCA, Figure 5B) shows that the taxonomic composition differs between the two groups. As revealed in Figure 5C–E, MFGM results in advantageous changes in the gut microbiota from the phylum to the genus level. Although the changes in the microbiota within the group are not completely consistent due to individual differences, we mainly studied the bacteria that have varied as a whole between the groups. At the phylum level, the main compositions of the microbial community are Bacteroidota, Firmicutes, Proteobacteria, Verrucomicrobiota, and Actinobacteriota, accounting for more than $98\%$ of the total. Among them, the relative abundance of the phylum Verrucomicrobiota significantly increases to $2.25\%$ ($p \leq 0.05$) after the MFGM supplementation. In a previous study, an increase in Verrucomicrobiota in C57bl/6 mice fed a six amino acid peptide from Jinhua ham was associated with decreased MDA, ROS, and antioxidant enzymes [45]. The peptide benefits were attributed to increased tight junction protein and reduced lipopolysaccharide (LPS)-induced inflammation. The relative abundance of *Firmicutes is* increased in the mice of the MFGM group. Matsumoto et al. reported that increased phylum Firmicutes (SM$\frac{7}{11}$ and T2-87) produced greater colonic butyrate in rats participating in a voluntary running exercise [46]. To further characterize the gut microbiota changes, besides observing the gut microbiota in each mouse in Figure 5C–E, the LEfSe analysis was used to distinguish the taxa with significant differences in abundance (LDA score > 3.0, $p \leq 0.05$, Figure 6F,G).
At the family level, there is an increased relative abundance of Bacteroidaceae and decreased relative abundance of Helicobacteraceae after the MFGM supplementation. Bacteroidaceae produce short-chain fatty acids (SCFAs) [47]. Carbohydrates are digested and subsequently fermented into SCFAs in the colon, where they are carried through the bloodstream to various organs (e.g., muscle and adipose tissues) and used as substrates for gluconeogenesis and lipogenesis [48]. In a BALB/c allergen mouse model, reduction of the Helicobacteraceae family (Proteobacteria phylum) reduced inflammatory biomarkers by disrupting the recycling of tight junction proteins that form the physical and immunologic barrier of the intestinal epithelium [49]. Disruption of the tight junction of the mucosal epithelial cells may damage the water transport and mucosal hydration function. The decreased hydration status of the intestine may reduce endurance performance [6].
The relative abundance of Bacteroides, Butyricimonas, and Anaerostipes increases after the MFGM supplementation at the genus level. The MFGM reduces the relative abundance of Lactobacillus compared to the Control group. Bacteroides play a significant role in producing SCFAs, mainly propionate and acetate, to satisfy the energy requirements in endurance exercises [50]. Meanwhile, members of Bacteroides are oxygen tolerant and can survive oxidative stress when the host faces high-intensity exercise [51]. As a genus predominant in the gut of endurance athletes [52], Lactobacillus can promote the utilization of protein and maintain the homeostasis of energy metabolism in muscle. However, the relative abundance of Lactobacillus decreases in the mice of the MFGM group. This contradiction might be due to the production of tryptophan by Lactobacillus and the subsequent synthesis of 5-HT in the gut. 5-HT transported to the brain affects the pituitary gland and mental mood, thereby resulting in central fatigue [7]. Butyricimonas and Anaerostipes are the main genera that produce butyrate, which regulates the neutrophil function and migration, and increases the expression of tight junction proteins in colon epithelia. Butyrate can be utilized as an important source of energy for other microbes and the host’s organs [6]. The current study’s results show that MFGM reshapes the gut microbiome of mice associated with antifatigue capacity. However, further research is needed to clarify the detailed mechanism.
## 3.7. Effects of MFGM on the Functional Changes in Microbial Communities
To gain insight into the shifts of molecular functions of bacterial microbiota in response to the changes in the gut microbial communities, the KEGG analysis was performed via a phylogenetic investigation of the community through the reconstruction of unobserved states (PICRUSt). As presented in Figure 6, Genetic Information Processing, Organismal Systems, Human Disease, Cellular Processes, Environmental Information Processing, and Metabolism exhibit significant alternations at level 1 of the KEGG after the MFGM supplementation ($p \leq 0.05$). Especially at level 3 in Metabolism (Figure 6F), it is observed that some pathways associated with increased antifatigue capacity are enriched in the MFGM group.
In lipid metabolism, sphingolipid metabolism and glycerophospholipid metabolism are enriched after the MFGM supplementation. In previous research, sphingolipid metabolism was significantly altered after exercise in rats with chronic fatigue syndrome [53]. Sphingolipid was also demonstrated to play a critical role in muscle contraction and protection from fatigue [54]. As bioactive molecules are involved in the recognition and signal transduction of proteins, glycerophospholipid is one of the phospholipids in the cell membrane. He and coworkers declared that combining Astragali Radix and Codonopsis Radix with Jujubae Fructus could protect cell membrane structure from oxidative damage and ameliorate fatigue by regulating glycerophospholipid metabolism in mice [55]. In amino acid metabolism, the metabolism of BCAAs increases in the MFGM group. As mentioned above, decreasing the free Trp/BCAA ratio can reduce the synthesis and release of 5-HT in neurons, thereby delaying central fatigue [25]. The proportion of BCAAs reaching $19.73\%$ in the MFGM group may be the reason for the increase in BCAA metabolism. D-Arginine and D-ornithine metabolisms are higher in the MFGM group. In a previous study, the supplementation of arginine and ornithine increased the EST of rats, and the potential mechanism was associated with ammonia buffering [56]. In terms of vitamin metabolism, MFGM significantly promotes the biosynthesis and metabolism of various B vitamins, for example, folate, biotin, and vitamin B6. B vitamins have been suggested to directly stimulate ROS and modulate immune cytokines to reduce oxidant stress [57]. Vitamin B complex has been claimed to relieve oxidative tissue injury related to stress-induced neurobehavioral changes in rats [58]. Moreover, the apoptosis pathway of Cellular *Processes is* significantly increased in the Control group, which is similar to the result obtained from the immunohistochemical analysis of gastrocnemius muscle in Figure 4. However, a more in-depth study is needed to confirm the potential relationship between MFGM and the abovementioned pathway.
## 4. Conclusions
The present study demonstrated that chronic MFGM supplementation for 18 weeks significantly increased the antifatigue capacity of mice. According to the high correlation between antioxidant enzymes and fatigue-related indices, as well as the increased level of Nrf2 in the liver after the MFGM supplementation, this effect might be related to the regulation of the Nrf2 pathway, which is associated with resistance against oxidative stress, thereby relieving fatigue. MFGM improves gut bacterial composition at the phylum, family, and genus levels, while altering microbiota function associated with antifatigue capacity. This study expands the application fields of MFGM and provides a theoretical basis for the development and application of new antifatigue products.
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|
---
title: Analytical Models of Intra- and Extratumoral Cell Interactions at Avascular
Stage of Growth in the Presence of Targeted Chemotherapy
authors:
- Evgeniia Lavrenteva
- Constantinos Theodoropoulos
- Michael Binns
journal: Bioengineering
year: 2023
pmcid: PMC10045748
doi: 10.3390/bioengineering10030385
license: CC BY 4.0
---
# Analytical Models of Intra- and Extratumoral Cell Interactions at Avascular Stage of Growth in the Presence of Targeted Chemotherapy
## Abstract
In this study, we propose a set of nonlinear differential equations to model the dynamic growth of avascular stage tumors, considering nutrient supply from underlying tissue, innate immune response, contact inhibition of cell migration, and interactions with a chemotherapeutic agent. The model has been validated against available experimental data from the literature for tumor growth. We assume that the size of the modeled tumor is already detectable, and it represents all clinically observed existent cell populations; initial conditions are selected accordingly. Numerical results indicate that the tumor size and regression significantly depend on the strength of the host immune system. The effect of chemotherapy is investigated, not only within the malignancy, but also in terms of the responding immune cells and healthy tissue in the vicinity of a tumor.
## 1. Introduction
Cancerous malignancies are among the most lethal diseases affecting humanity over the last century. Moreover, they mutate and get more complex with time, which puts them at the top of the list of concerns for scientists and the medical society [1]. Conventional treatment techniques, such as chemotherapy, radiotherapy, immunotherapy, virotherapy, and surgical intervention, frequently do not lead to the full eradication of tumors [2]. Thus, there is an immense need for the precise theoretical prediction of the course of the disease under certain types of treatment and ways to make those therapies work in coordination, in order to achieve the most effective and tolerable results. The purpose of the mathematical modeling of cell interactions within tumors and affected tissues is to predict the effects of external factors, such as treatments, and to design or optimize these treatments (treatment dosages, treatment duration and combinations of different treatments) to minimize, stop or reverse tumor growth.
Tumor-immune interactions and the effects of chemotherapeutic drugs are both of serious research interest, aiming to understand the dynamics of natural physiological responses to malignant formations and discover ways to use their benefits, along with therapeutic applications. To develop theoretical models that will closely represent in vivo conditions, many different quantitative approaches have been developed.
The most basic models of tumor growth consider only general tumor cells as a formation. An example of this is the study of Song et al. [ 3], which was able to predict how tumor cells survive and evolve in terms of their encounters with the immune system, but does not account for other crucial phenomena, such as the effect of nutrition or the presence of differently behaving cell groups on the formation of this encounter, which could significantly alter the results. These types of models are typically represented by a small set of ordinary differential equations (ODEs). More sophisticated studies, still being general as regards the tumor dynamics, have also included the effect of the host immune system response and/or a treatment, leading to more realistic results, which fit well with experimental data [4,5]. Gupta et al. show how certain species of the immune system facilitate tumor destruction, using fractional order derivatives [4]. Elkaranshawy et al. includes the immune response and the effect of immunotherapy to make the realistic model [5]. These models extend the ODE set of equations by adding additional interaction terms.
A considerable number of authors has developed more complex descriptions and involved diverse cell populations into the model of a tumor [6,7]. Thus, Sherratt and Chaplain formulated a new extended model that consists of all cell types present in clinically observed tumor samples, and take into account the role of nutrition received from the underlying tissue distributed among live cells [6]. Taghibakhshi et.al. investigated the effects of the concentrations of the essential nutrients and of the initial spheroid radius on the tumor growth [7]. These models essentially describe diffusion processes and are given as sets of partial differential equations (PDEs).
Separately, a large body of research has looked at the effect of chemotherapeutic drugs on tumor growth and regression. As an example, Ansarizadeh et al. [ 8] suggest a complex model, which describes the interaction among normal, immune and tumor cells in a tumor with a chemotherapeutic drug, using a set of coupled PDEs. Moreover, there are approaches such as that in [9], designed by Pourhasanzade et al., which even look into the microenvironmental factors, as well as at separate types of nutrients and various cell groups of a tumor. These types of models appear to be more detailed and sophisticated PDE sets.
As can be seen from Table 1, different models have been developed for the prediction of tumor growth, considering the effects of different factors employing a range of assumptions. For example, many studies consider a single type of generic tumor cells [3,4,5,8,10,11,12,13,14,15], while others consider separate proliferating, quiescent and necrotic cells within the tumor [6,7,9,16,17,18]. The benefit of considering these separate types of cells is a full understanding of intra-tumoral interactions, which prove to be more realistic than a generalized approach.
Nowadays, a clear picture of a tumor can hardly be complete and competent in its applications, if the major latest findings and clinical observations are neglected. Thus, to produce more realistic model predictions and to aid the development of treatments, this study develops a model based on the one in [6], including a larger number of factors than previous studies, leading to a more complex, but more useful, model, which can account for various interactions between different cell types, as well as for interactions with nutrient levels, immune response and chemotherapeutic treatment. A few of the previous models similarly based on the model in [6] attempted to consider certain aspects of tumor growth, but they often lacked one or another crucial parameter, or used some degree of generalization, in order for the model to be realistic compared to physiological observations.
This paper is divided into four sections: in Section 2, we present a formulation of the mathematical model governed by a partial differential equations system, and the description of its parameters, modifications, and extensions, developed to include innate immunity response, chemotherapy, and glucose/oxygen consumption. Section 3 delves into the model's responses to varying conditions of the tumor microenvironment and immune-chemotherapy treatment strategies, while also conducting model validation against existing experimental results. Our assessment of the computational results and graphical data obtained by altering model parameters considers potential biological outcomes and offers optimal treatment predictions. Finally, Section 4 concludes the article with a summary of our findings and directions for future work.
## 2. Mathematical Model and Methodology
In this segment, we have developed and verified a new, complex, extended model of an avascular tumor formation, representing the interactions and mutual relations of intratumoral cell populations—proliferating cells (PC), quiescent cells (QC), necrotic cells (NC)—with extratumoral cell populations—surrounding healthy tissue cells (SC) and immune system cells, cytotoxic to the active (PC and QC) cancerous cells (IC)—as well as nutrient consumption and chemotherapeutic drug intake over time.
By considering the formation of cancer as one cluster of atypical cells or neglecting its interaction with other tissues in contiguity, gives an opportunity of constructing a simpler model, but apparently it does not illustrate the process to the fullest extent. Therefore, we suggest a system that takes cell species inside and outside of the tumor, as well as the presence of nutrients, under chemotherapy’s effect, into account. The model parameters are estimated using experimental data, and reflect clearly how the proposed PDE system describes the system dynamics and predicts tumor growth. Sensitivity analysis of the system provides the characterization of a significantly improved microenvironment, and therapeutic conditions needed for a tumor to shrink in the smallest time interval.
A numerical model was developed, with the aim of investigating the interactions between the participating intra-tumoral and extra-tumoral cell populations. This model considers proliferating, quiescent and necrotic cells, in addition to healthy host cells and generic immune response cells. Additionally, the model accounts for nutrient sufficiency and the individual immune response strength, as well as the effect of a chemotherapeutic medicament on avascular stage cancer tumor growth.
The model is constructed as a set of seven partial differential equations, and it is well-defined in terms of cell densities of proliferating (living) cells P(x,t), quiescent (nonproliferating live) cells Q(x,t), necrotic cells N(x,t), surrounding cells S(x,t), attacking immune cells L, nutrient supply influx C(x,t) and a chemotherapeutic drug influx D(x,t). The chemotherapy schedule is assumed to be the same as in the work of [8].
## 2.1. Model Assumptions
The proposed model is based on the following assumptions:Chemotherapy and innate immune responses decrease proliferation. All living cells receive nutrients (consisting of glucose and oxygen) from the underlying tissue, and divide depending on the level of nutrient supply. Chemotherapeutic drugs attack proliferating cells, surrounding healthy cells and immune cells. One cell population limits the movement of the cell population of another type and vice versa—this phenomenon is called ‘contact inhibition of migration’ [6].The effectiveness of the nutrient source term decreases with overall cell density. We assume that the nutrients, the immune response, and the drug react, and diffuse over the spatial domain. Nutrients diffuse into the tumor space at a diffusion rate that allows for the concentration of nutrient supply to reach a steady state. Immune cells are generated through a steady influx into the tumor area, and proliferate within it.
## 2.2. Modelling Equations
The cell density, P, dynamics of the proliferating rim can be described as a diffusion equation:[1]∂P∂t=∂∂xPP+Q+S∂∂xP+Q+S+gCP1−P−Q−N−S−fCP−k5LP−kDP1−e−DP The cell density, Q, of the quiescent cell population is given by [2]∂Q∂t=∂∂xQP+Q+S∂∂xP+Q+S+fCP−hCQ−kDQ1−e−DQ and cell density, N, of the necrotic core by:[3]∂N∂t=hCQ The cell density of healthy tissue surrounding the tumor, S, dynamics is described by:[4]∂S∂t=∂∂xSP+Q+S∂∂xP+Q+S+gCSΓ−P−Q−N−S−kDS1−e−DS The nutrient supply from the underlying tissue, C, is given by:[5]∂C∂t=Dc∂2C∂x2+k1C01−αP+Q+N+S−k1C−k2PC−k3SC−k4LC
The response of the innate immune system, L, dynamics can be computed as:[6]∂L∂t=DL∂2L∂x2−vL∂L∂x+g(C)LΓ−P−Q−N−S−L−k5PL−kDL(1−e−D)L The chemotherapy drug intake, D, is as follows:[7]∂D∂t=DD∂2D∂x2+vD(t)−kDD Here kDP(1−e−D)P, kDQ(1−e−D)Q, kDS(1−e−D)S, andkDL(1−e−D)L indicate the fractional cell kill rate, and term (1−e−D) represents saturation. For each type of cell, the fractional cell kill rates under the chemotherapy effect are denoted by kDP, kDQ, kDS and kDL, for proliferating, quiescent, healthy surrounding, and immune cells, respectively. The source term k1C01−αP+Q+N+S in Equation [5] represents the access of nutrition from the surrounding tissue and C0 is the nutrient concentration in the absence of a tumor cell population. The term −k1C describes the nutrient natural decay, whereas −k2PC, −k3SC, and −k4LC define the nutrient consumption by proliferating, surrounding and immune cells, respectively. In Equations [1]–[3] and [6], the functional form h(C) denotes the rate of quiescent cells turning to necrotic ones, g(C) is the mitosis rate of proliferating cells, whereas f(C) is the rate of proliferating cells turning quiescent. Furthermore, f(C) and h(C) are decreasing functions, whereas function g(C) is an increasing function. These functional forms are selected with parameter values g(C)=1+0.2C, h(C)=0.5f(C), f(C)=0.5[tanh(4C−2)]. Γ and α are dimensionless parameters, Γ<1 and α∈0,1. Equations [5]–[7] introduce the diffusive terms DC∂2C∂x2, DL∂2L∂x2, and DD∂2D∂x2 into the system, where diffusion coefficients for nutrients, immune system cells and the chemotherapeutic drug are represented by DC, DL and
DD, respectively.
The external pulsing source of the chemotherapeutic drug vD(t) in Equation [7] is denoted as the dosage strength:vD(t)=1for n−1×i<t<n−1×i+τ, else 0, where $i = 7$ days is the interval, τ = 0.25 days is the dosage duration and the number of pulses is taken as $$n = 1$$, 2, 3, according to the work of Ansarizadeh et al. [ 8]. Additionally, the term −kDD represents the excretion of the drug.
The initial conditions for our model are used in accordance with the original model [6]:Px,0=0.125e−0.1x,Qx,0=0,Nx,0=0,Sx,0=Γ×(1−0.01e−0.1x),Lx,0=1,$C = 1$,$D = 1.$
The boundary conditions for $x = 0$ and $x = 265$ are assumed as follows:∂P∂$x = 0$,∂Q∂$x = 0$,∂S∂$x = 0$,∂L∂$x = 0$,∂C∂$x = 0$,∂D∂$x = 0$ where for $x = 0$, these conditions represent symmetry, while for $x = 265$, the selected simulated boundary, they represent a “flat” profile. The original model this is based on does not mention the units used [6], but through comparison with other studies such as [8], it can be determined that 100 units of x equate to 1 cm, and the units of time are days.
In its turn, N requires no boundary condition. In order to compute a solution for the system described in Equations [1]–[7], the space-step and time-step were selected, obtaining the values of Δx = 0.5 and Δt = 0.05, respectively. The model’s parameters are given in Table 2.
## 3. Model Results and Validation
This section explains the model results for five different cell populations, a set of parameters and a range of initial and boundary conditions. The numerical simulations were conducted by employing the finite difference method (FDM), as it is robust and simple for solving partial differential equations, where the solution region consists of regular geometries. To determine the dynamics behavior of the system, an explicit Euler method was employed. Although more sophisticated numerical integration methods, such as those employing variable step length and implicit methods exist, for simplicity, the simpler, explicit Euler method was used here. If a larger time-step is used, this can lead to inaccuracies, so different time steps were tested, and a small enough time-step was used, such that any integration error was negligible.
## 3.1. Chemotherapy Effect on Malignant Proliferation and Tumor Regression
In the present model, the growth of a tumor was evaluated by the density of the proliferating cell population P, which is an indicator of malignancy. Figure 1 displays that the proliferating cell density decreases when the innate immune system is involved (b) and, logically, decreases even more in the presence of chemotherapy (c); these results suggest that the model gives the expected response trends. In this study, the chemotherapeutic drug was assumed to be an agent with the parameters presented in the previous related works [8,22,23], and does not represent a specific drug. The model is supposed to give the opportunity to fit and estimate the drug parameters, so the particular medication can be chosen according to its kinetic features.
The cell density in the basic model goes up to the value of 0.14 × 109 cells/cm3, a point at which the immune cells affect the proliferation rate, while density slightly decreases. In turn, the drug intake results in a decrease of density to 0.13 × 109 cells/cm3. We assume that, in all cases, the nutrient supply C is accessed by the proliferating rim of the tumor from the underlying tissue, and functionally regulates the ability of proliferating cells to enter quiescence; the nutrient coefficient α value tends to be relatively small. It can clearly be seen that the incorporation of the diffusive terms ∂L∂t and consequently ∂D∂t, for the immune response and the chemotherapy effect respectively, provides a realistic representation of tumor growth and regression dynamics.
In Figure 2, the necrotic cell density N is presented for all three considered steps of the model. Figure 2a shows the density of a simple avascular formation without any side effects, yielding approximately 0.7 × 109 cells/cm3 (after 30 days). With the addition of the immune system cells’ effects in Figure 2b, the number of necrotic cells stops growing, meaning that the number of quiescent turning necrotic and, therefore, the number of proliferating cells turning quiescent, has declined as well, to 0.69 × 109 cells/cm3. After the introduction of a chemotherapeutic drug in Figure 2c, the necrotic cell density slightly decreases, which supports the earlier finding in Figure 1c, regarding the decrease in proliferation.
## 3.2. The Immunity Power Effect on Tumor Evolvement under Chemotherapy
We have considered three different levels of immune response, varying the rate of the external immune cells influx from the medium value vL2 = 0.2, used in recent relevant works, and testing sensitivities with low and high values: vL1 = 0.1 and vL3 = 0.5. Figure 3 shows that patients with different immune system power experience different effects on tumor formation under the treatment of the same drug. The graph suggests that malignant proliferation is faster when the immune system response is weaker (P ≈ 0.143 × 109 cells/cm3, vL1 = 0.1, after 30 days). Otherwise, a patient with a stronger immune response has a decline in proliferation speed (P ≈ 0.123 × 109 cells/cm3, vL3 = 0.5, after 30 days). The model results are consistent with clinical findings that treatment is more effective in cases where a stronger innate immune system response is present. Therefore, we suggest that immunotherapy, which boosts the immune response, should be combined with other treatments and models, such as the one presented in this study, which should be used to determine the optimal combination of immunotherapy and other treatments.
## 3.3. Chemotherapy’s Effect on the Surrounding Healthy Tissue and Immune System
Chemotherapy medications cannot differentiate between tumor cells and healthy cells. This is the reason why chemotherapy damages healthy cells, lowers immune cell counts and causes other negative side effects. The model output presented in Figure 4 demonstrates the influence of a chemotherapeutic drug on cells that are healthy, and preferably should not be affected be the treatment; in the present case the drug promotes considerable changes in the non-malignant cell populations of this model.
In Figure 4a the graphs show cell densities’ progression over time before the implementation of a chemotherapeutic drug, with the surrounding tissue cells S and the immune system cells L. Meanwhile, Figure 4b provides the graphs of the same terms under the effect of chemotherapy. It is obvious from the solution that the drug affects both healthy cell species and tumor cells, as denoted by the decreases in their densities.
## 3.4. Model Validation
Our model was compared for prediction accuracy with the available models that describe the tumor growth in terms of cell densities (Table 3). The obtained data were also compared against experimental data, as well as other modeling results. For this purpose, the model of Swanson et al. [ 24] was chosen, as it presents results that quantitatively describe the dynamics of avascular aggressive tumor formation, visualized by the medical imaging of multiple patients. Next, the model of Hinow et al. [ 25] allows us to compare the tumor (proliferating) cell densities after chemotherapy treatment was carried out. Another piece of evidence that helps us validate some terms of the new model is an experiment of Chicoine and Silbergeld [26]; this was an in vitro tumor cell assay cultivated in a Petri dish. Here, the medium cell density of the tumor cells is considered.
The absence of models with the same number and type of variables makes it possible to validate several terms present in the developed model, but not all. However, the results (cell density profiles) match those given in the original model of Sherratt and Chaplain [6] if the extra terms added here are neglected, which gives us the confidence to assume that our model shows the right trends.
The work of Wang et al. [ 27] shows that the normalized drug concentration in our model tends to have a stable, constant distribution rate, similar to their findings. Additionally, [27] is supported by solid experimental validation. The immune response rate can be validated through the article of Ku-Carillo et al. [ 28], where the immune cell density for the low-level immune strength (vL1 = 0.1) appears to be I ≈ 0.4 × 109 cells/cm3, whereas for the case of an intermediate level of immune strength, I significantly increases. In a similar way, this model, in terms of the immune system response, results in I ≈ 0.26 × 109 cells/cm3 (vL1 = 0.1), which increases with the increase in immune strength.
Future work should involve increasing the model’s complexity and adding more supportive experimental validation, in order to prove its potential, in terms of its application to certain types of cancer.
## 4. Conclusions
We constructed an extended PDE-based model to represent the interactions between intra- and extratumoral cell populations limited by nutrition level and chemotherapeutic drug intake. The proposed model has been created based on an earlier developed model [6], which was extended by incorporating the effects of immune response and chemotherapy, including their coupling/interactions and using avascular tumor growth experimental data. Having reviewed a number of existing models, we suggest a new model, which integrates all relevant terms into one larger system that is equally realistic and practically applicable. We tested the performance of the designed model through numerical calculations, which match the literature, as well as experimental data shown in the works of Swanson et al. [ 24], Hinow et al. [ 25], Chicoine and Silbergeld [26]. The set of equations was implemented by using a finite difference method in MatLab.
According to the simulation results, in the created interactive system, a tumor’s progression drastically depends on the following: (a) a patient’s innate immunity level—a powerful immune response significantly changes the proliferation rate; (b) the nutrient supply—the limitations in nutrition obviously affect the proliferating cells’ spread but also the immune response and the healthy tissue around the formation. These findings establish our model’s accuracy, adequacy and effectiveness. The graphical depiction demonstrates logical trends for cell population interactions, proliferation and death. In view of these recounted facts, we suggest that the proposed model is suitable for predicting the distribution and behavior of the different cell types in this complex multicellular formation. In conclusion, the constructed model can be improved and utilized to further examine the malignant processes in the damaged tissue on early stages, the sensitivity of the drug diffusion and decay, nutrient supply and the level and duration of chemotherapy, in order to suggest the most optimal treatment. In our future research, we plan to incorporate other factors, such as radiotherapy and/or immunotherapy, in order to simulate and analyze the most-effective combined treatment strategies.
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|
---
title: 'New Times, New Ways: Exploring the Self-Regulation of Sport during the COVID-19
Pandemic and Its Relationship with Nostalgia and Well-Being'
authors:
- Heetae Cho
- Mun Yip Kinnard Chen
- Hyoung-Kil Kang
- Weisheng Chiu
journal: Behavioral Sciences
year: 2023
pmcid: PMC10045768
doi: 10.3390/bs13030261
license: CC BY 4.0
---
# New Times, New Ways: Exploring the Self-Regulation of Sport during the COVID-19 Pandemic and Its Relationship with Nostalgia and Well-Being
## Abstract
Coronavirus disease (COVID-19) has negatively affected individuals’ participation in sport activities, while sport participation is an important regulator of well-being. The current study investigated the effects of the nostalgia for sport activities and self-regulation of sport activities on subjective well-being. A total of 302 responses were collected from participants who had engaged in sport activities before the lockdown period. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings showed that nostalgia positively affected the self-regulation of sport and subjective well-being. In addition, self-regulation of sport was positively associated with subjective well-being. Based on the findings of this study, policymakers can implement interventions that promote an individual’s feelings of nostalgia, as it might lead them to engage in sport or promote self-regulation.
## 1. Introduction
Coronavirus disease (COVID-19) is a highly infectious virus that caused a global pandemic [1]. By 30 June 2021, this respiratory virus had caused more than 3.9 million deaths worldwide [2]. Many countries had adopted public health measures, such as social distancing, hygiene practices, and even complete lockdowns for the general population to reduce the spread of this virus and the strain on healthcare systems [3]. For example, the Singapore Parliament passed the COVID (Temporary Measures) Act on 7 April 2020, and implemented a similar set of public health measures for “preventing, protecting against, delaying, or otherwise controlling the incidence or transmission of COVID-19 in Singapore” [2] (p. 1). Singapore was in Phase Two during the conceptualization of this study in November 2020 [4]. During this phase, several activities resumed, and more facilities could remain open, although certain health protection measures remained in effect [4]. However, owing to various health measures and restrictions on sport and sport facilities, engaging in sport activities was a challenge. In particular, under the “Advisory for the Resumption of Sports and Physical Exercise and Activity for Phase Two”, individuals were required to maintain a physical distance of two meters between each other while exercising or playing sports [5]. Sport events that required close contact with others were either canceled or modified to accommodate the health measures. In other words, the imposition of several restrictive health measures made it challenging to engage in sport activities in Singapore during Phase Two. Thus, the increased difficulty in engaging in sport might have reduced individuals’ level of sport participation [6].
These increased difficulties in engaging in sport activities might cause some people to feel nostalgic toward the past when they were able to participate in sport without any restrictions but are now unable to do so. Batcho [7] showed that feelings of nostalgia arise during times of transition as a way of finding continuity. Similarly, Gibbs and Egermann [8] found that nostalgia increases during transitional periods such as the COVID-19 lockdown and serves as a form of psychological resource that provides a buffer against the anxieties and difficulties that accompany such changes. It is essential to study nostalgia, as it has been associated with numerous psychological responses [9]. For instance, individuals who reported a greater intensity and frequency of nostalgia tended to report greater meaning in life [10] and positive emotionality [11]. In addition, researchers found that feelings of nostalgia increase an individual’s optimism, self-esteem, social-connectedness, and meaning in life [10,12,13].
Although previous studies investigated how nostalgia influences various psychological outcomes [7,8,9,10,11,12], there have been only a few studies on the effect of nostalgia on sport participants’ self-regulation and well-being by considering the novelty of the COVID-19 pandemic and the consequent restrictions placed on sport activities [14]. Therefore, this study aimed to examine the relationships among an individual’s nostalgia for sport activities, self-regulation of sport activities, and subjective well-being during the COVID-19 situation. This study contributes to enhancing our understanding of the role of nostalgia and its effects on individuals’ self-regulation of sport behavior and well-being. In addition, the findings of this study can help policymakers adjust and adapt the restrictions to optimize and improve individuals’ well-being.
## 2.1. Broaden-and-Build Theory of Positive Emotions
Fredrickson [15] asserted that experiences of positive emotions broaden individuals’ thought–action repertoires, which builds their enduring personal resources over time. Two central aspects of this theory are: [1] the broadening of thought–action repertoires and [2] the building of one’s personal resources. The first proposition asserts that positive emotions broaden an individual’s awareness and encourage new, exploratory thoughts and behaviors. Previous literature has shown that individuals experiencing positive emotions often exhibit thought patterns that are creative [16], flexible [17], and open to new information [18]. Such individuals often have a “broad, flexible cognitive organization and the ability to integrate diverse material” [19] (p. 89). Kahn and Isen [20] found that individuals who experience positive emotions also have an increased preference for novelty and are more open to trying new behaviors. This leads to the second proposition, which asserts that the broadening of an individual’s thought–action repertoires builds their personal resources.
According to the existing literature, when an individual’s attention and cognition are broadened, it permits flexible, creative, and integrative thinking, which can enhance their coping resources [21]. For instance, individuals who experience positive emotions during bereavement are more likely to develop long-term plans and goals, both of which predict greater psychological health [22]. Fredrickson and Joiner [23] showed that individuals who experience more positive emotions tend to be more resilient during adversity due to their broad-minded coping. These improved coping resources then increase the likelihood of experiencing positive emotions, which further improves their personal resources, possibly leading to an upward spiral of improved well-being and coping resources [15,23]. That is, positive emotions contribute to personal well-being [15]. Nostalgia is considered a bittersweet emotion but is still predominantly positive [11]. Thus, nostalgia, as a positive emotion, might broaden an individual’s thought–action repertoires and help build personal resources.
## 2.2. Nostalgia for Sport Activities
The increased difficulty in engaging in sport activities might have led some individuals to experience nostalgia for the pre-COVID-19 state of sport, where access to sport activities was not limited by any mandatory restrictions. Cho et al. [ 24,25] defined nostalgia as a sentimental longing for a positive past; comparisons with negative current or future situations can induce feelings of nostalgia. According to Cho et al. ’s [24] classification of nostalgia, nostalgia consists of two dimensions: [1] the purpose of nostalgia and [2] the structure of nostalgia. The ‘purpose of nostalgia’ dimension encompasses an individual’s priorities and values that are based on past experiences. The ‘structure of nostalgia’ dimension asserts that nostalgia can be evoked by objects or interpersonal relationships. Object-based nostalgia includes places, symbols, or facilities, whereas interpersonal-relationship-based nostalgia involves social experiences with other people [24].
This classification is further divided into four factors: [1] experience, [2] socialization, [3] personal identity, and [4] group identity. The first factor, experience, refers to the fact that nostalgic feelings can be induced by one’s past experiences [24]. Reminiscing about certain sport athletes, teams, facilities, or atmospheres in the past can evoke a sense of nostalgia [26]. The second factor, socialization, states that nostalgia can be experienced through past experiences of social interactions with others [27]. For instance, recollecting past sport experiences with friends and family members can evoke feelings of nostalgia. The third factor, personal identity, states that an individual’s identification with sport can generate nostalgia. In other words, individuals who view themselves as sport fans can have nostalgic feelings regarding their roles and identities as fans [28]. Last, similar to the previous factor, the fourth factor, group identity, states that individuals with group identities in sport, such as being in a sport fan club, can experience nostalgic feelings when recollecting information about the past [29]. More specifically, it asserts that nostalgic feelings are evoked by the norms, rituals, and culture of the social group.
The current study selected and adapted this classification of nostalgia since it focused on nostalgia in the context of sport [24]. As previously mentioned, the increased difficulty in engaging in sport activities might have led individuals to experience nostalgia about the pre-COVID-19 state of sport activities [14]. In other words, protective health measures might have led them to long for the past when no restrictions were imposed on sport activities.
## 2.3. Self-Regulation of Sport Activities and Subjective Well-Being
The broadening and building of personal resources can be measured by an individual’s self-regulation of sport activities. Self-regulation refers to the process by which individuals engage in goal-directed behavior through strategies such as behavioral monitoring, selective processing of information, and self-evaluation [30,31]. That is, the self-regulation of sport activities indicates a personal resource that an individual may have developed and involves strategies that an individual can learn to employ to effectively manage goal-directed behavior [31]. These strategies can be considered personal resources that enable individuals to effectively reach their goals. Therefore, the self-regulation of sport activities can serve a broadening function under the broaden-and-build theory of positive emotions [14], as it encourages an individual to determine new self-regulatory sport activities.
Based on the broaden-and-build theory [14], feelings of nostalgia, as a predominantly positive emotion, might lead individuals to self-regulate their sport activity by identifying new sport activities (broadening function) and building new sources of sport activities (building function). Previous studies have shown that feelings of nostalgia motivate future-oriented behaviors [32,33], such as engagement in sport activity and healthy eating [34,35]. Kersten et al. [ 34] explained that nostalgia shifts an individual’s mindset toward improving future outcomes, of which their health is one important domain. Thus, individuals who experience stronger feelings of nostalgia often have higher levels of health optimism, develop stronger health-related attitudes, and consequently adopt better health habits such as engaging in sport activity [33,34]. Additionally, Bocincova et al. [ 36] found that feelings of nostalgia lead an individual to be in a psychological state of pursuit rather than avoidance. In other words, individuals experiencing feelings of nostalgia are more likely to approach and face any potential problems instead of avoiding them. This indicates that when faced with restrictions on sport activities, individuals experiencing feelings of nostalgia are more likely to engage in and identify new self-regulatory sport behaviors instead of avoiding the problem of sport restrictions. Therefore, nostalgia might increase an individual’s self-regulation of sport activities. Thus, this study hypothesized the following: Based on the broaden-and-build theory of positive emotions, building personal resources, such as the self-regulation of sport activities, can result in improved subjective well-being. Subjective well-being (SWB) refers to individuals’ appraisals and evaluations of their lives [37]. It includes cognitive judgments (e.g., life satisfaction) and emotional aspects (e.g., positive or negative emotions) [37]. Additionally, the personal well-being index—adult (PWI-A) can be used to measure SWB [38]. It is essential to study SWB because higher levels of SWB have been associated with numerous desirable outcomes such as higher levels of happiness [39], improved work performance [40], and enhanced physical health and longevity [41].
According to the broaden-and-build theory [15], positive emotions can improve an individual’s subjective well-being through the development of personal resources. As mentioned previously, nostalgia is a future-oriented emotion [33] that can improve well-being through the adoption of health-related attitudes. Furthermore, it can affect new health-related behaviors, such as sport activity [34]. Considering the broaden-and-build theory [14], engagement in sport activity is a personal resource that can improve an individual’s well-being. Moreover, nostalgia enhances an individual’s subjective well-being [42] by building other personal resources [10,43] that can serve as a psychological buffer against adversity [44]. This is consistent with existing literature that has shown the association of nostalgia with numerous psychological benefits [9], such as positive emotionality [45], optimism, and self-esteem [12,13]. Therefore, this study proposed the following hypothesis: Fredrickson [15] noted that personal resources can lead to improved well-being. For instance, self-regulation positively predicts long-term health [46] and educational outcomes, including academic achievement among adolescents [47]. Moreover, it is inversely related to poor health outcomes such as obesity [48]. This suggests that the self-regulation of sport activities can be a personal resource that has been built up, and improvement in self-regulation can promote physical and psychological health [49]. Previous studies have shown that individuals who can self-regulate and regularly engage in sport activity are often healthier and have higher levels of subjective well-being [50,51,52]. In a recent meta-analysis of sport activity and subjective well-being, Buecker et al. [ 53] found that engaging in sport activity improves an individual’s subjective well-being regardless of their prior fitness level and the type of sport activity they engaged in. Furthermore, other studies have shown that this positive relationship between regular sport activity and subjective well-being persists among individuals in different age groups [54,55]. Considering the existing literature, we proposed the following hypothesis:
## 3.1. Data Collection
This study collected data from individuals aged 21 and above who participated in sport activities before the COVID-19 lockdown. In this study, we defined sport activity as a physical activity engaged in for pleasure [56]. Data were collected in Singapore using a snowball sampling method from 1 February to 31 May 2021. Specifically, online survey forms were disseminated through multiple social media platforms (e.g., Facebook and WhatsApp) using Google Forms. In addition, participants were encouraged to share the survey link with their friends and family members who were eligible for this study. Participants of the online survey have to be at least 21 years old in the year 2021. Participants were informed about the voluntary nature of participation and that they could withdraw from the study at any time. Furthermore, they were informed that there would be no monetary benefit for their participation. Participants spent approximately 15 min on average completing the questionnaire. The response rate was estimated to be approximately $20\%$; finally, this study recruited 302 participants from Singapore.
## 3.2. Measures
The survey was conducted using Google Forms. The questionnaire consisted of six sections that collected [1] participants’ demographic information, while also assessing their levels of [2] nostalgia for sport activities, [3] self-regulation for sport activities, and [4] subjective well-being. The questionnaire included items from existing scales that measured these constructs with minimal modifications to suit the context of sport activities.
## 3.2.1. Nostalgia for Sport Activities
Cho et al. ’s [57] leisure nostalgia scale was borrowed and modified to measure nostalgia for sport activities instead of leisure activities. For instance, the term “leisure” was replaced with “sport activity” in this study. This 33-item scale measures five subfactors: [1] Nostalgia as Sport Experience (e.g., *Remembering a* sport activity that I enjoyed evokes my nostalgic feelings), [2] Nostalgia as Environment (e.g., The architectural design of my favorite sport activity place evokes my nostalgic feelings), [3] Nostalgia as Socialization (e.g., Positive feelings for building friendships with others during my favorite sport activity evokes my nostalgic feelings), [4] Nostalgia as Personal Identity (e.g., Pride in being a fan of my favorite sport activity evokes my nostalgic feelings), and [5] Nostalgia as Group Identity (e.g., Pride of being a part of my group at the sport activity place evokes my nostalgic feelings). The responses on this scale were scored using a 7-point Likert scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree).
## 3.2.2. Self-Regulation of Sport Activities
Fleury’s [58] index of self-regulation was borrowed to measure an individual’s self-regulation of physical activity. This 9-item scale measures three factors: [1] Reconditioning (e.g., I think of the benefits of regular sport activity), [2] Stimulus Control (e.g., I keep track of the ways that I can stay active in sport), and [3] Behavioral Monitoring (e.g., I have learned new habits that help me participate in sport activity). Participants were required to indicate their level of agreement with the items using a 6-point Likert scale ranging from 1 (Strongly Disagree) to 6 (Strongly Agree).
## 3.2.3. Subjective Well-Being
The personal well-being index—adult (PWI-A) developed by the International Wellbeing Group [38] was used to measure participants’ subjective well-being. This scale comprises seven items: [1] Standard of Living Domain (e.g., How satisfied are you with your standard of living?), [ 2] Personal Health Domain (e.g., How satisfied are you with your health?), [ 3] Achieving in Life Domain (e.g., How satisfied are you with what you are achieving in life?), [ 4] Personal Relationship Domain (e.g., How satisfied are you with your personal relationships?), [ 5] Personal Safety Domain (e.g., How satisfied are you with how safe you feel?), [ 6] Community-Connectedness Domain (e.g., How satisfied are you with feeling part of your community?), and [7] Future Security Domain (e.g., How satisfied are you with your future security?). The items were scored on an 11-point Likert scale ranging from 0 (No satisfaction at all) to 10 (Completely satisfied).
## 3.3. Data Analysis
Data were analyzed using partial least squares structural equation modeling (PLS-SEM) via SmartPLS 4 [59]. Before the analysis, the data were screened to identify missing values and outliers. Missing values were treated using the expectation-maximization (EM) algorithm. Standardized z-scores were used to identify univariate outliers, whereas the Mahalanobis distance was used to identify multivariate outliers. Subsequently, Anderson and Garbing’s [60] two-step approach was used to investigate the proposed model. First, the measurement was examined to evaluate the scale reliability and validity. Second, the structural model was carried out to test the hypotheses.
## 4. Results
No univariate or multivariate outliers were detected in the data screening, leaving 302 responses for further analysis. Of the 302 respondents, $49.0\%$ ($$n = 148$$) were women and $51.0\%$ ($$n = 154$$) were men. The participants’ average age was 24 years (SD = 4.034), ranging from 21 to 56 years. Most participants were Chinese ($$n = 262$$, $86.8\%$), followed by Malay ($$n = 16$$, $5.3\%$), Indian ($$n = 16$$, $5.3\%$), Latina ($$n = 3$$, $1.0\%$), Filipino ($$n = 3$$, $1.0\%$), and Eurasian ($$n = 16$$, $0.7\%$).
## 4.1. Measurement Model
The measurement model was first assessed to determine the indicator reliability, composite reliability (CR), and internal consistency reliability (Cronbach’s α). However, it was found that one item of ‘nostalgia as environment’ and two items of ‘subjective well-being’ revealed low indicator loadings (<0.60). Therefore, these items were removed to improve the overall reliability and validity of the measures without compromising the meaning of the constructs [61]. As shown in Table 1, reliability was supported due to the adequate internal consistent reliability (α > 0.70) and CR (>0.50). The average variance extracted (AVE) values were examined to evaluate convergent validity. The AVE values ranged from 0.865 for reconditioning to 0.951 for behavioral monitoring, indicating good convergent validity (AVE > 0.50) [62] (Table 1). Moreover, the disjointed two-stage approach [63] was applied to evaluate the second-order constructs of nostalgia and self-regulation. Table 2 showed that all the indicators (i.e., α, CR, and AVE) exceeded the suggested values [61]. Finally, discriminant validity was assessed by the heterotrait–monotrait ratio of correlations (HTMT) to see if the HTMT coefficients were greater than 0.85 [64]. Table 3 indicated the acceptable discriminant validity of the measures in this study.
## 4.2. Structural Model
The relationships between the participants’ COVID-19 nostalgia for sport activities, self-regulation of sport activities, and subjective well-being were examined (Table 4). The results indicated that nostalgia was positively associated with the self-regulation of sport (H1: β = 0.602, SE = 0.083, $t = 7.258$, $p \leq 0.001$) and subjective well-being (H2: β = 0.239, SE = 0.117, $t = 2.049$, $p \leq 0.05$). Moreover, the self-regulation of sport was positively associated with subjective well-being (H3: β = 0.256, SE = 0.105, $t = 2.424$, $p \leq 0.05$). The predictors explained $44.9\%$ and $24.5\%$ of the variance in the self-regulation of sport activities and subjective well-being, respectively (Figure 1). Additionally, mediation analysis revealed that nostalgia had a significant indirect effect on subjective well-being (β = 0.154, SE = 0.063, $t = 2.456$, $$p \leq 0.014$$).
## 5. Discussion
This study investigated the relationships between the nostalgia for sport activities, self-regulation of sport activities, and subjective well-being based on the broaden-and-build theory of positive emotions [15]. More specifically, nostalgia for sport activities was found to positively predict an individual’s self-regulation of sport activities (H1). This result is consistent with the broaden-and-build theory of positive emotions, which asserts that individuals who experience positive emotions are more likely to build new and enduring personal resources [15]. These resources include determining new ways to engage in sport activities. Furthermore, FioRito and Routledge [33] noted that nostalgia is a future-oriented emotional experience that motivates certain behaviors, emotions, and goals that enhance an individual’s future outcomes. For instance, nostalgia increases certain motivation-related emotions that promote future-oriented actions such as inspiration [45], optimism [10], and a sense of purpose in life [12]. Additionally, Routledge et al. [ 65] found that individuals’ nostalgic memories often contain a sense of hope for the future. Kersten et al. [ 34] found that hopefulness for one’s health induced by feelings of nostalgia is also associated with stronger intentions to engage in sport activity or a healthy diet. In other words, nostalgia triggers emotional states that promote actions and health [33]. Similarly, a recent neurological study by Bocincova et al. [ 36] found that feelings of nostalgia shift individuals toward approach-related psychological states.
Our results supported the hypothesis that nostalgia would be positively related to subjective well-being (H2). This is consistent with numerous previous studies that showed nostalgia to be significantly and positively associated with an individual’s subjective well-being in many domains [9], such as enhanced meaning in life [10] and positive emotionality [11]. In the context of sport, FioRito and Routledge [33] have asserted that the future-oriented nature of nostalgia indirectly improves an individual’s well-being by promoting future-oriented emotions and behaviors such as caring for their physical health [34]. However, it should also be noted that nostalgia can improve an individual’s subjective well-being through other aspects as well. For instance, Rao et al. [ 43] stated that nostalgia could improve an individual’s subjective well-being by constructing or strengthening their meaning in life. Meaning in life can serve as an essential psychological coping resource and can thus be both an indicator of [66] and a contributor to an individual’s higher subjective well-being [44]. Routledge et al. [ 65] noted that nostalgia promotes feelings of purpose and meaning in life, as it involves the perception of important life experiences with certain objects or significant individuals.
Finally, self-regulation positively predicted subjective well-being (H3). This is consistent with the broaden-and-build theory of positive emotions, which states that the development of new personal resources, such as finding new sport activities, can enhance an individual’s well-being [15]. This is also consistent with numerous existing studies that have shown that regular engagement in sport activity promotes an individual’s health and subjective well-being [50,51,52,67,68]. Furthermore, the previous literature has shown that certain types of motivation toward sport activity have a differential impact on an individual’s well-being [69]. More specifically, autonomous motivation toward sport activity has been more strongly associated with higher levels of subjective well-being through numerous domains, such as increased happiness [70], quality of life [71], and physical self-worth [72]. As mentioned previously, Yeom et al. [ 31] indicated that an individual’s self-regulatory sport behavior involves autonomous motivation. Thus, previous research on the relationship between autonomous motivation toward sport activity and subjective well-being explains the positive relationship between the self-regulation of sport activities and subjective well-being.
## 5.1. Practical Implications
The current study has some practical implications in the context of the self-regulation of sport behaviors during the COVID-19 pandemic. First, policymakers should implement interventions to promote feelings of nostalgia toward the pre-lockdown state of unrestricted access to sport activity as it might lead them to engage in sport activities or promote the self-regulation of sport activities.
Second, the findings suggest that it is essential for an individual to begin participating or continue to participate in sport activity, even during a pandemic, because it can significantly affect their well-being. In other words, one should seek alternative sport activities that do not violate the restrictions. For instance, even though contact sport was not allowed during Phase 2 in Singapore, other sport activities, such as running or exercising in public parks, were allowed [5]. Thus, individuals should identify alternatives to reach and maintain an optimal level of sport activity that meets the international guidelines provided by the World Health Organization [73]. Lastly, policymakers should explore and allow alternative sport activities that are relatively safe for the general population. Additionally, they should encourage the general population to continue their engagement in sport activities that are within the limits of restrictions. These self-regulatory sport behaviors would then enhance the well-being of the general population, an aspect that is crucial during a pandemic situation that negatively impacts individuals’ well-being [1].
## 5.2. Limitations and Future Research
Although the current study has yielded some significant insights into the nature of people’s sport behaviors and well-being, it has a few limitations. First, this study utilized a cross-sectional design that measured individuals’ sport behaviors and subjective well-being at a specific time point, namely, during Phase Two of the restrictions. Thus, changes in individuals’ sport behaviors and subjective well-being cannot be traced across different phases to determine how restrictions on sport activity might have affected the two aforementioned constructs. Therefore, future research should adopt a longitudinal design to trace the changes in individuals’ sport behaviors and subjective well-being across different phases of restrictions. This would provide further evidence and an understanding of how factors such as nostalgia, the self-regulation of sport activities, and subjective well-being interact with one another and change depending on the environmental context. Next, this study did not include items related to respondents’ sport experience (e.g., weekly exercise frequency, duration, and intensity) in the questionnaire. Moreover, most participants were young adults (mean age = 24.00) and Chinese ($86.8\%$). Therefore, the present study cannot be generalized to other cultures and a larger population. Future research should compare the current results to samples from other populations or cultures to determine whether the findings persist across different populations. Cross-cultural studies could also be conducted to provide additional support for the present findings. Finally, the current study utilized a quantitative approach. Thus, future research can employ a qualitative approach to provide a deeper understanding of the factors that could affect individuals’ self-regulation of sport activities during pandemics [74]. It would further provide a more contextual understanding of the impact of different restrictions imposed on sport activities, particularly on individuals’ self-regulatory sport behaviors.
## 6. Conclusions
In summary, this study explored the relationships between the nostalgia for sport activities, self-regulation of sport activities, and subjective well-being in the context of sport restrictions during Phase 2 in Singapore. Our results supported all three hypotheses. This suggests that policymakers should investigate the restrictions on sport activities, encourage the general population to continue to maintain or increase their sport activity level, and provide alternative sport activities. Unfortunately, the proposed findings are deemed to be less plausible than is reasonably accepted, given the poor model fit. Therefore, future studies should improve the data by improving the data collection procedures or proposing other models for a better data fit.
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|
---
title: Telomere Shortening in Three Diabetes Mellitus Types in a Mexican Sample
authors:
- Pavel Cuevas Diaz
- Humberto Nicolini
- German Alberto Nolasco-Rosales
- Isela Juarez Rojop
- Carlos Alfonso Tovilla-Zarate
- Ester Rodriguez Sanchez
- Alma Delia Genis-Mendoza
journal: Biomedicines
year: 2023
pmcid: PMC10045771
doi: 10.3390/biomedicines11030730
license: CC BY 4.0
---
# Telomere Shortening in Three Diabetes Mellitus Types in a Mexican Sample
## Abstract
This study aimed to explore the role of telomere length in three different diabetes types: latent autoimmune diabetes of adulthood (LADA), latent autoimmune diabetes in the young (LADY), and type 2 diabetes mellitus (T2DM). A total of 115 patients were included, 72 ($62.61\%$) had LADA, 30 ($26.09\%$) had T2DM, and 13 ($11.30\%$) had LADY. Telomere length was measured using real-time Polymerase Chain Reaction. For statistical analysis, we used the ANOVA test, X2 test, and the Mann–Whitney U test. Patients with T2DM had higher BMI compared to LADA and LADY groups, with a BMI average of 31.32 kg/m2 ($$p \leq 0.0235$$). While the LADA group had more patients with comorbidities, there was not a statistically significant difference ($$p \leq 0.3164$$, $$p \leq 0.3315$$, $$p \leq 0.3742$$ for each of the previously mentioned conditions). There was a difference between those patients with T2DM who took metformin plus any other oral antidiabetic agent and those who took metformin plus insulin, the ones who had longer telomeres. LADA patients had shorter telomeres compared to T2DM patients but not LADY patients. Furthermore, T2DM may have longer telomeres thanks to the protective effects of both metformin and insulin, despite the higher BMI in this group.
## 1. Introduction
Diabetes mellitus (DM) is a metabolic disease characterized by chronic hyperglycemia due to a lack of insulin secretion, insulin sensitivity/action, or both, thus requiring frequent monitoring and proper control with both lifestyle changes and pharmacotherapy [1,2]. Diabetes mellitus can be classified into various types; however, the most common types are type 1 (T1DM) and type 2 (T2DM). Generally speaking, T1DM is due to the destruction of pancreatic β-cells by T-cell-mediated autoimmunity, usually leading to absolute insulin deficiency; this category includes latent autoimmune diabetes of adulthood (LADA) and latent autoimmune diabetes in the young (LADY). Otherwise, T2DM is due to a progressive loss of adequate β-cell insulin secretion, usually in the background of some degree of insulin resistance. Despite classification being important, DM is a heterogeneous disease, in which clinical presentation and progression may vary considerably among individuals, leading to some patients not being clearly classified as having T1DM or T2DM at the time of diagnosis [3].
Mexico is one of the leading countries in the incidence of diabetes worldwide. According to the International Diabetes Federation Atlas from 2021 [4], diabetes prevalence in Mexican adults is $16.9\%$ (14.2 to 22.1, $95\%$ CI), with a possible increase to $18.3\%$ by 2030, meaning 17,062.7 in 100,000 s Mexicans with diabetes. Considering that T1DM and T2DM are also polygenic diseases [5], the heritability is 15 for T1DM and 3 for T2DM [6], resulting in a lifetime risk of $5\%$ of developing T1DM if a parent has type 1 diabetes, higher if the father has the disease [7], and $40\%$ T2DM if one parent has type 2 diabetes, higher if the mother has the disease [8]. One of the genetic compounds included in DM pathophysiology is telomere length, which is found to be heritable [9].
Telomeres are double-stranded DNA protein regions with a variable length located at the end of all chromosomes, except for the very end of the strand, which is single-stranded [10,11]. Telomeres are composed of several repeats of the hexanucleotide TTAGGG, and thus are rich in guanine [12]. This sequence maintains the stability of chromosomes, prevents the end fusion of chromosomes, protects chromosome structure, and determines the lifespan of cells [13]. To maintain the length of the telomeres, there is a ribonucleoprotein named Telomerase, which has a reverse transcriptase activity, adding telomeric DNA repeats to the single-strand overhang of telomeres, playing a crucial role in cell proliferation, differentiation, and survival [14,15]. Experimental and clinical evidence indicates that the lymphocyte telomere length corresponds to the telomere length of the stem cells and endothelial progenitor cells. Therefore, the lymphocyte length in studies, could be used as a biomarker of cell ageing [16].
The first team to demonstrate an association between type 2 diabetes mellitus (T2DM) and shortened telomere length were Jeanclos et al. back in 1998 [17]. Since then, numerous studies have shown a relationship between telomere attrition and diabetes, such as the meta-analysis of prospective, case-control, and cross-sectional studies performed by Zhao et al. which included nine population cohorts, 5759 cases, and 6518 controls, and demonstrated that leukocyte telomere length is associated with T2DM risk [18].
One of the possible mechanisms by which DM can cause telomere shortening is oxidative stress. It is well established that hyperglycemia elicits an increase in reactive oxygen species production and triggers maladaptive response by affecting several metabolic and signaling pathways leading to DNA damage, such as single-strand breaks and telomere erosion [19]. Short telomeres may lead to premature β-cell senescence, resulting in reduced β-cell mass and, subsequently, impaired insulin secretion and glucose tolerance [20]. Oxidative stress induces an abnormal telomere–telomerase system function, leading to DNA damage and b-cell dysfunction, resulting in further aggravation or development of diabetes mellitus, being a cyclical process [11,13,21,22].
A 2016 meta-analysis found telomere length variation by geographical region, European patients displaying a reduced telomere length compared with Asian and American populations. In addition, patients with T1DM were found to have a shorter telomere length compared to patients with T2DM, and females had shorter telomeres than males [23]. However, other published studies have demonstrated the opposite [24,25]. However, little is known about the relationship between telomere length and the Mexican population. Furthermore, we are still unfamiliar with the link between telomere length and different diabetes classifications among Mexicans. Therefore, the aim of this study was to investigate the relationship between telomere shortening and different types of diabetes in a Mexican population sample.
## 2.1. Patients Data
We performed a cross-sectional study with samples obtained from Mexican patients attending an external diabetes consult in the Hospital Regional de Alta Especialidad Dr. Gustavo A. Rovirosa Pérez. A total of 200 patients with diabetes were initially considered for this study, but after adjusting them to the inclusion and exclusion criteria mentioned later, only 115 were finally included in this study. Patients were previously evaluated and diagnosed by an endocrinologist according to the American Diabetes *Association criteria* 2021. From the total of patients, 72 ($62.61\%$) had latent autoimmune diabetes of adulthood (LADA), 30 ($26.09\%$) had type 2 diabetes mellitus (T2DM), and 13 ($11.30\%$) had latent autoimmune diabetes in the young (LADY). The mean age was 55.30 years, $71.30\%$ ($$n = 82$$) being women and $28.70\%$ ($$n = 33$$) men.
The inclusion criteria were: (a) patients previously diagnosed with type 1 or type 2 diabetes mellitus, (b) Mexican patients with both Mexican parents and grandparents, and (c) patients that agree to be part of the study. The exclusion criteria were: (a) foreign patients and (b) patients who did not accept the blood sample collection. During the data collection, we also registered socioeconomic data (age, sex, occupation, educational status, and civil status), clinical characteristics, including other comorbidities and diabetes complications, alcohol consumption, smoking status, and pharmacologic treatment data.
In Table 1, we show the values for Glutamic Acid Decarboxylase Antibodies (GADA), which were measured in all patients. GADA were used to determine the type of diabetes of each patient with the use of the Human Anti-Glutamic Acid Decarboxylase Antibodies ELISA Kit, following the manufacturer’s instructions. Patients with GADA > 5 IU/mL and >30 years old were diagnosed as LADA, those with GADA > 5 IU/mL and <30 years old were diagnosed as LADY, and those with GADA < 5 IU/mL as T2DM.
## 2.2. Anthropometric and Laboratory Assessment
At the time of the study, the mean diagnosis time was 16.02 years, and at physical exploration, there was a mean body mass index (BMI) of 30.26 kg/m2. At the time of blood sample extraction, biochemical tests were performed, resulting in a mean glycated hemoglobin (HbA1c) of $6.48\%$ and a mean fasting plasma glucose of 133.03 mg/dL. Out of the total of patients, 52 ($45.22\%$) had hypertension, 28 ($24.35\%$) had dyslipidemia, and 5 ($4.35\%$) had neuropathy, the LADY population having the most comorbidities (Table 1). Among the antidiabetic agents, 110 ($95.65\%$) used metformin, 45 ($39.13\%$) used glibenclamide, 29 ($25.22\%$) used any dipeptidyl peptidase 4 inhibitors, and 84 ($73.04\%$) used any kind of insulin (Table 2). Other laboratory parameters were assessed (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, insulin and glomerular filtration rate, human islet antigen-2 antibody, and human zinc transporter8 antibody) but were not taken into consideration for this study as they were not relevant to the study.
## 2.3. Measurement of Telomere Length
First, blood samples were obtained from all patients in the Diabetes Clinic of the Regional Hospital of High Specialty “Dr. Gustavo A. Rovirosa Pérez” in Hospital Re-gional de Alta Especialidad “Dr. Gustavo A. Rovirosa Pérez”. After getting one 4 mL blood tube of each patient, the samples were sent to the National Institute of Genomic Medicine in Mexico City.
DNA extraction was obtained from peripheral leukocytes using the Gentra Puregene (Qiagen) commercial method. Quantity and quality were assessed using spectrophotometry (Nanodrop 2000). To determine telomere length, we performed a real-time Polymerase Chain Reaction (rt-PCR), along with an SYBR Green master mix. To obtain the relative telomere to single copy gene (T/S) ratio, sample T/S was divided by reference T/S, using Succinate dehydrogenase complex flavoprotein subunit A (SDHA) as the gene control. The primers were as follows:Telomere-F 5′-CGG TTT GTT TGG GTT TGG GTT TGG GTT TGG GTT TGG GTT-3′Telomere-R 5′-GGC TTG CCT TAC CCT TAC CCT TAC CCT TAC CCT TAC CCT-3′SDHA-F 5′-TCT CCA GTG GCC AAC AGT GTT-3′SDHA-R 5′-GCC CTC TTG TTC CCA TCA AC-3′
## 2.4. Statistical Analysis
The values of continuous variables between groups were compared using the ANOVA test, and the categorical variables were compared using an X2 test. The Mann–Whitney U test was performed if the data had skewed distributions. To achieve an approximately normal distribution, log transformation for telomere length was used. All statistical analysis and graphics were created using GraphPad Prism 8.0 for Windows, setting $p \leq 0.05$ as statistically significant.
## 3. Results
A total of 115 patients with any diabetes classification were included in this study, only divided into latent autoimmune diabetes of adulthood (LADA), latent autoimmune diabetes in the young (LADY), and type 2 diabetes mellitus (T2DM). Each group had its distinctive characteristics. Patients with T2DM had higher BMI compared to LADA and LADY groups, with a BMI average of 31.32 kg/m2 ($$p \leq 0.0235$$). On the other hand, the T2DM group had more glucose control, with a mean HbA1c of $5.28\%$. However, these differences among the three groups were not statistically significant ($$p \leq 0.8736$$).
It is worth noting that all three groups had patients with comorbidities. In this study, we only included systemic arterial hypertension, dyslipidemia, and neuropathy. While the LADA group had more patients with comorbidities, there was not a statistically significant difference ($$p \leq 0.3164$$, $$p \leq 0.3315$$, $$p \leq 0.3742$$ for each of the previously mentioned conditions).
A correlation analysis between telomere length and three variables, age, BMI, and HbA1c, was performed to explore which factors affected telomere length. None of the variables were significantly associated with telomere length (Table 3, Figure 1). However, in the LADA group, both age and age of disease diagnosis were correlated with telomere length, with a $p \leq 0.0001$ using the Mann–Whitney test (Table 4).
In the first sight, the telomere length of the LADA group was significantly shorter than those of the T2DM and LADY group. However, after performing the Mann–Whitney test, only the LADA group had significantly shorter telomeres than those of the T2DM group ($$p \leq 0.0121$$, Figure 2a), while it was not significant against those with LADY ($$p \leq 0.8221$$, Figure 2b). Between T2DM and LADY groups, there was no statistical difference ($$p \leq 0.1671$$, Figure 2c). After comparing the three groups simultaneously, there was a significant difference in telomere lengths ($$p \leq 0.0424$$, Figure 2d).
As all individuals were taking at least two antidiabetic agents, we explored the relationship between metformin plus any other oral antidiabetic agent (OAA) and metformin plus insulin. There was a difference between those patients with T2DM who took metformin plus any other OAA and those who took metformin plus insulin, the ones who had longer telomeres ($$p \leq 0.0256$$, Figure 3b).
Regardless of the diabetes group, there was not any difference between metformin plus any other OAA and metformin plus insulin ($$p \leq 0.1478$$). When dividing into subgroups, no analysis was performed in the LADY group, as all of them except for one were taking insulin. As for the LADA group, there was no difference ($$p \leq 0.3759$$, Figure 3a).
## 4. Discussion
As previously mentioned, diabetes is a heterogeneous disease affecting the entire globe, and is prevalent in Mexico. A meta-analysis of 17 papers conducted by Wang et al. determined that telomere length in diabetic patients varied based on geographical region. This study found that European patients with diabetes displayed a more pronounced telomere length compared with Asian and US patients. Additionally they demonstrated that telomere length was affected by diabetes type, BMI, age and sex [26]. These results agree with other meta-analyses that concluded that a significant association between diabetes and telomere length was influenced by geographical region and diabetes type [18,27]. Additionally, telomere shortening is widely accepted as a hallmark of cellular senescence. In our study, LADA patients were found to have shorter telomere lengths compared with T2DM patients, and no difference when compared to LADY patients. The previous results were independent of age, BMI, HbA1c, and comorbidities, as these were not significant. BMI was the only significant clinical variable, mainly in the T2DM group, which was found to have a higher body mass index in comparison to the LADA and LADY groups. BMI and sex have been described as strong predictors of the association between diabetes and telomere length [26]. This is supported by a study conducted by Al-Thuwaini, who found that higher waist circumference, along with higher HbA1c levels and fasting blood glucose and lower high-density lipoprotein to be correlated with shortened telomere length in diabetic patients [28]. Other studies have supported these conclusions [29]. In fact, patients with T2DM who took metformin and insulin had longer telomere lengths. This is supported by previously published evidence, suggesting that insulin and metformin have protective effects [30,31]. Metformin has been found to delay the senescence of renal tubular epithelial cells in diabetic nephropathy [32] and reduce inflammation [33], irrespective of diabetes status, and alleviate the effects of aging on diabetic progression [34,35]. Even exposure to metformin and insulin can prevent telomere attrition in individuals with prediabetes [36,37]. A profound review on molecular mechanisms can be found in Kulkarni et al. [ 38], Hu et al., and Mohammed et al. [ 39].
Not only metformin appears to have a beneficial effect on telomere length. Other antidiabetic agents such as glibenclamide and sitagliptin have also been studied. Glibenclamide has been found to remarkably decrease the telomere shortening rate. This may be due to the fact that glibenclamide stimulates insulin secretion from pancreatic β-cells and reduces free fatty acid concentrations [40]. A randomized controlled trial reported a significant improvement in glucose control, reduced insulin resistance, and elongated telomere length in T2DM patients treated with sitagliptin for 2 months. It was also reported to have no effect on telomerase activity between patients and control groups [41].
Insulin and other antidiabetic agents have been found to possess anti-inflammatory effects. This can be due to reductions in nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) oxidase expression, reactive oxygen species generation, and NF-κB binding [42], in the case of insulin. On the other hand, Zeng et al. found that insulin therapy may accelerate telomere shortening. It is possible that this may be due to the association between insulin and weight gain, aggravating the insulin resistance, promoting oxidative stress, and shortening the telomere length [43].
The advantage of our study is mainly being the first population-based study comparing three different diabetes types. First, the LADA group was demonstrated to have a shorter telomere length than the T2DM group. Second, our study confirmed that metformin plus insulin may have a role preventing shortening in the T2DM group.
We must not forget the limitations of this study. First, this is a cross-sectional study; thus, speculating the causal relationship between telomere lengths in these diabetes groups is difficult. Second, the number of subjects was not very large, considering the overall population. Finally, subjects were obtained from a specialized diabetes clinic, meaning that patients in our study had access to an antibody-detecting facility, nutrition counseling, and constant check-ups; most of the patients were under stricter control than those who diabetes can be found in frequently.
## 5. Conclusions
A great variety of molecular pathways have been sketched in an attempt to elucidate the mechanisms by which diabetes and telomere attrition are linked. Discovering new pathways could be useful as potential therapy targets and even more personalized treatments. All available antidiabetics have shown some degree of protection against telomere shortening in diabetic patients. Epigenetic variables may also contribute to glycemic and metabolic control in order to make a synergic effort to minimize cellular damage. It is evident that further studies will be needed to analyze the possible variables that could be biased, including more gender, age, and nationality studies.
This study indicated that LADA patients had shorter telomeres compared to T2DM patients but not LADY patients, and T2DM may have longer telomeres thanks to the protective effects of both metformin and insulin, despite the higher BMI in this group. To fully elucidate these results, more research is needed in the future with more subjects, taking into consideration the antibodies testing to fully clarify the proper diabetes mellitus groups.
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|
---
title: 'Protein Fractions from Flaxseed: The Effect of Subsequent Extractions on Composition
and Antioxidant Capacity'
authors:
- Katarzyna Waszkowiak
- Beata Mikołajczak
- Katarzyna Polanowska
- Marek Wieruszewski
- Przemysław Siejak
- Wojciech Smułek
- Maciej Jarzębski
journal: Antioxidants
year: 2023
pmcid: PMC10045795
doi: 10.3390/antiox12030675
license: CC BY 4.0
---
# Protein Fractions from Flaxseed: The Effect of Subsequent Extractions on Composition and Antioxidant Capacity
## Abstract
Flaxseed proteins exhibit functionalities interesting for the food industry, including antioxidant capacity. Antioxidant activity depends on the protein composition and the presence of phenolic compounds extracted with them from the matrix. The research focused on the effect of subsequent protein extractions (water, salt and alkaline) of flaxseed meals (of three cultivars) on the protein fraction composition and its relations to antioxidant capacity. The protein and phenolic profiles and antioxidant functionalities (in antiradical ORAC and emulsion assays) were analysed. Spectroscopic characteristics of the fractions (fluorometric and FT-IR analysis) were also included. Our study has shown the effect of fractionation on the share of proteins at MW from 56–38 kDa (globulin-like) and <15 kDa (albumin-like) in the protein profiles. The highest globulin share was in the alkaline-extracted fractions (AEF) and albumin in the salt-extracted (SEF) ones. SDG (secoisolariciresinol diglucosides) and phenolic acids (p-coumaric and ferulic) were extracted with flaxseed proteins. Their contents were fraction-dependent and the highest in AEF. The concentration of phenolics in AEF corresponded with the highest antiradical capacity (ORAC) compared with the other fractions. However, the SEF showed a higher ability to inhibit oxidation in emulsions than AEF, which could be associated with the higher content of the low MW proteins.
## 1. Introduction
Flaxseed (*Linum usitatissimum* L.) is one of the crucial oilseeds [1], which is a source of oil rich in essential n-3 fatty acids (α-linolenic acid, ALA). Flaxseed, as well as flaxseed cakes or meals, products remaining after pressing flaxseed oil, are also a source of various compounds, such as proteins, mucilage and lignans [2]. These compounds can be valuable for food production from both nutritional and functional points of view [3].
After industrial oil extraction, the protein content in defatted flaxseed meals is high and amounts to between approximately 35 and $45\%$ of the dry oil-free mass [4]. The biological value of flaxseed protein is also significant compared to other plant proteins. Their essential amino acid composition and digestibility are comparable to pea protein and soy protein [5,6]. The flaxseed protein-derived peptides (products of their hydrolysis) showed various biological activities, such as antioxidant capacity, angiotensin-converting enzyme inhibition, antibacterial activity, anti-inflammatory and anti-diabetic effects [7]. Moreover, the flaxseed protein’s primary structure, relatively ordered secondary structure, tunable structural properties and suitable interfacial behaviours indicate that their isolates and concentrates can exhibit interesting functional properties for food production [8]. They include foaming and emulsifying properties [6]. All these nutritional and functional benefits encourage researchers to focus on methods of efficient extraction and isolation or concentration of flaxseed proteins [9,10]. In studies by Qin et al. [ 11] and Nwachukwu et al. [ 12], the flaxseed proteins were extracted and fractionated to obtain albumin and globulin and then their composition and selected functional properties were compared.
In the flaxseed matrix, the proteins occur in natural associations/complexes with mucilage and phenolic compounds. The compounds can be extracted with the flaxseed proteins, influencing their functional properties, including antioxidant activity. Flaxseed mucilage is a viscous seed coat gum composed of neutral and acidic polysaccharides [13]. The neutral polysaccharides are arabinoxylans consisting of L-arabinose, D-xylose and D-galactose, while the acidic polysaccharides are rhamnogalacturonans containing L-rhamnose, L-fucose, L-galactose and D-galacturonic acid. Flaxseed mucilage shows various interesting functional properties, including water holding and oil binding capacity, emulsifying activity, water–oil emulsion stability and gelling and coating properties [8]. A ratio of neutral to acidic polysaccharides influences the physicochemical properties of flaxseed mucilage, such as viscosity. It may affect the listed functionalities. Because mucilage is a soluble dietary fibre, it also possesses probiotics, anti-obesity, anti-cholesterol and anti-diabetic activities [14,15,16].
The main flaxseed phenolic compounds are lignans—secoisolariciresinol diglucosides (SDG) and phenolic acid glucosides (including ferulic and p-coumaric acid derivatives) [17]. In the flaxseed matrix, these phenolic compounds form large complexes (oligomers), which are composed of SDG molecules ester-linked with 3-hydroxy-3-methylglutaryl (HMG) residues and other hydroxycinnamic acid derivatives [18]. SDG has confirmed antioxidant capacity, which is crucial for the health benefits provided by this molecule [19]. Some of the flaxseed phenolic compounds are probably extracted together with mucilage or proteins during the extraction process and may affect the antioxidant properties of the resulting concentrates [20,21].
Although flaxseed contains significant amounts of SDG and phenolic acids, it remains a question whether their extraction together with flaxseed proteins is efficient and how much they participate in the antioxidant activity of the received protein fractions. Recent studies focused on the extraction process and structural modification of flaxseed proteins to improve their functionalities, e.g., emulsifying properties [6]. Some researchers also studied the effect of these processes on the antioxidant activity of flaxseed proteins [11,20] using various analytical assays, e.g., spectrophotometric 2,2-diphenyl-1-picryl-hydrazyl radical (DPPH•) or ferric reducing antioxidant power (FRAP) assays. However, there is a lack of information on whether the results correspond with the protein antioxidant capacity in an emulsion.
The article showed the research results on the effect of the subsequent protein extractions (water extraction, salt extraction and alkaline extraction) of flaxseed meals on the protein fraction composition (including spectroscopic characterisation, amino acid, electrophoretic and phenolic compound profiles) and antioxidant functionalities (studied both in analytical ORAC assay and emulsion).
## 2.1. Material and Reagents
The research materials were the protein fractions of flaxseed (*Linum usitatissimum* L.) obtained from of three cultivars: golden-seed Jantarol, Oliwin and brown-seed Szafir (IHAR, Borowo, Poland; year of production: 2017; for the chemical composition of the material see Waszkowiak and Mikołajczak [22]). The material preparation included grinding in a ZM 200 mill (1 mm sieve, Retsch, Haan, Germany), double cold oil extraction (ground seed to n-hexane ratio of 1:3 w/v) and then grinding in a colloidal mill (Foss, Hilleroed, Denmark) to standardise the material composition. The defatted meals were cold-stored at 4 °C.
Folin–Ciocalteu reagent (FCR), 6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox), 8-anilinonaphthalene-1-sulfonic acid (ASN), butylated hydroxytoluene (BHT) and Tween 20 were from Sigma-Aldrich (Munich, Germany). Analytical standards of SDG were from PhytoLab (Vestenbergsgreuth, Germany) and standards of phenolic acids and enzyme β-glucosidase from almond (2 units mg−1 solid) were from Merck (Darmstadt, Germany). Other solvents and reagents (analytical (ACS) or HPLC grade) were purchased from Merck (Darmstadt, Germany), SERVA Electrophoresis (Munich, Germany) or POCH (Gliwice, Poland).
Spectra/*Por molecularporus* membrane tubing (membrane 1, MWCO:6–8 kDa) were purchased from Spectrum Medical Industries Inc. (Huston, TX, USA).
## 2.2. Extraction Process
Protein extraction from defatted flaxseed meals and subsequent fractionations were carried out according to the procedure described by Kwon et al. [ 23] with some modifications. In this procedure, protein extraction and fractionation are based on differences in solubility (Figure 1).
Briefly, defatted meal (20 g) was mixed with deionised water (1:10 and 1:15 w/v for Szafir/Oliwin and Jantarol cultivar, respectively) and stirred continuously for 1 h at room temperature. The higher meal-to-water ratio for Jantarol seed extraction was due to the higher content of water-soluble mucilage in these seeds, which increased the viscosity of the solution compared to the other cultivars.
Then, the pH of solution was adjusted to 4.2 using 1 mol L−1 hydrochloric acid and centrifuged (12,000× g for 30 min at 4 °C; Heraeus Megafuge 40R centrifuge, Thermo Fisher Scientific, Santa Clara, CA, USA). The precipitate (residue) was used for further extraction, while the supernatant was collected, filtered using glass wool and dialysed against deionised water using from 6–8 kDa MWCO dialysis membrane tubing for approximately 24 h at 4 °C (with at least six changes of water). The content of the dialysis membrane tubing was freeze-dried (Alpha 1–4 LSC Freeze dryer; Christ, Germany) and labelled as WEF (water-extract fraction).
The residue after first extraction was dissolved in 150 mL of 0.5 mol L−1 sodium chloride, and the pH of solution was adjusted to 8.0. The solution was stirred continuously for 1 h at room temperature, and then centrifuged (12,000× g for 30 min at 4 °C). The residue was used for further extraction, while the supernatant was collected and dialysed against deionised water using from 6–8 kDa MWCO dialysis membrane tubing for approximately 24 h at 4 °C. The content of the dialysis membrane tubing was freeze-dried and labelled as SEF (salt-extracted fraction).
The residue after the second extraction was dissolved in 150 mL of 0.1 mol L−1 sodium hydroxide. The pH of solution was adjusted to 11.0 using 1 mol L−1 sodium hydroxide. The next extraction of proteins was performed for 1 h at room temperature with continuous stirring, followed by centrifugation (12,000× g for 30 min at 4 °C). The supernatant was collected and dialysed as previously described. The obtained fraction labelled AEF (alkaline-extract fraction) was freeze-dried. All flaxseed protein factions were stored at 4 °C in the dark for further use.
## 2.3. Amino Acid and Electrophoretic Profile Analyses
The amino acid profiles of protein fractions were determined by an high-performance liquid chromatography (HPLC) gradient system with precolumn phenylisothiocyanate (PITC) derivatisation after acid hydrolysis in 6 mol L−1 hydrochloric acid with $1\%$ phenol under nitrogen at 110 °C over 24 h, as proposed by Kwanyuen and Burton [24]. The tryptophan content was examined after alkaline hydrolysis of proteins in 4 mol L−1 sodium hydroxide at 110 °C over 18 h under nitrogen and analysed according to the method proposed by Çevikkalp et al. [ 25] with slight modifications. The analysis was performed using the LC Agilent Technologies 1200 Rapid Resolution (Santa Clara, CA, USA) system equipped with a UV-Vis detector DAD 1260 and a reversed-phase column Zorbax Eclipse Plus C18 (4.6 × 150 mm, 5 µm). Two independent samples for each fraction were prepared and each sample was injected twice.
The electrophoretic protein separation (SDS-PAGE) of protein fractions was carried out according to the procedure described by Waszkowiak et al. [ 26] under reducing conditions. The separation was performed in $15\%$ polyacrylamide separating gel. The PageRuler Plus Protein Ladder from 10–250 kDa (Thermo Fisher Scientific, Waltham, MA, USA) was run as the standard. The protein content in each separated sample was 12 μg (it was measured with a 2-D Quant Kit, GE Healthcare Bio-Sciences, Marlborough, MA, USA). The gels obtained as a result of electrophoretic separation were scanned using a Molecular Imager® Gel DocTM XR+ System scanner (Bio-Rad Laboratories, Inc. Hercules, CA, USA). Gel path analysis was developed using Image Lab 6.0.1 software (Bio-Rad Laboratories, Inc.). All bands accounted for $100\%$, the shares of individual protein bands were determined and analysed statistically.
## 2.4. SDG and Phenolic Acid Analysis
The qualitative and quantitative analysis of lignans and main phenolic acids of flaxseed protein fractions was preformed according to the procedure described by Renouard et al. [ 27] and Fuentealba et al. [ 28] with some modification. The procedure included alkaline and enzymatic (β-glucosidase) hydrolyses, followed by high-performance liquid chromatography (HPLC).
An amount of 0.100 g of each fraction was weighed and 1.8 mL of deionised water was added. The samples were dissolved by periodically mixing (for 30 min at room temperature by vortex) and then underwent sonication using an ultrasonic bath (10 min at room temperature). The procedure of mixing and sonication was repeated.
A volume of 0.2 mL sodium hydroxide at a concentration of 1 mol L−1 was added to the dissolved samples (0.1 mol L−1 final concentration). Hydrolysis was carried out at 50 °C for 12 h (Thermoblock TB-951U, JW Electronic, Warszawa, Poland). Then, the sample was cooled down and neutralised by the addition of 2 mol L−1 hydrochloric acid. The 0.5 mL of neutral hydrolysate sample was transferred to an Eppendorf test tube and methanol was added (volume ratio 1:1) to precipitate proteins and mucilage. The sample was left for 24 h at 4 °C, and then centrifuged at 10,000× g for 10 min (Espresso Personal Microcentrifuge, Thermo Scientific, Waltham, MA, USA). The supernatant was filtered by an RC syringe filter (diameter: 13 mm, pore size: 0.45; Thermo Fisher Scientific, Santa Clara, CA, USA) and submitted to lignan SDG analysis by HPLC.
After alkaline hydrolysis, 0.5 mL of neutralised samples were taken, evaporated to dryness at 38 °C (Genvac miVac Duo Centrifugal Concentrator, Thermo Fisher Scientific), and suspended in 0.5 mL of β-glucosidase solution (2 U mL−1 of enzyme at 0.1 mol L−1 sodium acetate buffer, pH 5). The samples were hydrolysed for 12 h at 40 °C in the SIF 6000R control temperature oven (Lab Companion, Billerica, MA, USA) under gentle shaking. Methanol was added (volume ratio 1:1) to stop hydrolysis and the samples were cleaned as was described above. The supernatants were submitted to phenolic acid analysis by HPLC.
The main flaxseed phenolic compounds, i.e., secoisolariciresinol diglucosides (SDG), ferulic acid and p-coumaric acid, were identified and quantified by the HPLC method using an Agilent 1290 Infinity LC System (Agilent, Santa Clara, CA, USA) equipped with a Luna Omega 5 µm Polar C18 100A LC Column (4.6 × 75 mm; Phenomenex, Torrance, CA, USA). The injection volume was 20.0 µL. A mobile phase gradient with acetonitrile (solvent A) and $1\%$ aqueous solution of acetic acid (solvent B) was developed: linear increment from $12\%$ to $60\%$ of solvent A in 18 min, further increase of A to $70\%$ in 1 min and followed by a decrease to $12\%$ of A in 1 min at a constant flow rate of 0.3 mL∙min−1. The eluate was monitored using a Diode-Array detector (DAD) set at the wavelength characteristic for the analysed compounds. The identification and quantification of phenolic compounds was conducted by comparing their retention times with those of corresponding standards; additionally, a DAD detector was applied to identify the compounds on the basis of their absorption spectra. Two independent samples for each fraction were prepared and each sample was injected twice. The results were expressed in mg per gram of fraction.
## 2.5.1. Fourier Transform Infrared (FT-IR) Analysis
FT-IR spectra were determined by the spectrum Two FT-IR spectrometer with a Universal ATR with a diamond crystal (PerkinElmer, Waltham, MA, USA). The experiment was set up for all available spectral ranges (500–4000 cm−1). Typically, a microgram of the sample was put on the diamond crystal in the ATR system with a controlled force gauge. The measurements were conducted three times for each fraction sample.
## 2.5.2. Fluorometric Analysis
The fluorescence spectra (emission and excitation) of each fraction sample (solution in double-deionised water at a concentration of 0.1 g L−1) were recorded by the Shimadzu RF 5001PC fluorometer (Kyoto, Japan) at ambient conditions. All the spectroscopic measurements were conducted at ambient conditions in a 1 cm × 1 cm quartz cuvette. Samples were excited at the UV and VIS region (200–500 nm), and emission was measured over the range up to 990 nm with 3 nm excitation and emission slits. Geometry excitation to the emission beam was 90° (L-shaped).
## 2.6. Functional Properties of Flaxseed Protein Fractions
Stock solutions of the protein fractions in deionised water (10 g L−1) were prepared by periodical mixing in a vortex. Next, they were centrifuged at 15,000× g for 15 min at 4 °C (Heraeus Megafuge 40R centrifuge) and used for the further determination of protein surface hydrophobicity, total phenolic content and antioxidant activity by ORAC_FL assay and in emulsion. Two independent samples for each fraction were prepared and each sample was analysed twice.
## 2.6.1. Protein Surface Hydrophobicity (Ho)
Protein surface hydrophobicity was analysed by the method using anionic (8-anilinonaphthalene-1-sulfonic acid, ASN) fluorescent probes [29].
The stock solutions of the protein fractions were serially diluted to final concentrations ranging from 0.0063–0.1 g L−1 (w/v) in 0.01 mol L−1 sodium phosphate buffer (pH 7.0). A 15 µL aliquot of ANS solution (11 mg mL−1) was added to 3 mL of serially diluted sample solutions, mixed thoroughly, and the fluorescence intensity was measured using a florescence spectrophotometer (Hitachi F-2700, Hitachi High Technologies, Pleasanton, CA, USA; 390 nm excitation and 490 nm emission wavelength, 5 nm widths of excitation and emission slit).
The results were expressed as the index of protein surface hydrophobicity Ho, which is defined as the initial slope of the plot of the relative fluorescence intensity (RFI) vs. protein concentration, calculated by the simple linear regression. The relative fluorescence intensity was counted as follows [30]: RFI = (F − F0)/F0, where F is the fluorescence of the protein–ANS conjugate, and F0 is the fluorescence of the ANS solution without protein fractions.
## 2.6.2. Antiradical Activity (ORAC_FL Assay)
The oxygen radical absorbance capacity assay with fluorescein sodium salt (Fluka, Everet, WA, USA) as a fluorescent probe (ORAC_FL) by Ou et al. [ 31] was carried out following the previously described procedure [32]. The fluorescence was recorded using a florescence spectrophotometer (Hitachi F-2700; 493 nm excitation and 515 nm emission wavelength).
The stock solutions were diluted with deionised water to obtain appropriate concentrations within the assay activity range. Trolox solutions (0–100 μmol L−1) were used as standards. The results were calculated by subtracting the areas under the fluorescein decay curves for the control sample (without standard or protein fraction) and the test sample (net area) and were expressed as μmol of Trolox equivalents (TE) per gram of the sample.
## 2.6.3. Total Phenol Content (TPC)
The total phenol content was determined by the Folin–Ciocalteu method [33,34]. The method is based on the spectrophotometric measurement of the intensity of the colorimetric reaction that takes place between the phenolic groups (derived from the standard or sample) with the Folin–Ciocalteu reagent (FCR) in the sodium carbonate environment.
The sample (0.040 mL of standards, 0.100 mL of WEF and 0.080 mL of SEF and AEF, respectively) was mixed with deionised water up to a total volume of 3.200 mL. As a blank, 3.200 mL µL of deionised water was used. Then, 0.200 mL of Folin–Ciocalteu reagent was mixed with the sample and, after 5 min, 0.200 mL of aqueous sodium carbonate solution (200 g L−1) was added. After 120 min of incubation in the dark at room temperature, the absorbance was measured at 765 nm against a blank. The calibration curve of gallic acid (the standard solution: 0–0.9 mg mL−1) was used to calculate the total phenol contents and the results were expressed as gallic acid equivalents (GAE) in milligrams per gram of the sample.
## 2.6.4. Ability to Inhibit Oxidation in Emulsion System
The test emulsion containing $2.5\%$ flax oil (received from a local manufacturer) and $0.25\%$ Tween 20 in 0.05 mol L−1 potassium phosphate buffer (pH 7.2) was prepared as described by Waszkowiak and Barthet [35]. Fresh emulsion was prepared on the day the oxidation test was performed. An aliquot (5 mL) of the emulsion was transferred into 25 × 95 mm glass screw cap tubes. Then, 25 µL of additive was added and the emulsion mixture was vortexed thoroughly. The additives were (per amount of oil in emulsion) the known antioxidant butylated hydroxytoluene (BHT; 0.1 g L−1) and three protein fractions (10 g L−1). A control emulsion without additive was run with each oxidation study.
The oxidation test was performed over 24 h at 40 °C in the dark in an incubator (SIR6000R, Lab Companion, Billerica, MA, USA) with constant mixing (110 random per min). In fresh emulsions (initial value), and after a 24 h-oxidation test, the extractions of oil were performed by adding 2.5 mL of hexane and 2.5 mL of isopropanol to 5 mL of emulsion mixture. The sample was thoroughly shaken for 5 min, and then centrifuged for 10 min at 5500× g (Heraeus Megafuge 40R centrifuge). The top layer, which contained the oil was removed, diluted with hexane to an appropriate concentration and analysed for conjugated dienoic derivatives (CD) and trienoic derivatives (CT) according to the AOCS Official Method Th 1a-64 [36] and Pegg’s protocol [37], respectively Two sample tubes of each emulsion (test and control) were analysed to monitor lipid oxidation and two independent analyses were performed for each sample.
The ability of the selected additives to inhibit oxidation in the emulsion system was expressed as PI_24 h (the protective index after 24-h incubation of emulsion at 40 °C). The index was defined as the CD/CT value of the sample with the additive divided by the CD/CT value of the control sample. PI < 1 denotes an acceleration effect on the oxidation in emulsion, PI = 1 denotes no effect on oxidation, and PI > 1 denotes a protective effect against oxidation [38].
## 2.7. Statistical Analysis
The experiments were performed for two independently prepared fraction samples. Each analysis was determined at least in duplicate. All experimental results were expressed as mean ± standard deviation. Statistical analyses were conducted using Statistica software (v.13.3, Tibco Software Inc., Palo Alto, CA, USA). The effects of subsequent extractions of protein fractions ($L = 3$; water-extraction—WEF, salt extraction—SEF and alkaline-extraction—AEF) and flax cultivars ($L = 3$; Szafir, Oliwin, Jantarol) on dependent variables were analysed. Analysis of variance (ANOVA) for a completely randomised design (CRD) experiment was carried out, and post hoc Tukey’s HSD test was applied for multiple comparisons of the means (at a significance level of α = 0.05). A Pearson correlation coefficient (r) was computed to assess the linear relationship between variables. In the statistical analysis of the results, p-values < 0.05 were taken as statistically significant.
## 3.1. Electrophoretic and Amino Acid of Profiles of the Flaxseed Protein Fractions
The results confirm that the subsequent extractions and fractionation affects protein contents, amino acid compositions and SDS-PAGE electrophoretic profiles of the flaxseed protein fractions (Table 1 and Figure 2, respectively).
The total protein content (based on the sum of amino acids, Table 1) was the highest in AEF (alkaline-extracted fractions) compared to WEF (water-extracted) and SEF (salt-extracted) fractions, irrespective of the flax cultivar. It ranged from 500.4 mg g−1–708.1 mg g−1 for AEF, 369.7–505.8 mg g−1 for SEF (the lowest for Jantarol fractions in both cases), and 311.2–332.5 mg g−1 for WEF. The results showed that water extraction and subsequent salt extraction probably removed the mucilage resulting in protein concentration in alkaline-extracted fractions. However, its removal was not total since the protein concentration in AEF fractions did not exceed between 50 and $70\%$.
The lowest protein concentration in AEF of Jantarol probably resulted from differences in the chemical composition of the selected flaxseed. Our previous study showed [19] that the seeds of Jantarol were characterised by a lower protein content (170.4 g kg−1) than the seeds of Szafir and Oliwin cultivars (220.9 kg−1 and 213.7 kg−1, respectively). Moreover, the Jantarol seeds had a higher amount of soluble fibre (mucilage, 221.0 g kg−1) when compared with the other cultivars used in the study (about 209.4 g kg−1 and 212.8 g kg−1 for Szafir and Oliwin, respectively). The high mucilage content, forming a viscous solution, may have influenced the extraction process from Jantarol seeds.
Eleven protein bands were found and marked in the SDS-PAGE electrophoretic profiles of flaxseed protein fractions (Figure 2). In our study, the globulin monomers (11S) were represented by band 2, and subunits α (35–22 kDa) and β (~18 kDa) were bands 4–6 and 8, respectively. Proteins of the albumin-like group (bands 9–11) were characterised by a molecular weight (MW) <15 kDa. The protein profile and molecular weight distribution are similar to the data presented in previous reports regarding the albumin and globulin fractions [11,12].
The electrophoretic profiles of AEF differed from WEF and SEF, irrespective of the cultivar. In the AEF samples, the lowest percentage share of those albumin-like proteins at low MW (<15 kDa) was observed ($8.10\%$; see Supplementary Materials, Table S1), and proteins at MW ranging from 35 kDa to 16 kDa constituted the largest share ($85.79\%$). It is worth emphasising that bands 7 (~20 kDa), 9 and 11 were not present in the AEF electrophoretic profiles. Fractions WEF and SEF compared to AEF were distinguished by the absence of two bands (1 and 3) at the MW range from 56–38 kDa. WEF and SEF fractions showed similar SDS-PAGE protein profiles. However, the fractions differed in the proportion of particular protein bands. The significantly increased intensities of bands 5 and 8 were in the WEF profile in comparison to SEF.
The statistical analysis of variance (two-way ANOVA, simply main effect) confirmed the significant effect ($p \leq 0.001$) of subsequent extractions on the share of proteins at MW from 56–38 kDa (globulin-like proteins) and <15 kDa (albumin-like proteins) in the protein electrophoretic profiles. When the fraction type was adopted as a factor, the following dependencies were found for the proteins at MW from 56–38 kDa: WEF < SEF < AEF and for the proteins at MW < 15 kDa: AEF < WEF < SEF, respectively.
When comparing the amino acid profiles, the differences among the fractions were also observed (Table 1). The crucial differences were for the AEF profiles compared to WEF and SEF. AEF fractions were characterised by the highest content of branched-chain amino acids (BCAA; isoleucine and phenylalanine). However, the low amounts of cysteine (sulphur-containing amino acid, SCAA) were in AEF; its percentage share amounted to between 1.6 and $1.7\%$ in the amino acid profiles of AEF. The explanation is the removal of soluble and easy-extractable albumin-like proteins at low MW during water and salt extractions (as shown in the electrophoretic profiles; Figure 2). As it has been proved in previous studies [12,39], the proteins are rich in cysteine. The AA profiles of SEF were characterised by the lower percentage share of hydrophobic amino acids (HAA) in comparison to the AEF profiles, such as alanine, proline and valine, and the highest percentage share of negatively charged amino acids (NCAA).
Our study showed that the subsequent protein extractions allowed to obtain flaxseed protein fractions differed in shares of albumin-like and globulin-like proteins in SDS-PAGE profiles and, therefore, differed in contents of BCAA, SCAA, HAA and NCAA. These differences in the protein fractions resulting from subsequent extractions can influence both their nutritional values and functional properties. The SCAA, HAA and NCAA relate to disulfide bonds, hydrophobic contact, the charged patch on a surface and electrostatic interactions [40,41], which affect protein structural properties and behaviour at the oil–water or air–water interfaces (i.e., emulsifying or foaming properties). The presence of flaxseed proteins at low MW (<15 kDa) in the WEF and SEF fractions and the domination of proteins at higher MW (36–16 kDa) in the AEF fractions can influence those behaviours.
## 3.2. SDG and Phenolic Acid Contents of the Flaxseed Protein Fractions
In flaxseed, most phenolic compounds have a form of glucoside derivatives [42,43]. They form a macromolecular complex in which SDG is ester-linked to HMG; the phenolic acid glucosides (p-coumaric acid and ferulic acid derivatives) are linked to this SDG-HMG oligomer [44]. In our study, two-step hydrolysis was applied, which included alkaline hydrolysis followed by enzymatic hydrolysis (with β-glucosidase). Alkaline hydrolysis allows the release of SDG and phenolic acid glucosides from the complex, and enzymatic hydrolysis helped convert phenolic acid derivatives into identifiable free forms [27,28]. The contents of the main flaxseed phenolic compounds in the flaxseed protein fractions, i.e., SDG, ferulic acid and p-coumaric acid, are shown in Table 2.
The statistical analysis of variance showed the significant effects of fraction type ($p \leq 0.001$) and cultivar ($p \leq 0.001$) on the compound contents in the flaxseed fractions. The highest concentrations of SDG and phenolic acids were in the AEF, and the lowest contents were in WEF, regardless of the flaxseed cultivar used as the extraction material. When the variety was adopted as a factor, the following dependencies were found for SDG: Oliwin < Szafir < Jantarol, and for phenolic acids: Oliwin = Szafir < Jantarol, respectively.
The presence of phenolic compounds (lignans and phenolic acids) reported in our study could significantly influence the functional properties of the extracted protein fractions. Pham and co-authors’ study [45] proved that the complexation of flaxseed protein with phenolic compounds gives extra benefits to proteins and protein-based emulsions. They carried out the covalent modification of the flaxseed protein isolate by phenolic compounds (i.e., flaxseed polyphenols (FPP), ferulic acid and hydroxytyrosol (HT)) and the results showed that the surface hydrophobicity could be modified (increase or decrease) by adding selected phenolic compounds. The authors also discovered that the flaxseed protein isolate–FPP-based emulsion had higher oxidative stability than the isolate-based emulsion.
The accumulation of phenolic compounds during extraction, particularly lignan–SDG, could be valuable for the potential antioxidant properties of the flaxseed protein fractions. Our study showed that the highest content of SDG in the protein fractions was after alkaline extraction (AEF). SDG and its aglycone and oligomers extracted from flaxseed shows antioxidant capacity [19,45]. As previously reported, SDG acts, for example, as a radical scavenger and lipid peroxidation inhibitor [18,46,47,48], and its antiradical activities are attributed to the 4-hydroxy-3-methoxy phenyl moiety [47]. The antioxidant capacity of the flax lignan is one of the crucial elements of its beneficial effect on human health [19]. SDG presence in concentrates of flaxseed proteins can enhance their functionalities associated with oxidation inhibition in food.
## 3.3.1. FT-IR Spectra Analysis
Further information about individual protein fractions provided infrared spectra (Figure 3). Among the signals common to all groups of samples were those originating from stretching vibrations in -OH, =NH and -NH2 groups (between 3000 and 3500 cm−1), additionally broadened due to the presence of hydrogen bonds. In addition, signals from analogous vibrations of carbon–hydrogen bonds in aliphatic chains (2800–3000 cm−1) and the CO bond present in the bonds of amide and ester functional groups (between 1600 and 1750 cm−1) were visible. In addition, region between 1500 and 1650 cm−1 was characteristic of NH deformation vibrations. At wave number values above 1000 cm−1, an intense signal from carbon–oxygen bond vibrations was evident [49].
Regardless of the source, the spectra of WEF (the water-extracted fractions) showed a much more intense signal from CH bonds than SEF (salt-extracted) and AEF (alkaline-extracted). Similarly, there is a signal from the carbonyl group at about 1700 cm−1, which may indicate a relatively higher amount of ester groups and free carboxylic groups. It could be related to better solubility compounds with a free carboxylic group in the water, which caused them to be in WEF. Moreover, the spectra of WEF showed a more intense sequence of signals in a region between 1200 and 750 cm−1 compared with the other fractions (followed by SEF and the lowest in AEF), characteristic of flaxseed mucilage [49,50].
The spectra for all WEF samples were very similar, and the differentiating features were much more pronounced in the other two fractions. In the group of the SEF samples, the relatively high intensity of signals from CH bonds were characterised by the sample from the Oliwin seeds and, among AEF, the fractions extracted from the Szafir cultivar. Since these differences do not relate directly with the data in Table 1, the assumption can stay that they are due to the presence of associated substances extracted with the proteins [51].
## 3.3.2. Fluorimetric Characterisation
For further investigations, we have chosen fluorometric characterisation of the protein fractions to determine significant changes originating from eventual impurities or specific inner- or/and intermolecular interactions of compounds. Therefore, several sets of fluorescence emission spectra at different excitation wavelengths (especially UV region: 200–300 nm) were recorded with the step of 10 nm. Additionally, fluorescence excitation spectra were also collected. It was found that the shape of fluorescence emission spectra remained unchanged for individual samples, regardless of the excitation wavelength in the range of excitation from 200–300 nm. For other excitation wavelengths, no emission spectra were recorded. Therefore, the normalised fluorescence excitation (dashed lines) and emission spectra (solid lines) are presented in Figure 4.
Emission spectra of WEF and SEF fractions were characterised by a broad band centred at 340 nm with no regard to the cultivar. However, the blue shift of the fluorescence band was for AEF (band centre from 328–335 nm, cultivar dependent). Moreover, this blue shift was noticeably more pronounced for the Jantarol cultivar (band peak at 328 nm), accompanied by an additional fluorescence band placed at 430 nm, manifested as a shoulder of the main fluorescence band. Similar results to the described, but much less pronounced, were also observed for the WEF of Jantarol. The recorded emission arises mainly from tryptophan with much less contribution of phenylalanine and tyrosine (only those three proteins are fluorescent among all the proteins present in samples). The changes in the spectra can provide information on the molecular organisation of peptides and their interactions within the samples.
The results of the fluorescence excitation spectra allow us to conclude that all recorded emissions come from the same group of chromophores/fluorophores because the fluorescence excitation spectra show the same shape for each sample, regardless of the extraction method, cultivar and wavelength of observation. The observed changes in fluorescence emissions are evidence of the influence of the extraction method on the electronic structure of sample molecules and/or the possibility of the occurrence of intermolecular interactions of compounds. It may occur due to the other compounds’ presence extracted with the proteins, which supports the conclusion drawn from the FT-IR spectra analysis, or conformational changes of proteins due to the extraction conditions [52]. The observed blue shift of fluorescence maximum for AEF and WEF Jantarol cultivars can also suggest the shielding of tryptophan from the environment capable of forming hydrogen bonds. It is the evidence of the different conformation of proteins for the WEF and AEF Jantarol cultivar from proteins in other samples. Among all tested fractions, the AEF from the Jantarol cultivar is the most susceptible to conformational changes. Since observed effects are the most efficient at a high pH, the Jantarol cultivar is the most susceptible to the extraction environment (especially at a higher pH).
## 3.4. Functional Properties of the Flaxseed Protein Fractions
Table 3 shows the results of the analysis of selected protein fractions’ properties, i.e., protein surface hydrophobicity, antiradical activity and total phenolic content.
Protein surface hydrophobicity (Ho): Higher surface hydrophobicity Ho shows that more hydrophobic patches are outside protein molecules. Its value is valuable for studying protein aggregation behaviours and interfacial properties [6].
The results showed that the lowest Ho had WEF from the Oliwin and Szafir cultivar, and the highest had AEF from seeds of the Jantarol cultivar. The Ho values for the WEF fractions were twice as low as the SEF fractions and between 8 and 9 times lower than AEF, irrespective of the flax cultivar. The Ho values of AEF (50.8–80.9) were similar to those reported in previous studies for flaxseed protein concentrates (43.3–68.5; [10]), but lower than those shown for flaxseed protein isolates (120.6; [53]). The study by Nwachukwu and Aluko [12] showed that flaxseed globulins had higher Ho than albumins.
The results are consistent with the amino acid composition of the fractions—the significantly higher Ho values were for AEF, which were characterised by the higher percentage shear of HAA and lower NCAA in the AA profile compared to the others (Table 1). The statistical analysis (Pearson correlation test) confirmed that protein surface hydrophobicity of the flaxseed protein fractions obtained with subsequent extractions was significantly positively correlated with HAA contents (Pearson correlation coefficient $r = 0.754$, $$p \leq 0.019$$).
Antiradical activity (ORAC_FL): ORAC_FL assay measures antioxidant scavenging activity against peroxyl radicals. The analysis (Table 3) showed the lowest activity of WEF fractions. For fractions of the particular flax cultivar, the antiradical activity of SEF was from 1.9 to 3.3 times, and AEF was from 4.8 to 14 times as high as WEF (the lowest for those from the Oliwin cultivar and the highest for the Jantarol fractions, respectively). The results of statistical analysis revealed a strong relationship (correlation coefficient $r = 0.963$, $p \leq 0.001$) between the antiradical activity of the flaxseed protein fractions and the SDG content (main phenolic compounds, Table 2).
Total phenolic content (TPC)/compounds reducing Folin–Ciocalteu reagent (FCR): In our study, the positive correlation was found between the TPC values and the results of ORAC_FL assay ($r = 0.974$, $p \leq 0.001$). As for antiradical activity results, the TPC of WEF were the lowest (Table 3). The TPC values of SEF were from 1.4–3 times as high as WEF, and the TPC of AEF were from 2.7–6 times higher than WEF. In the Folin-Ciocalteu method, a phosphotungstate–phosphomolybdate complex is reduced by phenols to blue reaction products measured at 765 nm. Under proper conditions, the assay gives predictable reactions with various phenolics in plants. However, the FCR is significantly reactive towards other nonphenolic compounds, e.g., amino acids (cysteine, tryptophan, tyrosine) and proteins [54]. Therefore, the results for protein fractions reported in our study may have resulted from the reduction of FCR by both phenolic compounds and proteins. This assumption confirmed the results of the statistical analysis, which showed the positive correlation between the TPC values for the protein fractions and SDG contents (Pearson correlation coefficient $r = 0.938$, $p \leq 0.001$), as well as between the values and hydrophobic amino acid (HAA) contents ($r = 0.722$, $$p \leq 0.028$$).
The statistical analysis of variance showed statistically significant ($p \leq 0.001$) effects of the subsequent extraction (water/salt/alkaline), as well as the cultivars on the above properties. In the analyses performed for each of the studied functional properties (two-way ANOVA, simply main effect), adopting the fraction type and cultivar as a factor, respectively, the following dependencies were: WEF < SEF < AEF and Oliwin < Szafir < Jantarol.
## 3.5. Ability of Flaxseed Protein Fractions to Inhibit Oxidation in Emulsion
Due to the growing interest of researchers and the food industry in the application of flaxseed protein as emulsifiers [6,8,55], we investigated the ability of the flaxseed protein fractions to inhibit oxidation in the emulsion system (protective effect). CD and CT contents were monitored in oil extracted from the emulsion after 24 h of incubation at 40 °C. Changes in CD content (measured at a wavelength of 234 nm) were related to changes in primary oxidation products and changes in CT (measured at 268 nm) to changes in secondary oxidation products during flaxseed oil oxidation [56].
All flaxseed protein fractions showed a protective effect against oil oxidation in the tested emulsion system (Table 4). After 24 h of incubation, the values of CD and CT indexes for the emulsions with the addition of 10 g L−1 flaxseed fraction (per oil in emulsion) were significantly lower than the control emulsion (the protective indexes PI_24 h > 1).
The WEF and AEF were characterised by a similar protective effect against oil oxidation in the emulsion. The effects were also close to the BHT (at concentration of 0.1 g L−1). The SEF had a statistically significantly higher protective effect than the other additives, particularly toward CD formation—the CD_PI_24 h values for SEF were 1.5 times as high as the others, irrespective of the flax cultivar. The PI_24 h values counted based on changes in CT were also slightly higher for SEF.
Contrary to antiradical activity (ORAC_FL assay results described in the above section), there was no statistically significant relationship between the ability to inhibit oxidation in emulsion (CD or CT_PI_24 h) and the phenolic compound (SDG) content in flaxseed protein fractions (Pearson correlation coefficients: r = −0.152, $$p \leq 0.696$$ and r = −0.064, $$p \leq 0.870$$, respectively). There was also no statistically significant relationship between CD or CT_PI_24 h and TPC (r = −0.165, $$p \leq 0.671$$ and r = −0.188, $$p \leq 0.628$$, respectively).
The analysis of variance confirmed a statistically significant ($p \leq 0.001$) effect of the subsequent extractions of protein fractions (fraction type) and the extraction material (cultivar) on the ability to inhibit oxidation in emulsion (CD/CT_PI_24 h index). However, the order was different compared to antiradical activity (ORAC) results. When the fraction type effect was adopted, the following dependencies found for CD_PI_24 h were AEF = WEF < SEF and for CT_PI_24 h were AEF < WEF < SEF.
Our study showed different behaviour of the flaxseed protein fractions in antiradical assay and emulsion, with AEF showing the best antiradical activity in ORAC_FL and SEF having the best ability to inhibit oxidation in the emulsion system.
The better protective effect against oxidation of the salt-extracted protein fractions (SEF) over the alkaline-extracted fractions (AEF) in the emulsion can be a result of their composition. SEF was characterised by lower protein concentration (Table 1). Moreover, the relative differences in the intensities of infrared signals characteristic for chemical bonds present in mucilage components may suggest its higher content in SEF than in AEF. Flaxseed mucilage is a good emulsifier [57]. Previous studies [58,59] showed that protein-to-mucilage proportion in the flaxseed concentrates influenced their functional properties, including water-binding and emulsifying capacities, and the high-mucilage protein concentrates had better properties than the low-mucilage protein isolates.
Moreover, the SDS-PAGE profiles showed the highest concentration of proteins at low MW (Figure 2) in SEF and the lowest share of such proteins in the AEF profile. They are probably an albumin-like (1,6-2S) conlinin, which together with globulin-like (11-12S) linin, are the main groups of flaxseed proteins. Water-soluble conlinin has a higher content of SCAA and NCAA but lower HAA content than flaxseed globulin [12], providing better emulsion-forming ability. The study by Liu and co-authors [60] proved that conlinin is the major protein associated with flaxseed gum and plays a crucial role in its emulsifying properties (conlinin hydrolysis decreased the gum emulsifying activity and emulsion stability). The higher content of SCAA and NCAA (enhanced reducing property) also suggests a better capacity of albumin-like conlinin to protect against lipid oxidation in a food than globulin-like linin.
Both mentioned compounds (mucilage and low MW proteins) can be crucial to enhance the observed protective effect of SEF against oxidation in the emulsion system compared to AEF. They probably allow for improvement in the oil-in-water emulsion stability, preventing the aggregation of oil droplets, as well as helping to locate the compounds with potential antioxidant activity at the oil–water interface. Further research is necessary to investigate this problem.
The food industry is increasingly interested in flaxseed protein applications, among others, due to their functional (e.g., emulsifying) properties. The presence of phenolic compounds in flaxseed protein fractions, especially SDG with proven antioxidant capacity, indicates another crucial functionality related to lipid oxidation inhibition.
Previous studies have mainly focused on producing only one protein concentrate/isolate in one process from a given batch of seeds/meal [9,10]. Few studies have applied the procedure of subsequent extractions and fractionation of flaxseed proteins [11]. In our research, we use the subsequent extractions (first with water, then salt and alkaline solutions) of the same raw material to investigate and understand the relationship between the fraction composition (protein and phenolic profiles) and antioxidant capacity. However, this approach to the extraction process allows for the full use of one raw material batch for the production of protein concentrates with different functional properties compared to separate ones (e.g., three separate extractions from three flaxseed batches). It is in line with the current trend of food and food additive manufacturing.
Our study shows the influence of the fractionation method on the composition and antioxidant capacity of protein fractions. The knowledge can help to design the process to obtain flaxseed protein concentrates with different potential applications in food. The high concentration of SDG (with proven antiradical potential) and globulin-like proteins at high MW (with a high content of HAA and surface hydrophobicity) in an alkaline-extracted fraction suggest its application in food with high-fat content. The salt-extracted fractions can be better for emulsion-like food. The practical application of this approach needs further study.
## 4. Conclusions
The research provides novel information about the influence of protein fraction composition on antioxidant activity and its relation to extraction solvent. Our study has proved there is a significantly higher content of SDG in alkaline-extracted fractions (AEF), which is related to their higher antiradical capacity (ORAC assay) compared with the others (WEF and SEF). However, the AEF showed a lower ability to inhibit oxidation in the emulsion assay than the salt-extracted fraction (SEF). The SDS-Page separation showed that SEF characterised the higher percentage share of the flaxseed proteins at MW < 15 kDa in the protein profile than AEF. It suggests the crucial role of low-MW proteins in protection against oxidation in oil-in-water emulsions.
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|
---
title: Cytoprotective Effect of Pteryxin on Insulinoma MIN6 Cells Due to Antioxidant
Enzymes Expression via Nrf2/ARE Activation
authors:
- Junsei Taira
- Ryuji Tsuda
- Chika Miyagi-Shiohira
- Hirofumi Noguchi
- Takayuki Ogi
journal: Antioxidants
year: 2023
pmcid: PMC10045797
doi: 10.3390/antiox12030693
license: CC BY 4.0
---
# Cytoprotective Effect of Pteryxin on Insulinoma MIN6 Cells Due to Antioxidant Enzymes Expression via Nrf2/ARE Activation
## Abstract
The low-level antioxidant activity of pancreatic islets causes type 1 diabetes due to oxidative stress, which is also the cause of failure in the pancreatic islets’ isolation and cell transplantation. In our previous study, pteryxin was found to be a natural product as a nuclear factor-erythroid-2-related factor (Nrf2) activator. This study focused on elucidation that the potentiality of pteryxin can activate the antioxidant enzymes, even under oxidative stress, by hydrogen peroxide (H2O2). Pteryxin treated with mouse insulinoma MIN6 cells was enhanced the antioxidant gene expressions in the ARE (antioxidant response element) region for HO-1 (Heme Oxygenase-1), GCLC (Glutamate-cysteine ligase catalytic subunit), SOD1 (Super Oxide dismutase1), and Trxr1 (Thioredoxin reductase1), and those enzymes were also expressed during the nuclei transference of cytoplasmic Nrf2. In fact, the cells exposed to H2O2 concentrations of a half-cell lethal in the presence of pteryxin were then induced main antioxidant enzymes, HO-1, GCLC, and Trxr1 in the ARE region. The increased glutathione (GSH) levels associated with the GCLC expression also suggested to be cytoprotective against oxidative stress by activating the redox-metabolizing enzymes involving their increased antioxidant activity in the cells. In addition, *Akt is* a modulator for Nrf2, which may be responsible for the Nrf2 activation. These results allowed us to consider whether pteryxin or its synthesized congeners, an Nrf2 activator, is a potential preservative agent against islet isolation during cell transplantation.
## 1. Introduction
Oxidative stress, with the excess production of reactive oxygen species (ROS), is related to the increased risk of developing several diseases, including obesity and diabetes mellitus [1]. Oxidative stress generates in the diabetic status and may contribute to the progressive pancreatic β-cell dysfunction in diabetes due to the low expression levels of the antioxidant enzymes [2]. Thus, the pancreatic β-cells that express low levels of many antioxidant enzymes, such as catalase, superoxide dismutase, and glutathione peroxidase, are hypothesized to be susceptible to oxidative damage induced by ROS associated with diabetes mellitus [2,3]. Oxidative stress is also a major cause of islet damage and loss during the islet isolation process or preservation of isolated cells. This is particularly problematic in the pancreatic cell transplantation, as it significantly affects the survival of β-cells [4]. Therefore, the preservation solution for the cell transplantation has been required in the medical practice field [5].
The antioxidant response element (ARE) is activated by the nuclear factor E2-related factor2 (Nrf2) in a major cellular defense mechanism against the oxidative stress or response to electrophilic chemicals. The Nrf2 dissociates from the Kelch-like ECH-associated protein 1 (Keap1) by electrophiles and oxidative stress [6]. The Nrf2 performs a significant role in the regulation of adipocyte differentiation, obesity, and insulin resistance [7]. In addition, the Nrf2 induction restored insulin secretion from pancreatic β-cells due to suppression of the accumulation of intracellular ROS in the isolated islets and pancreatic B-cells through alteration of the gene expression related to the antioxidant, energy consumption, and gluconeogenesis in metabolic tissues [8]. Another Nrf2 study has revealed that the Keap1-Nrf2 system is a key regulator in the protection of pancreatic β-cells as it preserves the islet size by both enhancement of the β-cell proliferation and repression of β-cell apoptosis in diabetic model mice under oxidative or nitrative stress [9]. Therefore, a more effective Nrf2 activator is required to reduce the oxidative stress, particularly the low-level of the antioxidant status of the pancreatic islets. There is a significant potential for clinical applications of the Nrf2 activators in patients with diabetes. Based on these medicinal backgrounds, the Nrf2 activators, such as CDDO-9,11-dihydro-trifluoroethyl amide (Dh404), and dimethyl fumarate (DMF), have been significantly associated with inflammation involving oxidative stress and diseases of the heart, kidney, and pancreas [10,11,12]. They increased the expression of the key antioxidant enzymes, decreased inflammatory mediators in the islets, and conferred protection against oxidative stress in β-cells.
Recent studies have demonstrated that the ethanol (EtOH) extract of P. japonicum has an anti-obesity effect and it contains coumarin-related compounds, including pteryxin, that affect diabetes and obesity, both of which are bioaccessible to the systemic tissues [13,14,15,16,17,18]. For example, anti-diabetic and anti-obesity effects of cis-3’,4’-diisovalerylkhellactone suppressed adipocyte differentiation and stimulated glucose uptake via activation of AMPK and down-regulation of adipogenic transcription factors [17]. Pteryxin inhibited the transcription factors for lipid synthesis in the differentiated adipocytes and in hepatocytes [18]. Additionally, our recent study found the highest Nrf2 activity in the EtOH extract of P. Japonicum Thunb leaves, and its Nrf2 active compound was identical to that of pteryxin, then it induced the expression of the antioxidant protein, HO-1 [19]. In addition, the Nrf2 active function, due to pteryxin, was suggested to hold electrophillicity due to the α,β-carbonyl and/or substituted acyl groups in the molecule modulating the dissociation of Nrf2 from the Keap 1. The highest Nrf2-activating compound, pteryxin, will be expected as a preservation solution for transplantation under oxidative stress state. Therefore, this study placed aim to elucidate the antioxidant ability of pteryxin inducing the expressions of antioxidant gene and enzyme on the ARE region in insulinoma MIN6 cells, and its potentiality as a transplant preservation solution to reduce the cell damage caused by the oxidative stress in cell transplantation.
## 2.1. Materials
Primers for HO-1 (Heme Oxygenase-1), Nqo1 (NAD(P)H dehydrogenase quinone 1), Akt (Protein Kinase B), GCLC (Glutamate-cysteine ligase catalytic subunit), GST (Glutathione S-transferase), SOD1 (Super Oxide Dismutase-1), Snxin1 (Sorting nexin-1), Trxr1 (Thioredoxin reductase1), Bcl-xL (B-cell lymphoma-extra-large), and GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) are commercially available (Assays-on-Demand Gene Expression Products, Thermo Fisher Scientific, Waltham, MA, USA). Antibodies of Nrf2, HO-1, Akt, and GAPDH were obtained from Cell Signaling Technology (Danvers, MA, USA), and GCLC and Trxr1 were purchased form Cosmo Bio Co., Ltd. (Tokyo, Japan). SOD1 and Goat anti-rabbit IgG H&L were obtained from Abcam Plc. ( Cambridge, UK).
## 2.2. Preparation of Pteryxin
In this study, pterixin was isolated using a supercritical fluid extraction method to obtain pterixin in a convenient manner. Pteryxin was isolated from dried leaf powder of P. japonicum [19]. The dried leaf powder (150 g) of P. japonicum was extracted with supercritical carbon dioxide using the Supercritical Fluid Extraction Screening System (X-10-05. Thermo Separation Products, West Palm Beach, MA, USA) at 30 MPa and 43 °C for 3 h, then the extract of 133 mg was obtained. The extract was separated using a centrifugal chromatography with a two-phase solvent system of n-hexane/chloroform/$70\%$ methanol (9:1:10 in v/v/v) (Easy-PREPccc, coil column 318 mL, Kutuwa Sangyo, Hiroshima, Japan), The lower layer (mobile phase) was eluted at 3.0 mL/min at 1110 rpm, then the crude pteryxin extract (41.6 mg) was obtained (retention time at 75–90 min,). The crude pteryxin fraction was purified by a reversed-phase chromatography column (XBridge C18 column, 150 × 19 mm, I.D., 5 μm particle size, Waters Corp., Milford, MA, USA) by formic acid/H2O/acetonitrile ($\frac{0.1}{55}$/45 in v/v/v) elution at a flow rate of 12.0 mL/min using a HPLC apparatus (PU-980 HPLC pump, Japan Spectroscopic Corporation, Tokyo, Japan). Finally, 32.8 mg of pteryxin was obtained as a purified product.
## 2.3. Analysis of Pteryxin
The purity of the isolated pteryxin was determined by the isocratic condition with a mobile phase consisting of acetonitrile/H2O (45:55) at a flow rate of 0.4 mL/min for 5 min by a quadrupole LC/MS/MS (Xevo TQD equipped with H-class and eλ PDA detector, Waters Corp., Milford, MA, USA) on a reversed phase chromatography column (ACQUITY UPLC BEH C18, 50 × 2.1 mm I.D., 1.7 µM particle size, Waters Corp.) at 40 °C.
The structure of pteryxin was confirmed as previous procedures using 1H and 13C-NMR spectra (Avance III HD Ascend 400 MHz spectrometer, Bruker Billerica, MA, USA) [13].
## 2.4. Cell Culture
Mouse insulinoma MIN6 cells were cultured in DMEM medium (including $10\%$ FBS, 100 U/mL penicillin, and 100 µg/mL streptomycin) at 37 °C in a $5\%$ CO2 atmosphere.
## 2.5. Immunohistochemistry
Cells were treated with pterixin (10 μM and 50 μM, respectively) for 1 h, then the Nrf2 translocation from the cytoplasm to the nucleus due to pterixin in the cells was examined as follows. Briefly, the cells were fixed with $4\%$ paraformaldehyde in PBS, then they were blocked using $20\%$ AquaBlock (EastCoast Bio, MO, USA) for 30 min at room temperature. After washing the cells with PBS and incubated at 4 °C with the anti-Nrf2 antibody (1:100), they were subsequently incubated with Goat anti-rabbit IgG H&L (1:200) for 1 h. at room temperature. The cells were treated with mounting medium to detect the fluorescence emitted by the DAPI (Vector Laboratories, Peterborough, UK).
## 2.6. Quantitative Polymerase Chain Reaction (qPCR)/Reverse Transcription PCR (RT-PCR)
Cells (1.0 × 105 cells/mL) were pre-cultured overnight. The pre-cultured cells with or without pterixin (2 and 50 μM) were incubated for 24 h, then the total RNA was extracted from cells with or without test compounds by a RNeasy Mini Kit (QIAGEN, Venlo, Netherlands) using commercially available procedures. Briefly, the RNA extract (2.5 μg) was heated for three minutes at 85 °C, then it was reverse-transcribed into cDNA using Superscript II RNase H-RT (Invitrogen, Waltham, MA, USA). Polymerization of 20 ng cDNA was manually carried out using DNA polymerase (Invitrogen) under amplification cycles for denaturation at 94 °C for 1 min, annealing at 57–62 °C for 1 min, and extension at 72 °C for 1 min with a final extension step at 72 °C for 10 min (Perkin-Elmer 9700 Thermocycler, Perkin Elmer, Inc., Waltham, MA, USA). Quantification of the mRNA levels was carried using a TaqMan real-time PCR system, according to the manufacturer’s instructions (Applied Biosystems, Inc., Waltham, MA, USA). The PCR was performed for 40 cycles, including the initial step at 50 °C for 2 min and at 95 °C for 10 min for denaturation, 15 s at 95 °C, annealing/extension, and 1 min at 60 °C. Each mRNA expression was normalized by the GAPDH mRNA expression level.
## 2.7. Cytotoxicity
The cell viability treatment, with or without a test sample in a well, was examined by an MTT assay, as previously reported [14]. Cells (1.0 × 105 cells/mL) were pre-cultured overnight. Then, the cells were treated with or without pteryxin (1 μM, 5 μM, and 10 μM, respectively) for 24 h. After the culture, MTT ($0.05\%$) was added to each well and incubated for 3 h. The formazan reduced from the MTT was extracted with DMSO (100 µL) and was determined as an index of the surviving cells at 570 nm using a microplate reader (BIO-RAD Model 550, BIO-RAD, Hercules, CA, USA).
## 2.8. Glutathione (GSH) Content
Cells (1.0 × 105 cells/mL) were pre-cultured overnight. The pre-cultured cells with or without a test sample was incubated for 24 h. The total glutathione including GSH and GSSG in the cell lysate was determined by available instructions using the GSH/GSSG-Glo™ Assay kit (Promega K. K., Madison, WI, USA) and a microplate reader (GLOMAX MULTI Detection system, Promega K. K., Madison, WI, USA). Briefly, the determination of the total glutathione measured the luciferin production from the GSH-dependent GSH probe was reduced by glutathione-S-transferase with the firefly luciferase reaction. In addition, GSSG was measured by adding GSH blocking reagent for the lysate, separating only the oxidized GSSG, then the GSSG content was quantified by a luminescence reaction with luciferase. The luminescence intensity depending on the amount of the total GSH and GSSG was measured using a microplate reader. The standard curve of GSH (0–16 µM) was made by luminescence for each GSH and GSSG concentration, then the GSH content of test sample concentrations were expressed as the amount of the total GSH except for the GSSG content.
## 2.9. Protein Expression with or without H2O2 Treatment
The expressions of antioxidant proteins due to pteryxin, with or without H2O2 (100 μM) treatment in a well, was examined by Western blot analysis. Cells (1.0 × 105 cells/mL) were pre-cultured overnight. The pre-cultured cells were incubated with pteryxin (1 μM, 5 μM, and 10 μM, respectively) or without for 24 h, then cells were exposed to H2O2 (100 μM) for 24 h. The cells were washed with PBS, then treated with the lysis buffer. The cellular lysates were centrifuged at 13,800 g for 5 min. The total cellular extracts were separated on SDS-polyacrylamide gels (4–$12\%$ SDS-polyacrylamide, Invitrogen) and transferred to a nitrocellulose membrane (iBlot Gel Transfer Mini, Invitrogen) using an iBlot Gel Transfer Device (Invitrogen). The protein detection was carried out using an immunodetection system (Invitrogen) with the antibodies.
## 2.10. Statistical Analysis
The data were expressed as the means ± SD. F test was used the degree of variability in two groups, then they were compared using the Student’s t-test. The differences between each group were considered to be significant, *$p \leq 0.05$ and **$p \leq 0.01.$
## 3.1. Pteryxin and Its Cell Viability
Pteryxin was isolated from the leaf extract of P. Japonicum Thunb using supercritical carbon dioxide by super critical extraction method (Figure 1a). Pteryxin is an angular type khellacton coumarin with substituted acyl groups [19]. The cytotoxicity of pteryxin (2 μM, 10 μM, and 50 μM, respectively) was evaluated for 24 h incubation in MIN6 cells. No cytotoxicity of pteryxin was observed in the range of the test concentrations (Figure 1b). The similar result was obtained from mouse macrophages RAW264.7 cells [19]. Thus, these pteryxin concentrations as a reference were used throughout this study.
## 3.2. Nrf2 Translocation from Cytoplasm to Nucleus by Pteryxin
The Nrf2 translocation from the cytoplasm to the nucleus due to pteryxin in the cells was examined. Cells treated and untreated with pteryxin were stained with the Nrf2 antibody (green color) and DAPI (blue color) as shown in Figure 2. The Nrf2 translocation from the cytoplasm to the nucleus was clearly observed in the confocal microscope with the pteryxin (10 μM and 50 μM) in the 1 hr treated cells. The previous study supported the result that the Nrf2-ARE signaling using the reporter assay was activated in the presence of pteryxin [13].
## 3.3. Antioxidant Genes Expression
*The* gene expression in the presence of pteryxin was investigated in the ARE regions and the genes of HO-1, GCLC, SOD1, Srxn1, Trxr1, Nqo1, and Gstp1 were detected (Figure 3). The expression of HO-1 was remarkably higher than those of the other genes and the Keap1 expression was included. This result suggested that the pteryxin as the Nrf2 activator strongly acted on Keap1, then the Nrf2 signaling activated GCLC, SOD1 and Trxr1 in the ARE regions. In addition, pteryxin also expressed the antiapoptosis gene, Bcl-xL and its upper regulating protein, Akt, contributing to cell survival.
## 3.4. Antioxidant Enzymes Expression
The antioxidant protein expression in the presence of pteryxin was investigated concerning the antioxidant enzymes in the ARE regions: i.e., HO-1, GCLC, SOD1, and Trxr1, including Keap1 and Nrf2 (Figure 4). As shown in Figure 4, the antioxidant enzymes expressed in the presence of pteryxin in a dose dependent-treated concentration. A weak the Nrf2 expression was also detected, then the Nrf2 dissociation proceeded due to the pteryxin modulation, resulting in the decreased Keap1 expression. These enzyme expressions are almost related to those of the antioxidant gene expressions in the ARE region (Figure 3).
## 3.5. GSH Content
The tripeptide GSH participates in many critical cellular functions, including antioxidant defense and cell growth. GCLC is a key catalytic enzyme that produced GSH from glutamate cysteine and glycine. The GSH content was determined with or without pteryxin. As shown in Figure 5, the GSH content was enhanced in the presence of pteryxin, which is related to that of the GCLC expression (Figure 4d). This result indicated that the production of GSH contributes to the cytoprotective effect due to the cellular redox reactions, such as its antioxidant activity for the reactive oxygen and nitrogen species and thioether formation [20,21,22].
## 3.6. Antioxidant Enzyme Expressions under H2O2-Treated Cells
It was found that pteryxin has the activating effects of the antioxidant enzyme (Figure 3). Based on this fact, the effect of pteryxin was evaluated in cells under oxidative stress caused by H2O2 treatment. The results showed that the expressions GCLC, Trxr1, and HO-1 antioxidant enzymes were observed in the presence of pteryxin (Figure 6). Thus, the antioxidant enzyme expression activity of pteryxin was shown to be useful against oxidative stress.
## 4. Discussion
The low-level antioxidant activity of pancreatic islets causes type 1 diabetes due to oxidative stress, thus the cause of failure is the pancreatic islets isolation and cell transplantation. This is particularly problematic in pancreatic cell transplantation as it significantly affects the survival of the β-cells [2,3]. Therefore, the preservation solution for the cell transportation has been required in the medical practice field. The ARE region is activated by the Nrf2 in a major cellular defense mechanism against oxidative stress.
Previous studies have reported the Nrf2 activators of dh404 and methyl fumarate, which prevent damage during pancreatic oxidative stress via the Nrf2 pathway. These Nrf2 activators markedly increase the expression of the key major antioxidant enzymes and protect β-cells by reducing inflammatory mediators [23,24]. In this study, pteryxin as a natural a Nrf2 activator was used mouse insulinoma MIN6 cells (Figure 1) [19]. As shown in Figure 2, the dissociated Nrf2 due to pteryxin transferred from the cytoplasm to the nucleus and promoted the expression of the genes, such as HO-1, GCLC, SOD1, Srxn1, and Trxr1 encoded in the ARE region (Figure 3). Whereas pteryxin had no effect on the gene expressions of Gstp1 and Nqo1, which perform an important role in detoxification by catalyzing the conjugation of quinones and electrophilic compounds. Pteryxin is an angular-type khellacton coumarin, which has an electrophilicity due to the α,β-carbonyl and/or substituted acyl groups in the molecule modulating the dissociation of Nrf2 from the Keap1, but pteryxin was not detoxified; therefore, it could contribute to the Nrf2 activation in the cells. In addition, the expression of HO-1 involved in the Keap1 expression was remarkably higher than those of the other gene expressions which suggested that pteryxin acts to dissociate the Nrf2 from the Keap1. Consequently, pteryxin would activate the Nrf2-ARE signaling in our previous report [19].
When the cysteine residue in the Keap1 is oxidized by an electrophile, the Nrf2 part from Keap1 binds to the ARE region in the DNA sequences. A multitude of Nrf2 inducers have been reported, most of which are electrophilic and directly react with the cysteine thiol groups in Keap1 [25]. The functional significance of these cysteine residues, Cys151, Cys273, and Cys288, perform a fundamental role in the sensing of the electrophilic Nrf2 activators [26]. Based on the functional necessity of these three cysteine residues, various Nrf2 activators, such as sulforaphane, dimethyl fumarate, and 1-[2-cyano-3,12-dioxooleana-1,9[11]-dien-28-oyl] imidazole, are Cys151-dependent compounds and 15-deoxy-D12,14-prostaglandin J2 (15d-PGJ2) combines with Cys288 also consisting of 4-hydroxy-nonenal, sodium meta-arsenite, and 9-nitro-octadec-9-enoic acid, can react with any of the three sensor cysteines of Cys151, Cys273, and Cys288 [27,28]. Pteryxin has a similar structure to dimethyl fumarate, which has the α,β-carbonyl moiety its molecule, therefore target of pteryxin may be Cys151 on the sequences of Keap1.
Pteryxin was expressed the antioxidant enzymes related to the following genes: HO-1, GCLC, and Trxr1 involving Akt and the antioxidant gene expressions (Figure 5). GSH was enhanced in the presence of pteryxin, which is related the expression of the enzyme, GCLC (Figure 4d). GSH is an important intracellular peptide with multiple functions ranging from antioxidant defense to the modulation of cell proliferation. The production of GSH contributes to the cytoprotective effect due to cellular redox reactions against the excess production of ROS and nitrogen oxide, and is also an SH donor for various enzymes [20]. GSH is synthesized in the cytosol of all mammalian cells in a tightly regulated manner. The major determinants of the GSH synthesis are the availability of cysteine, the sulfur amino acid precursor, and the activity of the rate-limiting enzyme, GCLC. Many conditions alter the GSH level via changes in the GCLC activity and GCLC gene expression (Figure 3 and Figure 4) [21,22]. These include oxidative stress, activators of the Phase II detoxifying enzymes, antioxidants, drug-resistant tumor cell lines, hormones, cell proliferation, and diabetes mellitus.
Based on these results, the effect of pteryxin was examined in cells under oxidative stress caused by the H2O2 treatment, then the expression of GCLC, Trxr1, and HO-1 was enhanced. A previous study showed an enhanced HO-1 expression in dh404, the Nrf2 activator treated islets, but not in the other main antioxidants, suggesting that the antioxidant potentiality of pteryxin may be a high activity (Figure 6). Therefore, the potential cytoprotective activity by upregulating the genes mechanism against cell damage causing oxidative stress. In other words, pteryxin was shown to be effective in the cytoprotective action for pancreatic cells during islet cell transplantation. The potential of pteryxin or similar chemical synthesis as a protective agent in the transplantation may be a candidate dependent on the research progress in the future.
## 5. Conclusions
This study showed that the Nrf2 activator, pteryxin, has the potential to prohibit cellular damage related to the expression of antioxidant genes and enzymes on the ARE region in the nuclei of insulinoma MIN6 cells. In addition, the effect of pteryxin was examined in cells under oxidative stress caused by the H2O2 treatment, then the expression of enzymes, such as GCLC, Trxr1, and HO-1 was enhanced. This suggests that pteryxin or its synthesized congeners may be useful as a preservation reagent during islet cell transplantation.
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---
title: Combined Intake of Fish Oil and D-Fagomine Prevents High-Fat High-Sucrose Diet-Induced
Prediabetes by Modulating Lipotoxicity and Protein Carbonylation in the Kidney
authors:
- Lucía Méndez
- Silvia Muñoz
- Lorena Barros
- Bernat Miralles-Pérez
- Marta Romeu
- Sara Ramos-Romero
- Josep Lluís Torres
- Isabel Medina
journal: Antioxidants
year: 2023
pmcid: PMC10045798
doi: 10.3390/antiox12030751
license: CC BY 4.0
---
# Combined Intake of Fish Oil and D-Fagomine Prevents High-Fat High-Sucrose Diet-Induced Prediabetes by Modulating Lipotoxicity and Protein Carbonylation in the Kidney
## Abstract
Obesity has been recognized as a major risk factor for chronic kidney disease, insulin resistance being an early common metabolic feature in patients suffering from this syndrome. This study aims to investigate the mechanism underlying the induction of kidney dysfunction and the concomitant onset of insulin resistance by long-term high-fat and sucrose diet feeding in Sprague Dawley rats. To achieve this goal, our study analyzed renal carbonylated protein patterns, ectopic lipid accumulation and fatty acid profiles and correlated them with biometrical and biochemical measurements and other body redox status parameters. Rats fed the obesogenic diet developed a prediabetic state and incipient kidney dysfunction manifested in increased plasma urea concentration and superior levels of renal fat deposition and protein carbonylation. An obesogenic diet increased renal fat by preferentially promoting the accumulation of saturated fat, arachidonic, and docosahexaenoic fatty acids while decreasing oleic acid. Renal lipotoxicity was accompanied by selectively higher carbonylation of proteins involved in the blood pH regulation, i.e., bicarbonate reclamation and synthesis, amino acid, and glucose metabolisms, directly related to the onset of insulin resistance. This study also tested the combination of antioxidant properties of fish oil with the anti-diabetic properties of buckwheat D-Fagomine to counteract diet-induced renal alterations. Results demonstrated that bioactive compounds combined attenuated lipotoxicity, induced more favorable lipid profiles and counteracted the excessive carbonylation of proteins associated with pH regulation in the kidneys, resulting in an inhibition of the progression of the prediabetes state and kidney disease.
## 1. Introduction
Kidney disease constitutes a serious world health problem whose prevalence is rising with the spread of obesity and obesity-related disorders [1]. Among those disorders, insulin resistance (IR), together with oxidative stress and inflammation, promotes kidney disease [2]. IR is considered an early metabolic alteration in chronic kidney disease (CKD) patients and is related to increased risk for CKD in nondiabetic patients. In fact, IR emerges when the glomerular filtration rate is still within the normal range [3] and occurs at the molecular level of the kidney tissue even before the blood accumulation of nitrogenous substances, like urea and creatinine [1]. The underlying mechanism remains elusive, and the exhaustive description of these molecular changes becomes a critical issue in designing more efficient preventive/palliative treatments, besides finding accurate biomarkers for early kidney disease diagnosis.
Oxidative stress is a concomitant factor linking obesity, diabetes and CKD [4]. It is defined as a disturbance in the pro-oxidant and antioxidant balance in favor of the former, driving the disruption of redox signaling and control and/or molecular damage [5]. Among other alterations, oxidative stress can induce oxidative protein modifications, protein carbonylation being a major hallmark of oxidative stress-related disorders [6]. Protein carbonyls can be directly formed by the attack of ROS to the side chains of proline, arginine, lysine, and threonine or indirectly by adduction to the side chains of arginine, cysteine, histidine, or lysine residues of lipid peroxidation and carbohydrate oxidation products [7]. The oxidation of lipids and reducing sugars, drive the formation of a heterogeneous mixture of reactive carbonyl species (RCS), including α,β-unsaturated aldehydes, (e.g., 4-hydroxynonenal (HNE) and acrolein), keto-aldehydes (e.g., methylglyoxal and 4-oxo-nonenal), and di-aldehydes (e.g., malondialdehyde (MDA) and glyoxal). The reaction of those RCS with proteins forms advanced lipoxidation products (ALEs), which are now recognized to play relevant roles in numerous oxidative stress-related diseases [8]. Protein carbonylation often results in protein fragmentation, aggregation, and enhanced susceptibility to proteolytic digestion, leading to a loss in its functions [6]. Moreover, carbonylated proteins are also related to the inflammatory response. At least, it has been described that some reactive carbonyls-bound proteins act as damage-associated molecular patterns (DAMPs), which are endogenous danger molecules that are released from damaged or dying cells and interact with pattern recognition receptors (PRRs), activating the innate immune system [9,10,11]. Therefore, protein carbonylation is associated with various diseases, such as obesity, chronic renal failure, and diabetes, among others [7]. In a close relationship with oxidative stress and inflammation, lipotoxicity takes place. Lipotoxicity, as the ectopic accumulation of lipids in organs different from adipose tissue [12], is mainly associated with dysfunctional signaling and insulin resistance response in these non-adipose tissues. Particularly, renal ectopic lipid accumulation has been linked with kidney diseases, especially diabetic nephropathy, because lipotoxicity promotes podocyte injury, tubular damage, mesangial proliferation, endothelial activation, and the formation of macrophage-derived foam cells [13]. Some of the proposed mechanisms to explain those pathological effects are that excessive lipid accumulation alters cellular homeostasis, activates lipogenic and glycogenic cell-signaling pathways, and promotes oxidative stress, mitochondrial dysfunction, inflammation, and cell death [12,13].
It has been well established that the excessive consumption of fat, which is characteristic of the so-called “westernized” diets, is a major risk factor for CKD [14]. These high-energetic diets cause multi-tissue lipotoxicity, including an aberrant fat deposition in the kidney [15] and, thus, the previously mentioned subsequent renal alterations leading to renal injury and the development of CKD. Recent evidence indicates that the quantity and the quality of the fat accumulated in kidneys influence the severity of renal damage. In fact, lipotoxicity induced by saturated fatty acids (SFAs) causes podocyte death, while the monounsaturated (MUFAs) oleic fatty acid prevents it [16]. Moreover, rat kidneys enriched in polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), were resistant to the progress of early-stage CKD, likely due to an improvement of the antioxidant and anti-inflammatory renal status that attenuates nephropathy [17]. Other pathological renal alterations, directly or indirectly related to lipotoxicity, induced by long-term high-energetic diet feeding are increased mitochondrial fission in tubular cells, which leads to cell apoptosis [18], and renal lysosomal dysfunction, which impairs autophagic flux and contributes to lipotoxicity [19]. High-fat diets also regulate gene expression in the kidneys. Ha et al. [ 14] demonstrated that a high-fat diet activates protease-activating receptor 2 (PAR2) in renal tubule epithelial cells, being an important contributor to kidney inflammation, oxidative stress, and fibrosis induced by the diet.
Increasing knowledge regarding the impact of diet on human health is resulting in a growing consumer demand for a healthy diet by means of natural, health-promoting products [20]. Numerous marine-derived nutrients and bioactive compounds have been identified as having diverse biological activities (such as an anticancer or anti-inflammatory activity), with some described to interfere with the pathogenesis of different diseases [21]. Among these marine compounds, fish oil has been reported to possess beneficial health effects, which are mainly attributed to ω-3 fatty acids, particularly to eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids [22]. The EPA/DHA ratio has already been demonstrated to modulate liver carbonylome and lipid profiles, controlling liver oxidative stress and inflammation [23,24], as well as gut microbiome populations from rats fed high-fat and high-sucrose (HFHS) diets [25]. Regarding kidney disease, some authors have recently reported an improvement in kidney function and structure after EPA/DHA supplementation, less oxidative stress, inflammation, and tubulointerstitial fibrosis [26,27]. However, their effects on kidney carbonylome are highly unknown.
The combination of the bioactivities of diverse food compounds has offered very promising results in health promotion research because it allows to compensate for some individual deficiencies/adverse effects or potentiate some interesting properties of each particular bioactive [28]. For example, the combination between fish oils and proanthocyanidins from grape seed extracts showed additive and even synergistic effects in decreasing insulin resistance, modulating liver carbonylome, switching lipid and lipid mediator profiles towards less proinflammatory ones, and regulating gut microbiome populations, among others [29,30,31,32,33,34,35,36]. Another interesting bioactive compound is iminociclitol D-Fagomine (FG), a food bioactive component which can be found mainly in buckwheat-based products [37]. FG is known for its properties in improving glucose tolerance and low-grade chronic inflammation, in part because it is able to inhibit intestinal glycosidases and regulate gut microbiota populations [38,39,40]. In previous studies, the combined supplementation of FG and ω-3 PUFA altered the gut microbiome by promoting the growth of Lactobacilliales and Bifidobacteriales populations, as well as the production of short-chain fatty acids (SCFAs) in rats fed HFHS diet, while decreasing visceral adipose tissue and fasting glucose concentration, hyperinsulinemia and lobular inflammation in the liver [41].
The aim of this present study is to investigate early metabolic alterations in rat kidneys induced by the long-term feeding of a HFHS diet, which is concomitant with the onset of a prediabetic state. This study particularly addresses kidney lipotoxicity, the quantity and quality of the aberrant accumulated fat, the changes in protein carbonylation patterns, and the consequent metabolic pathways altered in the kidney. The second objective of this present study is to test if the supplementation of a HFHS diet with ω-3 EPA and DHA from fish oil, FG, or both, can exert a preventive/protective effect on lipotoxicity and protein carbonylation in kidneys. Rats fed a standard (STD) diet with fish oil and FG supplementation will be included as controls. Results will shed light on the renal metabolic alterations induced by the consumption of obesogenic diets, even before their clinical manifestation. Moreover, results can offer a deeper insight into the use of food natural antioxidants in combination with other bioactive compounds to design nutritional strategies for the prevention and palliation of kidney injury induced by diet.
## 2.1. Animals, Experimental Design and Sample Collection
A total of 72 male Sprague Dawley rats (Envigo, Indianapolis, IN, USA) aged 8–9 weeks were used in the experiment. Following the acclimatization period, the rats were randomly assigned to one of eight experimental dietary groups (nine rats each). Groups were established depending on the type of background diet they fed and the type of supplement they received. According to the background diet, half of the rats ($$n = 36$$) fed a standard diet (STD) (Teklad Global $14\%$ Protein Rodent Maintenance Diet, Harlan Laboratories, UK), and the other half fed a high-fat high-sucrose diet (HFHS) (TD.08811 $45\%$ kcal Fat Diet, Harlan Laboratories, UK). According to the supplement received, rats can be classified into four groups: (a) control oil, which was soybean oil (STD-C and HFHS-C); (b) D-Fagomine (STD-FG and HFHS-FG); (c) fish oil (STD + ω3 and HFHS + ω3; and (d) D-Fagomine and fish oil (STD-FG + ω3 and HFHS-FG + ω3). Control and fish oils were administered by oral gavage with a gastric probe at the same dose defined in previous studies (0.8 mL/kg body weight once a week) [42]. D-Fagomine was included in the feed at a proportion previously defined as well (0.96 g/kg feed) [41]. The complete description of diets is shown in Supplementary Table S1. The fatty acid composition of the different diets is shown in Supplementary Table S2 and is the same used for research by Dasilva et al. [ 43].
Diets and supplements were given for 24 weeks, and rats had free access to food and water during the whole experiment. Housing conditions were: 3 rats per cage under constantly controlled conditions of temperature (22 ± 2 °C) and humidity (50 ± $10\%$) in a 12 h light/dark cycle. Water and feed consumption were recorded daily, and body weight was monitored weekly throughout this study.
At the end of the experiment, rats were fasted overnight, anesthetized intraperitoneally with xylazine and ketamine (10 mg/kg and 80 mg/kg body weight, respectively), and sacrificed by exsanguination. Blood was collected by cardiac puncture from each animal. Then, plasma was immediately obtained by centrifugation at 850× g (15 min at 4 °C) to remove erythrocytes and stored with 5 mM PMSF (protease inhibitor) at –80 °C until analysis. Both kidneys were removed, washed with $0.9\%$ NaCl solution, weighed, and examined for macroscopic abnormalities. Then, the kidneys were snap-frozen in liquid nitrogen and stored at –80 °C until analysis. Perigonadal fat were also excised, weighed, and stored at −80 °C.
Animal experiments and all procedures rigorously adhered to the European Union guidelines for the care and management of laboratory animals. The animal study protocol was approved by the Research Council (CSIC) Subcommittee of Bioethical Issues (ref. AGL2013-49079-C2-1-R) and licensed by the regional Catalan authorities (reference no. DAAM7921).
## 2.2. Biochemical and Antioxidant System Evaluation in Blood
Plasma triglycerides, total cholesterol, and high-density lipoprotein cholesterol (HDL-C) were measured by spectrophotometric methods, as described by Bucolo et al., [ 44] and using SpinReact Kits (SpinReact S.A., Girona, Spain). Glucose concentration was evaluated by an Ascensia ELITE XL blood glucose meter (Bayer Consumer Care AG, Basel, Switzerland). Plasma insulin was measured using a Rat/Mouse Insulin ELISA kit (Millipore Corporation, Billerica, MA, USA) following the manufacturer’s instructions. Hemoglobin concentration was measured according to the Drabkin method [45], and the percentage of hematocrit was calculated in capillary tubes after centrifugation. Plasma urea levels were determined by using the Urea Assay Kit (Sigma, St. Louis, MO, USA) according to manufacturer’s instructions. Plasma non-enzymatic antioxidant capacity was measured as the oxygen radical absorbance capacity (ORAC) [46]. Oxidized and reduced glutathione balance (GSSG/GSH) was assayed according to Hissin and Hilf [47].
## 2.3. Renal Fat Deposition and Fatty Acid Analysis in Plasma and Kidney
Renal lipotoxicity was evaluated by quantifying the amount of fat accumulated in the kidneys and the fatty acid profiles of the fat. Briefly, total lipid amount from kidneys and plasma were extracted by a modification of Bligh and Dyer protocol [48], using dichloromethane/methanol/water (2:2:1, v/v) as extraction solvent. Total fat accumulated in kidneys was determined by gravimetric quantification and normalized by g of tissue. To obtain fatty acid profiles, 0.6 mg of the organic phase was transesterified, and total fatty acids were analyzed by gas chromatography coupled with a flame ionization detector (GC-FID, Clarus 500, PerkinElmer, Waltham, MA, USA) according to Lepage and Roy [49]. Nonadecanoic acid (19.0) was used as internal standards (IS).
FAD indexes and elongase activities from total fatty acid data of kidney and plasma were calculated as product/precursor ratio. Therefore, elongase-6 (Elovl-6) activity, enzyme that initially determines the rate of FA elongation, was calculated as Elovl-6 = [18:$\frac{0}{16}$:0]. The activities of ∆9 Stearoyl-CoA Desaturases SCD-16 and SCD-18, which regulate the desaturation of SFA to MUFA, were measured as SCD-16 = [16:1ω$\frac{7}{16}$:0], SCD-18 = [18:1ω$\frac{9}{18}$:0]. ∆4, ∆5, and ∆6 Desaturases (∆4D, ∆5D, and ∆6D), which desaturate linoleic (LA) and α-linolenic (ALA) acids to form ARA, EPA and DHA, were estimated as follows: Δ4D = [22:6ω$\frac{3}{22}$:5ω3], Δ5D = [20:4ω$\frac{6}{20}$:3ω6]; Δ6D = [20:3ω$\frac{6}{18}$:2ω6]; and Δ5D + Δ6D = [20:5ω$\frac{3}{18}$:3ω3].
## 2.4. Lipid Peroxidation Levels in Kidney
Lipid peroxidation levels in the kidney were measured through conjugated dienes hydroperoxides (intermediate lipid oxidation product) following the American Oil Chemists’ Society (AOCS) method [50]. Kidney lipids were extracted and quantified as described in 2.3. subsection. Then, conjugated dienes were measured using a spectrophotometer set at 234 nm (Beckman Coulter DU 640 Spectrophotometer UV/Vis Reader, Brea, CA, USA).
## 2.5. Extraction and Fluorescent Labeling of Renal and Plasma Protein Carbonyls
Approximately 300 mg of renal tissue was minced on ice and homogenized with a tissue homogenizer by sonication for 1 min under 0.6 s cycle and $100\%$ of amplitude with a Labsonic M ultrasonic sonifier (Sartorius AG, Goettingen, Germany) in 25 mL of buffer (20 mM sodium phosphate, pH 6.0, 0.5 mM MgCl2, 1 mM EDTA) containing 10 uL/mL of ProteoBlock protease inhibitor cocktail, which comprised 100 mM AEBSF–HCl, 80 mM aprotinin, 5 mM bestatin, 1.5 mM E64, 2 mM leupeptin, and 1 mM pepstatin A. The homogenate was centrifuged at 100,000× g (60 min at 4 °C) to recover proteins that remain in the supernatant solution. Protein concentration was determined by bicinchoninic acid assay (BCA) [51].
To evaluate protein oxidation, the carbonyl residues generated in vivo were tagged and measured by a fluorescence-based assay, as previously described [52]. Briefly, proteins were incubated with 1 mM fluorescein-5-thiosemicarbazide (FTSC) at 37 °C for 2.5 h in the dark. Then, proteins were precipitated with $20\%$ chilled trichloroacetic acid, centrifuged (16,000× g, 10 min at 20 °C), and the pellets were washed 5 times with the mixture ethanol/ethyl acetate (1:1) to remove the FTSC excess. Finally, proteins were resuspended in urea buffer (7 M urea, 2 M thiourea, $2\%$ 3,3-cholaminopropyldimethylammonio-1-propanesulfonate (CHAPS), $0.5\%$ Pharmalyte 3–10, $0.5\%$ IPG 3–10 buffer, and $0.4\%$ DTT) and stored at −80 °C until use. Protein concentration was measured by Bradford’s method [53].
## 2.6. Total and Specific Protein Carbonylation Relative Quantification
Total and individual protein carbonylation were completed as previously described [29]. Briefly, to study the global protein carbonyl levels in the kidney, an equal amount (30 μg) of FTSC-tagged protein of each sample were subjected to $12\%$ self-made monodimensional SDS-polyacrylamide gel electrophoresis (1D SDS-PAGE) and run in a Mini-PROTEAN 3 cell (Bio-Rad, Hercules, CA, USA). To visualize the protein carbonyl levels of individual proteins, 400 μg of FTSC-labeled renal proteins were resolved in two dimensional (2D) gels. For first dimension separation according to the isoelectric point of proteins, protein samples were loaded onto 11 cm IPG 3–10 Immobiline DryStrip gels (IPG strips) by using an Ettan IPGphor II isolectric focusing system (GE Healthcare Science, Uppsala, Sweden), and protein focusing was accomplished by using the appropriate voltage/time profiles indicated in the manufacturer’s instructions (GE Healthcare, Chicago, IL, USA). After focusing, cysteines on proteins were sequentially reduced and alkylated. Then, IPG strips were run in $12\%$ laboratory-made SDS-PAGE to separate the proteins according to their molecular weights by using an Ettan *Daltsix electrophoresis* system (GE Healthcare Science, Uppsala, Sweden).
FTSC-tagged proteins were visualized by exposing 1D and 2D gels to a UV transilluminator UVP BioDoc-It2 Gel Imaging System (Analytik Jena AG, Upland, CA, USA). After the fluorescent signal from FTSC bound to oxidized proteins was measured, the gels were stained overnight with Coomassie dye PhastGel Blue R-350 to visualize the total protein amount in each sample. The manipulation of gels from the same experiment in parallel and the optimization of staining and destaining cycles to minimize size changes guarantee the correct matching between the gels.
Total protein carbonylation measures were obtained by analyzing the total lane pixel intensity of 1D gels using the software LabImage 1D (Kapelan Bio-Imaging Solutions, Halle, Germany). Individual carbonylation levels of proteins were calculated by measuring the pixel intensity of the corresponding protein spot in 2D gels using the PDQuest software version 7.4 (Bio-Rad, Hercules, CA, USA). Both total and specific protein carbonylation were normalized by dividing the FTSC signal intensity in the FTSC-stained gel and the Coomassie signal intensity obtained in the corresponding Coomassie-stained gel, as previously described [29].
## 2.7. Identification of Carbonylated Proteins by NanoLC–ESI–IT–MS/MS
To identify carbonylated proteins in the kidneys, spots of interest were manually cut from the 2D gels directly onto the UV transilluminator to unequivocally assure the identification of carbonylation protein and guarantee the correct superposition of gels for quantification. After several cycles of washing with water and acetonitrile, the protein presented in each spot was submitted to tryptic digestion (0.5 µM trypsin in 50 mM NH4HCO3 buffer, pH 8, overnight at 37 °C). The consequential peptides were vacuum dried (Centrifugal Vacuum Concentrator MiniVac, GYROZEN Co., Daejeon, Republic of Korea) and dissolved in $1\%$ formic acid.
Protein identification was accomplished by the analysis nano-LC ESI-IT-MSMS analysis of the tryptic peptides. The analysis was performed on a Dionex UltiMate 3000 Series (ThermoFisher, Rockford, IL, USA) coupled to a mass spectrometer LTQ Velos Pro with electrospray ionization (ESI) (Thermo Fisher, Rockford, IL, USA). A loading solvent of water $0.1\%$ of formic acid with a flow rate of 10 μL/min was used to concentrate and clean injected samples on a μ-precolumn cartridge (μ-Precolumn C18 PepMap; 300 μm i.d. × 5 mm) (Thermo Scientific, San Jose, CA, USA). Peptides were separated at a flow rate of 300 nL/min along a C18 PepMap RSLC column (Acclaim PepMap RSLC C18, 2 μm, 100 Å, 75 μm i.d. × 15 cm) (Thermo Scientific, San Jose, CA, USA) set to 35 °C using a binary eluent system of water $0.1\%$ of formic acid (phase A) and acetonitrile $0.1\%$ formic acid (phase B). An increasing proportion of solvent B was used along a 30 min linear gradient from $5\%$ to $40\%$.
Peptide MS/MS analysis was performed in positive ionization mode, with the mass spectrometer operated in data-dependent acquisition (DDA) mode. MS1 survey scan acquisition was set between 400 to 1600 Da, followed by MS/MS analysis of the 6 most intense peaks with ≥2 charge state. Fragmentation was performed in collision-induced dissociation (CID) mode with a normalized collision energy of $35\%$ and an isolation width of 2.0 Da. A dynamic exclusion of 30 s for fragmented masses after the second fragmentation event was selected. Instrument and data acquisition were controlled through Xcalibur 2.0 and Tune 2.2 software (Thermo Fisher Scientific, Inc.).
For protein identification, raw data were searched against the *Rattus norvegicus* UniprotKB/Swiss-Prot database (downloaded on 1 October 2022) using PEAKS DB (Bioinformatics Solutions Inc., Waterloo, ON, Canada). The search criteria were stated as follows: methionine oxidation and carbamidomethylation of cysteine as fixed modifications; trypsin as proteolytic enzyme with up to 2 missed cleavage sites per peptide; and peptide precursor mass tolerance ±1.0 Da and ±0.6 Da for MS/MS fragment ions. The false discovery rate (FDR) selected for identification was maintained below $1\%$.
## 2.8. Gene Ontology (GO) and KEGG Pathway Enrichment Analysis of Carbonylated Proteins Identified in Kidney
GO functional enrichment analysis was conducted using the tool g:GOSt of the freely online g:Profiler (https://biit.cs.ut.ee/gprofiler (accessed on 30 December 2022)) software, by submitting gene names of the carbonylated proteins identified in the kidney and selecting *Rattus norvegicus* as organism and a significant threshold Benjamin–Hochberg FDR 0.05 [54]. KEGG pathway enrichment analysis was also performed by submitting the carbonylated protein gene list to the freely online STRING (Search Tool for the Retrieval of Interacting Genes) software version 11.5 (http://stringdb.org/ (accessed on 30 December 2022)), selecting Rattus norvegicus, and considering a significant enrichment when FDR < 0.05, which correspond to the p-value corrected for multiple testing using the Benjamini–Hochberg procedure [55].
## 2.9. Statistical Analysis
Data are mean ± standard deviation (SD). Statistical analyses were performed by two-way ANalysis Of VAriance (ANOVA) with the freely available R Studio [56] software version 1.4.1103-4. Normal distribution and homogeneity of variance were evaluated through Shapiro–Wilk’s and Levene’s test, respectively. Nonparametric Kruskal–Wallis analyses were applied when data distribution did not fit a Gaussian model or heterogeneity was found in variances. The post hoc test Tukey HD was used to compare the means and significant differences were considered when $p \leq 0.05.$
## 2.10. Materials and Reagents
Fish oil dietary supplement with 1:1 EPA/DHA ratio and EPA + DHA $50\%$ of total fatty acids was obtained by mixing appropriate quantities of the commercial fish oils AFAMPES 121 EPA (AFAMSA, Vigo, Spain) and EnerZona Omega 3 RX (Milan, Italy). Soybean oil, obtained from unrefined organic soy oil after first cold pressing, was from Clearspring Ltd. (London, UK). D-Fagomine (>$98\%$) was elaborated by Bioglane SLNE (Barcelona, Spain) and provided by Taihua Shouyue (HK) International Co. Ltd. (Hong Kong, China).
Ketamine-HCl was obtained from Merial Laboratorios S.A. (Barcelona, Spain). Xylazine (Rompun $2\%$) was obtained from Química Farmacéutica S.A. (Barcelona, Spain). Protease inhibitor cocktail ProteoBlock was purchased from Thermo Fisher Scientific Inc. (Rockford, IL, USA). Bicinchoninic acid (BCA) assay for protein quantification and Bio-Rad protein assay were obtained from Sigma (St. Louis, MO, USA) and Bio-Rad Laboratories (Hercules, CA, USA), respectively. Bio-Rad Laboratories also provided acrylamide and bis-N,N-methylene-bis-acrylamide. Sigma also provided the reagents dithiothreitol (DTT), iodoacetamide (IA), phenylmethylsulfonyl fluoride (PMSF), trichloroacetic acid (TCA), Tris Hydrochloride (Tris–HCl), ethylenediaminetetraacetic acid (EDTA), and CHAPS detergent. Coomassie dye PhastGel Blue R-350, Immobiline DryStrip gels (IPG strips) for isoelectric focusing (IEF) of pH range 3–10 and lengths 11, pharmalyte 3–10, IPG buffer, TEMED, and bromophenol blue were all acquired purchased from GE Healthcare Bio-Sciences AB (Uppsala, Sweden). Fluorescein-5-thiosemicarbazide (FTSC) labeling for fluorescent imaging was bought in Invitrogen (Carlsbad, CA, USA) and trypsin sequencing-grade for protein digestion in Promega (Madison, WI, USA). Internal standard of nonadecanoic acid (19:0) was purchased from Larodan Fine Chemicals (Malmö, Sweden). The rest of the reagents were of analytic/LC-MS grade.
## 3.1.1. Long-Term High-Fat and High-Sucrose Diet Feeding Induces Prediabetes and Increases Oxidative Stress and Lipotoxicity in Kidney
As shown in Table 1, HFHS-fed rats significantly showed higher values of specific rate of body mass gain, body mass index (BMI), perigonadal adipose tissue, and adiposity index, in comparison to STD-fed rats. These biometric determinations were concomitant with a statistically significant increase in plasma insulin and glucose, lower concentration of total cholesterol and HDL-Cholesterol (HDL-C), and higher percentage of fat in plasma. HFHS-diet intake provoked the ectopic accumulation of fat in erythrocytes, liver, and skeletal muscle, as previously described [43]. HFHS feeding elevated the levels of urea in plasma as well. These metabolic alterations were accompanied by worse general antioxidant status due to the HFHS intake, especially according to the GSSG/GSH ratio in erythrocytes.
Therefore, long-term HFHS-fed rats developed a general prediabetic state, as previously described [43,57]. That state was characterized by the augment of plasma insulin concentration needed for maintaining glucose levels into the normal range, the significant increase in the perigonadal white adipose tissue and adiposity, and the general worsening of the antioxidant system. These metabolic alterations were accompanied by a significant increment of urea level in plasma in rats fed the HFHS diet, in agreement with the prediabetic state, because insulin resistance is a characteristic feature of uremia [58]. These results pointed towards a slight but relevant deterioration of renal function, which was also supported by the direct effects of the HFHS on the kidney. As Figure 1 shows, HFHS diets increased renal lipotoxicity, evaluated as the amount of ectopic fat accumulated in the kidney, and oxidative stress, demonstrated by the superior formation of lipid peroxidation products and, especially, the enhanced protein carbonylation. The stronger effect of the HFHS diet on oxidative stress parameters in the kidney, in comparison to the one found in the plasma, may be due to the presence of the anesthetics (ketamine and xylazine) in the second. Ketamine has been used in sub-anesthetic doses to create a rat model of schizophrenia because it can induce oxidative stress in brain tissues [59,60]. However, anesthetic doses of ketamine, especially in combination with xylazine, have shown some free radical scavenger activity in blood samples of sheep [61] and rabbits [62], increasing the stability of blood samples during storage. Nevertheless, some studies did not find any effects on redox blood status of sheep [63]. In this current study, the presence of the anesthetics in the plasma samples could have affected some oxidative stress measurements, and the effect of the HFHS diet could be attenuated in this fluid.
## 3.1.2. Effect of Fish Oil and D-Fagomine on Prediabetes, Oxidative Stress, and Lipotoxicity Induced by the High-Fat and High-Sucrose Diet
As Table 1 shows, the effect of the diet, i.e., STD and HFHS, was the factor responsible for the changes in most of the biometrical and biochemical measurements of the rats, whereas the supplements had a more limited effect on them. However, rats fed FG (especially in the context of the STD diet) and its combination with fish oil in both dietary frameworks significantly reduced their specific rate of body mass gain, BMI, perigonadal adipose tissue, and adiposity index compared to the rest of the supplemented groups. These beneficial effects of the supplements were accompanied by significant decreases in the level of plasma insulin and glucose. Whole-body lipid metabolism seemed to be greater regulated by the quality of the fat’s supplement, and data reflected that fish oil, alone or in combination with FG, modulated HDL-C and plasma fat percentage. Furthermore, they tended to diminish urea in plasma when both bioactive compounds were eaten together and added to HFHS diet.
Finally, supplements were the main factor behind the changes found inoxidative stress parameters. Results revealed that the rats supplemented with fish oil, especially in combination with FG and when the HFHS was the background diet, improved their antioxidant status, showing lesser albumin carbonylation and GSSG/GSH ratios, particularly in erythrocytes and superior antioxidant capacity (ORAC values). Consequentially, the HFHS diet supplemented with the combination between fish oil and FG significantly ameliorated the whole-body oxidative stress of the rats, in agreement with the general metabolic improvement previously described for biometrical and biochemical determinations. Therefore, the combination of fish oil with FG successfully prevented the development of these alterations in HFHS-fed rats and also promoted healthier values in STD-fed ones, in comparison to corresponding controls (Table 1), supporting the protective effect of the combination of both bioactive products for developing prediabetes.
Besides the general improvement in the health status of rats, the inclusion in the HFHS diet of supplementation of fish oil, especially when it was accompanied by FG, drastically decreased renal lipotoxicity and protein carbonylation (Figure 1), likely preventing renal alterations induced by diet while ameliorating the prediabetic state.
## 3.2.1. Modulation of Lipid Profiles in Kidneys by the High-Fat and High-Sucrose Diet Intake
Results showed that long-term HFHS feeding had a strong effect on renal fatty acid composition (Table 2). This modulatory effect seemed partially due to the fatty acid composition of the HFHS diet (Table S2), particularly if it is considered the renal increase in SFAs. The fat from the HFHS diet used in this study mainly comes from milk fat, which is rich in even-numbered SFAs, such as myristic (14:0), palmitic (16:0), and stearic (18:0) [64]. It has been described that the type of fatty acid accumulated influences the severity of the metabolic alterations promoted by the ectopic fat. For instance, the lipotoxicity induced by SFAs causes insulin resistance and podocyte death [14]. Milk fat also contains odd-chain saturated fatty acids (OCFAs) [64], which can explain the rise of these fatty acids in the kidneys of HFHS-fed rats compared to the STD-fed ones.
The elevation of SFAs was also observed in the whole body, as the analysis of total fatty acid profiles in plasma demonstrated (Table 3). Results indicated that the effect of the HFHS diet was even more dramatic than in the kidney. Besides the direct influence of the fatty acid composition of the diet (Table S2), the higher increase of SFA in blood plasma, especially in the case of palmitic and stearic acids, could respond to the stimulation of de novo lipogenesis in the liver, mainly as a consequence of the high carbohydrate content of the HFHS diet [65], and, on the other hand, the decomposition of adipose tissue, which may start to overcome its lipid buffering capacity in parallel with the first stage of kidney dysfunction [66].
Regarding MUFAs, results showed significant differences between STD and HFHS-fed rats, but the direct influence of fatty acid content of the diet on these fatty acids was much lesser, at least in the kidneys (Table 2). In fact, there was a decrease in the proportion of MUFAs in HFHS-fed rats, although the background diet is richer in these fatty acids than the STD control diet. Additionally, this decrement also affected the oleic acid (18:1ω9) renal amount, despite being the main MUFA in milk fat. Some studies have demonstrated that oleic acid, which is present in other fats, typically olive oil, prevents the death of podocytes, which, in turn, can prevent and/or delay the development of kidney disease [67]. However, our results indicated that the one coming from milk was not efficiently incorporated into the kidneys, avoiding that protecting effect. On the contrary, plasma lipid profiles reproduced more faithful MUFAs diet intake, and the levels of MUFAs, especially oleic acid, were higher in HFHS-fed rats than STD-fed ones (Table 3). Interestingly, there was an increase in nervonic acid (C24:1) in plasma in the groups feeding the HFHS diet compared to STD-fed rats. Higher amounts of nervonic acid, which indicates demyelination and loss of axons, were found in patients suffering from metabolic syndrome and, at a higher concentration, in CKD patients [16].
As for PUFAs, the kidneys of STD- and HFHS-fed rats showed similar proportions of total ω6 and ω3 (Table 2). However, STD-fed rats accumulated significantly more ω6 linoleic acid (LA) than HFHS-fed ones, which can be explained by the higher content of LA in STD than HFHS chow (Table S2). On the other hand, HFHS-fed rats accumulated significantly more arachidonic acid (ARA; 20:4ω6) and dihomo-gamma-linolenic acid (DGLA; 20:3ω6), although they were not directly provided by the diet (Table S2). The modulation of ω6 PUFAs in plasma (Table 3) was different to the one described in the kidney. In plasma, the concentration of ARA and DGLA, like the total ω6 content, was higher in STD than HFHS. It has been hypothesized that this opposite behavior of the kidney to the whole body, mainly the liver and adipose tissue, can compensate for the metabolic activity of those tissues, a fact that supports the important role of the kidney in lipid metabolism control, especially when the organism is dealing with excessive levels of fatty acids [68]. With regard to ω3 PUFAs, both kidneys and plasma showed a similar modulation of lipidome in response to the diet (Table 2 and Table 3). HFHS-fed rats accumulated more EPA and DHA and showed less proportion of the precursor ALA. This higher accumulation of EPA and DHA induced by the HFHS diet was also observed in the liver and adipose tissue of the same cohort of rats [23,43]. Thus, HFHS-fed kidneys accumulated more PUFAs, especially ARA and, to a lesser extent, DHA, to the detriment of MUFAs. Considering the higher level of oxidative stress induced by the HFHS diet and the highly cytotoxic action of PUFAs lipid peroxidation products (mainly HNE from ARA, but also HHE from DHA), this enrichment in PUFAs could favor a more prooxidative environment in HFHS-kidneys, rather than offering a protective effect.
To evaluate the influence of diet on endogenous lipid metabolism, fatty acid elongase 6 (Elovl-6) activity, stearoyl-CoA (SCD), and fatty acid desaturase indexes (FAD) were measured in the kidneys and then compared with those in plasma. Results are shown in Table 4.
Elovl-6, a membrane-bound enzyme, is a condensing enzyme that converts palmitic acid (16:0) to stearic acid (18:0) and, thus, initially determines the rate of FA elongation [69]. Stearoyl-CoA desaturase (SCD), an endoplasmic reticular enzyme, biosynthesizes MUFAs from SFAs [70]. In this present study, the HFHS diet significantly increased SCD-18 in plasma, and SCD-16 and Elovl-6 followed similar tendencies. Up-regulated activities of both SCD-1 and Elovl-6, and more oleic acid levels were found in obese Zucker rats [71] and in the liver of a cyclosporine-induced nephropathy rat model [68]. In addition, the same HFHS diet increased SCD-1 indexes in adipose tissue and liver for the same cohort of rats [23,43] while increasing oleic acid too. Increased SCD-1 activities has been related to more adiposity and progression of the obesity syndrome in humans [72]. This association between SCD-1 and obesity and insulin resistance was also found in animal studies [73].
In spite of the abundant studies regarding the regulation of elongases and desaturases in fatty acid metabolism in the liver and the adipose tissue, this subject is little known in the kidney. Our results showed that the Elovl-6 activity increased significantly because of the HFHS diet in the kidney. Besides the diet, the more elongase-6 activity in the kidney may explain the increases in 18:0 and 20:0. Moreover, this higher renal elongase-6 activity seems to be addressed towards the production of long-chain SFAs rather than oleic acid, a pathway that was not activated in the kidney. As for SCD-1 activities, SCD-16 and SCD-18 indexes were significantly reduced by the HFHS diet in the kidney, in contrast to what was found in plasma. Discrepancies in lipogenic enzyme activities found in our study between the kidneys and the rest of the body have been previously described in a rat model of nephropathy [68]. It might be deduced that along with the liver and the adipose tissue, the kidney also partly contributes to lipogenic metabolism, and it may act as a compensatory lipogenic organ at the first stages of metabolic alteration. However, it is difficult to explain why SCD-1 indexes were decreased in the kidney, while increasing the Elovl-6 activity. This is because the interaction between Elovl-6 and SCD-1 in the kidney is still not clear. In the same previous study, authors also reported the opposite expression between Elovl-6 and SCD-1 in the kidney [74]. Our findings reinforce the differences in the mechanisms of regulation of the renal expression of Elovl-6 and SCD-1 and highlight the need for more investigation of them.
Additionally, the consumption of HFHS diets altered FAD indexes. There was an increment of Δ6D and a decrease of Δ5D activities in plasma, leading to the accumulation of DGLA (20:3 ω6) and lesser formation of ARA (20:4 ω6). In the kidney, HFHS diet also increased Δ6D, but Δ5D activity did not significantly diminish, explaining the superior amount of ARA in this tissue in contrast to the rest of the body. Finally, there was an up-regulation of plasma and renal Δ4D index that led to an accumulation of DHA. Additionally, superior activity of both Δ5D + Δ6D on ω3 series to produce EPA was observed in the kidneys, while it did not significantly change in plasma. FAD activities in plasma were in agreement with the ones described in the adipose tissue [43] and the liver [23]. The down-regulation of Δ5D activity found in plasma, adipose tissue and liver was the biggest difference between those tissues and the kidney regarding FAD modulation, and this discrepancy agrees with the different roles of each tissue in lipid metabolism.
## 3.2.2. Modulation of Total Lipid Profiles in Kidneys by the Effect of Supplementation with Fish Oil and D-Fagomine
The effects of FG and fish oil supplementation on total SFAs amount were generally low, especially in the context of HFHS diets, in both the kidney (Table 2) and plasma (Table 3). In spite of this, FG supplementation increased the incorporation of OCFAs into the kidneys and elevated their levels in plasma when added to the STD diet. This may be explained by the changes induced in the gut microbiota by the FG supplementation of the STD diet, which promoted the growth of taxa that produce OCFAss, as previously reported for the same cohort of rats [41]. Increased circulating concentrations of OCFAs have been associated with lower risks of cardiometabolic diseases, and it has been reported, at least for C15:0, that they can attenuate inflammation, anemia, dyslipidemia, and fibrosis in vivo [64].
There was also a limited influence of supplements on MUFAs modulation. Only in the kidneys of STD-fed rats (Table 2), the double supplementation reduced the proportion of palmitoleic and oleic fatty acids. Several studies have reported the effect of ω3 supplementation on decreasing oleic fatty acid content in erythrocyte membranes [68], plasma, liver, and adipose tissue [31]. Moreover, rats supplemented with FG, irrespective of the background diet, decreased the concentration of nervonic acid in plasma.
The different supplements induced deep changes in renal (Table 2) and plasma (Table 3) PUFAs profiles. *In* general, the incorporation of fish oil into the diet, which was the main responsible for the changes, significantly decreased the amount of ω6 PUFAs and increased the ω3 in both STD and HFHS dietary contexts. Consequently, the inflammatory index ω6/ω3 was significantly reduced after fish oil supplementation. The decrease in ω6 PUFAs was mainly noticed for ARA in HFHS diets. Meanwhile, the other major ω6, LA, was scarcely altered by fish oil supplementation, with the exception of the kidneys of STD-fed rats. In these rats, LA suffered a significant drop, especially in the double supplemented group. It is worth noting that the levels of 22:4 ω6, described as a marker of the progression of CKD [16], and 22:5 ω6, significantly diminished in the kidneys of HFHS-fed rats after fish oil supplementation. As for ω3 PUFAs, there was an enrichment in DHA and, above all, EPA due to fish oil supplementation. Consequently, the EPA/DHA ratio became more balanced in the body of these rats, according to the EPA/DHA 1:1 ratio of the fish oil. Additionally, fish oil supplements significantly increased the level of docosapentaenoic acid (22:5 ω3; ω3 DPA), even if there was a negligible amount of external supplementation of this fatty acid (Table S2). A significant increment of DPA in the adipose tissue was also found after fish oil supplementation [43]. FG alone did not have any significant effect, neither on ω6 nor ω3 PUFAs in none of the tissues.
Regarding desaturases and elongases activities (Table 4), results showed that the fish oil, especially in combination with FG, significantly reduced the activities of SCD-1 when added to the STD diet. In a previous study, fish oil supplementation, alone or in combination with grape polyphenols, led to a minor activity of SCD-1 in the liver of STD-fed rats [31]. Lesser activities for SCD-1 were also found in the liver and adipose tissue for the same cohort of rats [23,43]. Rats supplemented with fish oil, with or without FG, showed significantly higher indexes of Δ5D + Δ6D and lower activity of Δ5D in tissues and dietary frameworks. Kidneys from STD-fed rats also increased Δ6D activity. Similar effects on PUFAs desaturase activities for the supplementation with fish oil, combined or not with a grape polyphenol extract, were previously described in the liver and plasma of rats fed STD and HFHS diets [31]. Those results support the consistent effect of the supplementation with ω3 from marine sources on PUFAs desaturase activity, which seems highly independent from the rest of the food composition. It is worth noting that individuals with metabolically healthy phenotype had lower estimated SCD-16 and SCD-18 activities, whereas estimated Δ6D activity was higher compared to metabolically unhealthy phenotypes [74]. The supplementation with FG alone had limited influence of FAD indexes, and there was not any significant effect.
## 3.3. Identification and Functional Enrichment Analysis of Carbonylated Proteins in the Kidney
FTSC-labeling assay and 2D gel electrophoresis (Figure S2) were used to characterize carbonylated levels of individual proteins and study in detail the possible effect of HFHS and supplements in this regard. Despite a large number of visualized protein spots on the Coomassie-stained gels (~300), only a minor portion was distinctively attached to carbonyl-specific FTSC tags showing a visible carbonylation those which are marked by numbers in the gels (Figure S2). Protein carbonylation profile was the same for all dietary groups with a total of 36 carbonylated spots visualized, analyzed, and identified by mass spectrometry, as shown in Table 5.
Gene ontology (GO) and KEGG pathway functional enrichment analysis for carbonylated proteins identified in the kidney provided a general overview of their cellular distribution, molecular function, and pathways involved. Results are summarized in Figure 2.
Protein targets for carbonylation were essentially proteins from mitochondria ($48.9\%$), cytosol ($42.2\%$), and peroxisome ($8.9\%$) (Figure 2a). Carbonylated proteins located in mitochondria were phosphatidylethanolamine-binding protein 1 (Pebp1), superoxide dismutase 2 (Sod2), glutathione S-transferases (Gstp1 and Gsta4), peroxisomal trans-2-enoyl-CoA reductase (Pecr), Electron Transfer Flavoprotein Subunit Beta (Etfb), Enoyl-CoA delta isomerase 1 (Eci1), 3-Hydroxyisobutyrate dehydrogenase (Hibadh), aspartate aminotransferase 2 (Got2), malate dehydrogenase 2 (Mdh2), Hydroxyacyl-coenzyme A dehydrogenase mit. ( Hadh), Sorbitol dehydrogenase (Sord), Isocitrate dehydrogenase [NADP] 1 (Idh1), Alpha-aminoadipic semialdehyde dehydrogenase (Aldh7a1), glutamate dehydrogenase 1 mit. ( Glud1), Alanine--Glyoxylate Aminotransferase 2 (Agxt2), 3-Oxoacid CoA-Transferase 1 (Oxct1), Dihydrolipoamide Dehydrogenase (Dld), Methylmalonate-semialdehyde dehydrogenase [acylating] mit. ( Aldh6a1), Catalase (Cat), and Aconitate hydratase 1 and 2 (Aco1 and Aco2). Cysolic proteins were: Gstp1, Triosephosphate isomerase (Tpi1), Carbonic anhydrase 2 (Ca2), omega-amidase NIT2 (Nit2), Malate dehydrogenase (Mdh1), Actin cytoplasmic 1 (Actb), Heat shock cognate 71 kDa protein (Hspa8), Fructose-bisphosphate aldolase B (Aldob), Glyceraldehyde-3-phosphate dehydrogenase (Gapdh), Alcohol dehydrogenase [NADP(+)] (Akr1a1), Sord, aspartate aminotransferase 1 (Got1), Isocitrate dehydrogenase [NADP] cytosolic (Idh1), Aldh7a1, Cat, Triokinase/FMN cyclase (Tkfc), Transketolase (Tkt), and Aco1 and 2. Finally, Quinone oxidoreductase (Cryz)/Hydroxyacid oxidase 2 (Hao2) belongs to the peroxisome. Pecr, Idh1, and Cat can be found in the peroxisome as well.
Considering the molecular function (Figure 2a), almost $92\%$ of carbonylated proteins had catalytic activity, predominantly oxidoreductase activity ($29.3\%$), but also transferase ($16\%$) and lyase ($9.3\%$) activities, followed, in less proportion, by de/hydrogenase, de/hydratase and aminocyclase activities. The $32\%$ of carbonylated proteins showed binding capacities to diverse molecules, including nucleotides, such as NAD/NADP, toxic substances, vitamin B6, pyridoxal phosphate, ion, and sulfur. Carbonylated proteins with oxidoreductase activity in the kidney were identified as Gstp1, Pecr, Etfb, Hibadh, Mdh1, Mdh2, Gapdh, Hadh, Hao2, Akr1a1, Sord, Idh1, Aldh2, Aldh9a1, protein disulfide isomerase family A member 3 (Pdia3), Aldh7a1, Glud1, Aldh1a1, Dld, Aldh6a1, and Cat. Among those, Gapdh, Aldh2, Aldh9a1, Aldh7a1, Aldh1a1, Aldh6a1, and Dld act on the aldehyde or oxo group of donors NAD or NADP as acceptor. Proteins exhibiting transferase activity were mainly subdivided into those that transfer nitrogenous groups (Gapdh, Agxt2, and Got1 and 2) or glutathione (Gsta3, Gstp1, and Gsta4). Carbonylated proteins with lyase activity were Tpi1, Ca2, Aldob, Got1, Tkfc, and Aco1 and 2.
Finally, KEGG pathway enrichment analysis (Figure 2b) of renal carbonylated proteins indicated that carbonylation, as oxidative posttranslational modification (oxPTM), was involved in numerous signaling and metabolic processes in the kidney. Those pathways were mainly related to carbon metabolism and energy production (glycolysis/gluconeogenesis, TCA, pyruvate, and 2-oxocarboxilic acid metabolism or fatty acid degradation, among others). There was also an important enrichment in pathways devoted to amino acid metabolism and oxidative defense. Thus, carbonylation control proteins involved in glutathione metabolism and peroxisome. It is worth noting that a significant enrichment in proteins participating in the proximal tubule bicarbonate reclamation was found as well. The metabolism of xenobiotics by cytochrome P450, an important metabolic process that the kidneys are also responsible for [75], was under the control of carbonylation.
## 3.4. Quantitative Changes Induced on Renal Carbonylome by High-Fat and High-Sucrose Diet and the Effect of Fish Oil and D-Fagomine Supplementations
Long-term HFHS feeding and the supplements fish oil and FG altered several proteins identified as carbonylation targets in the kidney and captured in Figure 3. The completed list of identified proteins and carbonylation indexes is detailed in Supplementary Table S3.
## 3.4.1. Quantitative Changes Induced on Renal Carbonylome by High-Fat and High-Sucrose Diet
The intake of the HFHS diet induced significant changes in renal carbonylome according to the increased lipotoxicity found in these rats (Figure 1). In common with prior studies conducted on various tissues from animals fed HFHS diets [5,18], the increased carbonylation was highly selective for some proteins, supporting the fine metabolic control that this oxPTM can exert on cells, likely as powerful as the better-described phosphorylation or acetylation.
The strong influence of the intake of the HFHS diet on total carbonylation levels found in the kidney (Figure 1c) was reflected in the specific modulation of proteins. All the proteins responsive to HFHS diet were more carbonylated in comparison to the STD diet. Therefore, enhanced carbonylation because of the HFHS diet intake was found in twenty-four out of thirty-six protein spots (Figure 3). One of the most relevant targets was Pebp1, belonging to PEBP family of proteins, which has a high affinity towards ethanolamines and has diversified functions in maintaining cell integrity. Deregulated Pebp1 has been implicated in diabetic nephropathy through the inhibition of NF-κB activation [76]. An increased level of carbonylation of Pebp1 could partially explain that deregulation in the progression of kidney diabetic nephropathy. On the other hand, Pebp1 and Gpx4 are protein master regulators of ferroptosis. Moreover, the oxidized phosphatidyl ethanolamine 15-hydroperoxy-eicasotetraenoyl-phosphatidylethanolamine (15-HpETE-PE), coming from oxidation of ARA-PE by the activity of 15 lipoxigenase (15LO), seems to exert a triggering role in ferroptosis, and several studies have found that the PEBP$\frac{1}{15}$LO-driven ferroptosis occurs in kidney injury [77]. The higher content of ARA in kidney membranes together with the defective glutathione system indicated that HFHS kidneys could be more prone to ferroptosis than STD-fed ones. Nevertheless, the contribution of carbonylation of Pebp1 to its scaffold activity in ferroptosis during kidney injury is currently unknown [78] and needs more investigation.
Another group of proteins significantly more carbonylated in the HFHS-fed kidneys was involved in the antioxidant defense and detoxification of cytotoxic lipid peroxides. For instance, catalase was more carbonylated in HFHS-fed rats. So any diminution of activity through the oxidation of the enzyme could lead to an increase in intracellular H2O2. Catalase was among the carbonylated proteins detected in the liver of rats exhibiting higher levels of oxidative stress in a rat model of Metabolic Syndrome (SHROB) [79] and in a model induced by HFHS-diet intake [23,29,80]. HFHS diet also increased the carbonylation of proteins with ROS scavenger activity, such as albumin and actin. Increased levels of carbonylation in these proteins were previously described in the liver of rats fed HFHS [23,29,80]. Acy1a, which is involved in the degradation of N-acetylated proteins and expressed in high levels in the kidney, was more carbonylated in rats fed HFHS as well [81]. This protein has been linked to the antioxidant defense too [82]. Besides being a target of carbonylation in the kidney of Spontaneously Hypertensive rats (SHR) [83], other authors found that its expression may be redox sensitive because Acy1 was suppressed in murine models of ischaemia–reperfusion injury [84]. Other proteins significantly more carbonylated in HFHS-fed rats was the isoform 2 of the carbonic anhydrase. This isoform, the major one in the kidney, catalizes the formation of H2CO3 from CO2 and H2O for regulating blood pH. Additionally, it has been suggested that carbonic anhydrases would have an antioxidant role as well [85].
Regarding detoxification of lipid peroxides, the HFHS diet significantly enhanced carbonylation of several renal aldehyde dehydrogenase and aldo-keto reductase enzymes. Those enzymes catalyze the conversion of a variety of aldehydes, including the harmful HNE, to their corresponding acids for their detoxification [86]. Those aldehydes, which are advanced lipoxidation end-products (ALEs), are strong electrofiles, highly reactive towards nucleofile amino acids in proteins (His, Arg, and Lys) and one of the main aldehydes responsible for protein carbonylation in vivo. Their ability to impair enzyme function when they react with the protein has been well established [87]. Therefore, HFHS-induced elevation in ROS could cause losses in those aldehyde dehydrogenase functions and result in the accumulation of those aldehydes, in agreement with the above-reported lipid peroxides production in kidneys (Figure 1b). Previous studies performed in the liver of rats fed HFHS also found increased carbonylation of those aldehyde dehydogrenaes and suggested a role of these enzymes in oxidative stress-related pathologies induced by obesogenic diets [80].
The high levels of carbonylation induced by the HFHS in the kidney also affected chaperon proteins (Pdia3 and Hspa8). These enzymes are responsible for inhibiting misfolded protein aggregation and disulfide bridge formation through the reduction and isomerization of incorrect disulfide bonds to maintain the new synthesized protein native structure [88,89]. Thus, losses of their activity due to carbonylation would contribute to oxidative-induced kidney disease. They were also found as carbonylation targets in the liver of HFHS-fed rats [23,29] and in the kidney of hypertensive rats [83].
The rest of the proteins with enhanced carbonylation in HFHS-fed rat kidneys in comparison to STD-fed rats were metabolic enzymes (Figure 3), such as Tpi1, Etfb, Eci1, Aldob, Gapdh, Hadh, Hao2, Sord, Got1, Idh1, Glud1, Oxct1, Dld, Dak, Acsm2, and Tkt. Oxidative damage induced by HFHS diet on these enzymes could hamper renal energy production, as it has been previously reported in the liver of rats fed HFHS diets where the hepatic isoform of these kidney proteins was found as carbonylation targets as well [23,29]. Higher cabonylation of these kind of enzymes, as Dld, was also found in the medulla of kidneys of SHR rats [83].
## 3.4.2. Quantitative Changes Induced on Renal Carbonylome by Fish Oil and D-Fagomine Supplementation
As Figure 3 shows, there were significant changes in carbonylome induced by the supplementation with fish oil, FG, or both. However, the magnitude of the effects of both supplements was markedly dependent on the background diet, being higher when they were added to the obesogenic diet. Interestingly, the combination of fish oil with grape polyphenols was also more effective in modulating liver carbonylated proteins in rats fed an HFHS diet than an STD one [29]. Differences in the modulation of the gut microbiota induced by each kind of diet and the influence of food matrix [25,40,41], which determine the bioavailability and bioaccessibility of the bioactive compounds, could partially explain these results. Considering the STD framework, supplements just induced significant changes in the carbonylation of four proteins compared to the STD control group. Moreover, FG had a bigger influence on renal carbonylome than fish oil on its own in this dietary group. Thus, FG, alone and in combination with fish oil, decreased carbonylation of the proteins Eci1, Gapdh, and Glud 1. The fourth protein sensitive to supplementation of STD diets was Sord, the only one responding to fish oil supplementation in STD diets. These proteins have metabolic functions, mainly participating in glucose and lipid metabolism.
Much more changes were found when supplements were added to the HFHS diet, and thirteen proteins were modulated. In contrast to what was found for the STD context, fish oil was the main supplement responsible for carbonylome changes (affecting ten proteins) rather than FG (six proteins). Moreover, the combination of both fish oil and FG was the most successful supplement to control renal carbonylome and counteract the effect of HFHS intake (affecting thirteen proteins). It is also noteworthy that all the proteins that positively responded to supplementation were proteins significantly more carbonylated due to the HFHS intake, indicating a counteracting effect of supplementation to deal with the alterations induced by the obesogenic diet in the kidney. Thus, the supplementation of the high-caloric diet with fish oil, alone, or in combination with FG, significantly decreased Pebp1 carbonylation. This decrease was accompanied by a significant reduction of ARA, which may indicate an important role of fish oils in modulating renal ferroptosis, but it needs to be further investigated. Supplements led to lesser carbonylation of proteins involved in aldehyde detoxification and ROS scavenger, and several metabolic enzymes as well, reaching carbonylation indexes similar to the ones found in the STD-group kidneys.
## 3.5. Pathways Modulated by Diet and Supplements through Carbonylome Changes in the Kidney
To fully understand the extent of the effect of HFHS intake and the antioxidant role of the supplements on renal function by modulating carbonylome, the KEGG pathway enrichment analysis for carbonylated proteins responsive to dietary interventions was conducted. Results are shown in Table 6. Due to the limited effect of supplements in modulating carbonylation in the context of STD diets, there were not any renal metabolic pathways significantly enrichement in this dietary framework. Therefore, all the pathways significantly altered by supplements and shown in Table 6 correspond to the pathways modulated in the HFHS dietary context.
## 3.5.1. Pathways Modulated by High-Fat and High-Sucrose Diet through Carbonylome Changes in Kidney
KEGG pathway enrichment analysis of the proteins that were significantly more carbonylated because of the intake of HFHS diet, revealed that almost all the pathways identified as likely modulated by carbonylation in the kidney were affected by the diet (Table 6). Results could help understand how lipotoxicity and the subsequent oxidative stress drive kidney disease and related metabolic alterations.
One of the most relevant outcomes concerns one of the main functions of the kidney, i.e., the regulation of blood pH. To maintain systemic acid–base balance, the first step is the reclamation of all filtered bicarbonate entering the proximal tubule [90]. Interestingly, our data showed that the consumption of HFHS diets increased the carbonylation of proteins participating in this process of proximal tubule bicarbonate reclamation, likely driving to a lower capacity of buffering of kidney and, thus, the acidification of blood pH.
Results also highlighted important alterations of pathways involved in amino acid metabolism in the kidney due to the intake of HFHS diet. Renal amino acid metabolism plays a key role in the acid–base balance regulation via glutamine hydrolysis to produce “new” bicarbonate and ammonia excretion in a process known as ammoniagenesis [91]. Other amino acids different from glutamine are also used in ammoniagenesis, representing up to $20\%$ of ammonia production in normal conditions [92]. It is likely that branched-chain amino acids (BCAA) are involved in ammoniagenesis during metabolic acidosis, and alterations of BCAA plasma profiles have been found in hyperinsulinemia [93].
The significant alteration of the amino acid metabolism induced by the HFHS diet in our study—especially the pathways related to the alanine, aspartate, and glutamate metabolism and the BCAA degradation—as well as an increase of urea and insulin in plasma, supports the deregulation that this diet induced on the renal function of blood pH regulation. Moreover, when glutamine and other amino acids are metabolized to form two ammonium ions and α-ketoglutarate, this α-ketoglutarate is metabolized to two bicarbonate anions in the TCA cycle [94]. The other resulting molecule from this cycle, malate, is an intermediate of glycolysis/gluconeogenesis, demonstrating the involvement of that pathway in the regulation of pH as well. Accordingly, both TCA and glycolysis/gluconeogenesis pathways showed significantly enhanced protein carbonylation induced by the HFHS diet.
Taken together, our results indicated that HFHS diet caused functional damage to key proteins involved in the process of blood pH regulation and the induction of metabolic acidosis, a common consequence of the intake of westernized diet. In fact, diet is the most important contributing factor to body acidity and alkalinity. Westernized diets are particularly more prone to these acid-base abnormalities and are often associated with metabolic acidosis [95]. On the other hand, acidification is considered a mechanism of insulin resistance, although the precise mechanisms are not well known. Lesser binding affinity capacity or poor recycling of insulin receptor induced by acidosis are some examples of the proposed mechanisms [92]. It is noteworthy that acidification could support a mechanism to explain the link between kidney protein carbonylation and the incipient insulin resistance detected in the HFHS-induced prediabetic rats of this current study. The increased carbonylation of the HIF-1 proteins found in the functional enrichment analysis also support it because these proteins have been involved in the insulin resistance induced by acidosis [96]. Finally, the carbonylation of proteins from the peroxisome and ascorbate and aldarate metabolism, which are involved in the antioxidant defense in kidneys, could lead to a defective renal antioxidant capacity that contributes to the general prooxidant environment.
## 3.5.2. Pathways Modulated by Fish Oil and D-Fagomine Supplementation through Carbonylome Changes in Kidney
The inclusion of the diet of fish oil rich in EPA and DHA significantly ameliorated most of the changes in the metabolic pathways induced by the high-caloric diet (Table 6). There was a significantly decreased carbonylation in proteins involved in amino acid metabolism, especially in the alanine, aspartate, and glutamate metabolism, which was highly altered by the HFHS diet, as described above. There were also significant counteracting effects on glycolysis/gluconeogenesis, fructose and mannose metabolism, 2-oxocarboxylic acid metabolism, and the pentose phosphate pathway. Improvements in the peroxisome oxidative status and in the HIF-1 signaling pathway were also found after fish oil supplementation. This metabolic influence supports the improvements in metabolic parameters described for these rats, especially those regarding better insulin sensitivity, lipotoxicity, and oxidative stress. FG also exerted a significant effect on modulating amino acid metabolic pathways in the kidney (Table 6). In this case, FG acted mainly on essential amino acid metabolism, especially BCAA. Given the important role of BCAA on insulin resistance, the influence of FG on this pathway may be explained by the well-known antidiabetic properties of this compound. FG also had some capacity to modulate carbon metabolism, especially glycolysis/gluconeogenesis, fructose and mannose metabolism, and TCA cycle, as well as ascorbate and aldarate metabolism, peroxisome, and HIF-1 signaling. Interestingly, FG counteracted the carbonylation of proteins from the propanoate metabolism in the kidney. Although the mechanisms of SCFAs in the gut–kidney axis have not been fully investigated yet and are still controversial, they seem to exert beneficial effects on kidney function, including anti-inflammatory and anti-oxidant outcomes [97]. Previous results showed that FG promoted the growth of SCFA-producing bacteria and increased the amount of propionic acid in fecal content in the same cohort of rats in comparison with the HFHS-fed rats [25]. Therefore, the lesser carbonylation of the proteins involved in propanoate metabolism could assist the beneficial modulation exerted by propionate on renal function. The modulation of those metabolic pathways in the kidney was in agreement with the healthier status of these rats, especially the improved insulin sensitivity.
The most relevant finding was that the combination of both fish oil and FG showed additive and synergic effects on the modulation of kidney metabolic pathways through modulating carbonylation levels (Table 6). Almost all of the total individual effects were found in the combined group, but also new pathways were significantly modulated by the combination of supplements, as shown in Table 6. Those new pathways significantly enriched after the combination of both bioactive agents were arginine and proline metabolism, lysine degradation, fatty acid degradation, and butanoate metabolism. It is noteworthy that previous results indicated that the combination of fish oil and FG increased the formation of SCFAs in cecal content of the rats, including butyric and isobutyric acid [25]. The beneficial effects of butyrate on renal function have been confirmed by Felizardo et al. [ 98]. These authors demonstrated that butyrate preserves the glomerular basement membrane and ameliorates glomerulosclerosis and inflammation to prevent proteinuria in mice. Thus, the lesser carbonylation of this pathway found in HFHS-fed rats eating the combination of fish oil and FG may facilitate the action of these SCFAs. Therefore, HFHS-fed rats supplemented with the combination of both bioactive compounds exhibited healthier biochemical and biometrical values, the lowest lipotoxicity and whole protein carbonylation, as well as the more favorable kidney and plasma lipid profiles.
## 4. Conclusions
Long-term intake of high-fat and high-sucrose diets induced a prediabetic state in rats concomitant with significant alterations of renal function. The highly energetic diet provoked lipotoxicity in the kidneys, favoring the aberrant accumulation of SFAs and ARA but also DHA. The enrichment in PUFAs, together with the deficient antioxidant defense induced by the HFHS diet, led to more oxidative stress and the increasing formation of lipid peroxidation products and protein carbonyls. Interestingly, results reflected selective oxidative damage in renal proteins involved in the regulation of the acid–base balance. These results provide a mechanism based on the specific formation of protein carbonyls in key renal pathways that may contribute to explaining the incipient onset of insulin resistance diet-induced in rats. Additionally, the results of the present study indicated that the combination of fish oil with FG may have suppressive effects on the progression of renal dysfunction in a prediabetic state. The possible mechanisms are a) the amelioration of renal fat accumulation with the switch of lipid profiles towards less cytotoxic ones and b) the diminution of oxidative stress and oxidative damage of renal pathways mainly devoted to the regulation of blood pH and antioxidant defense. Therefore, rats fed the combination of both bioactive compounds presented a healthier phenotype with significantly improved insulin sensitivity in a mechanism closely dependent on their antioxidant properties. The information provided in this study will be useful to find new early biomarkers of kidney disease and design successfully personalized nutritional strategies for the prevention and palliation of kidney alteration induced by a diet based on the use of fish oil and FG as natural antioxidants.
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|
---
title: 'Functional Correlates of Microglial and Astrocytic Activity in Symptomatic
Sporadic Alzheimer’s Disease: A CSF/18F-FDG-PET Study'
authors:
- Chiara Giuseppina Bonomi
- Agostino Chiaravalloti
- Riccardo Camedda
- Francesco Ricci
- Nicola Biagio Mercuri
- Orazio Schillaci
- Giacomo Koch
- Alessandro Martorana
- Caterina Motta
journal: Biomedicines
year: 2023
pmcid: PMC10045805
doi: 10.3390/biomedicines11030725
license: CC BY 4.0
---
# Functional Correlates of Microglial and Astrocytic Activity in Symptomatic Sporadic Alzheimer’s Disease: A CSF/18F-FDG-PET Study
## Abstract
Glial and microglial cells contribute to brain glucose consumption and could actively participate in shaping patterns of brain hypometabolism. Here, we aimed to investigate the association between 18F-fluorodeoxyglucose (18F-FDG) uptake and markers of microglial and astrocytic activity in a cohort of patients with Alzheimer’s Disease (AD). We dosed cerebrospinal fluid (CSF) levels of soluble Triggering Receptor Expressed on Myeloid cells (sTREM2), Glial Fibrillary Acidic Protein (GFAP), a marker of reactive astrogliosis, and β-S100, a calcium-binding protein associated with a neurotoxic astrocytic profile. No associations were found between sTREM-2 and 18F-FDG uptake. Instead, 18F-FDG uptake was associated negatively with CSF β-S100 in the left supramarginal gyrus, inferior parietal lobe and middle temporal gyrus (Brodmann Areas (BA) 21 and 40). Increased β-S100 levels could negatively regulate neuronal activity in the temporo-parietal cortex to prevent damage associated with AD hyperactivity, or rather they could reflect neurotoxic astrocytic activation contributing to AD progression in key strategic areas. We also identified a trend of positive association of 18F-FDG uptake with CSF GFAP in the right fronto-medial and precentral gyri (BA 6, 9 and 11), which has been reported in early AD and could either be persisting as an epiphenomenon tied to disease progression or be specifically aimed at preserving functions in the frontal cortex. Overall, CSF markers of astrogliosis seem to correlate with cortical glucose uptake in symptomatic sporadic AD, highlighting the role of astrocytes in shaping regional hypometabolism and possibly clinical presentation.
## 1. Introduction
Alzheimer’s Disease (AD) is marked by the accumulation of amyloid-β (Aβ) peptides, which according to the amyloid hypothesis triggers tau-hyperphosphorylation and its intraneuronal deposition [1]. Despite these two pathological changes being the main actors of AD, it is widely recognized that many transversal mechanisms participate in its pathophysiology, acting alongside Aβ and tau pathology [2]. For instance, AD is often complicated by comorbidities, including diabetes and other metabolic conditions, which further increase the complexity of disease mechanisms [3,4].
Specifically, unravelling the interplay between cellular neuroinflammatory contributions—i.e., microglial activation and astrocytic reactivity—amyloid pathology and tauopathy has been the object of many efforts [5,6,7], because of their interesting and possibly ambivalent role in the progression of the disease.
Microglia regulates neuronal proliferation and the phagocytic removal of apoptotic neurons [8], de-facto shaping brain circuitry/connectivity, and its alterations play a key and intricate role in AD [9]. Indeed, loss-of-function mutations of Triggering Receptor Expressed on Myeloid Cells 2 (TREM-2)—a gene encoding a transmembrane receptor expressed on brain microglia—are the second genetic risk factor for sporadic AD, after Apolipoprotein E (APOE) [10]. The TREM-2 cascade has been discussed for its seemingly dual role since, on the one hand, it favors the phagocytic removal of Aβ while on the other it could mediate and sustain chronic damaging effects [11,12]. Similarly, reactive astrocytosis, marked by changes of astrocytic morphology within and around neuritic plaques, is also a typical finding in AD, despite whether its role is protective or detrimental still being vastly debated [13,14].
These cellular mechanisms have been much explored through their cerebrospinal fluid (CSF) correlates. Indeed, an increase of the soluble form of the TREM-2 receptor (sTREM-2) has been thoroughly reported from the early phases to more advanced stages of AD [15,16,17,18] and linked with reduced rates of amyloid accumulation in several studies [16,19], configuring it as a reliable marker of AD-related microglial activation. Many studies have also reported changes in the astrocytic secretome profile occurring in response to AD pathophysiology [20,21], including, among others, the increased production of both plasma and CSF levels of Glial Fibrillary Acidic Protein (GFAP)—the main intermediate filament protein in mature astrocytes—[22,23] and β-S100, a calcium-binding protein tied to neurotoxic astrocytic activity [24,25]. In a recent work, we ourselves investigated these CSF correlates of microglial and astrocytic activation, namely CSF sTREM-2, GFAP and β-S100, with our results supporting a dynamic switch in microglial functions, from neuroprotective to neurotoxic, depending on disease stage and APOE genotype, and a tight association of microglial activity with astrocytic reactivity, and with the acquisition of a more neurotoxic astrocytic phenotype (in this study).
The importance of the glial compartment has also been validated by recent studies highlighting the contribution of astrocytic metabolism to 18F-fluorodeoxyglucose (18F-FDG) uptake during Positron Emission Tomography (PET) scans [26,27]. Strong evidence supports the hypothesis that glutamate recycling in astrocytes activates aerobic glycolysis, with neurons being partially fueled by lactate derived from astrocytes [28], and astroglial glutamate transport triggering glucose uptake by astrocytes [29]. On the other hand, microglial cells have been shown to consume more glucose than neurons and astrocytes, and their activation state has likewise been linked to FDG-PET alteration in AD mouse models and AD patients [30]. This leads to speculation that microglial and astrocytic activity might shape patterns of regional hypometabolism of 18F-FDG-PET and, hence, cognitive manifestations of AD, proving the relevance of investigating the associations between astrogliosis, microgliosis and cortical metabolism.
Previous works also investigated the relationship between measures of cerebral glucose consumption and some markers of neuroinflammation, focusing especially on pre-symptomatic or very early AD, highlighting the presence of higher 18F-FDG-PET uptake in relation to neuroinflammatory processes in early AD [31,32,33]. We aimed to implement these findings by evaluating a cohort of symptomatic patients diagnosed with sporadic AD, by exploring possible correlations between regional hypometabolism of 18F-FDG-PET and changes in CSF biomarkers of microglial and astrocytic reactivity (sTREM-2, GFAP and β-S100).
## 2.1. Subjects’ Enrolment
Between September 2021 and December 2021, we enrolled 35 outpatients that had been referred to the UOSD Centro Demenze of the University Hospital “Policlinico Tor Vergata” in Rome upon suspicion of Alzheimer’s Disease (AD). After initial assessment, all patients underwent a complete diagnostic work-up including Mini Mental State Examination (MMSE), corrected by age and education, laboratory testing to rule out secondary cognitive decline, 3T brain MRI, and lumbar puncture. All patients also underwent 18F-FDG-PET at the Nuclear Medicine Unit of University Hospital “Tor Vergata”.
Eventually, 31 patients received a CSF biomarker-based diagnosis of AD according to the most recent NIA-AA research framework (i.e., as having an increase of CSF p-tau alongside Aβ42 decrease, A+T+, or sole evidence of amyloid pathology, A+T−). Genetic testing for Apolipoprotein E (APOE) was also performed on all subjects. All patients showed neuropsychological profiles compatible with classical AD [34].
We excluded patients ongoing treatment with antipsychotic drugs or having major comorbidities, such as oncological history, inflammatory/autoimmune systemic conditions, decompensated diabetes, or organ failure. Other exclusion criteria were a history of manifest acute stroke (i.e., Hachinski scale score > 4 or radiological evidence of focal ischemic lesions) and clinical evidence or suspicion of other neurological disorders. Thus, our final sample included 27 patients within the Alzheimer’s continuum (ADc) [35], namely 6 A+T− and 21 A+T+. All subjects included were right-handed. Demographics from patients are reported below (see Table 1).
We obtained written consent from all participants and/or legally authorized representatives. The ethical committee of the Santa Lucia Foundation accounted for the study protocol as an observational retrospective design (Prot. CE/AG4/PROG.392–08).
## 2.2. CSF Sampling and Laboratory Analysis
All lumbar punctures were performed with sterile technique between 8 and 10 am. A 10 mL CSF sample was collected for each patient in polypropylene tubes. A total of 2 mL was used for routine biochemical analysis and we centrifuged the remaining 8 mL at 2000× g at +4 °C for 10 min and aliquoted them in 1 mL portions. The aliquots were frozen at −80 °C for further analysis of CSF, AD, and glial biomarkers. We used commercially available kits for biochemical analysis. CSF Aβ42, p-tau and t-tau concentrations were determined using a sandwich enzyme-linked immunosorbent assay (EUROIMMUN ELISA©), LUMINEX© Multiple assays ELISA was used for CSF GFAP and β-S100 concentrations.
Blood samples were also drawn in EDTA tubes. The DNA was extracted automatically and APOE genotyping was conducted by allelic discrimination technology with real-time PCR, according to the manufacturer’s instructions (TaqMan; Applied Biosystems).
## 2.3. 18F-FDG-PET Data Acquisition
All acquisitions were performed at the Nuclear Medicine Unit of the University Hospital “Policlinico Tor Vergata” in Rome with a General Electric VCT PET/CT scanner (GE Medical Systems, Tennessee, USA). All subjects fasted for at least 5 h before i.v. injection of FDG, and serum glucose levels were in range according to European Association of Nuclear Medicine guidelines [36]. Patients were injected intravenously with 18F-FDG (dose range 185–295 MBq) and then hydrated with 500 mL of saline ($0.9\%$ sodium chloride), according to a previous similar report of our group in this field [37]. The scan started 30 min after the injection and lasted ten minutes. Acquisition and reconstruction parameters are reported elsewhere and were followed according to the report cited above. [ 37].
Upon imaging evaluation, all 27 subjects showed typical findings of brain cortical hypometabolism in key regions compatible with a diagnosis of classical AD (parietal and posterior cingulate cortices, precuneus, or a combination of the above) [38].
## 2.4. Statistical Analysis
The relationship between levels of CSF GFAP, sTREM-2 and βS100 biomarkers and brain 18F-FDG uptake were analyzed through separate correlation models for each biomarker and direction, performed using Statistical parametric mapping (SPM) 12 (Wellcome Department of Cognitive Neurology, London, UK; https://www.fil.ion.ucl-.ac.uk/spm/software/spm12/, accessed on 1 January 2023) implemented in Matlab 2018 (Mathworks, Natick, MA, USA).
PET data were converted from DICOM to Nifti format using the MRIcron software (available at https://www.nitrc.org/projects/mricron/, accessed on 1 January 2023) and then subjected to normalization. Bias regularization was applied (0.0001) to limit distortions due to smooth, spatially varying artifacts that can modulate image intensity and interfere with automated image processing. The FWHM of Gaussian smoothness of distortion (to prevent the algorithm from trying to model out intensity variations due to different tissue types) was set to a limit of 60 mm; the tissue probability map implemented in SPM12 was used (TPM.nii). An affine registration with mutual information with the tissue probability maps [39] was used to achieve approximate alignment with the ICBM spatial template—European brains [40,41]. Warping regularization was set with the following 1 × 5 arrays (0, 0.001, 0.5, 0.05, 0.2); smoothing (to cope with functional anatomical variability not compensated by spatial normalization and to improve signal-to-noise ratio) was set to 5 mm; sampling distance (encoding the approximate distance between sampled points in estimating model parameters) was set to 3.
We used an 8 mm isotropic Gaussian filter to blur individual variations (especially gyral variations) and increase the signal-to-noise ratio. Prior to regression analysis, the following parameters and post-processing tools were used: global normalization (which brings the images to a global value) = 50 (using proportional scaling); masking threshold (which helps identify voxels with acceptable signal in them) was set to 0.8; transformation tool of statistical parametric maps to normal distribution; correction of SPM coordinates to match Talairach coordinates, subroutine implemented by Matthew Brett (http://www.mrc-cbu.cam.ac.uk/Imaging, accessed on 1 January 2023). Brodmann surfaces (BA) were determined within a range of 0 to 3 mm from the corrected Talairach coordinates of the SPM output isocenter using a Talairach client (available at http://www.talairach.org/index.html, accessed on 1 January 2023). As suggested by Bennett et al. [ 42], sPM t-maps were calculated for multiple comparisons using the false discovery ($p \leq 0.05$) and corrected for multiple comparisons at the cluster level ($p \leq 0.001$).
The level of significance was set at 100 (5 × 5 × 5 voxels, i.e., 11 × 11 × 11 mm) contiguous voxels. The voxel-based analyses were performed in the subgroup of patients using regression analyses. We designed independent models assessing the effects of each CSF biomarker (levels of GFAP, sTREM-2 and βS100) as regression factors—independent variables—on cortical 18F-FDG uptake, using age and sex as covariates, as well as t-tau levels in order to adjust for another marker of neurodegeneration. Positive and negative associations were tested in all cases.
The cluster obtained in the regression analysis was then exported by means of the WFU Pickatlas tool implemented in SPM 12. Specifically, the mean signal intensities calculated from each cluster within each subject were normalized to the average intensities of the Pons volume of interest. The use of normalization based on activity in the pons, rather than whole-brain counts as in the reference region, has been reported to result in greater accuracy in discriminating patients from controls in neurodegenerative diseases. As previously suggested by Pagani and colleagues [43]), a dataset of normalized 18F-FDG values relevant to the cluster under study was exported. To determine whether the normalized 18F-FDG values for the studied cluster were Gaussian distributed, the D’Agostino K-squared normality test was applied (with the null hypothesis being normal distribution). We then performed linear univariate regression analyses using GraphPad Prism© version 9.3.1 for Windows (GraphPad Software, San Diego, California USA, www.graphpad.com, accessed on 1 January 2023) to visualize significant findings, namely the association between CSF glial biomarkers and normalized continuous data representing metabolism in the specific Broadman Areas of interest.
## 3. Results
First, we retrieved a trend of positive association between 18F-FDG uptake and CSF levels of GFAP in the right frontal-medial and precentral gyri (see Figure 1), with peaks in BA 6, 9 and 11 [see Table 2].
Moreover, a significant negative relationship between brain glucose consumption and CSF levels of β-S100 was also found in a wide cluster that included the left supramarginal gyrus, the inferior parietal lobe and middle temporal gyrus (see Figure 2), with peaks in Brodmann Areas 21 and 40 [see Table 3]. We did not find any significant relationships between 18F-FDG uptake and CSF levels of sTREM-2.
Lastly, we performed linear regression analyses to assess the association between glial CSF biomarkers and metabolism in the specific Broadman Areas of interest.
Figure 3a shows regressions between β-S100 and 18F-FDG brain uptake in BA40 (R2 = 0.1593, F [1,27] = 4.737, $$p \leq 0.0392$$), BA21 (R2 = 0.1440, F [1,27] = 4.207, $$p \leq 0.0509$$) and the widened cluster of the parietal lobe (R2 = 0.1460, F [1,27] = 4.275, $$p \leq 0.0492$$).
Figure 3b shows the linear regression between CSF GFAP and 18F-FDG brain uptake in BA9 (R2 = 0.1806, F [1,27] = 5.069, $$p \leq 0.0342$$), BA11 (R2 = 0.1705, F [1,27] = 4.727, $$p \leq 0.0402$$) and BA6 (R2 = 0.0313, F [1,27] = 0.9420, $$p \leq 0.3419$$).
## 4. Discussion
Despite its widespread use in both clinical settings and basic research, the identity of the cell types contributing to 18F-FDG-PET signal is still debated. Traditionally, 18F-FDG-PET signal has been attributed only to neuronal uptake, with hypometabolism being considered as a direct index of neuronal dysfunction—loss of neuropil, synapse, or functional impairment—or death. However, evaluating glucose consumption in AD is made complex by other additional factors, including changes in the expression of glucose transporters (GLUT) (e.g., a reduction of GLUT-3), insulin/insulin-growth-factor 1 axis dysregulation and defects due to the disruption of the neurovascular unit [44,45,46]. Moreover, other metabolically active cells are thought to contribute to glucose consumption in the brain [26,30].
Firstly, we observed a strong negative association between levels of CSF β-S100 and 18F-FDG cortical uptake in a wide cluster including the left supramarginal gyrus, the parietal lobe, and the middle temporal gyrus, with peaks in Brodmann Areas 21 and 40. To our knowledge this association is a novel finding, since relationships between this component, CSF β-S100, and cortical metabolism have never been described in symptomatic AD. Of note, one previous work reported no association between these two variables in a group of around 89 subjects (of which 25 A+T+) from the ALFA+ cohort, which includes pre-symptomatic and very early symptomatic patients with AD [31]. Thus, this result seems to be tightly bound to disease mechanisms that apply to late stages of the disease, after the onset of cognitive decline.
As a calcium binding protein, CSF β-S100 plays an active role in excitatory neurotransmission, as its exogenous form increases calcium concentrations in both cultured neurons and astrocytes [47]. By affecting intracellular Ca2+-dependent processes, extracellular β-S100 modulates synaptic plasticity, especially long term potentiation [48], and is linked to both hippocampal and non-hippocampal cognitive symptoms of AD [49].
Extensive data confirms that AD is marked by the presence of excitatory/inhibitory imbalance supported by Aβ-induced hyperexcitability [50,51] and built on pyramidal neuron hyperexcitability—that is also unsupervised due to GABAergic dysfunction [52]—and inhibition of glutamate reuptake [53]. Indeed, both AD-derived astrocytes and neurons cultured with AD astrocyte conditioned medium show aberrant intracellular Ca2+ dynamics in response to glutamate [54]. In light of this, speculations can be made that the increase of CSF β-S100 could reflect an attempt to negatively regulate regional neuronal activity, with the beneficial effect of preventing the neuronal damage associated with hyperactivity, which would result in decreased cortical metabolism and reduced 18F-FDG uptake. Alternatively, increased β-S100 could account for the local presence of pro-apoptotic neurotoxic astrocytes, favoring disease progression in areas that are typically involved in AD. In either scenario, our findings seem to point to the presence of increased regional vulnerability and/or incipient local neurodegeneration tied to astrocytic activation, with the increase of β-S100 acting either as a partial buffer or inducing neurotoxicity.
Moreover, since the association has been retrieved in key strategic areas of the dominant hemisphere, such as left BA40 and BA21, it could have repercussions on clinical presentation. Both areas belong to linguistic hubs, and are involved with language comprehension, semantic processing, and sentence generation (BA21, middle temporal gyrus) and with phonological abilities and verbal creativity (BA40, inferior parietal lobe, supramarginal gyrus) [55]. BA40 also plays a key role in several other functions, among which are working memory, motor functions (executive control of behavior and imitation, visuomotor transformation/motor planning), deductive reasoning, social perception and empathy [56]. This implies the need for a better understanding of the relationship between CSF levels of β-S100 and cognitive profile, to explore its possible role as a specific biomarker of these dysfunctions. Indeed, subtle impairment in both comprehension and strategic thinking, as well as all other cited cognitive domains, are often underrecognized in AD. Moreover, the identification of specific regional hypometabolic changes/hypoactivity suggests that targeted therapeutic approaches, such as the restoring of excitatory/inhibitory balance, could be beneficial to these symptoms.
Our second finding was the trend of positive association between CSF GFAP levels and glucose consumption in the right medial frontal gyrus, with maximum z-score of correlation peaking in BAs 9, 6 and 11. Since astrocytic reactivity is characterized by both morphological changes—resulting in cellular hypertrophy—and upregulation of GFAP expression and production, our finding supports the presence of active astrogliosis and higher metabolic demands in those areas of the frontal lobes. Previous studies focused on early AD, reported the presence of the same positive association between CSF GFAP and regional metabolism in both the frontal and the temporo-parietal lobes [32]. The loss of this linear association in the temporo-parietal and left frontal regions, which are among the first hubs to be damaged in AD [57], could be due to disease progression in our cohort of patients with symptomatic AD. Conversely, the presence of GFAP-related astrogliosis in the frontal lobe could either be an epiphenomenon resulting from astrocytic dysfunction in other areas—incapable of sustaining higher metabolic demands—or rather be specifically aimed at preserving functions in the frontal cortex in the course of symptomatic AD, supporting the hypothesis of its delayed involvement.
Interestingly, a previous work reported that higher CSF t-tau levels correlate with hypometabolism in the right frontal cortex [58], and the concordance between these and our findings suggests that the frontal lobe is a hot spot for the progression of AD. Astrocytic reactivity might sustain but also be exacerbated by tau/neurodegenerative changes in the frontal lobe, with increased GFAP production and higher metabolic demands possibly trying to sustain damaged circuitry.
In a work from Salvadó et al. [ 2022] [32], the positive GFAP/glucose consumption association turned negative when switching from isolated amyloidopathy (A+T-) to patients with full-blown AD (hence, with tauopathy: A+T+), which they interpreted as an uncoupling of astrogliosis from metabolism due to failure to sustain elevated energetic demands when tauopathy sets in. Our results suggest that the positive association between CSF GFAP and glucose uptake could uncouple in regions with higher damage but could also be partially maintained in the frontal lobes in symptomatic AD, even in the presence of tau pathology. In light of this, it would be very interesting to explore stage-dependent glucose metabolic changes associated with reactive astrogliosis separating symptomatic A+T- from A+T+, to solve this discrepancy.
Finally, in our cohort, we did not retrieve any significant correlation between CSF sTREM-2 and cortical glucose uptake. This finding contrasts with previous literature showing a link between sTREM-2 and FDG-PET signal in AD patients with Mild Cognitive Impairment (MCI). Biel and colleagues reported that sTREM-2 positively associates with FDG-PET hypermetabolism in patients with CSF findings of amyloidopathy but negative amyloid-PET, while, in case of amyloid-PET positivity—reflecting higher levels of fibrillary Aβ—sTREM-2 associates with hypometabolism instead [33]. Nevertheless, this discrepancy could be due to our limited sample size and to the different clinical and AT profiles of our cohort.
We are aware that this study has limitations. First, our study design is observational and cross-sectional, therefore no assumptions on direct causal relationships can be made, and it is not possible to completely decipher the mechanisms underlying these associations. Also, widening the study cohort could be useful to confirm and to strengthen our results, by repeating the assessments in A+T- and A+T+ patients. In addition, a larger sample size would allow the assessment of direct repercussions on specific cognitive domains.
Nonetheless, our results open several interesting future directions. The difference between our own findings and previous literature on cognitively unimpaired patients with AD stimulates the urge for follow-up longitudinal data, to add meaningful information on the burden of glial and microglial contributions on disease progression. Since it seems to be more sensitive to amyloid changes and have widespread correlation with cortical hypometabolism in early AD [32], it would also be interesting to use plasma GFAP instead of its CSF counterpart. Lastly, implementing the use of novel PET radiotracers such as TSPO for microglial activation could add more information on the bond between microglial inflammation and reactive astrogliosis.
## 5. Conclusions
Our results suggest that astrogliosis (CSF GFAP) in the frontal lobe is associated with higher local metabolic demands, while a negative association of CSF β-S100 with cortical metabolism was found in the parieto-temporal lobe, likely reflecting regional vulnerability to incipient damage. No microglial involvement with cortical glucose uptake has been identified in our study.
Overall, these findings represent glucose metabolic changes associated with reactive astrogliosis, adding evidence to the role of astrocytes in shaping 18F-FDG-PET signal in vivo. This also fuels the need to implement measures of glial and microglial activity in the stratification and profiling of patients, to allow a tailored understanding of pathophysiology and to better account for their contribution in the biological evolution of AD.
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|
---
title: Bioinformatics-Based Analysis of Key Genes in Steroid-Induced Osteonecrosis
of the Femoral Head That Are Associated with Copper Metabolism
authors:
- Baochuang Qi
- Chuan Li
- Xingbo Cai
- Luqiao Pu
- Minzheng Guo
- Zhifang Tang
- Pengfei Bu
- Yongqing Xu
journal: Biomedicines
year: 2023
pmcid: PMC10045807
doi: 10.3390/biomedicines11030873
license: CC BY 4.0
---
# Bioinformatics-Based Analysis of Key Genes in Steroid-Induced Osteonecrosis of the Femoral Head That Are Associated with Copper Metabolism
## Abstract
Osteonecrosis of the femoral head (ONFH) is a common disabling disease. Copper has positive effects on cells that regulate bone metabolism. However, the relationship between copper metabolism (CM) and steroid-induced ONFH (SONFH) remains unclear. The GSE123568 dataset was downloaded from the Gene Expression Omnibus. The differentially expressed CM-related SONFH genes (DE-CMR-SONFHGs) were identified via differential analysis and weighted gene coexpression network analysis (WGCNA). Receiver operating characteristic (ROC) analysis was performed for the predictive accuracy of key genes. Targeting drugs and the copper death-related genes (CDRGs) relevant to key genes were investigated. The bioinformatics results were confirmed via quantitative real-time polymerase chain reaction (qRT–PCR) and Western blot (WB) analysis. Two out of 106 DE-CMR-SONFHGs were identified as key genes (PNP and SLC2A1), which had diagnostic value in distinguishing SONFH from control samples and were related to various immune cell infiltrations. Eleven PMP-targeting drugs and five SLC2A1-targeting drugs were identified. The qRT–PCR, as well as WB, results confirmed the downregulation PNP and SLC2A1 and high expression of the CDRGs DLD, PDHB, and MTF1, which are closely related to these two key genes. In conclusion, PNP and SLC2A1 were identified as key genes related to SONFH and may provide insights for SONFH treatment.
## 1. Introduction
Osteonecrosis of the femoral head (ONFH) is a common disabling disease [1]. Approximately 150,000–200,000 new cases per year are reported in China [2], and approximately 20,000 to 30,000 new patients per year are diagnosed in the USA [3]. Nontraumatic ONFH is mainly the result of glucocorticoid use and chronic alcohol consumption [4]. Significant osteonecrosis results in the collapse of the articular cartilage of the femoral head, followed by early osteoarthritis (OA) of the hip. In clinical practice, individuals who are affected by steroid-induced osteonecrosis of the femoral head (SONFH) are usually young and middle-aged individuals [5]; the exact pathogenesis is still unclear, and effective prevention and early treatment options are scarce. Hence, it is vital to investigate the exact pathogenesis of SONFH and to discover ideal methods for the early diagnosis and treatment of SONFH.
Copper is a double-edged sword in cells. Copper is an indispensable co-factor for all organisms, and active homeostatic mechanisms work across copper concentration gradients to maintain very low intracellular copper concentrations in order to avoid the intracellular accumulation of free copper that can be hazardous to cells. Copper levels in mammals are tightly controlled through intracellular or systemic homeostatic mechanisms. However, excessive levels of intracellular copper ions cause damage because these copper ions produce free radicals and induce oxidative stress [6]. Copper metabolism includes copper uptake, distribution, sequestration, and excretion at the cellular and systemic levels in multicellular organisms. Excessive copper intake can cause undesired modulation of the immune response [7]. Chen et al. [ 8] showed that prolonged exposure of cells and tissues to excessive copper levels could activate p53-dependent or p53-independent pathways that lead to “programmed cell death” or “apoptosis”. Copper-induced “apoptosis” has been verified in spleen cells, thymocytes, and hepatocytes [9,10]. Copper (Cu) ions stabilize the expression of hypoxia-inducible factor-1α (HIF-1α) and upregulate vascular endothelial growth factor (VEGF) expression; high VEGF expression induces neovascularization to further promote bone development [11,12]. However, it has also been demonstrated that higher concentrations of copper significantly reduce the value-added of osteogenic precursor cells and decrease new bone formation [13]. Physically, steroid hormone application can lead to a disturbance in copper metabolism, causing changes in serum copper levels [14,15]. However, the relationship between disorders of copper metabolism and SONFH has not yet been revealed.
With the development of bioinformatics in recent years, microarray analysis using high-throughput platforms has been applied as effective means in exploring the molecular mechanisms of disease and identifying biomarkers [16]. Based on the microarray data in GSE123568 datasets, as did the previous literature [17,18], this study mainly focused on identifying the key genes in SONFH from the perspective of copper metabolism on the basis of bioinformatics and supplemented by the experimental verification of gene expression; this study aimed to provide new ideas for the treatment and prevention of SONFH.
## 2.1. Data Source
The SONFH-related dataset GSE123568 (30 SONFH blood samples and 10 control blood samples) was extracted from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/, accessed on 10 May 2022). Furthermore, 2062 copper metabolism-related genes (CMRGs) were derived from the GeneCards database (https://www.genecards.org/, accessed on 10 May 2022) with the search term “Copper metabolism”.
## 2.2. Differential Gene Expression Analysis
The differential gene expression analysis between SONFH samples and control samples in the GSE123568 dataset was performed using the R package “limma” [19]. Multiple testing correction was implemented using the method described by Benjamini and Hochberg, and criteria for identifying the differentially expressed SONFH genes (DE-SONFHGs) were an adjusted p value < 0.05 and |log2fold change (FC)| > 0.5.
## 2.3. Weighted Gene Coexpression Network Analysis (WGCNA)
To identify genes of which expression is highly correlated with SONFH in the GSE123568 dataset, WGCNA [20] was performed. The samples were clustered to determine the overall correlation of all samples and to exclude outliers in order to ensure the accuracy of the analysis. To ensure that the interactions between genes fit the scale-free distribution to the greatest extent, a soft threshold was determined. The minimal number of genes in each module was fixed at 30. The modules that positively and negatively correlated with SONFH were selected as key modules. Then, the module members (MMs) and gene significance (GS) were estimated, and a scatter plot was generated to identify the key module genes according to the criteria |MM| > 0.8 and |GS| > 0.2.
The DE-SONFHGs, CMRGs, and the key module genes that were identified were intersected with the jvenn tool to identify the differentially expressed CM-related SONFH genes (DE-CMR-SONFHGs).
## 2.4. Functional Enrichment Analysis of DE-CMR-ONFHGs
The DAVID database (https://david-d.ncifcrf.gov/, accessed on 10 May 2022) [21] was utilized to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the DE-CMR-SONFHGs [22,23,24]. A significance threshold of $p \leq 0.05$ and a number of enrichments (counts) of at least 2 were considered to indicate significantly enriched results.
## 2.5. PPI Network Construction
To investigate the interactions among DE-CMR-SONFHGs, a protein–protein interaction (PPI) network was developed with the Search Tool for the Retrieval of Interacting Genes (STRING) database. The confidence level was 0.4, and discrete proteins were eliminated to identify interaction relationship pairs.
## 2.6. Screening for Key Genes using a Machine Learning Algorithm
Least-Absolute Shrinkage and Selection Operator (LASSO) logistic regression and Support Vector Machine (SVM) algorithms were used to screen the key DE-CMR-SONFHGs. The R software “glmnet” package (version 4.0-2) [25] was used to perform 10-fold cross-validation, and the error rate was calculated under different features to select strongly correlated genes. Similarly, the SVM algorithm in “e1071” (version 1.7-9) [26] was utilized to sort the DE-CMR-SONFHGs. The recursive feature elimination (RFE) method was used to determine the importance and importance ranking of each gene, and the error rate and accuracy rate were calculated. The lowest point of the error rate was selected as the best combination, and the corresponding genes were considered candidate genes. *The* genes that intersected with the candidate genes that were identified via the LASSO analysis and SVM analysis were obtained using the jVenn tool. Additionally, to further explore the diagnostic value of key genes in the discrimination of SONFH samples and control samples, the R package “pROC” [27] was used to plot the receiver operating characteristic (ROC) curves of key genes in the GSE123568 dataset [28].
## 2.7. Single-Gene Gene Set Enrichment Analysis (GSEA)
To study the molecular mechanisms associated with the diagnostic key genes, GSEA software (V4.0.3) [29] was utilized to perform single-gene GSEA [30]. When the parameter “c2.cp.kegg.v7.4.symbols.gmt” was set, the KEGG pathway gene set was used as the enrichment background [22,23,24]; when the parameter “c5.go.bp.v7.4.symbols. Gmt” was set, the GO biological process gene set was set as the enrichment background; when the parameter “Phenopyte labels: *Use a* gene as the phenotype” was used, the expression values of two key genes were considered the phenotype file. Then, all other genes, separately in the gene sets, were used to calculate the correlation with each key gene. Arranged according to the correlation coefficient from high to low, these genes were used as the new gene sets to be tested. The enrichment of the GO and KEGG terms in the tested gene sets was examined, and the significant enrichment threshold was set as an NOM p value < 0.05.
## 2.8. Immune Cell Infiltration Analysis
The single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analyze the abundance of 28 infiltrating immune cell populations in all samples in the GSE123568 dataset. Then, the R package “ggplot2” [31] was used to reveal the immune cell populations with differential abundance between SONFH samples and control samples using the Wilcoxon test method. The correlation between differential immune cell infiltration was calculated with “corrplot”. The correlation between key genes and differential immune cell infiltration was calculated via Spearman analysis.
## 2.9. Drug Predictive Analysis
According to the previous literature, I drugs that target the key genes were identified with The Drug Gene Interaction Database (DGIdb; www.dgidb.org, accessed on 10 May 2022), and Cytoscape software [32] was used to build a drug targeting network.
## 2.10. Analysis of the Relevance of Copper Death-Related Genes and Differentially Expressed Genes in Disease
Based on the 10 copper death-related genes (FDX1, LIPT1, LIAS, DLD, PDHA1, DLAT, PDHB, GLS, MTF1, and CDKN2A) that have been reported in the literature [33,34], the expression of 2 key genes (PNP and SLC2A1) and 10 copper death-related genes were extracted from the GSE123568 dataset, and the Pearson correlation coefficient between the key genes and the copper death-related genes was calculated.
Moreover, the expression of 10 copper death-related genes was extracted from the GSE123568 dataset and combined with the grouping information of the samples, and the R package “ggplot2” was used with the Wilcox test method to generate graphs showing copper death-related gene expression in the disease and control samples.
## 2.11. Verification of the Expression of Key Genes in Clinical Samples
Human samples were acquired from the Department of Orthopaedics of the 920th Hospital of the People’s Liberation Army Joint Security Force. A total of 13 peripheral blood samples were collected, 6 of which were collected from SONFH patients and 7 were collected from healthy participants [35]. Eight bone tissue samples (including 4 hormonal osteonecrosis and 4 normal femoral head bone tissue) were obtained from femoral head removed during surgery [36]. The Ethics Committee of the 920th Hospital of the Chinese Pe’ple’s Liberation Army Joint Security Force approved this study, and the individuals who participated in the study provided written informed consent.
Preoperatively, 3 mL of fasting peripheral anticoagulated blood was collected, and lymphocytes were separated using lymphocyte isolation solution. Total RNA was extracted with the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the protocol. cDNA synthesis was performed using a reverse transcription kit (Takara, Tokyo, Japan) according to the manufacturer’s instructions. The thermal procedure of polymerase chain reaction (PCR) used in this study was initial denaturation at 95 °C for 1 min, denaturation at 95 °C for 20 s, annealing at 55 °C for 20 s, and extension at 72 °C for 30 s. The PCR was performed for 40 cycles. GAPDH was used as the positive control for this reaction, and each sample was calculated using the comparative Ct method (Table 1).
Bone tissue samples (including 4 hormonal osteonecrosis and 4 normal femoral head bone tissue) were collected. An appropriate amount of RIPA cracking solution was added, and the tissues was cracked on ice for 30 min. Total protein was conducted using a BCA protein concentration assay kit (Biyuntian, Shanghai, China) according to regulations. Samples with equal amounts of proteins were separated by performing electrophoresis and were probed with primary antibodies against DLD, PDHB, MTF1, SLC2A1, and PNP (Affinity or Proteintech). Membranes were then incubated with a horseradish peroxidase-conjugated secondary antibody, and proteins were visualized with enhanced chemiluminescence reagents. The target protein abundance was normalized to actin.
## 2.12. Statistical Analysis
All bioinformatics analyses were performed in R language. The Wilcoxon test was used to compare the data from different groups. The qRT–PCR and WB data were analyzed using the 2−△△Ct method. Student’s t test was utilized to compare the differences in the RT–qPCR and WB data. If not otherwise stated, a p value smaller than 0.05 indicates significance.
## 3.1. Screening of Differentially Expressed Genes
The workflow diagram (Figure 1) clearly shows the major steps of this paper. A total of 2036 DE-SONFHGs between the SONFH samples and control samples were identified, and these DE-SONFHGs included 1383 upregulated and 653 downregulated genes. The distribution of these DE-SONFHGs is presented Figure 2.
## 3.2. WGCNA
The overall clustering of the dataset samples was good, so no samples were removed (Figure S1A). Then, the traits of the samples were classified, and sample clusters and clinical feature heatmaps were generated (Figure S1B). According to the position of the blue line, the power threshold was determined to be 24 (R2 = 0.85), and 48 modules were obtained (Figure 3A,B and Figure S1C,D). Subsequently, similar modules were analyzed and merged with the dynamic cutting tree algorithm with a MEDissThres equal to 0.2, and 16 modules remained after merging (Figure 3C). The module with the strongest positive correlation (bisque4 module, cor = 0.67, $$p \leq 3$$ × 10−6) and the module with the strongest negative correlation (dark-green module, cor = −0.83, $$p \leq 5$$ × 10−11) were selected as key modules; the bisque4 module included 2382 genes, and the dark-green module included 245 genes (Figure 3C). Furthermore, 896 hub genes were authenticated in the bisque4 module, and 145 hub genes were authenticated in the dark-green module (Figure 3D). Finally, a grand total of 1041 hub genes were identified. Moreover, 106 DE-CMR-SONFHGs were identified by taking the intersection of 1041 hub genes, 2036 DE-SONFHGs and 2062 CMRGs (Figure 3E).
## 3.3. Functional Enrichment Analysis of DE-CMR-ONFHGs
The 106 DE-CMR-SONFHGs were enriched in 143 GO-Biological Process (GO-BP), 50 GO-Cellular Component (GO-CC), and 34 GO-Molecular Function (GO-MF) terms, and 51 KEGG pathways (Figure 4A,B). *These* genes were mainly enriched in various GO terms related to protein binding, the membrane, and stimulus response, such as identical protein binding, plasma membrane, and inflammatory response. Furthermore, these DE-CMR-SONFHGs were mainly enriched in phagosomes, the PI3K-Akt signaling pathway, and neutrophil extracellular trap formation.
## 3.4. Linkage between Proteins
A PPI network was constructed to explore the interactions of 106 DE-CMR-SONFHGs using the STRING website; 387 protein interaction pairs, including 92 nodes, were identified (Figure 5). TLR4 had the greatest connectivity in the PPI network. In addition, 15 downregulated genes were included in the PPI network.
## 3.5. Screening for Key Genes using a Machine Learning Algorithm
LASSO logistic regression and the SVM algorithm were utilized to identify key DE-CMR-SONFHGs. When lambda.min was 0.1073883, the error rate was the lowest, and two candidate genes, namely PNP and SLC2A1, were identified via LASSO (Figure 6A,B). Additionally, eight candidate genes, including RNASET2, PNP, SLC2A1, REXO2, CYBA, SOAT1, TFDP1, and LYZ, were selected using SVM (Figure 6C and Table 2). Finally, two genes (PNP and SLC2A1) were identified in the results of both LASSO and SVM (Figure 6D). The ROC curves revealed that the area under the curve (AUC) values of PNP and SLC2A1 in the GSE123568 dataset were both above 0.9, indicating that each key gene had diagnostic value in distinguishing between SONFH samples and control samples (Figure 7).
## 3.6. Single Gene Enrichment Analysis
To investigate the underlying functions of PNP and SLC2A1, GSEA was performed. PNP was mainly enriched in “positive regulation of smoothened signaling pathway”, “positive regulation of cytokine production involved in immune response”, “*Glycosphingolipid biosynthesis* lacto and neolacto series”, and “B-cell receptor signaling pathway” (Figure 8A,B and Tables S1–S4). SLC2A1 was mainly enriched in “cgmp metabolic process”, “vacuolar acidification”, and “glycosphingolipid biosynthesis lacto and neolacto series” (Figure 8C,D and Tables S5–S8).
## 3.7. Immune Cell Infiltration Analysis
To explore the abundance of 28 infiltrating immune cell populations in all the samples, ssGSEA was performed between the SONFH and control samples. Twenty immune cell populations, such as MDSCs, eosinophils, and CD56bright NK cells, were significantly different between the SONFH and control samples (Figure S2A). Among these differentially abundant cell populations, the abundance of immature dendritic cells was strongly negatively correlated with the abundance of activated B cells, and the abundance of CD56bright natural killer cells was strongly positively correlated with the abundance of activated B cells (Figure S2B). PNP expression had a significant and the strongest negative association with the abundance of plasmacytoid dendritic cells, and PNP expression had a significant and the strongest positive association with the abundance of CD56bright natural killer cells (Figure S2C). SLC2A1 expression had a significant and the strongest negative association with the abundance of T follicular helper cells, and SLC2A1 expression had the strongest positive association with the abundance of T helper 17 cells (Figure S2D).
## 3.8. Drug Prediction Analysis
After the drug prediction analysis, 16 drugs in the DGIdb database were predicted to target PNP and SLC2A1; of these drugs, 11 drugs were predicted to target SLC2A1, and five drugs were predicted to target PNP (Figure S3).
## 3.9. Relevance of Copper Death-Related Genes and Differentially Expressed Genes to Disease
To investigate the correlation between copper death-related genes and differentially expressed genes in femoral head necrosis, we calculated correlations based on the expression of the two key genes that were identified and the expression of 10 copper death-related genes, and we determined the corresponding p values and correlation coefficients, r. Significantly correlated relationship pairs were identified based on the correlation threshold p value < 0.05 and |r| > 0.3 (Table S9). The correlation results were visualized through the R package ‘ggplot2’ (Figure 9) according to the correlation ranking, where a significant and the strongest positive correlation was observed among SLC2A1, PNP, and CDKN2A, and a significant and the strongest negative correlation was observed among SLC2A1, PNP, and PDHB.
The expression of the 10 copper death-related genes was extracted from the GSE123568 dataset and combined with the sample grouping information. The expression of copper death-related genes in the disease samples and control samples was evaluated via the wilcox.test method (Figure 10). The results showed that four copper death-related genes (LIPT1, DLD, PDHB, and MTF1) were significantly different and were differentially upregulated in the disease samples.
## 3.10. Verification of the Expression of Key Targets through qRT–PCR and WB
qRT–PCR was performed on human blood samples to analyze the expression of PNP and SLC2A1. Compared to that in the normal group, the expression of PNP ($$p \leq 0.0362$$) and SLC2A1 ($$p \leq 0.0094$$) was notably reduced in the SONFH group (Figure 11A).
Compared to that in the normal group, the expression of LIPT1 ($$p \leq 0.4129$$) showed no significant difference, but DLD ($p \leq 0.0001$), PDHB ($$p \leq 0.0149$$), and MTF1 ($$p \leq 0.0326$$) was increased in the SONFH group (Figure 11A).
The results of WB were similar to those of qRT–PCR. Compared to that in the normal group, the expression of PNP ($$p \leq 0.0005$$) and SLC2A1 ($$p \leq 0.0025$$) was notably reduced in the SONFH group (Figure 11B,C).
Compared to that in the normal group, the expressions of DLD ($$p \leq 0.0279$$), PDHB ($$p \leq 0.0391$$), and MTF1 ($$p \leq 0.0009$$) was notably increased in the SONFH group (Figure 11B,C).
## 4. Discussion
Several in vitro investigations have shown the positive effects of copper on cells that regulate bone metabolism. Li and Yu [37] showed that copper ions can suppress the resorption of osteoclasts. Several investigators have confirmed that copper exerts positive effects in a dose-dependent manner. Low concentrations ($0.1\%$ w/w) of copper increased the viability and growth of osteoblasts, while higher concentrations (2.5 and $1\%$ w/w) were shown to be toxic to cells [38]. In addition, the existence of copper stimulates the differentiation of MSCs toward the osteogenic lineage [39]. The copper content of femoral head necrosis tissues reported in previous studies varied. Milachowski [40] observed a decrease in copper levels in his study of the relationship between idiopathic femoral head ischemic necrosis and trace element metabolism. Y’mazaki’s [41] study showed the exact opposite results, with an increase in copper levels in subchondral bone and cartilage in ischemic femoral head necrosis. Therefore, we speculate that SONFH may be accompanied by an imbalance in copper metabolism. A study by Gonzalez-Reimers et al. [ 42] confirmed, through experiments in rats, that steroids increase muscle copper, iron, and zinc levels, as well as bone copper levels. In conclusion, we hypothesize that disorders of copper metabolism may be one of the mechanisms underlying the pathogenesis of SONFH. To test our hypothesis, we searched for SONFH-related and copper metabolism-related genes in public databases and identified PNP and SLC2A1 as DE-SONFHGs that are associated with copper metabolism through a series of analyses, such as GO and KEGG analyses. We also discovered a significant and the strongest negative correlation between PNP expression and plasmacytoid dendritic cell infiltration. The strongest negative correlations were found between PNP expression and plasmacytoid dendritic cell infiltration, between SLC2A1 expression and T follicular helper cell infiltration, between PNP expression and CD56 bright natural killer cell infiltration, and between SLC2A1 expression and T helper 17 cell infiltration. It was experimentally verified that both PNP and SLC2A1 were significantly downregulated in the peripheral blood of SONFH patients, as well as in the hormonal osteonecrosis samples, which was consistent with the results of our bioinformatics analysis.
Purine nucleoside phosphorylase (PNP) is a vital enzyme in purine metabolism. A missense SNP (rs1049564) in the PNP gene was found to be associated with high IFN levels in SLE. The rs1049564 T allele of PNP is a loss-of-function variant that triggers blockade of the S phase and activation of the IFN pathway in lymphocytes [43]. PNP deficiency induces apoptosis mediated by the p53 pathway in human pluripotent stem cell-derived neurons [44]. In our study, it was firstly determined that the down-regulation of PNP at the mRNA and protein levels resonated with the activation of the p53-mediated endogenous apoptotic signaling pathway in SONFH patients. We speculate that PNP downregulation in SONFH may mediate femoral head necrosis through the P53 pathway.
SLC2A1 is a gene that encodes a glucose transporter protein (GLUT1) that controls glucose uptake and is encoded on 1p34.2 [45]. *This* gene is essential for glucose metabolism, is involved in normal and tumor cell glycolysis, and can play a key role in the cell growth and proliferation of many tumor cells [46,47,48]. One study confirmed that in mice deficient in SLC2A1, the Wnt7b-induced bone anabolic function is blocked and bone formation is affected [49]. SLC2A1 (GLUT1) is one of the targets of miR-140-5p [50], and miR-140-5p may promote the development of femoral head necrosis through the ubiquitin proteasome system [51]. This study confirmed that SLC2A1 is downregulated in SONFH tissues as did the previous literature [52], suggesting that the biological process through which miR-140-5p targets SLC2A1 may be closely related to the development of SONFH.
Both PNP and SLC2A1 were enriched in signaling pathways associated with SONFH pathogenesis. The PNP gene was mainly enriched in “positive regulation of smoothened signaling pathway”, “positive regulation of cytokine production involved in immune response”, “glycosphingolipid biosynthesis lacto and neolacto series”, and “B-cell receptor signaling pathway”. SLC2A1 was mainly enriched in “cgmp metabolic process”, “vacuolar acidification”, and “glycosphingolipid biosynthesis lacto and neolacto series”. The main pathways clearly associated with SONFH in this study included “neutrophil extracellular trap formation”, “PI3k-Akt signaling pathway”, “mTOR signaling pathway”, “TNF signaling pathway”, and “HIF-1 signaling pathway”. Drug-induced glucocorticoid administration is considered to be a risk factor for femoral head necrosis. It has been proposed that glucocorticoid-induced platelet activation leads to disruptions in the local blood flow in the femoral head. Activated platelets can trigger neutrophil extracellular trap (NET) formation, leading to the ischemic necrosis of bone cells [53]. AKT/mTOR signaling pathway components are upregulated in a glucocorticoid-induced ONFH model, and human umbilical cord MSCs reduce macrophage polarization by inhibiting the AKT/mTOR signaling pathway and thus ameliorate necrosis and osteoclast apoptosis in a GC-induced model of ONFH [54]. In contrast to that in normal tissues, the expression of TNF-α in ONFH bone tissues was notably upregulated, and autophagy, apoptosis, and the p38 MAPK/NF-κB signaling pathway were significantly activated, suggesting a significant effect of the TNF signaling pathway in the pathogenesis of femoral head necrosis [55]. Some scholars have also confirmed the involvement of the HIF-1 signaling pathway in the pathogenesis of hormonal osteonecrosis. Animal experiments have confirmed that bone health supplements can expedite the formation of new bone, promote the resorption of damaged bone, inhibit the inflammatory response, and ultimately ameliorate SONFH through the HIF-1α/BNIP3 pathway [56].
Many studies have confirmed that copper plays a vital role in the function of the mammalian immune system; for example, animals with copper deficiency are more vulnerable to infections, whereas animals that consume an oral diet that is rich in copper are more resistant [57]; oral copper supplementation helps to maintain T-cell function in rats with acute spinal cord injury [58]. From the perspective of the immune system, the body needs balanced copper intake; moderate copper intake is sufficient for optimal immune function, while too much may be harmful to the organism [59]. Our study also found that the pathogenesis of SONFH is associated with multiple immune regulatory pathways. Therefore, we hypothesize that in the pathogenesis of SONFH, an imbalance in copper metabolism may cause disordered immune regulation in the body and thus induce femoral head necrosis. Recently, studies have shown that immune cell infiltration is associated with the progression of SONFH [35,60,61]. In the present study, we concluded that PNP expression had a notable and the strongest negative association with plasmacytoid dendritic cell infiltration, while PNP expression had a notable and the strongest positive association with CD56 bright natural killer cell infiltration. SLC2A1 had a notable and the strongest negative association with T follicular helper cell infiltration, and SLC2A1 had the strongest positive association with T helper 17 (Th17) cell infiltration. T follicular helper cells (Tfhs) are currently a research hotspot in basic immunology. Tfhs may contribute to the bone destruction that is associated with osteoporosis [62]. The balance between Treg and Th17-cell activity directly affects osteoclastogenesis and osteoblast/osteoblast coupling regulation [63]. In summary, immune cells may influence osteoblasts and osteoclasts in the pathogenesis of SONFH. Therefore, assessing the differences in the proportions of infiltrating immune cells in SONFH is valuable for elucidating the molecular mechanisms underlying SONFH and verifying molecular markers that are associated with immune infiltration.
Among the drugs predicted to target the key genes in this study and thus to affect SLC2A1, genistein (genistein) may be one of the most promising drugs. Genistein is an isoflavone that is also known as genistein, and it is an estrogen-like compound that is widely found in legumes [64]. It can prevent bone loss in human and rat models of osteoporosis directly by acting through estrogen receptors (ERs) on bone cells and indirectly by affecting thyroid follicular cell activity [65]. Phytoestrogens prevent methylprednisolone-induced femoral head necrosis and secondary osteoporosis in rats [66] The specific mechanism by which genistein ameliorates SONFH requires further investigation.
In the present study, we found notable differences in the expression levels of three copper death-related genes (DLD, PDHB, and MTF1) that are associated with PNP and SLC2A1 in SONFH. Dihydrothioctanamide dehydrogenase (DLD), which is also referred to as the E3 subunit of pyruvate dehydrogenase complex (PDHC) EC 1.6.4.3, is the third catalytic enzyme of PDHC, and it is a multifunctional mitochondrial matrix enzyme [67]. It has been demonstrated that DLD gene silencing prevents lipid peroxidation and iron-related death in vitro and in vivo [68]. Pyruvate dehydrogenase B (PDHB) encodes pyruvate dehydrogenase, which is a constituent enzyme of the pyruvate dehydrogenase multienzyme complex in mitochondria [69]. Recessive PDHB mutations cause pyruvate dehydrogenase complex (PDC) deficiency, which mainly affects the nervous system, such as developmental delays, seizures, and peripheral neuropathy [69]. Recent studies have found that DLD and PDHB positively regulate copper-related death [34]. Similarly, the current study revealed for the first time the high expression of DLD and PDHB in SONFH patients at the mRNA and protein levels, and we hypothesize that these gene alterations may disrupt pyruvate metabolism and thus induce the copper-related death of osteoblasts in the pathogenesis of SONFH. Further experimental studies are needed to elucidate the related mechanisms. Metal-regulated transcription factor 1 (MTF1) is a highly conserved zinc (Zn)-binding transcription factor in eukaryotes that responds to both metal overload and metal deficiency to protect cells from oxidative and hypoxic stress. A comparable Cu+-binding center was confirmed to be present in mammalian MTF1, suggesting that it may also respond to Cu [70]; however, in vitro experiments also confirmed that copper ions enhance MTF1 expression in myogenic cells [71]. The application of hormones can increase copper contents in muscle, bone, and even femurs [41,42]. Recent studies have confirmed that MTF1 is a negative regulator of copper-related death [34]. MTF-1 is one of the targets of miR-148-3p, which inversely regulates MTF-1 transcriptional activity [72]. Moreover, miR-148-3p is downregulated in bone marrow mesenchymal stem cells of mice with steroid-induced femoral head necrosis [73], which echoed the over-expression results of MTF in SONFH patients in this study. Thus, we speculate that low miR-148-3p might -increase the MTF-1 transcriptional level in the pathogenesis of SONFH. It is highly likely that MTF-1 is closely related to SONFH.
There were some limitations in the current study. For example, the number of peripheral blood samples and femoral head tissue samples is not large enough, and meanwhile, more in-depth functional analyses combining key genes with potential functions and processes, as well as vital copper death-related genes, are exigent for SONFH, which needs to be verified by collecting more clinical samples in the future.
## 5. Conclusions
In summary, this study offers new perspectives on the relationship between copper metabolism and SONFH. Steroids may target PNP and SLC2A1 to induce cuproptosis in osteoblasts and thus trigger femoral head necrosis; PNP and SLC2A1 may be potential drug targets and biomarkers for the diagnosis of SONFH. In the future, we will further study the specific mechanism by which these genes are involved in SONFH. In-depth studies on genistein as an effective agent for the treatment of hormonal femoral head necrosis should be continued. The present study also had the following limitations. First, the data used in this study were extracted from a single source, and the results may be biased to a certain extent. Second, functional experiments should be carried out to further elucidate the potential molecular mechanisms underlying hormonal femoral head necrosis.
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|
---
title: 'The relationship between the baseline geriatric nutritional risk index (GNRI)
and neurological function at the convalescence stage in patients with stroke: a
cross-sectional study'
authors:
- Lielie Zhu
- Jianning Xia
- Xiangzhi Shao
- Xinyu Pu
- Jiajun Chen
- Jiacheng Zhang
- Xinming Wu
- Jinyihui Zheng
- Dengchong Wu
- Bing Chen
journal: BMC Geriatrics
year: 2023
pmcid: PMC10045810
doi: 10.1186/s12877-023-03919-w
license: CC BY 4.0
---
# The relationship between the baseline geriatric nutritional risk index (GNRI) and neurological function at the convalescence stage in patients with stroke: a cross-sectional study
## Abstract
### Background
Malnutrition is a common complication after stroke and may worsen neurological outcomes for patients. There are still no uniform tools for screening nutritional status for the patients with stroke. We aimed to explore the relationship between the baseline geriatric nutritional risk index (GNRI) and neurological function at the convalescence stage for patients with stroke and assessed the predictive value of the GNRI for adverse neurological outcomes.
### Methods
A total of 311 patients with stroke were enrolled retrospectively. Basic information and laboratory results on admission since onset of stroke were collected. The GNRI on admission was calculated and neurological outcomes evaluated by the Barthel index at 1 month after the onset of stroke. Statistical analyses, including correlation coefficient tests, multivariate regression analyses, and receiver operating characteristic (ROC) analyses, were applied in this study.
### Results
Compared with the good outcome group, the poor outcome group showed a significantly lower GNRI on admission ($P \leq 0.05$). GNRI was associated with Barthel index ($r = 0.702$, $P \leq 0.01$). The GNRI was independently correlated with the Barthel index (Standardization β = 0.721, $P \leq 0.01$) and poor outcome 0.885 ($95\%$ CIs, 0.855–0.917, $P \leq 0.01$) after adjusting for covariates. Compared with no nutritional risk grades (Q4), the OR of GNRI to poor neurological outcome increased across increasing nutritional risk grades of GNRI (OR = 2.803, $95\%$ CIs = 1.330–5.909 in Q3, 7.992, $95\%$ CIs = 3.294–19.387 in Q2 and 14.011, $95\%$ CIs = 3.972–49.426 in Q1, respectively, P for trend < 0.001). The area under ROC curves (AUC) of the GNRI was 0.804, which was larger than that of the NIHSS, BMI, or Albumin ($P \leq 0.01$), with an optimal cut-off value of 97.69, sensitivity of $69.51\%$ and specificity of $77.27\%$. Combined GNRI with NIHSS gained the largest AUC among all the variables (all $P \leq 0.05$), with an AUC of 0.855, sensitivity of 84.75 and specificity of $72.73\%$.
### Conclusions
For patients with stroke, higher nutritional risk grades at baseline indicated worse neurological function at the convalescence stage. Compared with NIHSS, BMI, and Albumin, GNRI was a competitive indicator for the risk of poor neurological outcome. The predictive property of GNRI for adverse neurological outcomes might be more powerful when combined with NIHSS.
## Background
As a common complication after stroke, malnutrition is closely related to the concomitant factors of stroke, such as age, neurological defects, swallowing dysfunction, and a decline in daily living activities [1, 2]. It is reported that the incidence of malnutrition post stroke can reach $16\%$-$66.7\%$ [1, 3]. Malnutrition can exacerbate stroke, hinder functional recovery, prolong hospital stay, and even increase mortality [4]. For the patients with stroke at the convalescence stage, malnutrition may can also hamper rehabilitation and worsen neurological outcomes [1, 5].
A study revealed that nutritional improvement in stroke patients with malnutrition was associated with the resumption of activities of daily living [6]. However, malnutrition for patients with stroke remains incompletely recognized, which leads to an undertreated problem [4]. One simple strategy for quickly identifying patients with nutritional problems who may benefit from nutritional intervention is to use a validated nutrition screening tool (NST). Current guidelines recommend that all stroke patients should be screened for risk of malnutrition at admission [7]. Patients identified as at risk of malnutrition should be subjected to further assessment and subsequently receive an appropriate nutritional intervention [8]. It is believed that early screening or identification of malnutrition of patients with stroke and predicting the neurological outcome in stroke rehabilitation patients could facilitate appropriate nutritional intervention, which is important to regain functional capacity and activities of daily living, as well as to improve the quality of life [5].
Nevertheless, there are still few NSTs designed and validated specifically for stroke patients [2, 5]. For most of common NSTs, their prediction forpoor clinical outcome was also seldom performed based on the neurological function of stroke [9]. In recent years, the Geriatric Nutritional Risk Index (GNRI) [10], which is based on serum albumin and actual weight and ideal weight ratio, has been used as a simple and effective NST in a variety of clinical environments, such as evaluating nutritional status [2] and predicting outcome events for many illnesses, such as tumor, trauma, hemodialysis and heart failure [11–14]. However, the relationship between GNRI and adverse neurological outcomes in patients with stroke is unclear, and it is still unknown whether the GNRI plays a predictive role for adverse neurological outcomes. In this study, we explored the relationship between the GNRI and neurological function of the convalescence stage in patients with stroke and assessed the predictive value of the GNRI.
## Study design
This was a single-centre study to explore the relationship between the GNRI and neurological functionin patients with stroke at the convalescence stage. A medical record review of the patients admitted to Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Traditional Chinese Medicine from January 2022 to September 2022 was performed. This study was approved by the Ethics Committee of Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Traditional Chinese Medicine (No. WZY2021-KT-072).
## Participants
Inclusion criteria: [1] age between 50 and 85 years; [2] met the diagnostic criteria for ischaemic stroke, listed in the *Diagnostic criteria* of cerebrovascular disease in China (version 2019) [15]; [3] patients admitted to the hospitals within one week after the onset of stroke confirmed by magnetic resonance imaging or computerized tomography scans; [4] the patients’ baseline data within 1 week since onset of stroke were intact; and [5] information about neurological function evaluation at 1 month after onset of stroke was available. Exclusion criteria: [1] patients who died within 1 month after the onset of stroke; and [2] patients whose medical information was missing or incomplete.
According to the above criteria,we enrolled a total of 311 patients with stroke at the convalescence stage (225 males and 86 females subjects) aged 50.5–85.5 years with complete data. All participants received the necessary supportive care for ischaemic stroke patients, including antiplatelet, lipid-lowering, blood pressure-controlling, and blood glucose-regulating medications, and at the same time, specific treatments for comorbidity were implemented according to the patient’s condition. Meanwhile, all of the participants underwent a routine rehabilitation program, which included physiotherapy, occupational therapy, physical agent therapy, and speech or swallowing therapy, depending on the patient's condition. All patients were divided into the poor outcome group (Barthel index < 60, $$n = 223$$) and the good outcome group (Barthel index ≥ 60, $$n = 88$$) according to the Barthel index evaluated at 1 month after the onset of stroke.
## Data collection
A retrospective review was conducted on the patients’ clinical records at baseline (within 1 weeks since the onset of stroke), including the characteristics of demographic, clinical and medical history variables, such as age, sex, height, body weight, smoking history, drinking history, comorbidity, the infarct type, thrombolytic therapy or not, CRP (C-reactive protein), serum Albumin and National Institute of Health Stroke Scale (NIHSS) scores. BMI was calculated by dividing weight (kg) by the square of height (m2). According to the Oxfordshire Community Stroke Study (OSCP) [16], infarction was divided into three types in this study: total anterior circulation infarct (TACI), partial anterior circulation infarction (PACI), and posterior circulation infarction (POCI). The NIHSS is a 15-item impairment scale used to measure and assess stroke severity, recommended by National Stroke Foundation guidelines. The NIHSS includes the following domains: level of consciousness, eye movements, integrity of visual fields, facial movements, arm and leg muscle strength, sensation, coordination, language, speech and neglect. Each impairment is scored on an ordinal scale ranging from 0 to 2, 0 to 3, or 0 to 4. Item scores are summed toa total score ranging from 0 to 42 (the higher the score, the moresevere the stroke) [17].
## GNRI calculation
The GNRI was calculated using the following formula [18]: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{GNRI}=\lbrack1.489\times\mathrm{albumin}\;(\mathrm g/\mathrm l)\rbrack+41.7\times\lbrack\mathrm{body}\;\mathrm{weight}\;(\mathrm{kg})/\mathrm{ideal}\;\mathrm{body}\;\mathrm{weight}\;(\mathrm{kg})\rbrack$$\end{document}GNRI=[1.489×albumin(g/l)]+41.7×[bodyweight(kg)/idealbodyweight(kg)]; the ideal weightwas calculated using the following formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Ideal}\;\mathrm{body}\;\mathrm{weight}\;(\mathrm{men})=\mathrm{height}\;(\mathrm{cm})\times0.75-62.5$$\end{document}Idealbodyweight(men)=height(cm)×0.75-62.5; and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Ideal}\;\mathrm{body}\;\mathrm{weight}\;(\mathrm{women})=\mathrm{height}\;(\mathrm{cm})\times0.60-40$$\end{document}Idealbodyweight(women)=height(cm)×0.60-40.
The radio of bodyweight to ideal body weight was set as “1” when the body weight exceeded the ideal body weight.
Based on the above values of GNRI, four grades of risk related to nutrition were defined and used as subgroups of GNRI for further analysis [19]: high nutritional risk (Q1): GNRI < 82; medium nutritional risk (Q2): 82 ≤ GNRI < 92; low nutritional risk (Q3): 92 ≤ GNRI ≤ 98; no nutritional risk (Q4): GNRI > 98.
## Outcome measurements
Barthel index [20]: The index is specifically divided into 10 items: eating, dressing, toilet use, stool control, urine control, going up and down stairs, bed and chair transfer, walking on the flat ground, bathing and modification. The total score of the Barthel index scale is 0 to 100. A higher score indicates better daily self-care ability. Generally, a total score of 40 indicates severe dependence, 41 to 60 indicates moderate dependence, 61 to 99 indicates mild dependence, and 100 indicates that the patients can fully care for themselves and are not dependent. According to a previous study [21], the cut-off of the Barthel index was set at 60 because patients with a Barthel index < 60 were indicated to have functional dependency. A Barthel index of < 60 was considered an indicator of poor outcome, and a Barthel index ≥ 60 was considered a good outcome, which was used as the basis for grouping in this study.
According to the regular clinical evaluation procedure, the Barthel index was evaluated at 1 months after onset of stroke which was recorded in the institutional database.
## Statistical analysis
Statistical analysis was performed using the IBM SPSS software version 23. For continuous variables, normally distributed variables were described as means ± SDs while obviously skewed variables are expressed as the median (interquartile range, IQR). The proportion or prevalence was used to describe categorical variables. The univariate analysis included t-test and Pearson’s χ2-test was used to compare the mean and proportion. Spearman partial coefficient analysis was applied to assess the correlations between GNRI and other clinical characteristics or biomarkers. Multivariate logistic regression analysis was used to analyse the independent correlation between variables and neurological outcomes. The significant variables in univariate analysis and covariates considered clinically influential or potential risk factors according to the literature published previously were then analysed by multivariate stepwise logistic regression (backwards stepwise) to identify significant variables affecting neurological outcomes. The odds ratio (OR) and $95\%$ confidence intervals (CIs) were calculated. To evaluate the effect of biomarkers in predicting neurological outcomes, receiver operating characteristic (ROC) curves were plotted with the area under the curve (AUC) and the best cut-off values were calculated. The sensitivity and specificity were used to show the predictive value of the GNRI. Statistically significant differences were defined as a two-tailed $P \leq 0.05.$
## Basic characteristics
The 311 patients included in the analysis had a mean GNRI of 95.42 ± 11.97. According to the nutritional risk grades related to GNRI at baseline, there were 51 patients ($16.4\%$) at high nutritional risk (Q1), 75 patients ($24.1\%$) at medium nutritional risk (Q2), 54 patients ($17.4\%$) at low nutritional risk (Q3), and 131 patients ($42.1\%$) at no nutritional risk (Q4). The basic characteristics of the study population are presented in Table 1. Compared with the good outcome group, the poor outcome group showed a significantly higher proportion of drinking, a higher level of NIHSS scores, and lower BMI, GNRI, and Barthel index ($P \leq 0.05$). No significant difference in age, the proportion of sexes, smoking, the distribution of comorbidities, the infarct types, the proportion of thrombolytic therapy, or CRP level was found between the two groups (all $P \leq 0.05$).Table 1Basic characteristics of the subjectsCharacteristicThe poor outcome group (Barthel index<60)($$n = 223$$)The good outcome group (Barthel index ≥ 60)($$n = 88$$)t/χ2/z P Median age (years)67.7 ± 9.367.0 ± 9.10.6800.497Sex (male/female)$\frac{158}{6567}$/210.8810.348Smoking (Yes),[n(%)]103(46.2)36(40.9)0.7110.399Drinking (Yes),[n(%)]92(41.3)20(22.7)9.4000.002BMI (kg/m2)22.81 ± 4.1725.84 ± 4.77-5.536<0.001Comorbidity,n(%) Hypertension153(68.6)61(69.3)0.0150.903 Diabetes192(86.1)72(81.8)0.9010.342 Hyperlipidaemia158(70.9)56(63.6)1.5310.216 Heart failure12(5.4)4(4.5)-a 1.000 COPD30(13.5)11(12.5)0.0500.823 Pneumonia29(13.0)8(9.1)0.9220.337 Chronic kidney disease21(9.4)5(5.7)1.1490.284 Other diseases38(17.0)11(12.5)0.9800.322Infarct type,[n(%)] TACI46(20.6)17(19.3)2.1270.345 PACI100(44.8)33(37.5) POCI77(34.5)38(43.2)Thrombolytic therapy (Yes),[n(%)]70(31.4)36(40.9)2.5450.111NIHSS scores14.24 ± 2.6611.90 ± 2.397.195<0.001CRP (mg/L)17.11(12.48,27.64)15.10(11.54,27.60)-1.6150.106Albumin(g/L)35.17 ± 7.2743.06 ± 7.50-8.540<0.001GNRI91.68 ± 10.18104.87 ± 10.97-10.065<0.001GNRI group,[n(%)] Q1(GNRI<82)48(21.5)3(3.4)53.360<0.001 Q2(82 ≤ GNRI<92)68(30.5)7(8.0) Q3(92 ≤ GNRI ≤ 98)40(17.9)14(15.9) Q4(98<GNRI)67(30.0)64(72.7)Barthel index35.40 ± 15.0078.01 ± 12.83-23.464<0.001Other diseases included gastrointestinal haemorrhage, other infections, and pressure sores Abbreviations: BMI body mass index, TACI total anterior circulation infarct, PACI partial anterior circulation infarction, POCI posterior circulation infarction, NIHSS National Institute of Health Stroke Scale, GNRI geriatric nutritional risk index a Fisher’s exact test
## Case distribution characteristics of poor and good outcome in different GNRI subgroups
The constituent ratio of cases in different subgroups of GNRI between the poor outcome group and the good outcome group are presented in Table 1. In the good outcome group, more cases were distributed in the lower nutritional risk grades of GNRI (constituent ratios were $3.4\%$, $8.0\%$, $15.9\%$, and $72.7\%$ in Q1, Q2, Q3, and Q4, respectively, $P \leq 0.05$). Additionally, it was found that most cases in the Q1 subgroup had poor outcomes ($94.1\%$), and with elevated GNRI grades, the proportion of cases with poor outcomes decreased $90.7\%$, $74.1\%$ and $51.1\%$ in Q2, Q3, and Q4, respectively ($z = 49.268$, $P \leq 0.001$, according to linear by linear association). Meanwhile, the proportion of cases with good outcomes increased with elevated GNRI grades ($5.9\%$, $9.3\%$, $25.9\%$, and $48.9\%$, respectively, $P \leq 0.001$), which are presented in Table 2. Figure 1 intuitively shows the case distribution of poor and good outcomes stratified by GNRI subgroups. Table 2Comparison of the case distribution between poor and good outcomes in different subgroups of GNRIGNRInχ2z P Q1(GNRI<82)Q2(82 ≤ GNRI<92)Q3(92 ≤ GNRI ≤ 98)Q4(98<GNRI)Poor outcome cases (Barthel index<60)48($94.1\%$)68($90.7\%$)40($74.1\%$)66($51.1\%$)22353.39049.268▲<0.001Good outcome cases (Barthel index ≥ 60)3($5.9\%$)7($9.3\%$)14($25.9\%$)64($48.9\%$)88n517554131311▲$P \leq 0.05$, according to linear by linear association Abbreviations: GNRI geriatric nutritional risk indexFig. 1Case distribution with poor and good outcome categorized by GNRI subgroups. Abbreviations: GNRI: geriatric nutritional risk index
## Spearman’s partial correlations between GNRI, Albumin, BMI, NIHSS and Barthel index
After adjusting for age, sex, drinking, smoking, the infarct type, and comorbidity, Spearman’s partial correlation analysis showed that GNRI, BMI, and Albumin were all positively and significantly associated with the Barthel index ($r = 0.702$, 0.211, and 0.666, respectively, all $P \leq 0.01$), and NIHSS was negatively associated with the Barthel index (r = -0.407, $P \leq 0.01$). In addition, GNRI grades also showed a positive relationship similar to that of GNRI with the Barthel index ($r = 0.611$, $P \leq 0.01$), which verified a robust result. The details of the Spearman’s correlation analysis are presented in Table 3. The relationships between GNRI, Albumin, BMI, NIHSS and Barthel scores are presented as scatter diagrams in Fig. 2.Table 3Spearman’s correlations of between GNRI, BMI, NIHSS, Ablumin, and Barthel indexVariableBarthel index r a P GNRI 0.702<0.001 GNRI grades 0.611<0.001 NIHSS -0.407<0.001 BMI 0.211<0.001 Albumin 0.666<0.001 Abbreviations: GNRI geriatric nutritional risk index, NIHSS National Institute of Health Stroke Scale, BMI body mass index aadjusted for age, sex, drinking, smoking, the infarct type, comorbidity, thrombolytic therapy, and CRPFig. 2Scatter diagrams showing relationship between GNRI(a),NIHSS(b),BMI(c), Albumin(d) and Barthel index. Abbreviations: GNRI: geriatric nutritional risk index; NIHSS: National Institute of Health Stroke Scale; BMI: body mass index
## Line regression analyses for the Barthel index
Table 4 shows the multivariate linear regression analysis for the Barthel index. It was shown that GNRI, NIHSS, BMI, and Albumin all correlated with the Barthel index according to the univariate analysis (all $P \leq 0.01$). After adjusting for age, sex, drinking, smoking, the infarct type, and comorbidity, the correlations still existed. It was found that GNRI, NIHSS, BMI, and Albumin were all independently correlated with the Barthel index (Standardization β = 0.721, -0.406, 0.205, and 0.673, respectively, all $P \leq 0.01$).Table 4Line regression analysis of GNRI, NIHSS, BMI, and Albumin to the Barthel indexVariablesModel1Model2βStandardization β P βStandardization β P GNRI 1.4470.721<0.0011.4470.721<0.001 NIHSS -3.518-0.408<0.001-3.496-0.406<0.001 BMI 1.0950.207<0.0011.0810.205<0.001 Albumin 2.0270.687<0.0011.9850.673<0.001Model1:crude model; Model2: adjusted for age, sex, drinking, smoking, the infarct type, comorbidity, thrombolytic therapy, and CRP Abbreviations: GNRI geriatric nutritional risk index, NIHSS National Institute of Health Stroke Scale, BMI body mass index
## Logistic regression analyses for poor outcome
The GNRI was analysed in the logistic regression model as a continuous and categorical variable,separately. Logistic regression analyses to examine the relationship between GNRI, NIHSS, BMI, Albumin and poor outcome are listed in Table 5. In model 1 (crude model), the odds ratio(OR) of GNRI (continuous variable) for poor outcome was 0.891 ($95\%$ CIs, 0.865–0.918, $P \leq 0.01$), and in multivariate model 3, which adjusted for age, sex, drinking, smoking, the infarct type, comorbidity, thrombolytic therapy, CRP, BMI, and NIHSS, the OR of GNRI (continuous variable) for outcome was 0.885 ($95\%$ CIs, 0.855–0.917) ($P \leq 0.01$). NIHSS, BMI, and Albumin exhibited independent associations with poor outcome (OR = 1.447, 0.856, and 0.847, respectively, $P \leq 0.01$) after adjusting for covariates, which were similar to GNRI.Table 5Logistic regression analysis of GNRI, NIHSS, BMI, and Albumin to poor outcomeVariablesModel1Model2Model3βOdds ratios($95\%$CI) P βOdds ratios($95\%$CI) P βOdds ratios($95\%$CI) P GNRI -0.1150.891(0.865–0.918)<0.001-0.1180.889(0.862–0.916)<0.001-0.1220.885(0.855–0.917)a <0.001 NIHSS 0.3691.447(1.284–1.631)<0.0010.3761.456(1.291–1.644)<0.0010.3701.447(1.248–1.678)b <0.001 BMI -0.1470.863(0.814–0.915)<0.001-0.1460.865(0.814–0.919)<0.001-0.1560.856(0.797–0.919)c <0.001 Albumin -0.1400.869(0.836–0.904)<0.001-0.1400.869(0.834–0.906)<0.001-0.1660.847(0.806–0.891)a <0.001Model1:crude model; Model2: adjusted for age, sex, drinking, smoking, the infarct type, comorbidity, thrombolytic therapy, and CRPModel3: aadjusted for Model2 + NIHSS + BMI; badjusted for Model2 + GNRI + BMI; cadjusted for Model2 + GNRI + NIHSS The relationship between the GNRI grade and poor outcome was further analysed and is presented in Table 6. After adjusting for age, sex, drinking, smoking, the infarct type, comorbidity, thrombolytic therapy, and CRP, the ORs of GNRI to poor outcome across increasing nutritional risk grades were 1.00 in Q4, 2.803 ($95\%$ CIs, 1.330–5.909) in Q3, 7.992 ($95\%$ CIs, 3.294–19.387) in Q2 and 14.011 ($95\%$ CIs, 3.972–49.426) in Q1 (P for trend < 0.001). The details are presented in Table 6.Table 6The associations between GNRI subgroups and poor outcomeGNRI subgroupsn(%)Model1Model2Odds ratios($95\%$CI) P Odds ratios($95\%$CI) P Q151(16.4)15.284(4.532–51.545)<0.00114.011(3.972–49.426)<0.001Q275(24.1)9.279(3.966–21.712)<0.0017.992(3.294–19.387)<0.001Q354(17.4)2.729(1.357–5.487)0.0052.803(1.330–5.909)0.007Q4131(42.1)1.0-1.0- P for trend <0.001<0.001Model1:cruded model; Model2: adjusted for age, sex, drinking, smoking, the infarct type, comorbidity, thrombolytic therapy, and CRP Abbreviations: GNRI geriatric nutritional risk index
## Predictive property of the GNRI for poor outcome
The ROC was used to analyse the predictive value of GNRI, NIHSS, BMI, and Albumin to poor outcome. The AUC of those potential predictors was evaluated and plotted (see in Fig. 3 and Table 7). There were all statistically significant differences in the AUC values for GNRI, NIHSS, BMI, and Albumin (all $P \leq 0.05$). The AUC of GNRI was greater than the AUC values of NIHSS, BMI, or Albumin (0.804 vs. 0.738, 0.769, or 0.699) with a statistically significant difference (both $P \leq 0.05$). In addition, it was found that the AUC of the combined GNRI with NIHSS was largest among all the variables with statistically significant differences (all $P \leq 0.05$). The AUC of all variables is shown in Fig. 3. The optimal cut-off of the GNRI for predicting poor outcome was 97.69, with a sensitivity of $69.51\%$ and specificity of $77.27\%$. The AUC of the combined GNRI with NIHSS was 0.855, with a sensitivity of 84.75 and specificity of $72.73\%$. The details of the ROC parameters of GNRI, Albumin, NIHSS, BMI, and GNRI + NIHSS for poor outcomes are listed in Table 7.Fig. 3ROC analysis of GNRI, Albumin, NIHSS, BMI, and GNRI + NIHSS for poor outcome. Abbreviations: GNRI: geriatric nutritional risk index; NIHSS: National Institute of Health Stroke Scale; BMI: body mass indexTable 7ROC parameters of GNRI, Albumin, NIHSS, BMI, and GNRI + NIHSS for poor outcomeVariablesStatistical valueYoudenCut-offSensitivity(%)Specificity(%)AUC($95\%$CI) z P GNRI 0.804(0.755–0.846)10.980 < 0.0010.467897.6969.5177.27 NIHSS 0.738(0.685–0.786)7.687 < 0.0010.32481184.7547.73 Albumin 0.769(0.718–0.815)9.153 < 0.0010.402937.661.8878.41 BMI 0.699(0.644–0.749)5.989 < 0.0010.346722.5157.4077.27 GNRI + NIHSS 0.855(0.811–0.892)14.398 < 0.0010.57480.65984.7572.73 Abbreviations: GNRI geriatric nutritional risk index, NIHSS National Institute of Health Stroke Scale, BMI body mass index
## Discussion
Initially, used for estimating the risk of malnutrition-related complications in an ageing population, the GNRI was the preferred tool for screening the nutritional status of hospitalized elderly individuals [2]. In recent years, the relationship between malnutrition and poor outcome in a variety of diseases has been well developed [22–24]. It is generally accepted that the GNRI has a stronger prognostic effect than traditional nutritional indicators [2, 25].
According to those previous studies, the GNRI was adopted in the present study, and the associations of the GNRI with neurological outcome were explored in populations with stroke. The results showed that lower GNRI or higher nutritional risk grades of GNRI on admission showed an independent correlation with poor neurological function at 1 month since onset of stroke, which was still significant after adjusting for the covariates. In addition, compared with NIHSS, BMI, and Albumin, the predictability of GNRI for poor neurological outcome was more powerful. The GNRI might serve as a promising potential predictor for neurological outcome for patients with stroke at the convalescence stage. To the best of our knowledge, this is the first study regarding the directly predictive property of the GNRI for poor outcomes of convalescence.
With the integration of information derived from both serum albumin and bodyweight, GNRI reflected the nutrition and BMI, and enabled comprehensive assessment of the above variables [24]. As a consequence, the GNRI value was a complementary indicator that improved the diagnostic accuracy and reduced the limitations. The AUC of the GNRI was greater than the AUC of BMI or Albumin (0.804 vs. 0.769 or 0.698), with a statistically significant difference (both $P \leq 0.05$), and increased sensitivity without loss of specificity compared with Albumin or BMI alone.
The reason for the GNRI exhibiting powerful predictive validity in the present study is still unclear. This study merely enrolled patients with stroke in the rehabilitation phase. Albumin not only reflects the state of nutrition, but is also affected by some factors, such as inflammation or disease stress [26]. Therefore, the use of albumin alonely may have a narrow effect for prediction, which was verified by the lower sensitivity of albumin according to our ROC analysis. Nishioka [5] advised using the other NSTs for patients with stroke unless accompanied by assessment of oedema and inflammatory status, as well as excluding the presence of diseases that caused hypoalbuminemia. In this study, a lower proportion of comorbidities, such as pneumonia, other infections, gastrointestinal haemorrhage, and pressure sore, may partly explain the robustness of the GNRI in predicting the neurological outcome, which indicated that further stratification analysis of the predictive efficiency of the GNRI was necessary when accumulating more cases. The relationship between BMI and functional outcome of stroke is complex. In this study BMI had a weak association with the Barthel index. However, a previous study found that functional outcomes after stroke have a nonlinear relationship to patient adiposity [27]. As an effect of obesity paradox, BMI had a U-shaped/J-shaped relationship to unfavorable disability and stroke-related quality of life outcomes [28]. These differing patterns suggest distinct pathophysiologic mechanisms, including greater importance of metabolic reserve against nutritional challenge for survival and greater frequency of atherosclerotic and thromboembolic infarcts in individuals with higher BMI [29]. Limited by small samples, we did not carry out further analysis for BMI.
Studies have confirmed that malnutrition has a negative impact on stroke rehabilitation [3, 30–32]. Several studies have also suggested that enhanced nutritional support is associated with improved independence in activities of daily living and quality of life [33, 34]. However, only a few studies have involved nutritional screening and neurological outcomes. Among them, many previous studies employed length of hospital stay, mortality and complication rates as outcomes of predictive validity [4]; fewer studies used functional outcomes and discharge destination [5]. For inpatient rehabilitation, the latter outcomes may be preferred to the former for predictive validity, similar to using the Barthel index as a functional outcome in this study..
Compared with traditional NSTs such as NRS 2002 [35], the application of the GNRI in stroke patients has the following advantages: [1] the objectivity of the components of the GNRI avoided the difficulty of obtaining subjective indicators when evaluating stroke patients with consciousness or cognitive impairment; [2] the evaluation of the GNRI was very quick and convenient for the needs of stroke patients in bedside and repeated evaluation; and [3] the validity and reliability of the GNRI had been verified in a variety of clinical settings [11, 12, 14, 23, 24]. Thus it was believed that the GNRI might be used as an appropriate NST for identifying nutritional risk and predicting neurological outcome in the rehabilitation setting.
The maximum value of the NIHSS scores among all the participants in this study was 22, and the IQR was 12–16. The scores reflected a moderate-severe severity of stroke, which has the most rehabilitation value [36]. The retrospective review helped us to find that the population with poor outcomes at 1 month after onset presented lower levels of GNRI at baseline compared with the population with good outcomes. According to the grades of GNRI, more cases with poor outcomes distributed in the higher nutritional risk grades of GNRI further verified the positive relationship between GNRI and neurological function ($z = 49.268$, $P \leq 0.001$). After adjusting for covariates, *Spearman analysis* showed a negative relationship between the GNRI and poor outcome. The "r" coefficient in GNRI was higher than it in NIHSS, which indicated a more force weight for GNRI in influence to neurological outcome. This was also verified by line regression analysis with a higher standardization β for the GNRI than for the NIHSS. To exclude the influence of other factors, this study further fitted a multivariate logistic regression analysis, and the negative association between the GNRI and poor outcome of stroke remained (OR = 0.885, $P \leq 0.01$), which indicated that a high GNRI was a protective factor against poor outcome. When constructing multivariate regression equations, an independent correlation between NIHSS, BMI, and Albumin and the poor outcome of stroke was found ($P \leq 0.05$), which was the basis of further exploring the predication value of these indexes. To acquire a robust consequence, the relationship between the GNRI grade and poor outcome was further analysed. After adjusting for covariants, the OR of GNRI to poor outcome increased across increasing nutritional risk grades (P for trend < 0.001). The OR of the patients with GNRI < 82 was 14 times higher than that of the patients with GNRI > 98, which further verified that malnutrition was a powerful risk factor for poor neurological function. At present, no similar research is available for comparison. Early, a prospective study indicated the OR of a lower GNRI was 2.55 for a poor outcome 3 months after stroke [18]. However the cut-off of GNRI in that study was 92 and the poor outcome was evaluated by modified Rankin Scale (mRS), which was quite different from our study.
The study further explored the predictive effects of GNRI, NIHSS, BMI, and Albumin on poor outcome in convalescence stage for patients with stroke. The ROC analysis found that the AUCs of GNRI and NIHSS were 0.804 and 0.738, respectively, which were good in terms of prediction efficacy. The GNRI had a sensitivity of $69.51\%$, which was lower than the NIHSS, and had a specificity of $77.27\%$, which was higher than the NIHSS. NIHSS is generally recognized as a clinical tool for evaluating changes in the condition of patients with stroke. The NIHSS scores was also used as a predicator for neurological function prognosis [17]. However, studies have shown that the prediction efficiency of the NIHSS is insufficient for patients with POCI due to the lack of a relevant index forthe NIHSS [36, 37]. In this study, POCI proportion of stroke lesions was up to nearly $40\%$, which may affect the predictive efficiency of NIHSS. Our study suggests that the GNRI may remedy the defect of the NIHSS in predicting neurological outcomes for patients with POCI stroke.
To determine the total predictive value of the combined GNRI and NIHSS, we brought GNRI and NIHSS scores simultaneously into bivariate logistic regression fitting and returned logit (p) as a predictive probability, which was used as an independent predictive variable for ROC analysis. It was found that the AUC of the combined GNRI with NIHSS was largest among all the variables with statistically significant differences (all $P \leq 0.05$). In addition, the combination of the GNRI and the NIHSS scores could improve the sensitivity and specificity compared with the use of either score alone, which indicated that we could acquire more predictive effectiveness by adding the GNRI to regular NIHSS scores when predicting poor neurological outcomes in clinical practice, especially for patients suffering from POCI stroke.
It was difficult to explain the causality of between the GNRI and poor neurological outcome of stroke. There are several plausible mechanisms for this. Patients with stroke often experience a reduction in their body weight, approximately 3 kg, in the acute phase [38]. A decreased body weight could be attributed to muscle atrophy that is primarily caused in paretic limbs and the diminished nutritional intake because of dysphagia [5]. Individuals with a lower GNRI in the study may also include patients who had developed malnutrition before the onset of stroke. Meanwhile, the malnutrition hampers neurological self-recovery and disturbs routine rehabilitation procedures for patients after stroke [4, 5]. Both the factors may be reciprocally causative and even create a vicious cycle. Regardless, nutritional improvement in stroke patients with malnutrition was associated with the resumption of activities of daily living [6].
There are some limitations in this study. First of all, the small sample size of the study affects the statistical efficiency. Second, this study is a cross-sectional study, and the direction of cause and effect between GNRI and poor outcome may be uncertain. As our retrospective study was based on the former data, some potential risk factors and key parameters could not be available, and the dynamic evaluation for GNRI and neurological function was lacking, which affects the persuasiveness of the conclusions. Third, all the serum samples were collected only once from the participants, which may lead to the bias in the analysis.
## Conclusions
In conclusion, for patients with stroke, there was an independent relationship between a lower GNRI on admission and poor outcome at 1 month after the onset of stroke, which suggested that higher nutritional risk grades at baseline may indicate worse neurological function at the convalescence stage. Compared with NIHSS, BMI, and Albumin, GNRI was a competitive indicator for the risk of poor neurological outcome. The predictive property of GNRI for adverse neurological outcomes might be more powerful when combined with NIHSS.
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|
---
title: LMP2 deficiency causes abnormal metabolism, oxidative stress, neuroinflammation,
myelin loss and neurobehavioral dysfunctions
authors:
- Xingyong Chen
- Yanguang Mao
- Yueting Guo
- Dongyun Xiao
- Zejing Lin
- Yiyi Huang
- Ying Chun Liu
- Xu Zhang
- Yinzhou Wang
journal: Journal of Translational Medicine
year: 2023
pmcid: PMC10045813
doi: 10.1186/s12967-023-04071-0
license: CC BY 4.0
---
# LMP2 deficiency causes abnormal metabolism, oxidative stress, neuroinflammation, myelin loss and neurobehavioral dysfunctions
## Abstract
### Background
Substantial evidence suggests that immunoproteasome is implicated in the various neurological diseases such as stroke, multiple sclerosis and neurodegenerative diseases. However, whether the immunoproteasome itself deficiency causes brain disease is still unclear. Therefore, the aim of this study was to explore the contribution of the immunoproteasome subunit low molecular weight protein 2 (LMP2) in neurobehavioral functions.
### Methods
Male LMP2 gene completed knockout (LMP2-KO) and littermate wild type (WT) Sprague–Dawley (SD) rats aged 12-month-old were used for neurobehavioral testing and detection of proteins expression by western blotting and immunofluorescence. A battery of neurobehavioral test tools including Morris water maze (MWM), open field maze, elevated plus maze were used to evaluate the neurobehavioral changes in rats. Evans blue (EB) assay, Luxol fast blue (LFB) and Dihydroethidium (DHE) staining were applied to explore the blood–brain barrier (BBB) integrity, brain myelin damage and brain intracellular reactive oxygen species (ROS) levels, respectively.
### Results
We firstly found that LMP2 gene deletion did not cause significantly difference in rats’ daily feeding activity, growth and development as well as blood routine, but it led to metabolic abnormalities including higher levels of low-density lipoprotein cholesterol, uric acid and blood glucose in the LMP2-KO rats. Compared with the WT rats, LMP2-KO rats displayed obviously cognitive impairment and decreased exploratory activities, increased anxiety-like behavior and without strong effects on gross locomotor abilities. Furthermore, multiple myelin loss, increased BBB leakage, downregulation of tight junction proteins ZO-1, claudin-5 and occluding, and enhanced amyloid-β protein deposition were observed in brain regions of LMP2-KO rats. In addition, LMP2 deficiency significantly enhanced oxidative stress with elevated levels of ROS, caused the reactivation of astrocytes and microglials and markedly upregulated protein expression levels of interleukin (IL)-1 receptor-associated kinase 1 (IRAK1), IL-6 and tumor necrosis factor-α (TNF-α) compared to the WT rats, respectively.
### Conclusion
These findings highlight LMP2 gene global deletion causes significant neurobehavioral dysfunctions. All these factors including metabolic abnormalities, multiple myelin loss, elevated levels of ROS, increased BBB leakage and enhanced amyloid-β protein deposition maybe work together and eventually led to chronic oxidative stress and neuroinflammation response in the brain regions of LMP2-KO rats, which contributed to the initial and progress of cognitive impairment.
## Introduction
It is generally accepted that the ubiquitin–proteasome-system (UPS) controls numerous cellular pathways including signal transduction, inflammatory processes, cell differentiation and apoptosis by degradation of misfolded or damaged proteins. Due to three proteolytic active subunits β1, β2 and β5, the proteasome exerts caspase-like, trypsin-like, and chymotrypsin-like activity, respectively. However, in cells exposed to a variety of stimulate factors such as inflammatory cytokines (for example, interferon-γ) and ischemia, these three catalytic β-subunits are replaced by their immunosubunits β1i (LMP2/proteasome subunit beta 9, PSMB9), β2i (multicatalytic endopeptidase complex-like 1, MECL-1) and β5i (LMP7) and then forms new subtype of proteasome called the immunoproteasome [1, 2].
The immunoproteasome is regarded as a special type of proteasome which involves in both immune and non-immune responses [2]. Apart from MHC-I antigen processing, immunoproteasome mediate proinflammatory cytokine production observed in different animal disease models. Bone marrowderived dendritic cells from LMP2/β1i-deficient mice infected with influenza virus displayed lower levels of interleukin (IL)-1β, IL-6 and TNF-α compared with the WT counterparts. Our previous study found that LMP2 and LMP7 were evident in astrocytes and microglia/macrophage cells in the ipsilateral ischemic hemisphere after cerebral ischemia/reperfusion in rats, respectively. Furthermore, inhibition of LMP2 by shRNA showed the suppression of neuroinflammation response and reduced infarction volume [3].Similarly, Administration of a high selective proteasome inhibitor PR-957 or LMP7 gene knockout significantly attenuates inflammatory cytokines secretion and disease manifestation observed in animal experimental models of autoimmune encephalomyelitis and rheumatoid arthritis [4, 5]. Taken together, these findings suggest a key role for the immunoproteasome participating in innate immune response and regulation of inflammatory cytokines, which possibly involves in compromised NF-κB signaling or NF-κB-independent signal pathways [6, 7].
Beyond the involvement of the immunoproteasome in the immune system, recent studies have begun to unravel the non-immune functions of the immunoproteasome. Due to the rapid induction properties and the enhanced proteolytic activities compared to the standard proteasome, the immunoproteasome prevents accumulation of the oxidatively-damaged and ubiquitylated proteins and maintains protein homeostasis [1, 8]. For example, oxidized damage proteins were accumulated in the liver and brain of LMP7/β5i- and LMP2/β1i-deficient mice [8]. In addition, increased expression of LMP2 has also been observed in brain of Alzheimer’s disease (AD) [9–11], which is a hallmark of amyloid-β protein deposition accompanying with oxidative stress and neuroinflammation response. A mechanism of amyloid-β protein deposition is due to the inability of the damaged UPS to efficiently remove the excess amyloid-β proteins. From this point of view, increased immunoproteasome expression may have a compensatory effect in protecting neurocytes against stress-induced injury and death by clearing toxic amyloid-β protein more efficiently than the standard proteasome. Increased proteasome activities and messenger RNA and protein expression of immunoproteasome was observed in reactive glias in the cortex of the transgenic mouse AD model [11]. However, these data are in contrast with other studies results [9]. In other words, there is still a controversy about the expression and roles of immunoproteasome in AD context. More interestingly, recent research confirmed a relationship between immunoproteasome and emotion in animal experiment. Gorny X, et al. reported that β5i/LMP7 gene deletion mice displayed more anxiety after mild stress and increased cued fear after fear conditioning compared with the WT mice [12]. These findings suggest that the basal proper formation of immunoproteasome in healthy mice seem to be involved in the regulation of anxiety and cued fear levels. Our previous work found that elevated level of plasma LMP2 during the acute phase of ischemic stroke was high risk of poor functional outcome and post-stroke cognitive impairment (PSCI) at 90 days [13]. Although several lines of evidence have supported an involvement of immunoproteasome in ischemia stroke and multiple sclerosis pathogenesis [14, 15], whether the immunoproteasome itself (such as LPM2) defects causes brain disease is still unclear. Therefore, the aim of this study was to explore the contribution of LMP2 in neurobehavioral functions in LPM2 gene knockout rats. The following issues would be addressed in the present study. First, the effect of LMP2 gene defect on neurobehavioral functions need to be clarified. Second, does LMP2 defect induce abnormalities in brain structure and cellular function? Third, what is the possible mechanism underlying these changes. Our study contributes to a further understanding of immuneproteasome deficiency leading to central nervous system abnormalities, which provides preliminary data for further research.
## Ethical approval and experimental animals
All experiments were approved by the Institutional Animal Ethical Committee of Fujian Medical University (No. FJMUIACUC2020-0059) (Animal license No. SYXK (Min) 2020–0005). LMP2 heterozygous type (LMP2±) Sprague–Dawley (SD) rats by use of CRISPR/Cas9 genome editing technology were purchased from Cyagen Biotechnology Co., LTD. ( Animal License No. SCXK (Yue) 2018–0032). LMP2± rats were bred in SPF grade animal room. They were housed five per cage to maintain social interaction. LMP2± rats were mated. The tail genomic DNA of offspring rats were extracted and identified by polymerase chain reaction (PCR).The obtained homozygous rats were mated with the opposite sex heterozygous rats to obtain more homozygous rats with knockout LMP2 gene. Experiment rats aged 12-month-old were divided into two groups: LMP2 gene knockout (LMP2-KO) rats and littermate wild type (WT) rats (each group $$n = 15$$).
## Morris water maze (MWM)
The MWM system (RD1101-MWM, Mobiledatum Co., LTD., Shanghai, China) was used to evaluate spatial learning and reference memory as described previously [16, 17]. The lab room was kept dark and quiet during the test. During acquisition phase, each rat underwent four trials daily (10–15 min break between trials) for 5 consecutive days. Briefly, each trial started from a different location and lasted for 60 s. Each rat was trained to escape from the water by swimming to the hidden platform. Once the platform was found, the rat was allowed to stay on the platform for 10 s. If the rat could not locate the platform within 60 s, it would be guided to the platform and allowed to stay there for 15 s (assigned a latency of 60 s). On the sixth day, the probe test of platform location memory retention was conducted with the platform removed. Rats were placed into the water from the opposite side of the original platform quadrant and were allowed to swim freely for 60 s to find the location of the original platform. All parameters including the latency and path length to platform, the time spent in the target quadrant, average swimming speed, and the numbers of crossings over the platform site were calculated using system software (*Mobile datum* Co., LTD., Shanghai, China).
## Open field test (OFT)
The OFT was performed to assess locomotor activity and anxiety-like behavior according to a previous report [18]. Briefly, the test was conducted for 5 min in a dimly lit room. The box system consists of an arena measuring 100 cm × 100 cm × 40 cm (L × W × H). The arena was made of black high-density polyethylene panels that were fastened together. A video tracking system was suspended over the box to record the rats’ movements’ orbits. The rats were placed in the center of arena and allowed to freely explore the area for 5 min. The total traveled distance, distance and the time in the central area of each rat were calculated.
## Elevated plus maze (EPM)
The elevated plus maze test was applied to assess anxiety-like behavior as described previously [19]. A decrease in the open arm activity (duration and/or entries) reflects anxiety-related behaviors. The system consists of four black polypropylene arms, including two opposing open arms (50 cm × 10 cm × 0.5 cm), two opposing closed arms (50 cm × 10 cm × 40 cm) and a central platform (10 cm × 10 cm) and elevated 50 cm above the ground. The test was performed in a quiet, dimly lit room. Rats were placed at the central platform facing an open arm and allowed 5 min to explore the maze. Their behavior was recorded with a camera mounted above the maze. The entries into the open arms and duration in the open arms were calculated.
## Luxol fast blue (LFB) staining
LFB is widely used to detect demyelination in the CNS. Coronal Sects. ( 20 μm) were stained with Luxol Fast Blue Stain Kit according to the manufacturer’s instructions (Abcam, USA) and conducted as described previously [20]. The severity of the white matter lesions was graded as described [21].
## Evaluation of BBB Permeability
Evans blue (EB) dye (Sigma-Aldrich, USA) extravasation and FITC-dextran cerebral fluorescent angiography were used to evaluate BBB permeability using spectrophotometry and fluorescence microscopy as described previously [22–24].
## Blood routine and blood biochemicalexamination
After the animals were deeply anesthetized, the thoracic cavity was exposed, and 5 ml of blood was drawn from the right atrial vein with a sterile syringe. Blood routine and blood biochemical examination were conducted by the biotech company of Zolgene Biotech, Inc. (Fuzhou, China).
## Immunofluorescence (IF)
Immunofluorescence was performed as described previously [22]. After blocking with $10\%$ normal goat serum (Sigma-Aldrich, USA), slices were incubated with primary antibodies as following: rabbit anti-GFAP (1:300; Cell Signaling Technology, USA), rabbit anti-IBA1 (1:400; Abcam, Cambridge, MA, USA), rabbit anti-beta Amyloid (1:50; Abcam, Cambridge, MA, USA), and mouse anti-Olig1 (1:100; Santa Cruz, USA). After incubated overnight at 4 °C and washed three times in 0.01 M PBS (3 × 5 min), slices were incubated with the following secondary antibodies for 1 h at room temperature: Alexa Fluor® 594 conjugated goat anti-rabbit IgG or Alexa Fluor® 488 conjugated goat anti-rabbit IgG or Alexa Fluor® 488 conjugated goat anti-mouse IgG(1:1000; Cell Signaling Technology). Slices were mounted in ProLong® Gold antifade reagent (Thermo Fisher Scientific, USA) prior to imaging.
## Western blotting
Approximately 30 μg of total protein was loaded on an SDS–polyacrylamide gel as described previously [22]. The primary antibodies were used as follows: rabbit anti-myelin basic protein (MBP) and rabbit anti-IRAK1 (1:1000; Abcam, Cambridge, MA, USA), mouse anti-IL-6 (1:500; Abcam, Cambridge, MA, USA), mouse anti-Olig1 and mouse anti-TNF-a (1:300; Santa Cruz, USA), mouse monoclonal anti-β-actin (1:3000; Cell Signaling Technology, USA). Membranes were incubated with the secondary antibodies for 1 h at room temperature: horseradish peroxidase (HRP)-conjugated goat anti-rabbit or HRP-conjugated goat anti-mouse IgG antibody (1:3000; Cell Signaling Technology, USA). The bands were quantified by densitometry with ImageJ software (ImageJ 1.4, NIH, USA).
## Statistical analysis
All datawere analyzed using SPSS 19.0 software (version 19, IBM Corp., Armonk, NY, USA) and are presented as the mean ± standard deviation. The water maze escape latency and the path length were analyzed using repeated measures two-way ANOVA. Other parametric data from different groups were compared using one-way ANOVA. The least-significant difference post hoc test was used for comparison within groups. GraphPad Prism 8.0 (GraphPad Software Inc., La Jolla, CA, USA) was used to make statistical graph. A value of $P \leq 0.05$ was considered statistically significant.
## Comparison of basal parameters between the WT and LMP2-KO rats
First, there was no significant difference of daily variability in feeding, drinking, and movement behaviors, growth and development between the two groups of rats. Besides, it seemed no obviously difference in gross brain appearance between the WT and LMP2-KO rats (Fig. 1A). Furthermore, LMP2 gene deletion did not result in significant changes in heart rate and blood pressure compared with the WT group (Fig. 1B). In addition, blood routine examination indicated there were no comparable in white blood cell count, lymphocyte count, hemoglobin concentration and platelet count between the two groups (Fig. 1C). Interestingly, blood biochemical examination showed that the levels of low-density lipoprotein cholesterol (LDL-C), uric acid (Uric) and blood glucose in the LMP2-KO group (LDL-C:4.29 ± 0.98 mmol/L; Uric:552.96 ± 65.79 umol/L; GLU 12.68 ± 2.62 mmol/L) were higher than those in the WT group (LDL-C: 3.24 ± 0.89 mmol/L; Uric: 474.80 ± 85.61 umol/L; GLU 8.94 ± 1.21 mmol/L), respectively (*$P \leq 0.05$); but there were no significant differences in alanine aminotransferase (ALT), total cholesterol and creatinine levels between the two groups (Fig. 1D) (Table1) (#$P \leq 0.10$,*$P \leq 0.05$).Fig. 1Comparison of basal parameters between the LMP2-KO and WT rats. A There was no significant difference in gross brain appearance between the LMP2-KO and WT rats. B There were no significant differences in heart rate and blood pressure between the two groups (#$P \leq 0.05$). C The levels of ALT, total cholesterol, LDL-C, blood glucose, creatine and uric acid from blood samples of LMP2-KO and WT rats were comparison, and there were significant increased levels of blood LDL-C, uric and glucose in the LMP2-KO rats compared with the WT rats (#$P \leq 0.10$,*$P \leq 0.05$). ( D) Blood biochemical examination showed that the levels of low-density lipoprotein cholesterol (LDL-C), uric acid (Uric) and blood glucose in the LMP2-KO group were higher than those in the WT group, respectively (*$P \leq 0.05$)Table 1Comparison of basal parameters between the WT and LMP2-KO ratsItemWT ratsLMP2-KO ratsP valueItemWT ratsLMP2-KO ratsP valueHeart rate374.47 ± 43.65384.42 ± 50.490.574LDL-C (mmol/L)3.24 ± 0.894.29 ± 0.980.023Systolic pressure (mmHg)123.67 ± 16.14128.43 ± 9.850.350Uric acid (umol/L)474.80 ± 85.61552.96 ± 65.790.034Diastolic pressure (mmHg)94.33 ± 12.3594.42 ± 11.170.983Blood glucose (mmol/L)8.94 ± 1.2112.68 ± 2.620.001White blood cell count (× 109/L)7.38 ± 2.947.46 ± 1.160.962ALT (U/L)45.57 ± 9.6040.34 ± 9.920.246Number of lymphocytes (× 109/L)4.36 ± 0.914.94 ± 1.550.492Total cholesterol (mmol/L)2.82 ± 0.643.05 ± 0.760.478Concentration of hemoglobin (g/L)160.80 ± 12.68135.20 ± 42.450.232Creatinine (umol/L)53.89 ± 23.7054.14 ± 22.300.975Number of platelets (× 109/L)690.60 ± 347.83688.60 ± 446.560.994
## LMP2 gene knockout results in cognitive impairment in rats
As shown in Fig. 2, there was no significant difference in the latency to platform and path length during the initial 2 days of acquisition/learning period between the WT group and the LMP2-KO group. However, with the extension of training time, rats in the WT group found the platform position earlier than those in the LMP2-KO group, and LMP2-KO rats spent more time and longer path length looking for a platform, compared to the WT controls from the 3rd day to the 5th day in the acquisition/learning phase. For example, on the 5th day of training, the latency to platform and path length before finding the platform in the LMP2-KO group was significantly higher than those in the WT group, respectively. However, there was no significant difference in the average swimming speed between the two groups during the 5 days training period (Fig. 2) (#$P \leq 0.10$,*$P \leq 0.05$).Fig. 2LMP2 defect increases the latency and path length to find the hidden platform in place navigation. LMP2-KO and WT rats were subjected to Morris water maze test (MWM) for place navigation for 5 consecutive days. The escape latency and the path length were analyzed using repeated measures two-way ANOVA. A Representative track plots of MWM for place navigation. B The latency significantly increased in the LMP2-KO rats as compared with the WT rats. C The path length of finding the hidden platform significantly increased in the LMP2-KO rats as compared to the WT rats. D The average swimming speed was not significantly different between the LMP2-KO and the WT rats (#$P \leq 0.10$,*$P \leq 0.05$) In the probe test, as shown in Fig. 3, the frequency of crossing over the platform was significantly lower in the LMP2-KO group than that of the WT group ($P \leq 0.05$). Although the data demonstrated that there was no significant difference in the total path length between the two groups, the path length in the target quadrant in the LMP2-KO group was shorter than that in the WT group. In addition, there was no significant difference in the average swimming speed between the two groups (Fig. 3) (#$P \leq 0.10$,*$P \leq 0.05$).Fig. 3LMP2 defect decreases the frequency over the target quadrant in probe test. Both LMP2-KO and WT rats were subjected to Morris water maze test (MWM) for probe tests on the 6th day followed place navigation. The data were analyzed using one-way ANOVA. A Representative track plots of MWM for probe tests. B The frequency crossing through the platform significantly lower in LMP2-KO rats than in WT rats (*$P \leq 0.001$). C The total path length and D the percentage of path length in target quardrant and E the average swimming speed was compared between LMP2-KO and WT rats, respectively (#$P \leq 0.10$,*$P \leq 0.05$)
## Deletion of the LMP2 gene reduces rats’ exploratory activities.
As shown in Fig. 4, the results showed that, there was no comparable in the total distance traveled during five minutes between the WT and LMP2-KO rats (#$P \leq 0.05$). However, compared with the WT rats, both the distance traveled in center area and the time spent in the center were significantly lower in LMP2-KO rats, respectively (Fig. 4) (*$P \leq 0.001$).Fig. 4Deletion of the LMP2 gene reduces rats’ exploratory activities. A Representative tracks for open field test. B There was no significant difference in the total distance between the WT rats and the LMP2-KO rats (#$P \leq 0.10$). C–D Compared with the WT rats, both the distance traveled in center area and the time spent in the center were significantly lower in the LMP2-KO rats, respectively (*$P \leq 0.001$)
## Deletion of the LMP2 gene increases rats’ anxiety-like behavior
The above preliminary data suggested that LMP2-KO rats seemed to have anxiety-like behavior. Next, we used elevated plus maze experiment to further assess whether these rats showed signs of anxiety. As shown in Fig. 5, WT rats repeatedly walking between the open and closed arms, but LMP2-KO rats preferred to walk in the closed arms, less chance to entry to the open arms. Compared with the WT rats, both the percentages of the entries in open arms and the time spent in open arms were significantly less in LMP2-KO rats than those in WT rats, respectively (Fig. 5) (*$P \leq 0.05$).Fig. 5Deletion of the LMP2 gene increased rats’ anxiety-like behavior measured by elevated plus maze. A Representative tracks for the elevated plus maze test. B–C Compared with the WT rats, both the percentages of the entries in open arms and the time spent in open arms were significantly less in the LMP2-KO rats than that in WT rats, respectively (*$P \leq 0.05$)
## LMP2 gene deficiency results in myelin loss and remyelination coexisted in brain of rats
Our previous study suggested that white matter demyelization accompany with cognitive impairment in eNOS deficiency mice [25]. Therefore, we were interested to explore whether there exists similar phenomenon in brain of LMP2 gene deficiency rats. As shown in Fig. 6A, LFB staining showed severe myelin loss of cerebral cortex, corpus callosum and striatum in the LMP2-KO rats compared with the WT rats, respectively. These finding were also supported by measuring the protein levels of MBP detected by western blotting. MBP protein expression in the LMP2-KO group was further decreased compared with that in the WT group ($P \leq 0.001$) (Fig. 6B, C).Fig. 6LMP2 gene deficiency results in myelin loss and remyelination coexisted in brain of rats. A LFB staining showed myelin loss of the cortex, corpus callosum and striatum of WT, LMP2-KO rats, respectively. B–C Representation image of MBP protein expression detected by western blotting and quantification data. D–F Immunofluorescence and western blotting showed that increased protein expression of oligodendrocytes-1(Olig1) in the LMP2-KO rats compared with the WT rats. Scale bars: 500 μm and 100 μm. * $P \leq 0.001$ OLIG1, a member of the oligodendrocyte lineage-specific basic helix-loop-helix (OLIG) family of transcription factors, is a gene regulator and expressed in oligodendrogenesis which are the source cells of myelination in the central nervous system (CNS). To our surprise, we observed demyelination and remyelination coexisted in brain of LMP2-KO rats. Both IF and western blotting showed that increased protein expression of oligodendrocytes-1(Olig1) in forebrain of LMP2-KO rats compared with the WT rats ($P \leq 0.001$) (Fig. 6D–F).
## LMP2 gene deficiency significantly induces BBB leakage and decreases tight junction proteins expression compared to the WT rats
Recent evidence has suggested that BBB disruption is an early biomarker of human cognitive impairment, including the early clinical stages of AD and vascular cognitive impairment [26]. Our previous work confirmed the correlation between LMP2 and BBB dysfunction under ischemia stroke conditions [14, 27]. To our surprise, LMP2 gene deletion led to BBB leakage indicated by Evans blue (EB) exudation evaluation. A small amount of EB dye was observed to exude outside the blood vessels in the brain of WT rats under fluorescence microscope and further supported by quantification of EB content in the cerebral of rats. However, EB exudation was significantly increased in the LMP2-KO group than that in the WT group (*$P \leq 0.001$) (Fig. 7A, B). Similarly, FITC-dextran angiographic micrographs indicated the tracer was confined to the capillaries in wild-type littermates, whereas LMP2-KO rats showed large amounts of tracer leakage in the brain parenchyma (Fig. 7C). The BBB which consists of brain endothelial cells interconnected by tight junctions is essential for the homeostasis of the CNS. Tight junctions are consisted of a number of proteins, including zonula occludens-1 (ZO-1), occluding and claudin-5. LMP2 deficiency significantly decreases the protein levels of ZO-1, claudin-5 and occluding detected by western blotting (*$P \leq 0.001$) (Fig. 7D, E).Fig. 7LMP2 gene deficiency significantly induces BBB leakage and decreases tight junction proteins expression compared to the WT rats. A–B Evans blue (EB) exudation was observed under fluorescence microscope and quantificated, respectively. The level of EB exudation significantly increased in the LMP2-KO group than that in the WT group. C FITC-dextran angiographic micrographs indicated the tracer was confined to the capillaries in wild-type littermates, whereas LMP2-KO rats showed large amounts of tracer leakage in the brain parenchyma. D–E Representation images of tight junction proteinsZO-1, claudin-5 and occluding expression detected by western blotting and quantification data. Scale bars: 500 um; 100 μm.*$P \leq 0.001$
## LMP2 gene deficiency enhances Amyloid-β protein deposition compared to the WT rats
The BBB dysfunction induces the failure of amyloid-β (Aβ) protein transport from brain to the peripheral circulation across the BBB and involves in the pathogenesis of Alzheimer's disease (AD) [28]. In addition, increasing evidence has suggested that immunoproteasome participates in the pathology of AD [29]. Interestingly, we observed that LMP2 deficiency significantly enhanced Aβ protein deposition. Immunofluorescence showed that there was more Aβ protein deposition in the hippocampus and cerebral cortex of LMP2 gene deficiency rats compared with the WT rats (Fig. 8).Fig. 8LMP2 gene deficiency enhances Amyloid-β protein deposition compared to the WT rats. LMP2 deficiency significantly enhanced Aβ protein deposition. Immunofluorescence showed that there was more Aβ protein deposition in the hippocampus and cerebral cortex of LMP2 gene deficiency rats compared with the WT rats. Scale bars: 500 um; 100 μm
## LMP2 deficiency significantly enhances oxidative stress, reactive glia and proinflammation compared to the WT rats
The immunoproteasome participates in the regulation of oxidative stress and neuroinflammation under different context [3, 30]. Importantly, oxidative stress and inflammation are strongly related to a variety of neurological diseases, including stroke, AD and Parkinson's disease (PD). Interestingly,LMP2-KO rats exhibited significantly enhanced oxidative stress, as shown in Fig. 9A, compared to the WT rats, the levels of ROS, as indicated by DHE fluorescence staining, were markedly elevated in the cerebral cortex and corpus callosum of LMP2-KO rats. Fig. 9LMP2-KO rats exhibit significantly enhanced oxidative stress, increased expression of astrocyte, microglial expression and inflammation compared to WT rats, respectively. A DHE staining showed ROS levels in the cortex and corpus callosum of WT and LMP2-KO rats, respectively. B Expression of astrocytes (GFAP), microglias (IBA1) in brain of WT and LMP2-KO rats, respectively. C–D Representation images of IRAK1, IL-6 and TNF-a proteins expression detected by western blotting and quantification data. Scale bars: 500 um; 100 μm. * $P \leq 0.001$ In addition, immunofluorescence showed that increased expression of astrocytes and microglials, labeled by GFAP and IBA1, respectively (Fig. 9B). Gliosis leads to the activation of inflammation-related signaling pathway and releases proinflammatory cytokines. As shown in Fig. 9C, D, the levels of IRAK1, IL-6 and TNF-a proteins expression detected by western blotting were markedly upregulated in the LMP2-KO group compared with the WT group, respectively (*$P \leq 0.001$) (Fig. 9C, D).
## Discussion
The major finding of this study is that LMP2 gene global deletion caused significant neurobehavioral dysfunctions including cognitive impairment and decreased exploratory activities, increased anxiety-like behavior. Along with this, the signs of metabolic abnormalities including higher levels of LDL, uric acid and blood glucose, multiple myelin loss, elevated levels of ROS, increased BBB leakage and enhanced amyloid-β protein deposition were obviously observed in the LMP2-KO rats. All these factors maybe interact with each other and eventually led to chronic oxidative stress and neuroinflammation response in the brain regions of LMP2-KO rats, which contributed to the initial and progress of cognitive impairment.
Although the biological importance roles of the ubiquitin proteasome system (UPS) in the control of myriad cellular processes have been well documented, the significance of the immunoproteasome has not been well comprehended until now. The immunoproteasome possesses broader biological roles including immune and non-immune functions attributed to its three immunosubunits β1i, β2i and β5i [2]. Particularly, LMP2 has recently drawn considerable attention in many studies. Structurally, LMP2 is a critical component for proteasome activity in that LMP2 is essential for the proper incorporation of the immunoproteasome [31]. LMP2 is expressed in neuron, astrocyte, microglia and endothelial cells in brain tissue of human or rodent animal [3, 9, 32]. For example, LMP2 were increased expression in brain areas affected by AD or Huntington's disease which often displays cognitive dysfunction [9, 33]. However, it is not clear whether LMP2 change is the cause or result of these neurodegenerative diseases. Interestingly, in this study, we provided new evidence that LMP2 gene deletion resulted in cognitive dysfunction, reduced rats’ exploratory activities, increased rats’ anxiety-like behavior and without strong effects on gross locomotor abilities (such as swimming speed). Similarly, a recent reported that LMP7-deficient mice expressed more anxiety and increased cued fear and no strong effects on gross locomotor abilities [12]. Taken together, these data suggest that the immunoproteasomeis closely related to cognitionand emotional behavior. However, the related mechanism underlie this remains unclear. Recent reported that immunoproteasome inhibitor ONX-0914 affected long-term potentiation in murine hippocampus, a form of synaptic plasticity thought to contribute to learning and memory [34].Induction and maintenance of long-term potentiation is directly dependent on selective targeting of proteins for proteasomal degradation. Therefore, we postulated one of possible mechanisms is that the immunoproteasome plays an important role in synaptic plasticity which underlie learning and memory processes [35].
Several factors are related with the development and progress of cognitive impairment, including abnormal metabolism of lipids and glucose [26, 36], BBB damage [37], mitochondrial dysfunction [38, 39], white matter demyelination [25, 40]. For example, BBB injury induces the failure of Aβ proteins transport from brain to the peripheral circulation across the BBB, and eventually Aβ proteins aggregate in the brain further aggravate BBB damage, forming a vicious cycle [28, 41]. This phenomenon can also be seen in the present study, we observed that Aβ proteins deposition in the hippocampus and cerebral cortex of LMP2-KO rats compared with the WT rats. Interestingly, it seems that oxidative stress and inflammation reaction are the two leading causes for these changes. Brain neurons are sensitive to oxidative stress and inflammation damage. Indeed, oxidative stress and inflammation response are closely together with each other and are involved in the pathogenesis of stroke and neurodegenerative diseases [42, 43]. Alongside immune functions, immunoproteasome play a crucial role in removing oxidant-damaged proteins and protect cell viability against oxidative stress [1, 44]. In the absence of LMP2, accumulation of oxidized and polyubiquitinated proteins were observed in the liver and brain of LMP2-KO mice [8]. In line with this observation, LMP2-KO rats exhibited significantly enhanced oxidative stress; the levels of ROS indicated by DHE fluorescence staining were markedly elevated in the cerebral cortex and corpus callosum of LMP2-KO rats. In addition, our previous study found that inhibition of LMP2 decreased the protein levels of IL-1β and TNF-α in rat stroke model [3]. However, to our surprise, LMP2 gene deletion led to the augmentation of neuroinflammation accompanied with upregulation protein expressions of IL-1R-associated kinase 1 (IRAK1), IL-6 and TNF-a. Notably, IRAK1 is a key signaling mediator in the TLR/IL-1R signaling pathway which initiates a cascade of diverse downstream proinflammatory events [45]. Astrocyte and microglia can be polarized to proinflammatory phenotype by increasing IRAK1 expression and induce inflammation response [46]. Taken together, this evidence supports the view of LMP2 deficit promoting oxidative stress and inflammation response, but a great deal of work need to do insight into the detail molecular mechanisms underlying this.
In the present study, LFB staining showed severe myelin loss of cerebral cortex, corpus callosum and striatum in the LMP2-KO rats compared with the WT rats. Besides, the change of myelin loss was also confirmed by MBP protein expression decreased in the brain tissue of LMP2-KO rats. Unexpectedly, we observed demyelination and remyelination coexisted in the LMP2-KO rats. In fact, dynamics of myelin loss and generation can be observed in many CNS diseases, particularly in multiple sclerosis (MS). The innate immune response contributes to promoting remyelination observed in clinical MS disease [47]. However, despite this increase, overall levels of myelination indicated by MBP protein expression were decreased in the brain tissue of LMP2-KO rats. We assumed this kind of endogenous remyelination would not be enough to compensate the lost myeline and reverse the symptoms associated with demyelination and axonal death. Recent reported that genetically or pharmacologically enhancing myelin renewal could improve the memory-related tasks performance of APP/PS1 AD model mice [40]. In summary, this evidence suggests the potential of enhancing myelination maybe as a promising therapeutic strategy to improve cognitive impairment.
The present study has some limitations. First, we observed neurobehavioral abnormalities in the LMP2 knockout rats at a specific age (12 months old), but did not further explore the age at which these rats began to develop neurobehavioral abnormalities. Second, we did not administrate some therapeutic approach involves increasing the endogenous antioxidant activity and/or reducing ROS production as well as anti-inflammatory drug to treat these experimental animals, which would provide supplemental data to support the contribution of oxidative stress and inflammation response involved in the mechanisms of neurobehavioral abnormalities in rats. In addition, this study explored the phenomenon of LMP2-KO in rats, but we did not investigate its potential profound mechanisms. Cognitive impairment has been linked to several factors, including the excessive formation of ROS, mitochondrial dysfunction, inflammation and oxidative damage [38, 48]. Especially, whether mitochondrial abnormalities occurred in these knockout rats requires further investigation in the future.
## Conclusion
Our study demonstrates for the first time that LMP2 gene global deletion resulted in cognitive impairment and decreased exploratory activities, increased anxiety-like behavior. Chronic oxidative stress and inflammation response in the brain regions of LMP2-KO rats maybe work together and lead to the initial and progress of neurobehavioral dysfunctions. Future studies to reveal the detail molecular mechanisms underlying this are warranted.
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|
---
title: Biomarkers of Neutrophil Activation in Patients with Symptomatic Chronic Peripheral
Artery Disease Predict Worse Cardiovascular Outcome
authors:
- Giacomo Buso
- Elisabetta Faggin
- Alessandro Bressan
- Silvia Galliazzo
- Francesco Cinetto
- Carla Felice
- Michele Fusaro
- Andreas Erdmann
- Paolo Pauletto
- Marcello Rattazzi
- Lucia Mazzolai
journal: Biomedicines
year: 2023
pmcid: PMC10045814
doi: 10.3390/biomedicines11030866
license: CC BY 4.0
---
# Biomarkers of Neutrophil Activation in Patients with Symptomatic Chronic Peripheral Artery Disease Predict Worse Cardiovascular Outcome
## Abstract
Neutrophils play a role in cardiovascular (CV) disease. However, relatively scant evidence exists in the setting of peripheral artery disease (PAD). The aims of this study were to measure biomarkers of neutrophil activation in patients with symptomatic chronic PAD compared with healthy controls, to assess their association with PAD severity, and to evaluate their prognostic value in patients with PAD. The following circulating markers of neutrophil degranulation were tested: polymorphonuclear neutrophil (PMN) elastase, neutrophil gelatinase-associated lipocalin (NGAL), and myeloperoxidase (MPO). Neutrophil extracellular traps (NETs) were quantified by measuring circulating MPO–DNA complexes. Patients with PAD underwent a comprehensive series of vascular tests. The occurrence of 6-month major adverse CV (MACE) and limb events (MALE) was assessed. Overall, 110 participants were included, 66 of which had PAD. After adjustment for conventional CV risk factors, PMN-elastase (adjusted odds ratio [OR]: 1.008; $95\%$ confidence interval [CI]: 1.002–1.015; $$p \leq 0.006$$), NGAL (adjusted OR: 1.045; $95\%$CI: 1.024–1.066; $p \leq 0.001$), and MPO (adjusted OR: 1.013; $95\%$CI: 1.001–1.024; $$p \leq 0.028$$) were significantly associated with PAD presence. PMN-elastase (adjusted hazard ratio [HR]: 1.010; $95\%$CI: 1.000–1.020; $$p \leq 0.040$$) and MPO (adjusted HR: 1.027; $95\%$CI: 1.004–1.051; $$p \leq 0.019$$) were predictive of 6-month MACE and/or MALE. MPO displayed fair prognostic performance on receiver operating characteristic (ROC) curve analyses, with an area under the curve (AUC) of 0.74 ($95\%$CI: 0.56–0.91) and a sensitivity and specificity of 0.80 and 0.65, respectively, for a cut-off of 108.37 ng/mL. MPO–DNA showed a weak inverse correlation with transcutaneous oximetry (TcPO2) on proximal foot (adjusted ρ −0.287; $$p \leq 0.032$$). In conclusion, in patients with symptomatic chronic PAD, enhanced neutrophil activity may be associated with an increased risk of acute CV events, rather than correlate with disease severity. Further research is needed to clarify the role of neutrophils in PAD natural history.
## 1. Introduction
Peripheral artery disease (PAD) is a prevalent condition that affects over 200 million people around the world, including 40 million people in European countries [1]. This condition, which is most often secondary to atherosclerosis, is characterized by reduced blood flow and oxygen supply to the lower limbs due to progressive narrowing of the arteries. This can impair muscle function and overall quality of life, as well as significantly increase the risk of limb loss in affected patients.
From a pathophysiological perspective, circulating leukocytes, particularly neutrophils, have been shown to play a crucial role in atherosclerosis and atherothrombosis [2,3]. Neutrophils are the most abundant type of leukocytes and represent the first line of defense of innate immunity, capable of capturing and destroying microorganisms through phagocytosis and intracellular degradation. They also participate as mediators of inflammation [4].
Accumulation of neutrophils in atherosclerotic plaques increases plaque rupture risk. In particular, activated neutrophils may release proteolytic enzymes through degranulation, promoting endothelial cell detachment and exposure of sub-endothelial collagen and fibronectin to platelets, thus leading to plaque destabilization [5].
Moreover, recent studies showed that neutrophils stimulated by microbes, inflammatory agents, reactive oxygen species or activated platelets, release nuclear material, forming a web-like extracellular network known as neutrophil extracellular traps (NETs), made of DNA, histones, and granule constituents. In vitro and animal studies suggest that NETs may play a role in thrombus organization/stability [6,7,8], endothelial damage [9], and atherosclerosis progression [10]. Some human studies also demonstrated that markers of NETs, such as nucleosomes and myeloperoxidase (MPO)–DNA complexes, are increased in patients with severe coronary atherosclerosis and predict risk of cardiac events [11]. Similar findings were observed in patients with venous thromboembolism (VTE) [12,13,14].
Although the role of neutrophil activation, including neutrophil degranulation and NETs release, in cardiovascular (CV) disease is well-documented, relatively scarce information exists in the context of symptomatic chronic PAD to date [15].
Accordingly, the aims of the present study were to measure markers of neutrophil activation in patients with symptomatic chronic PAD compared with healthy controls, evaluate their association with disease severity, and assess their prognostic value.
## 2.1. Study Design and Participants
This is a multicenter, multinational, prospective case-control study conducted in the University Hospital of Lausanne (“Centre Hospitalier Universitaire Vaudois”, CHUV), Switzerland, in collaboration with both the University Hospital of Padua and the University Hospital of Treviso in Italy.
In the Angiology Division of the CHUV, consecutive patients aged 40–75 years with an established diagnosis of symptomatic chronic PAD were recruited. Symptomatic chronic PAD was defined as a Leriche–Fontaine stage from II to IV [16], associated with an ankle-brachial index (ABI) ≤ 0.9, or a toe-brachial index (TBI) ≤ 0.7 (in case of incompressible arteries).
Sex and BMI-matched subjects without evidence of thrombotic events and coronary artery disease (CAD) were enrolled as a control group at Cà Foncello University Hospital of Treviso in Italy. In particular, we recruited patients without a history of CV disease that underwent computed tomography angiography scan by using a “triple rule out protocol” for atypical chest pain to exclude the presence of pulmonary thromboembolism, CAD, and acute aortic dissection. As for controls, the inclusion criteria were: electrocardiogram free of abnormalities compatible with ischemia, necrosis, or myocardial injury; negative troponin enzyme levels; baseline left ventricular ejection fraction ≥ $45\%$; absence of history of cardiopathy and cardiac arrhythmias including atrial fibrillation, supraventricular tachycardia, and extrasystoles; estimated mortality risk < $2\%$ according to the GRACE Risk Score.
Overall exclusion criteria were: active or previous diagnosis of cancer; presence of rheumatic disease or immunosuppressive therapy (including chronic steroid treatment); concomitant exacerbations of chronic disease (such as chronic obstructive pulmonary disease, inflammatory bowel disease or immunological disorders); clinical and biochemical evidence of active infectious disease; recent surgery (less than three months); pregnancy.
The aims of the present study were: [1] to measure levels of circulating markers of neutrophil activation (neutrophil degranulation and neutrophil-derived NETs) in patients with symptomatic chronic PAD compared with healthy controls; [2] to explore the association between circulating markers of neutrophil activation and PAD severity; and [3] to evaluate the predictive value of circulating markers of neutrophil activation for adverse outcomes at 6-month follow-up in patients with symptomatic chronic PAD.
Research has been performed in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and local ethic committee-approved study protocol (CER-VD, BASEC Project-ID: 2016-01250). All subjects gave their written, informed consent upon enrollment in the study.
## 2.2. Clinical Data Collection and Biochemical Analysis at Baseline
The following data were collected from each participant: age; sex; height; weight; body mass index (BMI, calculated as body weight [kg] divided by the square of the height [m]); family history of atherosclerotic CV events (ischemic stroke, transient ischemic attack, acute coronary syndrome); presence of diabetes (defined as a documented diagnosis of diabetes or patient on antidiabetic treatment with no other clear indication), and self-reported history of active smoking (if more than 100 cigarettes lifetime); current use of antihypertensive drugs, antiplatelet drugs, and lipid lowering treatment (statins).
In patients with symptomatic chronic PAD, Leriche–Fontaine stage, arterial sites involved (aortoiliac, femoral-popliteal, and below-knee arteries), as well as personal history of CV disease (CAD and cerebrovascular disease [CeVD]), VTE, and lower limb revascularization, were recorded. At enrollment, the blood pressure (BP) (mmHg) was assessed in all participants as the mean of three measures one minute apart. Hypertension was defined as systolic BP (SBP) ≥ 140 and/or diastolic BP (DBP) ≥ 90 mmHg or ongoing antihypertensive medications. All the subjects underwent fasting blood sampling to measure creatinine level (mg/dl) and glomerular filtration rate (GFR) (ml/min/1.73 m2), estimated through the 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation. Total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, as well as triglycerides (mg/dl), were measured. Blood serum samples were collected from each patient and stored at −70 °C until further analysis.
## 2.3. Quantification of Circulating Markers of Neutrophil Activation and NETs Target by ELISA
Dedicated enzyme-linked immunosorbent assay (ELISA) kits were used for quantitative measurement of neutrophil degranulation markers, including polymorphonuclear neutrophil (PMN)-elastase (ab119553; Abcam, Cambrige, UK), neutrophil gelatinase-associated lipocalin (NGAL) (KIT 036RUO; BioPorto Diagnostics A/S, Hellerup, Denmark), and MPO (BMS2038INST; Invitrogen, Waltham, MA, US).
NETs-associated MPO–DNA complexes were measured by adding serum into 96-well plates coated with a monoclonal anti-human MPO antibody (MABX4043-10KC; Millipore, Burlington, MA, US) after saturation of no specific binding site with bovine serum albumin, followed by incubation with peroxidase-labelled anti-DNA monoclonal antibody included in the Cell Death ELISA kit (11774425001; Roche Diagnostics, Mannheim, Germany). The optical absorbance was measured at 405 wavelength by using Berthold Mithras multimode reader (Berthold Technologies GmbH, Bad Wildbad, Germany).
## 2.4. Vascular Assessment in Patients with PAD
All study participants with symptomatic chronic PAD underwent a comprehensive series of vascular tests at enrollment (V0) and 6 months later (V1). The visit V0 included: ABI calculation (and TBI calculation, in case of incompressible arteries); transcutaneous oximetry (TcPO2) measurement on the distal and proximal foot; constant-load treadmill test; 6-min walking test; pulse wave velocity (PWV); and flow-mediated dilatation (FMD) of the brachial artery. The visit V1 included ABI calculation (and TBI calculation, in case of incompressible arteries) and 6-min walking test. Details of such tests are provided in Supplementary Methods.
## 2.5. Clinical Outcomes
At the 6-month follow-up, the following outcomes were assessed in patients with PAD: [1] presence of major adverse CV (MACE) and/or limb events (MALE); [2] ABI reduction ≥ 0.15 or (or TBI reduction ≥ 0.1, in case of incompressible arteries); and [3] 6-min maximal walking distance (6MWD) reduction ≥ 20 m.
MACE was defined as the composite of all-cause death, non-fatal myocardial infarction, non-fatal stroke, and heart failure leading to hospitalization, as well as coronary revascularization, including percutaneous coronary intervention, and coronary artery bypass graft. Deaths were regarded to be attributable to a cardiac cause unless a non-cardiac death could be confirmed. MALE was defined as disabling claudication or severe limb ischemia leading to an intervention as well as major vascular amputation. Only patients with no intercurrent lower limb revascularization were included in the analysis of the remaining two outcomes, as surgery could have improved both ABI (or TBI) and 6MWD.
All patients who did not show up for the V1 were interviewed by telephone in order to ascertain their clinical conditions as well as the development of MACE and MALE. If patients did not attend the scheduled follow-up visit planned and were not reachable by telephone, they were considered as lost to follow-up.
## 2.6. Statistical Analysis
Patients were classified according to whether they had PAD or not. First, we compared groups in terms of demographics, concomitant diseases, and laboratory data. According to the normality of the distribution, the Student’s t-test or the Mann–Whitney test were used to compare groups in terms of continuous variables, whereas the Chi-square test or the Kruskal-Wallis test were applied for categorical variables. Continuous variables were reported as mean ± standard deviation (SD), regardless of the normality of the distribution, for the sake of simplicity. Categorical variables were reported as a percentage.
We then performed univariable logistic regression for associations between clinical variables and PAD status, expressed as odds ratios (ORs) with a $95\%$ confidence interval (CI). As for multivariable regression, covariates entering the models were selected by a significance level of $p \leq 0.10$ on bivariate analysis using the stepwise regression model with backward elimination.
In patients with PAD, we further performed linear correlation analysis with the estimation of the Pearson (or Spearman, for those variables found not to follow a normal distribution) coefficient (ρ) to test the association between the levels of circulating markers of neutrophil degranulation and NETs and the above vascular parameters assessed on V0. Multiple linear regression analysis was carried out including biomarkers selected by a significance level of $p \leq 0.10$ on bivariate analysis adjusted for covariates with a well-known correlation reported in the literature.
We then performed a multivariable analysis using a Cox model to identify the predictors for MACE and/or MALE during the first 6 months of follow-up. As for the other outcomes (assessed at a fixed time point of 6 months from enrollment), univariable logistic regression was carried out to identify their predictors at baseline. Covariates entering into the models were selected by a significance level of $p \leq 0.10$ on bivariate analysis or by a well-known association reported in the literature.
For all biomarkers showing significant predictive ability in terms of MACE and/or MALE at multivariable analysis, the prognostic performance and optimal value for identifying patients at risk were defined based on receiver operating characteristic (ROC) curves by calculating the area under the curve (AUC), sensitivity, specificity, and Youden’s index (= sensitivity + specificity − 1).
All the statistical tests were two-tailed and conducted at a significance level of 0.05.
Sample size has been estimated on the basis of previous investigation performed in patients with VTE [12] and CAD [11].
We conducted statistical analyses using SPSS (IBM SPSS Statistics for Windows, v. 25.0. IBM Corp., Armonk, NY, USA).
## 3.1. General Study Population Characteristics
From 13 October 2016 to 18 May 2020, 110 participants were included in the study, of which 66 had symptomatic chronic PAD and 44 were healthy controls. General participants’ characteristics are summarized in Table 1. The mean age was 59 (±11) years and the mean BMI was 27.3 (±5) kg/m2. Twenty-four ($21.8\%$) participants had a BMI compatible with obesity class I or more (according to the World Health Organization classification). Overall, no significant difference was observed between participants with and without PAD in terms of BMI, waist, and obesity rates. Male sex was prevalent in both groups (68.2 vs. $72.2\%$, respectively; $$p \leq 0.766$$), while patients with PAD were significantly older (64 vs. 51 years; $p \leq 0.001$) and were more likely to have diabetes (36.4 vs. $4.7\%$; $p \leq 0.001$), hypertension (69.7 vs. $40.9\%$; $$p \leq 0.003$$), and active smoking (51.5 vs. $29.5\%$; $$p \leq 0.037$$).
The Leriche–Fontaine stage was IIa in 50 ($75.8\%$) and IIb in 15 ($22.7\%$) patients, whereas one patient displayed PAD stage IV. Twenty-six ($39.4\%$) patients with PAD had aortoiliac involvement, while 51 ($77.3\%$) and 20 ($30.3\%$) of them had femoral-popliteal and below-knee arteries involvement, respectively. Thirty-three ($50\%$) patients with PAD had a history of previous lower limb revascularization. At baseline, mean ABI and TBI were 0.72 (±0.13) and 0.54 (±0.14), respectively. Mean TcPO2 was 41 (±12) and 37 (±12) mmHg on distal and proximal foot, respectively.
As for the laboratory parameters at baseline, total and LDL cholesterol levels were significantly lower in patients with PAD than healthy controls ($p \leq 0.001$ each), while no significant differences were found between groups in terms of creatinine and GFR, HDL cholesterol, and triglycerides (Table 1).
## 3.2. Circulating Markers of Neutrophil Degranulation in Patients with PAD and Controls
Levels of all markers of neutrophil degranulation were significantly higher in patients with symptomatic chronic PAD, compared with healthy controls ($p \leq 0.001$ for each) (Table 1, Figure 1).
Uni- and multivariable regression analyses, the latter including age, diabetes, hypertension, and active smoking as covariates, confirmed a significant association with the presence of PAD for PMN-elastase (adjusted OR: 1.008; $95\%$CI: 1.002–1.015; $$p \leq 0.006$$), NGAL (adjusted OR: 1.045; $95\%$CI: 1.024–1.066; $p \leq 0.001$), and MPO (adjusted OR: 1.013; $95\%$CI: 1.001–1.024; $$p \leq 0.028$$).
Correlation analyses between the above markers and PAD severity showed a significant, though weak, negative correlation between NGAL and the following parameters: 6-min pain-free walking distance (6PFWD) (ρ −0.298; $$p \leq 0.025$$), pain-free walking distance (PFWD) (ρ −0.256; $$p \leq 0.046$$), and maximal walking distance (MWD) (ρ −0.305; $$p \leq 0.017$$). No significant correlation was found between NGAL and 6MWD (ρ −0.243; $$p \leq 0.066$$), as well as between the other markers of neutrophil activation and further vascular tests (Figure 2, Supplementary Table S1).
After inclusion in a multivariable regression analysis model including age, diabetes, and active smoking, no significant correlation was found between NGAL and the above parameters of PAD severity.
## 3.3. Circulating NETs in Patients with PAD and Controls
Levels of MPO–DNA were not significantly different in patients with symptomatic chronic PAD compared with healthy controls ($$p \leq 0.241$$) (Table 1, Figure 1), which was also confirmed by univariable regression analysis (OR: 1.022; $95\%$CI: 0.969–1.077; $$p \leq 0.426$$).
Correlation analyses in patients with PAD found a significant, though weak, negative correlation between MPO–DNA and TcPO2 on proximal foot (ρ −0.284; $$p \leq 0.029$$) (Figure 2, Supplementary Table S1), which remained significant after inclusion in a multivariable regression analysis model including age, diabetes, and active smoking (adjusted ρ −0.287; $$p \leq 0.032$$).
## 3.4. Clinical Outcomes in Patients with PAD
Of the included 66 patients with symptomatic chronic PAD, three were lost to follow-up. Within the first 6 months of follow-up, 11 of the remaining 63 patients developed MACE and/or MALE (two myocardial infarctions, and nine disabling claudication or severe limb ischemia leading to an intervention). After exclusion of patients with symptomatic chronic PAD who underwent intercurrent limb revascularization or were lost to follow-up, data on ABI at V1 were available on 49 patients; 14 of them had a significant ABI (≥0.15) or TBI (≥0.1) reduction at 6 months. As for the 6MWT, data on 6MWD at V1 were available on 40 patients, 10 of whom had a 6MWD reduction ≥ 20 m at 6 months.
Compared to patients without MACE and/or MALE, those who experienced this outcome displayed significantly higher levels of both MPO (150.17 vs 98.52 ng/mL, respectively; $$p \leq 0.020$$) and MPO–DNA (0.362 vs 0.256 abs 405–490; $$p \leq 0.014$$) at baseline (Table 2), whereas clinical features including age, PAD staging, comorbidities, and baseline treatment were similar between groups (Supplementary Table S2).
Multivariable Cox regression analyses found that both PMN-elastase (adjusted hazard ratio [HR]: 1.010; $95\%$CI: 1.000–1.020; $$p \leq 0.040$$) and MPO (adjusted HR: 1.027; $95\%$CI: 1.004–1.051; $$p \leq 0.019$$) were predictive of MACE and/or MALE at 6-month follow-up. Conversely, no significant association was found between the above markers at baseline and both ABI reduction ≥ 0.15 (or TBI reduction ≥ 0.1, in patients with incompressible arteries) and 6MWD reduction ≥ 20 m at 6-month follow-up (Table 3).
In terms of MACE and/or MALE, ROC curve analyses showed fair and poor prognostic performance for MPO and PMN-elastase, respectively. In particular, MPO displayed an AUC of 0.74 ($95\%$CI: 0.56–0.91), as well as a sensitivity and specificity of 0.80 and 0.65, respectively, for a cut-off of 108.37 ng/mL, determined using Youden’s index. Lower values of AUC were found for PMN-elastase, as shown in Figure 3.
## 4. Discussion
Patients with symptomatic chronic PAD showed significantly increased levels of circulating markers of neutrophil degranulation compared to healthy subjects, whereas levels of MPO–DNA, a specific marker of NETs, were similar in the two groups. In patients with symptomatic chronic PAD, markers of neutrophil degranulation (PMN-elastase and MPO) were predictive of worse CV outcome at 6 months. Conversely, none of the neutrophil activation markers correlated strongly with a series of PAD severity parameters at baseline, nor were they significantly associated with a reduction of ABI or 6MWD at follow-up.
MPO is a heme-containing peroxidase stored in neutrophilic granules and released upon neutrophil activation. This extracellular protease is expressed in atherosclerotic lesions [17], and MPO-containing macrophages were found to be particularly abundant in vulnerable and ruptured plaques [18]. In humans, MPO deficiency was found to be associated with a reduced risk of CV disease [19]. Conversely, individuals with genetic polymorphisms of MPO are at an increased risk of CAD [20]. Circulating levels of MPO are associated with both the presence [21] and severity [22,23] of CAD on angiography. Moreover, both in patients presenting to an emergency department with chest pain [24] and those with established CAD diagnosis [23], MPO circulating levels are predictive of adverse outcomes. Of note, inhibition of MPO improved blood flow in a diabetic mouse model of hind-limb ischemia [25], while higher serum MPO levels were associated with increased risk of myocardial infarction or stroke in patients with PAD [26]. Intriguingly enough, in a recent prospective cohort study including patients with PAD undergoing digital subtraction angiography [27], baseline MPO levels were 3.68 times higher in patients with all-cause death and MACE, and 1.48 times higher in those with MALE than those without such outcomes at 24-months follow-up. Moreover, the authors found higher MPO levels in patients with multi-bed vascular disease compared to those with PAD alone, suggesting that this biomarker may reflect the extent of vascular damage in patients with atherosclerotic disease [27].
In line with these findings, our study found higher levels of MPO and other circulating markers of neutrophil degranulation in patients with symptomatic chronic PAD than in heathy subjects. Adjustment for conventional risk factors associated with PAD presence on bivariate analysis, namely age, diabetes, hypertension, and active smoking, did not attenuate the association, indicating that these markers are not strongly associated with such covariates. Importantly, two of the markers of neutrophil degranulation, namely MPO and PMN-elastase, were found to be predictive of MACE and/or MALE within the following 6 months. In particular, ROC curve analyses showed fair prognostic performance for the former, underlining their possible role in CV risk definition in patients with PAD.
Neutrophil elastase plays a crucial role in bacterial killing [28] and seems to be involved in several noninfectious diseases, such as respiratory diseases and arthritis [29], whereas its role in CV disease is less clear. Higher levels of circulating neutrophil elastase have been reported in patients with myocardial infarction [30], while an animal model suggested that this protease enhances myocardial injury by inducing an excessive inflammatory response in cardiomyocytes, thus worsening the prognosis post-myocardial infarction [31]. Our study seems to suggest a role for PMN-elastase in the natural history of PAD as well, although further research will be needed to clarify this aspect.
In addition to classical strategies such as degranulation, neutrophils are also able to release their nuclear contents into the extracellular space. This chromatin mesh called NETs is highly cytotoxic [27], and recent evidence revealed a potential role of NETs in linking sterile inflammation with thrombosis, including atherothrombosis [32,33]. In a murine model of myocardial ischemia/reperfusion, NET-induced no-reflow was described [34], whereas another study showed significantly reduced infarct size in protein arginine deiminase 4 (PAD4) knockout mice with myocardial infarction, which are unable to undergo at least one form of NETs generation [35]. In humans, NETs are an abundant component of coronary thrombi [36], and coronary NETs burden is a strong independent predictor of adverse clinical outcome in patients with ST-elevation myocardial infarction [36]. Moreover, NETs excess may be associated with impaired wound healing and seems to predict poor wound outcomes in patients with diabetes [37]. In the setting of PAD, NETs have been associated with several ischemic outcomes, particularly in patients undergoing peripheral angioplasty and stenting [38]. In human research, several surrogate markers of NETs generation have been used so far, including citrullinated histones, complexes of double-stranded DNA (dsDNA), and MPO–DNA complexes. Nonetheless, the specificity of dsDNA in this setting may be questionable, since extracellular nucleosomes may be released during necrosis and apoptosis of cells other than neutrophils [39], whereas the role of histone citrullination in NET generation is still under debate [40]. Accordingly, MPO–DNA should be a more reliable, specific marker of NETs generation. In this respect, previous studies found that MPO–DNA is a component of coronary thrombus in acute myocardial infarction [41] and correlates with the presence of severe coronary atherosclerosis [11]. Moreover, MPO–DNA may be associated with both CV disease severity and prognosis [42,43].
In our study, MPO–DNA levels were not significantly higher in patients with symptomatic chronic PAD compared with healthy subjects. As most of the above studies on NETs were carried out in the setting of acute vascular events, NETs generation may therefore not be significantly enhanced in patients with chronic, stable CV disease, including PAD. Another hypothesis to be considered in this respect concerns the use of statins. Besides decreasing cholesterol synthesis, these drugs are known for a series of pleiotropic effects, including antioxidant and inflammatory properties, improvement of endothelial function, and stabilization of atherosclerotic plaques [44]. Importantly, increasing evidence has demonstrated that statins have modulatory effects on neutrophil function, and particularly on NETs generation [45,46]. Furthermore, research on patients with carotid artery stenosis demonstrated lower circulating NGAL levels in those on statins compared to the non-statin group [47], whereas no difference was found between patient with and without statin treatment in terms of serum MPO and neutrophil elastase [48]. Such evidence could play a relevant role in the interpretation of our results. In particular, the widespread use of statins in patients with PAD might have influenced the circulating levels of MPO–DNA complexes and significantly attenuated differences between groups. Since our patient cohort is too small to speculate further on this, future studies on larger cohorts with a greater representation of patients not receiving statin therapy may clarify these issues.
Of note, MPO–DNA was not predictive of MACE and/or MALE at Cox regression analysis in our study, though its levels at baseline were significantly higher in patients developing such an outcome within the next 6 months. Further research on larger cohorts using longer follow-up periods is warranted to clarify this apparent contradiction.
In order to evaluate lower limb perfusion, walking functional capacity, arterial stiffness, and endothelial function, we performed a comprehensive assessment of the patients’ vascular status at baseline. Previous research reported that MPO is strongly associated with both ABI values and PAD presence [49]. However, no strong correlation was found between markers of neutrophil activation, including MPO and these vascular tests. At multivariable regression analysis, only MPO–DNA was significantly, though weakly, inversely correlated with TcPO2 on proximal foot, whereas no significant correlation was found with TcPO2 on distal foot. The clinical significance of such findings is thus uncertain and need to be further explored. Additionally, it should be noted that none of the markers of neutrophil activation were associated with worsening of ABI and 6MWD at follow-up. Taken together, these data seem to suggest a role for neutrophils in acute CV events, rather than in disease severity and its progression, in the setting of symptomatic chronic PAD.
Our study has several limitations worth noting. First, the small sample size and low number of outcomes significantly limit the interpretability of our findings. Second, our results cannot be extended to all subjects with PAD, as we did not enroll patients with asymptomatic disease (Leriche–Fontaine stage I) nor rest pain in legs (Leriche–Fontaine stage III), and only one patient displayed PAD stage IV. The measurement of the above markers could be particularly important in patients with asymptomatic disease, who represent the majority of PAD patients and whose CV risk may be similar to that of symptomatic patients [50]. Third, the great majority on MACE and/or MALE at 6-month follow-up consisted in disabling claudication or severe limb ischemia leading to an intervention, whereas myocardial infarctions were scarcely represented, and no patient developed stroke. Our results should thus be verified in larger studies with longer follow-ups, in order to ascertain the predictive value of the markers of neutrophil activation in the whole spectrum of adverse CV events. Another limitation is that the implementation of MPO–DNA measurements in clinical routine lacks standardization. Furthermore, the inclusion in regression analyses of diabetes, hypertension, and hypercholesterolemia as dichotomous covariates is a potential bias. However, we could not use them as continuous variables, as these might be strongly confounded by antidiabetics, antihypertensive, and lipid-lowering therapies. Lastly, our study was mostly descriptive and it is not possible to determine the pathomechanisms underlying neutrophil degranulation and NET generation in patients with PAD. Basic science studies are warranted to better understand these aspects.
Despite these limitations, our study has strengths. To the best of our knowledge, this is the first study assessing a large spectrum of markers of neutrophil activation, including a highly specific surrogate marker of NET generation, in patients with symptomatic chronic PAD compared with healthy controls. Moreover, the prospective nature of the study and the inclusion of a comprehensive vascular assessment of patients with PAD are additional strengths of our study and significantly increase the relevance of our findings.
## 5. Conclusions
Our study suggests enhanced neutrophil degranulation in patients with symptomatic chronic PAD compared with healthy subjects, even after adjustment for conventional CV risk factors. Conversely, NETs generation may be similar in the two groups, at least in stable conditions. In patients with symptomatic chronic PAD, biomarkers of neutrophil activation may not correlate with disease severity but rather be associated with an increased risk of acute CV events. These data suggest a role of neutrophils in PAD natural history that needs to be further elucidated.
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|
---
title: Antioxidant Defense Expressed as Glutathione Status and Keap1-Nrf2 System Action
in Relation to Anthropometric Parameters and Body Composition in Young Women with
Polycystic Ovary Syndrome
authors:
- Magdalena Chełchowska
- Justyna Jurczewska
- Joanna Gajewska
- Joanna Mazur
- Dorota Szostak-Węgierek
- Ewa Rudnicka
- Jadwiga Ambroszkiewicz
journal: Antioxidants
year: 2023
pmcid: PMC10045817
doi: 10.3390/antiox12030730
license: CC BY 4.0
---
# Antioxidant Defense Expressed as Glutathione Status and Keap1-Nrf2 System Action in Relation to Anthropometric Parameters and Body Composition in Young Women with Polycystic Ovary Syndrome
## Abstract
Metabolic disorders present in women with polycystic ovary syndrome (PCOS) and the associated risk of obesity may result in increased oxidative stress and reproductive failure. Therefore, we evaluated the concentrations of reduced glutathione (GSH), oxidized glutathione (GSSG), glutathione peroxidase (GPx), and reductase (GR), as well as nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associating protein1 (Keap1) in the serum of 56 women with PCOS divided according to the visceral to subcutaneous fat surface ratio (VAT/SAT) and waist-to-hip ratio (WHR) values. Antioxidant parameter levels were measured by competitive inhibition enzyme immunoassay technique. As the VAT/SAT ratio and WHR increased, we observed significantly higher concentrations of GSSG and Keap1 protein and a lower value of the GSSG/GSH ratio (R-index), which is considered an index of cellular redox ($p \leq 0.05$). Negative correlations were found between the R-index and body weight, BMI, WHR, subcutaneous and visceral fat surface and the VAT/SAT ratio, and total body fat; positive links were found with fat free mass and total body water. Opposite associations were noted between GSSG level and the aforementioned body composition parameters. Oxidative stress characterized by a depleted reduced-to-oxidized glutathione index is associated with anthropometric and body composition parameters in women with PCOS. In particular, abdominal obesity expressed by the VAT/SAT ratio and/or WHR seems to have a negative impact on glutathione status, which may lead to a disruption of many biological cell processes. The observed negative association of Keap1 with R-index suggests that the elevated oxidative changes dependent on the VAT/SAT ratio may lead to Nrf2 activation to promote antioxidant enzyme expression. Although the GSH/GSSG index as well as the VAT/SAT ratio appear to be good indicators of oxidative status, studies on a larger group of patients should continue to confirm these links among women with PCOS.
## 1. Introduction
Polycystic ovary syndrome (PCOS) is one of the most common endocrinopathies and its prevalence—depending on the adopted diagnostic criteria and the population studied—ranges from 4 to $16\%$ of reproductive-age women [1,2,3]. Characteristic features of the syndrome include menstrual disorders, hyperandrogenism, and often obesity and infertility [4,5,6]. In addition to primary and secondary infertility, pregnancy complications are more common in this patient group, including gestational diabetes, hypertension, preeclampsia, and a higher risk of miscarriage or low-birth-weight offspring [7,8,9].
It is estimated that up to 60–$70\%$ of patients with PCOS are overweight or obese, with particularly increased central distribution of adipose tissue. Central accumulation of adipose tissue is characteristic of both lean and obese women with this disease entity [1]. Visceral adipose tissue is a highly active endocrine organ secreting hormonally active proteins—adipokines, pro-inflammatory cytokines and growth factors involved in the regulation of energy homeostasis, and carbohydrate and lipid metabolism [10,11]. The adipose tissue of women with PCOS shows many abnormalities in the secretion of these compounds, which affect the clinical signs of the disease and exacerbate existing endocrine and metabolic disorders, often leading to reproductive failure [2,3,12].
Insulin resistance in PCOS resulting in hyperglycemia and higher levels of free fatty acids can lead to increased reactive oxygen species (ROS) production, especially when accompanied by overweight and obesity. Because PCOS is also associated with reduced antioxidant levels, it is considered a state of increased risk for oxidative stress. Research conducted in this area suggests that there may be a strong relationship between impaired adipose tissue metabolism, insulin resistance, hyperandrogenism, inflammation, and oxidative stress in the pathogenesis of PCOS [1,2,3,13,14,15,16,17,18] (Figure 1).
A commonly known measure of oxidative stress is the ratio of reduced glutathione (GSH) to oxidized glutathione (GSSG). The GSH/GSSG system is the main “redox buffer” that protects cellular structures from the damaging effects of free oxygen radicals, and the reactivity of this compound is conditioned by the presence of a thiol group. Glutathione in reduced form in the presence of glutathione peroxidase (GPx) reacts with hydrogen peroxide (H2O2) and lipid peroxide, oxidizing to disulfide. Regeneration of the active thiol form occurs with the participation of NADPH-dependent glutathione reductase (GR), belonging to flavoproteins. In addition to the regeneration of GSH from GSSG, the second process affecting the increase in glutathione concentration is its neosynthesis. The de novo synthesis of GSH in the cell is limited by the availability of its constituent amino acids, and in particular by the availability of the sulfur amino acid precursor, cysteine [19,20]. Modification of the oxidation state of protein cysteine residues is significantly responsible for the role of GSH in redox-dependent cell signaling. The process of protein glutathionylation is a mechanism that protects sensitive protein thiols from irreversible oxidation, and thus from irreversible loss of their biological activity [18]. One of the transcription factors involved in cell signaling and containing selected cysteine residues is nuclear factor erythroid 2-related factor 2 (Nrf2). Under physiological conditions, Nrf2 exists in the cytoplasm in the form of a complex with Keap1 protein (Kelch-like ECH-associating protein 1). Reactive oxygen and nitrogen species (RNS) formed in excess can oxidize the specific cysteine residues of Keap1, leading to a conformational change of this protein, the release of Nrf2, and its translocation to the cell nucleus. Through the activation of the antioxidant response element (ARE) in the nucleus, it participates in the transcriptional regulation of many antioxidant genes—including, i.e., glutathione S-transferase, NAPH-oxidoreductase, and glutamate-cysteine ligase, the rate-limiting enzyme in glutathione synthesis [19,21,22]. Given the important role of GSH in cellular defense mechanisms, the induction of regulatory enzymes involved in its synthesis plays a key role in protecting cells from excessive oxygen free radical activity [20].
According to the meta-analysis conducted by Murri et al. [ 1], patients with PCOS had approximately $50\%$ lower glutathione levels compared with healthy women and no changes in glutathione peroxidase activity. There are no systematic data on the relationships between glutathione status and adipose tissue and the risk of metabolic disorders in women with PCOS. There are also limited reports on the Keap1-Nrf2 system action in this disease, and these mainly concern animal models [23,24].
In this study, we aimed to evaluate glutathione status and the function of the Keap1-Nrf2 system in relation to anthropometric parameters and body composition in young women with polycystic ovary syndrome. Therefore, the serum concentrations of GSH, GSSG, GPx and GR, as well as the values of Nrf2 and Keap1 proteins in patients with PCOS, were determined. The interrelationships between the tested antioxidants and the link between antioxidants and body composition parameters were also investigated.
## 2.1. Participants
The study was conducted in accordance with the ethical standards established by the Declaration of Helsinki after obtaining approval from the Ethical Committee of the Medical University of Warsaw (consent no. KB/$\frac{170}{2019}$). All participants were acquainted with the objectives and procedures of the study. Patients gave written consent for the analysis of biological samples, anthropometric and body composition measurements, and the use of clinical information collected from their medical records.
The study included 56 Caucasian women with polycystic ovary syndrome, recruited in the Department of Gynecological Endocrinology of the Medical University in Warsaw in the years 2021–2022. The inclusion criteria for the study group were PCOS diagnosed according to the Rotterdam diagnostic criteria (presence of at least two of the following three criteria: oligo-/amenorrhea, clinical and/or biochemical hyperandrogenism, image of polycystic ovary according to ultrasound exam) [25]. Exclusion criteria included: diabetes, chronic hypertension, cardiovascular diseases, thyroid dysfunction, endometriosis, congenital adrenal hyperplasia, Cushing’s syndrome, androgen releasing tumor, use of lipid-lowering or insulin-sensitizing drugs, exacerbated state of chronic and acute somatic disease and/or contagious diseases, mental disorders, genetic defects, pregnancy, and lactation. Due to the method of body composition measurement (BIA—bioelectrical impedance analysis), the additional exclusion criteria were: diagnosed epilepsy, implanted pacemaker or defibrillator, and metal endoprostheses. For further analysis, the patients were divided into two groups according to their visceral to subcutaneous fat surface ratio and the waist-to-hip ratio values.
## 2.2. Anthropometric Measurements
Body weight and height were measured according to the established standards [26]. Body mass index (BMI) was calculated as the ratio between body weight and height squared (kg/m2). Interpretation of these results followed the international classification provided by the World Health Organization (WHO): <18.5kg/m2 = underweight; 18.5–24.9 kg/m2 = normal weight; 25.0–29.9 kg/m2 = overweight; ≥30.0 kg/m2 = obese [27].
In addition, waist and hip circumference were measured. According to the WHO recommendations [28], waist circumference was measured at the midpoint between the lower margin of the least palpable rib and the top of the iliac crest, using a stretch-resistant tape. Hip circumference was measured around the widest part of the buttocks. The cut-off point for high values of the waist circumference was >80 cm. Additionally, to measure abdominal obesity, the waist-to-hip ratio (WHR) was calculated by dividing the waist circumference by the hip circumference. Abdominal obesity was defined as WHR ≥ 0.85 [28].
## 2.3. Body Composition Analysis with Bioelectrical Impedance
Whole body composition of the women was measured using a Maltron BioScan 920-II multi-frequency bioelectrical impedance analyzer according to the manufacturer’s instructions (Maltron International Ltd., Rayleigh, UK). Body composition analysis was performed in the supine position with the limbs separated by 30° from the body. Before starting the examination, the participants were recommended to rest for about three minutes. The electrodes were placed on the top middle part of the right hand and on the top middle part of the right foot. Before placing the electrodes, the placement sites were cleaned using isopropyl alcohol to limit possible errors and ensure adherence.
Quantitative analysis of abdominal adipose tissue (subcutaneous and visceral) was performed in a standing position with the upper limbs separated from the body. The configuration of electrode placement was strictly defined by the device’s manufacturer [29]. Based on the anthropometric measurements and body composition tests, the following parameters were determined: subcutaneous fat surface (SAT in cm2), visceral fat surface (VAT in cm2), and the ratio of visceral to subcutaneous fat (VAT/SAT ratio). The cut-off for VAT was >120 cm2 and that for SAT was >225 cm2. At the same time, a VAT/SAT ratio above 0.90 was adopted as a risk factor for metabolic diseases [29]. The obtained results were processed using the Maltron BioScan 920 v. 1.1.135 software. According to the guidelines of the European Society of Parenteral and Enteral Nutrition (ESPEN) for body composition tests, the subjects had to meet the following conditions: entering the test on an empty stomach, emptying the bladder 30 min before the test, lack of physical activity for 12 h before the test, no alcohol and no fluids containing caffeine for 24 h before the test [30].
## 2.4. Biochemical Analysis
For biochemical analysis, 5 mL of venous blood was collected in the morning from all participants after 12 h of overnight fasting during the follicular phase between days two and six of their menstrual cycle.
Blood samples were prepared in a manner appropriate for the planned biochemical analyses. The serum samples obtained after centrifugation were divided into small portions, some of which were used for testing on the day of collection, and the remaining part was frozen at −80 °C until the rest of the biochemical analyses were performed (stored no longer than three months).
Serum fasting glucose concentrations were determined by enzymatic reference method with hexokinase using commercial kits on Integra 400 plus a biochemical analyzer (Roche Diagnostics, Basel, Switzerland).
Serum insulin, testosterone (T), luteinizing hormone (LH), follicle stimulating hormone (FSH), thyroid stimulating hormone (TSH), estradiol (E2), androstenedione (A), 17-hydroxyprogesterone (17-OHP), dehydroepiandrosterone sulfate (DHEA-S), and sex hormone binding globulin (SHBG) levels were measured using one- or two-step chemiluminescent microparticle immunoassay (CMIA; Alinity I analyzer, Abbott Diagnostics GmbH, Wiesbaden, Germany).
To determine insulin resistance, the homeostasis model assessment for insulin resistance (HOMA-IR) was calculated using the following formula: HOMA-IR = [fasting insulin (µU/mL) × fasting glucose (mmol/L)]/22.5.
Serum concentrations of antioxidant defense parameters were measured using immunoenzymatic methods following the manufacturer’s instructions.
GSH and GSSG levels were determined using kits based on a sandwich enzyme-linked immunosorbent assay (ELISA) with two specific and high affinity monoclonal antibodies (Human GSH ELISA Kit Cat. No.: 201-12-5407; Human GSSG ELISA Kit Cat. No.: 201-12-5444, SunRed Bio-technology Company, Shanghai, China), which we have previously described in detail [31]. To assess the cellular redox index, the GSH/GSSG ratio was calculated.
Nuclear factor (erythroid-derived 2)-like 2, also known as NFE2L2 or Nrf2 was measured using human an NFE2L2 ELISA kit (cat. No. EH3417, Fine Biotech Co., Ltd., Wuhan, China) based on the double-antibody sandwich ELISA technique. The pre-coated antibody was an anti-human NFE2L2 monoclonal antibody, while the detection antibody was a biotinylated polyclonal antibody. Samples and biotinylated antibodies were added into ELISA plate wells. Then, avidin–peroxidase conjugates (HRP-streptavidin) were added to the wells. TMB substrate was used for coloration after the enzyme conjugate had already been thoroughly washed out of the wells. TMB (3,3′,5,5′-tetramethylbenzidine) reacts to form a blue product from the peroxidase activity, and finally turns to yellow after adding the stop solution. The color intensity and quantity of target analyte in the sample were positively correlated. The levels of GR, GPx, and Keap1 proteins were determined in an analogous manner using a human glutathione reductase ELISA kit (cat. No. MBS2703164, MyBioSource Inc., San Diego, CA, USA), a human glutathione peroxidase ELISA kit (cat. No. MBS167041, MyBioSource Inc., San Diego, CA, USA), and a human KEAP1 (Kelch-like ECH-associated protein 1) ELISA kit (cat. No. EH4240, Fine Biotech Co., Ltd., Wuhan, China), respectively. The concentrations of NFE2L2, Keap1, GR, and GPx in the samples were calculated by comparing the O.D. of the samples to the standard curve. The intra- and inter-assay CVs were less than $8.0\%$ and $10\%$ for NFE2L2, Keap1, and GPx, whereas they were $10\%$ and $12\%$ for GR, respectively. Assay sensitivity was less than 0.094 ng/mL for NFE2L2, 14.063 pg/mL for Keap1, 0.260 ng/mL for GPx, and 35.000 pg/mL for GR, respectively.
## 2.5. Statistical Analysis
Statistical analysis included 40 parameters in four groups: baseline clinical features [13], anthropometric data and body composition [20], and antioxidant defense parameters [7]. The normality of each parameter was checked using the Kolmogorov–Smirnov test. The study group was divided into two according to the visceral to subcutaneous fat surface ratio, with a cut-off point of 0.90, and independently into two groups according to the waist-to-hip ratio values, with a cut-off point of 0.85. For each of the identified subgroups, the mean values of the respective parameters were presented with the standard deviation (SD) if the distribution did not deviate from normal, or the median along with the interquartile range (1-3IQR) if the hypothesis of normality of distribution was rejected. In the first case, the groups were compared using the parametric Student’s t-test and in the second case using the nonparametric Mann–Whitney test. The level of correlation between GSH, GSSG, as well as the R-index and anthropometric parameters was also calculated using Spearman’s rho. The relationship between Keap1 and the three aforementioned antioxidant defense parameters are presented graphically as a scatter plot.
All reported p-values were two-tailed, and values = <0.05 were considered significant. IBM SPSS v.28 statistical software was used (IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY, USA: IBM Corp).
## 3. Results
Table 1 shows a comparison of the clinical characteristics of the 56 participants with PCOS stratified twice: once based on the VAT/SAT ratio (≤0.90; >0.90) values and a second time based on the WHR values (<0.85; ≥0.85). Patients in each group were of similar age (median 25 years). We observed significantly higher levels of fasting insulin and HOMA-IR values in the serum of women with increased VAT/SAT index (VAT/SAT > 0.90) and WHR ratio (WHR ≥ 0.85) when compared with women with normal values of these parameters ($p \leq 0.01$). Fasting glucose levels were also higher in these groups, but the difference was not statistically significant.
We found that patients with higher VAT/SAT and WHR ratios had significantly lower levels of SHBG than patients with VAT/SAT ≤ 0.90 ($p \leq 0.001$) and WHR < 0.85 ($p \leq 0.01$). Additionally, lower VAT/SAT values were associated with lower levels of 17-OHP ($p \leq 0.01$). We did not observe significant differences in LH, FSH, T, E2, A, TSH, and DHEAS concentrations between the studied groups.
As expected, anthropometric and body composition indices differed significantly in both groups divided by VAT/SAT and WHR values. The PCOS group with increased VAT/SAT ratios had statistically higher subcutaneous and visceral adipose tissue content, BMI, total body fat, and muscle mass compared with the PCOS group with normal VAT/SAT ratio ($p \leq 0.001$). Additionally, this group of women was characterized by a significantly higher WHR compared with women with VAT/SAT ≤ 0.90 ($p \leq 0.001$). Similar relationships were observed in the group of women with WHR ≥ 0.85. The subjects’ detailed anthropometric data and body composition measures are shown in Table 2.
Serum antioxidant parameters in women with PCOS from each subgroup are summarized in Table 3 and Table 4. We found that GSSG and Keap1 protein concentrations were statistically higher, while the R-index value was significantly lower in the serum of women with increased VAT/SAT compared with the group with normal values of this ratio ($p \leq 0.001$; Table 3).
Similar differences were shown for GSSG and R-index between the WHR ≥ 0.85 and WHR < 0.85 groups ($p \leq 0.05$ and $p \leq 0.01$, respectively; Table 4). Other antioxidant defense parameters were not significantly different between all the studied groups. As the VAT/SAT and WHR ratio increased, we observed lower concentrations of reduced glutathione; however, these differences were not statistically significant (although in the case of the WHR ratio, they were at the limit of significance $$p \leq 0.053$$; Table 3 and Table 4).
The correlations of GSH, GSSG, and the R-index with anthropometric parameters and body composition in women with PCOS are shown in detail in Table 5 (whole group). Serum GSH concentrations were negatively correlated with hip circumference, WHR, and VAT/SAT ratio values. Serum GSSG levels were positively associated with weight, BMI, WHR, subcutaneous and visceral adipose tissue content, the VAT/SAT ratio, total body fat, and cell mass, and inversely correlated with fat-free mass and total body water. In contrast, we found negative correlations of R-index values with weight, BMI, WHR, subcutaneous and visceral adipose tissue content, the VAT/SAT ratio, and total body fat mass, whereas we found a positive correlation of R-index values with fat-free mass and total body water. We also observed positive relations between Keap1 and VAT/SAT ratio values ($r = 0.263$; $$p \leq 0.05$$).
Increased concentrations of insulin and HOMA-IR levels observed in women with PCOS were positively correlated with oxidized glutathione concentrations ($r = 0.418$, $$p \leq 0.001$$; $r = 0.405$, $$p \leq 0.002$$, respectively) and negatively correlated with R-index values (r = −0.304, $$p \leq 0.003$$; r = −0.380, $$p \leq 0.004$$, respectively). In addition, associations between Nrf2 concentrations and insulin ($r = 0.256$, $$p \leq 0.057$$) and HOMA-IR levels ($r = 0.260$, $$p \leq 0.053$$) were on the border of significance.
Figure 2A–C shows the relationships between glutathione status parameters and Keap1 protein in women with PCOS. There was a statistically significant positive correlation between levels of GSSG and Keap1 concentrations (A), no correlations between GSH and Keap1 levels (B), and a significant negative correlation between Keap1 levels and R-index values (C). Additionally, increased values of reduced glutathione to oxidized glutathione ratio were significantly associated with increased levels of glutathione reductase in the serum of patients with PCOS ($r = 0.384$, $$p \leq 0.009$$). Other relationships between the selected antioxidant defense parameters were not confirmed.
## 4. Discussion
Compared with healthy counterparts, body fat distribution is different in women with PCOS due to the predominant accumulation of visceral fat and abdominal obesity [3,12,32]. Accompanying PCOS, abnormal adipose tissue metabolism and associated chronic low-grade inflammation are a significant source of reactive oxygen species [14,33]. Excess free radicals with reduced antioxidant activity—including glutathione, an important small-molecule antioxidant observed in patients with PCOS—may be responsible for exacerbating oxidative stress [18].
Studies determining glutathione status in patients with PCOS mainly concern its reduced form or the activity of glutathione peroxidase [1,17]. In the PCOS group, GSH concentrations were lower by half compared with healthy women [16,34,35]. Data on GPx activity are inconclusive and show both lower, higher, and unchanged activity of this enzyme in patients with polycystic ovary syndrome [5,15,17]. The most frequently analyzed relations were glutathione and GPx, with indicators of insulin resistance, hyperandrogenism, and infertility [3,4,18,36]. Associations between parameters of glutathione status and body composition have not been systematically studied and require attention. This is particularly important given that visceral obesity occurs in both overweight and normal-weight women [32].
In the present study, we showed differences in the levels of the tested oxidative stress markers both when dividing the groups due to WHR, determining abdominal obesity, and VAT/SAT, determining an additional increased risk of metabolic syndrome. We demonstrated that reduced glutathione was slightly higher in the group of PCOS women with low WHR and VAT/SAT ratios. This finding is consistent with the results obtained by Uckan et al. [ 35], who found significant differences in the level of this association between nonobese and obese patients with PCOS. Similarly to others, we revealed a close association of glutathione with WHR and the VAT/SAT ratio, while we did not confirm an association of GSH with insulin parameters [16,18,35]. As a result of GPx catalyzing the neutralization of toxic H2O2, reduced glutathione is oxidized to disulfide (oxidized glutathione), which—with the participation of glutathione reductase and NADPH coenzyme—is reduced back to thiol, which is an important redox cycle in the cell [36]. The effect of obesity on GPx activity has not been clearly confirmed [3,35]. Data determining oxidized glutathione levels in patients with PCOS are also unknown. In our study, GSSG levels were significantly higher in patients with elevated WHR and VAT/SAT with unchanged GPx and slightly lower GR levels. Due to the high lipolytic activity of visceral tissue and the release of large amounts of fatty acids and pro-inflammatory cytokines, peroxidative damage and the accumulation of GSSG may be enhanced in patients with PCOS [37]. Oxidized glutathione is a potentially toxic metabolite for cells. Elevated oxidized glutathione concentrations lead to a disruption of the GSH/GSSG ratio, which is crucial for many biological cell processes, such as the regulation of gene transcription or enzyme and receptor activity [36]. In the present study, high GSSG concentrations were accompanied by low GSH/GSSG ratio levels, which may suggest a shift in the balance toward oxidative processes. The close positive associations of GSSG and negative correlations of the GSH/GSSG ratio with most anthropometric data (e.g., WHR, BMI, WC, and HC) and body composition parameters (e.g., VAT/SAT ratio and fat mass in kg and %) may suggest the severity of oxidative stress in PCOS patients with abdominal and visceral obesity. The low R-index value may indicate an impaired process of reducing excessive GSSG to the active form of GSH with the involvement of glutathione reductase.
In our study, GR levels were the lowest in patients with the highest VAT/SAT ratio, while GR correlated positively with GSSG concentration and negatively with the R-index value. This confirms the important role of this enzyme in the glutathione redox cycle.
As we noted in the introduction, in addition to the regeneration of GSH from GSSG via GR, the concentration of this compound in cells is dependent on de novo synthesis. The neosynthesis of GSH in the cells is limited by the availability of cysteine, whose oxidative modifications are largely responsible for glutathione’s role in redox-dependent cell signaling. Containing cysteine residues, Nrf2 participates in the transcriptional regulation of many antioxidant genes, including enzymes that regulate the rate of glutathione synthesis. Activation of Nrf2 by pro-oxidant factors or specific Nrf2 activators (e.g., resveratrol, quercetin, and astaxanthin) is associated primarily with conformational changes in the Keap1 inhibitory protein [21,38,39,40]. In a rat model of PCOS, Li et al. [ 23] confirmed that granulosa cells (GCs) under oxidative stress show high levels of Nrf2 and heme oxygenase-1 (HO-1). Additionally, Wang et al. [ 24] documented that humanin, a mitochondrial-derived peptide, regulates oxidative stress in the ovaries of patients with polycystic ovary syndrome via the Keap1-Nrf2 pathway. Similarly, Gharaei et al. [ 21] showed that astaxanthin supplementation in women with PCOS undergoing assisted reproductive techniques positively affected antioxidant status in the blood and Keap1-Nrf2 pathway activation in GCs.
The links between oxidative stress, glutathione, and the Keap1-Nrf2 system have not yet been described. We observed slightly increased Nrf2 levels in patients with abnormal VAT/SAT and WHR ratios. In addition, in the group with VAT/SAT > 0.90, we found significantly higher Keap1 protein levels. Moreover, the negative association of Keap1 with the R-index may suggest that the elevated oxidative changes observed in this group may lead to Keap1 dissociations from Nrf2 and the activation of this factor to promote antioxidant enzyme expression. The unchanged GPx levels observed in all the groups with PCOS and only slightly reduced GR in the groups with increased risk of metabolic disorders may be the result of a compensatory antioxidant response associated with the activation of the Keap1-Nrf2 system. It is currently known that, in addition to Keap1, there are other factors that can regulate *Nrf2* gene transcription. These include phosphorylation of Nrf2 by protein kinases or acetylation of Nrf2 [41,42]. For this reason, our research in this area should be considered preliminary and should be continued to confirm possible links between Nrf2 action and antioxidant response in women with PCOS.
## Strengths and Limitations
A strength of the presented research was taking comprehensive measurements of glutathione status markers in the blood of women with PCOS in relation to body composition, with particular emphasis on visceral adipose tissue storage. Moreover, determining the ratio of GSH to GSSG allowed us to estimate the severity of oxidative stress in these patients. Assessment of Nrf2 and Keap1 proteins in the serum of patients with PCOS also seems to be important, although, on the other hand, it may be a certain limitation. Serum concentrations of this factor may not reflect the true cellular antioxidant response as Nrf2 functions mainly in the cell nucleus, and released forms do not always represent its free or active status. Another limitation of the study is that we did not measure serum levels of sulfur amino acids, which are important for the synthesis of this compound. However, we are planning to assess the amino acid profile—such as cysteine, cysteamine, cysteinyl-glycine, and homocysteine—in a future study of patients with PCOS. Finally, the lack of lipid profile and inflammatory markers may be a further limitation of our study. However, it is well known that patients with PCOS often have abnormal lipid levels, and chronic low-grade systemic inflammation is an important factor in this disease [6,14,33,43,44].
## 5. Conclusions
In conclusion, oxidative stress characterized by a depleted reduced-to-oxidized glutathione index is associated with anthropometric and body composition parameters in women with PCOS. In particular, abdominal obesity expressed by the VAT/SAT ratio and/or WHR seems to have a negative impact on glutathione status, which may lead to a disruption of many biological cell processes. The observed negative association of Keap1 with the R-index suggests that the elevated oxidative changes dependent on the VAT/SAT ratio may lead to Nrf2 activation to promote antioxidant enzyme expression. Although the GSH/GSSG index, as well as the VAT/SAT ratio, appear to be good indicators of oxidative status, studies on a larger group of patients should continue to confirm these links among women with PCOS.
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|
---
title: Urinary Phenolic Metabolites Associated with Peanut Consumption May Have a
Beneficial Impact on Vascular Health Biomarkers
authors:
- Isabella Parilli-Moser
- Inés Domínguez-López
- Anna Vallverdú-Queralt
- Sara Hurtado-Barroso
- Rosa M. Lamuela-Raventós
journal: Antioxidants
year: 2023
pmcid: PMC10045820
doi: 10.3390/antiox12030698
license: CC BY 4.0
---
# Urinary Phenolic Metabolites Associated with Peanut Consumption May Have a Beneficial Impact on Vascular Health Biomarkers
## Abstract
Phenolic compounds in peanuts may moderate inflammation and endothelial function. Thus, the aim of this study was to evaluate the association of urinary phenolic metabolites (UPMs) with vascular biomarkers after peanut product consumption. A three-arm parallel-group randomized controlled trial was conducted in 63 healthy young adults who consumed 25 g/day of skin roasted peanuts (SRP), 32 g/day of peanut butter (PB), or 32 g/day of a control butter for six months. UPMs were analyzed by liquid chromatography coupled to mass spectrometry. Additionally, urinary eicosanoids, prostacyclin I2 (PGI2), and thromboxane A2 (TXA2) were determined using two competitive enzyme-linked immunosorbent assay kits. Consumers of SRP and PB presented significantly higher excretion of UPMs (enterodiol glucuronide ($$p \leq 0.018$$ and $$p \leq 0.031$$), 3-hydroxybenzoic acid ($$p \leq 0.002$$ and $p \leq 0.001$), vanillic acid sulfate ($$p \leq 0.048$$ and $$p \leq 0.006$$), p-coumaric acid ($$p \leq 0.046$$ and $$p \leq 0.016$$), coumaric acid glucuronide I ($$p \leq 0.001$$ and $$p \leq 0.030$$) and II ($$p \leq 0.003$$ and $$p \leq 0.036$$), and isoferulic acid ($$p \leq 0.013$$ and $$p \leq 0.015$$) in comparison with the control group. An improvement in PGI2 ($$p \leq 0.037$$) levels and the TXA2:PGI2 ratio ($$p \leq 0.008$$) was also observed after the peanut interventions compared to the control. Interestingly, UPMs with significantly higher post-intervention levels were correlated with an improvement in vascular biomarkers, lower TXA2 (r from −0.25 to −0.48, $p \leq 0.050$) and TXA2:PGI2 ratio (r from −0.25 to −0.43, $p \leq 0.050$) and higher PGI2 (r from 0.24 to 0.36, $p \leq 0.050$). These findings suggest that the UPMs with higher excretion after peanut product consumption could have a positive impact on vascular health.
## 1. Introduction
The regular consumption of nuts and peanuts has been associated with a reduced risk of developing cardiovascular diseases [1,2,3] and diabetes [4,5,6], with improvements in the lipid profile, inflammation markers, and preservation of endothelial function [7,8,9,10]. However, the results of studies evaluating the impact of nut consumption on inflammation are discrepant, as clinical trials have not been able to consistently verify the anti-inflammatory effects found in observational studies [8,11,12].
Peanuts are edible seeds classified as legumes, nevertheless, they are frequently include in the nuts group, since they share a similar nutritional composition, being nutrient-dense and rich in monounsaturated fatty acids [13,14]. They are the most consumed nuts worldwide [15], and are regarded as a convenient, tasty, and easy snack that contributes to a healthy lifestyle [14]. The wide range of nutrients and bioactive compounds found in peanuts include fiber, folate, and arginine [13,16], and also, they are a well-known source of antioxidants such as polyphenols, whose concentration have been reported to be highest in their skins [17,18]. Phenolic acids, mainly p-coumaric and isoferulic acids, were the most abundant polyphenols found in skin roasted peanuts and peanut butter with skin added, representing more than 60–$70\%$ of the total polyphenols [19].
Polyphenols represent the main antioxidants and anti-inflammatory compounds in our diet and have been related to antithrombotic and vasodilatory effects [20]. They are also reported to play a beneficial role in the prevention of inflammation-related chronic diseases such as type 2 diabetes, obesity, cancers, and neurodegenerative and cardiovascular diseases [21,22,23]. Phenolic compounds appear to regulate the expression of several pro- and anti-inflammatory genes and cytokines through MAPK, NF-kB, and arachidonic acid pathways, contributing to the inhibition of enzymes involved in eicosanoid production and enhancing anti-inflammatory activities [24,25]. However, the role of polyphenols in modulating inflammatory pathways needs further investigation. It is believed that the immunoprotective and anti-inflammatory activities of polyphenols are initiated in the gut, with subsequent systemic effects [21].
The eicosanoids prostacyclin I2 (PGI2) and thromboxane A2 (TXA2) are the major arachidonic acid products in the vascular endothelium and platelets, respectively [26,27,28]. TXA2 has prothrombotic and vasoconstrictor properties, as it stimulates inflammation and platelet aggregation [28]. In contrast, PGI2 acts as a potent vasodilator and inhibitor of platelet aggregation [26,29], counteracting the activities of TXA2 and playing an important role in preventing atherosclerosis and thrombosis [28,30]. Despite being well-known vascular biomarkers, the association of PGI2 and TXA2 with peanut consumption and urinary phenolic metabolites (UPMs) has not been studied to date. Thus, the aim of the present study was to evaluate UPMs’ concentrations after daily intake of peanuts or peanut butter and their potential effect on vascular health through the analysis of urinary eicosanoids.
## 2.1. Study Population and Recruitment
Healthy young adults aged 18–33 years were recruited into the ARISTOTLE study from the Food and Nutrition Torribera Campus at the University of Barcelona and the surrounding area through poster boards in different settings, flyer distribution, and word of mouth. Potential participants were screened using the following exclusion criteria: body mass index (BMI) over 25 kg/m2, history of chronic diseases (cardiovascular diseases, cancer, diabetes, and others), peanut allergy, active smoking, excessive alcohol consumption, and other toxic habits.
## 2.2. Study Design
The present study includes data from a three-arm randomized controlled trial (ARISTOTLE study), described elsewhere [31]. All participants signed an informed consent form and were randomized to one of three intervention groups, consuming either 25 g/day of skin roasted peanuts (SRP) or two tablespoons (32 g)/day of peanut butter (PB) or two tablespoons (32 g)/day of a control butter (CB) based on peanut oil and free of fiber and polyphenols. Prior to the baseline visit they followed a two-week peanut-free run-in period. The intervention lasted 6 months, extended in some cases to 7 months due to the COVID-19 pandemic. To facilitate the intervention compliance, the participants were supplied with the three intervention products and requested to follow their habitual diet, excluding wine, grapes, dark chocolate (>$70\%$ cocoa), berries, and nuts.
The study was conducted in compliance with the principles of the Declaration of Helsinki. Ethical approval for the involvement of human subjects was granted by the Ethics Committee of Clinical Investigation of the University of Barcelona (Barcelona, Spain) and the clinical trial was registered at https://register.clinicaltrials.gov (NCT04324749).
## 2.3.1. Anthropometric and Clinical Measurements
Anthropometric and clinical measurements were obtained in fasting conditions at the beginning and end of the trial. Height was measured in the standing position using a portable stadiometer. Weight and body composition (body fat and muscle percentages) were measured using a tetrapolar OMRON BF511 bioelectrical device, with the participants wearing light clothes and no shoes. BMI was calculated as weight divided by height squared (kg/m2). Using an inelastic flexible tape, waist circumference was measured at the midpoint between the lower margin of the last rib and the top of the iliac crest, and hip circumference on the upper trochanters. Both measurements were used to calculate the waist-to-hip ratio, dividing waist circumference by hip circumference. Diastolic and systolic blood pressure (DBP and SBP, respectively) were measured in triplicate using an OMRON M6 digital monitor with the volunteer in a sitting position.
## 2.3.2. Sample Collection and Biochemical Analysis
Blood and urine samples were collected at baseline and at the end of the intervention. Overnight fasting blood was obtained from the arm via venipuncture into tubes containing ethylenediaminetetraacetic acid (EDTA) to separate serum after centrifugation at 3000 g for 10 min at 4 °C. Urine from 24 h before each visit was provided by participants. All samples were aliquoted and stored at −80 °C until analysis. Biochemical markers in serum (lipid profile) were measured in an external laboratory (Cerba international, Barcelona, Spain) using enzymatic methods.
## 2.3.3. Dietary Intake and Physical Activity
Diet and physical activity were recorded by professional staff members using validated questionnaires. Dietary intake was quantified using a semi-quantitative 151-item food frequency questionnaire (FFQ) and Spanish food composition tables [32]. Physical activity was measured as the metabolic equivalent of task-minutes per week (MET/week) using the Spanish version of the Minnesota Leisure-Time Physical Activity Questionnaire [33].
## 2.4.1. Standards and Reagents
Protocatechuic acid, 4-hydroxybenzoic acid, o-coumaric acid, m-coumaric acid, p-coumaric acid, enterodiol, urolithin-A, and urolithin-B were purchased from Sigma-Aldrich (St. Louis, MO, USA). Dihydroresveratrol and the internal standard (+) cis, trans-abscisic acid D6 were obtained from Santa Cruz (Santa Cruz Biotechnology, Santa Cruz, CA), and 3-hydroxybenzoic acid, vanillic acid, syringic acid, and enterolactone from Fluka (St. Louis, MO, USA). The reagents were purchased from the following commercial suppliers: methanol and acetonitrile of HPLC grade from Sigma-Aldrich, formic acid (≥$98\%$) from Panreac Química S.A. (Barcelona, Spain), and ultrapure water (Milli-Q) generated by a Millipore system (Bedford, MA, USA).
## 2.4.2. Urine Treatment for Phenolic Metabolite Analysis
In each visit, 24 h urine samples were collected and stored at −80 °C until analysis. All samples and standards were always handled under filtered light and cool conditions to prevent phenolic oxidation. UPMs were determined following a validated method developed by our research group [34]. Briefly, 50 μL of urine was diluted with ultrapure water to 1 mL, acidified with 2 μL of formic acid and centrifuged at 15,000 g at 4 °C for 4 min. The acidified urines underwent a solid-phase extraction in Water Oasis HLB 96-well plates (30 µm) (Water Oasis, Milford, MA, USA). The 96-well plate was activated with 1 mL of methanol and 1 mL of 1.5 M formic acid, added consecutively. Then, 1 mL of sample was loaded onto the plates together with 100 μL of the internal standard. Sample clean-up was performed with 500 μL of 1.5 M formic acid and $0.5\%$ methanol, and elution was achieved using 1 mL of methanol acidified with 1.5 M formic acid. After evaporation under nitrogen stream, it was reconstituted with 100 μL of $0.05\%$ formic acid and the extract was filtered with a 0.22 µm polytetrafluoroethylene 96-well plate filter (Millipore, MA, USA).
## 2.4.3. Chromatographic Conditions
The analysis was performed by liquid chromatography coupled to linear trap quadrupole Orbitrap high-resolution mass spectrometry (LC-LTQ-Orbitrap-HRMS) (Thermo Scientific, Hemel Hempstead, UK) equipped with electrospray ionization and working in negative mode, as previously described by Laveriano-Santos et al. [ 34]. Chromatographic separation was performed using a Kinetex F5 100A (50 × 4.6 mm × 2.6 µm) from Phenomenex (Torrance, CA, USA). The gradient elution was performed with two mobile phases, A, water ($0.05\%$ formic acid), and B, acetonitrile ($0.05\%$ formic acid), using the following non-linear gradient: 0 min, $2\%$ B; 1 min, $2\%$ B; 2.5 min, $8\%$ B; 7 min, $20\%$ B; 9 min, $30\%$ B; 11 min, $50\%$ B; 12 min, $70\%$ B; 15 min, $100\%$ B; 16 min, $100\%$ B; 16.5 min, $2\%$ B; and 21.5 min, $2\%$ B. The flow rate was set at 0.5 mL/min and the injection volume was 5 µL.
## 2.4.4. Identification and Quantification of Urinary Phenolic Metabolites
Aglycones were identified by comparing retention times with those of available standards and phase II metabolites by comparison with accurate mass MS/MS spectra with an error of 5 ppm found in the literature. As standards for glucuronidated and sulfated UPMs were unavailable, these metabolites were quantified with their respective aglycone equivalents. Xcalibur 3.0 and Trace Finder version 4.1 (Thermo Fisher Scientific, San Jose, CA, USA) software were used for the instrument control and chromatographic data analysis. In this study, 38 UPMs were identified and quantified (aglycones and phase II metabolites in glucuronide and sulfate form). Values below the limit of detection were replaced by half the limit of detection, and values below the limit of quantitation were replaced by the midpoint between the limit of detection and the limit of quantitation.
## 2.5. Determination of Eicosanoids in Urine
The concentration of urinary PGI2 and TXA2 was indirectly quantified by measuring the prostaglandin I metabolite and 11-dehydro thromboxane B2, respectively. Both molecules were determined in urine using two competitive enzyme-linked immunosorbent assay (ELISA) kits acquired from Cayman Chem. Co. (Ann Arbor, MI, USA, ref. 501,100 and 519,510). The PGIM assay has a range from 39 to 5000 pg/mL and a sensitivity ($80\%$ B/B0) of approximately 120 pg/mL. The 11-dehydro thromboxane B2 assay has a range from 15.6 to 2000 pg/mL and a sensitivity ($80\%$ B/B0) of approximately 34 pg/mL. The urine samples were diluted 1:10 and 1:3, respectively, and assayed in triplicate. The TXA2:PGI2 ratio was also calculated. Concentrations are expressed as pg/mL.
## 2.6. Statistical Analyses
Continuous variables are expressed as mean ± standard deviation (SD) and categorical variables are expressed as number (n) and proportion (%). Normality of distribution was assessed by the Shapiro–Wilk test. Non-parametric tests were used due to the non-normality of most variables and the small sample size (<30 in each group). Differences between groups in the general characteristics of participants at baseline were measured by the chi-square test for categorical variables and the Kruskal–Wallis test for continuous variables. The effect of peanut and peanut butter interventions on UPMs and eicosanoids was estimated by performing a generalized estimating equation on Poisson regression models for repeated measures. Identity link function, autoregressive correlation, and robust standard error parameters were specified due to the low number of clusters and the nature of the variables. Analyses were adjusted for age, sex, and physical activity. Finally, Spearman’s correlation coefficient was estimated to study linear associations between UPMs and eicosanoids. All statistical analyses were conducted using the Stata statistical software package version 16.0 (StataCorp, College Station, TX, USA). Differences were considered significant when the p-value was lower than 0.050.
## 3.1. Baseline Characteristics
Among the 90 subjects who enrolled and were randomized to each arm, 63 participants (19 males and 44 females) completed the study. Table 1 shows their general characteristics. The average age was 22.71 ± 3.13 years; around $70\%$ were female and $46\%$ had graduated from a 4-year degree course. The mean physical activity was higher than 4000 METs/week. At baseline, no significant differences between groups were found, except in the level of plasmatic high-density lipoprotein cholesterol (HDL-c) ($$p \leq 0.006$$) and urinary concentrations of urolithin B and dihydroresveratrol glucuronide II ($$p \leq 0.022$$ and $$p \leq 0.008$$ respectively, data not shown).
## 3.2. Effect of the Intervention on Urinary Phenolic Metabolite Levels
The concentration of UPMs by polyphenol class (lignans, hydroxybenzoic acids, hydroxycinnamic acids, stilbenes, and hydroxycoumarins) is presented in Table 2. A total of 38 metabolites were identified in urine and many of them were detected in the form of glucuronides and sulfates. Overall, the most abundant UPMs were hydroxybenzoic acids, and the least abundant were stilbenes. After adjustment for sex and age, the excretion of some UPMs was found to be higher after peanut or peanut butter consumption compared with the control butter.
Compared to the CB group, lignan excretion was significantly higher in the SRP group (enterodiol glucuronide, $$p \leq 0.018$$; enterolactone glucuronide, $$p \leq 0.045$$; and enterolactone sulfate, $$p \leq 0.020$$) and the PB group (enterodiol glucuronide, $$p \leq 0.031$$, and enterolactone glucuronide, $$p \leq 0.032$$) after full adjustment (Table 2). In regard to the hydroxybenzoic acids, higher excretion levels after the consumption of PB (3-hydroxybenzoic acid, $p \leq 0.001$; hydroxybenzoic acid sulfate, $$p \leq 0.014$$; vanillic acid sulfate, $$p \leq 0.006$$; and syringic acid glucuronide II, $$p \leq 0.023$$) and SRP (3-hydroxybenzoic acid, $$p \leq 0.002$$; vanillic acid sulfate, $$p \leq 0.048$$; and syringic acid sulfate, $$p \leq 0.041$$) were found compared to the CB group. Interestingly, post-intervention levels of hydroxycinnamic acids such as p-coumaric acid, coumaric acid glucuronide I, coumaric acid glucuronide II, and isoferulic acid were significantly higher in both the PB ($$p \leq 0.016$$, $$p \leq 0.030$$, $$p \leq 0.036$$, and $$p \leq 0.015$$, respectively) and SRP groups ($$p \leq 0.046$$, $$p \leq 0.001$$, $$p \leq 0.003$$, and $$p \leq 0.013$$, respectively) in comparison with the control. Regarding stilbenes, the only increase observed was in dihydroresveratrol glucuronide II after PB consumption versus CB ($$p \leq 0.004$$) (models of adjustment are shown in Table A1).
## 3.3. Effect of the Intervention on Eicosanoid Levels in Urine
The urinary levels of eicosanoids are presented in Table 3. Compared to the control, a significant change in PGI2 levels was observed after SRP consumption ($$p \leq 0.037$$), whereas the TXA2:PGI2 ratio decreased after both SRP and PB interventions ($$p \leq 0.021$$ and $$p \leq 0.047$$, respectively) after adjustment. However, no change in TXA2 levels was observed after 6 months or between groups (models of adjustment are shown in Table A2).
## 3.4. Relationship between Urinary Phenolic Metabolites and Eicosanoids
Correlations were generated to evaluate the association between UPM and eicosanoid levels. Of the 38 quantified metabolites, 17 showed a significant correlation with one or two of the vascular biomarkers (Figure 1). The participants with a higher excretion of enterodiol, enterolactone, enterolactone glucuronide, enterolactone diglucuronide, enterolactone sulfate, syringic acid glucuronide I, syringic acid sulfate, dihydroresveratrol glucuronide II, urolithin A, and urolithin B presented lower levels of TXA2 (r = −0.44, $p \leq 0.001$; r = −0.36, $$p \leq 0.003$$; r = −0.25, $$p \leq 0.045$$; r = −0.41, $p \leq 0.001$; r = −0.36, $$p \leq 0.005$$; r = −0.31, $$p \leq 0.015$$; r = −0.28, $$p \leq 0.029$$; r = −0.25, $$p \leq 0.046$$; r = −0.48, $p \leq 0.001$; and r = −0.38, $$p \leq 0.002$$, respectively). Moreover, significant direct correlations were observed between levels of PGI2 and enterodiol glucuronide, 3-hydroxybenzoic acid, vanillic acid, p-coumaric acid, coumaric acid glucuronide II, and isoferulic acid ($r = 0.26$, $$p \leq 0.045$$; $r = 0.26$, $$p \leq 0.042$$; $r = 0.36$, $$p \leq 0.006$$; $r = 0.27$, $$p \leq 0.032$$; $r = 0.24$, $$p \leq 0.046$$; and $r = 0.31$, $$p \leq 0.014$$, respectively). Similarly, higher levels of enterodiol, enterolactone, enterolactone sulfate, 3-hydroxybenzoic acid, vanillic acid sulfate, p-coumaric acid, o-coumaric acid, coumaric acid glucuronide III, isoferulic acid, dihydroresveratrol glucuronide II, and urolithin A were associated with a lower TXA2:PGI2 ratio (r = −0.26, $$p \leq 0.042$$; r = −0.29, $$p \leq 0.019$$; r = −0.28, $$p \leq 0.023$$; r = −0.27, $$p \leq 0.031$$; r = −0.25, $$p \leq 0.046$$; r = −0.27, $$p \leq 0.038$$; r = −0.30, $$p \leq 0.017$$; r = −0.29, $p \leq 0.022$; r = −0.43, $p \leq 0.001$; r = −0.28, $$p \leq 0.027$$; and r = −0.41, $$p \leq 0.001$$; respectively).
## 4. Discussion
In this randomized controlled trial, a significant increase in urinary UPMs was observed in healthy young adults who consumed SRP and PB daily for 6 months compared to those who consumed CB (a cream without fiber or polyphenols). Similarly, the ratio between the eicosanoids TXA2 and PGI2 improved in the consumers of SRP or PB compared to CB. Interestingly, we found that several UPMs with significantly higher post-intervention levels were associated with improvements in vascular biomarkers (lower TXA2 and TXA2:PGI2 ratio and higher PGI2).
Compared to the control group, participants consuming SRP and PB were found to excrete higher levels of lignans (enterodiol glucuronide, enterolactone glucuronide, and enterolactone sulfate), hydroxybenzoic acids (3-hydroxybenzoic acid vanillic acid sulfate, hydroxybenzoic acid sulfate, syringic acid glucuronide II, and syringic acid sulfate), hydroxycinnamic acids (p-coumaric acid, coumaric acid glucuronides I and II, and isoferulic acid), and stilbenes (dihydroresveratrol glucuronide II). To date, few studies have investigated the bioavailability of peanut polyphenols. In a recent study published by our group, we showed that the most abundant polyphenols in the two intervention products (SRP and PB) are p-coumaric and isoferulic acid [19]. In a comparative study with tree nuts, Rocchetti et al. found the total phenolic content was highest in peanuts, especially phenolic acids such as 3,4-dihydroxyphenylacetic, 4-hydroxybenzoic, and protocatechuic acids, after in vitro fecal fermentation [35].
In vitro and in vivo studies have provided evidence for the anti-inflammatory, antiadipogenic, and antidiabetic potential of nut polyphenols [36,37,38]. Nevertheless, the biological properties of these phytochemicals are highly dependent on their bioavailability. After ingestion, 85–$90\%$ of dietary polyphenols reach the large intestine, where they become fermentable substrates for bacterial enzymes, leading to the breakdown of their original structures into several smaller absorbable metabolites [39,40,41]. These compounds reach the bloodstream and can have a biological effect on target organs. [ 39]. Maintaining a healthy gut microbiota has emerged as a key factor for protection against inflammatory-related diseases. Polyphenol activity is thought to principally take place in the gut, where phenolic immunoprotective and anti-inflammatory effects are initiated before acting at a systemic level [21]. Regarding microbial metabolites, participants in the present study who consumed SRP or PB presented a higher excretion of enterolactone glucuronide and enterodiol glucuronide, both important biomarkers of microbiota diversity [42]. In previous studies, higher post-intervention levels of phenolic metabolites have been associated with beneficial health effects. For example, hydroxycinnamic acids were related with lower odds of depression in an Italian cohort [43] and of developing metabolic syndrome in a Polish cohort [44]. Hydroxybenzoic acids were inversely associated with cardiovascular disease in a Spanish study [45]. In addition, associations have also been found between urinary lignan metabolites and a lower risk of type 2 diabetes (enterolactone) and diabetes mortality (enterodiol) [46,47].
The eicosanoids PGI2 and TXA2 are the major arachidonic acid products in the vascular endothelium and platelets, synthesized by cyclooxygenase isoforms [26,27,28]. As PGI2 counteracts the pro-aggregatory and vasoconstrictor activities of TXA2 [29,31], the ratio of the two molecules is an important regulator of the interaction between platelets and vessel walls in vivo, and crucial for vascular health [48,49]. Previous research indicates that peanut consumption may have a positive effect on cardiometabolic risk factors and reduce the risk of developing cardiovascular diseases [3,5,50,51]. However, this is the first study to report an improvement in vascular biomarkers related to antithrombotic and vasodilatory effects in healthy young adults after peanut product consumption. We found a significant reduction in the TXA2:PGI2 ratio in participants who daily consumed SRP and PB compared to the control group. Regarding PGI2, a higher level was found in the SRP group and an increasing tendency in PB consumers, whereas no changes were observed in TXA2 levels after the intervention compared to the control. Our results agree with those of Canales et al., who reported an increase in PGI2 serum levels and a decrease in the TXB2:PGI2 ratio after consumption of walnut-paste-enriched meat [52]. Similarly, a long-term decrease in inflammatory markers was observed in healthy volunteers after the consumption of 20 g and 50 g of Brazil nuts [53].
The role of phenolic compounds in anti-inflammatory reactions and the modulation of enzymatic activities related to eicosanoid synthesis and degradation has been reported [54,55]. They are thought to be involved in the expression of several pro- and anti-inflammatory genes and cytokines through different pathways (MAPK, NF-kB, and arachidonic acid) [24,25]. To shed light on the vascular effects of polyphenols, we correlated UPM with eicosanoid levels and found that participants who excreted more p-coumaric acid, o-coumaric acid, coumaric acid glucuronide III, and isoferulic acid (the major polyphenols in peanuts) presented higher levels of PGI2 and a lower TXA2:PGI2 ratio. In addition, those who excreted more enterodiol, enterodiol glucuronide, enterolactone, enterolactone sulfate and glucuronide, and urolithins A and B (microbial phenolic metabolites) presented a lower TXA2:PGI2 ratio and TXA2 levels. These results suggest that an improvement in vascular function is associated with a higher excretion of UPMs from a dietary source (in this case peanut consumption) or from the gut microbiota.
In line with our results, it has been demonstrated that plant polyphenols can enhance PGI2 release from endothelial cells [56], and the consumption of high-procyanidin chocolate was related to an increase in plasma PGI2 [55]. Additionally, a reduction in serum TXA2 was determined in healthy subjects after the consumption of extra virgin olive oil, a typical polyphenol-rich product of the Mediterranean diet [57]. After an intervention with cranberry juice, Rodriguez-Mateos et al. found that twelve polyphenol metabolites, including ferulic and caffeic acid sulfates, quercetin-3-O-ß-D glucuronide, and γ-valerolactone sulfate, were significantly correlated with improved vascular function in healthy volunteers [58]. They also observed an amelioration of endothelial function after the acute intake of blueberry drinks containing different levels of polyphenols [59] and raspberries [60]. Additionally, metabolites such as caffeic acid, ferulic acid, isoferulic acid, vanillic acid, and 2-hydroxybenzoic acid, measured in plasma after the intake of a whole-grain biscuit rich in phenolic acids, were associated with a reduced inflammatory status in overweight subjects [61]. Moreover, after consumption of a low-polyphenol diet by healthy young men, a higher ratio of TXA2:PGI2 versus a usual diet (Mediterranean diet) was observed [62]. Nevertheless, conflicting results have also been published. For example, no changes in TXA2 levels were found in healthy subjects who consumed 40 g of dark chocolate [63]. Moreover, no effects on TXA2 and PGI2 metabolites in urine, or the ratio of both molecules, were found in healthy subjects consuming an American diet supplemented with procyanidin-enriched cacao [64].
## Strengths and Limitations
To our knowledge, this is the first study to analyze UPMs after peanut consumption and to provide promising results regarding the effect of peanut consumption on vascular function in healthy young adults. Another strong point of the present study is the randomized and controlled design, as well as the use of a precise extraction of phenolic metabolites from urine samples and the novel method based on liquid chromatography coupled to mass spectrometry used for the accurate identification and quantification of UPMs. However, several limitations should also be acknowledged, including the small sample size for each intervention group and the lack of blinding. The sample size was calculated to ensure $80\%$ of statistical power, but this value decreased to $60\%$ due to dropouts. Finally, the scope of the study did not include the elucidation of molecular mechanisms underlying the observed associations, and hence, causality cannot be determined.
## 5. Conclusions
In conclusion, the present study shows for the first time that regular peanut and peanut butter consumption could have a positive impact on vascular biomarkers in healthy young adults. Our results suggest that the urinary phenolic metabolites whose production increased after peanut product consumption, especially hydroxycinnamic acids, may contribute to the maintenance of vascular health, as could microbial phenolic metabolites such as enterolignans and hydroxycoumarins. However, further studies, mainly clinical trials, are needed to elucidate the association between metabolites and vascular function, as well as to understand the plausible mechanisms.
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|
---
title: 'Mid-Infrared Spectroscopy as a New Tool for Ruling Out Spontaneous Bacterial
Peritonitis: A Proof-of-Concept Study'
authors:
- Marwin A. Farrugia
- Maëna Le Corvec
- Christophe Renou
- Jean-Baptiste Nousbaum
- Dann J. Ouizeman
- Olivier Sire
- Olivier Loréal
- Hugues Tariel
- Jérôme Bernard
- Thierry Piche
- Albert Tran
- Hafid Ait-Oufella
- Luce Landraud
- Philippe Gual
- Rodolphe Anty
journal: Biomedicines
year: 2023
pmcid: PMC10045833
doi: 10.3390/biomedicines11030838
license: CC BY 4.0
---
# Mid-Infrared Spectroscopy as a New Tool for Ruling Out Spontaneous Bacterial Peritonitis: A Proof-of-Concept Study
## Abstract
Background and aims: A highly sensitive and specific point-of-care method for diagnosing spontaneous bacterial peritonitis (SBP) is currently lacking. The objective of the present study is to evaluate the diagnostic value of a rapid, easy-to-use, mid-infrared fiber evanescent wave spectroscopy (MIR-FEWS) method for ruling out SBP. Patients and Methods: Cirrhotic patients ($$n = 256$$) at five centers in France were included for suspected SBP or for the scheduled evacuation of ascites fluid. The mid-infrared spectrum of 7 µL of an ascites fluid sample was recorded using a MIR-FEWS system. To define a model for the diagnosis of SBP, the patients were divided into a calibration group ($$n = 170$$) and a validation group ($$n = 86$$). Results: Most of the patients were male ($71\%$). The mean age was 60.25 years. Alcohol-related liver disease was the most common cause of cirrhosis. SBP was observed in $18\%$ of the patients. For the diagnosis of SBP in the calibration and validation groups, respectively, the model gave areas under the receiver operating characteristic curves of 0.87 and 0.89, sensitivities of $90\%$ and $87\%$, specificities of $78\%$ and $80\%$, positive predictive values of $48\%$ and $50\%$, negative predictive values of $97\%$ and $96\%$, positive likelihood ratio of 4.09 and 4.35, negative likelihood ratio of 0.13 and 0.16, Youden index of 0.68 and 0.67, and correct classification rates of $80\%$ and $81\%$. Conclusion: The results of this proof-of-concept study show that MIR-FEWS is a highly sensitive diagnostic method for ruling out SBP. The method warrants further investigation.
## 1. Introduction
Spontaneous bacterial peritonitis (SBP) is a frequently lethal complication in patients with ascites and cirrhosis. Although the mortality rate has decreased over the last 30 years, it remains between $23\%$ and $58\%$ [1,2]. As with other infections in cirrhotic patients, earlier treatment is associated with a lower likelihood of death [3].
Several point-of-care (POC) tests (such as urine dipsticks in ascites fluid) have been developed but are not sensitive enough [4]. It has been suggested that sophisticated ascites cytokine profiles can help the clinician to manage patients with suspected SBP [5,6,7]. However, given the lack of international guidelines on the use of these tools, there is an unmet need for a specific, sensitive POC test for SBP [1].
Mid-infrared (MIR) spectroscopy measures the vibrational interactions between a sample and mid-infrared light. The MIR spectrum of a patient’s biofluid may constitute a metabolic fingerprint that can be used for diagnostic purposes [8]. Several mid-infrared spectroscopy techniques have been developed. In mid-infrared fiber evanescent wave spectroscopy (MIR-FEWS), the sample to be analyzed is deposited on an optical fiber capable of transmitting MIR light. In the present study, a sensor with a chalcogenide glass optical fiber and an appropriate spectrometer (DIAFIR, Rennes, France) were used [8]. The MIR spectra were analyzed with specific validated software, in order to give the clinician a result within 15 min; no engineering know-how is required. This MIR-FEWS technique has recently been described as a POC diagnostic for infections in patients with septic arthritis [9,10].
The objective of the present study is to develop a new spectral model for ruling out SBP in a French multicenter study of a cohort of patients with ascites and cirrhosis.
## 2.1. Patients
An initial group of 123 patients with ascites and cirrhosis was recruited prospectively at four centers (Brest, Hyères, Nice, and Monaco) between 2010 and 2014, during a French multicenter study funded by the French national hospital-based clinical research program (forming the PHRC cohort; ClinicalTrials.gov NCT01193426) [11]. The goal was to define a specific cytokine profile produced during SBP [11]. Clinical and laboratory data were collected for all patients. Laboratory data included analyses of blood and ascites samples.
A second group of 133 patients with ascites and cirrhosis admitted to an intensive care unit (at Saint-Antoine University Medical Center, Paris, France; the Paris cohort) was studied prospectively. The study was approved by an institutional review board (CPP IV Ile de France, Paris, France; reference $\frac{2014}{04}$NI) and performed in accordance with the principles of the Declaration of Helsinki. The goal was to define specific ascitic cytokines during the development of SBP, and the corresponding results have been published elsewhere [11]. Detailed clinical and laboratory data were not available for the Paris cohort.
The main inclusion criteria applied to the two cohorts were as follows: Aged 18 or over, social security coverage, the provision of informed consent, and admission for the treatment of ascites or complications of cirrhosis. The exclusion criteria applied to the two cohorts were abdominal surgery within the previous month, the presence of chylous ascites or ascites not related to portal hypertension (pancreatic ascites, hemoperitoneum, ascites observed during acute heart failure, peritoneal tuberculosis, and hepatocellular carcinoma), and severe obesity (body mass index ≥35 kg/m2).
Spontaneous bacterial peritonitis was diagnosed as a polymorphonuclear (PMN) leukocyte count in the ascites fluid ≥250/mm3, in line with the current guidelines, [12,13].
Ascites cultures were obtained for all patients.
All patients gave their written, informed consent to participate.
## 2.2. Ascites Sample Analysis
For each patient in the PHRC and Paris cohorts, ascites samples were collected on the day of hospital admission, in dry sterile tubes (BD Vacutainer® ESTTM N°362725 13 × 75 mm). The cell count was determined using a magnification microscope with the KOVA™ Glasstic™ Slide 10 with Grids device. The samples were collected from the two cohorts between 2010 and 2014. After centrifugation at 5000 g/min, the supernatants were frozen, stored at −80 °C at each investigating center, and subsequently (in 2015) analyzed centrally using MIR-FEWS.
## 2.3.1. Acquisition and Pre-Treatment of Spectra
The MIR absorbance spectra (frequency range: 4000–800 cm−1) were recorded for 15 min, using LS23 single-use sensors and a SPIDTM FT-IR spectrometer (DIAFIR, Rennes, France). A FEWS infrared sensor was placed in the spectrometer, the background signal was recorded, and 7 μL of ascites fluid were deposited on the sensor for acquisition of the spectrum.
Only spectra that passed quality controls (sufficient signal amplitude at background and acquisition stage, final signal-to-noise ratio, and the water to other elements signal ratio) were selected. In order to reduce physical and environmental sources of bias, the spectra were preprocessed and normalized.
The data’s homogeneity (i.e., the possible presence of outliers) was checked by visual inspection of a principal component analysis plot.
## 2.3.2. MIR-FEWS Analysis
The objective of the statistical analyses of the spectra was to identify patients with an ascites fluid PMN leukocyte count <250/mm3 (i.e., no SBP) or ≥250/mm3 (i.e., SBP). The patients’ spectra were randomized 2:1 into a calibration group and a validation group. The two randomized groups had the same proportions of patients with SBP.
Firstly, the most informative spectral variables (i.e., those that best distinguish between a PMN leukocyte count <250 vs. ≥250) were selected. The initial set of 615 spectral variables was reduced by applying a least absolute shrinkage and selection operator, a random forest algorithm, and factor-adjusted discriminant analysis. Secondly, a logistic regression model (hereafter referred to as the spectral model) was defined. It produced a score ranging from zero (fluid PMN leukocyte count <250/mm3) to one (fluid PMN leukocyte count ≥250), which corresponded to the probability of identifying a patient with an ascites fluid PMN leukocyte count ≥250/mm3. The optimal threshold was chosen according to the “closest.topleft” method [14] i.e., the point closest to the top-left part of the area under the curve plot with perfect sensitivity or specificity.
The model’s diagnostic performance with a given threshold was characterized by calculating the area under the receiver operating characteristic curve (AUROC), the sensitivity, the specificity, the negative predictive values (NPV), the positive predictive value (PPV), positive likelihood ratio (LR+), negative likelihood ratio (LR−), Youden index and the correct classification rate.
## 2.4. Statistical Analysis
Continuous variables were expressed as the median [interquartile range]. The Shapiro–Wilk test was used to determine whether or not data were normally distributed. Groups were compared using Student’s t-test (for normally distributed data) or the Mann–Whitney U-test. Categorical data were assessed using Fisher’s exact test. Spearman’s test was used to study the correlations between variables.
The $95\%$ confidence intervals for the performance indicators and the AUROCs were estimated from 2000 bootstrap replicates (using the pROC package in R software [14]). The threshold for statistical significance was set to $p \leq 0.05.$ All statistical analyses were performed with R software [15].
## 3.1. Characteristics of the Study Participants
Spontaneous bacterial peritonitis was found in 47 ($18\%$) of the 256 patients. The characteristics of the study participants with and without SBP are summarized in Table 1. Data on most of the clinical and laboratory variables were only available for the PHRC cohort ($$n = 123$$).
The majority of the patients were male ($71\%$), and the study population’s mean age was 60.3 years. The causes of cirrhosis were variously alcohol ($$n = 83$$, $67.4\%$), viral hepatitis and alcohol ($$n = 19$$; $15.4\%$), viral hepatitis ($$n = 8$$; $6.5\%$), non-alcoholic fatty liver disease ($$n = 4$$, $3.3\%$), biliary cirrhosis ($$n = 3$$, $2.4\%$), and other causes ($$n = 6$$; $5\%$). There were no significant differences between the calibration and validation groups (Table S1).
## 3.2. The Spectral Model’s Ability to Identify SBP
All the spectra passed the quality controls, and the principal component analysis did not reveal any outliers. The ascites fluid-specific spectral model was defined with four spectral variables (the absorption values at four different wavelengths). The model’s AUROCs for the diagnosis of SBP were 0.87 and 0.89 in the calibration and validation groups, respectively (Figure 1).
At the optimal threshold, the calibration model had a sensitivity of $90\%$, a specificity of $78\%$, a PPV of $48\%$, an NPV of $97\%$, a Youden index of 0.68, and a correct classification rate of $80\%$. The validation model gave a sensitivity of $87\%$, a specificity of $80\%$, a PPV of $50\%$, an NPV of $96\%$, a Youden index of 0.67, and a correct classification rate of $81\%$ (Table 2). A comparison of the AUROCs and the scores did not detect any significant differences between the PHRC cohort and the Paris cohort (Figure 2).
The spectral model’s scores were significantly correlated with the PMN leukocyte count in the calibration samples ($r = 0.44$; $p \leq 0.001$), the validation samples ($r = 0.64$; $p \leq 0.001$), and the samples as a whole ($r = 0.48$; $p \leq 0.001$).
None of the studied variables accounted for the misidentification of some patients, i.e., the five false-negative patients (three in the calibration group and two in the validation group; Table S2) and the 44 false-positive patients (30 in the calibration group and 14 in the validation group). A positive culture did not appear to have a significant influence on the spectral model’s score (Figure S1). An analysis of cases as a function of the bacteria involved would not have been statistically robust, given the small size of each subgroup.
## 4. Discussion
In the present study, an innovative MIR-FEWS technique was used to rule out SBP in cirrhotic patients. The AUROCs in the calibration and validation groups were 0.87 and 0.89, respectively.
Spontaneous bacterial peritonitis is a frequent and highly lethal complication of end-stage liver disease [12]. According to the guidelines, specific treatment of SBP must be initiated as soon as possible [12,13]. This is only possible if the diagnosis is actively sought by obtaining an ascites fluid sample and an immediate cytology assessment. The result should then be sent as soon as possible to the clinician, so that he/she can initiate appropriate treatment. Problems at any of these steps can delay the initiation of specific treatment for SBP. In this situation, a highly sensitive and specific POC test would be helpful for the immediate initiation of treatment of SBP. Several POC technologies (including urine test strips) have been assessed. However, a large prospective study showed that the Multistix test strip was not sensitive or specific enough [16]. Use of a second-generation test strip improved the specificity but not the sensitivity [17]. Specific markers in the ascites or serum have also been investigated; these include various cytokines, calprotectin, lactoferrin, and triggering receptor expressed on myeloid cells 1 (TREM-1) [11,18,19,20,21]. A synthesis of the results of these studies and the current study is presented in Supplementary Table S3. Up to now, a specific, easy-to-measure marker is not yet available, and the international guidelines do not recommend any diagnostic markers [12].
The SPIDTM MIR-FEWS system can be easily operated by the clinician. Its value in the diagnosis of septic arthritis has already been demonstrated in a pilot study and a large prospective study [9,10]. It has been suggested that MIR-FEWS will be of clinical value in other fields of hepatology, such as the diagnosis of non-alcoholic steatohepatitis or the prognostic evaluation of patients with ascites and cirrhosis [8,22].
Our present results suggest that MIR-FEWS is useful and has a very high NPV for ruling out SBP within 15 min. A negative result can thus avoid the initiation of inappropriate antibiotic treatment. Of course, a cytology assessment of the ascites fluid sample should be performed as soon as possible. The diagnostic accuracy appeared to be equivalent or superior to that of other tests described in the literature in this clinical setting (Supplementary Table S3). The negative predictive value was high, which enabled us to unambiguously detect patients without SBP.
The present study has several strengths. Firstly, the patients were prospectively included, and the ascites fluid samples were prospectively collected. Secondly, the patients came from several general hospitals and two university hospitals. Thirdly, the large number of patients made it possible to establish validation and calibration groups and thus avoid overfitting.
The study also had some limitations. Firstly, the MIR-FEWS technique was not tested as a true POC test (i.e., at the time when the patients were being cared for in the various investigating centers; the analyzed samples had been collected and frozen several years previously). Secondly, the PPV was low; half of the patients were wrongly considered to have SBP. Thirdly, long-term data on the morbidity and mortality of the study population were lacking. It would be valuable to investigate the possible impact of this rapid diagnostic approach on the three-month morbidity and mortality of patients in a prospective multicenter study.
The present proof-of-concept study highlighted the diagnostic value of an innovative MIR-FEWS technique. To validate this new technology as an accurate POC test, our results will now have to be confirmed in a large, prospective, multicenter study. The test’s specificity could perhaps be increased by combining the spectral model with one or several clinical or laboratory variables.
## 5. Conclusions
An innovative MIR-FEWS technique might be of value for rapidly ruling out SBP (within 15 min) and with a high degree of sensitivity. A large, prospective, multicenter study is now required to confirm these findings. MIR-FEWS might be a new tool to help the clinician manage patients with ascites, cirrhosis and SBP; as emphasized in the treatment guidelines, every hour gained is precious.
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---
title: Perceived Stress Is Directly Associated with Major Consumption of Sugar-Sweetened
Beverages among Public University Students
authors:
- Cesar Campos-Ramírez
- Jorge Palacios-Delgado
- Maria del Carmen Caamaño-Perez
- Nicolas Camacho-Calderon
- María Elena Villagrán-Herrera
- Adriana Aguilar-Galarza
- Teresa García-Gasca
- Miriam Aracely Anaya-Loyola
journal: Behavioral Sciences
year: 2023
pmcid: PMC10045845
doi: 10.3390/bs13030232
license: CC BY 4.0
---
# Perceived Stress Is Directly Associated with Major Consumption of Sugar-Sweetened Beverages among Public University Students
## Abstract
Stress is a condition that has been related to the development of risk behaviors for health such as sugar-sweetened beverages (SSBs) consumption. The aim of this study was to examine the link between SSBs consumption and perceived stress level in university students. This was an observational, cross-sectional and single-time-point study where the subjects were recruited as a non-probabilistic sample of first-year university students. The students reported their SSBs consumption through a validated questionnaire, as well as their perceived stress level, evaluated through the Cohen scale. Comparisons were made between the means of all variables. Factorial analysis of variance was conducted to explore the effect of the variables’ interaction on the stress level. One-way analysis of variance was performed to assess differences between the sexes. Men consumed more SSBs (6101.17 ± 3772.50 mL/week) compared to women (4294.06 ± 3093.8 mL/week). However, women had higher scores of perceived stress and showed a strong association of stress with the SSBs consumption pattern (r and p-value). This study shows for the first time the association that exists between stress and SSBs consumption and indicates that it is related to sex in the young population.
## 1. Introduction
Obesity is an increasingly major health issue in developing countries. This condition can be driven by a complex set of factors including inherited (genetic) and adaptive (epigenetic) biological traits, behavioral and emotional life experiences, as well as environmental and social variables. Nevertheless, the impressive increase in obesity rates over the past few decades, in which we dramatically changed our natural and food-related environment, strongly suggests that obesity is mainly driven by environmental adaptive, rather than genetic inherited factors [1,2].
On the other hand, energy intake has also increased in our modern society through the ready availability and low cost of energy-dense foods and drinks [3], such as nutrient-poor foods and, especially, sugar-sweetened beverages (SSBs), which are highly palatable products (HPP) that can be experienced with intense pleasure. Interestingly, HPP consumption is upregulated by the intensity and duration of the stress experience of the organism [4,5,6]. Also important is to note that the pleasure experience associated with palatability is considered necessary and sufficient for stress dampening [7]. In other words, we eat and drink these products to cope with our stress.
Stress is a complex set of the organism’s responses to stimuli or events that are interpreted as factors jeopardizing its psychological and/or physical integrity. This complex response includes biological, cognitive and behavioral actions, involving the autonomic, neuroendocrine, metabolic and immune systems [8]. When this response allows achieving an integral balance, the organism is able to learn, and this adaptation makes it more able to confront with the same or a similar treat with ease and even to confront harder challenges [9].
During this response, the organism typically develops vulnerabilities expressed in the progression of several types of psychiatric conditions, such as addiction, anxiety and depression, but also in the development of chronic diseases [10,11].
More important, metabolic stress has also been linked to situations in which food is restricted or consumed in excess [7]. Similarly, caloric restriction and/or fasting, as metabolic stressors, promote a resilient metabolic adaptation, while an excess of food or drink consumption promotes maladaptation and the development of vulnerabilities [12].
All these adaptations occur mainly throughout the individual history of food and drink consumption, and these types of biological adaptations are currently studied by two modern disciplines, i.e., nutrigenetics, which studies how genetic variations affect the organism response to a given diet, and nutrigenomics, which focuses on how dietary components affect the genome function. For example, it has been demonstrated that dietary components of popular beverages, such as green tea, red wine or SSBs, affect different metabolic pathways [13]. SSBs consumption interacts with genetic signals associated with obesity risk factors; therefore, a genetic predisposition towards a higher body mass index (BMI) in people showing a higher consumption of SSBs was found [14]. Thus, this association widens the differences between individuals with and without genetic predisposition to obesity [15]. However, the consumption of food and drinks also modifies gene expression through epigenetic changes [16].
Moreover, mounting evidence indicates that a frequent SSBs consumption promotes metabolic and inflammatory abnormalities [17]. Environmental changes that affect genome regulation, also called epigenetic changes, are commonly associated with diet and can be considered as an example of cellular plasticity of different tissue and organs, affecting their function and occurring throughout the individual diet history. Among the many organs, these adaptations affect the central nervous system, consequently regulating food and drink intake as well as appetite/satiety regulatory mechanisms, but this goes along with metabolic adaptations, such as lipid accumulation, as well as immune, endocrine and autonomic functions [18].
According to the scientific literature, stress and SSBs consumption are closely associated. SSBs can act as stress regulators directly on the brain reward system, which is involved in the eating behavior and influences metabolic or biological stress responses [4,5,6], as well as the effect of SSBs consumption in stress dampening [7].
In this study, we were interested in understanding the link between SSBs consumption and the stress response in Mexican freshmen university students, which are part of one of the populations with the highest SSBs consumption worldwide [13]. Particularly, we examined the consumption per week of the different SSBs groups available in the market, as well as if their association with the stress response experience differed by sex. It is of vital importance to highlight that there are no studies that address the relationship between these two factors in the Latin population from Mexico, which is of great interest since it is the population with the highest consumption of SBBs worldwide. In addition, the present investigation has an added value because the findings could be extrapolated to other populations worldwide, since the effects of SSBs consumption on human physiology do not differ in a general way between races.
## 2.1. Participants
This was an observational, cross-sectional and single-time-point study where the subjects were recruited by non-probability sampling of first-year students from the Autonomous University of Queretaro (UAQ) located in central Mexico during the fall of 2019. The recruitment of students was through the “Su Salud” program, which is a comprehensive health program that performs an integral assessment of all freshmen students of UAQ. The inclusion criteria were: (a) to be an enrolled student in one of the schools in UAQ participating in the Su Salud program; (b) to have signed the informed consent letter; (c) to attend the instructional talk for the study and (d) to answer all the questionnaires. The exclusion criteria were: (a) the omission of or a partial questionnaire response and (b) the pre-existence of a medical condition disabling the evaluation of anthropometric (weight, total and visceral fat) or other measures. The study was approved by the ethics committee of the Natural Sciences Department, Autonomous University of Queretaro, with registration number 98FCN2017.
The freshmen that met the eligibility criteria and agreed to participate in the study were asked to sign the informed consent letter. The enrolled students did not obtain any type of remuneration, either monetary or in any kind, for their participation in the study. A total of 632 students participated in this study, 265 were men ($42.0\%$), and 367 were women ($57.9\%$).
## 2.2. Measures
The SSBs Consumption *Questionnaire is* a questionnaire based upon several other validated questionnaires [19,20,21,22]. It consists of a list of beverages grouped as follows: [1] cola soft drinks (caloric and non-caloric), [2] all other flavored soft drinks (caloric and non-caloric), [3] industrialized juices (caloric and non-caloric), [4] industrialized teas or infusions (caloric and non-caloric), [5] milk (whole, partially skimmed and skim), [6] sports drinks (caloric and non-caloric), [7] energy drinks (caloric and non-caloric) and [8] homemade sweetened drinks (caloric and non-caloric).
The SSBs consumption questionnaire asked each participant whether they consumed any of the different beverage groups in either their caloric or non-caloric versions. The SSBs consumption questionnaire gives the choice to select the portion (250 mL, 355 mL, 600 mL or 1 L) or commercial presentation based on the observed available drinking containers of the different products found in the university campus and in the stores nearby. This questionnaire was also designed to report SBBs consumption during an average week throughout the last month. To quantify the consumed amount of beverages, including non-caloric and caloric SSBs, the questionnaire responses were transformed into mL/week for each of the beverages groups (caloric and non-caloric) included in the questionnaire. Total liquids consumption was considered as the sum of the caloric and non-caloric versions of all beverages groups evaluated plus natural juices and non-sugar-sweetened beverage (NSSBs). NSSBs included natural water and infusions without any sweetener.
Stress scale. The Perceived Stress Scale (PSS) [23] was used to measure the perception of stress, which is the degree of appraised stress in situations of life. The items are developed to determine how unpredictable, uncontrollable and overloaded the respondents find their life’s events. The scale also includes direct queries about the current levels of experienced stress. The items are easy to understand, and the response options are simple to select. Importantly, the questions are of a general nature and, hence, are relatively free of content specific to any subpopulation group. The questions in the PSS ask about feelings and thoughts during the last month. In each case, the respondents were asked about how often they felt in a certain way.
The PSS total score is obtained by reversing the response (e.g., 0 = 4, 1 = 3, 2 = 2, 3 = 1 and 4 = 0) of the seven positively stated items (4, 5, 6, 7, 9, 10 and 13) and then summing these scores with those of the seven negative stated items (1, 2, 3, 8, 11, 12 and 14). The score obtained from the positive stated items represents the ability to control stressful or threatening situations, while that from the negative stated items represents the perception of helplessness or loss of control and its consequences. All items are valued on a five-point Likert-type scale (0 = never, 1 = almost never, 2 = occasionally, 3 = often, 4 = very often). The total scale has a range of scores from 0 to 56. It is important to note that the higher scores on the total PSS indicate higher levels of perceived stress. In order to assess the stress level in this sample, the 14-item PSS version was used, which was translated and adapted for the young Mexican population [24]. This adaptation was made after conducting a pilot study among university students, 20.48 years old (SD = 3.62), which showed adequate internal consistency (α = 0.83) and convergent validity with the Beck depression inventory (Beck, Ward, Mendelson, Mock, 1961)—rs = 0.553, $$p \leq 0.001$$—and with the emotional exhaustion scale (Ramos, Manga and Moran 2005)—rs = 0.521, $$p \leq 0.001$$—in addition to a confirmatory factor analysis with acceptable adjustment statistics (KMO = 0.87), which indicated that the cultural adaptation of the scale used in this study was satisfactory. Finally, the participants were classified according to the reported score into three categories: Low stress (0–18), moderate stress (19–36) and high stress (37–56).
Since the PSS scale scores and the SSBs consumption pattern appeared to be different in men and women, it was decided to carry out separate analyses. Based on the PSS total score, the participants were categorized in three different groups according to their stress level (low, moderate and high) [25]; then, comparisons were made for the consumption of each SSBs group with the category of stress as an independent variable.
Pearson correlations, odds ratio and quartile categorization were performed to assess the association between SSBs consumption and the PSS scores. The quartiles for women were established at Q1 ≤ 2000, Q2, 2001–3715, Q3; 3716–6010 and Q4 > 6010 mL/week, for men, the quartiles were established at Q1 ≤ 3340, Q2, 3341–5177, Q3, 5178–8395 and Q4 > 8395 mL/week.
Anthropometric data. All measures were performed by a trained nutritionist. Weight determination and body composition data were obtained using a multifrequency bioelectric impedance device (Seca mBCA 515, model 0123; Hamburg Germany). We used for this study the total body fat percentage (TBF%) and visceral fat percentage (VF%).
## 2.3. Statistical Analyses
Descriptive statistics for the PSS scores, as well as for the SSBs intake data, were performed. Data are presented in tables as the mean ± standard deviation or as percentages. Comparisons were made between the means of all the variables analyzed according to sex. Factorial analysis of variance was conducted to evaluate the effect of SSBs intake on the stress level and anthropometric variables. One-way analysis of variance was performed to assess differences between sexes. All analyses were completed in SPSS v22.
## 3.1. Participants’ Charateristics
A total of 632 freshmen students participated in this study; 266 of them were men ($42.0\%$), and 366 were women ($57.9\%$). The participants came from different schools: Chemistry, Engineering, Languages and Letters, Natural Sciences and Political Sciences (Table 1). The participants had an age between 17 and 25 years, with a mean of 18.96 ± 1.52 years.
## 3.2. PSS Scores of Men and Woman
The factorial analysis of variance did not show a significant interaction between the effects of age, stress level and sex on SSBs consumption. The PSS scores were clearly different between sexes; the women’s scores revealed higher perceived stress according to the negative factors and the total score, but a lower score for the positive factors, (Table 2).
## 3.3. SSBs Scores of Men and Woman
Men reported a higher consumption of almost all SSBs than women, except for industrialized teas or infusions and drinkable yogurt, whose reported consumption was similar between men and women (Table 3).
## 3.4.1. Comparisons of SSBs Groups Consumption According to the Stress Level
For women, a significant difference was observed regarding industrialized juice and total SSBs consumption, with women who reported a higher stress level consuming statistically more of these beverages than women in the low stress level group. For men no differences in SSBs consumption were found among the stress level groups (Table 4 and Table 5).
## 3.4.2. Comparison Using SSBs Quartile Categorization
The relation between stress level and SSBs total consumption quartiles was analyzed according to the sex of the participants (Figure 1).
A significant difference was observed for Q4 in the negative factors’ score, with 2.12 points more than for Q1, in women. For total stress, Q4 presented an average of 3.5 points more than Q1. No significant differences were found for men.
## 3.4.3. Correlations
For women, significant correlations between SSBs total consumption and the positive factors’ score, the negative factors’ score and the total scale score were detected (Pearson R = −0.115 ($p \leq 0.05$); 0.137 ($p \leq 0.01$) and 0.156 ($p \leq 0.01$), respectively). No significant correlations were found between perceived stress and SSBs total consumption in men.
## 3.4.4. Odds Ratio
Finally, an association was made between the SSBs consumption quartiles and the stress level reported by the students. Regarding the men, $31.5\%$ were located in the group with high perceived stress, and the odds ratios for presenting beverage consumption within the highest quartile were 1.33 ($95\%$ CI: 0.742–2.395) compared to men who did not have high perceived stress. For women, $29.2\%$ reported high perceived stress, and the odds ratios for presenting beverage consumption within the highest quartile were 2.007 ($95\%$ CI: 1.219–3.304) compared to women who did not have high perceived stress.
## 3.5. Stress and NCSBs Consumption in Men and Woman
Regarding the consumption of the NCSBs (artificial sweetened), we found that it was significantly lower than the consumption of SSBs. NCSBs consumption in men was 356.25 ± 942.12 (mean ±SD) mL/week, while the average in women was 440.41 ± 1065.66 mL/week. No associations were found between the level of stress and NCSBs consumption overall and within the two sexes.
## 3.6.1. Natural Juices
The consumption of natural juices was on average in men of 559.00 ± 902.50 mL/week, while in women it was of 485.89 ± 802.25 mL/week. No association between stress and natural juice consumption was found in the overall participants.
## 3.6.2. Total Liquids
The average of total liquids consumed by men was 12,564.18 ± 7215.45 mL/week. The SBBs consumption was $55.71\%$ of the total liquids consumption for men and $48.86\%$ for women. Additionally, for men, NCSBs overall mean consumption amounted to 5540.09 ± 5889.73 mL/week.
## 4. Discussion
Emotional well-being has multiple associated factors; however, it has been observed that one of the main factors in young population is stress, which is associated with (HPP) consumption and unhealthy eating habits [26]. The results of the present study showed a difference in the levels of perceived stress between sexes, with higher scores for women, and very similar levels to those found in other populations in the same age range [27].
When analyzing the perceived stress by categories, it was found that women tended to increase the consumption of most SSBs groups, but this increase was only significant for industrialized juices and total SSBs consumption, with, on average, 1.2 L higher consumption by women who presented a high perceived stress score compared to those with low perceived stress. These findings are consistent with previous reports where a positive correlation was found between stress values and sugar and fast-food consumption [27,28,29]. This can be explained by the fact that exposure to a stressor can cause impaired control in the mechanisms responsible for limiting the intake of high-sugar and high-fat foods, mainly in women [30]. Another important aspect to note is that the individuals who are susceptible to increasing their calorie and HPP intake in response to stress are those that normally maintain restriction diets [30]. In a study on a young population [27], correlations were found between high-sugar food consumption and the PSS score; however, when analyzing the association between high-sugar food consumption and depression symptoms, they found no association, which could be indicating that the high-sugar or HPP consumption could decrease the signs and symptoms of depression. However, the possible mechanisms involved is not clear, nor if SSBs consumption has the ability to prevent this mental illness.
To corroborate the association observed between perceived stress level and SSBs consumption, it was decided to group the participants in quartiles according to consumption. It is important to note that different values were designated in the construction of the quartiles for men and women, because the SSBs consumption patterns clearly differed between sexes, as in men, higher consumption was observed for the majority of the SSBs groups evaluated, as well as a consumption of almost 2 L higher when evaluating all SSBs consumed, which agrees with previous reports with similar populations [31].
The items belonging to the positive factors of the Cohen scale used in this work were focused on evaluating the individual control over stressful or threatening situations. Very similar constructs were previously evaluated. For instance, some studies [32,33] found the construct called “perceived behavioral control” (perception of the ease or difficulty to perform a behavior) was positively associated with soft drinks consumption in adolescents, while another study [34] found that a very similar construct, previously associated with healthy eating behaviors, i.e., self-efficacy (individual capacity to successfully execute a behavior), was the main predictor of soft drinks consumption in university students. Women belonging to the highest SSBs consumption quartile presented higher scale scores for negative factors and total scale, which corroborated a consumption trend depending on the levels of perceived stress. For men, none of the comparisons were significant. One of the possible explanations for this association is that stress-induced feeding has the ability to activate the reward system and decrease the hypothalamic–pituitary–axis (HPA) activity by releasing opioids [35]; in addition, HPP consumption activates a negative feedback mechanism from the HPA axis, mimicking the effect of stress recovery [36]. However, repeated exposure to this type of foods can lead to an excess of calorie intake and to obesity development [37]. The mechanisms that explain the connection/relation between stress and eating behavior are not fully characterized. Hormones such as leptin, insulin and glucocorticoids, which are involved in food intake regulation and energy balance in the hypothalamus, have been studied, and resulted to be important biomarkers of the mechanisms of this relation [38].
Further, it has been observed through functional magnetic resonance imaging that chronic stress in women causes brain activity increase in areas related to reward, motivation and decision making in response to visual stimuli of HPP; in addition, the same stimuli caused an activity decrease in strategic planning and emotional control areas, suggesting that chronic stress can alter the brain response to food in a way that predisposes individuals to unhealthy eating habits and obesity development [39]. The processing of emotions in areas such as the amygdala, striatum and hippocampus has also been significantly related to behavior-determined stress and reward [40], and dysfunctions in these structures could be related to eating disorders as well as to obesity and addictions [38]. Therefore, and in accordance with the results of this study, other factors such as emotional, motivational, and executive information from limbic system areas, the striatum, and the pre-frontal cortex may influence food intake, particularly, SSBs consumption.
Although a relationship between stress and eating behavior in humans has been widely observed, the mechanisms by which these phenomenon are linked have not been fully understood. However, is accepted that some general characteristics such as being a woman, overweight or obese and having followed or following a restrictive diet are predisposing factors to overeating under stress conditions, that is, these characteristics give the individual greater reactivity to stress [41]. However, analyses in individuals on the calorie restriction regimens produced contradictory results [42]. Although this relationship may be bidirectional, the majority of individuals ($70\%$) increase their caloric intake during or after stressful events [43]. Furthermore, a “medication” with HPP could have some beneficial effects on an individual functioning [44]. However, in the long term, it is a counterproductive strategy to relieve stress, since the high caloric content of SSBs contributes to obesity and therefore to the dysfunction of appetite-regulating hormones [45], inflammation [46] and various metabolic complications [47].
One of the main findings of this study is the difference in the manifestations of SSBs consumption between sexes. In men, it seems to be mainly associated with an increase in visceral fat, but not in women [48]. One explanation is related to the anthropometric differences between men and women, with men having less total fat and greater abdominal fat, and premenopausal women having greater subcutaneous femoral/gluteal fat [49]. When men present a higher SSBs consumption and therefore a higher sugar consumption, a positive association with visceral fat is expected, i.e., SSBs consumption would cause an increase in visceral fat in men and not in women. These differences in fat distribution are also attributable to hormonal differences, particularly of estrogens, which are found in much lower concentration in men [50]. Visceral adipose tissue has estrogen receptors whose activation mediate lipolysis via the activation of hormone-sensitive lipase in women; on the contrary, in men, this activation would have an antilipolytic effect, since this tissue does not have estrogen receptors. Therefore, the accumulation of visceral fat would be determined by the activation of lipoprotein lipase, which is the key enzyme for the accumulation of fat in this area [51]. The net result of the above is a lower accumulation of abdominal fat in women in response to an increase in caloric or sugar intake. Other factors such as greater release of leptin in the gluteal/femoral zone and the effects at the hypothalamic level on the regulation of appetite by estrogens would function as a protective for women who have less capacity to consume food in large quantities [52] and explains why a higher SSBs consumption in men is reflected in an increase in abdominal fat. Therefore, premenopausal women in general have a lower prevalence of metabolic diseases, since the visceral adipose tissue is associated with a higher associated of metabolic disorders than subcutaneous fat [49] due to the protective effect of ovarian hormones, which agrees with the results of this study.
Finally, when obtaining the odds ratios as an association measure for the studied factors, the theory of stress influencing SSBs consumption was reinforced, as we observed that women who self-reported a high perceived stress were more likely to belong to the highest SSBs consumption group.
## 5. Conclusions
This study showed for the first time the association that exists between stress and SSBs consumption in university students. This population is in an important period of biological and social maturation. Freshmen university students face important lifestyle changes due to their integration into university life, a time window with a great sensitivity to stressors and to the development of mental illnesses or risk behaviors that can last for the rest of their adult life [50,51,52]. This work shows an important area for the identification of risk factors and the subsequent implementation of prevention strategies for this phenomenon. Future global public policies should focus on the identification of mental disorders that affect the young population inside and outside universities, since in recent years this type of illness has been on a dramatic increase. In particular, it is proposed to use questionnaires similar to those used in this study for the early detection of these conditions in larger populations and thus establish strategies that truly have an impact on public health. It is important to highlight that the results presented here agree with reports from other countries besides Mexico; therefore, it is certain that the main recommendation is the drastic reduction in the consumption of any SSBs due to their wide and consistent side effects. The above can be used in clinical practice by health professionals and for the theoretical support of future research in the field.
The study design and the lack of measurements of other factors that influence SSBs consumption make the results unable to demonstrate causality. More research is needed on stress-mediated eating in order to fully understand its impact on risk behaviors development and diseases related to obesity.
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|
---
title: Polyunsaturated and Saturated Oxylipin Plasma Levels Allow Monitoring the Non-Alcoholic
Fatty Liver Disease Progression to Severe Stages
authors:
- Miguel D. Ferrer
- Clara Reynés
- Margalida Monserrat-Mesquida
- Magdalena Quetglas-Llabrés
- Cristina Bouzas
- Silvia García
- David Mateos
- Miguel Casares
- Cristina Gómez
- Lucía Ugarriza
- Josep A. Tur
- Antoni Sureda
- Antoni Pons
journal: Antioxidants
year: 2023
pmcid: PMC10045849
doi: 10.3390/antiox12030711
license: CC BY 4.0
---
# Polyunsaturated and Saturated Oxylipin Plasma Levels Allow Monitoring the Non-Alcoholic Fatty Liver Disease Progression to Severe Stages
## Abstract
Hepatic fat accumulation is the hallmark of non-alcoholic fatty liver disease (NAFLD). Our aim was to determine the plasma levels of oxylipins, free polyunsaturated fatty acids (PUFA) and markers of lipid peroxidation in patients with NAFLD in progressive stages of the pathology. Ninety 40–60-year-old adults diagnosed with metabolic syndrome were distributed in without, mild, moderate or severe NAFLD stages. The free PUFA and oxylipin plasma levels were determined by the UHPLC–MS/MS system. The plasma levels of oxylipins produced by cyclooxygenases, lipoxygenases and cytochrome P450, such as prostaglandin 2α (PGF2α), lipoxinB4 and maresin-1, were higher in severe NAFLD patients, pointing to the coexistence of both inflammation and resolution processes. The plasma levels of the saturated oxylipins 16-hydroxyl-palmitate and 3-hydroxyl-myristate were also higher in the severe NAFLD patients, suggesting a dysregulation of oxidation of fatty acids. The plasma 12-hydroxyl-estearate (12HEST) levels in severe NAFLD were higher than in the other stages, indicating that the hydroxylation of saturated fatty acid produced by reactive oxygen species is more present in this severe stage of NAFLD. The plasma levels of 12HEST and PGF2α are potential candidate biomarkers for diagnosing NAFLD vs. non-NAFLD. In conclusion, the NAFLD progression can be monitored by measuring the plasma levels of free PUFA and oxylipins characterizing the different NAFLD stages or the absence of this disease in metabolic syndrome patients.
## 1. Introduction
Non-alcoholic fatty liver disease (NAFLD) is identified in approximately $10\%$ of children and 20–$30\%$ of adults in the Western world [1,2,3,4]. NAFLD is a disease with no well-defined signs or symptoms, with a histological spectrum ranging from fatty liver alone to non-alcoholic steatohepatitis (NASH) [5]. Liver steatosis is the pathologic accumulation of fat inside the hepatocytes (mainly as triglycerides), and it is strongly linked to insulin resistance, obesity and overweight [6]. Patients affected by NAFLD may evolve to NASH, cirrhosis and end-stage liver failure [5]. Hepatic fat accumulation is therefore the hallmark of NAFLD [7]. Grading NAFLD patients provides prognostic information and identifies patients who may benefit from therapy. There are currently no effective pharmacological therapies against NAFLD, insulin resistance being the main pharmacological target [8]. The current interventions include dietary and lifestyle modifications to control body weight, metabolic syndrome and cardio-metabolic risk factors [9]. The reference standard for the diagnosis and grading of hepatic steatosis is liver biopsy [10,11], although magnetic resonance imaging–estimated proton density fat fraction (MI-PDFF) has also been validated to grade the disease [12], and it is preferably used for NAFLD screening in epidemiologic studies [13].
The NAFLD/NASH pathophysiology involves at least two steps. The first step involves insulin resistance and is associated with an increased rate of lipolysis and the release of free fatty acids from adipose tissue, which are available for hepatic uptake and re-esterification to triacylglycerols [14,15,16]. The second step is oxidative stress, which produces lipid peroxidation and activates inflammatory pathways, causing the progression of the pathology to NASH [17]. Lipotoxicity exerts a key role in the pathogenesis of NASH [7,18,19,20,21]. Oxidized low-density lipoproteins (LDL-c), as well as fatty acid oxidized metabolites and other reactive metabolites, are increased in patients with NASH compared with those with NAFLD [16,18,22,23,24,25]. Since most polyunsaturated fatty acids (PUFA) and saturated fatty acids (SFA) oxidized metabolites are endogenous signaling molecules [26], the identification of changes in specific lipid mediators depending on the different degrees of NAFLD might shed light onto the mechanisms contributing to the progression of this disease and reveal novel therapeutic targets and biomarkers.
Oxylipins are a family of peroxidation products of PUFA and SFA with bioactive properties, including inflammation and immune regulatory properties [27]. These compounds are formed via mono- or dioxygen-dependent catalyzed or non-catalyzed reactions [28]. Oxylipin biosynthesis requires cytosolic phospholipase A2 (cPLA2)-mediated release of free fatty acids from cell membranes and enzymatic or non-enzymatic oxidation [29,30]. Some of the enzymes that catalyze the oxidation of free PUFA into oxylipins are cyclooxygenases (COXs), lipoxygenases (LOXs) and cytochrome P450 (CYP450) [31,32], which are expressed in a variety of cells and tissues [33]. The action of these enzymes on SFA to synthetize saturated oxylipins is poorly known. CYP450 catalyzes the formation of ω-hydroxide PUFA and SFA in the liver [34]. The β-hydroxy-saturated fatty acids could be synthetized as intermediate products of the β-oxidation of long-chain fatty acids catalyzed by the trifunctional protein (MTP), a multienzyme complex in the inner mitochondrial membrane. The assessment of 3-OH-fatty acids (C14 to C18) in serum is used as a hallmark of MTP deficiency [35]. Hydroxy-saturated fatty acids, such as 12-hydroxystearic acid, are esters of hydroxyl fatty acids (FAHFAs), endogenous lipids that exert anti-inflammatory and anti-diabetic action [36]. The enzymes responsible for FAHFA biosynthesis in vivo remain unknown, although an adipose triglyceride lipase has been identified as being possibly responsible for their biosynthesis [36]. The major pathway for the synthesis of oxylipins are COXs, enzymes which convert PUFAs into isoprostanes, such as prostaglandins (PG) and thromboxanes (Tx). Other oxylipins, such as hydroxy-eicosatetraenoic acids (HETEs) and their derived metabolites, including eoxins, leukotrienes (LTs), lipoxins (LXs), maresins (MaRs), protectins and resolvins (Rv), are synthetized by LOXs.
In recent years, the roles of oxylipins in the inflammatory process have been described [5,15,37]. In this instance, the plasma oxylipin levels are the result of the biosynthesis/degradation balance associated with the inflammation status. Macrophages, neutrophils, mononuclear cells, adipose tissue, muscle and liver can synthetize and secrete oxylipins [38], which are used as autocrine and paracrine signaling molecules for interorgan communication and inflammation management. The hepatic inflammation present in NAFLD is related to macrophage recruitment. In fact, the hepatic recruitment of macrophages promotes the development of NASH [17]. The recruited macrophages are responsible for the production of inflammatory mediators, including oxylipins [39]. In this study, we aimed to measure the plasma levels of polyunsaturated and saturated oxylipins, free PUFA and markers of lipid peroxidation and oxidative stress in patients with NAFLD in progressive stages of severity of the pathology. We also aimed to find the associations of certain plasma free fatty acids and oxylipins and the intrahepatic fat content in NAFLD patients and to estimate the potential diagnostic value of these plasma markers to range NAFLD steatosis.
## 2.1. Design and Participants
Ninety 40–60-year-old adults recruited in the Balearic Islands, Spain, with NAFLD diagnosed by magnetic resonance imaging were selected. The inclusion criteria consisted of meeting at least three of the five metabolic syndrome traits described by the International Diabetes Federation (IDF) consensus [40]: [1] body mass index (BMI) 27–40 Kg/m2 or an increased waist circumference of ≥94 cm in men and ≥80 cm in women; [2] triglyceride levels ≥ 150 mg/dL; [3] reduced HDL cholesterol < 40 mg/dL in men and <50 mg/dL in women; [4] increased blood pressure (BP), systolic BP ≥ 130 mmHg or diastolic BP ≥ 85 mmHg; [5] fasting serum glucose level ≥ 100 mg/dL. The following exclusion criteria were applied: liver diseases (other than NAFLD); viral, autoimmune and genetic causes of liver disease; previous cardiovascular disease; active cancer or a history of malignancy in the previous five years; previous bariatric surgery; non-medicated depression or anxiety; pregnancy; primary endocrinological diseases (other than hypothyroidism); alcohol (>21 and >14 units of alcohol a week for men and women, respectively) and drug abuse; weight loss medications in the past six months; concomitant therapy with steroids; inability or unwillingness to give informed consent or communicate with study staff.
All the procedures were approved by the Ethics Committee of the Balearic Islands (ref. IB $\frac{2251}{14}$ PI). The study protocol followed the ethical standards of the Declaration of Helsinki. All participants were informed of the purpose and the implications of the study, and all provided written consent to participate. This study was registered in Clinicals Trials.gov ref. NCT04442620 [41].
NAFLD diagnosis was performed by abdominal magnetic resonance imaging–estimated proton density fat fraction (MI-PDFF) (Signa Explorer 1.5T, General Electric Healthcare, Chicago, IL, USA) [42]. Participants were grouped according to their intrahepatic fat content. Four stages of NAFLD were defined according to hepatic steatosis measured as percentage of intrahepatic fat content (IFC): IFC0 (stage 0 or control group without steatosis) IFC < $6.4\%$; IFC1 (stage 1 with mild steatosis) $6.4\%$ ≤ IFC < $17.4\%$; IFC2 (stage 2 with moderate steatosis) $17.4\%$ ≤ IFC < $22.1\%$; and IFC3 (stage 3 with severe steatosis) IFC ≥ $22.1\%$, following previous criteria for steatosis grade classification [12]. The proportions of hepatocytes containing macrovesicles of fat in these steatosis grades were: grade IFC0 for less than $5\%$, grade IFC1 for 5–$33\%$, grade IFC2 for 33–$66\%$ and grade IFC3 for more than $66\%$ [12].
## 2.2. Anthropometric Characteristics, Blood Collection and Biochemistry Analysis
Weight (kg) was measured with subjects in bare feet and light clothes using calibrated scales. An amount of 0.6 kg for their clothing was subtracted from the total weight. Height (m) was determined with the participant’s head in the Frankfurt plane with a wall-mounted stadiometer (Seca 213, SECA Deutschland, Hamburg, Germany) to the nearest millimeter. Body mass index (BMI) was calculated in kg/m2. Blood pressure was measured in triplicate with a validated semi-automatic oscillometer (Omron HEM, 750CP, Hoofddorp, The Netherlands) in a seated position.
Venous blood samples were collected from the antecubital vein in suitable vacutainers with ethylenediaminetetraacetic acid (EDTA) as anticoagulant after 12 h of fasting conditions. Plasma was obtained after centrifugation of the fresh blood at 1700 g, 15 min at 4 °C. Biochemical and blood cell parameters, including glucose, glycosylated hemoglobin (Hb1Ac), total cholesterol, high-density lipoprotein cholesterol (HDL-c), LDL-c, triglycerides (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyltransferase (GGT) and platelet count, were determined using standardized clinical procedures [13]. The fibrosis-4 index (FIB-4) was calculated from the data on age, AST and ALT activities and the platelet count according to the following formula:FIB-4 = (age × AST)/[PLT × (ALT)$\frac{1}{2}$] where ALT: alanine aminotransferase; AST: aspartate aminotransferase; FIB-4: fibrosis-4 index; PLT: platelet count.
The FIB-4 cut-off values for diagnosing the patients in terms of liver fibrosis were: FIB-4 lower than 1.3, no fibrosis; FIB-4 between 1.3 and 2.67, liver fibrosis; patients with FIB-4 higher than 2.67, severe liver fibrosis [43].
## 2.3. Malondialdehyde Assay
Malondialdehyde (MDA), a plasma marker of peroxidation of PUFAs, was measured using a specific colorimetric assay kit (Sigma-Aldrich Marck®, St. Louis, MO, USA), following its instruction manual. The method is based on the reaction of MDA with n-methyl-2-phenylindole, generating a stable chromophore, and measuring the absorbance at 586 nm. Plasma samples and standards were reacted with n-methyl-2-phenylindole in acetonitrile:methanol (3:1) and HCl (12 N) at 45 °C for 1 h. A standard curve of known MDA concentrations was used to calculate the concentration in the plasma samples.
## 2.4. Oxylipin Determination
The plasma oxylipin levels were determined in plasma by an adaptation of a method developed for the simultaneous determination of PUFA and SFA oxylipins in immune cells, based on solid phase extraction (SPE) and HPLC-MS/MS technology, and using deuterated oxylipin (d4-PGF2α) as an internal standard [28].
Oxylipins were purchased from Cayman Chemical (AnnArbor, MI, USA), Santa Cruz Biotechnologies (Finell ST, Dallas, TX, USA) and Sigma Aldrich (Darmstadt, Germany). Cayman Chemical (Ann Arbor, MI, USA) provided (Z)-7-[(1R,2R,3R,5S)-3,5-dihydroxy-2-[(E,3S)-3-hydroxyoct-1-enyl]cyclopentyl]hept-5-enoic acid (PGF2α, Prostaglandin 2a) and deuterated internal standard ProstaglandinF2a-d4 (d4-PGF2α). SigmaAldrich provided: 5,8,11,14-Eicosatetraenoic acid (AA, Arachidonic acid; 16-Hydroxy-hexadecanoic acid (16-hydroxy-palmitic acid, 16−HPAL); 3-Hydroxy-tetradecanoic acid (3-hydroxy-myristic acid, 3-HMYR); 12-Hydroxyoctadecanoic acid (12-hydroxy-stearic acid, 12-HEST). Santa CruxBiotechnology provided: 15-Hydroxy-5,8,11,13-Eicosatetraenoicacid (15-HETE), 17-Hydroxy-4,7,10,13,16,19-docosahexaenoicacid (17-DoHE), Resolvin D2 (RvD2); 5,8,11,14,17-eicosapentenoicacid (EPA); 8,11,14,17-eicosatetraenoic acid (ETA); 7R,14S-dihydroxy-4Z,8E,10E,12Z,16Z,19Z-docosahexaenoic acid (MaR1,Maresin-1); (5S,6E,8Z,10E, 12E,14R,15S)-5,14,15-Trihydroxyicosa-6,8,10,12-tetraenoic acid (LXB4, LipoxinB4); (5S,6Z,8E, 10E,12R,14Z)-5,12-Dihydroxyicosa-6,8,10,14-tetraenoic acid (LTB4, Leukotriene B4).
Strata®C-8 cartridge (100 mg, 55 μm, 70 Å from Phenomenex®) for solid phase extraction and the analytical column Luna C8 (150 mm × 2.0 mm, 5 μm) were purchased from Phenomenex® (Torrance, CA, USA). Products for HPLC (acetonitrile, methanol, ammonium formiate of HPLC Grade) were purchased from Fisher Scientific (Pittsburgh, PA, USA).
Oxylipins and free PUFAs were extracted from plasma after the addition of deuterated d4-PGF2α at a final concentration of 1 ng/mL as internal standard, following a procedure previously described [28]. The oxylipins and free fatty acids were extracted and concentrated with a Strata®C-8 cartridge (previously washed with 1 mL of formic acid $0.1\%$) connected to a Visiprep SPE vacuum manifold (Supelco Co., St. Louis, MO, USA). Samples with internal standards were prepared by diluting 1:2 with formic acid $0.1\%$ (0.5 mL of the sample and 1 mL of formic acid) and centrifuged at 1200× g to eliminate the proteins. The deproteinized samples where then eluted trough the column. After elution, the columns were washed with 1 mL of $0.1\%$ formic acid, and the analytes were finally eluted with 1 mL of methanol. The collected eluate was evaporated in an Eppendorf Concentrator 5301® at 30 °C; the residues were dissolved in 50 μL of $50\%$ methanol and injected into the LC–MS/MS system.
A UHPLC system (Ultimate 3000, Thermo Fisher Scientific, Waltham, MA, USA) coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (ThermoFisher®Scientific, Waltham, MA, USA) operating with a heated electrospray interface (HESI) was employed. Spectra were recorded in negative mode. The analytical column was Luna C8 (150 mm × 2.0 mm, 5 μm; Phenomenex, Torrance, CA, USA) maintained at 40 °C. The mobile phases used in this separation were 0.5 mM ammonium formiate (pH 3.3) (A) and acetonitrile with 0.5 mM ammonium formiate (B). The stepwise linear gradient was 5–$35\%$ B from 0 min to 5 min, 35–$65\%$ B from 5 to 15 min, 65–$75\%$ from 15 to 20 min, 75–$100\%$ B from 20 to 24 min and held at $100\%$ B from 24 to 28 min. The system was returned to initial conditions from 100 to $5\%$ B from 28 to 29 min and held at $5\%$ until 34 min to equilibrate the column. A flow rate of 0.3 mL/min and injection volume of 10 μL were used. The temperature of ion transfer capillary, spray voltage, sheath gas flow rate, auxiliary gas flow rate and S-lens RF level were set to 350 °C, 3.1 kV in negative mode, 35 arbitrary units (AU), 10 AU and 55 AU, respectively. Full scan acquisition was performed over a range of 150–700 m/z with a resolution of 70,000. During the MS/MS scans, precursors were fragmented with a normalized collisional energy of 60 AU. Ions were selected for MS/MS analysis from an inclusion list in accordance with the m/z found by using each standard oxylipin and free fatty acid. XcaliburTM4.1, Trace Finder 4.1 SP2 software were used for data processing (Thermo Fisher Scientific).
The concentrations of fatty acids and oxylipins in plasma samples were calculated using deuterated d4-PGF2α as internal standard. Pure commercial free fatty acids or oxylipins were used to analyze the individual response of each oxylipin and free fatty acid with respect to the d4-PGF2α internal standard. A mixture of these oxylipins and fatty acids and deuterated d4-PGF2α internal standard at 50 ng/mL in water was processed together with the plasma samples. The differential response of each oxylipin or fatty acid with respect to the d4-PGF2α internal standard (internal standard factor, ISF) was calculated with the LC–MS/MS signal. The ISFs were calculated following the formula previously described [28]. Once the ISF had been calculated, this value was used to calculate the oxylipin or fatty acid concentration in the plasma following the procedure previously described [28].
## 2.5. Statistical Analysis
Statistical analysis was carried out using the Statistical Package for Social Sciences (SPSS v.21.0 for Windows). The results were expressed as mean ± SEM, and p ≤ 0.05 was considered statistically significant. A Kolmogorov–Smirnov test was applied to assess the normal distribution of the data. Data were normally distributed, and the statistical significance was assessed by a one-way analysis of variance (ANOVA) using the steatosis degree as a statistical factor. When a significant effect of the ANOVA was found, a Bonferroni post hoc test was performed to identify differences between the groups. The correlations between plasma oxylipin and fatty acid concentrations and between IFC and MDA, oxylipin and fatty acid concentrations were calculated using the bivariate correlation Pearson test. The discriminatory capability of plasma oxylipin or fatty acid concentrations for different steatosis grades was tested by using the following dichotomizations: IFC0 vs. IFC1 or greater; IFC1 or less vs. IFC2 or greater; IFC2 or less vs. IFC3. For each set of dichotomized steatosis grades, the area under the receiver operating characteristic curve (AUCROC) was calculated. The oxylipins or fatty acids with an AUCROC significantly different from 0.5 were selected as parameters with discriminant capability. The lowest plasma oxylipin or fatty acid threshold value that provided a specificity to distinguish between dichotomized steatosis grades equal or greater than $90\%$ was selected. At that oxylipin or fatty acid threshold value, the raw sensitivity, specificity, accuracy, positive predictive value and negative predictive value to distinguish between dichotomized steatosis grades were calculated.
## 3. Results
A total of 90 metabolic syndrome patients underwent MI-PDFF and were classified into the different hepatic steatosis degrees. The IFC of 19 patients was lower than $6.4\%$ (IFC0, without NAFLD); 42 patients presented an IFC between $6.4\%$ and $17.4\%$ (IFC1, NAFLD grade 1); 19 patients presented an IFC value between $17.4\%$ and $22.1\%$ (IFC2, NAFLD grade 2); and 10 patients presented an IFC value higher than $22.1\%$ (IFC3, NAFLD grade 3). No differences in BMI were observed between IFC0, IFC1, IFC2 and IFC3 groups, all these values being indicative of obesity (Table 1). The mean values of systolic and diastolic blood pressure and total and LDL-c in serum were indicative of hypertension and hypercholesterolemia in all groups, with no differences observed between groups. The circulating triglycerides, glucose and HbA1 in the IFC3 group were significantly higher than in the other groups. The HDL-c levels were significantly higher in the IFC0 group compared to the other groups. The plasma MDA levels, indicative of degradation of PUFA by peroxidation, were increased in the IFC2 group compared to the IFC0 group. The markers of hepatic function were affected differently. AST was not affected by the degree of steatosis. However, patients with IFC2 and IFC3 presented increased ALT activity compared to patients with IFC0 and IFC1. Patients with IFC3 presented higher GGT activity than patients with IFC0. GGT activity in patients with IFC1 and IFC2 was no different to that of patients with IFC0 and IFC3. The number of patients without liver fibrosis (FIB-4 < 1.3) was 71, while 19 patients presented liver fibrosis (FIB-4 ≥ 1.3). Of the patients with liver fibrosis, only four had severe fibrosis (FIB-4 ≥ 2.67). The 19 patients who presented liver fibrosis were distributed between the different IFC groups without a clear pattern (5 patients in the IFC0 group, 7 patients in the IFC1 group, 5 patients in the IFC2 group and 2 patients in the IFC3 group). The FIB-4 mean values in all steatosis groups were lower than 1.3, and there were no statistical differences between groups. An effect of the degree of liver steatosis was observed in the plasma levels of MDA. The three groups with steatosis (IFC1, IFC2 and IFC3) presented equivalent MDA levels, but only in the IFC2 group were these levels significantly increased compared to the IFC0 group.
The NAFLD grade significantly influenced the plasma levels of AA, EPA, ETA, MaR1, LXB4, 3HMYR, 16HPAL, 12HEST and PGF2α (Table 2), whereas the 15HETE, 17DoHE, RvD2 and LTB4 plasma levels were not affected by the degree of steatosis. The plasma free fatty acids and oxylipin levels of IFC0 were similar to those of IFC1 and IFC2, which were also similar between them. The main feature observed was the significantly higher AA, EPA, ETA, Mar1, LXB4, 3HMYR, 16HPAL, 12HEST and PGF2α plasma levels in IFC3 NAFLD patients compared to the other groups. The IFC was significantly correlated with 12HEST, 17HDoHE, 15HETE, AA, EPA, 16HPAL, MaR1, ETA, 3HMYR, LXB4, PGF2α and MDA plasma levels (Table 3). The MDA plasma levels were significantly correlated with MaR1, 15HETE, 12HEST, 16HPAL, 3HMYR, RvD2, LTB4 and AA plasma levels.
The correlations between the circulating levels of all the free fatty acids and oxylipins are shown in Table 4. All correlations between plasma free fatty acids and oxylipins were positive; high levels of the fatty acids were associated with high levels of their corresponding oxylipin metabolite. Additionally, the free AA, EPA and ETA plasma levels were significantly correlated between them (Table 4). In summary, free AA plasma levels correlated with 16HPAL, 12HEST, 15HETE, MaR1, 17HDoHE and 3HMYR. Free EPA plasma levels correlated with 16HPAL, 12HEST, MaR1 and 3HMYR. 15HETE plasma levels were correlated with 17HDoHE, 12HEST, MaR1, LXB4, 3HMYR, PGF2α, 16HPAL and AA. The 17DoHE plasma levels were correlated with 15HETE, MaR1, 12HEST, LTB4, LXB4, 3HMYR, PGF2α and 16HPAL. RvD2 plasma levels correlated with 3HMYR, 16HPAL, PGF2α ¡, MaR1, LXB4 and 12HEST. The MaR1 plasma levels were correlated with LXB4, 3HMYR, 16HPAL, 12HEST, PGF2α, 15HETE, 17DoHE, AA, EPA, RvD2 and LTB4. LXB4 plasma levels were correlated with PGF2α, 3HMYR, 12HEST, 16HPAL, RvD2, 15HETE, 17HDoHE and LTB4. The LTB4 plasma levels were correlated with 17HDoHE, 12HEST, MaR1, PGF2α, 3HMYR, LXB4 and 16HPAL. The 3HMYR plasma levels were correlated with PGF2α, LXB4, 12HEST, 16HPAL, Mar1, RvD2, free AA and EPA, 15HETE, 17HDoHE and LTB4. The plasma levels of 16HPAL were correlated with free AA and EPA, Mar1, LXB4, 3HMYR, PGF2α, free ETA, RvD2, 12HEST, 15HETE, 17HDoHE and LTB4. The 12HEST plasma levels were correlated with 3HMYR, LXB4, PGF2α, MaR1, 15HETE, 17HDoHE, LTB4 and 16HPAL. PGF2α plasma levels were correlated with 3HMYR, LXB4, Mar1, 16HPAL, 12HEST, RvD2, LTB4, 15HETE and 17HDoHE.
The raw estimates of accuracy, true positive predictive value and false positive predictive value to detect specific grades of steatosis through plasma free fatty acids or oxylipin levels were calculated by using receiver operating characteristic curve analysis (ROC) (Table 5). The plasma levels of 12HEST, PGF2α, 15HETE and free ETA showed certain diagnostic accuracy in distinguishing patients with different grades of steatosis. The plasma levels of 12HEST allowed differentiating patients with IFC0 or IFC1 grades from patients with IFC2 or IFC3 grades with an AUROC of 0.661 ($95\%$ confidence interval: 0.537, 0.788). A plasma concentration of 12HEST greater than 12.3 nM allowed diagnosing IFC2 or IFC3 hepatic steatosis with $83\%$ of true positives, although $72\%$ of false positives were also diagnosed in this category (patients with steatosis grade IFC0 or IFC1 who were diagnosed with grade IFC2 or IFC3). Similarly, 12HEST plasma concentration allowed differentiating patients with IFC0 or IFC1 or IFC2 grades from patients with IFC3 grade with an AUROC of 0.694 ($95\%$ confidence interval: 0.477, 0.910). A plasma concentration of 12HEST greater than 30 nM allowed diagnosing IFC3 degree hepatic steatosis with $80\%$ of true positives (IFC3 patients who were correctly diagnosed with IFC3 degree), although $53\%$ of false positives were also diagnosed in this category (patients with steatosis grade IFC0, IFC1 or IFC2 who were diagnosed with grade IFC3). The plasma levels of PGF2α had similar sensitivity but more specificity than the 12HEST plasma levels in diagnosing IFC3 steatosis. The PGF2α plasma levels allowed diagnosing IFC3 patients with respect to IFC0, IFC1 or IFC2 patients with an AUROC value of 0.748 ($95\%$ confidence interval: 0.537, 0.958). A plasma concentration of PGF2α greater than 0.675 nM allowed diagnosing IFC3 hepatic steatosis with $80\%$ of true positives, with only $17\%$ of false positives. The plasma levels of 15HETE presented a diagnostic value similar to PGF2α in distinguishing IFC3 patients from IFC0, IFC1 or IFC2 patients. Finally, the plasma levels of free ETA also had a diagnostic value in distinguishing patients without hepatic steatosis from those with hepatic steatosis (AUROC value of 0.647; $95\%$ confidence interval: 0.525–0.768). Free ETA plasma levels greater than 0.42 nM indicated hepatic steatosis IFC1, IFC2 or IFC3 degree, with $82\%$ of true positives but $79\%$ of false positives detected.
## 4. Discussion
NAFLD, defined as steatosis affecting ≥ $5\%$ of hepatocytes, progresses by increasing the hepatic fat content through different severity stages until advanced forms of liver injury are reached. The clinical progression of NAFLD is associated with fibrosis, cirrhosis and hepatocellular carcinoma. A pathohistological grading system for hepatic steatosis depending on the percentage of hepatocytes with fatty infiltration has been developed [44,45]. Hepatic steatosis, in the absence of inflammation and fibrosis, is visualized as a general benign state, which is in accordance with the similar values of glycemia, triglyceridemia, cholesterol levels and blood pressure observed in this study between the IFC0, IFC1 and IFC2 stages. However, the IFC3 severe steatosis stage is associated with higher values of glycaemia and triglyceridemia, which points to the pathological relevance of the high fat accumulation in the hepatocytes. The higher glycemia observed in the group of patients with severe NAFLD probably evidences a worsening of insulin resistance at this stage of the disease. The metabolic pathways involved in NAFLD progression are not well characterized, but low-grade chronic inflammation and oxidative stress take part in the pathogenesis and progression of NAFLD [46,47]. MDA, the PUFA peroxidation marker of oxidative stress [48], is progressively increased with the stage of NAFLD, and its plasma levels are significantly correlated with the IFC, reflecting that low-grade oxidative stress is secondary to the accumulation of fatty acids in the liver. The activities of hepatic markers, such as AST, ALT and GGT, were in accordance with previously reported data, which indicate that ALT is commonly increased in NAFLD patients [49], and the AST/ALT ratio is below 0.8 in these patients [50]. GGT is another biochemistry marker, which is usually increased in NAFLD patients, and the results obtained in this study confirm that patients categorized in groups with higher levels of steatosis also present higher GGT activity and are therefore at a more advanced stage of the disease. Regarding the degree of fibrosis, the subjects with NAFLD included in this study did not present fibrosis or presented incipient fibrosis, as pointed out by the low values of the FIB-4 index. These results confirm that the FIB-4 index presents high accuracy and negative predictive value for ruling out advanced liver fibrosis but is more inaccurate in discriminating incipient liver fibrosis stages [51].
The plasma oxylipin profile is associated with oxidative stress and inflammation, since certain oxylipins are the result of enzymatic or non-enzymatic lipid peroxidation, and oxylipins have pro- or anti-inflammatory capabilities [7,27]. The main finding of this study is that the plasma oxylipin levels are influenced by the degree of steatosis in NAFLD patients. The plasma levels of AA, EPA, ETA, MaR1, LXB4, 3HMYR, 16HPAL, 12HEST and PGF2α were higher in patients with severe NAFLD (IFC3) than in patients without or with mild or moderate NAFLD. Severe NAFLD is associated with higher insulin resistance than the previous stages, which can result in increased lipolysis and release of free fatty acids [10,40,42] into circulation, as we observed for AA, EPA and ETA. Oxylipins derived from AA through the cyclooxygenase pathway (COX-1 and COX-2), such as PGF2α, or through the lipoxygenase pathway (5-LOX, 12-LOX and 15-LOX), such as LXB4, were higher in the plasma of severe NAFLD patients compared to the other groups, although 15HETE and LTB4 plasma levels, also derived from AA through lipoxygenases, were similar in all NAFLD stages. These results could indicate the enhanced activity of COX and LOX enzymes in the severe stage of NAFLD. Similar trends for most oxylipins derived from AA have been described comparing NAFLD subjects to healthy controls [52] and in NASH patients compared to NAFLD patients [7,28], evidencing the associations between the activation of LOX pathways and the progression of the disease to NASH. On the contrary, oxylipins derived from docosahexaenoic acid (DHA) through the LOX pathways, such as 17DoHE and RvD2, are present in the plasma of all patients at a similar concentration, although MaR1 plasma levels, also derived from DHA, are increased in severe NAFLD patients compared to the other NAFLD stages. Severe NAFLD is therefore related to the increased production of oxylipins from AA and DHA, such as PGF2α, LXB4 or MaR1, which can exert pro- and anti- or resolving inflammatory activities, respectively. PGF2α increases vascular permeability, recruits neutrophils to sites of injury and promotes the switch from leukotriene biosynthesis to specialized pro-resolving mediators (SPM) production in neutrophils [53,54]. MaR1 and LXB4 are SPM, which counter-regulate dysregulated resolution responses and moderate the proinflammatory phenotype of inflammatory diseases [55,56,57]. The levels of PGF2α, LXB4 and MaR1 are increased in the plasma of severe NAFLD patients, evidencing the activation of both pro-inflammatory and pro-resolving processes at this stage of liver steatosis.
Severe NAFLD patients also have increased plasma levels of saturated oxylipins, such as 3HMYR, 16HPAL and 12HEST. 16HPAL is synthetized from palmitic acid by CYP450 [58,59]. The ω-oxidation of monocarboxylic fatty acids generates the corresponding ω-hydroxycarboxylic acid and takes place predominantly in the kidney and liver. The higher 16HPAL levels observed in IFC3 compared to the other NAFLD stages could indicate an activation of the CYP450 pathway related to inflammation in this severe stage of NAFLD [60]. The increased 16HPAL levels could also be indicative of a decreased function of the peroxisomal ω-oxidation of fatty acids in the severe NAFLD stage. In fact, impaired peroxisomal PUFA metabolism is associated with the progression to NASH [7]. Under normal physiological conditions, the peroxisomal ω-oxidation pathway accounts for 5–$10\%$ of total fatty acid oxidation [58]. Peroxisomal ω-oxidation of fatty acids may function as an escape route to overwhelmed mitochondrial β-oxidation [61]. Impairment of β-oxidation pathway leads to the accumulation of β-hydroxy-fatty acids and the reduction in energy production [62]. In this sense, the increased levels of the β-hydroxy-fatty acid 3HMYR in the IFC3 NAFLD stage could indicate a dysfunction of the β-oxidation pathway. The 12HEST biosynthesis pathway has not been described yet [63], although the most probable source for this molecule might be its non-enzymatic acyl chain oxidation by different active forms of oxygen [64]. 12HEST is a component of branched fatty acid esters of hydroxy-fatty acids (FAHFAs), which are endogenous lipids with anti-inflammatory and anti-diabetic action [36]. FAHFA biosynthesis in vivo could be attributed to an adipose triglyceride lipase catalyzing the transacylation reaction, which esterifies hydroxyl fatty acids with a fatty acid from a triglyceride or a diglyceride to produce FAHFAs [36]. The increased 12HEST levels in severe NAFLD patients could be related to increased production of ROS, inducing oxidative stress and enzymatic and non-enzymatic lipid peroxidation. This could also indicate decreased 12HEST consumption to synthetize FAHFAs and, consequently, a low rate of FAHFAs synthesis at this stage of severe NAFLD. Some FAHFAs improve both insulin sensitivity and glucose tolerance in mice by enhancing insulin secretion, glucose transport and insulin action and reducing adipose tissue inflammation [37]. Reducing the rate of FAHFAs production, as indicated by the high levels of 12HEST, could therefore contribute to the progression of NAFLD.
Intrahepatic lipid saturation predisposes the liver to inflammation; however, the mechanisms by which this intrahepatic lipid saturation promotes NAFLD progression are still poorly understood [46]. The obtained data show that the plasma levels of 12HEST and 17HDoHE are the lipid mediators with the highest correlation with the intrahepatic fat content in NAFLD patients. The biosynthesis of these lipid mediators might be enhanced in a situation of intrahepatic lipid saturation. Both liver-resident cells (e.g., Kupffer cells, hepatic stellate cells, sinusoidal endothelial cells) and recruited immune cells (e.g., monocytes, macrophages, dendritic cells, natural killer cells) have metabolic pathways to synthetize pro-, anti- or resolving inflammatory signals [65]. Intrahepatic lipid saturation is related to the activation of 17HDoHE biosynthesis by liver 15-LOX. The 12HEST biosynthesis pathway might involve the non-enzymatic oxidation of stearate by the hydroxyl radical, directly generating the hydroxyl fatty acid 12HEST, since other ROS do not have the ability to react directly with SFA. Non-enzymatic fatty acid oxidation is associated with NAFLD progression to NASH [7]. In this study, we confirmed that the intrahepatic lipid saturation in NAFLD patients is associated with oxidative damage in lipids and with hydroxyl fatty acid production.
The free fatty acid and oxylipin metabolism is reflected in the correlations found between the plasma levels of free fatty acid and oxylipins. AA, ETA and EPA plasma levels are positively correlated between them, evidencing a common origin and destiny. The plasma levels of 15HETE and 17HDoHE are well correlated, pointing to the role of the same 15-LOX responsible for their synthesis from AA or DHA, respectively. The plasma levels of PGF2α, LXB4 and MaR1 are also positively well correlated, suggesting a common origin, although PGF2α exhibits pro-inflammatory, whereas LXB4 and MaR1 exhibit anti-inflammatory or resolving activities [66]. The synthesis of SPM (such as MaR1 and LXB4) by neutrophils is a pivotal process for the transition from inflammation to resolution [67,68]. SPM, in turn, counter-regulate the proinflammatory mediators, such as PGF2α [66]. The plasma levels of 16HPAL, 3HMYR and 12HEST are well correlated between them and with the plasma levels of PGF2α, LXB4 and MaR1. The origin of these oxylipins is different, as they are synthetized by the action of COX, LOX, CYP450, peroxisomal ω-oxidation, mitochondrial β-oxidation or non-enzymatic reactions with the hydroxyl radical. The high degree of correlation between these oxylipins could be related to their degradation pathway. Oxylipins are biological mediators that require strict control, and only recently, it was started being described how they are removed during inflammation [33]. It seems that oxylipins are removed via mitochondrial β-oxidation. Many oxylipins are removed by carnitine palmitoyl transferase, a mitochondrial importer of fatty acids driving toward β-oxidation [33]. The positive correlation of the plasma concentration of these oxylipins in NAFLD could be attributed to the difficulties in removing them by an overwhelmed β-oxidation pathway.
There is a lack of conclusive biomarkers for the non-invasive monitoring of NAFLD in humans [69]. NASH progression is based upon the NAFLD Activity Score (NAS) and fibrosis using liver biopsies. The Pathology Committee of the NASH Clinical Research Network designed NAS as a scoring system of 14 histological features [44]. Patients are diagnosed as ‘‘NASH’’ with a NAS ≥ 5, while they are diagnosed as “Not NASH’’ with a NAS ≤3. The MI-PDFF represents the standard gold reference for grading “Not NASH” NAFLD in four stages, based on the IFC [12]: without NAFLD, mild NAFLD, moderate NAFLD and severe NAFLD. The plasma content of 11,12-dihydroxyeicosatrienoic acid (11,12-diHETrE) was proposed as a single biomarker to differentiate NAFLD from NASH, with an AUCROC area of 1. A panel of other oxylipins, including 13,14-dihydro-15-keto prostaglandin D2 (dhk PGD2) and 20-carboxy arachidonic acid (20-COOH AA), was also proposed for the diagnosis of NASH [34]. However, no oxylipins have been proposed to date in order to differentiate the stage of “Not NASH” NAFLD. Here, we propose that the free fatty acids and oxylipin plasma levels correlating with the IFC could have diagnostic value to range NAFLD steatosis. We tested the discriminatory capability of circulating oxylipins as biomarkers to differentiate patients between different NAFLD grades. The plasma levels of 12HEST, PGF2α, 15HETE and free ETA showed significant diagnostic accuracy in distinguishing patients with different NAFLD grades. Among all the oxylipins analyzed, 12HEST is the most sensible biomarker to diagnose severe NAFLD (IFC3) but with low specificity. The PGF2α plasma levels exhibit similar sensitivity but higher specificity than 12HEST. The 15HETE plasma levels have a similar diagnostic value as PGF2α in diagnosing severe NAFLD (IFC3), and the ETA plasma levels could be useful in diagnosing a certain degree of NAFLD (IFC1, IFC2 or IFC3) against the absence of NAFLD (IFC0).
In summary, NAFLD progression can be monitored by measuring the plasma levels of free PUFA and oxylipins in metabolic syndrome patients. Severe NAFLD stage is characterized by higher glycemia, triglyceridemia and plasma levels of free PUFA compared to absent, mild or moderate NAFLD, evidencing increased insulin resistance. The plasma levels of oxylipins produced by COX, LOX and CYP450 enzymes (PGF2α, LXB4, MaR1) are higher in severe NAFLD patients than in patients with mild and moderate NAFLD or in patients without NAFLD, pointing to the coexistence of both inflammation and resolution processes associated with this severe stage of the disease. The plasma levels of saturated oxylipins 16HPAL and 3HMYR are higher in severe NAFLD than in the preliminary stages of the disease, which could be indicative of dysregulation of both the mitochondrial β-oxidation and the peroxisomal ω-oxidation pathways. Plasma 12HEST levels in severe NAFLD are higher than in the other stages, indicating that the non-enzymatic hydroxylation of saturated fatty acid produced by activated oxygen species is more present in this severe stage of NAFLD. Finally, the plasma levels of 12HEST and PGF2α could be considered as novel potential biomarkers for diagnosing the severe, moderate or mild stages of NAFLD.
## 5. Conclusions
NAFLD progression can be monitored by measuring the plasma levels of free PUFA and oxylipins in metabolic syndrome patients. The severe NAFLD stage is characterized by increased insulin resistance, dysregulation of both the mitochondrial β-oxidation and the peroxisomal ω-oxidation pathways and the coexistence of both inflammation and resolution processes associated with the severe stage of the disease.
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|
---
title: Analysis of the Influence of IL-6 and the Activation of the Jak/Stat3 Pathway
in Fibromyalgia
authors:
- Ylenia Marino
- Alessia Arangia
- Marika Cordaro
- Rosalba Siracusa
- Ramona D’Amico
- Daniela Impellizzeri
- Rosalia Cupi
- Alessio Filippo Peritore
- Enrico Gugliandolo
- Roberta Fusco
- Salvatore Cuzzocrea
- Rosanna Di Paola
journal: Biomedicines
year: 2023
pmcid: PMC10045851
doi: 10.3390/biomedicines11030792
license: CC BY 4.0
---
# Analysis of the Influence of IL-6 and the Activation of the Jak/Stat3 Pathway in Fibromyalgia
## Abstract
Background: *Fibromyalgia is* a medical condition that affects a small percentage of the population, with no known effective treatment. There is evidence to suggest that inflammation is a key factor in the nerve sensitization that characterizes the disorder. Therefore, this paper concentrates on the role of IL-6 in fibromyalgia and the related pain-like symptoms. Methods: This work aimed to evaluate Sprague–Dawley rats, which were injected for three consecutive days with 1 mg/kg of reserpine; IL-6-R Ab was intraperitoneally injected at 1.5 mg/kg seven days after the first reserpine injection. Behavioral analyses were conducted at the beginning of the experiment and at seven and twenty-one days from the first reserpine injection. At this timepoint, the animals were sacrificed, and tissues were collected for molecular and histological analysis. Results: *Our data* showed the analgesic effect of IL-6-R-Ab administration on mechanical allodynia and thermal hyperalgesia. Additionally, the reserpine + IL-6-R-Ab group showed a reduced expression of the pain-related mediators cFOS and NFG and reduced levels of pro-inflammatory cytokines (TNF-α, IL-1β and IL-6) and chemokines (Cxcl5, Cxcl10 and Cx3cl1). From the molecular point of view, the IL-6-R-Ab administration reduced the gp130 phosphorylation and the activation of the Jak/STAT3 pathway. Additionally, the IL-6-R Ab reduced the activation of neuroinflammatory cells. Conclusions: Our study showed that IL-6 plays a crucial role in fibromyalgia by triggering the Jak/STAT3 pathway, leading to an increase in chemokine levels and activating glial cells.
## 1. Introduction
Fibromyalgia is a chronic condition characterized by pervasive pain, fatigue and depression [1,2]. It is widespread: currently, it affects the $6\%$ of people [3,4,5]. Its main characteristic is the damaged transduction of nociceptive signaling. This disfunction leads to hypersensitivity to non-noxious stimuli [6,7].
Although peripheral sensitization certainly contributes to the sensitization of the nociceptive system and, thereby, to inflammatory-pain hypersensitivity at inflamed sites (primary hyperalgesia) [8], it nevertheless represents a form of pain elicited by the activation of nociceptors, albeit one with a lower threshold due to the increased peripheral transduction sensitivity, and generally involves ongoing peripheral pathology [9]. Peripheral sensitization appears to play a major role in altered heat, but not mechanical sensitivity, which is a major feature of central sensitization [10,11]. The etiology of fibromyalgia is still unclear: if central sensitization is considered to be the main mechanism involved, then many other genetic, immunological and hormonal factors may play an important role [12]. In the past, fibromyalgia was considered a non-inflammatory disorder [13,14]; currently, many pro-inflammatory pathways and mediators are known to be activated in fibromyalgia patients [15,16,17,18,19]. Several clinical and experimental reports displayed the key role of inflammation in fibromyalgia [20,21,22,23]. The increased release of pro inflammatory cytokines has been detected in suffering animals, and their overexpression is correlated with the development of pain-like behaviors [24,25,26]. Thus, neuropathic pain is characterized by a pro-inflammatory microenvironment [27]. These mediators are secreted by immune/inflammatory cells [28,29,30,31] and include interleukin-6 (IL-6), interleukin-1 beta (IL-1β), tumor necrosis factor-α (TNF), interleukin-10 (IL-10), monocyte chemoattractant protein 1 (MCP-1), glutamate, nerve-growth factor (NGF) and substance P (SP) [32,33,34]. Among these pro-inflammatory mediators, one of the most thoroughly studied is interleukin-6 (IL-6). Several papers described the relationship between neuropathic pain and IL-6 [35]. It is a pleiotropic cytokine responsible for many biological pathways, including neuropathologies [36,37]. Originally described as a B-stimulatory factor in the production of immunoglobulin [38], IL-6 binds its receptor IL-6-R on target cells [39,40] and activates the signal transducing membrane glycoprotein gp130 [41]. In turn, it dimerizes and activates several intracellular signaling pathways, including mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK), the Janus-activated kinase/signal transducer activator of transcription (JAK/STAT) and phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathways [42,43]. Persistent IL-6 increase is correlated with chronic pain in both patients [44] and experimental animals [45]. Its role has been described in rheumatoid-arthritis-induced pain [46], spinal-cord-injury induced pain [47], cancer pain [48], neuropathic pain [49], peripheral nerve injury [50], chemotherapy-induced peripheral neuropathy [51] and inflammatory pain [52]. In contrast with other sources of pathological pain, such as cancer and neuropathic pain, fibromyalgia syndrome is a chronic painful condition, which is characterized by widespread pain mainly perceived in deep somatic tissues, i.e., in the muscles and joints. The definition is based on the American College of Rheumatology (ACR)’s classification scheme [53]. Fibromyalgia (FM) is also characterized by abnormal pain sensitivity and frequent additional comorbidities, such as sleep disturbances and affective disorders [53]. In contrast to classic neuropathic pain syndromes, the general perception of fibromyalgia is that in this disease, nerve lesions are not demonstrable [54,55]. In particular, increased expression levels of IL-6, IL-6-R, and gp130 have been found in dorsal-root ganglia (DRG) and spinal-cord tissues from suffering animals. Moreover, experimental reports displayed that IL-6 injection induces thermal hyperalgesia and mechanical allodynia. Additionally, it enhances translations in sensory neurons and links to nociceptive plasticity [56,57], contributing to central sensitization [58,59,60]. These previous data suggest the key role of IL-6 in pathological pain. To the best of our knowledge, there is no complete description of IL-6’s role in the chronic pain related to fibromyalgia, although some authors previously approached this topic [61,62]. Thus, this paper focuses on the role of IL-6 fibromyalgia and associated pain-like behaviors. To perform this analysis, we employed an animal model of reserpine-induced fibromyalgia, a biogenic amine depletory. In particular, we employed a validated model comprising three reserpine administrations [63], although recently other authors reported higher numbers of reserpine injections [64]. In our model, long-lasting widespread nociceptive hypersensitivities were exhibited in rats [65,66]. It is noteworthy that the time course of pain-related behaviors was in parallel with the decrease in monoamine neurotransmitters after the reserpine treatment [66]. Indeed, reserpine promotes the appearance of the inflammatory process, including the expression of cytokines and growth factors by macrophages and mesangial cells (IL-1b, IGF-1 and TNF-α) [67]. Thus, monoamine depletion appears to cause nociceptive hypersensitivity in a rat-reserpine-induced pain model, which could be useful in the study of the pathological mechanisms of chronic, widespread pain, including fibromyalgia.
## 2.1. Animals
Sprague–Dawley male rats (200–220 g) were employed for this research. Food and water were managed ad libitum. The University of Messina Review Board for animal care approved the study ($\frac{212}{2021}$-PR). All experiments followed the USA (Animal Welfare Assurance No. A5594-01), European (EU Directive $\frac{2010}{63}$) and Italian (D. Lgs $\frac{2014}{26}$) guidelines.
## 2.2. Induction of Fibromyalgia
Reserpine was administered by 3 subcutaneous injections of 1 mg/kg for 3 consecutive days [26,63,65]. Reserpine was dissolved in distilled water with $0.5\%$ acetic acid (vehicle). Controls received the same volume as vehicle, but no reserpine was injected.
## 2.3. IL-6 Ab Administration
The IL-6-R Ab (R&D systems) was dissolved in PBS and intraperitoneally injected at 1.5 mg/kg seven days after the first reserpine injection [47].
## 2.4. Experimental Groups
Rats were randomly divided into several groups ($$n = 20$$ for each).
Control: animals were subcutaneously injected with vehicle instead of reserpine and treated with PBS seven days after the first reserpine injection.
Control + IL-6-R Ab: animals were subcutaneously injected with saline instead of reserpine and treated with IL-6-R Ab seven days after the first reserpine injection.
Reserpine: animals were subcutaneously injected with reserpine as described above.
Reserpine + IL-6-R Ab: animals were subcutaneously injected with reserpine as described above and treated with IL-6-R Ab seven days after the first reserpine injection.
Twenty-one days after reserpine injection, behavioral analyses were conducted, animals were sacrificed by isoflurane overdose and samples from L4–L6 area of spinal cord were collected for molecular analysis.
## 2.5. Behavioral Analyses
Behavioral analyses were performed on day 0 to obtain initial data, on day 7 before the IL-6-R-Ab administration to verify the reserpine-induced fibromyalgia and, subsequently, after 21 days from the beginning of the experiment to assess the effect of IL-6-R-Ab administration on behavioral changes. We decided to perform these analyses on these days based on previously published data on IL-6-R Ab [47] and on the experimental model [68].
## 2.5.1. Von Frey Hair Test
Mechanical allodynia was evaluated at 0, 7 and 21 days from the first reserpine injection, using a dynamic plantar von Frey hair esthesiometer (Bio-EVF4; Bioseb, Vitrolles, France) [69]. The device includes a force transducer with a plastic tip. The tip was applied to the plantar area and a rising, upward force was exerted. The withdrawal threshold was defined as the force, expressed in grams, at which the mouse removed its paw.
## 2.5.2. Hot-Plate Test
At 0, 7 and 21 days from the first reserpine injection, the hot-plate test was performed. The hot-plate latency was calculated using a metal surface maintained at 53.6 °C (Ugo Basile, Milan, Italy). Each rat was monitored, and the licking of a hind paw was set as the end point [70]. Maximal latency accepted was 45 s. Regarding thermal hyperalgesia, the hot-plate test is advantageous over tail flicking/tail withdrawal because it gives the opportunity to test thermal sensitivity unconfounded by stress-induced analgesia associated with restraint.
## 2.5.3. Tail-Flick Warm-Water Test
At 0, 7 and 21 days from the first reserpine injection, the tail-flick warm-water test was performed to evaluate pain threshold (IITC Life Science). Each rat’s tail was immersed in warm water (50 ± 0.5 °C) and the time between tail input and retraction was recorded. A maximum tail-flick latency of 10 s was employed to minimize tissue damage [71].
## 2.6. Quantitative Real-Time PCR
Total RNA from spinal-cord tissue was extracted according with the manufacturer’s instructions (Qiagen, Milan, Italy). The RNA was quantified using a Nanodrop spectrometer and cDNA was obtained using iScriptTM cDNA Synthesis Kit (Bio-Rad, Milano, Italy). according to manufacturer’s protocols [72]. Real-time PCR analysis was performed by the SYBR Green method using the QuantiTect Primer Assay (Qiagen) with b-actin as internal control [73]. Real-time PCR was performed using a Bio-Rad CFX Real-Time PCR ((Bio-Rad, Milano, Italy) Detection System [74].
## 2.7. Western-Blot Analysis
Western-blot analysis was performed on lumbar-spinal-cord tissues. Samples were prepared as described previously [75]. Briefly, tissues were suspended in buffer A (0.2 mM phenylmethylsulfonyl fluoride, 0.15 mM pepstatin A, 20 mM leupeptin and 1 mM sodium orthovanadate), homogenized for 2 min and centrifuged at 10,000× g for 10 min at 4 °C. Supernatants represented the cytosolic fraction. The pellets, containing nuclei, were re-suspended in buffer B (150 mM NaCl, $1\%$ Triton X-100, 1 mM EGTA, 1 mM EDTA, 10 mM Tris–HCl pH 7.4, 0.2 mM phenylmethylsulfonyl fluoride, 20 mM leupeptin and 0.2 mM sodium orthovanadate). After centrifugation for 30 min at 15,000× g at 4 °C, the supernatants contained nuclear proteins. Equal amounts of protein were separated on SDS-PAGE gel and transferred to nitrocellulose membrane [76]. The membranes were probed with specific antibodies: (sc-377573) p-gp130 antibody, (A94191) p-Jak1 antibody, (ab32143) p-STAT3 antibody, (sc-32300) NGF antibody, (sc-166940) cFOS antibody in 1x PBS, $5\%$ w/v non-fat dried milk and $0.1\%$ Tween-20 at 4 °C, overnight [77]. Filters were incubated with peroxidase-conjugated bovine anti-mouse IgG secondary antibody or peroxidase-conjugated goat anti-rabbit IgG (1:5000, Jackson ImmunoResearch, West Grove, PA, USA) for 1 h at room temperature. To ensure amounts of proteins were equal, blots also were probed with an antibody against the b-actin protein (Santa Cruz Biotechnology). Signals were examined with an enhanced chemiluminescence (ECL) detection-system reagent (Thermo Fisher, Waltham, MA, USA) [78]. The relative expression of the protein bands was quantified by densitometry with BIORAD ChemiDocTM XRS+ software and standardized to b-actin. Each blot was stripped with glycine $2\%$ and re-incubated several times to optimize the detection of the proteins and to visualize other proteins, minimizing the number of gels and transfers.
## 2.8. Immunohistochemical Analysis
Immunohistochemical analysis was performed on sections (7 μm) of spinal cord, as previously described [79]. Briefly, tissues were fixed in $10\%$ (w/v) PBS-buffered formaldehyde and embedded in paraffin. Seven-micrometer sections were prepared from tissues. After deparaffinization, endogenous peroxidase was quenched with $0.3\%$ (v/v) hydrogen peroxide in $60\%$ (v/v) water for 30 min. The slides were permeabilized with $0.1\%$ (w/v) Triton X-100 in PBS for 20 min. Tissue sections were incubated in $2\%$ (v/v) normal goat serum in PBS to block non-specific binding. The sections were incubated overnight with primary antibodies: anti-GFAP (SCB, #sc33673) and anti-IBA-1 (Thermo Fisher Scientific, Milan, Italy) antibodies. Slides were then washed with PBS and incubated with a secondary antibody. Specific labeling was identified with an avidin–biotin-peroxidase complex and a biotin-conjugated goat anti-rabbit immunoglobulin G (Vector Lab, Milan, Italy). Five histological sections were evaluated for each animal. Cells were enumerated by counting five high-power fields (40×) per section using Leica DM6 microscope (Leica Microsystems, Milan, Italy). Reactive glia cells were considered as highly ramified with hypertrophic processes [80,81].
## 2.9. ELISA Analysis
The concentrations of TNF-α, IL-1β and IL-6 were measured. Briefly, spinal-cord tissues were homogenized in 1 mL PBS with 10 μL of protease inhibitor at low speed. The samples were centrifuged at 14,000× g at 4 °C for 15 min; supernatants were employed, using respective ELISA kits according to the manufacturer’s protocol and analyzed using a microplate reader [82].
## 2.10. Statistical Analysis
All values in the figures and text are expressed as mean ± standard error of the mean (SEM) of $$n = 5$$ number of animals. The results of the behavioral analysis were analyzed by two-way ANOVA, while all other results were analyzed by one-way ANOVA. Both analyses were followed by a Bonferroni post hoc test for multiple comparisons. A p-value < 0.05 was considered significant; * $p \leq 0.05$ vs. control; # $p \leq 0.05$ vs. reserpine; ** $p \leq 0.01$ vs. control; ## $p \leq 0.01$ vs. reserpine; *** $p \leq 0.001$ vs. control; ### $p \leq 0.001$ vs. reserpine.
## 3.1. Experimental Timeline
In order to investigate the effects of IL-6-R Ab on fibromyalgia, the rats were subcutaneously administered reserpine for three consecutive days and were intraperitoneally injected at seven days after the first reserpine injection with IL-6-R Ab 1.5 mg/kg (Figure 1).
## 3.2. Analysis of Pain-like Behaviours
Seven days after the first reserpine injection, the animals displayed increased sensitivity to mechanical (Figure 2A, *** $p \leq 0.001$ vs. control) and thermal (Figure 2B,C, *** $p \leq 0.001$ vs. control) stimuli compared to the control and control + IL-6-R Ab groups. No statistical differences were found between the control and control + IL-6-R Ab groups. This hypersensitivity strongly increased 21 days from the first reserpine injection (*** $p \leq 0.001$ vs. control), while the animals administered IL-6-R Ab at the same timepoint showed reduced hypersensitivity to von Frey hair (Figure 2A, ## $p \leq 0.01$ vs. reserpine, * $p \leq 0.05$ vs. control), hot-plate (Figure 2B, # $p \leq 0.05$ vs. reserpine, *** $p \leq 0.001$ vs. control) and tail-flick warm-water (Figure 2C, # $p \leq 0.05$ vs. reserpine, ** $p \leq 0.01$ vs. control) tests. The analgesic effect of the IL-6-R-Ab administration on the long-term fibromyalgia-induced changes in the nociceptive pathways persisted. One of the characteristic hallmarks of suffering animals is the reduction in body weight. The control and control + IL-6-R Ab rats gained in body weight during the experiment and no differences were detected between them (Figure 2D). Fibromialgia induced a reduction in body weight in the rats from the reserpine group (*** $p \leq 0.001$ vs. control). The body weights of rats in the reserpine + IL-6-R-Ab group increased after the IL-6-R-Ab administration compared with those in the reserpine group (*** $p \leq 0.001$ vs. control, ### $p \leq 0.001$ vs. reserpine), suggesting that the IL-6-R-Ab-treated fibromyalgia rats did not experience chronic pain (Figure 2D).
## 3.3. Analysis of Pain-Related Mediators
To evaluate the nociceptive pathways, Western-blot analyses were performed 21 days from the first reserpine injection. The spinal-cord tissues harvested from the reserpine group showed increased cFOS (Figure 3A,A’, *** $p \leq 0.001$ vs. control) and NGF expression (Figure 3B,B’, *** $p \leq 0.001$ vs. control) compared to the control and control + IL-6-R-Ab rats. The animals from the reserpine + IL-6-R-Ab group showed a reduction in both nociceptive mediators in the lumbar-spinal-cord tissues (Figure 3A, ## $p \leq 0.01$ vs. reserpine, * $p \leq 0.05$ vs. control and Figure 3B, ## $p \leq 0.01$ vs. reserpine, ** $p \leq 0.01$ vs. control).
## 3.4. Analysis of Pro-Inflammatory Mediators
The expressions of proinflammatory cytokines and chemokines were assessed in the lumbar region of the spinal cords of the experimental animals 21 days from the first reserpine injection. Increased expressions of TNF-α (Figure 4A, *** $p \leq 0.001$ vs. control), IL-1β (Figure 4B, *** $p \leq 0.001$ vs. control) and IL-6 (Figure 4C, *** $p \leq 0.001$ vs. control) were found in the samples from the reserpine group compared to the control and control + IL-6-R-Ab rats. The IL-6-R-Ab administration strongly reduced these levels (Figure 4A, # $p \leq 0.05$ vs. reserpine, *** $p \leq 0.001$ vs. control Figure 4B, # $p \leq 0.05$ vs. reserpine, ** $p \leq 0.01$ vs. control and Figure 4C, # $p \leq 0.05$ vs. reserpine, ** $p \leq 0.01$ vs. control). The RT-PCR analysis was performed to evaluate the chemokine expression. Increased expressions of Cxcl5 (Figure 4D, *** $p \leq 0.001$ vs. control), Cxcl10 (Figure 4E, *** $p \leq 0.001$ vs. control) and Cxecl1 (Figure 4F, *** $p \leq 0.001$ vs. control) were found in the lumbar region of the spinal cords of the reserpine group compared to the control and control + IL-6-R-Ab groups. Samples from the reserpine + IL-6-R-Ab group showed reduced levels of Cxcl5 (Figure 4D, ## $p \leq 0.01$ vs. reserpine, *** $p \leq 0.001$ vs. control), Cxcl10 (Figure 4E, ## $p \leq 0.01$ vs. reserpine, * $p \leq 0.05$ vs. control) and Cx3cl1 (Figure 4F, ### $p \leq 0.001$ vs. reserpine, *** $p \leq 0.001$ vs. control).
## 3.5. Analysis of the Jak/Stat Pathway
A Western-blot analysis was performed 21 days from the first reserpine injection. The analysis showed increased gp130 phosphorylation in the tissues from the reserpine group compared to the control and control + IL-6-R-Ab rats (Figure 5A,A’, ** $p \leq 0.01$ vs. control). The same trend was found for the Jak phosphorylation (Figure 5B,B’, ** $p \leq 0.01$ vs. control). The IL-6-R-Ab administration strongly reduced the p-gp130 and p-Jak expression (Figure 5A, # $p \leq 0.05$ vs. reserpine and Figure 5B, # $p \leq 0.05$ vs. reserpine, * $p \leq 0.05$ vs. control). Additionally, the samples from the reserpine group showed increased STAT3 phosphorylation in the nuclear compartment compared to the controls (Figure 5C,C’, *** $p \leq 0.001$ vs. control). The reserpine + IL-6-R-Ab group showed reduced p-STAT3 expression (Figure 5C, # $p \leq 0.05$ vs. reserpine, ** $p \leq 0.01$ vs. control).
## 3.6. Analysis of Glial Activation
An immunohistochemical analysis was performed 21 days from the first reserpine injection on the lumbar spinal-cord tissues. In particular, higher-magnification imaging revealed a small morphology and low level of GFAP and Iba-1 immunoreactivity in the controls (Figure 6A,A’,B,B’ and Figure 7A,A’,B,B’, respectively), while in the reserpine-administered rats, astrocytes and microglia exhibited intense GFAP (Figure 6C,C’, *** $p \leq 0.001$ vs. control) and Iba-1 (Figure 7C,C’, *** $p \leq 0.001$ vs. control) immunoreactivity and adopted a highly ramified morphology with hypertrophic processes. The IL-6-R-Ab administration strongly reduced both the GFAP (Figure 6D,D’,E, ## $p \leq 0.01$ vs. reserpine, *** $p \leq 0.001$ vs. control) and the Iba-1 (Figure 7D,D’,E, ### $p \leq 0.001$ vs. reserpine, *** $p \leq 0.001$ vs. control) expressions, reducing the reactive-glial-cell number.
## 4. Discussion
This study employed a well-established experimental animal model of fibromyalgia to investigate the role of IL-6 in this syndrome.
The leading school of thought views fibromyalgia as a central sensitization syndrome. Nociplastic pain is the recently proposed term to mechanistically explain central sensitization. Accumulating research suggests an alternate explanation: fibromyalgia can be conceptualized as a neuropathic pain syndrome, with dorsal root ganglia (not the brain) as the primary pain source [83].
Firmly in line with the literature, after 3 weeks, the animals exhibited widespread nociceptive hypersensitivities and allodynia [84]. Based on the data presented by Guptarak et al. [ 47], the IL-6-R Ab was administered seven days after the first reserpine injection. Twenty-one days thereafter, pain-like behaviors were checked. The characteristic hypersensitivity to mechanical and thermal stimuli in fibromyalgia was significantly reduced by the IL-6-R-Ab administration. We also showed that the body-weight increases in the IL-6-R-Ab-administered animals were similar to those shown by the control animals, suggesting that the rats did not suffer chronic pain. Again clearly in line with the literature [68,85], the molecular analysis of the spinal-cord tissues confirmed the increased expression of neuro-sensitizing mediators, such as NGF and c-FOS, in the suffering animals compared to the controls. The IL-6-R-Ab administration reduced the expression of both these mediators, confirming the analgesic effects derived from the neutralizing IL-6-R Ab.
Reserpine is a monoamine depletor that exerts a blockade on the vesicular monoamine transporter for neuronal transmission or storage, promoting dopamine autoxidation and oxidative catabolism by monoamine oxidase [86]. This accelerated mechanism leads to the formation of dopamine quinones and hydrogen peroxide, related to the oxidative-stress process [87]. Bagis et al. [ 88] demonstrated significantly higher serum levels of pentosidine and malondialdehyde, together with serum superoxide dismutase reduction, in patients with chronic pain compared with normal controls. *This* generation of advanced glycation end products resulting from increased nitrosative stress activates transcription factor NF-kb, leading to pro-inflammatory gene expression [89]. It includes the expression of cytokines and growth factors by macrophages and mesangial cells (IL-1b, IGF-1, TNF-α). Thus, to evaluate the expression profile of IL-6 in fibromyalgia, further molecular analyses were performed. Increased levels of pro-inflammatory cytokines, such as TNF-α, IL-1β and IL-6 and their mediators, were detected in the suffering animals compared to the controls, confirming the key role of inflammation in fibromyalgia [90,91]. Among these circulating biomarkers, calcitonin gene-related peptide (CGRP) is also dysregulated and may be a reliable parameter to help diagnose this complex syndrome [92], while there were no differences in vascular endothelial growth factor (VEGF) and nitric oxide (NO) levels between patients and controls [93]. The IL-6-R-Ab administration reduced proinflammatory cytokine levels. In particular, the IL-6-R Ab blocked IL-6-related signal transduction.
Reserpine administration by increasing cytokine levels increases the activation of cytokines on the pathways they subtend [67]. Once it is bounded to its receptor, IL-6 recruits gp130 to its receptor complex, which is responsible for Jaks activation and STAT-protein recruitment [94]. The IL-6/IL-6-R complex induces gp130 homodimerization, which in turn activates Jaks and STAT3 downstream proteins. Phosphorylated gp130 provides docking sites for STAT3, which is then phosphorylated by JAKs. The P-STAT3 dimers translocate to the nucleus, where they regulate gene expression binding to specific DNA elements [95]. The IL-6-R-Ab administration reduced gp130 and Jak1 phosphorylation and, in turn, STAT3 phosphorylation. Furthermore, STAT3 has a key role in nociceptive transmission [96,97,98]; in our study, the inhibition of the Jak/STAT3 pathway by the IL-6-R-Ab administration reduced the pain-like behaviors induced by fibromyalgia. Recent studies underline the key role of STAT3 in chronic pain [99]. In particular, the Jak/STAT3 signaling was related to cellular proliferation and astrocyte activation in an animal model of chronic pain [100]. Emerging lines of evidence indicate that peripheral-nerve injury converts resting spinal-cord glia into reactive cells, which are required for the development and maintenance of neuropathic pain. It was found that nerve-injury-induced astrocyte proliferation requires the JAKs/STAT3 pathway.
The rats’ nerve injuries induced marked signal transduction and activation of STAT3 translocation and activation in the dorsal horn astrocytes. Intrathecally administering inhibitors of JAKs and STAT3 3 signaling to rats with nerve injuries reduced the number of proliferating dorsal horn astrocytes and produced recovery from established tactile allodynia, a cardinal symptom of neuropathic pain that is characterized by pain hypersensitivity evoked by innocuous stimuli. Moreover, recovery from tactile allodynia was also produced by the direct suppression of dividing astrocytes by the intrathecal administration of the cell -cycle inhibitor flavopiridol to the rats with injuries. Together, these results imply that the JAKs/STAT3 signaling pathways are critical transducers of astrocyte proliferation and the maintenance of tactile allodynia and may be a therapeutic target for neuropathic pain [101,102]. Strongly in line with previous data [103], the activation of the Jak/STAT3 pathway led to increased GFAP expression, while the IL-6-R-Ab administration reduced astrogliosis.
Small-fiber neuropathy is frequently described in fibromyalgia [104]. It refers to the selective loss of unmyelinated C and thinly myelinated Aδ fibers, which mediate pain, heat and cold sensation, respectively. Small-fiber neuropathy is classically slowly progressive and length-dependent in onset, although non-length-dependent forms exist. Therefore, the identification of a small fiber or early sensory neuropathy in the setting of widespread pain is important and carries clinical-management implications [105]. In this scenario, another crucial point is the overexpression of proinflammatory mediators, which are responsible for central sensitization during the development of pathologic pain [34]. In particular, when the STAT3 pathway is activated, reactive astrocytes become important sources of chemokines [106]. Our data showed the upregulation of Cxcl10, Cx3cl1 and Cxcl5 in the dorsal horns of the spinal cords of the suffering animals, while the IL-6-R-Ab administration significantly reduced their expression. Chemokines from reactive astrocytes induce multiple effects on neuroinflammation. It was reported that they are able to regulate the expression of proinflammatory genes [107], increasing nociceptive transmission through activated microglial cells [108]. Firmly in line with the literature, the increase in chemokines in the animals with fibromyalgia was correlated with increased Iba1 expression, while the IL-6-R-Ab administration significantly reduced microglial activation.
Various pathological-pain models showed increased expression levels of interleukin-6 and its receptor in the spinal cords and dorsal root ganglia. In summary, our study showed that IL-6 has a key role in fibromyalgia, activating the Jak/STAT3 pathway, chemokine overexpression and glial-cell activation. Our data suggest that the modulation of the IL-6 pathway could attenuate pain-like behavior in fibromyalgia-dependent pain. The findings from our preclinical study may have clinical implications for enhancing our understanding of the molecular mechanisms underlying the disease and identifying potential new targets for therapy.
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|
---
title: 'Association between multimorbidity patterns and incident depression among
older adults in Taiwan: the role of social participation'
authors:
- Hsin-En Ho
- Chih-Jung Yeh
- James Cheng-Chung Wei
- Wei-Min Chu
- Meng-Chih Lee
journal: BMC Geriatrics
year: 2023
pmcid: PMC10045862
doi: 10.1186/s12877-023-03868-4
license: CC BY 4.0
---
# Association between multimorbidity patterns and incident depression among older adults in Taiwan: the role of social participation
## Abstract
### Background
Previous research has found different multimorbidity patterns that negatively affects health outcomes of older adults. However, there is scarce evidence, especially on the role of social participation in the association between multimorbidity patterns and depression. Our study aimed to explore the relationship between multimorbidity patterns and depression among older adults in Taiwan, including the social participation effect on the different multimorbidity patterns.
### Methods
Data were retracted from the Taiwan longitudinal study on ageing (TLSA) for this population-based cohort study. 1,975 older adults (age > 50) were included and were followed up from 1996 to 2011. We used latent class analysis to determine participants’ multimorbidity patterns in 1996, whereas their incident depression was determined in 2011 by CES-D. Multivariable logistic regression was used to analyse the relationship between multimorbidity patterns and depression.
### Results
The participants’ average age was 62.1 years in 1996. Four multimorbidity patterns were discovered through latent class analysis, as follows: [1] Cardiometabolic group ($$n = 93$$), [2] Arthritis-cataract group ($$n = 105$$), [3] Multimorbidity group ($$n = 128$$) and [4] Relatively healthy group ($$n = 1649$$). Greater risk of incident depression was found among participants in the Multimorbidity group (OR: 1.62; $95\%$ CI: 1.02–2.58) than the Relatively healthy group after the multivariable analysis. Compare to participants in the relatively healthy group with social participation, participants in the arthritis-cataract group without social participation (OR: 2.22, $95\%$ CI: 1.03–4.78) and the multimorbidity group without social participation (OR: 2.21, $95\%$ CI: 1.14–4.30) had significantly increased risk of having depression.
### Conclusion
Distinct multimorbidity patterns among older adults in Taiwan are linked with the incident depression during later life, and social participation functioned as a protective factor.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-023-03868-4.
## Background
We are living in an ageing world. In 2020, the United Nations estimated approximately 727 million persons aged ≥ 65 years, which is projected to grow continuously to more than double by 2050. [ 1] Additionally, the global population proportion over 60 years will almost double from 12 to $22\%$ between 2015 and 2050. [ 2] This global ageing wave is changing healthcare systems and the manner of seeking better health in old age. [ 3] Currently, non-communicable diseases are the principal cause of death, leading to a significant disease burden and years lived with disability (YLD). [ 4].
Multimorbidity is among the major concerns among aged people. It refers to patients with more than two or three chronic diseases simultaneously. [ 5] Researchers have discovered that multimorbidity has increased the disease burden and medical costs over decades. [ 6] It also possesses several negative impacts on health and geriatric syndromes, such as falls, [7] being institutionalised, [8] major adverse kidney events, [9] frailty, [10] disability [11] and even mortality. [ 12, 13] Different patterns/clusters of multimorbidity in different populations have recently been discovered. [ 14] Hence, it is important to understand the composition of each multimorbidity pattern to study the aetiology of common diseases. Furthermore, different multimorbidity patterns with different characteristics were discovered in different countries to cause different health consequences. For example, Yao et al. found that people in China aged ≥ 50 years had primarily four multimorbidity patterns: [1] a vascular-metabolic group, [2] a stomach-arthritis group, [3] a cognitive-emotional group and [4] a hepatorenal group. [ 15] A previous study revealed that cardiometabolic multimorbidity of the four multimorbidity patterns in Taiwanese older people was linked with increased disability and mortality. [ 16] Furthermore, Zheng et al. showed that complex cardiometabolic multimorbidity pattern among five multimorbidity patterns was linked with higher mortality in the United States. [ 17].
Depression among the elderly is another crucial issue in an older population. [ 18] Clinically significant depressive symptoms are present in approximately $15\%$ of community-dwelling older adults. [ 19] Also, depression among the elderly causes great suffering with adverse effects on health, such as suicide-related ideation, [20] social isolation, [21] frailty, [22] disability [23] and mortality. [ 24].
Multimorbidity has been associated with depression. A systematic review showed that depression was two to three times more likely in people with multimorbidity than in those without multimorbidity or with no chronic physical condition. [ 25] Additionally, older adults with multimorbidity are more likely to have depression, anxiety and stress symptoms. [ 26] A study in China showed that multimorbidity was linked with elevated functional limitations and depression among people older than 45 years. [ 27] Another study in the UK revealed that multimorbidity and depression were strongly associated among female UK Biobank participants with a previous breast cancer diagnosis. [ 28].
Conversely, social participation is an important promoter of physical and mental health among the elderly. Social participation includes social connections, informal social participation and volunteering. [ 29] Previous research disclosed that the elderly with certain social participation patterns had less chance of developing physical disability. [ 30] Also, there was a negative association between social participation and depression in older adults. Liu et al. discovered that older adults who maintained or increased social participation had reduced depressive symptoms, whereas individuals with decreased social participation reported elevated depressive symptoms. [ 31].
However, social participation’s role in different multimorbidity patterns associated with depression remains unexplored. Moreover, no study concerning this question has been performed. Therefore, understanding the answers would help healthcare professionals rapidly identify the high-risk population and take measures to prevent depression. Therefore, we analysed the relationships between multimorbidity patterns and depression among older adults in Taiwan in this 16-year, population-based cohort study.
## Data source and study groups
The data were extracted from a population-based, nationally-representative study, Taiwan Longitudinal Study on Aging (TLSA), initiated in 1989 by the Taiwanese Bureau of Health Promotion and the Population Studies Center of the Institute of Gerontology at the University of Michigan, USA. Data were collected from systematically selected representative samples of the Taiwanese population, including institutionalised older adults. Respondents were longitudinally followed at every three to four years intervals. Highly trained interviewers performed all personal interviews with careful supervision to maintain quality. Nine study waves have been initiated, including 1989, 1993, 1996, 1999, 2003, 2007, 2011, 2015 and 2019. The current study used 1996 and 2011 data, excluding individuals with depression in 1996. The 10-item CES-D measures depressive symptoms. The scores on this scale ranged from 0 to 30, with ten or more as the cut-off point for depression. We also excluded participants who died or loss follow-up during the observation period. Detail of the study was shown in our previous work. [ 32].
This study is a retrospective study. The Institutional Review Board of Health Promotion Administration, Ministry of Health and Welfare approved this study. The approval number was BHP-2007-002.
## Independent variable
The multimorbidity patterns in 1996 were analysed by assessing 12 chronic conditions, including hypertension, diabetes mellitus, coronary artery disease, stroke, cancer, lung disease, arthritis or rheumatic disease, hepatobiliary disease, renal disease (including stone), gout, hip fracture and cataract. Additionally, participants were asked, ‘Have you ever had the disease of …?’ If they answered ‘No’ or ‘I don’t know’, they were classified as a disease-free group.
Another important independent variable is social participation. Participants who had engaged in paid (Participants were asked:” Are you currently in a job? Or not in a job?”) or voluntary work (Participants were asked:” Are you involved in any voluntary social service work?”) or had participated in community activities (Participants were asked:” Are you currently engaged in community activities, such as clubs of singing, dancing club, Tai Chi or karaoke?”) in 1996 were considered to have experienced social participation.
## Dependent variables
Depressive symptoms were assessed using the 10-item CES-D in the seventh wave [2011], [33, 34] a modified version of the original 20-item full-length CES-D. [35] Adequate reliability and validity have been demonstrated for the Chinese version of the 10-item CES-D among the Chinese elderly. [ 36] Ten depressive symptoms were examined, including [1] poor appetite; [2] bad mood; [3] everything was an effort; [4] could not sleep well; [5] lonely; [6] people were unfriendly; [7] could not get going; [8] sad; [9] happy and [10] life was good. Each respondent was asked if they had any among the ten depressive symptoms within the last seven days. A four-point scale was used for each item, indicating how often one experienced a given symptom (0 = never, 1 = rarely, 2 = sometimes, 3 = often or chronically). The positive effect of items was reverse-coded. The score ranges from 0 to 30, while a higher score indicates more severe depressive symptoms. We adopted a score of ten or higher as the cut-off point proved to have high sensitivity (0.85) and specificity (0.80), indicating clinically significant depression. [ 36].
## Confounding variables
We recorded and analysed the following variables in the third wave [1996]: age, sex, income level, social participation, self-rated health, health behaviour (smoking, drinking, betel nut chewing and exercise habits), disability and admission.
The income level was determined by asking, ‘Are you satisfied with your income?’ The answers were categorised as ‘good’ (very satisfied/satisfied), ‘fair’, or ‘poor’ (unsatisfied/very unsatisfied). Additionally, self-rated health was evaluated for each individual and categorised as ‘good’ (very good/good), ‘fair’, or ‘poor’ (poor/very poor). Furthermore, participants’ reported exercise habits were classified into ‘no exercise,’ ‘≤ 2 times’, ‘3–5 times’ and ‘≥ 6 times’ per week. Functional status is the ability to perform the activities of daily living (ADL), such as bathing, dressing, eating, getting out of bed, walking and using the bathroom. [ 37] Respondents were also defined as ‘with disability’ if they had difficulty in one of the six ADLs.
## Statistical analysis
We used latent class analysis (LCA) to estimate the disease patterns. Both models (lower Akaike information criterion or Bayesian information criterion values) fit and interpretability were used to select the most appropriate model. Demographic and clinical characteristics were descriptively analysed for each group. Continuous variables were assessed using analysis of variance and the chi-square test to assess categorical variables. Univariate and multivariable logistic regression analyses explored the relationships between disease patterns and depression. Test of interaction between distinct multimorbidity patterns and social participation was also performed by multivariable logistic regression. The SAS procedure PROC LCA 1.3.2 (https://www.latentclassanalysis.com/software/proc-lca-proc-lta) was used to perform LCA. All data were analysed using SAS version 9.4 software (SAS Institute, Cary, NC, USA). Statistical significance was considered at $p \leq 0.05.$
## Results
Table 1 shows the demographic and clinical characteristics of the participants in 1996. The participants with numbers of 1975 were enrolled in the final analysis with a mean age of 62.1 years. The LCA developed four groups and five group of multimorbidity patterns with AIC: 948.34 and 938.41, BIC: 1270.27 and 1342.40, adjusted BIC: 1108.21 and 1139.04, respectively). Therefore, we chose four groups of multimorbidity patterns which include the cardiometabolic group ($$n = 93$$), arthritis-cataract group ($$n = 105$$), relatively healthy group ($$n = 1649$$) and multimorbidity group ($$n = 128$$) (Fig. 1). The age distribution, gender, social participation, self-rated health, smoking, alcohol consumption, betel nut chewing, exercise, disability and admission in the past year differed between the groups after the statistical analysis. For example, there were more females in the cardiometabolic and multimorbidity group, whereas males predominated among all participants.
Table 1Demographic and clinical characteristics of the participants in 1996Multimorbidity PatternsCharacteristicsTotalCardiometabolicArthritis-cataractRelatively healthyMultimorbidity P-value 1975n = 93n = 105n = 1649n = 128Age62.1(7.6)64.1(7.1)64.3(7.9)61.5(7.6)66(6.7)< 0.0001SexMale1013($51.3\%$)42($45.2\%$)54($51.4\%$)868($52.6\%$)49($38.3\%$)0.0104Female962($48.7\%$)51($54.8\%$)51($48.6\%$)781($47.4\%$)79($61.7\%$)Income satisfaction0.2146Poor222($11.8\%$)7($18.1\%$)10($10.0\%$)193($12.3\%$)12($9.6\%$)Fair851($45.1\%$)48($55.8\%$)48($48\%$)706($44.8\%$)49($39.2\%$)Good814($43.1\%$)31($36.1\%$)42($42\%$)677($42.9\%$)64($51.2\%$)Social participation0.0093Yes1300($65.8\%$)51($54.8\%$)64($60.9\%$)1111($67.4\%$)74($57.8\%$)No675($34.2\%$)42($45.2\%$)41($39.1\%$)538($32.6\%$)54($42.2\%$)Self-rated health< 0.0001Poor312($16.4\%$)23($26.7\%$)27($26.7\%$)203($12.8\%$)59($47.2\%$)Fair677($35.7\%$)37($43\%$)44($43.6\%$)552($34.8\%$)44($35.2\%$)Good908($47.9\%$)26($30.2\%$)30($29.7\%$)830($52.4\%$)22($17.6\%$)Smoking0.0102Yes471($23.8\%$)15($16.1\%$)24($22.9\%$)414($25.1\%$)18($16.1\%$)No1503($76.2\%$)78($83.9\%$)81($77.1\%$)1235($74.9\%$)110($85.9\%$)Alcohol consumption0.002Yes485($246\%$)14($15.1\%$)19($18.1\%$)432($26.2\%$)20($15.6\%$)No1489($75.4\%$)79($84.9\%$)86($81.9\%$)1216($73.8\%$)108($84.4\%$)Betal nut chewing0.0126Yes141($7.1\%$)8($8.6\%$)14($13.3\%$)116($7\%$)3($2.3\%$)No1834($92.9\%$)85($91.4\%$)91($86.7\%$)1533($93\%$)125($97.7\%$)Admission in the past year< 0.0001Yes194($9.8\%$)12($12.9\%$)21($20\%$)134($8.1\%$)27($21.1\%$)No1781($90.2\%$)81($87.1\%$)84($80\%$)1515($91.9\%$)101($78.9\%$)Exercise0.0044No902($45.7\%$)34($36.6\%$)47($44.8\%$)774($46.9\%$)47($36.7\%$)≦ 2times/Week126($6.4\%$)4($4.3\%$)10($9.5\%$)107($6.5\%$)5($3.9\%$)3–5 times/Week175($8.9\%$)5($5.4\%$)6($5.7\%$)155($9.4\%$)9($7\%$)≧ 6times/Week772($39.1\%$)50($53.8\%$)42($40\%$)613($37.2\%$)67($52.3\%$)Disability0.0008Yes73($3.7\%$)8($8.6\%$)9($8.6\%$)49($3\%$)7($5.5\%$)No1902($96.3\%$)85($91.4\%$)96($91.4\%$)1600($97\%$)121($94.5\%$)Data in tables are numbers (%) for categorical variables and means (SD) for continuous variables Fig. 1Multimorbidity patterns by latent class analysis in 1996 Table 2 presents the univariate logistic regression that explored the relationship between each characteristic and incident depression. Different multimorbidity patterns, especially the arthritis-cataract group (OR: 1.70, $95\%$ confidence interval [CI]: 1.02–2.82) and the multimorbidity group (OR: 2.30, $95\%$ CI: 1.50–3.54), were linked with a significantly increased incident depression after 16 years. Additionally, being female, poor and fair income satisfaction, no social participation, poor and fair self-rated health, no alcohol consumption and admission during the past year was also significantly associated with incident depression.
Table 2Univariate logistic regression of demographic and clinical characteristics predicting depressionDepression P-value OR$95\%$ CIDisease patternsCardiometabolic1.6110.933–2.7820.0869Arthritis-cataract1.695*1.019–2.8200.0421Relatively healthyRefMultimorbidity2.302*1.497–3.5410.0001Age1.0100.993–1.0270.2692SexMaleRefFemale1.777*1.367–2.311< 0.0001Income satisfactionPoor1.694*1.126–2.5490.0115Fair1.394*1.045–1.8590.024GoodRefSocial participationYesRefNo1.584*1.219–2.0570.0006Self-rated healthPoor2.972*2.110–4.186< 0.0001Fair1.637*1.203–2.2270.0017GoodRefSmokingYes0.7760.565–1.0670.119NoRefAlcohol consumptionYes0.704*0.510–0.9710.0326NoRefBetel nut chewingYes0.80.468–1.3690.4153NoRefAdmission in the past yearYes1.634*1.117–2.3910.0114NoRefExerciseNo0.810.521–1.2590.3485≦ 2times/week1.0070.544–1.8620.98253–5 times/weekRef≧ 6times/Week0.6980.444–1.0980.1198DisabilityYes1.2590.669–2.3710.4751NoRefAbbreviations: OR, odds ratio; CI, confidence interval.
Table 3 demonstrates the multivariable logistic regression results. Participants in the multimorbidity group had an increased risk of incident depression (OR: 1.62, $95\%$ CI: 1.02–2.58) after adjusting for gender, social participation, alcohol consumption, income satisfaction, self-rated health and admission in the past year. Test of interaction between distinct multimorbidity patterns and social participation showed there was no interaction between each multimorbidity patterns and social participation (Supplementary Table 1).
Table 3Multivariable logistic regression of demographic and clinical characteristics predicting depressionDepressionOR$95\%$CI P-value Disease patternsCardiometabolic1.330.745–2.3750.3345Arthritis-cataract1.230.704–2.1470.4675Relatively healthyRefMultimorbidity1.623*1.019–2.5840.0413SexMaleRefFemale1.607*1.184–2.1820.0023Social participationYesRefNo1.2250.922–1.6260.1611Alcohol consumptionYes0.9960.686–1.4450.9816NoRefSelf-rated healthPoor2.093*1.434–3.0540.0001Fair1.3690.995–1.8830.054GoodRefIncome satisfactionPoor1.574*1.030–2.4050.0361Fair1.2860.954–1.7330.0986GoodRefAdmission in the past yearYes1.2950.856–1.9600.2215NoRef*, Significant (p-value < 0.05)Abbreviations: OR, odds ratio; CI, confidence interval.
Table 4 shows the association between multimorbidity patterns and incident depression among participants with and without social participation. Compared to participants in the relatively healthy group with social participation, participants in the arthritis-cataract group without social participation (OR: 2.22, $95\%$ CI: 1.03–4.78) and the multimorbidity group without social participation (OR: 2.21, $95\%$ CI: 1.14–4.30) had significantly increased risk of having depression after adjusting for gender, self-rated health, income satisfaction, alcohol consumption, admission in the past year.
Table 4Association of social participation with distinct multimorbidity patterns in the relationship of depressionMultimorbidity PatternsCardiometabolicArthritis-cataractRelatively healthyMultimorbidityWith social participation [1300]1.717 (0.820–3.592)0.817 (0.357–1.869)Ref1.410 (0.750–2.649)Without social participation [675]1.098 (0.437–2.759)2.218* (1.030–4.778)1.159 (0.837–1.605)2.211* (1.138–4.296)Model was adjusted with: age, gender, self-rated health, income satisfaction, admission in the past year, alcohol consumptionData are presented as odds ratios with confidence intervals (CI)*, Significant (p-value < 0.05).
## Discussion
LCA discovered four distinct multimorbidity groups in this 16-year population-based longitudinal study among older adults in Taiwan, including the cardiometabolic, Arthritis-cataract, multimorbidity and relatively healthy groups. Additionally, we found that older adults in the multimorbidity group had a greater risk of developing depression than the relatively healthy group after the multivariable logistic regression with adjustment for potential confounding factors.
There was few evidence regarding the association of multimorbidity or multimorbidity patterns with incident depression. Notably, our results were different from those of the previous study. Hsu and Hsu using also TLSA to analyse the trajectory of depressive symptoms among older adults with different chronic diseases and found that older adults with cardiovascular disease (CVD), gastrointestinal disease, chronic respiratory disease (CRD), and the combination of two of these three diseases had a greater effect on the intercept of depressive symptoms. Only the older adults with CRD combined with CVD or with GI disease had a significant negative effect on the slope of depressive symptoms [38]. They used different groups of multimorbidity and this could be the reason that our results were different. Yao et al. discovered four somatic multimorbidity patterns among older adults in China, including cardiometabolic, respiratory, arthritic-digestive-visual and hepatic-renal-skeletal patterns, with all multimorbidity patterns linked with a higher risk of having depressive symptoms after the 4-year follow-up. [ 15] The possible explanation for the different results is the different reference groups. Our previous work revealed that people in the relatively healthy group still had several chronic diseases, such as arthritis. [ 16] Another possible reason is the statistical method used to locate the multimorbidity patterns. Notably, the multimorbidity patterns vary depending on the method of analysis used, such as Hierarchical cluster analysis (HCA) or exploratory factor analysis (EFA). A previous comparative study found EFA was useful in describing comorbidity relationships, and HCA could be useful for an in-depth multimorbidity study. [ 39].
Several hypotheses of the mechanism between multimorbidity and depression exist, and the linkage may be bidirectional. [ 40] Additionally, for patients with multimorbidity, there was more care burden, [41] symptom burden, [42] disability, [43] poor quality of life, [44] poor self-rated health [45] and pain, [46] which all lead to depressive emotion. Depression-related poor self-care, [47] alcohol consumption [48] and suicidal ideation [49] could also lead to multimorbidity. Furthermore, evidence shows that the inflammation process was induced by multimorbidity and such inflammation could also become a risk factor for depression. [ 50] For the relationship between psychological burden and chronic diseases, a past study revealed that there is psychological burden at different times for different chronic conditions. In addition, minimizing the incidence of comorbidities, physical limitations, or psychiatric conditions may have the prospective effect of avoiding the trend of increased depressive symptoms. [ 51] Combine with our results, healthcare professional should put more emphasis on noticing possible depressive symptoms among older adults with multimorbidity burden.
Our results suggest that participants in the arthritis-cataract group without social participation and the multimorbidity group without social participation had significantly increased risk of having depression, compare with participants in the relatively healthy group with social participation. Social participation has proved to be related to less depression among the elderly in different populations. [ 52, 53] However, Galenkamp et al. demonstrated that older adults with multimorbidity had less social participation; higher socioeconomic status, widowhood, a larger network of friends, volunteering, transportation possibilities and fewer depressive symptoms were essential for social leisure participation. [ 54] Another study in the UK showed that physical multimorbidity reduced some aspects of social participation over time. [ 55] For the relationship between social participation and multimorbidity, previous study showed that older adults with higher emotional-social support had lower depressive symptoms, and the effect of this support could reduce depressive symptoms even more over time [38]. From our results, social participation seems to be a protective factor against incident depressive symptoms, especially among participants in arthritis-cataract group and multimorbidity group. Moreover, participants of arthritis-cataract group without social participation seemed to have much difference compared with those who with social participation. Interaction between social participation and arthritis-cataract may exist. Therefore, future study should focus on the interaction and intervention of social participation among these groups of older adults and measure the outcomes. Also, from our results, it seems that there is no benefit of social participation among participants with cardiometabolic multimorbidity. One hypothesis is there are more factors influencing the relationship between cardiometabolic multimorbidity and social participation. Previous study showed that there are multiple factors contributing to social participation among diabetes patients, including self-management of treatment, lifestyle, mobility, subjective assessment of health, and quality of life. [ 56] *There is* also a Japanese report that social participation is associated with prevention of hypertension, and physical activity may play as a connective role. [ 57] Further study is suggested with considering these factors among larger population.
Several strengths exist in this study. Notably, this is the first longitudinal study of how social participation affects late life depression among participants with different multimorbidity patterns in an Asian country. The 16-year retrospective cohort study was based on a representative national sample with high survey response rates. Data from a large and randomly selected population have high external validity. Finally, we adjusted for numerous confounding factors, including gender, social participation, self-rated health, health behaviours and recent admission.
Our study also has several limitations. First, multimorbidity patterns can change over time. For example, older adults may develop more chronic diseases as they age. Therefore, the relationship between the multimorbidity group and depression could have been underestimated. However, our previous work examined the trend of multimorbidity patterns for 16 years in a similar population and found that disease patterns remained similar in five waves of data (Supplementary Figs. 1–5). [ 16] We then proposed that the composition of disease groups was similar in subsequent years. Future study can be done using latent transition analysis (LTA) or group-base trajectory analysis [58] to include the multiple waves of multimorbidity. Second, self-reported data were used from TLSA without objective measurements, resulting in reporting bias. Third, the depression was related to poor cognitive performance. However, no relative information regarding cognition could be obtained in 1996 from TLSA. Future studies should explore the relationship between cognitive performance and depression in patients with different multimorbidity patterns. Third, we used data from 1996 to 2011, and the types and methods of social participation could change over time. We would like to conduct further study regarding multimorbidity patterns and social participation in the future with newest data. Fourth, because the characteristics of longitudinal study, the possible selection bias by death or loss follow-up of the participants could still happen.
## Conclusion
This 16-year population-based cohort study revealed that distinct multimorbidity patterns among older adults in Taiwan were associated with incident depression during later life. However, social participation can be a protective factor against future depression among older adults with different multimorbidity patterns. Therefore, further studies should focus on identifying multimorbidity and timely intervention of social participation encouragement.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1. Supplementary Figure 1. Multimorbidity patterns in 1996. Supplementary Figure 2. Multimorbidity patterns in 1999. Supplementary Figure 3. Multimorbidity patterns in 2003. Supplementary Figure 4. Multimorbidity patterns in 2007. Supplementary Figure 5. Multimorbidity patterns in 2011.
Supplementary Material 2. Supplementary Table 1. Multivariable logistic regression of demographic and clinical characteristics predicting depression with interaction of multimorbidity patterns and social participation.
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|
---
title: 'Calculated Whole Blood Viscosity and Albumin/Fibrinogen Ratio in Patients
with a New Diagnosis of Multiple Myeloma: Relationships with Some Prognostic Predictors'
authors:
- Melania Carlisi
- Rosalia Lo Presti
- Salvatrice Mancuso
- Sergio Siragusa
- Gregorio Caimi
journal: Biomedicines
year: 2023
pmcid: PMC10045865
doi: 10.3390/biomedicines11030964
license: CC BY 4.0
---
# Calculated Whole Blood Viscosity and Albumin/Fibrinogen Ratio in Patients with a New Diagnosis of Multiple Myeloma: Relationships with Some Prognostic Predictors
## Abstract
Background: *In this* single center study, we retrospectively evaluated the calculated hemorheological profile in patients with a new diagnosis of multiple myeloma, with the aim to evaluate possible relationships with some prognostic predictors, such as ISS, albumin levels, beta2-microglobulin, red cell distribution width, and bone marrow plasma cell infiltration. Methods: *In a* cohort of 190 patients, we examined the calculated blood viscosity using the de Simone formula, and the albumin/fibrinogen ratio as a surrogate of erythrocyte aggregation, and then we related these parameters to prognostic factors, using the Kruskal–Wallis and the Mann–Whitney tests, respectively. Results: From our analysis, it emerged that the evaluated hemorheological pattern differed in the three isotypes of multiple myeloma, and the whole blood viscosity was higher in IgA and IgG isotypes with respect to the light chain multiple myeloma ($p \leq 0.001$). Moreover, we observed that, as the ISS stage progressed, the albumin/fibrinogen ratio was reduced, and the same hemorheological trend was traced in subgroups with lower albumin levels, higher beta2-microglobulin and red cell distribution width RDW values, and in the presence of a greater bone marrow plasma cell infiltrate. Conclusions: Through the changes in blood viscosity in relation to different prognostic factors, this analysis might underline the role of the hemorheological pattern in multiple myeloma.
## 1. Introduction
Multiple myeloma (MM) is the second most common hematologic cancer, with a median age at diagnosis of 65 years. The risk of developing MM is higher in older age groups, whereas the diagnosis is more uncommon in patients under the age of 45 [1]. MM is a disease characterized by the presence of abnormal clonal plasma cells in the bone marrow, with potential uncontrolled growth causing destructive bone lesions, kidney injury, anemia, and hypercalcemia [2].
Despite improvements in treatment [3], MM remains an incurable disease and the affected patients may have poor quality of life due to disease-related symptoms and adverse events from therapies with cumulative toxicity. For this reason, one of the current directions of MM research is the identification of prognostic markers to stratify the patients into specific risk groups, with a significant impact on the accurate prognosis assessment and the selection of an appropriate therapeutic approach [4,5,6].
Many prognostic biomarkers have been identified over the years. These markers reflect host factors, tumor-related factors, tumor stage, disease burden, and the tumor response to treatment.
Among these prognostic factors, the staging systems play a key role. The International Staging System (ISS), which has surpassed the Durie–Salmon classification, is widely available, and it is based on two simple and routine laboratory tests: serum albumin and beta2-microglobulin (beta2-MG). It is robustly validated and applicable across geographical areas [7]. Considering the remarkable role of cytogenetic alterations, in 2015, the Revised International Staging System (R-ISS) was introduced. This new prognostic system includes, in addition to albumin and beta2-MG, too high-risk chromosomal abnormalities detected by interphase fluorescence in situ hybridization (FISH) (deletion (17p), translocation t(4;14) (p16;q32), or t(14;16) (q32;q23)) and serum lactate dehydrogenase (LDH) levels [8,9]. However, this revised staging system has a limitation represented by the fact that $62\%$ of patients were classified into the intermediate-risk category, when instead the patients could belong to different risk levels of progression/death. Recently, a second revision of the R-ISS, named R2-ISS, has been proposed. In this revision, 1q gain/amplification are included in the risk calculation [10].
In addition to ISS and its revisions, an important prognostic role in MM patients is also played by albumin levels alone. In fact, besides being used as a factor when calculating ISS, the albumin level has been found to be an independent predictor of mortality [11,12]. The same consideration can also be made for beta2-MG; elevated serum beta2- MG levels represent a tumor marker and may indicate the tumor burden in hematologic malignancies, especially in MM [13]. Additionally, beta2-MG is an independent predictor of survival in MM and an independent predictor of progression in patients with asymptomatic MM [14].
Furthermore, important prognostic information is also derived from the numerical and morphological assessment of bone marrow plasma cells (BMPC) [15,16], and from the pre-treatment red cell distribution width (RDW) [17].
Finally, in the MM setting, the hemorheological pattern can play a significant role, especially in terms of whole blood viscosity (WBV) and erythrocytes deformability and aggregation [18,19,20,21].
Generally, the measurement of WBV is carried out ex vivo using different types of viscometers, such as rotational, capillary, and oscillatory ones. However, WBV can also be indirectly calculated using specific formulas, from some simple laboratory parameters (such as hematocrit, total plasma proteins, and fibrinogen), overcoming the technical difficulties and the costs of use and maintenance of common viscometers [22,23,24,25]. In the de Simone formula, the calculation of WBV is obtained from the hematocrit and total plasma proteins. However, it must be emphasized that this formula was validated with specific reference ranges for the values of laboratory parameters.
Although these reference ranges are not often respected in MM patients, the possibility of using the calculated whole blood viscosity (c-WBV) in plasma cell dyscrasias was provided by the results of our recent clinical study in which, when evaluating the directly measured and calculated blood viscosity data in monoclonal gammopathy of undetermined significance (MGUS) and MM patients, it emerged that the c-WBV and the surrogate marker of erythrocyte aggregation showed a parallel trend [26].
In addition, this formula does not consider the deformability and the aggregation of erythrocytes, but the latter parameter may be indirectly calculated using the albumin/fibrinogen ratio, as mentioned above [27,28].
Therefore, considering the possible role of the hemorheological profile in MM, we performed a single center retrospective analysis with the aim to evaluate, in a cohort of patients with a new diagnosis of MM (NDMM), eventual associations between the c-WBV and albumin/fibrinogen ratio with some recognized prognostic predictors. The analysis was performed in the entire group of patients and by dividing the sample into three subgroups based on the MM isotype: light chains MM, IgA, and IgG MM (LCMM, IgA MM, and IgG MM). The evaluation of the hemorheological pattern was carried out in a calculated way, using the de *Simone formula* for the c-WBV and the albumin/fibrinogen ratio as a surrogate of erythrocyte aggregation.
## 2.1. Population
This is a retrospective single center study performed on 190 patients (102 women and 88 men; average age 69 ± 10) with a new diagnosis of MM, evaluated at the Hematology Division of the “Paolo Giaccone” University Hospital in Palermo between 1 January 2017 and 30 September 2022. Specifically, the sample is comprised of 107 patients with IgG MM (71 with IgG κ and 36 IgG λ isotype), 56 patients with IgA isotype (28 with IgA κ and 28 IgA λ isotype), and 27 patients with LCMM (13 with expression of κ light chain and 14 with λ light chain). Patients over the age of 18 who had a set of investigations performed on serum, urine, and bone marrow samples, who were indicated at the diagnosis for the evaluation of the underlying disease, were included.
## 2.2. Laboratory Tests
In this retrospective study, we evaluated the following parameters: hematocrit (Ht), obtained using an automated hematology analyzer; total plasma proteins, expressed in g/L and evaluated with the colorimetric method; fibrinogen, expressed in g/L and evaluated with the Clauss method; WBV at 208 s–1, calculated according to the de *Simone formula* ((0.12 × Ht) + 0.17(TP − 2.07)); albumin (g/L), evaluated using the colorimetric method; and, finally, the albumin (g/L)/fibrinogen (g/L) ratio.
## 2.3. Statistical Analysis
All of the statistical analyses were performed using GraphPAd Prism version 9.5. The data were expressed as medians and interquartile ranges. The one-way variance analysis, concerning the comparison between the different isotypes and between different stages of ISS, was performed using the Kruskal–Wallis test, integrated with the Dunn test. The median comparison was made using the Mann–Withney test. The correlation coefficient for Spearman ranks was used for the analysis of the different correlations. The null hypothesis was evaluated for values of p ≤ 0.1.
## 3. Results
The clinical data and baseline characteristics of the study population are summarized in Table 1. In the entire cohort of MM patients, we first evaluated the medians, interquartile ranges, and ranges of the hemorheological pattern (Table 2). Then, by comparing the whole blood viscosity and the remaining parameters in the MM isotypes, we observed that the c-WBV, total plasma proteins, albumin, and fibrinogen levels distinguished the three isotypes; particularly, the c-WBV and total plasma proteins were higher in the IgA and IgG MM with respect to LCMM, while the albumin level was reduced in the IgA and IgG isotypes in comparison with the LCMM (Table 3).
Dividing the entire cohort of MM patients based on ISS, the hematocrit value, albumin level, and albumin/fibrinogen ratio decreased from stage I to stage III, while the total plasma proteins increased; no changes in c-WBV and plasma fibrinogen were observed (Table 4).
In the whole cohort and in the three MM isotypes, we then calculated the medians of some prognostic predictors: albumin (all MM 36.39 g/L, LCMM 39.90 g/L, IgA 33.85 g/L and IgG 37.00 g/L), beta2-MG (all MM 4.70 mg/L, LCMM 4.40 mg/L, IgA 4.490 mg/L and IgG 4.70 mg/L), RDW (all MM $15.1\%$, LCMM $14.4\%$, IgA $16\%$ and IgG $14.8\%$), and BMPC (all MM $40\%$, LCMM $40\%$, IgA $60\%$ and IgG $30\%$) (Supplementary Table S1). Therefore, in the entire group of patients and in the different isotypes, the behavior of c-WBV and the other hemorheological parameters was evaluated in relation to these medians.
Dividing the whole study population according to the albumin level, in the subgroup with lower albumin values, we observed a reduction in hematocrit and albumin/fibrinogen ratio, and an increase in the total plasma proteins (Table 5). The same subdivision was carried out in patients with LCMM and, always in the group with lower levels of albumin, we observed a reduction in hematocrit, total plasma proteins, and c-WBV (Supplementary Table S2A). Evaluating the IgA isotype, there was observed a reduction in the hematocrit and albumin/fibrinogen ratio, and an increase in total plasma proteins and c-WBV (Supplementary Table S2B); however, for the IgG isotype, lower levels of albumin were associated with a decrease in hematocrit values and in the albumin/fibrinogen ratio (Supplementary Table S2C).
The distribution of the whole cohort of patients according to the beta2-MG levels, in the subgroup that exceeded the median value, showed a reduction in the hematocrit, albumin, and albumin/fibrinogen ratio, and an increase in the total plasma proteins (Table 6). The same evaluation performed in LCMM showed only a reduction in hematocrit in the subgroup with higher levels of this predictor (Supplementary Table S3A). No differences were observed in the IgA isotype (Supplementary Table S3B), while in IgG MM, a reduction in hematocrit, albumin, and albumin/fibrinogen, and an increase in total plasma proteins were associated with higher levels of beta2-MG (Supplementary Table S3C).
Still subdividing the entire group of patients according to the RDW, in the subgroup the exceeded the median, a decrease in hematocrit, albumin, and the albumin/fibrinogen ratio and an increase in total plasma proteins were observed (Table 7). In LCMM, the identical approach showed a reduction in hematocrit, c-WBV, and albumin levels (Supplementary Table S4A). In the IgA isotype, values exceeding the median of RDW were associated with a reduction in hematocrit and albumin levels (Supplementary Table S4B), and, in the IgG MM subgroup, with a decrease in hematocrit, albumin, and albumin/fibrinogen ratio (Supplementary Table S4C).
Finally, making a subdivision of the entire cohort of MM patients according to the BMPC percentage, in the subgroup that exceeded the median values, we observed a reduction in hematocrit and albumin levels (Table 8). The same analysis performed in the LCMM group revealed a decrease in c-WBV in the presence of BMPC values beyond the median (Supplementary Table S5A). In IgA MM, only a decrease in albumin levels was observed (Supplementary Table S5B), while in the IgG isotype, a reduction in hematocrit and albumin levels and an increase in total plasma proteins and c-WBV was observed (Supplementary Table S5C).
## 4. Discussion
In relation to the amount of data examined and considering that this retrospective single center study covered not only the whole group of MM patients but also the different MM isotypes, we mainly focused c-WBV (and the parameters that determine it) and the albumin/fibrinogen ratio (indirect indicator of erythrocyte aggregation) in the entire cohort of patients.
Indeed, it is necessary to consider that the different MM isotypes (LCMM, IgA, and IgG) are present in significantly different percentages, with the LCMM representing only $14\%$ of the entire sample. Regarding c-WBV, the three isotypes are different, with values of c-WBV being higher in the IgA and IgG isotypes with respect to LCMM.
The increase in c-WBV, observed in IgA and IgG MM, is mainly due to the higher levels of total plasma proteins present in these isotypes, with no changes in the hematocrit. In our analysis, we found that in the entire cohort of patients, as well as in IgA and IgG isotypes, c-WBV is only related to total protein levels (data not shown), and this explains the behavior of this hemorheological parameter. However, in LCMM, c-WBV is dependent not only on the protein levels, but also on the hematocrit values (data not shown).
Moreover, variations in the albumin and fibrinogen levels are evident among the different isotypes. Albumin is a pivotal prognosis predictor in MM, and its reduced levels are associated with an early mortality (less than 12 months) [29]. Albumin has sharply different values in the three MM isotypes, and, in fact, its levels in IgA and IgG isotypes are reduced compared with those observed in LCMM. The plasma fibrinogen level has the same behavior, and it is significantly higher in LCMM. Instead, among the three different isotypes there is no appreciable variation in the albumin/fibrinogen ratio, which shows a particular trend in relation to the prognostic predictors mentioned above.
The subdivision of the whole cohort of MM patients according to ISS shows some interesting findings. Proceeding from stage I to stage III, the decrease in hematocrit is evident, associated with a slight increase in total plasma proteins, with no changes in c-WBV. The albumin/fibrinogen ratio, which is significantly reduced when progressing from stage I to III stage, behaves similarly to the albumin levels. Therefore, from a hemorheological point of view, the above-described shows that, with the progress of the ISS, MM patients become more anemic and at the same time register a theoretical tendency to erythrocyte aggregation. The increase in erythrocyte aggregation, evaluated with different methods, turns out to be a constant in the hemorheological pattern of patients with plasma cell dyscrasias [30,31].
Red cell aggregation has a key role, especially in areas of circulation where low sliding gradients prevail, such as the venous system [32]. This hemorheological determinant depends on the degree of interrelation between plasma proteins and erythrocytes, and it is a function of the concentration, size, and density of the plasma proteins, electrical charge surface expressed as the potential zeta, the plasma dielectric coefficient, and the membrane properties. Erythrocyte aggregation is a reversible process that affects blood viscosity at low shear rates and it certainly does not play any role in blood viscosity at high shear rates. At the same time, the monoclonal immunoglobulins at a non-physiological concentration can significantly interfere with the behavior of this determinant.
In the subdivision performed based on the albumin levels, the patients with values below the median showed a reduction in hematocrit, an increase in total plasma proteins, and a rise, although not significant, in c-WBV; a decrease in the albumin/fibrinogen ratio was also markedly present. In MM patients, the reduction in albumin levels does not seem to depend on age and/or gender, nor does it appear to be affected by liver and kidney function, by the presence of bone osteolytic lesions, Bence−Jones proteinuria, hypercalcemia, and body weight [33]. Actually, the reduction in albumin levels is due to the fact that interleukin-6 (IL-6), but above all the altered cytokine network present in MM, reduces the hepatic synthesis of albumin, approximately equal to 200 mg per kilogram of body weight per day [34,35,36]. Even if in most recent nomograms related to the prognostic stratification of these patients’ cytokines are not taken into account [37,38], other authors have included the cytokines in prognostic nomograms, in particular, for newly diagnosed MM patients [39].
The division of the entire cohort of MM patients according to beta2-MG, which in the condition of normal renal function reflects the whole mass of MM cells, shows a reduction in the hematocrit, albumin levels, and albumin/fibrinogen ratio, with an increase in total plasma proteins and fibrinogen in the subgroup exceeding the median value. Considering that some nomograms related to prognostic stratification simultaneously consider the levels of serum albumin and beta2-MG, we examined the correlation between these two parameters (Spearman test) in the entire cohort of patients, observing a negative correlation between the above parameters (r = −0.37 $p \leq 0.001$).
The results obtained with the subdivision of the whole cohort of patients according to the median of the RDW were similar. In fact, in the subgroup with higher RDW values, we observed a decrease in hematocrit, in albumin levels, and the albumin/fibrinogen ratio, associated with an increase in the total plasma proteins. Many papers in the literature underline the prognostic role of RDW in MM [17,40,41]. Indicating the heterogeneity of the volume of erythrocytes, RDW is a simple and immediately inflammatory marker and it reflects the increase of some cytokines, such as IL-6 and tumor necrosis factor-alpha (TNF-alpha), but also of hepcidin in the blood [42,43]. It is possible that the increase in this predictor may affect the erythrocyte deformability of MM patients, also considering that in some of our previous research [18,19,20,21], this hemorheological determinant was reduced compared with the control group. In this regard, in a cohort of 298 normal adults, some authors found a negative correlation between RDW and erythrocyte deformability, and only when the RDW value exceeded $14\%$ was the reduction of this hemorheological determinant highlighted [44]. In addition to normal subjects, this correlation has been described in some hematological neoplasms [45].
The last subdivision, according to the percentage of BMPC, in the subgroup that overtook the median value, showed a significant reduction in the hematocrit and albumin levels. BMPC is a prognostic predictor in MM patients, and it is important to evaluate the cut-off in the BMPC percentage. In fact, a better prognosis was observed in patients with BMPC less than $50\%$, and this percentage was confirmed in several research works [46]. In more recent years, the issue of BMPC has been reconsidered by other authors, such as Qian in 2017 and Al Saleh in 2020 [47,48]. The latter hypothesized that a percentage of $60\%$ predicted both disease-free survival and overall survival in MM patients. In relation to these recent data, we divided the entire study population, considering $60\%$ BMPC as a limit, and we observed that in the 70 patients ($37.4\%$) with values equal to or greater than the above percentage, in addition to the reduction of albumin and hematocrit, an increase in the total plasma proteins was also present (data not shown).
## 5. Conclusions
In conclusion, in this retrospective single center study, it is evident that c-WBV, with the same hematocrit values, was higher in the IgA and IgG isotypes with respect to the LCMM. It is interesting what happens in relation to the subdivision by ISS; in fact, as the stage progressed, we observed a reduction in albumin, an increase in the values of the total plasma proteins, and a significant decrease in the albumin/fibrinogen ratio, but, above all, a worsening of anemia. The same results were evident when performing the analysis with other prognostic predictors.
With the evaluation of c-WBV using the de Simone formula, it was not possible to obtain direct information on erythrocyte aggregation and deformability and plasma viscosity. However, using the albumin/fibrinogen ratio, information on erythrocyte aggregation can be reliable, and the reduction in this ratio was evident when patients were stratified on the basis of evaluated prognostic predictors. RDW was increased in MM patients and this marker could be related to erythrocyte deformability.
In our experience, erythrocyte deformability was reduced in MM patients and this datum mainly depends on the membrane dynamic properties and its lipid composition. Considering the above, it cannot be excluded the reduced red cell deformability may also be dependent on the increase in RDW. However, it must be considered that the albumin/fibrinogen ratio and RDW are strongly influenced by the altered cytokine network described in MM. In fact, the increase in IL-6 and TNF-alpha, also through the role played by hepcidin [49,50], interferes with erythropoiesis and thus with the RDW value, while the same cytokine network, inhibiting the hepatic synthesis of albumin, involves a decrease in the albumin/fibrinogen ratio.
As a limit, in our study, no information was available on the plasma viscosity (PV). However, in relation to the data regarding the total plasma proteins, fibrinogen, and albumin, we assumed that PV may be different in the three MM isotypes, also considering the diverse percentage contribution that each of the protein fractions seemed to exert.
This analysis could underline the importance of blood viscosity in MM, which underwent important changes as the prognostic factors considered varied. In future work, to make the different MM isotypes numerically homogeneous and better deepen the hemorheological evaluation, we will collect a greater study sample, also considering other important information such as that relating to cytogenetic abnormalities, which were currently not evaluated.
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---
title: Healthy lifestyle, DNA methylation age acceleration, and incident risk of coronary
heart disease
authors:
- Jiahui Si
- Lu Chen
- Canqing Yu
- Yu Guo
- Dianjianyi Sun
- Yuanjie Pang
- Iona Y. Millwood
- Robin G. Walters
- Ling Yang
- Yiping Chen
- Huaidong Du
- Shixian Feng
- Xiaoming Yang
- Daniel Avery
- Junshi Chen
- Zhengming Chen
- Liming Liang
- Liming Li
- Jun Lv
- Junshi Chen
- Junshi Chen
- Zhengming Chen
- Rory Collins
- Liming Li
- Richard Peto
- Daniel Avery
- Ruth Boxall
- Derrick Bennett
- Yumei Chang
- Yiping Chen
- Zhengming Chen
- Robert Clarke
- Huaidong Du
- Simon Gilbert
- Alex Hacker
- Michael Holmes
- Andri Iona
- Christiana Kartsonaki
- Rene Kerosi
- Ling Kong
- Om Kurmi
- Garry Lancaster
- Sarah Lewington
- Kuang Lin
- John McDonnell
- Winnie Mei
- Iona Millwood
- Qunhua Nie
- Jayakrishnan Radhakrishnan
- Sajjad Rafiq
- Paul Ryder
- Sam Sansome
- Dan Schmidt
- Paul Sherliker
- Rajani Sohoni
- Iain Turnbull
- Robin Walters
- Jenny Wang
- Lin Wang
- Ling Yang
- Xiaoming Yang
- Zheng Bian
- Ge Chen
- Yu Guo
- Can Hou
- Jun Lv
- Pei Pei
- Shuzhen Qu
- Yunlong Tan
- Canqing Yu
- Zengchang Pang
- Ruqin Gao
- Shaojie Wang
- Yongmei Liu
- Ranran Du
- Yajing Zang
- Liang Cheng
- Xiaocao Tian
- Hua Zhang
- Silu Lv
- Junzheng Wang
- Wei Hou
- Jiyuan Yin
- Ge Jiang
- Xue Zhou
- Liqiu Yang
- Hui He
- Bo Yu
- Yanjie Li
- Huaiyi Mu
- Qinai Xu
- Meiling Dou
- Jiaojiao Ren
- Shanqing Wang
- Ximin Hu
- Hongmei Wang
- Jinyan Chen
- Yan Fu
- Zhenwang Fu
- Xiaohuan Wang
- Min Weng
- Xiangyang Zheng
- Yilei Li
- Huimei Li
- Yanjun Wang
- Ming Wu
- Jinyi Zhou
- Ran Tao
- Jie Yang
- Chuanming Ni
- Jun Zhang
- Yihe Hu
- Yan Lu
- Liangcai Ma
- Aiyu Tang
- Shuo Zhang
- Jianrong Jin
- Jingchao Liu
- Zhenzhu Tang
- Naying Chen
- Ying Huang
- Mingqiang Li
- Jinhuai Meng
- Rong Pan
- Qilian Jiang
- Weiyuan Zhang
- Yun Liu
- Liuping Wei
- Liyuan Zhou
- Ningyu Chen
- Hairong Guan
- Xianping Wu
- Ningmei Zhang
- Xiaofang Chen
- Xuefeng Tang
- Guojin Luo
- Jianguo Li
- Xiaofang Chen
- Xunfu Zhong
- Jiaqiu Liu
- Qiang Sun
- Pengfei Ge
- Xiaolan Ren
- Caixia Dong
- Hui Zhang
- Enke Mao
- Xiaoping Wang
- Tao Wang
- Xi zhang
- Ding Zhang
- Gang Zhou
- Shixian Feng
- Liang Chang
- Lei Fan
- Yulian Gao
- Tianyou He
- Huarong Sun
- Pan He
- Chen Hu
- Qiannan Lv
- Xukui Zhang
- Min Yu
- Ruying Hu
- Hao Wang
- Yijian Qian
- Chunmei Wang
- Kaixue Xie
- Lingli Chen
- Yidan Zhang
- Dongxia Pan
- Yuelong Huang
- Biyun Chen
- Li Yin
- Donghui Jin
- Huilin Liu
- Zhongxi Fu
- Qiaohua Xu
- Xin Xu
- Hao Zhang
- Youping Xiong
- Huajun Long
- Xianzhi Li
- Libo Zhang
- Zhe Qiu
journal: Clinical Epigenetics
year: 2023
pmcid: PMC10045869
doi: 10.1186/s13148-023-01464-2
license: CC BY 4.0
---
# Healthy lifestyle, DNA methylation age acceleration, and incident risk of coronary heart disease
## Abstract
### Background
DNA methylation clocks emerged as a tool to determine biological aging and have been related to mortality and age-related diseases. Little is known about the association of DNA methylation age (DNAm age) with coronary heart disease (CHD), especially in the Asian population.
### Results
Methylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip for 491 incident CHD cases and 489 controls in the prospective China Kadoorie Biobank. We calculated the methylation age using a prediction model developed among Chinese. The correlation between chronological age and DNAm age was 0.90. DNA methylation age acceleration (Δage) was defined as the residual of regressing DNA methylation age on the chronological age. After adjustment for multiple risk factors of CHD and cell type proportion, compared with participants in the bottom quartile of Δage, the OR ($95\%$ CI) for CHD was 1.84 (1.17, 2.89) for participants in the top quartile. One SD increment in Δage was associated with $30\%$ increased risk of CHD (OR = 1.30; $95\%$ CI 1.09, 1.56; Ptrend = 0.003). The average number of cigarette equivalents consumed per day and waist-to-hip ratio were positively associated with Δage; red meat consumption was negatively associated with Δage, characterized by accelerated aging in those who never or rarely consumed red meat (all $P \leq 0.05$). Further mediation analysis revealed that $10\%$, $5\%$ and $18\%$ of the CHD risk related to smoking, waist-to-hip ratio and never or rarely red meat consumption was mediated through methylation aging, respectively (all P for mediation effect < 0.05).
### Conclusions
We first identified the association between DNAm age acceleration and incident CHD in the Asian population, and provided evidence that unfavorable lifestyle-induced epigenetic aging may play an important part in the underlying pathway to CHD.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13148-023-01464-2.
## Background
The risk of coronary heart disease (CHD) strikingly increases with aging [1]. *Both* genetic and environmental factors are among the determinants of aging [2]. Chronological age, as a most informative marker of aging, cannot depict the remarkable heterogeneity of biological aging rates observed in peers. DNA methylation clocks form an interface between the genotype and the environment [3], emerging as a tool to determine biological aging objectively [4]. Previous studies provided evidence that accelerated DNA methylation age was predictive of mortality [5] and age-related diseases [6] even after adjusting for various known risk factors.
However, little is known about the association of DNA methylation age with CHD. Only a few studies were conducted in European ancestry and got mixed results [7–12]. No previous studies investigated the association of methylation aging with CHD risk in the Asian population, despite the mortality rate of CHD has been increasing continuously in the Asian population [13]. Studies are warranted to increase the genetic diversity of study participants to understand the mechanisms underlying DNA methylation aging across humans [14]. In addition, the current knowledge of how environmental factors affect methylation aging is still lacking [15]. Such factors may serve as intervention targets that might delay cardiovascular aging, thus approaching the goal of longevity.
To fill this gap, we aim to investigate the association between methylation aging and the incident risk of CHD in a Chinese population, and further quantify how much of the effects of lifestyle factors on CHD are mediated through methylation aging. We did a case–control study nested in the 10-year follow-up of the China Kadoorie Biobank (CKB) cohort, comprising 494 incident CHD cases and 494 matched controls.
## Results
The mean chronological age at baseline was 51.1 ± 7.7 years for incident CHD cases and 49.9 ± 7.2 years for matched controls (Table 1). Incident CHD cases were more likely to smoke, have higher measures of adiposity and total cholesterol (TC), and have a higher prevalence of hypertension and diabetes at baseline, compared with matched controls. Table 1Baseline characteristics of 980 participants according to the case and control statusBaseline characteristicsCases ($$n = 491$$)Controls ($$n = 489$$)Matched factorsAge, year51.149.9Female, %43.643.8Urban area, %20.620.4Fasting time, h4.04.0Middle school and above, %44.644.4Married, %90.394.1Family history of heart attack, %6.94.7Lifestyle factorsDaily tobacco smoker, %46.540.2Daily alcohol drinker, %8.910.1Physical activity, MET-h/day22.323.7Dietary habitsDays consumed fresh vegetables/week6.86.7Days consumed fresh fruits/week1.72.2Days consumed red meat/week2.93.0AdiposityBody mass index, kg/m223.923.3Waist circumference, cm82.279.7Waist-to-hip ratio0.910.88Fat percentage, %27.125.8Prevalent hypertension, %52.030.6Prevalent diabetes, %9.64.7Total cholesterol, mmol/L4.74.5The results are presented as means or percentages with adjustment for age, sex, and study area. MET Metabolic equivalent of task
## DNAm age and incident CHD
The correlation between chronological age and methylation age was 0.89 and 0.91 in cases and controls, respectively (Additional file 2: Fig. 1). The mean DNAm age was 51.4 ± 6.9 for incident CHD cases and 49.6 ± 6.3 years for matched controls. The corresponding mean value of DNAm age acceleration was 0.42 years for incident cases and − 0.42 years for controls at baseline. Fig. 1Subgroup analysis of the association between DNA methylation age acceleration and the risk of incident coronary heart disease according to potential baseline risk factors. Horizontal lines represent $95\%$ CIs. Odds ratio and $95\%$ CI are for the associations of 1-SD Δage increasing with CHD risk. Covariates in the multivariable model: age, sex, education level, marital status, smoking, alcohol drinking, physical activity, average days consuming fresh vegetables, fruits, and red meat per week, body mass index, fasting time, study area, batch, and cell type proportions. * To avoid misleadingly elevated risk, former smokers who stopped smoking for illness were categorized as the current smoker After adjustment for multiple risk factors of CHD and cell type proportion, Δage was associated with increased risk of incident CHD (Table 2). In all eligible participants, compared with participants in the bottom quartile of Δage, the odds ratio (OR) ($95\%$ confidence interval [CI]) for CHD was 1.84 (1.17, 2.89) for participants in the top quartile. One SD increment in Δage was associated with $30\%$ increased risk of CHD (OR = 1.30; $95\%$ CI 1.09, 1.56; Ptrend = 0.003). Further adjustment for baseline cardiometabolic risk factors only slightly attenuated the association (OR = 1.29; $95\%$ CI 1.08, 1.55; Ptrend = 0.005). The adjusted OR ($95\%$CI) for the risk of CHD associated with one SD increase in Δage was 1.36 (1.07, 1.74) among men and 1.18 (0.89, 1.56) among women. We did not observe statistically significant difference in the association of Δage with CHD between men and women (Pinteraction = 0.995). We performed conditional logistic regression accounting for matched study design, and additionally adjusted for family history of heart attack. We observed generally similar results in these sensitivity analyses (Additional file 3: Table S1 and S2).Table 2Odds ratios ($95\%$ confidence intervals) for incident coronary heart disease according to DNA methylation age accelerationQ1Q2Q3Q4per SD increaseP for trendWhole cohort No. of cases/controls$\frac{100}{145118}$/$\frac{127129}{116144}$/$\frac{101491}{489}$ Multivariable adjusted*1.001.37 (0.90, 2.08)1.72 (1.12, 2.62)2.02 (1.32, 3.11)1.36 (1.15, 1.61) < 0.001 + cell type proportion†1.001.38 (0.89, 2.14)1.68 (1.08, 2.62)1.84 (1.17, 2.89)1.30 (1.09, 1.56)0.003 + baseline cardiometabolic risk factor**1.001.34 (0.85, 2.11)1.78 (1.13, 2.80)1.78 (1.12, 2.82)1.29 (1.08, 1.55)0.005Men No. of cases/controls$\frac{62}{7862}$/$\frac{7876}{6877}$/$\frac{51277}{275}$ Multivariable adjusted*1.001.30 (0.73, 2.32)1.67 (0.95, 2.95)2.20 (1.22, 3.99)1.39 (1.10, 1.76)0.006 + cell type proportion†1.001.51 (0.81, 2.80)1.94 (1.06, 3.54)2.10 (1.12, 3.96)1.36 (1.07, 1.74)0.014 + baseline cardiometabolic risk factor**1.001.51 (0.80, 2.85)2.03 (1.09, 3.75)1.98 (1.04, 3.77)1.35 (1.05, 1.74)0.019Women No. of cases/controls$\frac{38}{6756}$/$\frac{4953}{4867}$/$\frac{50214}{214}$ Multivariable adjusted*1.001.54 (0.79, 2.98)1.91 (0.97, 3.76)1.79 (0.93, 3.46)1.29 (1.00, 1.67)0.053 + cell type proportion†1.001.38 (0.68, 2.79)1.49 (0.72, 3.10)1.47 (0.72, 3.00)1.18 (0.89, 1.56)0.247 + baseline cardiometabolic risk factor**1.001.40 (0.67, 2.89)1.66 (0.77, 3.57)1.51 (0.72, 3.15)1.17 (0.87, 1.56)0.301*Covariates in the multivariable model: age, sex, education level, marital status, smoking, alcohol drinking, physical activity, average days consuming fresh vegetables, fruits, and red meat per week, body mass index, fasting time, study area, and batch†The estimated proportion of CD4 + T cell, CD8 + T cell, B cell, natural killer, monocytes, and granulocyte**Baseline cardiometabolic risk factors included prevalent diabetes, hypertension, and total cholesterol level We examined the association between Δage and CHD risk according to potential baseline risk factors. The positive association between Δage and CHD was stronger in non-weekly drinkers (Pinteraction = 0.004; Fig. 1). No other statistically significant difference between groups was observed for the following baseline factors: chronological age, smoking status, physical activity, frequency of consuming red meat, body mass index (BMI), waist circumference (WC), waist-to-hip ratio, prevalent hypertension, prevalent diabetes, and TC level.
## Lifestyle factors, DNAm age acceleration, and mediation analysis for CHD
The average number of cigarette equivalents consumed per day was positively associated with Δage (effect size = 0.008; standard error [SE] = 0.003; $$P \leq 0.017$$; Table 3). Further mediation analysis revealed that $10.0\%$ of the smoking-associated CHD risk was mediated through Δage (P value for average causal mediation effect [ACME] = 0.025). The frequency of consuming red meat was negatively associated with Δage, with the adjusted SD difference (SE) being -0.041 (0.015) per one-day increase in days consuming red meat per week ($$P \leq 0.007$$). A total of $17.8\%$ of the red meat-associated CHD risk was mediated through Δage (PACME = 0.008). The total effect and average direct effect of the average number of cigarette equivalents consumed per day and red meat consumed days per week on CHD were not statistically significant (all P value > 0.05; Additional file 3: Table S3). One unit increase in waist-to-hip ratio was associated with a 1.53 SD increase in Δage (SE = 0.566, $$P \leq 0.007$$) after adjustment for BMI. The total effect and average direct effect of per 0.1 increase in waist-to-hip ratio were 1.79 (1.56, 1.94) and 1.74 (1.49, 1.90), respectively (all P value < 0.001, Additional file 3: Table S3). The proportions of CHD risk associated with waist-to-hip ratio mediated by Δage were $4.7\%$ (PACME = 0.017). Alcohol consumption, frequency of consuming fresh vegetables and fruits, physical activity, BMI, WC, and fat percentage were not significantly associated with Δage (all P values > 0.05; Table 3).Table 3Associations between lifestyle factors and Δage, and the risk of coronary heart disease mediated through ΔageLifestyle factors and Δage *Mediation effectEffect size (SE)P valueProportion mediated, %P value for ACMESmokingNo. of cigarette equivalents/day†0.008 (0.003)0.01710.00.025Alcohol drinkingAverage pure alcohol (g)/day0.0004 (0.001)0.777Dietary habitsFresh vegetables, days/week0.020 (0.033)0.551Fresh fruits, days/week0.014 (0.015)0.354Red meat, days/week−0.041 (0.015)0.00717.80.008Physical activity (METs-h)0.002 (0.002)0.387AdiposityBody mass index (kg/m2)0.012 (0.009)0.168Waist circumference, cm **0.008 (0.006)0.186Waist-to-hip ratio **1.530 (0.566)0.0074.70.017Fat percentage (%)**−0.008 (0.009)0.371ACME Average causal mediation effects; Bold text represents statistically significant results (P values < 0.05)*Basic adjustment included age, sex, study area, fasting time, education level, marital status, batch, and the estimated proportion of CD4 + T cell, CD8 + T cell, B cell, natural killer, monocytes, and granulocyte. All lifestyle factors were included in the model simultaneously† To avoid misleadingly elevated risk, former smokers who stopped smoking for illness were categorized as the current smoker**Additionally adjusted for body mass index (kg/m2) When we included smoking, alcohol consumption, and dietary habits as categorical variables in the analyses of lifestyle factors and Δage, participants who quit smoking because of illness and participants who never or rarely consumed red meat had statistically significantly higher Δage (Additional file 3: Table 4).
## Discussion
In this prospective study of middle-aged Chinese, an understudied population for epigenetics and cardiovascular disparities, we observed epigenetic age acceleration was associated with an increased risk of incident CHD after careful adjustment for potential confounders. One SD increase in DNAm age acceleration was associated with a $30\%$ increased risk of CHD. Further mediation analyses revealed the possible mediating role of DNAm age acceleration in the pathway from smoking, none or rare intake of red meat, and central obesity as measured by waist-to-hip ratio to the risk of CHD.
Individuals of different ethnicities may exhibit different aging rates, methylation profiles, and association effects [7]. Ethnic differences might limit the accuracy of methylation age predictors and also the generalization of the association results across populations. We adopted a methylation aging marker [20] established especially for Chinese populations and observed a strong correlation between methylation aging and chronological age.
Previous studies regarding the association between DNAm age acceleration and CHD were inconsistent. Studies conducted in the European ancestry got mixed results: null [7, 8] or positive association of methylation aging with CHD death [9] and incidence [10]. Only a few studies investigated the impact of methylation aging on CHD among other ancestries [11, 12]. These studies both adopted DNAm GrimAge [11], a composite surrogate biomarker of chronological age, sex, plasma protein levels, and smoking pack-years, and showed increased CHD incidence with accelerated DNAm GrimAge in 1,100 primarily hypertensive African Americans [12] and 6,935 individuals comprising three ethnic groups ($50\%$ European ancestry, $40\%$ African Americans, and $10\%$ Hispanic ancestry) [11]. To our knowledge, our study is the first to investigate the association of accelerated epigenetic age with the risk of developing CHD in the Asian population. Our findings expanded the diversity of epigenetic research to understand the mechanisms underlying methylation aging variability across humans.
Lifestyle factors that affect the DNAm markers of aging remain an open question. Studies reported null [23–26] or positive association [8, 27] between smoking behavior and DNAm age among the Western populations; only one study was conducted in African Americans [28]. The majority of previous studies reported null association when comparing the methylation aging of current smokers with others [23, 25, 26, 28]. A previous study showed that smoking pack-year was associated with methylation aging [27]. The present study identified methylation aging accelerated with the average number of cigarette equivalents consumed per day, suggesting that the amount a person smoked is of importance in the methylation aging process. The study of African Americans showed that accelerated methylation aging was observed in participants who quit ≤ 15 years prior, but not in those who quit > 15 years [28]. The present study further identified accelerated methylation aging in former smokers who stopped smoking for illness, but not in those who stopped for other reasons. Taken together, although studies have shown that DNA methylation changes can be reversed following smoking cessation, smoking cessation due to illness or for a short period might be the exception. The differences in methylation aging among former smokers (different cessation duration or reasons) and current smokers (how much has smoked) could be leveraged to offer new insights to explore the effect of smoking on methylation aging in future studies.
BMI was positively associated with DNAm age in previous studies among the Western populations. [ 8, 25, 27, 29] The present study reported no association of BMI with methylation aging but a positive association of waist-to-hip ratio with methylation aging. Of note, the pattern of obesity is different between Asian and White populations. Asians tend to put on abdominal fat preferentially [30]. Our results indicated that methylation aging might be more sensitive in response to the change in waist-to-hip ratio than BMI in the Asian population. We further quantitatively estimated how much lifestyle factors' effects on CHD are mediated through methylation aging. Our mediation analysis noted that around $10\%$ and $5\%$ of the increased CHD risk related to smoking and central obesity, as measured by waist-to-hip ratio, was mediated through methylation aging, respectively.
Regarding diet, previous studies in the European populations investigated the association between the amount of red meat intake (servings/day) and methylation aging, and found that excess intake of red meat may accelerate the methylation aging rate [25, 31]. Our baseline survey was conducted during 2004–2008. During this time period, the average intake of total meat was 80 g per day in China [32], far less than the 175 g in the U.S [33]. The present study collected data on the frequency of red meat intake (days/week) and found that participants who never or rarely consumed red meat had accelerated methylation aging. These findings might suggest that red meat consumption showed a U-shaped association with methylation aging.
In addition, we found that methylation aging mediated $18\%$ of the CHD risk related to none or rare red meat consumption. The analysis based on 500,000 CKB participants found that lower frequency of red meat intake was associated with a higher risk of intracerebral haemorrhage [34]. Our findings may partially explain the protective impact of moderate red meat consumption on this CVD subtype in Chinese and stimulate future studies toward a better understanding of disease mechanisms.
We firstly investigated the association between DNAm age acceleration and the risk of incident CHD in the Asian population, and quantitatively estimated how much of the effects of unfavorable lifestyles on the increased risk of CHD are mediated through DNAm age acceleration. These findings highlighted the importance of DNA methylation in the underlying mechanisms of cardiovascular disease, and the potential usage of epigenetic age as a biomarker of aging and CHD development. Other strengths included the prospective design that allowed us to preclude the possibility that the changes in DNAm age acceleration were a result of disease state; the use of an accurate methylation age predictor specific for the Chinese population; and careful adjustment of possible risk factors of CHD.
Our study has limitations that warrant discussion. First, the methylation aging was measured in whole blood, which might capture only particular aspects of aging. However, whole blood is easy to access and thus widely used in epidemiological studies. We have made adjustments for cellular compositions, suggesting that our findings were not significantly confounded by the mixed cellular nature of whole blood. These association results may not be generalized to methylation aging of other tissues. Multi-tissue age predictor that fits Chinese population might offer further insights into the aging process. Second, both lifestyle factors and DNA methylation were measured at baseline; therefore, the temporal order between lifestyle and aging acceleration cannot be inferred. However, behavioral lifestyles are long-term habits and less likely to be driven by DNA methylation levels. Changes in covariates during follow-up might lead to residual confounding in prospective studies.
## Conclusions
In this prospective cohort of the Chinese population, we have shown that epigenetic aging was associated with the incident risk of CHD in the next 10 years. Our results also provide evidence that smoking, central obesity, or never consumed red meat-induced epigenetic aging may play an important role in the underlying pathway to CHD. Our study adds to the evidence regarding the potential of using epigenetic clock as a marker of biological age across ethnic diversity humans. Our study also extended beyond the previous evidence that lifestyle intervention may attenuate methylation aging, and further lower the risk of CHD. Future research is warranted to translate epigenetic age acceleration measures into practical clinical and public health applications.
## Study population
The CKB cohort was established in 10 geographically diverse areas across China (5 urban and 5 rural sites) during 2004–2008. All participants ($$n = 512$$,715) had baseline data collected by laptop-based questionnaires, including sociodemographics, lifestyle factors, and medical and medication history. Trained staff measured body weight, height, WC, hip circumference, and body fat percentage using standard protocol and calibrated instruments. All participants provided a 10 ml blood sample for long-term storage, with the time since last meal recorded. Participants were not asked to be fasting. Further details of the CKB have been described elsewhere [16].
The study protocol was approved by the Ethics Review Committee of the Chinese Center for Disease Control and Prevention ($\frac{005}{2004}$, Beijing, China) and the Oxford Tropical Research Ethics Committee, University of Oxford (025–04, UK). All participants provided written informed consent.
## Study design and DNAm profiling
A total of 494 incident CHD cases occurring before the censoring date of 31 December 2015 and 1:1 matched controls were selected for genome-wide DNA methylation measurements in a case–control study nested in the CKB cohort. Matched factors included age at baseline (± 3 years), birth year (± 3 years), study area, sex, and hours fasting prior to blood draw (0 to < 6, 6 to < 8, 8 to < 10, and ≥ 10 h) at baseline. All these participants were free of heart disease, stroke, or cancer at baseline. Details of the inclusion and exclusion of participants have been provided previously [18].
Incident CHD cases were identified through linkage with the national health insurance system, with local death and disease registries, and by active follow-up. Linkage to the health insurance databases has been achieved $97\%$ of the participants since 2014. Trained staff coded all diagnoses by the International Classification of Diseases, Tenth Revision (ICD-10). Incident CHD included fatal ischemic heart disease (I20-I25 in ICD-10) and nonfatal acute myocardial infarction (I21). Trained staff reviewed hospital medical records for 134 reported cases for diagnosis adjudication, and confirmed $90\%$ of the diagnoses of CHD.
The Infinium Methylation EPIC BeadChip (Illumina, USA), which interrogates ~ 850,000 CpG sites, was used to measure epigenome-wide methylation levels for CHD cases and matched controls (BGI, China). DNA was extracted from baseline peripheral blood leukocytes. β-value for each CpG site was reported (ranging from 0 to 1.0) to signify the percentage of DNAm at each CpG site. The R package minfi [17] was used to process methylation data. Quality control, filtering, and normalization of the methylation data have been described previously [18]. These filtering processes resulted in 980 samples with 747,726 CpG sites retained.
## Assessment of lifestyle factors
Lifestyle factors included smoking, alcohol consumption, total physical activity level, dietary habits (fresh vegetables, fruits, and red meat), and adiposity levels (BMI, WC, waist-to-hip ratio, and fat percentage).
Lifestyle factors were collected based on questionnaires and physical measurements. We asked about smoking frequency, type and amount of tobacco smoked per day for ever smoker and calculated the average number of cigarette equivalents consumed per day. We also asked former smokers to report their reason for quitting smoking. Former smokers who stopped smoking for illness were included in the category of current smokers to avoid misleadingly elevated risk. Drinking frequency, type of alcoholic beverage, and volume of alcohol consumed on a typical drinking day were collected at baseline to calculate the average pure alcohol volume consumed per day. The usual type and duration of activities were collected to calculate daily level of physical activity by multiplying the METs value for a particular type of physical activity by hours spent on that activity per day and summing the MET-hours for all activities. Information on consumption frequency of three food items (fresh vegetables, fresh fruits, and red meats) that were mainly addressed in a 2013 guideline from the American Heart Association and the American College of Cardiology on lifestyle management to reduce cardiovascular risk [19] was collected. We then calculated average number of days consuming that food item per week. We calculated BMI (weight in kilograms divided by the square of the height in meters) to measure general adiposity. WC and waist-to-hip ratio (the ratio of WC to hip circumference) were used to measure central adiposity. Body fat percentage was estimated using a TBF-300 monitor (Tanita, Tokyo, Japan).
## Assessment of covariates
For each participant, sociodemographic characteristics (age, sex, education, and marital status) and personal health and medical history (hypertension and diabetes) were also collected based on questionnaire. Trained staff measured blood pressure with calibrated instruments. Participants with measured systolic blood pressure ≥ 140 mm Hg, measured diastolic blood pressure ≥ 90 mm Hg, or self-reporting prior diagnosis of hypertension or usage of antihypertensive medication at baseline were defined as having prevalent hypertension. Participants also provided blood samples for a quick on-site plasma glucose test at baseline. Participants with measured fasting blood glucose ≥ 7.0 mmol/l, measured non-fasting blood glucose ≥ 11.1 mmol/l, or self-reporting prior diagnosis of diabetes were defined as having prevalent diabetes. TC level was measured using standard clinical chemistry assays (Wolfson Laboratory at the University of Oxford, UK).
## DNA methylation age and age acceleration
We calculated the methylation age using an existing prediction model developed among Chinese [20]. This method has been shown to have higher accuracy and less error in Chinese populations [20]. Of all 239 CpGs used to calculate methylation age, 205 available CpGs passed the quality control process. DNA methylation age acceleration (Δage) was defined as the residual of regressing DNA methylation age on the chronological age. Δage was inverse normal transformed (SD = 1) for comparison purposes.
## Δage and incident CHD
Logistic regression was applied to estimate the OR and $95\%$ CI, with adjustment for chronological age (continuous, year), sex (male or female), education level (middle school and above, or others), marital status (married or not), smoking (continuous, average cigarettes or equivalents consumed per day), alcohol consumption (continuous, average pure alcohol volume consumed per day), physical activity (continuous, MET-h/d), average days consuming fresh vegetables, fruits, and red meat per week, BMI (continuous, kg/m [2]), fasting time (0- < 6, 6- < 8, 8- < 10, or ≥ 10 h), ten study area, and five DNAm measurement batchs (Model 1). We additionally adjusted for cellular proportions (CD4 + T cell, CD8 + T cell, B cell, natural killer, monocytes, and granulocyte) (Model 2) and baseline cardiometabolic risk factors (prevalent diabetes, hypertension, and TC level) (Model 3). The proportion of leucocyte cells was estimated from DNAm signature using the algorithm from Houseman et al. [ 21] by R package minfi [17]. To examine the robustness of the findings, we also performed conditional logistic regression, which accounts for matching, to estimate OR and CI. Matched factors (chronological age, sex, study area, and fasting time) were not included as covariates in the model. We also additionally adjusted for family history of heart attack (yes or no).
We examined the association of Δage with CHD among prespecified baseline subgroups based on sex (men or women), chronological age at baseline (< 50, or ≥ 50 years), smoking status (current daily smoker or not), alcohol consumption (current weekly drinker or not), level of physical activity (categorized using median cutoff), consumption of red meat (1–7 days/week, or ≤ monthly), BMI (< 24.0 or ≥ 24.0 kg/m2), WC (male ≥ 90 or female ≥ 85 cm, or others), waist-to-hip ratio (male ≥ 0.90 or female ≥ 0.85, or others), prevalent diabetes (presence or absence), prevalent hypertension (presence or absence), and TC level (≥ 5.17 mmol/l or not). The tests for interaction were performed employing a likelihood ratio test comparing models with and without cross-product terms.
## Lifestyle factors and Δage
Linear regression was applied to investigate the association between lifestyle factors and Δage. All five lifestyle factors were included in the model as continuous variables simultaneously. Covariates adjusted in the model included chronological age, sex, study area, fasting time, marital status, education level, batch, and the estimated proportion of CD4 + T cell, CD8 + T cell, B cell, natural killer, monocytes and granulocyte. In the analysis of central adiposity level or fat distribution with Δage, additional adjustment for BMI was made. Smoking behavior, alcohol consumption, and dietary habits were also treated as categorical variables in the analyses of lifestyle factors and Δage.
## Mediation analyses
In the subsequent mediation analyses, we focused on lifestyle factors associated with Δage ($P \leq 0.05$). Causal mediation analyses were performed to estimate how much of the lifestyle-associated CHD risk was mediated through Δage. We used the R package mediation [22] to perform parametric regression models. Two models were estimated for each lifestyle factor. The first was regressing Δage (mediator) on the lifestyle factor (exposure) and covariates. The second one was regressing the risk of CHD (outcome) on the lifestyle factor, Δage, and covariates, allowing for exposure-mediator interactions. Covariates included chronological age, sex, study area, fasting time, education level, marital status, batch, blood cell compositions, and four other lifestyle factors. The mediated proportion was calculated as the mediating effect (average causal mediation effect, ACME) of Δage divided by the total effect on log odds scale.
All analyses were performed with Stata version 14.2 (StataCorp) and R software version 3.5.2 (R Foundation for Statistical Computing). Statistical significance was set at two-tailed $P \leq 0.05.$
## Supplementary Information
Additional file 1. Supplemental Text Members of the China Kadoorie Biobank collaborative group. Additional file 2. Supplemental Figure. Additional file 3. Supplemental Tables 1–4.
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---
title: 'Evaluation of the effectiveness of a postnatal support education program for
husbands in promotion of their primiparous wives’ perceived social support: a randomized
controlled trial'
authors:
- Zahra Abbaspoor
- Foruzan Sharifipour
- Mojgan Javadnoori
- Zahra Behboodi Moghadam
- Bahman Cheraghian
- Mahin Najafian
journal: BMC Women's Health
year: 2023
pmcid: PMC10045875
doi: 10.1186/s12905-023-02270-x
license: CC BY 4.0
---
# Evaluation of the effectiveness of a postnatal support education program for husbands in promotion of their primiparous wives’ perceived social support: a randomized controlled trial
## Abstract
### Background
Primiparous women experience various challenges if not provided with social support in the early postpartum period. Support in form of postpartum education programs is needed to improve mental well-being in primiparous women. The aim of this study was to determine the effect of a postnatal supportive education program for husbands on the perceived social support (primary outcome), and stress and maternal self-efficacy (secondary outcome) of their primiparous wives.
### Methods
This randomized clinical trial was performed on pregnant women referring to healthcare centers for routine care from September to November 2021 in Kermanshah, Iran. One hundred pregnant women were randomly divided in to intervention and control groups. Four 45–90 min online training sessions were held weekly for the husbands of the intervention group. The primiparous women completed the Postpartum Partner Support Scale, Perceived Stress Scale, and Postpartum Parental Expectations Survey before (third day after delivery, immediately and one month after completing the intervention. Data were analyzed using Fisher's exact test, Chi-square test, independent t-test, and repeated measures analysis of variance in SPSS version 24, and $p \leq 0.05$ was considered statistically significant.
### Results
In the control and intervention groups before the intervention, socio-demographic characteristics ($P \leq 0.05$), the mean scores of perceived social support ($$P \leq 0.11$$), maternal self-efficacy ($$p \leq 0.37$$) and perceived stress ($$p \leq 0.19$$) were not statistically significant. However, in the intervention group compared to the control group the mean scores of perceived social support (79.42 ± 7.17 vs. 37.26 ± 7.99, $P \leq 0.001$), maternal self-efficacy (186.22 ± 39.53 vs. 106.3 ± 32.88, $P \leq 0.001$) and perceived stress (16.36 ± 6.65 vs. 43.3 ± 7.39, $P \leq 0.001$) immediately after the intervention and the mean scores of perceived social support (84.4 ± 5.91 vs. 37.14 ± 6.63, $P \leq 0.001$), maternal self-efficacy (191.24 ± 38.92 vs. 112.34 ± 37.12, $P \leq 0.001$) and perceived stress (13.98 ± 4.84 vs. 39.06 ± 7.25, $P \leq 0.001$) one month after the intervention changed significantly.
### Conclusion
The postpartum supportive education program for husbands was effective in promoting social support for primiparous women. Thus it can be introduced as routine care in the postpartum period.
### Trial registration
Clinical trial registration Iranian Registry of Clinical Trials; https://en.irct.ir/user/trial/56451/view (IRCT20160427027633N8), registered ($\frac{15}{06}$/2021).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12905-023-02270-x.
## Introduction
The early postpartum period is an exciting and joyful time for many parents, yet it is a stressful transient period during which most primiparous women face a variety of physical and emotional challenges, including fatigue and difficult responsibilities of caring for the baby and profound changes in the couple's roles and responsibilities [1].
The postpartum period is characterized by the vulnerability of primiparous women to stress. Postpartum stress not only impairs the health of primiparous women but also reduces their self-esteem, impedes adaptation to the role of mother, and impairs maternal bond with the baby [2]. Common stressors of the postpartum period include breastfeeding problems, sleep deprivation, fatigue, responsibility for caring for the baby, hormonal changes, and lack of social support [3].
Maternal self-efficacy refers to the mother’s belief in her abilities as an efficient mother and is greatly influenced by the sense of motherhood, self-confidence and perception of empowerment [4]. High self-efficacy is associated with positive thoughts, higher self-esteem, better adjustment, and more positive emotions [5]. Various studies have shown a negative relationship between maternal self-efficacy and maternal stress and postpartum depression [1, 6]. Some studies have shown that education during pregnancy is insufficient for the woman to play the maternal role and has no effect on maternal skills [7, 8].
Barnard [1994] emphasizes the importance of social support in the first year after childbirth [9]. Perceived social support refers to the amount of love, companionship, care, respect, attention, and help a person receives from others, such as family members, friends, etc. [ 10] There are three main types of social support: 1. Emotional support, which means having someone available to rely on and trust when needed. 2- Information support, which indicates receiving useful information (including guidelines, suggestions, advice, and feedback) from others to adapt to personal problems, and 3- Instrumental support, which is the material, objective and actual help received by a person from others [11]. A previous study found that pregnant women who received high social support from their husbands during pregnancy experienced less stress [12].
The results of a randomized controlled trial demonstrated the effectiveness of the postnatal psychoeducation program in improving maternal parental self-efficacy, social support and reducing postnatal depression of primiparous women [13]. Also, various studies have shown that social support is negatively correlated with the risk of postnatal depression [14, 15]. Hence, providing strong social support to primiparous women can potentially prevent them from developing postnatal depression (PND). Supportive interventions can be facilitated by providing social support in the form of education and maintaining the continuity of cares postnatal [16]. Shorey et al. [ 2017] demonstrated that the educational programs based on mobile applications are effective in improving parental self-efficacy and social support, recommending it to be introduced and performed by nurses in routine care [17]. However, the provision of supportive interventions is not widely practiced during the postnatal period as the focus has been mainly limited to pregnancy and childbirth [18]. Primiparous women often cite their family members, especially their husbands, as the main source of social support [1]. According to the opinion of most women, a woman's husband is the most important and closest person who can support her in coping with the problems by carefully understanding her sensitive psychological and physical condition [19]. Fletcher et al. showed that fathers’ participation in classes before delivery was effective in their supportive role [20]. The results of various studies have shown that fathers’ participation in pregnancy and childbirth care has positive consequences for the mother and baby, including reduced maternal stress, pregnancy complications (e.g., preterm delivery) and fear of childbirth, proper weight gain of premature infants, and successful breastfeeding. This is even true with long-term effects such as improved language learning and children’s academic achievement [21, 22]. Various studies recommended husband counseling and education as effective interventions to increase social support for pregnant and postpartum women [23–26].
In some Western countries, mothers and their infants are discharged early to facilitate family-centered postnatal care and encourage a sense of parental involvement. [ 27] In Iran, the average length of hospital stay of mothers after childbirth is 1 to 2 days [28]. Due to the mother’s short stay in the hospital, there is not sufficient time for educational interventions [17]. Moreover, the period immediately after delivery is not a good opportunity for education due to the discomfort that mothers experience during this period [29]. Parents are often overwhelmed by the amount of information they are given during their short hospital stay. Some complain that they have difficulty retaining this information after discharge and prefer to receive the information continuously after discharge early in the postpartum period [27]. To address these issues, healthcare providers need to develop the knowledge and tools needed to improve the quality of postnatal care that parents expect [30].
Given the importance of primiparous women’s mental health in facilitating adaptation to changes and new roles in the during the postpartum period, many husbands have a vital and decisive role in supporting their wives during this period. However, there is no sufficient time to provide all the necessary information in a short period of hospitalization, and according to a conducted survey, no study was found on the effectiveness of a postnatal supportive education program for husbands on perceived social support, stress and maternal self-efficacy of their primiparous wives. Therefore, the present study was conducted to determine the effect of a postnatal supportive education program for husbands on perceived social support (primary outcome), and stress and maternal self-efficacy (secondary outcome) of their primiparous wives.
## Hypothesis of study
Education to the husbands of primiparous women will lead increased perceived social support and maternal self-efficacy and reduced stress of these women in the postpartum period.
## Primary aim
Determine the effect of a postnatal supportive education program for husbands on perceived social support of their primiparous wives.
## Secondary aim
Determine the effect of a postnatal supportive education program for husbands on stress and maternal self-efficacy of their primiparous wives.
## Study design and participants
This randomized, controlled clinical trial study using two parallel groups was performed on 100 pregnant women referring to health centers in Kermanshah, Iran from September to November 2021. Figure 1 shows the CONSORT flowchart of the study. Women meeting the following criteria were eligible to participate in the study: first pregnancy, singleton term pregnancy, willingness to participate in the study, married and living with her husband, husband’s willingness to participate in the study, media literacy (familiarity with how the Skype app works), having a smartphone, knowing how to install and work with Skype, access to the Internet, and being available at least within the next 8 weeks. Exclusion criteria included: being husband's unable or unsure of the ability to attend all training sessions, presence of cardiovascular disease, high blood pressure, liver disease, diabetes or other chronic diseases (as reported by the woman), neuropsychiatric diseases, having recent calamities for the participant’s first-degree relatives (as reported by the woman) (such as death or incurable disease), and attending similar training or counseling classes by the husband before the study. Fig. 1CONSORT flowchart of the study
## Sample size calculation
The sample size was calculated based on the main variable of the study (social support) according to Khanzadeh and Moghaddam Tabrizi [31] using the following formula with type I error (α) set as two-sided $5\%$ (Z1 − α/2 = 1.96), type II error (β) set as $20\%$ (Z1 − β = 0.85) and power of $80\%$. The sample size for both groups was obtained 86 subjects and considering a $10\%$ attrition rate, the final sample size was calculated to be 96 for both groups (48 each). Based on the opinion of the research team, we rounded the number 96 to 100 (50 each group).
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=\;\frac{\left(z_{1-\frac\alpha2}+_{1-\beta}\right)^2+\left(SD_1^2+SD_2^2\right)}{\left(U_1-U_2\right)^{\wedge2}}$$\end{document}n=z1-α2+1-β2+SD12+SD22U1-U2∧2 μ1: mean difference of the social support in the intervention group (11.25), μ2: mean difference of the social support in the control group (4.95), SD1: standard deviation of the social support before intervention (14.21), and SD2: standard deviation of the social support after intervention (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.00$$\end{document}10.00).
## Sampling and random allocation method
The present study was approved by the committee of ethics of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1399.401) and was registered in Iranian Registry of Clinical Trials (IRCT20160427027633N8). First, According to the information of Kermanshah health center, Kermanshah city was divided into 4 geographical regions (or 4 classes) based on socio-economic status of the residents. These four regions included 76 healthcare centers, and from the healthcare centers of each region, one was selected by simple random method (drawing lots). These centers were Taleghani, Hafezieh, Shahid Souri, and Haj Daei, from which 28, 34, 22, and 16 women were selected by convenience sampling method (based on the share of each center). After making the necessary arrangements and obtaining a permit, the researcher went to these centers and selected eligible women. In this way, by referring to the Sib system, information about the women who were covered by the center and met the study entry criteria was extracted and they were contacted by phone to come to the health center to participate in the study. The study aims and method were explained to pregnant women and their husbands in a face-to-face meeting and informed written consent was gained from them (pregnant women and their husbands). The telephone numbers of the women and their husbands were taken, and the researcher gave her telephone number to them, and they were advised to inform the researcher when the mother gave birth to the baby. After the women completed the questionnaires online on the third day after delivery, the educational content was offered to the intervention group. It should be noted that the membership of the husbands in the virtual (Skype) group was monitored by checking whether or not they had installed Skype on their cellphones, and in case they had not, it was installed on their cellphones. Necessary training on how to use Skype was given to them.
The women were randomly divided into 6 blocks with an allocation ratio of 1:1 to the intervention and control groups. Blocked randomization was performed by a person who was not involved in data collection, and in order to conceal random allocation, opaque sealed packets in random sequence were used. To this aim, first, a random sequence was created by a table of random numbers and recorded on a card and the cards were placed in packets according to the random sequence. Packets were opened in the order of woman entry in the study and they were allocated into either the intervention or control group. Data analysis was performed by a statistician who was unaware of the nature of the codes. Due to the nature of the intervention, it was not possible to blind neither the participants nor the researchers.
## Intervention
Training was performed for intervention group in Skype. While the control group received only routine care. Training involved 4 online sessions of 45 to 90 min in Skype, held weekly (one session per week) by the researcher. Due to the different nature of education on natural childbirth and cesarean section, the women in the intervention group were divided according to the type of delivery (32 women who were supposed to have a normal delivery were in 4 groups of 8, and 18 women who were going to deliver their baby by a cesarean section were in 3 groups of 6). Topics of training sessions included: first session: the importance of maternal health in the postpartum period and the importance of social support for the mother in this period, second session: anatomical and physiological changes in the postpartum period, the impact of these changes on the mothers mental state, and the way she should be cared for, third session: baby care, the principles of breastfeeding and how to deal with its challenges, fourth session: the father’s responsibilities in the postpartum period and his supportive role in helping mothers adapt to changes (Interventional group educational content sessions are available in Additional file 1: Table 1).Table 1Socio-demographic characteristics of the intervention and control groupsVariablesInterventionn = 40Controln = 40P valueMean ± SDOr N (%)Mean ± SDOr N (%)Age (years)29.3 ± 6.1628.14 ± 5.98*0.34Husband’s age (years)35.08 ± 5.5833.56 ± 5.13*0.16Age gap with husband (years)5.56 ± 2.675.42 ± 2.73*0.79Weight at birth (grams)30.30 ± 180.9830.76 ± 171.2*0.19Education***0.6 Primary high school6[12]5[10] Secondary high school(diploma)8[16]12[24] University36[72]33[66]Occupation**0.7 Employed11[22]14[28] Housekeeper27[54]21[42] Student10[20]12[24] Other2[4]3[6]Husband’s education**0.22 Primary high school6[12]3[6] Secondary high school(diploma)19[20]17[34] University34[68]30[60]Husband’s occupation**0.84 Employed32[64]28[56] Jobless5[10]6[12] Worker12[24]14[28] Other1[2]2[2]Economic status***0.4 Weak4 [8]6[12] Medium39[78]33[66] Good7[14]11[22]Husband’s disease***0.18 No44[88]39[78] Yes6[12]11[22]Delivery method**0.4 Vaginal30[60]34[68] Cesarean section18[36]16[32] Vacuum delivery2[4]0[0]Type of baby feeding***0.55 Breast milk31[64]36[72] Milk powder4[8]2[4] Both15[30]12[24]Breastfeeding problems***0.42 Breast congestion5[10]8[16] Nipple sores8[16]11[22] No problem37[74]31[62]Pregnancy status***0.53 Planned pregnancy45[90]43[86] Unplanned pregnancy5[10]7[14]Baby’s gender***0.23 Girl21[42]27[54] Boy29[58]23[46]Satisfaction of the baby’s gender Yes50[100]50[100] No0[0]0[0]*Independent t-test, ** Fisher’s exact test, *** chi-square test The educational content was prepared by referring to the available library resources and with the guidance of supervisors and consultants and also based on the instructions and guidelines of the Iranian Ministry of Health. The validity of the educational content was evaluated qualitatively and approved by 10 relevant experts and faculty members related to the subject under study, including four reproductive health specialists, one midwifery specialists, three gynecologists and two psychologists. Each week after the review session, the content was presented in text, video and audio formats to the intervention group until the next session. Meetings were held upon agreement of the husbands. Each week, the researcher reminded the audience of the time of the training sessions by phone and text messages. In addition, the intervention group provided access to the researcher to ask questions via WhatsApp. Immediately and one month after completing the intervention (i.e., the fourth and eighth weeks after delivery), the questionnaire was completed by primiparous women online. In the end, in order to observe the ethical considerations, the educational materials were provided to the control group.
## Study outcomes
In this study, the study outcomes (primary outcome: social support and secondary outcome: maternal self-efficacy and stress) were measured at three time intervals: baseline or before intervention (third day after delivery), immediately after the intervention (fourth week after delivery), and one month after the intervention (eighth week after delivery). The outcome measures were completed by primiparous women online.
## Data collection tools
Data collection tools included socio-demographic questionnaire, Postpartum Partner Support Scale, Perceived Stress Scale, and Postpartum Parental Expectations Survey.
## Socio-demographic questionnaire
This questionnaire includes questions related to age, husband’s age, age gap with husband, educational of the woman and her husband, employment status of the woman and her husband, economic status, husband’s disease, weight at birth, type of baby feeding, breastfeeding problems, delivery method, pregnancy status, and baby’s gender and parental satisfaction with.
## Postpartum Partner Support Scale (PPSS)
This scale was developed in 2017 by Dennis et al. This questionnaire has a four-point Likert scale (from strongly disagree: 1 to strongly disagree: 4). Scores of this questionnaire range from 20 to 100, and higher scores indicate receiving higher husband support in the postpartum period [32]. The reliability of this questionnaire in the original version was reported by obtaining a Cronbach’s alpha coefficient of 0.96 [23]. In Iran, Moghaddam Tabrizi and Khanzadeh [2020] reported a Cronbach’s alpha of 0.96, an internal stability index was 0.7, and alpha and reliability of 0.98. Also, the content validity index (CVI) and the content validity ratio (CVR) of this scale were $70\%$ and $81\%$, respectively, which are both acceptable [31].
## Postpartum Parental Expectations Survey (PPES)
This scale designed by Reese in 1992, was used to measure maternal self-efficacy. This questionnaire has 25 affirmatively worded items scored based on a 10-point Likert scale (from I can't: 1 to I definitely can: 10). The minimum score of this questionnaire is 25 and the maximum is 250, with scores less than 25 indicating lack of self-efficacy of the mother and those higher indicating more self-efficacy of the mother. The validity and reliability of this questionnaire was first confirmed by Reese in 1992 [33]. The validity and reliability of this questionnaire in Iran was confirmed by Jafarnejad et al. in 2014 by obtaining a Cronbach's alpha coefficient of 0.87, a content validity index of 0.79, and a content validity ratio of 0.62 [34].
## Perceived Stress Scale (PSS)
Perceived Stress Scale was developed in 1983 by Cohen et al. to measure general perceived stress over the past month. Thoughts and feelings about stressful events, as well as controlling, overcoming, coping with stress. In the present study, the 14-item version was used which is answered based on a 5-point Likert scale (from Never: 0 to Most of the time: 4). Total Scores range from zero to 56, with higher scores indicating more perceived stress. The reliability of this questionnaire in the original version using Cronbach’s alpha coefficient method has been reported between 0.84 and 0.86, and its correlations with the constructs measured are also high (0.52 to 0.76) [35]. In Iran, the reliability of this questionnaire was calculated by Bastani et al. using the internal consistency method and obtaining a Cronbach’s alpha coefficient of 0.74 [36]. Dolatian et al. [ 2014] reported a content validity index of $95\%$ and a content validity ratio of $90\%$ for this questionnaire [37].
## Data analysis
The collected data were analyzed using descriptive statistics (frequency, percentage, mean and standard deviation) and inferential statistics using SPSS version 24. $P \leq 0.05$ was considered statistically significant. Kolmogorov–Smirnov test was used to evaluate the normality of quantitative data. Fisher’s exact test, Chi-square test, and independent t-test were used to examine the demographic characteristics. Independent t-test and analysis of variance with repeated measures were used to compare the mean scores of social support as well as perceived stress, and maternal self-efficacy in groups at each time interval and over time, respectively.
## Results
In this study, of the 373 primiparous women referring to health centers who were initially enrolled, 273 were excluded from the study due to not meeting inclusion criteria. No attrition was observed in both groups during the study period, and all participants (50 in the intervention group and 50 in the control group) were analyzed at two intervals (immediately and one month after the intervention) after the follow-up period (Fig. 1). The demographic characteristics of the participants are shown in Table 1. The mean age of women and their husbands were 29.3 ± 6.16 and 35.08 ± 5.58 in the intervention group and 28.14 ± 5.98 and 33.56 ± 5.13 in the control group, respectively. There was no significant difference between the two groups in terms of demographic characteristics.
Based on independent t-test, the mean scores of perceived social support ($$P \leq 0.11$$), maternal self-efficacy ($$p \leq 0.37$$) and perceived stress ($$p \leq 0.19$$) before intervention were not significantly different in the two groups. In the intervention group, the mean scores of perceived social support, self-efficacy and perceived stress of primiparous women immediately and one month after completing the intervention were significantly different from those in the control group ($P \leq 0.001$). Analysis of variance with repeated measures showed that the perceived social support (mean difference = 30.72; $95\%$ confidence interval = 28.61 to 32.82; $p \leq 0.001$), maternal self-efficacy (mean difference = 55.29; $95\%$ confidence interval = 41.85 to 68.73; $p \leq 0.001$) and perceived stress (mean difference = -16.7; $95\%$ confidence interval = -18.71 to -14.70; $p \leq 0.001$) of primiparous women in the intervention group changed significantly over time, and the interaction analysis showed that there was a significant cross impact between group and time ($P \leq 0.001$) (Table 2). As shown in Figs. 2, 3 and 4, the mean scores of perceived social support and maternal self-efficacy in the intervention group increased over time while the mean scores of perceived stress decreased over time. Table 2Comparisons of the mean scores social support, self-efficacy and perceived stress at three measurement time points (before intervention, immediately and a month after completing the intervention) in the studied groupsVariablesIntervention($$n = 50$$)Mean ± SDControln = 50Mean ± SDMean differencea(CI = $95\%$)aP value*Perceived social support (20–100) Before intervention8.8 ± 38.948.43 ± 36.32-2.74(-6.17–0.69)$$P \leq 0.11$$ Immediately after completing the intervention7.17 ± 79.4237.26 ± 7.99-42.16(-45.18- -39.15)$P \leq 0.001$ A month after completing the intervention5.92 ± 84.406.64 ± 37.14-47.26(-49.76- -44.77)$P \leq 0.001$P value**$P \leq 0.001$P = 0.6230.72(28.61- 32.82)$P \leq 0.001$Time effectP < 0.001Group*Time effectP < 0.001Maternal self-efficacy (25–250) Before intervention111.60 ± 42.43104.56 ± 37.04-7.04(-22.85–8.77)$$P \leq 0.37$$ Immediately after completing the intervention186.22 ± 39.53106.3 ± 032.88-79.92(-94.35- -65.49)$P \leq 0.001$ A month after completing the intervention191.24 ± 38.92112.34 ± 37.12-78.90(-93.99- -63.80)$P \leq 0.001$P value**$P \leq 0.001$P = 0.0655.29(41.85- 68.73)$P \leq 0.001$Time effectP < 0.001Group*Time effectP < 0.001Perceived postpartum stress (0–56) Before intervention48.04 ± 7.0646.14 ± 7.37-1.90(-4.76- 0.97)$$P \leq 0.19$$ Immediately after completing the intervention16.36 ± 6.6543.3 ± 07.3926.94(24.15–29.73)$P \leq 0.001$ A month after completing the intervention13.99 ± 4.8439.06 ± 7.2525.08(22.63–27.52)$P \leq 0.001$P value**$P \leq 0.001$P = 0.10-16.7(-18.71- -14.70)$P \leq 0.001$Time effectP < 0.001Group*Time effectP < 0.001*Independent t-test; **Repeated Measures ANOVAFig. 2The mean scores of perceived social support in the intervention and control groups across the three measurement timesFig. 3The mean scores of Maternal self-efficacy in the intervention and control groups across the three measurement timesFig. 4The mean scores of *Perceived postpartum* stress in the intervention and control groups across the three measurement times
## Discussion
The results of this study showed that the postnatal supportive education program for husbands enhanced the understanding of social support for their primiparous women in the fourth and eighth weeks after delivery. It can be argued that providing education and consultation services to husbands in order to increase their awareness about the characteristics and issues of women in the postpartum period is one of the tools for promoting husbands’ social support to their wives, which has a positive effect on the couple’s knowledge, attitude and practice with respect to postpartum health [31]. Various studies have introduced the husband as the most important source of support in stressful life situations of women [38, 39].
To the best of our knowledge, no study has yet been conducted on the effect of husband education or counseling on social support, maternal self-efficacy, and stress in postpartum women. However, previous studies have shown the overall impact of participation and counseling of pregnant women's husbands, primiparous women and couples in this respect. Mohammadpour et al. [ 2020], for instance, conducted a randomized clinical trial to determine the effect of husband counseling on social support perceived by their pregnant wives. Their intervention involved four 60-min sessions of group counseling (each group consisting of 7 to 10 people) once a week for four consecutive weeks. The results showed that the mean score of social support 4 weeks after the intervention in the intervention group increased significantly compared to the control group [23]. Another study in Singapore conducted by Shorey et al. [ 2017] examined the effectiveness of a postpartum psychiatric educational mobile application called "Home—but not Alone" in improving parenting outcomes. Parents in the intervention group received a training program in addition to routine care while the control group received only routine care. The results showed that parental self-efficacy and social support significantly improved four weeks after delivery in the intervention group compared to the control group [17]. The results of studies of Turan et al. and Sahip et al. showed that husbands’ education led to improvement their more appropriate supportive behaviors with women during pregnancy [25, 26]. The results of the present study are in line with those mentioned above, which indicates the positive effect of husband education or counseling on mothers’ social support. Therefore, it is necessary to increase the awareness of husbands about their positive role in the postpartum period and their participation in promoting the health of mother and baby. In fact, in the present study, online education through social networking provided a virtual community for husbands, and facilitated social support between members of this community through sharing personal information and experiences, and at the same time, and created the opportunity of making friends with other men in this community.
The results of the present study showed that the postnatal supportive education program for husbands of primiparous women promoted maternal self-efficacy in these women in the fourth and eighth weeks after delivery. It can be argued that educating men to promote support for their primiparous wives can play an effective role in reinforcing the mother’s abilities and her interpretation of the competence she has to play the role of a mother, thus increasing her self-efficacy [40]. The results of previous studies confirm our findings with respect to the positive effect of educational intervention on the promotion of maternal self-efficacy, and this was achieved in our study 4 and 8 weeks after delivery [34, 41]. In line with the present study, the results of a randomized clinical trial showed the significant effect of a postpartum psychological education program on increasing maternal self-efficacy and social support and reducing postpartum depression among primiparous women in their 6th and 12th weeks of postpartum. The final results of this trial indicated that the postpartum psychological education program is effective in improving maternal outcomes [13]. The main difference between this study and the present study was that in the former the training program involved a face-to-face meeting and presentation of a booklet for primiparous women, while in our study the training program included 4 online training sessions for husbands. In fact, the training sessions in our study were for husbands only. In these sessions, the need for spouse support in the postpartum period was taught for fathers and we assessed the effects of education on their primiparous women indirectly.
Salonen et al. [ 2010] investigated the effect of an online educational intervention on increasing maternal satisfaction and self-efficacy. Mothers in the intervention group had access to an educational website offering infant care training content from the 20th week of pregnancy whereas the control group received only the routine training. No statistically significant difference was observed between the two groups 6 to 8 weeks after delivery in terms of self-efficacy and parental satisfaction compared to before the intervention [42]. This finding is contrary to the results of the present study. This discrepancy can be attributed to the fact that participants in their study included a combination of primiparous and multiparous women with full-term or pre-term pregnancies as well as normal or abnormal birth weight. In addition, a number of women in both groups in their study had a depression score higher than 10, which was not the case in the present study.
In the present study, participants had access to information through education. This useful information helped the men to acquire the right knowledge independently and, by providing support to the mother, they managed to perform the challenging parenting tasks and increase the mother’s self-efficacy. The helpful feedback given to the husbands served as verbal encouragement, thus allaying their concerns and increasing their self-confidence and motivation to promote maternal self-efficacy.
The results of the present study showed that the postnatal supportive education program for husbands of primiparous women reduced the perceived stress in these women in the fourth and eighth weeks after delivery. This can be attributed to the fact that educating husbands on providing emotional, informational, and instrumental support to the mother reduces her physical and psychological stress. Therefore, among the available support sources, the support provided by sexual partners is very important [43]. This finding is consistent with the results of studies by Cohen et al. [ 2014] and Dafie et al. [ 2021] where providing counseling services to couples based on cognitive-behavioral methods during pregnancy has been found to exert a positive effect on reducing stress and depression. In this method, perinatal stress and depression is reduced and post-natal mental health is promoted by enhancing the couples’ understanding about pregnancy and its changes, minimizing negative behaviors and attitudes, increasing emotional support, and promoting empathetic communication during pregnancy. Given the relationship between mental health during pregnancy and postpartum mental health, the use of educational and counseling methods in care centers to promote maternal mental health is strongly recommended [44, 45].
Also, study of Alio et al. [ 2013] showed that the primary benefits of male partner involved during pregnancy were the reduction of maternal stress levels and the encouragement of positive maternal behaviors [46]. In another study, the results showed that 4 sessions of consultation with fathers had no effect on reducing stress in pregnant mothers [23], which is contrary to the results of the present study. It seems that the difference is attributable to the type of intervention, educational content, number and type of participants, follow-up period, or different assessment tools. Research has shown that educational information provided by health care providers during pregnancy is rarely adhered to by mothers themselves during the postpartum period [47], indicating the mothers’ need for support to cope with stress correctly, which explains the results of the present study.
One of the limitations of this study was that women in this study had no neuropsychiatric diseases. This may affect the generalizability of the results to women who are depressed. The second limitation was that data collection was based solely on participants’ own reports, and the researchers did not use other sources of data collection such as observation. The third limitation was that due to the nature of the intervention, the researchers were not blind to data collection after the intervention. Also, no data was collected from husbands. The last limitation was the cultural component. This prevents generalization to other realities. There is no homosexual culture in Iran, and families made up of a man and a woman and who are married. Other family models (single parent, mixed families, not married, formed by people of the same sex), which are not considered here and could be considered in other studies. These limitations notwithstanding, this study is worthwhile considering the following: Random allocation, concealment of allocation, random selection of women from health centers in 4 districts of the city, long-term follow-up, providing telephone numbers to answer participants’ questions, and providing educational content to the control group at the end of the study. Also, the intervention was designed around the planned delivery method. Therefore, we suggest that a similar study be done in women with a history of depression or suffering from that with more sophisticated statistical analysis.
## Conclusion
This study demonstrated the effectiveness of a postnatal supportive education program for husbands (male partners) in improving perceived social support, maternal self-efficacy, and stress in primiparous women. Therefore, policymakers in postpartum care are advised to consider the participation of husbands in the postpartum care process and to devise plans to increase the awareness of husbands and their role in promoting maternal and infant health. Also, to confirm the results of this study, a randomized controlled trial with a larger sample size from the whole population is suggested.
## Supplementary Information
Additional file 1: Table 1. Intervention group educational content sessions.
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|
---
title: Cognitive Function, and Its Relationships with Comorbidities, Physical Activity,
and Muscular Strength in Korean Older Adults
authors:
- Shinuk Kim
journal: Behavioral Sciences
year: 2023
pmcid: PMC10045882
doi: 10.3390/bs13030212
license: CC BY 4.0
---
# Cognitive Function, and Its Relationships with Comorbidities, Physical Activity, and Muscular Strength in Korean Older Adults
## Abstract
Background: *Little is* known regarding how much physical activity (PA) and lower-body muscle strength (LBMS) together can help to reduce the negative effect of comorbidities on cognitive function. This study examined the moderating effects of PA and LBMS in determining the relationship between comorbidities and cognitive function in older Korean adults. Materials and Methods: *This is* a population-based cross-sectional study. Data for this study were taken from the 2020 Korea Longitudinal Study on Aging (KLoSA) in South Korea using a computer-assisted personal interview. The 2020 KLoSA survey included a total of 10,097 older individuals aged 65 and older (6062 females and 4035 men). Comorbidities were determined based on physician-diagnosed chronic conditions. PA and LBMS were measured with a self-reported questionnaire and a sit-to-stand test, respectively. Cognitive function was assessed using the Korean version of the Mini-Mental Status Examination for dementia screening. Results: Multimorbidity was correlated with an increased risk (odds ratio, OR = 1.415, $p \leq 0.001$) of cognitive impairment. Insufficient PA and weak LBMS were correlated with an increased risk of cognitive impairment (OR = 1.340, $p \leq 0.001$; OR = 1.719, $p \leq 0.001$, respectively). Particularly, PA modulates the negative impact of comorbidities on cognitive function (β = −0.3833; $95\%$ CI = −0.4743 to −0.2023) independent of all measured covariates. Weak LBMS was found to be an independent predictor of cognitive function (β = −2.5078, $p \leq 0.001$) regardless of comorbidities. Conclusions: The study findings suggest that a lifestyle intervention targeting regular PA and muscular fitness should be a therapeutic means against cognitive decline associated with normal aging and/or chronic diseases.
## 1. Introduction
Population aging is a worldwide phenomenon that reflects a result of both falling birth rates and increasing life expectancy [1]. South Korea has seen the fastest increase in the number of elderly persons among all nations, from $7.2\%$ of the population older than 65 years in 2000 to $15.5\%$ of the population older than 65 years in 2017 [2]. A sudden rise in the elderly population raises the possibility of a range of medical disorders, such as chronic illnesses, physical and functional impairments, frailty, cognitive decline, and dementia [3]. Among the health conditions, cognitive decline is an important mental illness that warrants special care insofar as it might represent a preliminary clinical sign of dementia [4]. Therefore, it is essential to comprehend the etiology of cognitive decline so that a health policy can be developed to prevent cognitive impairment and reduce the likelihood that it will progress to dementia.
Comorbidities, which are defined as the coexistence of one or more chronic illnesses or diseases, are more common in older people and can raise the possibility of functional restrictions and/or limitations in both the physical and mental spheres [5]. Additionally, comorbidities are linked to a higher risk of cognitive decline, which may be connected to the pathophysiology and/or development of cognitive impairments and dementia [6], though the precise pathology underlying the documented association between exposures and outcomes is still poorly understood. From an etiological standpoint, it is well known that lifestyle risk factors, such as obesity, inactivity, frailty, excessive drinking, cigarette use, poor nutritional status, and others, are linked to a higher risk of having comorbidities in older adults [7].
Physical activity (PA) refers to any skeletal-muscle movement that requires a significant energy expenditure above that of rest. As a part of muscular fitness, muscle strength is influenced by the interaction of inherited and environmental factors [8]. It is well established that PA, including aerobic exercise, is crucial for enhancing cognitive function in people of all ages, possibly through increased cerebral blood flow and heightened neuromuscular activity [9,10]. Muscular strength is linked with better cognitive function among older adults. Resistance training has a favorable impact on cognitive ability, possibly via alterations in cerebral blood flow and neuronal activity of the central nervous system [11]. From a therapeutic standpoint, therefore, PA and muscle strength are two distinct factors that positively influence cognitive function [12].
Previous research has found links between cognitive function and PA and physical fitness. For example, the positive association between PA and cognitive function has been reported in previous studies involving Korean and other older Asian adults [13,14,15]. Likewise, lower-extremity muscular strength was an independent predictor of higher cognitive function in older adults who participated in the 1999–2002 National Health and Nutrition Examination Survey [16]. Handgrip strength and 6 min walk were two independent predictors of cognitive function in community-dwelling older Chinese adults [17]. Furthermore, it is probable that those who are physically strong may also be more prone to engaging in regular PA and vice versa, underscoring the importance of considering both as determinants of cognitive function.
Later in life, cognitive function, comorbidities, physical activity, and muscular strength all become intertwined. The nature of the relationships between these four variables, however, is unknown. In this light, the current study sought to investigate the relationships between cognitive function, comorbidities, physical activity, and lower-body muscle strength (LBMS). We hypothesized that PA and LBMS would both act as moderators in determining the impact of comorbidities on cognitive function in elderly Koreans.
## 2.1. Study Design, Setting, and Participants
This is a population-based cross-sectional study. Data for this study were taken from the 2020 Korea Longitudinal Study on Aging (KLoSA), which is a nationwide panel survey of the Korean population conducted by the Korea Employment Information Service (KEIS) in 2020 on behalf of the South Korean Ministry of Employment and Labor. The KEIS carried out the 2020 KLoSA survey across the country using a multi-stage, stratified sampling based on geographical areas and housing types. The 2020 KLoSA survey included a total of 10,097 older individuals aged 65 and older (6062 females and 4035 men) in 15 metropolitan cities and provinces of South Korea. The data collection was conducted using a computer-assisted personal interview. Other detailed information, such as weighting data and imputation methods for non-response, is available elsewhere (https://survey.keis.or.kr/klosa/klosa01.jsp) (accessed on 10 November 2022).
## 2.2.1. Cognitive Function
Cognitive function was assessed using the Mini-Mental Status Examination (K-MMSE), which was designed for dementia screening with a maximum total score of 30 points. The presence of cognitive impairment was determined using the previously described age- and education-specific cutoff points that were developed to identify individuals with cognitive impairments for dementia screening [18].
## 2.2.2. Comorbidities
Comorbidities were assessed based on the presence of at least one of the 25 chronic conditions listed in the other nationwide survey study [19]. The presence of a physician-diagnosed chronic condition(s) was determined by a self-reported questionnaire. Multimorbidity was defined as the presence of two or more chronic diseases.
## 2.2.3. Physical Activity and Lower-Body Muscle Strength
A self-reported questionnaire that inquired about participation in three domains of PA (i.e., occupational, transportation, and leisure–recreation) lasting at least 10 min per session was used to measure PA. For answers of “yes,” he or she was next prompted to record his or her weekly PA in terms of frequency and duration. The PA volume was then estimated by multiplying the weekly frequency by the duration of each session (minutes). In accordance with the global recommendations, PA was then categorized as being either sufficient (≥150 min per week) or insufficient (no PA or <150 min per week) [20].
A sit-to-stand test (STST), which was detailed elsewhere [21] and reported to have validity and reliability in elderly Koreans [22], was used to evaluate LBMS. Briefly, the participants were told to stand up from a chair 5 times as quickly as they could with both arms crossed over their chest. The performance was graded according to its completion (1 = successfully accomplished, 2 = attempted but failed to complete, and 3 = unable to perform at all). For the purposes of this study, a successfully completed STST was classified as normal, and attempts that could not be performed at all or could not be completed were combined and classified as weak.
## 2.2.4. Covariates
The covariates were age (years), gender (male vs. female), body mass index (kg/m2), marital status, education (elementary, middle/high school, or college), smoking (current/past smoker or non-smoker), and alcohol consumption (0, 1–6, 7 times/week).
## 2.3. Statistics
The normality of the data distribution and multicollinearity were checked using QQ plotting and the variance of the inflation component, respectively. The Student’s t-test and the chi-square test were applied to compare the cognitive function-based subgroups for continuous and categorical variables, respectively. Linear-regression analysis was conducted to estimate standardized coefficients of measured parameters in determining their relationships with cognitive function. Dummy codes were used for categorical variables such as gender (male = 1 and female = 0) and marriage (married with spouse = 1 and all others = 0). The odds ratios (ORs) and $95\%$ confidence intervals (CIs) of cognitive impairment by PA and LBMS were determined using binary logistic regression. Finally, as depicted in Figure 1, Andrew Hayes’ PROCESS macro with Model 2 was used to test the moderating effects of PA (moderator 1, W) and LBMS (moderator 2, Z) on the relationship between comorbidities (independent variable, X) and cognitive performance (dependent variable, Y). The statistical significance of the model was determined with $95\%$ CIs and bias-corrected bootstrapping ($$n = 10$$,000). All additional statistical significances were evaluated using SPSS-PC version 29.0 at a level of = 0.05. ( IBM Corporation, Armonk, NY, USA).
## 3. Results
Table 1 presents the descriptive statistics of the study participants by comorbidity. Those who had multimorbidity were likely to be older ($p \leq 0.001$), heavier ($p \leq 0.001$), live alone ($p \leq 0.001$), be smokers ($p \leq 0.001$), drink more frequently ($p \leq 0.006$), be less physically active ($p \leq 0.001$), have weaker LBMS ($p \leq 0.001$), have a lower cognitive function ($p \leq 0.001$), and have a higher rate of cognitive impairment ($p \leq 0.001$).
Table 2 presents the beta coefficients of linear regression for cognitive function. Cognitive function was inversely associated with age ($p \leq 0.001$), females ($p \leq 0.001$), smoking ($p \leq 0.001$), and multimorbidity ($p \leq 0.001$) and positively with married with a spouse ($p \leq 0.001$), BMI ($p \leq 0.001$), education ($p \leq 0.001$), alcohol intake ($p \leq 0.001$), PA ($p \leq 0.001$), and LBMS ($p \leq 0.001$). Although the positive correlation coefficient of alcohol consumption is surprising, it may reflect a gender difference in the association between alcohol consumption and cognitive function because the correlation coefficient for men (β = 0.088, $$p \leq 0.067$$) is not statistically significant whereas the correlation coefficient (β = 0.403, $p \leq 0.001$) for women is. In addition, the cognitive benefit of women might be associated with moderate alcohol consumption but not with heavy alcohol consumption [23]. In addition, the overall prevalence of chronic diseases was in the order of hypertension ($56.8\%$), diabetes ($24.2\%$), dyslipidemia ($17.1\%$), rheumatic arthritis ($16.5\%$), osteoarthritis ($8.5\%$), heart diseases ($4.5\%$), angina/myocardial infarction ($4.4\%$), stroke ($4.3\%$), thyroid disease ($3.3\%$), and others.
Table 3 represents the estimated risks for cognitive impairment by PA and LBMS. Multimorbidity was correlated with a higher risk of cognitive impairment (OR = 1.321, $95\%$ CI = 1.167~1.496, $p \leq 0.001$), which remained statistically significant (OR = 1.415, $95\%$ CI = 1.154~1.736, $p \leq 0.001$) even after adjustments for age, gender, body mass index, marriage, education, smoking, and alcohol consumption. Insufficient PA was correlated with a higher risk of cognitive impairment (OR = 1.325, $95\%$ CI = 1.219~1.441, $p \leq 0.001$) compared to sufficient PA, which remained statistically significant (OR = 1.340, $95\%$ CI = 1.160~1.547, $p \leq 0.001$) even after adjustments for all the covariates. Weak LBMS was correlated with a higher risk of cognitive impairment (OR = 3.240, $95\%$ CI = 2.607~4.027, $p \leq 0.001$) compared to normal LBMS, which remained statistically significant (OR = 1.719, $95\%$ CI = 1.380~2.143, $p \leq 0.001$) even after adjustments for all the covariates.
Table 4 displays the relationship between comorbidities (X) and cognitive function (Y) moderated by PA (W) and LBMS (Z). PA moderates the impact of comorbidities on cognitive function (β = −0.3753, $95\%$ CI = −0.5165 to −0.2341), which remained statistically significant (β = −0.3833; $95\%$ CI = −0.4743 to −0.2023) even after adjusting for all the covariates. As shown in Figure 2, individuals with insufficient PA had a substantially steeper slope for the link between comorbidities and cognitive function compared to individuals with sufficient PA. That is, individuals with insufficient PA were likely to experience a more severe influence of chronic diseases on cognitive decline as compared to people with sufficient PA. Likewise, LBMS moderates the impact of comorbidities on cognitive function (β = −0.2353, $95\%$ CI = 0.0774 to 0.3932). When all covariates were controlled for, the moderating effect of LBMS was no longer significant, despite remaining a significant predictor of cognitive function, implying that weak LBMS was an independent predictor of cognitive function regardless of comorbidities.
## 4. Discussion
This study examined the combined effects of PA and LBMS on the relationship between comorbidities and cognitive function in elderly Koreans. In this study population, cognitive function was inversely correlated with comorbidities and positively with PA and LBMS. It was especially interesting to note that having sufficient PA reduced the negative impact of comorbidities on cognitive function. However, muscle strength was found to be an independent predictor of cognitive function rather than a modulator in determining the impact of comorbidities on cognitive function.
In agreement with the current findings, previous studies reported inverse associations between comorbidities and cognitive function in community-dwelling older Korean adults [24], elderly Indian residents [25], and community-dwelling older Irish adults [26]. Furthermore, the negative impact of comorbidities on cognitive function has been observed in Canadian patients with dementia, mild cognitive impairment, and normal cognition [27]; European patients with type-2 diabetes [28]; Chinese patients with first-ever ischemic stroke [29]; Spanish patients with Parkinson’s disease [30]; and older Chinese patients with dementia [31]. Together, the findings from the present study and earlier ones concur that comorbid conditions have an adverse effect on cognitive function among older adults.
Nevertheless, comorbidities and their relationship with cognitive decline might be bi-directional [32]. By analyzing data obtained from community-dwelling adults in 2015–2017 through the Behavioral Risk Factor Surveillance System, the work of Taylor et al. [ 33], for example, showed that people with subjective cognitive decline were more likely to have a higher prevalence of chronic diseases, including coronary heart disease, stroke, diabetes, asthma, chronic obstructive pulmonary disease, cancer, arthritis, or kidney disease in comparison to people without subjective cognitive decline. In a similar vein, by conducting a secondary prevention study with 200 men aged 57.3±6.3 years, the work of Lutski et al. [ 34] showed that subjects with two or more chronic conditions had a greater probability of experiencing subjective cognitive impairment over a 20-year follow-up period. Taken together, it appears that comorbidities result in physical and functional limitations or changes in brain macrostructure and microstructure, which have a negative impact on cognitive function [35,36] or vice versa [37,38]. As a result, a prospective cohort study will be required to determine the precise direction of the relationship between comorbidities and cognitive function.
The cognitive benefit of PA observed in the current study has been reported in previous studies involving older Korean adults. For instance, Song and Park [39] examined changes in PA and its relationship with cognitive function using baseline and follow-up data from the KLoSA and showed that older adults who became or remained inactive had a higher risk of cognitive impairment compared to their counterparts who remained physically active. By analyzing the data obtained from 864 community-dwelling older adults from the Suwon Geriatric Mental Health Center, the work of Kim et al. [ 13] showed that PA positively contributed to cognitive function, perhaps via anti-depressant effects, in study subjects. Additionally, findings from the Longitudinal Ageing Study in India [14], French tri-city research [15], and the Mayo Clinic Study of Aging [40] also recognize the cognitive benefits of PA among community-dwelling older adults. Taken together, the findings from the current and previous studies support the cognitive benefits of PA later in life.
Additionally, muscular strength is just as important as PA in reducing the physical- and mental-health burden of chronic disorders, such as depression [41] and cognitive decline [36]. For example, by examining data taken from the KLoSA 2006–2015 and 2014–2018, respectively, Jeong and Kim [42] and Lee et al. [ 43] demonstrated that low handgrip strength was positively related to an elevated risk of cognitive impairment among older Korean adults. Similarly, Frith and Loprinzi [16], by evaluating data taken from the 1999–2002 National Health and Nutrition Examination Survey, examined the relationship between lower-extremity muscle strength and cognitive performance in a representative sample of older persons. That study shows that people with high muscle strength had better cognitive function than people with low muscle strength. Taken together, the findings from the current and previous studies strengthen our understanding of the cognitive benefits of muscular strength among older adults, regardless of comorbidities.
Although handgrip strength is more commonly tested in older adults, no sufficient evidence is available to support a definitive tool for measuring overall muscular strength and physical function [44]. Furthermore, other research suggests that lower-limb muscular strength is a better predictor of overall muscle strength or an alternative for people with hand disabilities [45]. The STST used in this study is yet another proxy for lower-extremity strength and muscular performance. A future study should look into the advantages and disadvantages of these surrogate measures of muscular strength. On the other hand, we believe that this is the first study to describe the moderating effect of sufficient PA in determining the association between comorbidities and cognitive function in elderly Asians, including Koreans. Therefore, the current findings of the study extend the cognitive benefits of PA reported in previous studies by demonstrating that regular PA may help older adults prevent and/or mitigate cognitive decline caused by chronic diseases.
The cognitive benefit of sufficient PA observed in the current study can be explained by several factors. First, having one or more chronic conditions may lead to an increased risk of depression, which may negatively contribute to cognitive function in older adults [46]. In contrast, sufficient PA has antidepressant effects that may attenuate the negative impact of comorbidities on cognitive function in older adults [47]. Second, the cognitive benefits of PA may be mediated via anti-inflammatory effects that may function to attenuate the impact of comorbidities on cognitive function [48]. Third, PA may function together to counteract cognitive decline associated with comorbidities via neuroplasticity, angiogenesis, neurogenesis, and mitochondrial biogenesis [49]. Lastly, PA is associated with better physical function [50], better health-associated quality of life [51], and emotional and physical well-being [52], which positively contribute to cognitive function among older adults.
The study has limitations. We are unable to propose a cause-and-effect explanation for the current findings due to the cross-sectional character of the study. Second, there may be a reciprocal association between comorbidities, PA, muscular strength, and cognitive performance, which remains to be confirmed in a subsequent study. Third, although the MMSE was used to assess cognitive function in this study, having additional reliable measures such as the Montreal Cognitive Assessment would be preferable in assessing various cognitive domains. Fourth, the assessment of PA using a self-reported questionnaire may either underestimate sedentary behaviors or overestimate moderate and high-intensity PAs among older adults [53]. Therefore, an objective measurement of PA using an accelerometer would be necessary for a future study.
## 5. Conclusions
This study examined the relationships between cognitive function, comorbidities, PA, and LBMS in older Korean adults. We found that PA is a modulator in determining the impact of comorbidities on cognitive function, and LBMS is an independent determinant of cognitive function, regardless of comorbidities. Therefore, the current findings of the study imply the clinical importance of having both sufficient PA and normal LBMS for a therapeutic strategy against cognitive decline associated with chronic diseases.
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|
---
title: Simple Novel Screening Tool for Obstructive Sleep Apnea in Inflammatory Bowel
Disease
authors:
- Alex Barnes
- Jane M Andrews
- Sutapa Mukherjee
- Robert V Bryant
- Peter Bampton
- Paul Spizzo
- Robert J Fraser
- Réme Mountifield
journal: Crohn's & Colitis 360
year: 2023
pmcid: PMC10045889
doi: 10.1093/crocol/otad016
license: CC BY 4.0
---
# Simple Novel Screening Tool for Obstructive Sleep Apnea in Inflammatory Bowel Disease
## Abstract
### Background
Inflammatory bowel disease (IBD) has been associated with an increased risk of obstructive sleep apnea (OSA). We aimed to examine the associations of obstructive sleep apnea, sleepiness, and IBD-related data and comorbidities, with the aim of developing a screening tool for sleep apnea in this population.
### Methods
An online survey of adults with IBD was administered which included measures of assessment of the risk of OSA, and measures of IBD activity, IBD-related disability, anxiety, and depression. Logistic regression was performed to investigate the associations between the risk of OSA and IBD data, medications, demographics, and mental health conditions. Further models were built for an outcome of severe daytime sleepiness and a combined outcome of risk of OSA and at least mild daytime sleepiness. A simple score was constructed for the purpose of screening for OSA.
### Results
There were 670 responses to the online questionnaire. The median age was 41 years, the majority had Crohn’s disease ($57\%$), the median disease duration was 11.9 years, and approximately half were on biologics ($50.5\%$). Moderate–high risk of OSA was demonstrated in $22.6\%$ of the cohort. A multivariate regression model for moderate–high risk of OSA included increasing age, obesity, smoking, and abdominal pain subscore. For a combined outcome of moderate–high risk of OSA and at least mild daytime sleepiness, a multivariate model included abdominal pain, age, smoking, obesity, and clinically significant depression. A simple score was constructed for screening for OSA utilizing age, obesity, IBD activity, and smoking status with an area under the receiver-operating curve of 0.77. A score >2 had a sensitivity of $89\%$ and a specificity of $56\%$ for moderate–high risk of OSA and could be utilized for screening for OSA in the IBD clinic.
### Conclusions
Over one-fifth of an IBD cohort met significantly high-risk criteria for OSA to warrant referral for a diagnostic sleep study. The risk of OSA was associated with abdominal pain, along with more traditional risk factors such as smoking, increasing age, and obesity. Consideration should be given for screening for OSA in IBD patients utilizing a novel screening tool that utilizes parameters typically available in IBD clinic.
## Introduction
Inflammatory bowel disease (IBD) is a chronic relapsing–remitting inflammatory condition that is increasing in frequency worldwide.1 The associated gastrointestinal symptoms may disrupt sleep leading to poor sleep quality. The prevalence of poor sleep in this population has been reported in a recent meta-analysis to be $56\%$.2 Poor sleep is more common in those with IBD than controls,3 more common in those with active IBD than inactive IBD,3,4 and more common in those with inactive IBD than controls.5 Mental health conditions such as anxiety and depression have been associated with poor sleep in an IBD population.6–13 Although IBD-associated upper airway obstruction is rare,14,15 obstructive sleep apnea (OSA) was shown to be more common in people with IBD in a study utilizing US-wide diagnostic coding data.16 This finding was supported by a previous study that utilized an online screening questionnaire in a UK population.17 OSA is associated with a variety of medical conditions including obesity, cardiovascular disease,18,19 Parkinson’s disease,20 and gastro-esophageal reflux disease.21,22 Relevant to IBD, OSA has been associated with an increase in circulating tumor necrosis factor-alpha (TNF-a) levels, with higher levels associated with more severe obstruction and hypoxia.23 Anti-TNF-a therapy has been associated with improved sleepiness in obese patients with OSA24 and a lower frequency of OSA spondyloarthropathy population.25 Active IBD is associated with elevated TNF-a levels and consequently may influence the course or development of OSA. Furthermore, obesity is prevalent in people with IBD26–28 and may somewhat explain the observed increased rates of sleep apnea. However, in a nationwide study in a US population, IBD remained associated with OSA after controlling for known risk factors such as obesity.16 This suggests that risk factors for OSA in an IBD population may be different from known traditional risk factors.
Our study aimed to examine the rates of OSA and sleepiness in an IBD population as well as examine factors associated with OSA and sleepiness. The study also aimed to construct a simple score for screening for OSA using typical IBD clinic parameters.
## Methods
Ethics approval for this study was obtained from the Southern Adelaide Human Research Ethics Committee (203.20). An online questionnaire was distributed to individuals with IBD through tertiary hospital IBD clinic patient email lists, a private gastroenterology practice patient email list, and through Crohn’s Colitis Australia, a charity organization, via email advertising and social media. Tertiary hospital IBD units and private gastroenterology groups routinely collect patient email addresses to allow communication between the IBD unit and the IBD cohort under care. Individuals with a self-reported diagnosis of IBD over 18 years of age were asked to participate. Demographic data such as age and sex were recorded, along with data on IBD which included disease duration, previous surgery, and current medications.
The OSA-50 is a validated screening tool for OSA in primary care.29,30 It contains 4 components with scores for waist circumference, age, snoring, and observed cessation of breathing during sleep. An OSA-50 score over 5 has been associated with moderate–severe OSA and is sufficient to justify direct referral for a sleep study in Australia (sensitivity $94\%$).29 The Epworth Sleepiness Scale (ESS) is a validated measure of daytime sleepiness.31 A score below 6 is considered below normal sleepiness, 6–10 is normal, 11–12 is mild sleepiness, 13–15 is moderate sleepiness, and a score over 15 is considered to represent severe daytime sleepiness. The ESS in combination with the OSA-50 has been shown to provide high specificity for OSA ($94\%$), however, with reduced sensitivity ($51\%$).32 IBD disease activity was assessed using the modified Harvey Bradshaw Index (HBI) in the case of Crohn’s disease with HBI > 5 considered an active disease,33 excluding the physical exam question. The Simple Clinical Colitis Activity Index (SCCAI) was utilized to assess disease activity in those with ulcerative colitis, with an SCCAI > 2 considered active disease.34 The abdominal pain subscore from HBI was utilized to form an abdominal pain dichotomous variable with an abdominal pain subscore of mild used as the cutoff value, with values above mild encoded as 1, and values mild or below encoded as a 0. The nocturnal diarrhea subscore from SCCAI was utilized to form a nocturnal diarrhea dichotomous variable with a nocturnal diarrhea subscore of >1 encoded as a 1, and scores 1 or less encoded as a 0.
Anxiety was assessed using the Generalized Anxiety Disorder 7-Item Scale (GAD-7)35 with a score over 10 used to indicate likely clinically significant anxiety. The Patient Health Questionnaire-9 (PHQ-9) was used to assess depression with a score of >15 used to indicate likely clinically significant depression.36 *Disability is* defined as any inability to perform an activity considered normal for a human.37 Disability was assessed using the IBD disability index self-report (IBD-DI-SR).38 The IBD-DI-SR is a validated self-reported measure of disability in an IBD population. It was developed as a self-report form and a short form of the IBD disability index.39 *Statistical analysis* was performed using Stata SE 16 (StataCorp). For normally distributed variables, mean and standard deviation were reported with comparisons made using the student t-test. For non-normally distributed variables, median and interquartile range (IQR) were reported, with comparisons made using the Mann–Whitney U-test. For categorical data, Pearson’s χ2 test was used, or Fisher’s exact test when appropriate. Univariate logistic regression was performed, and a multivariate logistic regression model was built for moderate–high risk of OSA (OSA-50 > 5) incorporating all variables from univariate analysis with $P \leq .10.$ The multivariate model was optimized by sequentially adding and removing variables to maximize the likelihood function. Separate models were constructed for an outcome of severe daytime sleepiness (ESS > 15) and for a combined outcome of moderate–high risk of OSA (OSA-50 > 5) and at least mild daytime sleepiness (ESS > 10). Variables common to the multivariate models and considered to be typically available in IBD clinic were then further analyzed to create a score for screening for OSA.
## Results
There were 670 responses to the online questionnaire. The median age was 41 years (32–70), with most being female ($78\%$). The majority had Crohn’s disease ($57\%$). The mean disease duration was 11.9 years (10.4), $30\%$ had undergone surgery for IBD, and around half were on biologics ($50.5\%$; see Table 1). Clinically significant depression (PHQ-9 > 15) was seen in $18\%$, and clinically significant anxiety (GAD-7 > 10) was seen in $29\%$. The mean IBD-DI-SR was −2.78 (6.01).
**Table 1.**
| Unnamed: 0 | Cohort | OSA-50 > 5 | OSA-50 ≤ 5 | P value |
| --- | --- | --- | --- | --- |
| Age, median IQR | 41 (32–70) | 49.9 (47.8–52.1) | 40.3 (39.1–41.5) | <.001 |
| Female gender, n (%) | 525 (78) | 73% | 78% | .266 |
| Crohn’s disease, n (%) | 384 (57) | 59% | 61% | .59 |
| Disease duration years, mean (SD) | 11.9 (10.4) | 13.8 (11.9–15.7) | 12.2 (11.2–13.1) | .095 |
| Previous surgery for IBD, n (%) | 201 (30.0) | 72% | 67% | .24 |
| Current steroid use, n (%) | 58 (8.6) | 11% | 9% | .30 |
| Current biologic use, n (%) | 339 (50.5) | 56% | 52% | .41 |
| Current immunomodulator use, n (%) | 228 (34.0) | 39% | 36% | .41 |
| Obesity, n (%) | 247 (36.9) | 66% | 26% | <.001 |
| Smoking, n (%) | 44 (6.6) | 12% | 5% | .004 |
| Alcohol usage, n (%) | 213 (31.8) | 36% | 33% | .13 |
| Opioid usage, n (%) | 93 (13.9) | 20% | 14% | .081 |
| Medications for sleep, n (%) | 85 (12.7) | 14% | 14% | .95 |
| Clinically significant depression, n (%) | 120 (17.9) | 31% | 23% | 0.069 |
| Clinically significant anxiety, n (%) | 193 (28.8) | 32% | 36% | .37 |
| Clinically active IBDSCCAI, mean (SD)HBI, mean (SD) | 7.18 (2.86)7.08 (3.29) | 7.90 (7.42–8.39)7.70 (7.13–8.28) | 6.95 (6.68–7.23)6.89 (6.58–7.20) | .0010.013 |
| IBD-DI-SR, mean (SD) | −2.78 (6.01) | −4.91 (−5.56 to −3.94) | −3.04 (−3.65 to −2.51) | .0008 |
The median OSA-50 score was 3, with $22.6\%$ having an OSA-50 score over 5 (see Table 1). Those who had an OSA-50 score over 5 were older ($P \leq .001$), smokers ($$P \leq .004$$), obese ($P \leq .001$), and had higher IBD activity scores ($$P \leq .001$$, and $$P \leq .013$$). Worse disability scores were seen in those with an OSA-50 score over 5 ($$P \leq .0008$$).
Univariate and multivariate logistic regressions for an outcome of moderate–high risk of OSA were performed including demographics, IBD data, IBD medications, and IBD clinical activity (see Table 2). Univariate regression was significant for increasing age, obesity, clinically significant depression, abdominal pain subscore, nocturnal diarrhea subscore, and clinically active IBD. A multivariate model was optimized with the final model including increasing age, obesity, smoking, and abdominal pain subscore (see Table 2). Male gender was not significant. Male gender was associated with lower rates of obesity ($$P \leq .002$$), lower rates of clinically significant depression ($$P \leq .004$$), and lower rates of clinically active IBD ($P \leq .001$) but was also associated with higher rates of smoking ($$P \leq .049$$), and males were on average older (45.8 years [43.3–48.4] vs 41.8 [40.7–42.9], $$P \leq .0013$$).
**Table 2.**
| Unnamed: 0 | Univariate regression (odds ratio, confidence interval, P value) | Multivariate regression (odds ratio, confidence interval, P value) |
| --- | --- | --- |
| Age | 1.05 (1.04–1.07), P < .001 | 1.06 (1.04–1.08), P < .001 |
| Male gender | 1.28 (0.83–1.99), P = .27 | |
| Crohn’s disease | 0.90 (0.61–-1.33), P = .60 | |
| Disease duration | 1.01 (0.99–1.03), P = .096 | |
| Previous surgery for IBD | 1.28 (0.84–1.96), P = .24 | |
| Current steroid use | 1.38 (0.75–2.54), P = .302 | |
| Current biologic use | 1.17 (0.80–1.72), P = .41 | |
| Current immunomodulator use | 1.18 (0.79–1.74), P = .42 | |
| Obesity | 5.39 (3.58–8.10), P < .001 | 6.42 (4.08–10.10), P < .001 |
| Smoking | 2.52 (1.31–4.81), P = .005 | 2.60 (1.26–5.39), P = .010 |
| Alcohol usage | 1.16 (0.78–1.73), P = .45 | |
| Opioid usage | 1.55 (0.94–2.55), P = .082 | |
| Medications for sleep | 0.98 (0.57–1.70), P = .95 | |
| Clinically significant depression | 1.76 (1.13–2.75), P = .013 | |
| Clinically significant anxiety | 0.92 (0.61–0.139), P = .69 | |
| Abdominal pain subscore | 1.33 (1.09–1.62), P = .004 | 1.32 (1.05–1.65), P = .017 |
| Nocturnal diarrhea subscore | 2.11 (1.11–3.99), P = .022 | |
| Clinically active IBD | 1.73 (1.01–2.93), P = .044 | |
The mean ESS score was 7.9 (4.75), with $13.4\%$ describing severe daytime sleepiness. Severe daytime sleepiness was associated with worse disability scores ($$P \leq .0001$$). Univariate and multivariate logistic regressions were performed including demographics, IBD data, IBD medications, and IBD clinical activity (see Table 3). Univariate regression was significant for smoking, clinically significant depression, clinically significant anxiety, and abdominal pain subscore. Multivariate regression was significant for clinically significant depression, clinically significant anxiety, and abdominal pain subscore (see Table 3).
**Table 3.**
| Unnamed: 0 | Univariate regression (odds ratio, confidence interval, P value) | Multivariate regression (odds ratio, confidence interval, P value) |
| --- | --- | --- |
| Age | 0.99 (0.97–1.01), P = .23 | |
| Male gender | 0.87 (0.49–1.54), P = .64 | |
| Crohn’s disease | 1.03 (0.64–1.67), P = .88 | |
| Disease duration | 0.99 (0.97–1.01), P = .46 | |
| Previous surgery for IBD | 0.95 (0.58–1.56,) P = .84 | |
| Current steroid use | 1.66 (0.82–3.36), P = .16 | |
| Current biologic use | 0.92 (0.58–1.47), P = .75 | |
| Current immunomodulator use | 0.96 (0.59–1.56), P = .87 | |
| Obesity | 1.41 (0.88–2.26), P = .15 | |
| Smoking | 2.48 (1.20–5.16,) P = .015 | |
| Alcohol usage | 0.88 (0.54–1.45), P = .63 | |
| Opioid usage | 1.17 (0.63–2.18), P = .62 | |
| Medications for sleep | 0.84 (0.41–1.70), P = .63 | |
| Clinically significant depression | 3.94 (2.38–6.53), P < .001 | 2.65 (1.46–4.80), P = .001 |
| Clinically significant anxiety | 2.99 (1.86–4,84), P < .001 | 1.66 (0.94–2.96), P = .082 |
| Abdominal pain subscore | 1.42 (1.13–1.78), P = .002 | 1.33 (1.04–1.69), P = .021 |
| Nocturnal diarrhea subscore | 1.66 (0.82–3.36), P = .16 | |
| Clinically active IBD | 1.69 (0.87–3.30), P = .12 | |
A combined outcome of moderate–high risk of OSA (OSA > 5) and at least mild daytime sleepiness (ESS > 11) was considered. Univariate regression was significant for age, current corticosteroid use, obesity, current smoking, clinically significant depression, abdominal pain, and clinically active IBD (see Table 4). Multivariate regression was significant for age, obesity, smoking, clinically significant depression, and abdominal pain (see Table 4).
**Table 4.**
| Unnamed: 0 | Univariate regression (odds ratio, confidence interval, P value) | Multivariate regression (odds ratio, confidence interval, P value) |
| --- | --- | --- |
| Age | 1.03 (1.01–1.05), P = .001 | 1.03(1.01–1.06), P = .002 |
| Male gender | 1.25 (0.68–2.29), P = .47 | |
| Crohn’s disease | 0.86 (0.50–1.47), P = .59 | |
| Disease duration | 0.99 (0.97–1.02), P = .60 | |
| Previous surgery for IBD | 1.44 (0.78–2.64), P = .24 | |
| Current steroid use | 2.12 (1.01–4.45), P = .046 | |
| Current biologic use | 1.12 (0.70–2.04), P = .50 | |
| Current immunomodulator use | 1.47 (0.86–2.51), P = .16 | |
| Obesity | 5.29 (2.96–9.43), P < .001 | 5.25 (2.86-9.64), P < .001 |
| Smoking | 3.21 (1.49–6.91), P = .003 | 3.33 (1.44–7.67), P = .005 |
| Alcohol usage | 0.82 (0.46–1.47), P = .52 | |
| Opioid usage | 1.46 (0.74–2.86), P = .27 | |
| Medications for sleep | 1.26 (0.61–2.58), P = .53 | |
| Clinically significant depression | 2.14 (1.24–3.70), P = .006 | 1.91 (1.04–3.48), P = .036 |
| Clinically significant anxiety | 1.55 (0.90–2.63), P = .11 | |
| Abdominal pain subscore | 1.44 (1.12–1.84), P = .004 | 1.37 (1.07–1.76), P = .011 |
| Nocturnal diarrhea subscore | 2.26 (0.96–5.33), P = .061 | |
| Clinically active IBD | 3.89 (1.38–10.96), P = .010 | |
A simple score was constructed (see Table 5) for the risk of OSA utilizing variables typically available in IBD clinic. The area under the receiver-operating curve for moderate–high risk of OSA (OSA-50 > 5) was 0.77 (0.73–0.81), with Youden’s index of 1.46. A score of >2 had a sensitivity of $89\%$ and a specificity of $56\%$. The area under the receiver-operating curve for moderate–high risk of OSA (OSA-50 > 5) and at mild daytime sleepiness (ESS > 10) was 0.77 (0.71–0.82), with Youden’s index of 1.41. A score of >2 had a sensitivity of $89\%$ and a specificity of $56\%$ for moderate–high risk of OSA.
**Table 5.**
| Variables | Score, if present |
| --- | --- |
| Obesity | 2 |
| Currently smoking | 1 |
| Age over 45 | 1 |
| Clinically active IBD | 1 |
## Discussion
In a large IBD cohort, the rate of and novel associations with OSA have been described. Over one-fifth of the cohort met the criteria for high risk of OSA and could on this basis be referred for diagnostic polysomnography. The risk of OSA was associated with increased age, obesity, smoking, and abdominal pain in a cohort of people with IBD. We also explored sleepiness, a common symptom of sleep apnea, with this associated in our cohort with mental health conditions and abdominal pain. We also proposed a novel simple score incorporating parameters typically used in IBD clinic to identify those at risk of OSA. The score incorporated parameters typically available in IBD clinic and could consequently be readily applied.
Well-established risk factors for OSA, including older age,40–42 smoking,43 and obesity44, were apparent in our data; however, no association with male gender,40,45,46 as previously well described with OSA, was seen. This may be a result of the female predominance of the cohort ($78\%$ female), noting that a previous study did note an association between male gender and OSA in an IBD cohort.16 We also note the perhaps confounding relationship between gender and other risk factors for OSA in our data set such as IBD activity and depression.
Abdominal pain was associated with OSA and sleepiness. Poor sleep has been associated with an increased perception of pain which may partly explain these results.47,48 Furthermore, OSA has been associated with irritable bowel syndrome49—known to be common in those with IBD.50–52 In addition, OSA-related nocturnal hypoxia may contribute to localized intestinal ischemia that has been previously postulated to play a role in the pathogenesis of IBD.53 OSA has been associated with cardiovascular disease54 and, in particular, stroke,55–57 which has been attributed to systemic inflammation and as well apnea-induced nocturnal ischemia.58–60 Identification of those with OSA will allow screening for associated cardiovascular complications and commencement of treatment such as continuous positive airway pressure (CPAP).61 Treatment of those with OSA is associated with improved daytime sleepiness, along with improved quality of life.62 Treatment of OSA has been shown to reduce blood pressure in those with hypertension63 and additionally, long-term observational data also suggest a reduction in ischemic heart disease and fatal cardiac events with the usage of CPAP,64 although no overall mortality benefit has been demonstrated and randomized controlled trial results have been mixed.65 IBD has been associated with increased risk of cardiovascular disease66,67 and consequently, consideration should be given to identifying and treating those with OSA to reduce cardiovascular risk. The OSA-50 is commonly used to screen for OSA in Australia; however, this incorporates parameters typically not available in IBD clinic such as apneic events, waist circumference, and snoring. We proposed a simple score incorporating parameters available in IBD clinic which could be used to screen for OSA and is consequently perhaps much more attractive to gastroenterologists. Further validation of this score in other IBD cohorts is required.
Limitations of this study include selection bias due to the use of an online questionnaire and the predominantly female cohort. The rates of anxiety and depression seen in our cohort were similar to that described elsewhere.68 Our cohort likely represents a moderate–severe IBD cohort with biologic usage in around half and previous surgery in almost a third. Given that OSA is more common in males, the prevalence of moderate–high risk of OSA seen here is likely lower than in the broader IBD population.69 Reporting bias may also be significant, noting a study of people with Crohn’s disease reported worse sleep quality than that observed by objective measures.70 The absence of objective measures of sleep quality and objective IBD activity is also considered a limitation. Further studies should consider objective measures of IBD activity and sleep quality.
## Conclusions
Over one-fifth of an IBD cohort met high-risk criteria for OSA to warrant referral for a diagnostic sleep study. Risk of OSA was associated with abdominal pain, along with more traditional risk factors such as smoking, increasing age, and obesity. Those at risk of OSA have worse disability scores. A simple score using typical IBD clinic data could be used to screen for OSA.
## Author Contributions
A.B.: Responsible for study concept and design, data acquisition, analysis and data interpretation, drafting of manuscript, critical revision of the manuscript. S.M.: Responsible for study concept, critical revision of the manuscript. J.A.: Responsible for study concept, responsible for critical revision of the manuscript. R.V.B.: Responsible for critical revision of the manuscript. P.B.: Responsible for critical revision of the manuscript. P.S.: Responsible for critical revision of the manuscript. R.J.F.: Responsible for critical revision of the manuscript. R.M.: Responsible for study concept and design, responsible for critical revision of the manuscript.
## Funding
None declared.
## Conflict of Interest
It include speakers fees and Ad Boards from Abbott, AbbVie, Allergan, Anatara, AstraZeneca, Bayer, BMS 2020, Celegene, Celltrion, Falk, Ferring, Gilead, Hospira, Immuninc, ImmunsanT, Janssen, MSD, Nestle, Novartis, Progenity, Pfizer, Sandoz, Shire, Takeda, Vifor, RAH research Fund, The Hospital Research Fund 2020-2022, The Helmsley Trust 2020-2023.
## Data Availability
The data underlying this article are available upon request to the author.
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---
title: Severity of Fatigue and Its Relationship with TSH before and after Levothyroxine
Replacement Therapy in Patients with Primary Hypothyroidism
authors:
- María Guadalupe Ruíz-Pacheco
- Irma Hernández
- Guadalupe Hernández-Estrella
- Lourdes Basurto
- Guadalupe Vargas-Ortega
- Baldomero González-Virla
- Mario Molina-Ayala
- Alex Francisco Hernández-Martínez
- Rosamaría Luengas-Mondragón
- Angel Alejandro Hernández-Allende
- Victoria Mendoza-Zubieta
- Lourdes Balcázar-Hernández
journal: Biomedicines
year: 2023
pmcid: PMC10045891
doi: 10.3390/biomedicines11030811
license: CC BY 4.0
---
# Severity of Fatigue and Its Relationship with TSH before and after Levothyroxine Replacement Therapy in Patients with Primary Hypothyroidism
## Abstract
Background: *Fatigue is* a common symptom in hypothyroidism; however, the effect of levothyroxine on fatigue has been little studied. The aim of this study was to evaluate the effect of levothyroxine on fatigue in Latino patients with primary hypothyroidism, as well as the association of TSH and free T4 (FT4) with the severity and persistence of fatigue. Methods: A prospective study was performed in 92 patients with primary hypothyroidism. Fatigue severity scale (FSS) scores and clinical and biochemical characteristics before and at 6 months of levothyroxine were evaluated. Results: After 6 months of levothyroxine, a reduction in FSS (53 (47–57) vs. 36 (16–38); $$p \leq 0.001$$) and fatigue frequency ($45.7\%$ vs. $26.1\%$; $$p \leq 0.008$$) was evident. Both before and after 6 months of levothyroxine, there was a positive correlation of the FSS score with TSH and a negative correlation with FT4. Persistent fatigue was associated with a pretreatment FSS score ($r = 0.75$; $$p \leq 0.001$$) and diabetes ($r = 0.40$; $$p \leq 0.001$$). An FSS > 34 (RR 3.9 ($95\%$ CI 1.43–10.73; $$p \leq 0.008$$)), an FSS > 36 (RR 3.23 ($95\%$ CI 1.21–8.6; $$p \leq 0.019$$)), and diabetes (RR 5.7 ($95\%$ CI 1.25–9.6; $$p \leq 0.024$$)) before treatment were risk factors for persistent fatigue. Conclusions: Levothyroxine improved fatigue in most patients. Diabetes and an FSS score >34 or >36 before treatment were risk factors for persistent fatigue.
## 1. Introduction
Fatigue is defined as “a sense of physical tiredness and lack of energy, distinct from sadness or weakness” [1]. The most recent definitions refer to fatigue as a suboptimal psychophysiological condition caused by exertion; this state causes changes that may reduce mental processing or physical activity [2]. Fatigue is related with alterations in muscle activity, proprioception, and cognitive function [3], and has been associated with chronic diseases such as hypothyroidism [4]. It has been reported that up to one-third of patients presenting with fatigue have thyroid disease, predominantly in women [5]. In addition, the frequency of fatigue is higher among patients with autoimmune thyroid disease compared to patients with post-thyroidectomy hypothyroidism due to differentiated thyroid cancer [6].
A previous study demonstrated that hormone replacement with levothyroxine is associated with improvement of fatigue in most patients with hypothyroidism, mainly in those with post-ablation hypothyroidism, proposing a possible relationship between thyrotropin (TSH) levels and fatigue [7]. Despite these data, there is no information available about the effect of levothyroxine on fatigue in the Latino population with primary hypothyroidism. The aim of our study was to evaluate the effect of levothyroxine replacement therapy on fatigue in Latino patients with primary hypothyroidism (autoimmune and unspecified hypothyroidism), the association of TSH and free T4 (FT4) with fatigue severity scale (FSS) score and the score related to the persistence of fatigue despite levothyroxine replacement therapy.
## 2. Materials and Methods
A prospective, longitudinal, clinical study in Latino patients with primary hypothyroidism due to autoimmune and unspecified etiology was conducted in a tertiary healthcare center. Patients with other causes of hypothyroidism such as iodine deficiency, post-ablative, post-thyroidectomy, and central hypothyroidism were excluded to avoid the effect of other diseases related to acquired hypothyroidism that could cause or worsen fatigue. Non-probabilistic sampling was performed, without gender preference.
Demographics and clinical characteristics, including the comorbidities (diabetes, dislipidemia, obesity, hypertension, rheumatologic/autoimmune diseases), were evaluated. All patients had adequate control of comorbidities. Clinical, fatigue severity, and biochemical characteristics of patients were evaluated both before and at 6 months after levothyroxine replacement therapy.
Levothyroxine replacement therapy: Patients were treated with L-thyroxine monotherapy at an initial dose of 1.6 μg/kg according to international recommendations. Dose adjustments were guided by serum TSH determinations between 4 and 8 weeks after initiation of therapy, with dose adjustments until normalization of the thyroid profile (TSH and FT4). According to the recommendations, patients with subclinical hypothyroidism were treated when TSH values were higher than 10 mIU/L, when elevated TSH levels were found accompanied by symptoms and positive antithyroid antibodies, as well as in women seeking pregnancy [8,9]. No patient was pregnant during the study. All patients included had a normal thyroid profile at 6 months after the beginning of levothyroxine replacement therapy.
Fatigue severity evaluation: The fatigue severity scale (FSS) was used to evaluate fatigue both before and at 6 months after the beginning of levothyroxine replacement therapy. The FSS is one of the most commonly used fatigue questionnaires in chronic diseases [10] and is validated in our population [11]. The FSS consists of nine items that measure how fatigue affects motivation, exercise, physical functioning, carrying out duties, work, family, or social life. The scores are averaged across the nine statements and a score ≥36 indicates a clinically significant trait level of fatigue [10,11]. The delta of FSS (ΔFSS) was the difference between the FSS after treatment and the FSS before treatment.
Biochemical and Hormonal Measurements: The thyroid profile included the measurement of TSH and FT4, and was evaluated before and after 6 months of levothyroxine replacement therapy. The electrochemiluminescence immunoassay “ECLIA” (Roche Diagnostics, Roche®, Indianapolis, IN, USA) was used to measurement TSH in serum, with a sensitivity of 0.014 μIU/mL, inter- and intra-assay coefficients of variation (CV) <$10\%$, respectively, and a normal reference range of 0.270 to 4.20 μIU/mL. FT4 was measured by an electrochemiluminescence immunoassay “ECLIA” (Roche Diagnostics, Roche®), with a sensitivity of 0.02 ng/dL, inter- and intra-assay coefficients of variation (CV) ≤$30\%$, respectively, and a normal reference range of 0.93 to 1.7 ng/dL. Thyroid peroxidase antibodies (TPOAb) (normal reference range <34 IU/mL) and thyroglobulin antibodies (TgAb) (normal reference range <1.8 IU/mL) were assessed by enzyme-linked immunosorbent assay.
Statistical Analysis: All quantitative variables were tested using non-parametric tests and described as medians with interquartile range (IQR). Proportions (expected frequency, prevalence) were used for the qualitative variables. For quantitative variables, the Wilcoxon test was used to compare two paired samples, and the Mann–Whitney U test was used to compare differences between two independent groups. For qualitative variables, we used the Fisher test. An ROC curve was designed to predict the best pretreatment FSS score cut-off point for predicting persistent fatigue. Spearman’s rank correlation coefficient was used to calculate the correlation between the variables. A multivariate, stepwise logistic regression analysis was carried out to explore which variables were associated with persistent fatigue. A $p \leq 0.05$ was considered as statistically significant. Statistical software consisted of SPSS (v.24, IBM®, Armonk, NY, USA) and STATA (v.16.1., StataCorp®, College Station, TX, USA).
Ethics: The study protocol was approved by our local ethics and scientific committees and all patients signed the appropriate informed consent.
## 3.1. Baseline Characteristics of the Population
A total of 92 patients with primary hypothyroidism were included; $94.6\%$ ($$n = 87$$) were women. The median age was 32 (IQR 31–41) years. The baseline characteristics of patients are included in Table 1.
## 3.2. Fatigue Severity Scale before and at 6 Months of Levothyroxine Replacement Therapy
After 6 months of levothyroxine replacement therapy, there was a reduction in TSH levels (52.4 (25–100) vs. 2.3 (1.7–3.5) mIU/L; $$p \leq 0.002$$), an increase in FT4 (0.38 (0.14–1.12) vs. 1.04 (0.99–1.15) pg/mL; $$p \leq 0.007$$), and a reduction in the score given to each of the FSS items and in the total FSS score (Table 2) compared to their pretreatment characteristics. The median dose of levothyroxine was 100 mg per day (IQR 25–150). All patients achieved the normalization of the thyroid profile.
According to the FSS score, a decrease in the frequency of fatigue was evident after 6 months of levothyroxine replacement therapy compared to its frequency before treatment ($45.7\%$ ($$n = 42$$) vs. $26.1\%$ ($$n = 24$$); $$p \leq 0.008$$). The ΔFSS was 17 (31–19). There was no association between the ΔFSS and the levothyroxine dose ($r = 0.14$; $$p \leq 0.25$$)
## 3.3. TSH and Free T4 According to the FSS Score after Levothyroxine Replacement Therapy
After 6 months of levothyroxine replacement therapy, patients with persistent fatigue, according to an FSS score greater than 36, had a higher pretreatment FSS score (48 (IQR 33–50) vs. 27 (IQR 16–48); $$p \leq 0.003$$) and higher pretreatment TSH (31 (IQR 6.8–31.9) vs. 8 (IQR 1.3–8.7) mIU/L; $$p \leq 0.001$$) compared to those with an FSS score less than 36. There were no differences in FT4 ($$p \leq 0.80$$).
## 3.4. FSS Score According to the Etiology and Presentation of Primary Hypothyroidism
According to the presentation of primary hypothyroidism, there were differences in pretreatment TSH and FT4, as well as in the doses of levothyroxine required for normalization of the thyroid profile in patients with overt hypothyroidism compared to subclinical hypothyroidism (Table 3). There was no difference in the FSS score both before and at 6 months of levothyroxine replacement therapy.
## 3.5. FSS Score between Autoimmune Hypothyroidism and Unspecified Hypothyroidism
Patients with autoimmune hypothyroidism had lower FT4 before levothyroxine and higher TPOAb and TgAb compared with patients with unspecified hypothyroidism. There was no difference in TSH and the FSS score before and after levothyroxine replacement therapy (Table 4). There was no association between the FSS score and the presence of autoimmune disease ($r = 0.15$; $$p \leq 0.84$$), TPOAb ($r = 0.34$; $$p \leq 0.43$$), or TgAb ($r = 0.29$; $$p \leq 0.37$$).
In a subanalysis of patients with autoimmune hypothyroidism ($$n = 51$$), normalization of TSH and FT4, as well as improvement of the FSS score after treatment was evidenced (Table 5). There were no differences in TPOAb and TgAb. Before levothyroxine, the FSS score was negatively associated with FT4 (r = −0.92; $$p \leq 0.001$$) and positively associated with TSH ($r = 0.30$; $$p \leq 0.03$$). After 6 months of levothyroxine, the FSS score was associated with TSH ($r = 0.67$; $$p \leq 0.001$$). There was no association between the FSS score and TPOAb or TgAb either before or after levothyroxine.
## 3.6. Association between FSS, TSH and FT4 before and at 6 Months of Levothyroxine Replacement Therapy
Prior to administration of levothyroxine, a positive correlation of the FSS score with TSH ($r = 0.45$; $$p \leq 0.001$$; linear regression β = 0.25: $$p \leq 0.036$$) and a negative correlation with FT4 (r = −0.39; $$p \leq 0.001$$; linear regression β = −0.080: $$p \leq 0.05$$) were evident.
After 6 months of levothyroxine replacement therapy, the positive correlation of the FSS score with TSH ($r = 0.34$; $$p \leq 0.001$$; linear regression β = 0.34: $$p \leq 0.021$$) and the negative correlation with FT4 (r = −0.35; $$p \leq 0.04$$; linear regression β = −0.15: $$p \leq 0.19$$) were constant.
The persistence of fatigue was positively associated with TSH before treatment ($r = 0.41$; $$p \leq 0.04$$), the FSS score before treatment ($r = 0.75$; $$p \leq 0.001$$), the FSS score after treatment ($r = 0.30$; $$p \leq 0.003$$), diabetes ($r = 0.40$; $$p \leq 0.001$$), hypertension ($r = 0.24$; $$p \leq 0.02$$), and fibromyalgia ($r = 0.36$; $$p \leq 0.001$$).
## 3.7. Risk Factors for the Persistence of Fatigue at 6 Months of Levothyroxine Replacement Therapy
An FSS > 34 before levothyroxine (AUC 0.72, $95\%$CI 0.58–0.81) had a sensitivity of $70\%$ and a specificity of $62\%$ to predict fatigue at 6 months of levothyroxine.
A multiple regression model adjusted by the comorbidities (diabetes, hypertension, obesity, rheumatoid arthritis, and fibromyalgia), the FSS score cut-off of >34 and the traditional cut-off of >36 before levothyroxine showed that diabetes (RR 5.7 ($95\%$CI 1.25–9.6; $$p \leq 0.024$$)), the FSS > 34 pre-treatment (RR 3.9 ($95\%$CI 1.43–10.73; $$p \leq 0.008$$)), as well as the FSS score cut-off of >36 (RR 3.23 ($95\%$CI 1.21–8.6; $$p \leq 0.019$$)), were risk factors for persistent fatigue at 6 months of levothyroxine replacement therapy (Table 6).
## 4. Discussion
This study demonstrates that levothyroxine replacement therapy improved fatigue (according to the FSS score) in most patients with primary hypothyroidism, and this score was positively associated with TSH and negatively associated with FT4 both before and after treatment. In addition, diabetes and an FSS score >34 before, as was the traditional cut-off point of an FSS score >36 before levothyroxine, were risk factors for persistent fatigue at 6 months of levothyroxine replacement therapy.
There is a wide spectrum of symptoms associated with hypothyroidism, and some may be nonspecific. Fatigue is one of the most common symptoms, regardless of the presentation of hypothyroidism (subclinical or overt hypothyroidism) [12]. Fatigue is a non-specific symptom; therefore, it is convenient to evaluate it objectively through different instruments, such as the FSS. In the context of fatigue and hypothyroidism, the associations with the etiology or type of presentation of hypothyroidism have been proposed.
Regarding disease etiology, one study demonstrated that fatigue and fatigue-related symptoms in hypothyroidism were more pronounced in patients with autoimmune diseases compared to patients with differentiated thyroid carcinoma, which was reflected in significantly higher scores on the MFI-20 questionnaire; however, these results could not be associated with thyroid hormone parameters [6]. Autoimmunity has been shown to have an impact on the quality of life of patients with autoimmune hypothyroidism, especially in terms of psychological symptoms; however, its direct association with fatigue has been unclear [13]. Despite these data, in our study, no differences in the FSS score were observed when comparing patients with autoimmune hypothyroidism versus unspecified hypothyroidism; similarly, no association was found between the fatigue severity scale score and the presence of autoimmune disease or TPOAb and TgAb levels. Specifically in our population of patients with autoimmune hypothyroidism, we only found association of the FSS score with TSH and FT4, with no association with TPOAb or TgAb. On the other hand, there was no difference in the clinical parameters or the FSS score when comparing patients with subclinical hypothyroidism and overt hypothyroidism, both before and after levothyroxine replacement therapy. Despite these findings, the greater decrease in the FSS score in patients with overt hypothyroidism (15 points) compared to those with subclinical hypothyroidism (7 points) is striking. This result, although not statistically significant, may be relevant in clinical practice, highlighting the importance of levothyroxine treatment in patients with subclinical hypothyroidism for the improvement of fatigue.
One of the expectations of both the physician and the patient when initiating treatment with levothyroxine is the improvement of symptoms related to hypothyroidism; however, the results of this goal are heterogeneous according to clinical studies. In a previous study, Watt, et al. used the ThyPRO questionnaire to measure a number of aspects of quality of life relevant to patients with benign thyroid disease. According to this instrument, there was no significant change in hypothyroid symptoms after 6 months of levothyroxine replacement therapy, including those related to fatigue [13]. One study evidenced that TSH level reduction was associated with fatigue relief; however, it was unclear whether fatigue relief was associated with the magnitude of TSH reduction or with absolute TSH levels after levothyroxine replacement therapy [7]. In patients with subclinical hypothyroidism, it has been reported that levothyroxine did not improve fatigue-related symptoms [14], and physical or mental fatigability [15] compared to the placebo, even a meta-analysis corroborated that levothyroxine was not associated with improvements in overall quality of life or hypothyroidism-related symptoms in patients with subclinical hypothyroidism [16]. In our study, we evidenced improvement in the FSS score after levothyroxine replacement therapy in most patients. Additionally, we found that absolute TSH and FT4 levels were associated with the FSS score, corroborating a relationship between the magnitude of fatigue, hypothyroxinemia, and hyperthyrotropinemia.
Our study showed that hypothyroidism could be one of multiple factors contributing to fatigue in these patients, and levothyroxine replacement therapy could improve fatigue in most of them. However, about a quarter persisted with fatigue despite normalization of the thyroid profile, implying that other contributing factors should be sought, with levothyroxine treatment being only one point of intervention among a sea of possibilities. According to comorbidities, we found that diabetes is a risk factor for persistent fatigue after levothyroxine replacement therapy, even though these patients had adequate glycemic control. Fatigue is a common symptom in diabetes, usually related to hyperglycemia. The prevalence of fatigue in people living with diabetes remains unclear due to the lack of a cut-off point for the instrument, and factors associated with fatigue such as BMI and glycosylated hemoglobin have been proposed, but the data are still inconclusive [17,18]. This demonstrates the importance of a comprehensive approach and treatment of both hypothyroidism and comorbidities.
It has been reported that 10–$15\%$ of patients persist with hypothyroidism-related symptoms despite treatment, mainly with impaired quality of life and mood disorders [19,20,21]. Studies focused on fatigue have been scarce so far; therefore, it is difficult to contrast the proportion of persistent fatigue found in our study. However, if we compared it with the reported proportion of persistence of all hypothyroidism-related symptoms, the one found in this study was higher (26.1 vs. 10–$15\%$), which may be related to a considerable proportion of patients with comorbidities in our population that may perpetuate fatigue, mainly diabetes, which was a risk factor for persistent fatigue. The persistence of symptoms related to hypothyroidism may be related to inadequate levothyroxine replacement with the resulting persistence of elevated TSH and/or hypothyroxinemia [22]; however, the persistence of symptoms can also occur in the context of normalization of the thyroid profile, as in our study. In this study, we did not find an association between the ΔFSS and the levothyroxine dose. Some cross-sectional studies have reported lower quality of life in patients with hypothyroidism and normal thyrotropin levels [22,23,24,25,26], including the reduction of vitality [26].
Some potential causes of residual symptoms in patients with normal thyrotropin values have been proposed, such as the presence of symptoms caused by accompanying conditions or comorbidities, incorrect attribution and unrealistic patient expectations, awareness of chronic condition, tissue hypothyroidism (low T3 levels at the tissue or cellular level), and alterations in the conversion of T4 to T3 or in the entry of thyroid hormone into cells [19]. There is no consensus about the best treatment approach for those patients who do not respond to standard treatment with levothyroxine alone. According to the recommendations, patients with hypothyroidism should be treated with levothyroxine monotherapy [8,9]; however, the addition of liothyronine has been proposed as a beneficial and safe therapy in the management of hypothyroidism symptoms, mainly in quality of life [20,21,27], weight management, fatigue, mood, and memory [28]. The development of clinical trials evaluating the effect of this intervention on persistent fatigue in different populations would be encouraging in the management of patients with hypothyroidism. Other strategies such as aromatherapy [29]; dietary intervention of green vegetables, beef, whole milk, and butter [30]; and yoga [31] have been associated with a reduction in fatigue in patients with hypothyroidism.
Among the strengths of the study are the exploration of a little-known issue in the Latino population with hypothyroidism, the use of a standardized scale that evaluates the presence and intensity of fatigue, the evaluation of the differences of this score according to the etiology, and presentation of hypothyroidism, as well as the evaluation of the association of the FSS score with biochemical parameters. Limitations of the study include a relatively small sample size due to the inclusion of patients only with primary autoimmune hypothyroidism or of unspecified cause, the lack of a control group, a short follow-up, as well as the absence of intervention with other strategies such as liothyronine in those with persistent fatigue. Another limitation was the majority inclusion of women despite non-random sampling, which could have a gender effect on the results, but could not be assessed due to the small number of men included. The inclusion of a greater number of women may be related to the 6- to 9-fold increased risk of hypothyroidism in women compared to men. Some of the factors that have been proposed are pregnancy, postpartum, menopause, and aging [8,32,33]. The development of future clinical studies that include patients with other causes of hypothyroidism, the evaluation of other associated factors for persistent fatigue, as well as the use of combination therapy with liothyronine, will allow overcoming these limitations and enrich the knowledge about fatigue in patients with hypothyroidism in order to improve their prognosis.
## 5. Conclusions
Levothyroxine replacement therapy improved fatigue in patients with primary hypothyroidism according to the FSS score, and this score was positively associated with TSH and negatively associated with FT4, both before and after treatment. Persistent fatigue was associated with a pretreatment FSS score and diabetes. The presence of diabetes, an FSS score >34, as well as the traditional FSS score cut-off point >36 before levothyroxine replacement therapy were risk factors for persistent fatigue at 6 months of treatment.
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|
---
title: 'GC-MS Profiling and Biomedical Applications of Essential Oil of Euphorbia
larica Boiss.: A New Report'
authors:
- Muddaser Shah
- Faizullah Khan
- Saeed Ullah
- Tapan Kumar Mohanta
- Ajmal Khan
- Rimsha Zainab
- Naseem Rafiq
- Hussan Ara
- Tanveer Alam
- Najeeb Ur Rehman
- Ahmed Al-Harrasi
journal: Antioxidants
year: 2023
pmcid: PMC10045896
doi: 10.3390/antiox12030662
license: CC BY 4.0
---
# GC-MS Profiling and Biomedical Applications of Essential Oil of Euphorbia larica Boiss.: A New Report
## Abstract
The present study explored *Euphorbia larica* essential oil (ELEO) constituents for the first time, obtained via hydro-distillation by means of Gas Chromatography-Mass Spectrometry (GC-MS) profiling. The essential oil was screened in vitro against breast cancer cells, normal cell lines, α-glucosidase, carbonic anhydrase-II (CA-II), free radical scavenging and in vivo analgesic and anti-inflammatory capabilities. The GC-MS screening revealed that the ELEO comprises sixty compounds ($95.25\%$) with the dominant constituents being camphene ($16.41\%$), thunbergol ($15.33\%$), limonene ($4.29\%$), eremophilene ($3.77\%$), and β-eudesmol ($3.51\%$). A promising antidiabetic capacity was noticed with an IC50 of 9.63 ± 0.22 μg/mL by the ELEO as equated to acarbose with an IC50 = 377.71 ± 1.34 μg/mL, while a 162.82 ± 1.24 μg/mL inhibition was observed against CA-II. Regarding breast cancer, the ELEO offered considerable cytotoxic capabilities against the triple-negative breast cancer (MDA-MB-231) cell lines, having an IC50 = 183.8 ± 1.6 μg/mL. Furthermore, the ELEO was also tested with the human breast epithelial (MCF-10A) cell line, and the findings also presumed that the ELEO did not produce any damage to the tested normal cell lines. The ELEO was effective against the Gram-positive bacteria and offered a 19.8 ± 0.02 mm zone of inhibition (ZOI) against B. atrophaeus. At the same time, the maximum resistance with 18.03 ± 0.01 mm ZOI against the fungal strain *Aspergillus parasiticus* was observed among the tested fungal strains. An appreciable free radical significance was observed via the DPPH assay with an IC50 = 133.53 ± 0.19 µg/mL as equated to the ABTS assay having an IC50 = 154.93 ± 0.17 µg/mL. The ELEO also offered a substantial analgesic capacity and produced $58.33\%$ inhibition in comparison with aspirin, a $68.47\%$ decrease in writhes, and an anti-inflammatory capability of $65.54\%$ inhibition, as equated to the standard diclofenac sodium having $73.64\%$ inhibition. Hence, it was concluded that the ELEO might be a natural source for the treatment of diabetes mellitus, breast cancer, analgesic, inflammatory, and antimicrobial-related diseases. Moreover, additional phytochemical and pharmacological studies are needed to isolate responsible chemical ingredients to formulate new drugs for the examined activities.
## 1. Introduction
Medicinal plants are the prime source of alternative therapy for diabetes mellitus (DM) and numerous other human health complications [1]. Essential oils (EOs) produced from plants have multiple pharmacological and pharmaceutical properties including microbial resistance [1,2,3], scavenging free radicals [2,3], curing inflammation [4,5,6], α-glucosidase inhibition [7], carbonic anhydrase-II inhibition [8,9], and relieving pain [10]. In recent eras, the EOs obtained from medicinal herbs have gained attention due to their diverse biomedical applications including antidiabetic, antifungal, antibacterial, and pharmacological capabilities, as well as effective natural remedies [7,8]. Furthermore, essential oils are widely utilized over the globe with less adverse effects, easy availability, affordability, and a high rate of efficacy [11,12], which have attracted considerable interest [13].
Cancer is an intricate genetic disease that retards the further growth and expansion of normal cells in the living body, even leading to death [14]. According to recent statistics [2020], cancer has been ranked second most common among the main causes of mortality over the globe, and around 9.6 million people died from cancer in 2018 [15]. The exploration of new medications using therapeutic plants that can heal cancer has become one of the most exhilarating demands through phytochemical screening [16]. However, medicinal plants have offered a significant role in developing traditional medicine systems, particularly to treat cancer [17]. In addition, EO constituents have also played an important role in cancer prevention and treatment [13]. In recent decades, most of the plants bearing essential oil displayed anticancer [13,14,18] and antitumor [19] activities to overcome the expansion of multidrug resistance complications [20]. Because of the considerable capabilities of the essential oils in cytotoxic treatment, the ELEO could be used as a complementary therapy to natural and chemotherapeutic drugs [21].
Carbonic anhydrases (CAs) are metalloenzymes that frequently persist in living organisms and catalyze the transformation of CO2 and H2O to HCO3− and H+ [22]. There are 16 different CA isozymes [23], and CAs play a vigorous role in mammals in several processes such as pH control, ion transport, calcification, stability, and secretion of electrolytes [22]. Classical CA (acetazolamide and brinzolamide) inhibitors have been used as commercial medications in the healing of numerous ailments comprising edema, cancer, glaucoma, epilepsy, obesity, and osteoporosis [24] for a long time with undesired side effects [25,26]. Thus, searching for new natural and safer CA inhibitors with fewer side effects is required.
A deficiency of insulin secretion usually causes type I diabetes (T1DM) [27]. Type I diabetes (T1DM) is typically instigated by the deficiency of insulin secretion. In contrast, type II diabetes (T2DM) is instigated by increased insulin resistance in the liver, decreased cell mass of peripheral organs, and insufficient insulin production [28]. Approximately $90\%$ of diabetic people have T2DM [29]. Furthermore, α-glucosidase (AGIs) inhibitors are efficient and perform a substantial role in reducing post-prandial glucose levels. The α-Glucosidase enzyme (EC 3.2.1.20) catalyzes the release of α-glucosides from the non-reducing side of the carbohydrates, consequently inhibiting the enzyme to control the raised glucose levels in the human blood [30]. α-Glucosidase (AGIs) inhibitors are effective and play a significant role in reducing post-prandial glucose levels. Numerous side effects, such as diarrhea and abdominal pain, are due to the regular use of synthetic drugs [17,18,19]. Hence, developing new natural AGIs with high efficiency is highly essential without imposing greater side effects.
Euphorbia is the largest genus, comprising around 2040 species in the family Euphorbiaceae [31]. Some plant species of Euphorbia have been documented for various health complications such as skin diseases, migraine, gonorrhea, intestinal parasites, and warts [32]. Euphorbia larica Boiss (Local name = Isbaq) is a significant native medicinal plant, mostly distributed over stony and rocky (up to one-meter-high) places of Dhofar (Salalah) and Ad Dakhiliyah areas of Oman. The native individuals of Oman habitually use sap or latex of E. larica to patch bites, burn wounds [33], as well as for treating camels with parasites [34]. Quercetin and kaempferol derivatives such as kaempferol-3-rutinoside, kaempferol-3-O-glucoside, quercetin 3-O-glucoside, rutin, and 6-methoxyapigenin were observed in ethanolic extracts of E. larica leaf [35]. In addition, saturated and oxygenated hydrocarbons and esters were identified and isolated from the aerial parts of E. larica [36]. Recently, an anthracene derivative (eupholaricanone) with α-glucosidase inhibition along with three steroids was reported from aerial parts of E. larica [28]. Hence, to highlight the significance of the plant essential oil under study, for the first time GC-MS profiling, α-glucosidase inhibition, and antioxidant, antimicrobial, analgesic, anti-inflammatory, and breast cancer activities are available in the literature. Therefore, our main aim was to examine and evaluate the chemical arrangement and biological capabilities of ELEO as a first step in evaluating the prospective benefits of the plant species.
## 2.1. Plant Material and Identification
The whole aerial fresh plant parts of E. larica (3.5 Kg) were gathered from different regions of the Ad Dakhiliyah in the Sultanate of Oman. The plant under study was identified by Dr. Syed Abdullah Gilani at the Department of Biological Sciences and Chemistry, College of Arts and Sciences, University of Nizwa, Oman. After identification, the sample was cleaned, washed with distilled water, air-dried, and then grinded into a fine powder via a stainless-steel blender. The voucher specimen of E. larica (EL/$\frac{03}{2019}$) was kept in the herbarium at the Natural and Medical Sciences Research Center (NMSRC), University of Nizwa (UON), Nizwa, Oman.
## 2.2. Extraction of EO
The hydro-distillation of fresh aerial parts (EL, 0.98 g, $0.32\%$) of the E. larica growing in Oman gives yellow-colored EO (3 × 8 h) via Clevenger-type apparatus [7,37]. ELEO was obtained from the burette, dried over anhydrous sodium sulphate (Na2SO4), then weighed and preserved in the fridge at 4 °C until GC/MS profiling and biological screening.
## 2.3. GC/MS Profiling of the ELEO
The GC-MS screening of ELEO was performed via GC-MS. The GC-MS apparatus comprises the Perkin Elmer Clarus (PEC) 600 GC system connected with the Rtx-5MS capillary column (30 m × 0.25 mm I.D × 0.25 µm film thickness; the extreme temperature of 260 °C, fixed to a PEC 600 MS) (Waltham, MA, USA). The ultra-high-purity helium (He, $99.99\%$) was utilized as carrier gas having a flow frequency of 1.0 mL/min. Furthermore, the ionization energy (IE) was observed at 70 eV, and the electron multiplier voltage was adjusted from the auto-tune, whereas the injection, transfer line, and ion source temperatures were 260, 270, and 280 °C, respectively. The tested samples were 1 µL each, having a split ratio of 10:1. The oven was adjusted at 60 °C (for 1 min) at a rate of 4 °C/min—260 °C held for 4 min. The sample completed its run in 65 min. All the data of the ELEO were obtained by collecting the full scan mass spectra within 45–550 a.m.u. scan range.
## 2.4. Identification of Compounds
GC-MS determined the basic ingredients in the ELEO, and the unidentified compounds were identified and authenticated via MS library software (NIST 2011 v.2.3 and Wiley MS, 9th edition) and available literature. The chemical ingredients were quantified via an external standard via calibration curves generated through applying the GC screening of the representative compound groups. In addition to comparing RI (obs.) with RI (Lit.), the co-injection with an available authentic sample of detected chemical ingredients to GC or GC/MS was similarly used to identify the chemical ingredients.
## 2.5. In Vitro Cytotoxic Capacity
In vitro cytotoxicity capability of ELEO was evaluated using the MTT (yellow tetrazolium salt, 3-(4,5-dimethylthizol-2-yl)-2,5-diphenyl tetrazolium bromide) test utilizing breast cancer cell lines (MDA-MB-231) [38]. This study used the human breast normal cell line (MCF-10A) as a control. The MDA-MB-231 cell lines were acquired from the American Type Culture Collection (ATCC) and MCF-10A was purchased from the Iranian Biological Resource Center (IBRC) (Tehran, Iran). Cells were uniformly cultured in DMEM (Dulbecco’s Modified Eagle Medium) and further augmented together with $1\%$ antibiotics (100 U/mL penicillin) and around $10\%$ FBS. The cells were equally injected in a 96-well plate at 1.0 × 104 cells/well density and then incubated for 24 h at 37 °C in $5\%$ CO2. The medium was discarded, and both the cell lines were tested using ELEO at dosages of 3, 10, 30, 100, and 300 μg/mL, respectively [39]. Right after 48 h of the incubation [17], around 20 μL MTT solution, 5 mg/mL, was inoculated into every well and incubated for a further 4 h. However, the medium was then discarded, and the formazan precipitate was carefully homogenized in DMSO (dimethyl sulfoxide). The absorbances of the mixture’s reagents were examined via a microplate reader at 570 nm. All tests were carried out in triplicate, and the cytotoxicity potential was represented as a % of cell viability equated to untreated control cells following Equation [1] [39]. [ 1]% viability =ODtest compoundODcontrol×100 where OD is the optical density of tested and controlled compounds.
## 2.6. In Vitro α-Glucosidase Potential
To evaluate the α-glucosidase inhibitory ability the ELEO was ensued at 37 °C using 0.5 mM phosphate buffer (pH 6.8) [7,30]. Furthermore, high to low dosages of the ELEO (60, 30, 15, 7.8, 3.90, and 1.95 µg/mL) were incubated through the α-glucosidase enzyme (2 U/2 mL) in phosphate buffer for 15 min at 37 °C. Later on, after the addition of 25 µL of substrate (p-nitrophenyl-a-D-glucopyranoside, 0.7 mM, final), the variations in absorbance at 400 nm for 30 min were noted using a microplate reader (Bio-Rad, Molecular Devices, CA, USA). DMSO-d (7.5 percent final) was applied as a positive control. In the current screening, acarbose with an IC50 377.7 1.34 µg/mL was applied as a standard. The results were obtained by estimating IC50 through EZ-fit software via Equations [1] and [2]. [ 2] SE=σn
## 2.7. In Vitro Carbonic Anhydrase-II Bioassay
The ELEO was screened for its CA-II inhibition significance using the most cited technique as stated by Rehman et al. [ 40]. To proceed with the assay, a freshly arranged aqueous solution of 20 bovine erythrocyte CA-II (0.1 mg/mL); 20 mL 4-Nitrophenyl acetate (4-NPA, 0.7 mM) in ethanol; and buffer solution (140 mL) HEPES-trisTris-HCl (20 mM) with pH 7.4 were properly homogenized in dimethyl sulfoxide (DMSO, $10\%$) in 96-well plates. The ELEO, standard, and enzyme (EC 4.2.1.1, Sigma-Aldrich, St. Louis, MO, USA) were carefully incubated for 15 min using a 96-well plate. Furthermore, the reaction proceeded by adding 20 μL 4-NPA and examining the rate of product formation for 30 min accurately with intervals of 1 min at 25 °C via a microplate reader. The significance was estimated through % inhibition using Equation [1].
## 2.8. Antimicrobial Assessment
Plants and their products are an alternative source of innovative drugs. Hence, to authenticate and highlight their importance, the E. larica essential oil was screened against the human pathogenic microbes using agar well diffusion techniques, and ZOIs were measured [41]. The microbial strains (clinical isolates) were identified by Dr. Hazir Rahman, Department of Microbiology, AWKUM, Mardan.
## 2.8.1. Antibacterial Screening
The ELEO was screened against the bacterial strains S. typhi, K. pneumonia, B. atrophaeus, and B. subtilis via agar well diffusion (AWD) techniques [41,42]. For the antibacterial valuation, the media were arranged by adding 28 g of nutrient agar (NA) media along with 1 L of distilled water (DW) and shaken until the media was entirely homogenized. All the required apparatus was sterilized including prepared media, well borer, Petri plates, and wire loop, properly autoclaved at 121 ℃ for 15 min. Then, around 20 mL of nutrient agar autoclaved media was loaded into every Petri plate using a laminar flow hood and kept undisturbed until their solidification. The tested bacterial strains were carefully inoculated following safety measures using a wire loop at a concentration of bacterial cell density of 1.5 × 108 CFU/mL. Five wells of equal magnitude (3 mm) and the same distance were made over the solidified media using a sterilized corn borer.
Furthermore, around 1 mg from the ELEO and respective standards were dissolved in 1 mL of dimethyl sulfoxide (DMSO) to obtain a 1000 ppm stock solution from which 50 and 100 µL samples were taken, which were represented as 50 µg/mL and 100 µg/mL.
The ELEO at doses of 50 µg/mL was inoculated into the 1st well and 100 µg/mL was into the 2nd well, individually. Furthermore, the 3rd and 4th wells in the Petri plate were filled with standard levofloxacin and erythromycin at the same concentrations for Gram-positive and Gram-negative bacterial strains, correspondingly. At the same time, DMSO was added to the 5th well as a negative control over the nutrient agar media. The Petri plates were packed appropriately, labeled, and retained overnight in the incubator at 37 °C. Then, the glass Petri plates were carried out from the incubator, and resistance produced by the tested samples was observed as the zone of inhibition (ZOI) around the wells, determined in millimeters. All of the data were taken in triplicate, authenticated statistically, and expressed as mean ± SEM.
## 2.8.2. Antifungal Evaluation
The ELEO was examined for its antifungal capabilities against A. parasiticus and A. niger from low to high doses via an agar well diffusion technique [41,42]. The antifungal activity was carried out by adding 39 g of the potato dextrose agar (PDA) to 1 L of distilled water in a glass conical flask and shaken until homogenized. Then, the antifungal media (PDA), Petri dishes, wire loop, and 3 mm steel borer were carefully autoclaved at 121 °C for around 20 min. The PDA media at about 20 mL was loaded into every glass Petri dish and afterward placed until media solidification. The tested fungal inoculum at a dosage of 108–109 CFU/mL was uniformly spread on the congealed PDA media. Later, around five wells using a borer of 3 mm were made at equal distances from one another. Low to high doses of ELEO were carefully inoculated into the 1st and 2nd wells over the media, and the standard fluconazole at the same doses was injected into the 3rd and 4th wells. At the same time, the 5th well was filled with DMSO, which was employed as a negative control. Next, all the Petri plates were carefully packed, labeled, and incubated for 72 h at 25 °C. Lastly, the Petri plates were brought out and the zone of inhibition was examined around the wells in millimeters. All of the data were taken in triplicate and expressed as mean ± SEM.
## 2.9.1. DPPH Assay
The ELEO was examined for its antioxidant significance using a DPPH bioassay [41,43]. The antioxidant assay proceeded by homogenizing 3 mg DPPH with 100 mL of distilled methanol (DM). Then, the reagent was kept in the shade for around 30 min to produce the free radicals to appraise the antioxidant potential of the ELEO. The ELEO and ascorbic acid were prepared at dosages of 1000, 500, 250, 125, and 62.5 µg/mL to screen their antioxidant effects. Moreover, around 2 mL of ELEO and standard was added to 2 mL of the previously organized DPPH stock solution and employed for incubation in the shade for 25 min. Eventually, the absorbance of the used samples was noted at 517 nm utilizing UV/Vis spectrophotometry (UV-1800, Shimadzu, Kyoto, Japan). The results were obtained via Equation [3]. % Antioxidant potential = A − B /A × 100[3] In the equation, A represents control absorbance and B denotes absorbance of the essential oil (EO) and the standard (ascorbic acid).
## 2.9.2. ABTS Test
The antioxidant capabilities of ELEO were also screened via ABTS bioassay. Around 383 mg of the ABTS and nearly 66.2 mg of K2S2O8 were individually homogenized in the 100 mL analytical-grade methanol and then mixed. Later, around 2 mL from the ABTS solution was carefully incubated with 2 mL of tested samples for 25 min utilizing equal dosages, as explained in the DPPH assay. Moreover, the absorbance of the analyzed samples was estimated at 746 nm utilizing UV/Vis spectrophotometry. The antioxidant ability was analyzed using formula 3.
## 2.10. In Vivo Activities
The in vivo activities were performed using Swiss albino mice with an average weight of 24–30 g bought from the Veterinary Research Institute Peshawar, KP Pakistan. The Swiss albino mice were placed in AWKUM animal’s houses at 20 °C for 45 days following ARRIVE guidelines strictly for the intake of rodent pellets, foodstuff, and water.
## 2.10.1. Analgesic Assessment
To authenticate the essential oil significance concerning pain therapy, the ELEO of the plant under study was tested following an acetic-acid-stimulated writhing bioassay (AAIWA) using Swiss albino mice as an experimental animals [41]. Furthermore, the experimental animals were equally divided into 5 groups ($$n = 6$$). The tested samples, including E. larica EO, control, and standard, were inoculated very carefully through the intraperitoneal muscle in experimental animals using the sterilized syringe. Around 1 mL of ($0.7\%$) acetic acid (AA) at dosages of 5 mL/kg body weight (BW) were infused into all the groups of the Swiss albino mice. Then, after 45 min, the mice of group 1 (control) and group 2 (standard) were given around 1 mL each of normal saline and aspirin, correspondingly, injected following ARRIVE guidelines. In addition to that, the ELEO was inoculated to the remaining groups of 3rd, 4th, and 5th experimental animals at concentrations of 25, 50, and 100 mg/kg BW dosages, respectively. Furthermore, writhes were calculated to determine the tested samples’ analgesic potential compared to normal saline and analgesic standard for 10 min. The obtained data were represented by % inhibition via Equation [4]. [ 4]% inhibition=A−BA×100……………. where A represents acetic acid induces writhes in Swiss albino mice, and B indicates the analgesic potential of the EO, analgesic standard, and negative control.
## 2.10.2. Anti-Inflammatory Evaluation
The ELEO was screened to determine its anti-inflammatory effects via carrageenan-induced paw edema assay in the experimental animals, as previously reported by Shah et al. [ 41]. The Swiss albino mice were properly grouped, as previously stated in the analgesic bioassay. However, inflammation was produced in all the groups of the Swiss albino mice by injecting 1 mL ($1\%$) of carrageenan [44]. Right after 30 min, the Swiss albino mice of group 1 were treated with 1 mL of normal saline (NS) as a negative control, whereas 1 mL of diclofenac sodium (50 mg/kg) was inoculated into group 2 mice following safety measure. The essential oil of EL at dosages of 25, 50, and 100 mg/kg of body weight (BW) were inoculated into the experimental animals of groups 3, 4, and 5, individually. In addition to that, the anti-inflammatory capabilities of examined samples were determined by calculating the Swiss albino mice right paw diameter in the same way as after the 1st, 2nd, and 3rd h, respectively, and resulting data were represented as % inhibition and computed applying Formula [4].
In Formula [4], A represents carrageenan-induced paw edema and B denotes the anti-inflammatory capabilities of essential oil, anti-inflammatory drug (standard), and control (NS) in the diameter of paw edema.
## 3. Statistical Analysis
The data for the in vitro and in vivo screening were taken in triplets and computed via one-way analysis of variance (ANOVA), examined through Bonferroni’s test at significance level $$p \leq 0.05$$ (*) and 0.01 (**) via two-way ANOVA. Furthermore, Sidak’s multiple comparisons tests (p = (ns > 0.9999, **** < 0.0001)) for statistical validity were performed. Furthermore, the free radical scavenging effects were determined through a nonlinear regression graph plotted amongst % inhibition and dosages of the experimental samples, and the IC50 was ascertained utilizing the GraphPad Prism 9.0.1 [2021] software for windows (San Diego, CA, USA, 2020) through the following formula. $Y = 100$/1 + (ˆHillSlope) where 1 represents the inhibitor concentration and denotes the inhibitor’s reaction. HillSlope shows curve steepness.
All experiments were conducted in triplicate to lower the likelihood of errors, and variations in the findings are described as Standard Error of Mean (SEM). The IC50 of all analyzed substances was determined utilizing EZ-FIT (Perrella Scientific, Inc., Amherst, MA, USA). The cytotoxic significance was calculated via IBM SPSS Statistics 26 software to examine the dose response and calculation of IC50.
## 4.1. Chemical Composition Identification of the ELEO
In the current context, the GC/MS profile demonstrated that ELEO contained sixty components [60], representing $95.25\%$ of the total oil constituents (Table 1). Camphene ($16.41\%$) and thunbergol ($15.33\%$) were the major components, followed by limonene ($4.29\%$), eremophilene ($3.77\%$), β-caryophyllene ($3.47\%$), β-eudesmol ($3.51\%$), and α-selinene ($3.26\%$) (Table 1; Figure 1). In addition to that, minor constituents noticed in the ELEO were cubebol ($2.97\%$), caryophyllene oxide ($2.85\%$), β-elemene ($2.71\%$), δ-cadinene ($2.48\%$), β-myrcene ($2.21\%$), and τ-cadinol ($2.45\%$). Hydrocarbons such as pentacosane, heptacosane, and nonacosane as well as hydroxy-acids such as 2-methyl-2,2-dimethyl-1,1-(2-hydroxy)-1-propanoic acid and 2-methyl-1,3-hydroxy-2,4,4-trimethylene propanoic acid were identified previously in the wax of E. larica [36]. These outcomes agree with those described in previous analyses [45,46,47]. The EOs of E. caracasana and E. cotinifolia harvested from Mérida, Venezuela, were found to be rich in β-caryophyllene ($33.7\%$), caryophyllene oxide ($6.4\%$), α-selinene ($7.6\%$), α-humulene ($18.8\%$), aromadendrene ($8.4\%$), α-copaene ($9.3\%$), and germacrene-D ($21.5\%$) [45]. A higher amount of β-eudesmol ($18.22\%$), caryophyllene oxide ($8.61\%$), and β-selinene ($4.21\%$) was determined in the E. fischeriana essential oil from China [46]. On the other hand, small quantities of camphene ($0.98\%$), β-selinene ($0.76\%$), τ-cadinol ($0.93\%$), caryophyllene oxide ($1.74\%$), and germacrene-D ($0.67\%$) were reported in the EO of E. mauritanica belonging to Egypt [47]. Moreover, the EO of E. mauritanica was found to be higher in cembrene A ($18.66\%$), verticiol ($17.05\%$), and limonene ($7.91\%$) [47] compared to our results. As Lokar et al. [ 48] reported, different habitats, seasons, geographical areas, and harvesting periods can affect the quantitative composition and variation of EO in similar plant species from diverse regions. The difference in the quantity of the active ingredients implies the variation in the ecosystem diversity where plant species grow. According to Salehi et al. [ 49], the EOs obtained from the plant species belonging to the genus Euphorbia consist of sesquiterpenes as a leading bioactive constituent in both oxygenated and non-oxygenated forms; however, β-caryophyllene was observed as the prime component up to $7\%$ in the EOs of the genus Euphorbia [49]. Furthermore, our results displayed a higher quantity of camphene and thunbergol, utilized as a distinctive indicator of E. larica from the southern areas of Oman.
## 4.2. In Vitro Cytotoxicity Potential
The ELEO was screened against the human breast cancer cell line MDA-MB-231 to find out the inhibition of growth in cancer cells from low to high doses and authenticate the literature previously published by Bhalla et al. [ 13] and Loizzo et al. [ 50] about the cytotoxic capabilities of plant essential oils. At the same time, human normal breast epithelial cell line MCF-10A was kept as a control in the experiment. The MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] bioassay was applied to evaluate the decrease in cancer cell viability persuaded through cytotoxic agents. For MDA-MB-231 and MCF-10A, the IC50, % inhibition, and % viability of ELEO are shown in Table 2. The results of the MTT assay revealed that the ELEO was effective against MDA-MB-231 cells with an IC50 =183.8 ± 1.6 μg/mL.
To find out whether the cytotoxic effects of ELEO were selective for malignant cells as equated to the non-malignant cells, the non-tumorigenic MCF-10A cells were exposed to the ELEO at dosages of 3, 10, 30, 100, and 300 μg/mL at the same concentration of the cancer cells. The findings revealed that these cells were less liable to the actions of the ELEO, especially at a higher dosage of 300 μg/mL. Furthermore, current outcomes also depicted that the triple-negative MDA-MB-231 cells, having an aggressive phenotype, responded more positively to ELEO and presented considerable cytotoxicity. In addition to that, less cytotoxic capacity was observed when non-tumorigenic MCF-10A cells were screened to the essential oil of the plant under study, indicating that ELEO has the significant potential to act as an effective remedy to cure breast cancer. Furthermore, essential oils also contain monomers which are well known for their cytotoxic significance, as stated by Yang et al. [ 51] and Maraveas et al. [ 52].
The current findings reflected that the tested sample offered appreciable capabilities against MDA-MB-231 cells with an IC50 value of 183.8 ± 1.6 μg/mL. The cytotoxic potential might be ascribed to the essential oil due to the presence of β-caryophyllene, β-elemene, and α-selinene, which were previously described by Compagnone et al. [ 53] in the EOs of Croton micans and *Croton matourensis* having cytotoxic potential. Thus, our findings favor the data reported by Azaat et al. [ 54] and Yagi et al. [ 55] for the essential oils extracted from the genus Euphorbia. The similarities observed in the mentioned literature with current data might be due to the essential oils extracted from the same genus and also the same approach used to examine the cytotoxic effects. However, our observed findings are not equated with the results presented by Estanislao et al. [ 56] and Lahmadi et al. [ 57] for Eucalyptus and Decatropis bicolor, respectively. The variation among the effects might be due to the differences in the plant genus and habitat, and the bioactive content in the plants also varies due to the quality and water availability, as reflected in the literature stated by Rawat et al. [ 58].
## 4.3. α-Glucosidase and Carbonic Anhydrase-II Bioassays
The essential oil of E. larica essential oil displayed potent antidiabetic capacity, with an IC50 = 9.63 ± 0.22 µg/mL as compared to the available marketed drug acarbose with an IC50 = 377.71 ± 1.34 µg/mL. The promising ability attributed to the tested sample is due to the presence of these active compounds such as linalool, limonene, and caryophyllene oxide in the essential oil, as earlier stated in the literature of Najibullah et al. [ 59], Basak et al. [ 60], and Jelassi et al. [ 61]. However, essential oils are affluent sources of monomers that have promising capabilities to act as antidiabetics, as reflected by Bigham et al. [ 62] in plant species *Teucrium polium* and *Musca domestica* and also revealed by Riyaphan et al. [ 63].
Our study authenticates the literature as earlier documented by Valarezo et al. [ 64] and Yu et al. [ 65] for the antidiabetic potential of some plant species of the genus Euphorbia. However, our data are not matched with the data presented by Salazar et al. [ 66] for Origanum vulgare and Dang et al. [ 67] for some *Vietnamese citrus* peel essential oils. The resemblances may perhaps be due to the genus similarities and disparities might be due to the differences in the plant family, as well as habitat, as these factors alter the chemical composition of the constituents within the plants. Furthermore, the same sample (ELEO) was investigated against the CA-II enzyme, displaying above $50\%$ inhibition, and was found to be active against CA-II with an IC50 = 162.82 ± 1.24 µg/mL (Figure 2) in comparison with standard acetazolamide (8.64 ± 0.27 µg/mL). These findings revealed that ELEO might be utilized as a therapeutic method for type 2 diabetes and other diseases associated with the CA-II enzyme.
## 4.4.1. Antibacterial Significance
In recent eras, with the advancement of therapeutic practices in the scientific field, the remedial properties of the plant’s essential oils have drawn great interest due to their fewer adverse effects, appreciable pharmacological implication, and economic practicability, especially to overwhelmed microbial infections, as stated by Chouhan et al. [ 68]. E. larica essential oil presented considerable activity against the tested microbes. However, the EO was found effective against the Gram-positive tested bacteria and offered 19.8 ± 0.02 mm resistance against B. atrophaeus followed by B. subtilis with ZOI 18.2 ± 0.04 mm, as equated to the Gram-negative bacterial strain S. typhi having 15.4 ± 0.02 mm ZOI. At the same time, 15.3 ± 0.07 mm ZOI was observed against K. pneumonia (Figure 3). In addition to that, the standard used for the Gram-positive and Gram-negative bacterial strains erythromycin and levofloxacin offered significant antibacterial resistance. The antibacterial feature attributed to the essential oils is due to the presence of α-selinene, as earlier reported by Silva et al. [ 69] in Myrcia alagoensis. The plant effluents in eremophilene and caryophyllene were also reported for their antibacterial capabilities, as revealed in the literature published by Utegenova et al. [ 70] and Moo et al. [ 71], which were also examined in the plant under study. In addition to that, essential oils also contain monomers that have antibacterial capabilities, as stated by Su et al. [ 72] and Elegir et al. [ 73]. Moreover, our outcomes agreed with the records expressed by Zhu et al. [ 74], Adedoyin et al. [ 75], and Olaoluwa et al. [ 76] regarding Euphorbia helioscopia, Euphorbia heterophylla, and Euphorbia hirta, as these plant species belong to the same genus and most of the chemical constituents are the same. In addition, the recent data do not match the data revealed by Carovic et al. [ 77] and Wilkinson et al. [ 78], as the plant species contain different phytoconstituents due to habitat variability, edaphic and climatic factors, as well as the quality and availability of water.
## 4.4.2. Antifungal Capabilities
The essential oils have multiple biomedical applications containing antifungal capabilities, as confirmed by Nazzaro et al. [ 79]. To further validate the previous literature as described by Hu et al. [ 80], the plant essential oil under study was examined for its antifungal significance and presented an appreciable resistance against the tested fungal strains. However, the maximum ZOI was observed against *Aspergillus parasiticus* at 18.03 ± 0.01 µg/mL followed by Aspergillus niger with 17.4 ± 0.03 ZOI as compared to the used antifungal standard; however, the negative control was examined as inactive (Figure 4). The significant resistance against the fungal strains is attributed to the presence of β-eudesmol, as earlier reported by Ho et al. [ 81], and camphene and thunbergol, as previously documented by Angioni et al. [ 82] and Mitic et al. [ 83], respectively. The role of monomers acting as an antifungal agents cannot be denied, which was earlier documented by Chen et al. [ 84] and Neves et al. [ 85].
Thus, our current findings agree with the data depicted by Rao et al. [ 86] and Mahmoud et al. [ 87], however, our results deviate from those as stated by Bansod et al. [ 88], and Oumzil et al. [ 89]. Different plant species vary in their chemical constituents, which can overcome various human health complications, as well as that the environmental gradients influence the bioactive constituents within a plant species, as earlier stated by Shah et al. [ 90].
## 4.5. Antioxidant Capability
Essential oils are a promising basis for neutralizing free radicals, as earlier documented by Amorati et al. [ 91] and Shah et al. [ 41]. To confirm this further, the essential oils obtained from the whole plant of E. larica Boiss. ( Euphorbiaceae) were investigated for their free radical scavenging capacities. The assessment revealed that the analyzed samples had considerable potential to scavenge the free radicals (Figure 5). However, E. larica essential oil was observed with utmost significance via DPPH assay with an IC50 = 133.53 ± 0.19 µg/mL, as equated to the ABTS assay having an IC50 = 154.93 ± 0.17 µg/mL. In addition to that, the standard ascorbic acid offered an IC50 = 73.72 ± 0.24 and IC50 = 83.07 ± 0.20 µg/mL via DPPH and ABTS assay, respectively. The appreciable ability attributed to ELEO is due to the presence of α-selinene, eremophilene, and caryophyllene in a high amounts, which was heretofore explained by Chandra et al. [ 92] and Ahmadvand et al. [ 93] in *Callicarpa macrophylla* and Artemisia persica, respectively. However, monomers also can neutralize free radicals, as documented previously by Maraveas et al. [ 52] and Su et al. [ 72].
Our current findings consented with the data stated by Elshamy et al. [ 94] and Essa et al. [ 47] for the essential oils of E. heterophylla and E. mauritanica L., as the plant species under study belongs to the same genus, having the majority of common bioactive constituents which can neutralize the free radicals. However, our data did not match with the study documented by Jie et al. [ 46], and the plant studied, Euphorbia fischeriana, belongs to the same genus collected from China. The chemical ingredients vary among the plants of the same genus due to their habitat, quality, and availability of water. In addition to that, plant species from a different genus, Ochradenus, presented similar antioxidant potential due to the same method of essential oil extraction, habitat, and a similar approach used to examine the free radical scavenging effect, as revealed in the literature of Ullah et al. [ 37].
## 4.6. Anti-Inflammatory Significance
The essential oil of E. larica was examined to highlight the anti-inflammatory capabilities induced via carrageenan in Swiss albino mice. The essential oil presented significant potential in the reduction in paw edema from low to high doses at $55.40\%$, $58.78\%$, and $65.54\%$, respectively, as equated to the standard with $73.64\%$ inhibition (Table 3). At the same time, the normal saline was observed to be inactive (Table 4). Our current findings authenticate the data confirmed by Miguel et al. [ 4] for EOs. The anti-inflammatory significance depicted by the plant essential oils, mainly caryophyllene oxide, were previously reported by Chao et al. [ 95] in *Cinnamomum osmophloeum* and β-caryophyllene and observed in the essential oils of Syzygium cumini and Psidium guajava, having promising anti-inflammatory activity, as stated by Siani et al. [ 96]. Furthermore, our study is supported by the details mentioned in the data explained by Sinan et al. [ 97] and De-Morais et al. [ 98] in the genus Euphorbia. The similarities in the aforementioned plants were due to the plant species from the same genus because of the common bioactive constituents and the extraction method of essential oils. Moreover, our current data do not match with the literature documented by Adeosun et al. [ 99], Souza et al. [ 100], and Aboluwodi et al. [ 101]. The variations observed might be due to the plant belonging to a different genus, environmental factors, collection time, and quality and availability of water, as well that the pollutants in the water contents also have adverse effects on the bioactive constituents, as explained by Shah et al. [ 102].
## 4.7. Analgesic Capabilities
The exploration of natural and green products is continuously expanding the use of EOs, and thus pressure is exerted on these materials from emerging nations. It is estimated that the requirement for EOs over the globe will increase by $7.5\%$ from 2020–2027 due to their diverse medical applications, including analgesic agents, as documented by Scuteri et al. [ 103]. To confirm the previous study, the essential oils of E. larica were examined to emphasize the capabilities to relieve pain induced in Swiss albino mice via acetic acid. The screening revealed that the E. larica essential oil presented significant potential of 36.95, 48.18, and $58.33\%$ reduction in writhes caused by the acetic acid in the Swiss albino mice from low to high doses of 25, 50, and 100 mg/kg, correspondingly (Table 4). However, the normal saline was observed to be inactive, and the analgesic standard (aspirin) depicted $68.47\%$ inhibition. The analgesic significance ascribed to the plant essential oil under study is due to the presence of limonene, as previously expressed by Guimaraes et al. [ 104], and caryophyllene, as elaborated by Bakir et al. [ 105]. Thus, our results consented with the data earlier reported by Ahmad et al. [ 106] and Majid et al. [ 107] for Euphorbia decipiens and Euphorbia dracunculoides, respectively. The similarities in our results were observed because the plant under investigation belonged to the same genus, Euphorbia, and the approach used to examine the analgesic activity was also the same. In addition to that, our recent results did not agree with the data presented by Kaskoos et al. [ 108] for *Lippia citriodora* and Citrus limon or the data reported in the literature stated for *Cyperus rotundus* by Chen et al. [ 109]. The variation in the data of our findings with the previously documented literature is due to the variation among the plant species, as well as that the different plant species possess various active constituents which are influenced by various aspects, such as habitat, climatic and edaphic factors, and water availability, as stated by Aboukhalid et al. [ 110].
## 5. Conclusions
From the current study, it is assumed that ELEO comprises promising bioactive constituents having multiple biomedical features. For the first time, we reported the GC-MS profiling and in vitro and in vivo pharmacological assessment of ELEO. The ELEO exhibited significant antimicrobial activity, antidiabetic and antiproliferative effects, the capability to scavenge free radicals, the potential to cure inflammation, and a significant effect on pain. The GC-MS analysis of the oil presented 60 bioactive compounds that contributed to $95.25\%$ of ingredients with dominant volatile constituents, i.e., camphene ($16.41\%$), thunbergol ($15.33\%$), limonene ($4.29\%$), eremophilene ($3.77\%$), and β-eudesmol ($3.51\%$). The ELEO attributed an appreciable analgesic significance ($58.33\%$) and an anti-inflammatory potential ($65.54\%$). The oil displayed prominent antioxidant activity using DPPH (IC50 133.53 ± 0.19 µg/mL) and ABTS (IC50 of 154.93 ± 0.17 µg/mL) assays. The EO determined effective resistance of 19.8 ± 0.02 mm ZOI against B. atrophaeus (Gram-positive bacteria) and *Aspergillus parasiticus* (18.03 ± 0.01 µg/mL, fungal strain). Moreover, the ELEO displayed significant cytotoxic potential with an IC50 = 183.8 ± 1.6 μg/mL against the triple-negative breast cancer (MDA-MB-231) cell lines and did not produce much harm to the normal cell lines (MCF-10A). Furthermore, significant inhibition was observed against α-glucosidase and carbonic anhydrase-II enzymes. In addition to that, further analyses are proposed to identify and isolate the responsible bioactive constituents for the investigated complications.
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|
---
title: Could Local Application of Hypoxia Inducible Factor 1-α Enhancer Deferoxamine
Be Promising for Preventing of Medication-Related Osteonecrosis of the Jaw?
authors:
- Gül Merve Yalcin-Ülker
- Murat Günbatan
- Gonca Duygu
- Merva Soluk-Tekkesin
- Ceyda Özcakir-Tomruk
journal: Biomedicines
year: 2023
pmcid: PMC10045901
doi: 10.3390/biomedicines11030758
license: CC BY 4.0
---
# Could Local Application of Hypoxia Inducible Factor 1-α Enhancer Deferoxamine Be Promising for Preventing of Medication-Related Osteonecrosis of the Jaw?
## Abstract
This experimental study investigates the prophylactic effect of deferoxamine (DFO) on medication-related osteonecrosis of the jaw (MRONJ). Thirty-six female Sprague Dawley rats received zoledronic acid (ZA) for eight weeks to create an osteonecrosis model. DFO was locally applied into the extraction sockets with gelatin sponge (GS) carriers to prevent MRONJ. The specimens were histopathologically and histomorphometrically evaluated. Hypoxia-inducible factor 1-alpha (HIF-1α) protein levels in the extraction sockets were quantified. New bone formation rate differed significantly between groups ($$p \leq 0.005$$). Newly formed bone ratios in the extraction sockets did not differ significantly between the control group and the GS ($$p \leq 1$$), GS/DFO ($$p \leq 0.749$$), ZA ($$p \leq 0.105$$), ZA-GS ($$p \leq 0.474$$), and ZA-GS/DFO ($$p \leq 1$$) groups. While newly formed bone rates were higher in the ZA-GS and ZA-GS/DFO groups than in the ZA group, the differences were not significant. HIF-1α levels differed significantly between groups ($p \leq 0.001$) and were significantly higher in the DFO and ZA-GS/DFO groups than in the control group ($$p \leq 0.001$$ and $$p \leq 0.004$$, respectively). While HIF-1α levels were higher in the ZA-GS/DFO group than in the ZA group, the difference was not significant. While HIF-1α protein levels and new bone formation rate were elevated in the DFO-treated group, the effect was not significant. Further large-scale studies are needed to understand DFO’s preventative effects on MRONJ and the role of HIF-1α in MRONJ pathogenesis.
## 1. Introduction
Bisphosphonate (BP) compounds are commonly used to reduce complications such as hypercalcemia and pathological fractures due to various malignant tumors by inhibiting bone osteoclastic activity, improving quality of life, and reducing pain in cancer patients with bone metastases. Intravenous BP therapy is effective in correcting hypercalcemia due to malignancy, and in treating tumors associated with breast, prostate, and lung cancers, and metastatic osteolytic lesions due to multiple myeloma [1,2]. The American Association of Oral and Maxillofacial Surgeons (AAOMS) describes BP-related osteonecrosis of the jaw (BRONJ) as a clinical condition characterized by the formation of exposed bone areas in the maxillofacial region that persist for ≥8 weeks in patients who have used or are still using BP-based drugs, and have not received radiotherapy to the head and neck region [3].
In recent years, BRONJ has been identified as a potential complication of nitrogen-containing BP (NBP)-based drugs, especially when administered intravenously. Since these clinical lesions can also be caused by antiresorptive drugs, such as denosumab and sunitinib, and antiangiogenetic drugs, such as bevacizumab, AAOMS has named this condition medication-related osteonecrosis of the jaw (MRONJ). AAOMS recently recognized other medications causing MRONJ, including fusion proteins (aflibercept), mammalian target of rapamycin (mTOR) inhibitors (everolimus), selective estrogen receptor modulators (raloxifene), immunosuppressants (methotrexate and corticosteroids), and antiresorptives prescribed for osteoporosis (romosozumab) [3].
While it remains unknown why BPs cause osteonecrosis, especially in the jaw, theories for MRONJ pathophysiology include changes in bone remodeling, excessive suppression of bone resorption, inhibition of angiogenesis, synergy between bone micro-fractures due to continuous microtrauma and periodontal bacterial invasion, suppression of innate or acquired immunity, vitamin D deficiency, toxicity, and inflammation or infection in soft tissues [3]. Studies have also shown that BPs inhibit vascular endothelial growth factor (VEGF) [4,5,6].
Previous experimental and clinical studies have shown that promoting angiogenesis is an important treatment strategy in the healing and regeneration of all tissues, including bone. Deferoxamine (DFO), which is indicated in the treatment of iron poisoning by the US Food and Drug Administration (FDA), has been shown in many studies to support bone healing, especially of irradiated bones [7,8,9]. DFO contributes to angiogenesis by inhibiting the prolyl-hydroxylase enzyme. It was observed that DFO causes iron chelation in the callus region in fracture healing models. In addition, it was observed that iron accumulation inhibits the prolyl-hydroxylation of hypoxia-inducable factor 1-alpha (HIF-1α), causing it to accumulate.
Elevated HIF-1α protein levels cause its nuclear translocation and dimerization with hypoxia-inducible factor 1-beta (HIF-1β), inducing the expression of VEGF and other mediators involved in neovascularization [10,11]. DFO has been shown to improve neoangiogenesis and neoangiogenesis via this mechanism when administered by local injection between bone fragments in fracture or distraction osteogenesis in experimental animal studies, facilitating osteogenesis [8,9,11].
In the current literature, there are experimental studies aiming at preventing MRONJ by inducing VEGF. In a study conducted by Tamari et al. [ 2019], they hypothesized that injection of endothelial progenitor cells to the surrounding soft tissue might stimulate MRONJ-like lesions [12]. Another study conducted by Sharma et al. [ 2021] on the effect of local delivery of hydrogel encapsulated VEGF for the prevention of medication-related osteonecrosis of the jaw has been investigated. They concluded that the application of locally delivered VEGF into the extraction sockets might induce bone healing and prevent MRONJ via a pro-angiogenic and immunomodulatory mechanism [13]. *In* general, the main aim of these studies was preventing MRONJ via a neoangiogenesis mechanism. Considering that the primary triggering factor of MRONJ is tooth extraction and the drugs that cause MRONJ formation are prescribed more and more each day, materials that are simpler to prepare for use in routine oral surgery practice and cheaper in terms of accessibility are needed.
Given the importance of vascularization inhibition via VEGF in MRONJ pathophysiology, these results suggest that the local application of an agent such as DFO to promote neoangiogenesis and healing by inducing VEGF could be protective against MRONJ after tooth extraction. This study investigates the effects of local application of DFO with a gelatin sponge (GS) carrier, which is used in daily oral surgery practice, on bone healing and neovascularization, and its protective effects against MRONJ in healthy and zoledronic acid (ZA)-treated rats.
## 2.1. Animal Care and Procedures
This study used 36 12-week-old female Sprague Dawley rats with an average weight of 200 ± 25 g from Yeditepe University Experimental Animals Research Center (YÜDETAM; Istanbul, Türkiye) and the study was approved by the Yeditepe University Experimental Animals Local Ethics Committee (protocol number 2020-845). Rats were randomly divided into six groups. The dosage and treatment duration of ZA injection was as described by Dayisoylu et al. [ 14]. Rats in groups I (control), II (GS), and III (GS/DFO) were given intraperitoneal (IP) sterile saline (SS) at 0.1 mg/mL three times a week for eight weeks, starting on the first day of the study. Rats in groups IV (ZA), V (ZA-GS), and VI (ZA-GS/DFO) were given IP ZA at 0.1 mg/kg (Zometa [4 mg/5 mL]; Novartis, Istanbul, Türkiye) three times a week for eight weeks, starting on the first day of the study. The procedures applied to rats in each group are shown in Table 1.
At the end of the eighth week, general anesthesia was induced by intramuscular injection of 80–100 mg/kg ketamine hydrochloride (Ketasol; Richterpharma, Wels, Austria) and 10 mg/kg $2\%$ xylazine hydrochloride (Rompun; Bayer, Türkiye) as both analgesic and anesthetic before tooth extraction under the supervision of a veterinarian. Bacitracin and neomycin sulfate ointment (Thiocillin [5 g]; Abdi İbrahim, Istanbul, Türkiye) was applied to the rat’s eyes to prevent ophthalmic complications during general anesthesia. Rats were prepared for surgery under aseptic and antiseptic conditions while under general anesthesia, and right upper first, second, and third molars were extracted.
The extraction sockets of rats in groups I (control) and IV (ZA) were left empty, and extraction socket healing was observed under normal and ZA-treated conditions. GSs (Surgifoam Gelatin Sponge; Ethicon, Johnson & Johnson, Raritan, NJ, USA) were cut to socket size and inserted into the extraction sockets of rats in groups II (GS) and V (ZA-GS). GSs cut to socket size and saturated with DFO (Desferal [0.5 g/7.5 mL]; Novartis) were inserted into the extraction sockets of rats in groups III (GS/DFO) and VI (ZA-GS/DFO). GS placement was fixed with 4.0 biodegradable sutures (Vicryl Rapide [polyglactin 910]; Ethicon, Johnson & Johnson, Raritan, NJ, USA).
The amount of DFO used to saturate GSs was calculated according to the volume of the rat extraction socket. In our previous study using the same physical conditions, it was observed that the mean socket bone volume following extraction of the lower left second molars of rats was 2.5834 ± 0.46697 mm3 under normal conditions (control group, equivalent to group I) and 2.6347 ± 0.2583 mm3 in ZA-treated rats (ZA group, equivalent to group IV) eight weeks after tooth extraction. A mean mandibular molar extraction socket volume was calculated based on these findings and was 2.60905 mm3 (~2.6 mm3) [15]. Given the extraction of 3 molars, the average volume was estimated to be 7.8 mm3, and a solution with a volume of 7.8 mm3 (7.8 µL) was prepared. The desired DFO concentration in the solution was calculated based on the dose used by Donneys et al. for pathological mandibular fractures (200 μg/300 μL) [9]. In this study, the DFO dose applied to the extraction socket was 5.2 μg/7.8 μL. Commercially available DFO (Desferal; Novartis, Istanbul, Türkiye) is sold as 0.5 g of sterile lyophilized powder in a 7.5 mL vial. When the ratios were compared, it was found that Donneys et al. used a solution with the same DFO concentration as the commercially available volume. Therefore, a 0.5 mg/7.5 mL solution containing lyophilized DFO powder and sterile distilled water was prepared and dropped on the sterile GS at a dose of 5.2 μg/7.8 μL with a sterile pipette tip.
All experimental rats were euthanized by decapitation eight weeks after tooth extraction and sixteen weeks after the start of the study (Table 1). Then, the maxilla of each rat was dissected and removed and placed in $10\%$ phosphate-buffered neutral formaldehyde for histopathological, histomorphometric, and immunohistochemical evaluation. Tissues were kept in this solution for two weeks for fixation.
## 2.2.1. Histopathological and Histomorphometric Examination
Dissected rat maxillae were kept in a prepared decalcification solution containing $50\%$ formic acid and $20\%$ sodium citrate. The solutions were changed once a week, and the decalcification process was completed after 30 days. After decalcification, sagittal sections covering the entire defect area were dissected. Paraffin blocks prepared from routinely processed decalcified specimens were cut into 4 μm slices and stained with hematoxylin and eosin (H&E). The stained sections were examined by a researcher blinded to the groups with an Olympus BX60 microscope connected to a computer with a color video camera (Tokyo, Japan). All measurements for histomorphometric analysis were made with the Olympus Image Analysis System 5. Images were taken at different magnifications via the camera, transferred to the computer screen, and calibrated. The histopathological presence of inflammation, foreign body reactions, and necrotic tissues was assessed. Inflammation was scored as 0 (absent), 1 (mild), 2 (moderate), and 3 (severe) according to its intensity. In the histomorphometric examination, all socket (total bone [TB]) and vital bone (VB) areas were measured. The rate of new bone formation was calculated using these data:New bone formation rate = (VB/TB) × 100
## 2.2.2. Immunohistochemical Staining and Evaluation
The paraffin blocks were cut serially into ~5 μm thick sections on charged slides for immunohistochemistry. Firstly, the sections were penetrated and dried overnight in an autoclave, then deparaffinized with xylene for 30 min, washed with $99\%$ alcohol for 15 min, followed by $96\%$ alcohol and distilled water. The Histostain-Plus Bulk Kit (Zymed Laboratories, South San Francisco, CA, USA) was used for analysis. The sections were microwaved four times for 5 min in a citrate buffer for antigen retrieval. Endogenous peroxidase activity was blocked by incubating the sections with $3\%$ hydrogen peroxide before washing with distilled water and phosphate-buffered saline for 5 min. Non-specific reactions were prevented by incubating sections with a blocking solution. Sections were incubated with a 1:50 dilution of the anti-HIF-1α primary antibody (GeneTEX, Irvine, CA, USA) for 120 min, followed by the secondary antibody for 25 min. The reaction was visualized using the chromogen 3-amino-9-ethylcarbazole (Zymed Laboratories, South San Francisco, CA, USA). Finally, the sections were counterstained with Mayer’s hematoxylin, coverslipped, and evaluated under a light microscope. A semiquantitative score system was used to evaluate immunostaining data:0(−): 0–$10\%$ staining immunopositivity1(+): 10–$25\%$ staining immunopositivity2(++): 25–$50\%$ staining immunopositivity3(+++): 50–$70\%$ staining immunopositivity4(++++): >$75\%$ staining immunopositivity
## 2.3. Statistical Evaluation
The data were analyzed using the Statistical Package for Social Sciences (SPSS) for Windows v.23 (IBM, Armonk, NY, USA). The normality of the data was assessed using the Shapiro–Wilk test. The chi-squared test was used to compare the inflammation and necrosis scores by group, and multiple comparisons were performed with a Bonferroni-correction Z-test. The histomorphometric analysis was statistically evaluated using a one-way analysis of variance to compare normally distributed data by group, and multiple comparisons were performed with the Tukey’s Honest Significant Difference test. The Kruskal–Wallis test was used to compare non-normally distributed data by group. Non-normally distributed immunohistochemical staining intensity scores were compared between groups using the Kruskal–Wallis test, and multiple comparisons were performed with Dunn’s test. All results are presented as mean ± standard deviation and median (minimum–maximum). All results with $p \leq 0.05$ were considered statistically significant.
## 3.1. Histopathological Analysis
Histopathological evaluations of extraction sockets are shown in Table 2, and histological images of H&E stained experimental groups are shown in Figure 1. The distributions of inflammatory responses did not differ significantly between groups ($$p \leq 0.108$$), and foreign body reactions were not observed in any group. However, necrotic area distributions did differ significantly between groups ($p \leq 0.001$). In groups I (control), II (GS), and III (GS/DFO), necrosis was not observed. There were necrotic areas in all specimens in group IV (ZA). Necrosis was present in $66.7\%$ of specimens in group V (ZA-GS) and $33.3\%$ in group VI (ZA-GS/DFO).
## 3.2. Histomorphometric Analysis
Newly formed bone rates in the extraction sockets were determined (Table 3) and differed significantly between groups ($$p \leq 0.005$$). They did not differ significantly between group I (Control) and groups II (GS; $$p \leq 1$$), III (GS/DFO; $$p \leq 0.749$$), IV (ZA; $$p \leq 0.105$$), V (ZA-GS; $$p \leq 0.474$$), and VI (ZA-GS/DFO; $$p \leq 1$$). However, they did differ significantly between group III (GS/DFO) and groups IV (ZA; $$p \leq 0.004$$) and V (ZA-GS; $$p \leq 0.037$$). While newly formed bone rates in groups V (ZA-GS) and VI (ZA-GS/DFO) were higher than in group IV (ZA), the difference was not significant ($$p \leq 0.946$$ and $$p \leq 0.193$$, respectively).
## 3.3. Immunohistochemical Analysis
Microscopic images of HIF-1α immunohistochemical staining are shown in Figure 2, and the median intensity scores for HIF-1α protein in the extraction sockets are shown in Table 4. HIF-1α protein levels differed significantly between groups ($p \leq 0.001$). HIF-1α intensity scores in group I (Control) did not differ significantly from those in groups II (GS; $$p \leq 1$$), IV (ZA; $$p \leq 0.442$$), and V (ZA-GS; $$p \leq 1$$). However, those of DFO groups III (DFO) and VI (ZA-GS/DFO) were significantly higher than those of group I (control; $$p \leq 0.001$$ and $$p \leq 0.004$$, respectively). In addition, HIF-1α intensity scores differed significantly between group II (GS) and DFO groups III (DFO; $$p \leq 0.005$$) and VI (ZA-GS/DFO; $$p \leq 0.011$$). However, the scores did not differ significantly between groups IV (ZA) and V (ZA-GS; $$p \leq 1$$). Moreover, while the scores were higher in group VI (ZA-GS/DFO) than in group IV (ZA), the difference was not significant ($$p \leq 1$$). Finally, HIF-1α intensity scores did not differ significantly between groups V (ZA-GS) and VI (ZA-GS/DFO; $$p \leq 0.442$$).
## 4. Discussion
With increasing life expectancy and various direct and indirect treatment modalities targeting bone and surrounding structures, modern clinicians have to cope with the side effects and complications of these drugs, including MRONJ. While some accepted MRONJ treatment strategies exist in the literature, none are entirely evidence-based. Furthermore, the systemic condition and host responses of patients taking these drugs vary. Therefore, preventing such complications is more logical than coping with them.
The most important factor in physiological or pathological wound healing processes is ensuring the adequate transportation of required defense cells, growth factors, cytokines, and progenitor cells to the affected area. The success of this process depends on the area’s adequate vascularization or sufficient neoangiogenesis. Angiogenesis is the formation of new blood vessels during endothelial cell growth, differentiation, and migration. During this mechanism, signaling molecules such as VEGF, the protein primarily inducing and regulating vascular growth, must bind to receptors on endothelial cells [16,17]. It has been shown that inhibiting angiogenesis is effective in causing MRONJ [3].
It is widely believed that BPs have antiangiogenic properties and suppress VEGF production via apoptosis [18,19]. NBPs such as ZA directly inhibit angiogenesis in vitro and in vivo, reducing vascularity in MRONJ lesions and quantitatively decreasing microvessels during early bone healing stages [5,6,13,20,21]. Furthermore, angiogenesis in post-extraction socket healing is inhibited by BPs, and both BPs and denosumab led to decreased arterial area, venous area, and overall vascularity in periodontal tissues during early and late MRONJ development [22]. These data suggest that microcirculation disorder in the lesion area may be an important contributor to MRONJ formation.
VEGF and transforming growth factor-β1 (TGF-β1) are synergistically effective in the MRONJ mechanism. While low-dose BPs increase osteoblast proliferation in the early stage, they reduce the cells’ differentiation capacity, resulting in damage to bone quality [23]. Manzano-Monero et al. [ 2018] reported that low-dose BP is effective by increasing levels of molecules such as TGF-β1 and VEGF, which affect cell growth, and decreasing levels of molecules such as bone morphogenetic protein 2 (BMP2) and receptor activator of nuclear factor kappa-Β ligand (RANKL), which are necessary for cell maturation [24,25,26]. Therefore, it can be hypothesized that VEGF inhibition is very important for MRONJ pathogenesis due to its direct and indirect effects.
Some studies have explored the importance of neovascularization of the region in treating and preventing MRONJ, both for inflammatory response regulation and growth factor migration to the region. One of the most important of these are the autologous platelet concentrates (APCs), frequently used for regenerative purposes in oral and maxillofacial surgery. APCs include growth factors such as platelet-derived growth factor (PDGF), TGF-β1, VEGF, and epidermal growth factor EGF [27,28,29]. APCs have local effects by being applied in combination with surgical treatments. These platelet-rich preparations accelerate tissue healing and bone regeneration [29]. The main APC role in tissue healing, which involves growth factor release in the necrotic bone area, is the stimulation of tissue healing through cell chemotaxis, proliferation, and differentiation, angiogenesis, and new bone matrix deposition [30].
APCs are classified based on leukocyte and fibrin content. Of these, platelet-rich plasma (PRP) and platelet-rich fibrin (PRF) are frequently used to treat MRONJ [27,28,29]. In addition, studies are reporting that PRF, which is placed and fixed in the socket following the tooth extraction, significantly reduces early complications when tooth extraction is planned in patients using antiresorptive drugs such as BP or denosumab [31]. Additionally, there are clinical reports supporting the curative effect of PRF in combination with photobiomodulation for MRONJ [32]. It has been shown that photobiomodulation has a contributing effect on new bone formation, and organization of deposition of collagen [33].
The disadvantage of using these autologous products is that they cannot maintain long-term stability. It has been reported that PRFs maintain stability for 3 to 7 days, depending on the method [34]. VEGF must be at a certain level for four weeks to stabilize endothelial cells in newly formed vessels. However, VEGF’s very short half-life precludes the effective use of its recombinant protein, either experimentally or clinically [35,36,37]. Therefore, the indirect induction of neoangiogenesis appears to be a more effective approach for maintaining recovery.
Physiologically, VEGF production is induced by HIF-1α. HIF-1α is one of four subunits of an αβ heterodimeric transcription factor called HIF (HIF-1α, HIF-2α, HIF-3α, and HIF-1β) that is active in hypoxic environments [38,39]. Wang and Semenza [1993] first suggested that DFO, an iron chelator agent, could induce HIF-1α activity [38]. DFO is a chelating agent used to treat iron poisoning and hemochromatosis. It causes the induction of HIF-1α expression, inducing the production of VEGF and other angiogenic factors [40]. It has been reported that DFO contributes to osteogenic and angiogenic responses in bone in surgical procedures targeting new bone formation, such as distraction osteogenesis applied to long bones [41].
Farberg et al. [ 2012] investigated the effect of DFO on radiation-induced hypovascularity and impaired bone healing in rats through distraction osteogenesis in irradiated jaws [8]. Examination with a microcomputed tomography angiography method found high neovascularization in the distraction osteogenesis spaces of the rat jaws treated with DFO. Furthermore, they observed new bone formation between the irradiated bone fragments of all DFO-treated rats. In addition, Donneys et al. [ 2013] investigated the healing effect of DFO injections into the space between fracture fragments in jaws reverted to a pathological healing pattern by radiation [9]. They observed that when DFO was applied in samples where pathological healing was expected, the healing was supported, and neoangiogenesis, a prerequisite for a healthy recovery, was realized in $42\%$.
Chung et al. [ 2013] found that DFO applied to human periodontal ligament cells induced osteoblastic activity and mineralization via the mitogen-activated protein kinase (MAPK), nuclear factor κB (NF-κB), and nuclear erythroid 2-related factor-2/antioxidant response element pathways [42]. Furthermore, Jia et al. [ 2016] reported that DFO does not affect mesenchymal stem cell proliferation in osteoporotic rats. However, it induced the expression of angiogenetic factors by inducing osteogenic differentiation and upregulating mRNA in mesenchymal stem cells [43].
Furthermore, some studies have reported that DFO promotes healing by inducing neoangiogenesis in tissues other than bone. Bonham et al. [ 2018] investigated pressure sores in diabetic rats and found that local DFO injections into the area may have a healing effect [44]. Another study on mature diabetic rats showed that DFO could regulate recovery by contributing to neovascularization in diabetic elderly rats [45]. A study by Sinder et al. [ 2018] investigated post-surgical radiotherapy treatment of breast cancer patients who had resective surgery by atomic force microscopy, showing that topical DFO affected collagen fibril organization and wound healing. It was concluded that DFO could eliminate the effects of radiation both macroscopically and microscopically in the areas where it was applied [46].
In this study, while GS increased new bone formation in the extraction sockets of non-ZA-treated rats, the difference was not significant. Similarly, DFO-saturated GS increased new bone formation in the healthy extraction sockets, but the difference was not significant. Nevertheless, immunohistochemical analysis of these groups showed significantly elevated HIF-1α protein levels in extraction sockets in group III (GS/DFO) compared with the control group ($$p \leq 0.004$$). We aimed to investigate the effect of DFO on MRONJ rat models, and performed histological evaluations eight weeks after tooth extraction in rats to evaluate the late phase of healing in extraction sockets. Consequently, the elevated HIF-1α protein levels might indicate the accelerating DFO effect on the early phase of extraction socket healing. However, this study’s findings are insufficient to discuss this effect, and further studies are needed to explore this phenomenon.
When the new bone formation rate was evaluated in ZA-treated groups, the effect was similar to the control groups. While GS increased new bone formation in the extraction sockets, the difference was not significant. Similarly, DFO-saturated GS increased new bone formation in the extraction socket of ZA-treated rats, but the difference was not significant. However, there was also no significant difference between the control group and the DFO-applied experimental group (VI). Furthermore, immunohistochemical analyses showed that ZA treatment increased HIF-1α protein levels compared with the control group. However, this increase was only significant between the control and DFO-treated experimental group (VI). The findings of the studies investigating the effect of ZA on HIF-1α protein levels are controversial. Minegaki et al. [ 2018] reported that hypoxic HIF-1a protein levels were unaffected by ZA-treatment [47].
Other studies have explored the possible connection between MRONJ pathophysiology and the HIF-1α/VEGF pathway. Ge et al. [ 2016] showed that ZA dose-dependently inhibited cell viability, migration, adhesion, and tube formation by decreasing VEGF expression and secretion. Here, ZA decreased HIF-1α protein levels but did not affect HIF-1α mRNA levels and promoter activity. In addition, they found that ZA decreased HIF-1α protein stability by reducing the activation of the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT)/mTOR and MAPK pathways [48]. Controversially, Trebec-Reynolds et al. [ 2010] investigated differences in signaling pathways between large and small osteoclasts. They found that VEGF-A mRNA and protein levels were elevated in large osteoclasts, found mostly in MRONJ and periodontitis specimens, compared to small osteoclasts, and that this increase was regulated by HIF-1α, whose mRNA levels were induced by RANKL-mediated activation of NF-κB [49].
We suggest that our findings could reflect our chosen experimental model in which teeth were extracted after eight weeks of ZA treatment. Some studies have explored the effect of DFO on the inflammatory process. Oses et al. [ 2017] used adipose tissue-derived mesenchymal stem cells (AdMSC) pre-conditioned with DFO under in vitro conditions to investigate the expression of specific factors and cytokines [50]. They reported that DFO increases the expression of proinflammatory cytokines such as interleukin (IL)-4 and IL-5 to indirectly increase the expression of proangiogenic factors such as VEGF and angiopoietin 1 by inducing HIF-1α. Another study used AdMSCs pre-conditioned with DFO and applied to the RAW 264.7 cells to examine DFO’s effect on macrophage polarization [51]. They concluded that DFO might have an immunomodulatory role by inducing macrophage polarization at the M2 phase. In addition, Hellwig-Bürgel et al. [ 2005] reported that HIF-1α is closely related to immune reactions due to its key mediatory role [52]. Studies on inflammation in MRONJ pathogenesis have recently started to be performed. Numerous studies have also reported that disrupted healing is caused by an insufficient or excessive inflammatory response [21]. Paschalidi et al. [ 2021] studied osteonecrotic tissue debrided from 30 post-operative patients with MRONJ, classifying M1 and M2 macrophages according to MRONJ stage using the immunofluorescence method [53]. They found that patients with early-stage MRONJ shifted toward M2 macrophages, while patients with advanced-stage MRONJ shifted toward M1 macrophages. They concluded that regulating macrophage function could be important in MRONJ treatment strategies.
In this study, we histopathologically examined inflammation with a semiquantitative method. We found that DFO did not contribute significantly to inflammation in the groups in which it was applied ($$p \leq 0.108$$). We believe this might be due to the suture material applied to fix the GS in the socket in the GS-applied groups affecting the inflammatory response. We applied DFO locally into the extraction sockets. Previous studies on DFO are either in vitro or animal-based. Several studies used distraction osteogenesis in irradiated jaws to examine the healing of the pathological fracture line, locally injecting DFO into the affected area [8,9]. They investigated a carrier molecule since the extraction sockets of rats and humans are not enclosed spaces, and it is not clinically feasible to inject DFO into the extraction socket. Another study by the same group examined pathological fracture healing in irradiated jaws using an implantable hyaluronic acid (HA–DFO) conjugate, finding that the healing properties of the HA–DFO conjugate were observed in $91\%$ of experimental pathological fractures [11].
We believe that conjugating DFO with an agent known to contribute to regeneration, such as HA, might be a very useful approach for increasing regeneration capacity and facilitating the local application of agents that promote healing, such as DFO, especially when a carrier is required [54]. However, using such conjugated agents requires significant investigation, and the difficulties associated with their production and storage in routine oral surgical applications such as tooth extraction are a significant disadvantage. Therefore, we opted to use GS, an inexpensive and accessible agent used in oral surgery practice.
While gelatin is the product of partial hydrolysis of natural collagen, it is used as a dressing material in clinical applications of tissue engineering applications and sponge form in drug delivery systems due to its non-toxic and non-carcinogenic properties, biocompatibility, and biodegradability [55]. Gelatin can be prepared in a spongy form suitable for tissue engineering applications. The porous 3D structure of GS scaffolds can provide multiple spaces for cell adhesion [55]. The mechanical properties of GS are improved using elements such as colloidal silver and gold nanoparticles, and chemicals such as antibiotics, collagen, transglutaminase, glutaraldehyde, and chitosan to enhance its anti-inflammatory and antibacterial properties [55,56,57]. In addition, it has been observed that GS can be used as a dressing material in conjugate form, or by absorbing the agent to facilitate healing.
In this study, a commercially available GS for routine oral surgery use known to contain only gelatin was chosen because it is believed that any additional material would make it difficult to understand the effect of DFO alone. While calculating the amount of DFO to be used, the concentration used by Donneys et al. [ 2013] in pathological mandibular fractures was used as its basis with the average extraction socket volume. The major limitation of this study was that it was unclear how many days the DFO-saturated GSs biodegraded, and how many days these materials continued to release DFO in the extraction sockets. Further studies are needed to understand this in more detail and provide scientific, evidence-based support for using GS, a cheap and practical material used in oral surgery practice, combined with drugs that enhance wound and bone healing, such as DFO.
## 5. Conclusions
This is the first experimental study investigating the prophylactic effect of local DFO application on MRONJ. GSs saturated with DFO were applied to the extraction sockets of rats following eight weeks of ZA treatment. While elevated HIF-1α protein levels and new bone formation were observed in the DFO-treated group, the effect was not significant. However, the absence of a significant effect on new bone formation rate but a significant effect on HIF-1α protein levels between the DFO-saturated GS and control groups suggests that local DFO application might be prophylactic for MRONJ after tooth extraction. Further studies are needed with more specimens to understand this effect. In addition, molecular studies are required to understand the importance of the RANKL/NF-κB/HIF-1α/VEGF pathway on pathological and inflammatory bone loss and MRONJ in the context of the DFO HIF-1α inducer. Such studies will be useful both for understanding the possible prophylactic and therapeutic effects of DFO, and for a deeper understanding of MRONJ pathophysiology.
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|
---
title: Polyphenol Profile of Cistus × incanus L. and Its Relevance to Antioxidant
Effect and α-Glucosidase Inhibition
authors:
- Aneta Starzec
- Maciej Włodarczyk
- Dominika Kunachowicz
- Andrzej Dryś
- Marta Kepinska
- Izabela Fecka
journal: Antioxidants
year: 2023
pmcid: PMC10045904
doi: 10.3390/antiox12030553
license: CC BY 4.0
---
# Polyphenol Profile of Cistus × incanus L. and Its Relevance to Antioxidant Effect and α-Glucosidase Inhibition
## Abstract
The European Food Safety Authority recommends C. incanus as a natural source of antioxidants. Its activity is essentially determined by polyphenols, although specific compounds are not finally indicated. The available plant material comes from different subspecies and locations, which can lead to differences in chemical composition and potency. For this reason, we conducted a detailed analysis of the polyphenol content and antioxidant activity of 52 different C. incanus teas from Turkey, Albania, Greece, and unspecified regions. We focused special attention on ellagitannins, which have not been properly determined so far. Besides oxidative stress, hyperglycemia is an essential component of cardiometabolic diseases. Therefore, in subsequent experiments, we evaluated the ability of C. incanus extracts and individual polyphenols to inhibit α-glucosidase. Using statistical methods, we analyzed how differences in chemical composition affect activity. The results showed that C. incanus is a rich source of ellagitannins (2.5–$19\%$), which dominate among polyphenols (5.5–$23\%$). Turkish-origin products had higher ellagitannin content and a greater antioxidant effect (FRAP, ABTS) than Albanian and Greek products. In contrast, the flavonoid and phenolic acid contents and DPPH values were at similar levels in all products. An in-depth analysis of their composition indicated that all groups of polyphenols are involved in the antioxidant effect, but a significant contribution can be attributed to ellagitannins and flavonoids. C. incanus extracts showed a high capacity to inhibit α-glucosidase activity (IC50 125–145 μg/mL). Ellagitannins were the most effective inhibitors (IC50 0.7–1.1 μM), with a potency exceeding acarbose (3.3 mM). In conclusion, C. incanus, due to the presence of ellagitannins and flavonoids, exhibits powerful antioxidant and α-glucosidase inhibitory effects.
## 1. Introduction
Cistus × incanus L. is widely used in the food and pharmaceutical industries as an excellent source of antioxidants. Preparations of C. incanus have been recommended as a natural product providing compounds with such effects by the European Food Safety Authority (EFSA) since 2010 [1]. Its antioxidant activity is mainly associated with the presence of polyphenols.
The taxonomy of the Cistus (rock-rose) genus is complex, which can cause problems identifying individual species and plant material. Frequent polymorphism of vegetative and generative organs and possible hybridization between related species result in numerous subspecies being distinguished. For example, C. incanus is considered a hybrid between C. albidus and C. crispus. At present, we distinguish three subspecies: incanus, corsicus, and creticus (=C. creticus) [2,3,4]. Plant material from different subspecies and regions can vary significantly in chemical composition and activity.
Plants of the genus Cistus have been widely used in the traditional medicine of Mediterranean countries. Rock-rose has been recommended for skin wounds, respiratory inflammation, circulatory and urinary disease, and diabetes. Its extracts have also been used for gastrointestinal disorders, peptic ulcers, and diarrhea [4,5].
An imbalance between endogenous antioxidants and prooxidants is associated with the occurrence of oxidative stress and the production of multiple reactive oxygen species (ROS), which is strongly correlated with the pathogenesis of non-communicable chronic diseases. Polyphenols can quench ROS, chelate transition metals, trap reactive dicarbonyls (e.g., methylglyoxal), and, as a result, reduce lipid peroxidation and post-translational modification of proteins and lipoproteins, and protect DNA from interaction with a wide range of metabolites [6,7,8]. Therefore, C. incanus preparations are suggested as dietary supplements to strengthen the immune system and prevent many chronic diseases such as type 2 diabetes and cardiovascular, liver, and kidney diseases [4,6,7].
Type 2 diabetes is a severe chronic metabolic disorder that causes many health complications and is one of the leading causes of mortality [9,10]. Excessive postprandial hyperglycemia (PPHG) is one of the most important factors in the development of diabetes. PPHG is also a strong predictor of cardiovascular disease; it increases oxidative stress through vascular endothelial dysfunction and subclinical inflammation, resulting in atherosclerosis and cardiovascular events [10]. High post-meal glucose levels are closely related to the diet’s carbohydrate content and the action of α-glucosidase and α-amylase in the gastrointestinal tract. These enzymes are crucial for carbohydrate digestion [10,11]. In light of this, agents that can reduce PPHG by delaying the digestion of complex carbohydrates in the diet may be an attractive preventative option and an adjunctive treatment for diabetes [10]. There are scientific reports suggesting that some polyphenols may have the ability to inhibit carbohydrate hydrolases, but there are few such studies on Cistus extracts [11].
Rock-rose is characterized by a wide variety of polyphenols. Despite the availability of literature data, its complete chemical profile is not yet known. The contents of individual components depend on genotype, environmental, cultivation, and storage conditions [4,12]. The main compounds found in C. incanus include flavonoids; among them, flavonols and flavan-3-ols are distinctive [13,14,15,16,17]. The non-flavonoid compounds include ellagitannins [15,18,19]. In addition, free phenolic acids are present [17,20]. All Cistus species also contain essential oil and a brown resin comprising labdane-type diterpenes [13,14,16].
In a study evaluating the effect of regular administration of C. incanus, Kuchta et al. [ 21] observed a reduction in cardiometabolic risk factors in healthy volunteers, including oxidative stress and dyslipidemia, by improving the lipid profile. Moreover, our previous in vitro studies showed that rock-rose flavonols exhibit antiglycative activity by inhibiting protein glycation [8]. However, the components responsible for the observed antioxidant, antidiabetic, and hypolipidemic effects have not been identified, while the market of Cistus products of different or unspecified origins is growing.
Therefore, we aimed to compare the antioxidant activity in relation to the chemical composition of C. incanus teas of different origins. We evaluated the content of polyphenols, including individual ellagitannins, flavonoids, and phenolic acids. Furthermore, in our study, we assessed in vitro the potential of C. incanus to modify polysaccharide metabolism through the effects on α-glucosidase by analysis of both the extracts themselves and individual polyphenols. Using statistical methods, we investigated how the chemical profile and its revealed differences affect antioxidant and anti-glucosidase activity.
## 2.1. Chemicals and Reference Materials
Folin–Ciocalteu reagent, iron (III) chloride, and iron (II) sulfate heptahydrate were obtained from Chempur (Poland). Aluminium chloride and 2,4,6-tris-(2-pyridyl)-s-triazine (TPTZ) was from Fluka (Switzerland). ABTS, DPPH, DMSO, Trolox, acarbose, 4-nitrophenol (p-NP), 4-nitrophenyl-α-D-glucopyranoside (p-NPG), α-glucosidase from Saccharomyces cerevisiae, LC and LC-MS grade solvents, and others were from Sigma-Aldrich (Germany).
Myricetin, myricetin-3-O-α-rhamnoside (myricitrin), myricetin-3-O-β-galactoside (gmelinoside I), quercetin, quercetin-3-O-α-rhamnoside (quercitrin), quercetin-3-O-β-glucoside (isoquercitrin), quercetin-3-O-β-galactoside (hyperoside), quercetin-3-O-β-glucuronoside, kaempferol, kaempferol-3-O-β-glucoside (astragalin), kaempferol-3-O-β-glucuronoside, kaempferol-3-O-β-(6″-O-p-coumaroyl)glucoside (tiliroside), gallic acid, ellagic acid, (+)-catechin, (−)-epicatechin, procyanidins A2, B1, B2 and C2 were purchased from Extrasynthese (France). Cistusin, punicalagin, and terflavin A were isolated from C. incanus by Fecka et al. [ 18], and myricetin-3-O-β-glucuronoside by Fecka [22]. All assayed compounds were at least of $95\%$ purity (HPLC-DAD).
Stock solutions (1 mg/mL) were prepared with methanol for most compounds and with DMSO for ellagic acid. The obtained solutions were then diluted with $50\%$ aq. methanol (v/v) to concentrations 20–200 μg/mL, and filtered by Durapore 0.45 and 0.22 μm filters (Millipore, Sigma-Aldrich).
Some minor polyphenols were identified by co-chromatography with the reference materials (Galke, Germany): helichrysoside → *Helichrysum arenarium* [23,24], kaempferol-3-O-β-galactoside → *Menyanthes trifoliata* [25], myricetin-3-O-β-glucoside → *Humulus lupulus* and *Vitis vinifera* [26,27], myricetin-3-O-α-arabinofuranoside → Polygonum aviculare [28], quercetin-3-O-β-xyloside → *Malus domestica* and *Pyrus communis* [29], quercetin-3-O-α-arabinofuranoside → Polygonum aviculare [28], punicalin → *Punica granatum* [30].
## 2.2. General Equipment
LC analysis was performed in a Dionex Ultimate 3000 liquid chromatograph with an autosampler (WPS-3000TSL), a pump (LPG-3400SD), a column thermostat (TCC-3000SD), and a diode detector (DAD-3000) (Thermo-Fisher Scientific, Sunnyvale, CA, USA). The system was connected with Chromeleon 7 software.
Spectrophotometric measurements were conducted using a Multiskan GO Spectrophotometer (Thermo-Fisher Scientific). The %Inhibition was calculated from the formula % inhibition=(AControl− ASampleAControl)×100 MS spectra in the negative mode were recorded on an ESI-qTOF Compact mass spectrometer (Bruker, Bremen, Germany). The mass spectrometer was re-calibrated for every run [31]. 1H-NMR, 13C-NMR, HSQC, HMBC, and COSY experiments were recorded on Avance 300 MHz spectrometer (Bruker) in DMSO-d6 and calibrated using the residual solvent peak. The data were processed with MestReNova 12 software (Mestrelab Research, Santiago de Compostela, A Coruña, Spain).
## 2.3. Plant Material and Extracts
The material used in this study consisted of 52 commercial products (teas) containing dried leaves or shoot tops (herbs) of Cistus × incanus L. (labeled as Ci1–Ci52). The products were purchased from various companies and came from different countries (Albania, Greece, Turkey) or had no specific origin. All C. incanus samples (approx. 10 g each) were homogenized with an analytical mill A 11 basic (IKA, Königswinter, Germany) and sieved through a 0.355 mm sieve (Multiserw-Morek, Marcyporęba, Poland).
Approx. 1 g of homogenized material was transferred into 50 mL of $55\%$ aq. methanol (v/v) and heated in a water bath under a reflux condenser for 15 min after reaching the boiling point. Next, the extracts were decanted and filtered through Durapore 0.45 and 0.22 μm filters. The drug-extract ratio was 1:50. Extracts were prepared in triplicate.
## 2.4. Preparative Separation of C. Incanus Flavonols
The main flavonols were isolated from C. incanus water-acetone extract on octadecyl by column chromatography and then purified on Sephadex LH-20. Mixtures of water-methanol (RP-18) and methanol (LH-20) were used as eluents. The procedure was described previously [18].
The resulting compounds were subjected to MS and NMR. Spectroscopic data were compared with data from the scientific literature and authentic standards.
## 2.5. UHPLC-ESI-qTOF-MS and HPLC-DAD Experiments
*In* general, the conditions for confirming the analyzed polyphenols’ identity were described before [18,31]. The separations were achieved on the Ultimate 3000 liquid chromatograph with DAD and ESI-qTOF Compact detectors and an octadecyl Kinetex column (150 × 2.1 mm; 2.6 μm; Phenomenex, Torrance, CA, USA). The resulting chromatograms, together with base and fragmentation mass spectra, were analyzed with Data Analysis 4.2 software (Bruker).
The content of polyphenols was analyzed on an octadecyl Hypersil Gold column (250 × 4.6 mm; 5 μm; Thermo-Fisher Scientific) by the method described previously [18]. Flavonols, ellagic acid and ellagitannins were detected at 254 nm, gallic acid and flavan-3-ols at 280 nm, and coumaroyl-flavonols at 320 nm (Table S5). For each polyphenol, the mean content in the test product and the standard deviation (SD) were determined. In addition, the mean compound content, SD, median, minimum, and maximum values were calculated for the groups of products. Due to the lack of authentic standards, some components were calculated from regression equations of related compounds. The amount of hexahydroxydiphenoyl-glucose and punicalin was calculated as punicalagin, myricetin-3-O-glucoside and myricetin-3-O-arabinoside as myricetin-3-O-galactoside, quercetin-O-arabinoside as quercetin-3-O-galactoside, helichrysoside and coumaroyl-tiliroside as tiliroside. Differences in molecular masses were taken into account in each conversion. Sums of ellagitannins (SET), phenolic acids (SPA), flavonoids (SF, flavonols+flavan-3-ols), and polyphenols (SPP) were obtained by summing the compounds in each group.
## 2.6. Total Phenolic (TPC) and Flavonoid (TFC) Content
TPC was estimated using the Folin–Ciocalteu method described by Singleton et al. [ 32], while TFC was estimated according to the European Pharmacopoeia method for *Betulae folium* [33], with some modifications. A detailed description of the procedures can be found in our publication [8]. TPC results were expressed as mg of gallic acid equivalents per gram of dry weight (mg GAE/g d.w.). The TFC was expressed as myricetin equivalents (mg ME/g d.w.).
## 2.7.1. DPPH and ABTS
The modified Blois method was used [34]. In a 96-well microplate, 200 µL of DPPH solution was added to 20 µL of each diluted extract (1:30). The plate was incubated for 30 min at ambient temperature in the dark. The absorbance was measured at 517 nm.
The method developed by Chen and Kang [35] was applied to evaluate the antioxidant effect using the ABTS radical. ABTS reagent was obtained according to the procedure described previously [8]. In a 96-well microplate, 200 µL of ABTS reagent and 2 µL of each diluted extract (1:30) were mixed. The plate was incubated for 15 min. Absorbance was measured at 734 nm.
Calibration was set for gallic acid (0.06–0.6 µM/mL). Results were expressed as a percentage of DPPH or ABTS radical inhibition and as mM of gallic acid equivalents per gram of dry weight (GAE/g d.w.).
## 2.7.2. FRAP
The FRAP assay, according to the method of Benzie and Strain [36] with slight modifications [8], was used. Then, 20 µL of diluted extracts (1:30) and 200 µL of FRAP reagent were each applied to the microplate. The plate was incubated for 4 min at ambient temperature in the dark. Absorbances at 593 nm were measured.
Calibration was performed for both ferrous ions Fe(II) (0.01–0.6 µM/mL) and gallic acid (0.1–2 µM/mL). Results were expressed as mM of Fe(II) per gram of dry weight and mM of GAE/g d.w.
## 2.8. α-Glucosidase Inhibitory Assay
The α-glucosidase inhibitory activity was conducted in a 96-well microplate using 4-nitrophenyl-α-D-glucopyranoside (p-NPG), as reported previously [37]. Briefly, 90 µL of 0.1 M sodium phosphate buffer (pH = 6.9) was mixed with 50 µL of sample (250 µg/mL, methanol-buffer 1:1) and 40 µL of enzyme solution (0.25 U/mL). Acarbose (5 mg/mL) was used as a positive control. The microplate with samples and enzyme solution was initially incubated for 5 min at 37 °C. After preincubation, the reaction was started by adding 20 µL of 5 mM p-NPG to the wells containing extracts/polyphenols or acarbose. The reaction was allowed to run for 10 min and afterward stopped by adding 50 µL of 0.1 M Na2CO3. The absorbance was measured at 405 nm. All of the samples were tested in triplicate.
The readings were reduced by absorbance of the sample blanks measured separately for each extract/polyphenol, which consisted of 50 µL of extract diluted in 150 µL of buffer solution only, while values obtained for uninhibited controls were reduced by absorbance values of the buffer solution (buffer blank). The activity was expressed as %inhibition. The half-maximal inhibitory concentration (IC50) values, defined as concentration of inhibitor necessary to reduce the rate of an enzymatic process by $50\%$, were calculated as follows:IC50=sample concentration ×$50\%$ inhibition
## 2.9. Kinetics of α-Glucosidase Inhibition
Polyphenols with >$70\%$ of α-glucosidase inhibition (gallic acid, ellagic acid, cistusin, punicalagin, terflavin A, tiliroside) were subjected to a kinetic study aiming to determine their mode of enzyme inhibition. Inhibition modes of α-glucosidase were determined according to Chu et al. [ 38]. Basically, the varying concentrations of polyphenols (0.5–250 µg/mL) and acarbose (2.5–10 mg/mL) were tested for inhibitory activity in the range of increasing p-NPG concentrations (1–20 mM) with a fixed enzyme concentration (0.25 U/mL). After 5 min of preincubation, the addition of the substrate started the reaction, and the changes in absorbance were measured at 30 s intervals. The uninhibited reaction was also run. The reaction was terminated after 10 min, and the absorbance values obtained in the kinetic study were used to create Lineweaver–Burk plots of 1/v against 1/[S].
## 2.10. Statistical Analysis
Statistica software (StatSoft, TIBCO Software Inc., Palo Alto, CA, USA) was used for statistical analysis. Normality of distribution was checked using the Shapiro–Wilk test. Analysis of variance (ANOVA) and post-hoc Fisher’s LSD test was performed. Correlation between data was checked using linear regression and Spearman’s rank correlation coefficient. Statistical significance for all tests was taken as $p \leq 0.05.$ For details, see the Supplementary Materials in Section S4.
## 3. Results and Discussion
C. incanus teas are popular as products with antioxidant potential. Therefore, they are used in the prevention and supportive therapy of many chronic diseases, including cardiometabolic diseases, in which oxidative stress and hyperglycemia are relevant to pathomechanism [5,6,7,16,21,39].
Rock-rose is also a source of many polyphenols, but their profile has not been fully defined, especially for materials of diverse origins [5,13,15,16,18,20,21]. Therefore, we carried out a detailed study of the polyphenol profile in 52 commercial C. incanus teas, of which 23 were of Turkish origin, 10 were from Albania, and 3 were from Greece (the data obtained for these materials may not be precise due to the small number of samples). The other products had no specified place of cultivation. However, our previous study showed that these products were possibly Albanian [8]. The present work is a continuation of that research.
Since C. incanus is well described in terms of antioxidant properties, we conducted antioxidant tests to compare the effect of the chemical profile on this action. It is likely that one of the groups of rock-rose polyphenols is ellagitannins, as described previously [18], so we focused on the determination of individual compounds from this group (punicalagin, cistusin, terflavin A). Flavonoids and phenolic acids were also quantified. In the first stage, we standardized the extraction method. Comprehensive information can be found in the Supplementary Materials (Section S1, Tables S1–S4). Extraction with $55\%$ aq. methanol was chosen for routine assays. Then, the content of polyphenols determined by chromatographic and spectrophotometric methods was compared. Since punicalagin is a known inhibitor of α-glucosidase [40], we decided to test C. incanus extracts and individual polyphenols as potential inhibitors of an enzyme involved in polysaccharide metabolism.
## 3.1. C. Incanus Polyphenols
The polyphenol profile was verified by UHPLC-ESI-qTOF-MS using extracts from six products (Ci5, Ci30, Ci38, Ci47, Ci48, Ci53) of different origins. In addition, two solvent gradients were used to enhance the information on co-eluting compounds. For polyphenol identification, authentic standards, ellagitannins, and flavonoids isolated from C. incanus (Section 2.4) were used. Details of the identified compounds are summarized in Table S5.
UHPLC-ESI-qTOF-MS analysis identified 54 polyphenolic compounds (peaks 1–54) based on reference substances, literature data, interpretation of UV/Vis spectra, retention times, and mass spectra. Among the tannins and related compounds were cistusin, punicalagin, punicalin, terflavin A, hexahydrodiphenoyl-glucose (HHDP-Glc), gallic and ellagic acids, ellagic acid glycosides, as well as flavan-3-ols such as catechin, epicatechin, and gallocatechin.
We isolated the main ellagitannins and flavonoids according to the procedure described before [18]. The following common flavonols were identified by HRMS and MS/MS together with NMR: myricetin-3-O-β-galactoside (=gmelinoside-I), myricetin-3-O-β-rhamnoside (=myricitrin) and myricetin-3-O-α-arabinoside. Of them, myricetin-3-O-β-galactoside was isolated by Gürbuz et al. [ 41] from C. salviifolius and reported by Riehle et al. [ 42] in C. incanus, myricetin-3-O-α-arabinoside was not confirmed in C. incanus previously while myricetin-3-O-β-rhamnoside was reported in the genus Cistus by Wittpahl et al. [ 15]. The other observed compounds with a myricetin core were myricetin-O-hexoside gallate and minor pentoside. The latest one was co-chromatographed with Polygonum aviculare herb extract and identified as myricetin-3-O-α-arabinoside (=betmidin, furanoside form) [28].
The main quercetin glycosides—3-O-β-galactoside (=hyperoside), 3-O-β-glucoside (=isoquercitrin), and 3-O-β-rhamnoside (=quercitrin)—from fractions were additionally compared with authentic standards. Of them, hyperoside was isolated by Gürbuz et al. [ 41] from C. salviifolius and reported by Wittpahl et al. [ 15] and Riehle et al. [ 42]. Among quercetin pentosides, the main isomer was presumed as 3-α-L-arabinopyranoside due to its highest intensity in this group and a previous report of this compound [41], and above its MS/MS typical for 3-O-substituted flavonols; quercetin-O-pentoside at 12.10 min with MS/MS in the manner of 3-O-substituted flavonols was presumed to be 3-O-xyloside of quercetin, following co-chromatography with its known source, i.e., pear and apple skin extracts [29], while the slower isomer at 12.35 min was confirmed to be avicularin (quercetin-3-α-L-arabinofuranoside) by co-chromatography with Polygonum aviculare extract [28]. Still, the amount was insufficient to perform an NMR analysis. Similarly to myricetin, tentatively identified quercetin-O-hexoside gallate was observed at 11.28 min. The following two compounds were previously reported in the genus Cistus: CAS: 1891055-64-3 and quercetin-3-O-α-arabinopyranoside in C. salviifolius [41].
Concerning kaempferol glycosides, two O-hexosides were observed, both giving MS/MS fragments typical for 3-O-substituted flavonols. The second, at 12.42 min, was confirmed as astragalin with the authentic standard, while the first, at 12.19 min, was confirmed to be trifolin (=kaempferol-3-O-β-D-galactopyranoside) by co-chromatography with *Menyanthes trifoliata* extract [25]. Previously, the sugars for kaempferol glycosides were attributed by Wittpahl et al. [ 15] tentatively only. A minor compound at 417 m/z was identified as kaempferol-3-O-pentoside.
The last distinctive group of flavonols comprised esters with coumaric acid. The most intense ions originated from a pair of E/Z-tilirosides (denoted in Table S5 as its isomers b and c, confirmed with the authentic standard) accompanied by a more rapidly eluting small one (probably 7-O-substituted kaempferol-6″-coumaroylglucoside, e.g., buddlenoid A) or tiliroside analog with galactose core instead of glucose. Three additional small molecules corresponding to coumaroyl esters of tiliroside were observed at the end of the chromatogram. Coumaroyl-tiliroside was previously described by Wittpahl et al. [ 15]. On the chromatogram, tiliroside was preceded by three molecules with 609 m/z and a fragmentation pattern analogous to tiliroside, except that the core molecule (MS/MS) was quercetin or its analog. Such compounds were helichrysoside (confirmed by co-chromatography) with *Helichrysum arenarium* extract [24] and its isomers, not previously reported in rock-rose.
All ellagitannins appeared on UHPLC chromatograms as two peaks each, most likely according to an equilibrium of α/β anomers of the central sugar. Other known polyphenols such as gallic acid, ellagic acid, catechin, and epicatechin were also identified compared to authentic standards. Additionally, minor amounts of glycosides such as ellagic acid pentosides, hexoside, and deoxyhexoside were observed in the extracts.
The used MS conditions were insufficient to observe and identify procyanidins that were reported previously [43]. It may be that other polyphenols present in larger quantities hindered the analysis. A different assay or extraction technique would have to be used to obtain the procyanidin fractions, which requires further experiments.
## 3.2. Quantification of Polyphenols, Flavonoids, Tannins, and Phenolic Acids in C. Incanus
In most scientific works, the total phenolic content (TPC) was determined by the Folin–Ciocalteu method; therefore, we initially determined the TPC of extracts using this method. In addition, the total flavonoid content (TFC) expressed as myricetin equivalents (ME) was determined. The same extracts were then subjected to quantitative analysis by HPLC-DAD. Detailed data (mean, SD, median, minimum, and maximum values) for products of different origins are shown in Table 1. It also includes the sum of polyphenols, flavonoids, ellagitannins, and phenolic acids determined by HPLC-DAD, while the content of individual polyphenolic compounds is shown in Table 2.
## 3.2.1. Quantification of TPC and TFC
Total phenolics in C. incanus extracts are presented as gallic acid equivalents per gram of dry weight of the product. The mean TPC for all samples (Ci1-Ci52) was estimated at 350 mg GAE/g d.w. ( range 14–623 mg/g). Samples of Turkish and Albanian origin had similar TPC, 354 mg/g and 328 mg/g, respectively. A high content was recorded in samples from Greece, with 403 mg/g. However, the differences were not statistically significant (Table S10). Slightly lower TPC values (269–347 mg/g) were recorded in the study of Gaweł-Bęben et al. [ 6] in $60\%$ hydromethanolic and methanolic extracts. In $80\%$ hydromethanolic extracts of a dozen C. incanus products of different origins, TPC ranged from 2 to 148 mg GAE/g d.w. As in our study, it was noted that products of Turkish origin had the highest content of polyphenols (84 mg/g), especially compared to products of unknown origin, which were the poorest in these compounds [20]. Lower TPC was also obtained for $50\%$ [44] and $30\%$ [45] hydroethanolic extracts, where the amount of polyphenols was 42–99 mg and 36–89 mg GAE/g d.w., respectively.
In addition to the high TPC content, samples of Turkish origin had the highest TFC (mean 45 mg ME/g d.w.) compared to Greek and Albanian origin (mean 40 and 33 mg/g, respectively). The TFC for all samples tested averaged 40 mg/g and ranged from 21 to 72 mg/g. In other studies, TFC values, converted to the selected flavonol, e.g., quercetin equivalents (QE) or rutin equivalents (RUE), were significantly lower than the results expressed as ME. Similar TFC values to those in the present study, expressed as quercetin, were obtained by Gaweł-Bęben et al. [ 6] (45–54 mg QE mg/g d.w.). A higher flavonoid content calculated as quercetin 41–138 mg QE mg/g d.w. was obtained in another study [45]. The TFC expressed as rutin was 11 mg (RUE/g d.w.) [ 46].
## 3.2.2. Quantification of Polyphenols, Flavonoids, Tannins, and Phenolic Acids by HPLC-DAD
Analyzing the mean contents of individual polyphenols in the C. incanus products, the largest amounts were recorded for ellagitannins, 73 mg/g d.w. ( SET range 26–189 mg/g). Turkish products had the highest SET content (94 mg/g) compared to the other countries, Greece (64 mg/g) and Albania (52 mg/g). The differences were statistically significant (Tables S10 and S11, Figure S3). A total of five compounds belonging to this group were determined, of which punicalagin was the main one with an average value of 29 mg/g d.w., followed by cistusin with 25 mg/g and terflavin A with 14 mg/g. In addition, the hexahydroxydiphenoyl-glucose isomers previously mentioned by Wittpahl et al. [ 15] were detected. Their amount was six times lower than that of punicalagin, with a mean 5 mg/g d.w. ( range 0.7–13 mg/g). Furthermore, small amounts of punicalin were reported (mean 0.4 mg/g). It was not possible to determine proanthocyanidins because their content was below the LOD (Table S7).
The mean phenolic acid content of C. incanus teas was estimated at ~10–11 mg/g d.w. ( SPA range 5–21 mg/g). Among these, only gallic and ellagic acids were present in higher amounts, averaging 7 and 3 mg/g, respectively. Both compounds were found in similar amounts in products of different origins.
The third group consisted of flavonoids, in which monomeric flavan-3-ols (epicatechin and catechin) and flavonols (glycosides of myricetin, quercetin, and kaempferol) were recorded. It was consistent with previous reports [13,14,15,16,17,20]. The sum of flavonoids (SF, flavonols+flavan-3-ols) was determined to be 18 mg/g d.w. ( range 12–23 mg/g). There were no statistically significant differences between products of various origins. Flavan-3-ols occurred at similar levels (1 mg/g) in the 0.4–2.4 mg/g. Products from different origins contained comparable amounts of catechin and epicatechin. C. incanus contained flavonol monoglycosides with sugars such as galactose, glucose, rhamnose, arabinose, and xylose. Larger amounts were recorded for rhamnosides and galactosides, i.e., myricitrin (mean 4 mg/g), hyperoside (mean 3 mg/g), myricetin-3-O-galactoside (below 3 mg/g), and coumaroyl-flavonols such as tiliroside (mean 2 mg/g). In addition, myricetin-3-O-arabinoside, myricetin-3-O-glucoside, quercetin-O-arabinoside, quercitrin, and kaempferol-3-O-glucoside were detected in smaller amounts. Other coumaroyl-flavonols were also detected in relatively large amounts—coumaroyl-tiliroside (mean 1 mg/g) and helichrysoside (mean 0.4 mg/g). The flavonoid content estimated by HPLC-DAD vs. spectrophotometric methods was ~2 times lower but reached the same order of magnitude (18 vs. 40 mg/g d.w., respectively). The ratio of TFC:SF varied around a value of 1.2–4 (median 2). It is believed that using an appropriately selected standard (myricetin) gives more realistic results than using rutin or quercetin, which were used in other studies, and the calculated flavonoid content was lower [6,20,46].
The sum of polyphenols (SPP), obtained by adding the determined contents for the individual compounds, in all C. incanus products was estimated at 102 mg/g d.w. ( range 54–229 mg/g), and the highest polyphenol sum was found in Turkish (124 mg/g), followed by Greek (92 mg/g) and Albanian (80 mg/g) products. The differences were statistically significant (Tables S10 and S11, Figure S3). SPP was more than three times lower compared to TPC. The ratio of polyphenol content for both methods ranged from 0.2 to 10 (median 3). The spectrophotometric method is used for the approximate estimation of polyphenols, and its results may be overestimated due to the presence of other substances in the extracts that can react with the Folin–Ciocalteu reagent. Chromatographic methods, on the other hand, allow quantitative analysis of individual polyphenols, which improves accuracy, especially if the appropriate profile of compounds is used as reference substances. In the statistical analysis, we detected a correlation between the results from the spectrophotometry and HPLC-DAD, which means that they reflect the difference in the content of components but do not accurately report their quantity.
In Ci1-Ci52 products, ellagitannins accounted for the majority of all polyphenols at ~$69\%$ (range 48–$85\%$, median $69\%$), followed by flavonoids at ~$20\%$ (range 8–$41\%$, median $18\%$) and phenolic acids at ~$11\%$ (range 5–$16\%$, median $12\%$). In products of Turkish origin, the ellagitannin content was the highest compared to the other countries, reaching $73\%$ (Greece $68\%$, Albania $63\%$). In these products, the greatest difference was also noted between ellagitannins and flavonoids, with five times lower content ($17\%$). In products from other countries, the percentage of flavonoids in SPP was higher, at $23\%$ for Albanian and $21\%$ for Greek products. Albanian products had the highest proportion of phenolic acids ($14\%$), followed by Greek ($11\%$) and Turkish ($10\%$) products. Most of the polyphenols determined by the chromatographic method were found in the highest amount among teas of Turkish origin (12 out of 21 compounds). Detailed data on their content can be found in the Supplementary Material in Tables S6 and S8.
There are only a few works in the scientific literature dedicated to the content of individual polyphenols in C. incanus. Wittpahl et al. [ 15] analyzed $50\%$ hydromethanolic extracts of four products. The sum of polyphenols in this study was 9–30 mg/g d.w., a significantly lower result than ours. The sum of ellagitannins (per gallic acid) in the German study ranged from 4 to 15 mg/g, and the dominant representative of this group was punicalagin (~4 mg/g). Nevertheless, these authors did not use authentic standards of ellagitannins. In the current work, the sum of ellagitannin significantly exceeded the results of the data cited above, but it is worth noting that punicalagin was also present in the highest amount (29 mg/g). The situation is similar for flavonoids. Results of Wittpahl et al. [ 15] ranged from 5 to 15 mg/g d.w. per rutin, with myricitrin in the highest amount (~4 mg/g). Our sum of flavonoids is slightly higher, while an identical amount of myricitrin was recorded. It should be noted that we determined a higher number of flavonoids. Their content, and simple glycosides, are influenced by the abundance of coumaroyl-flavonols, such as tiliroside and its analogs.
Viapiana et al. [ 20] determined 14 polyphenolic compounds (including flavonoids and phenolic acids) in hydromethanolic extracts but in lower amounts. The authors emphasize that higher contents of active compounds are found in products of Turkish and Albanian origin compared to products from Cyprus or of unknown origin. The results of the present study are significantly greater, while a similar correlation was noted between the content of polyphenols and the country of origin of the product.
The determination of polyphenol content is influenced by the choice of the test method, the parameters used, the apparatus settings, and the standard substances used. In addition, a very important factor influencing the content of polyphenolic compounds is the raw material itself—this mainly concerns environmental factors (place of origin, time of harvest, etc.) and factors related to the preparation of the commercial product (e.g., storage conditions, particle size, the ratio of leaf and stem content) [12,42,44,45,47].
## 3.3. In Vitro Antioxidant Potential of C. incanus
Frequently used methods to measure antioxidant activity include DPPH, ABTS, or FRAP tests. These methods work according to the single electron transfer mechanism; however, the principle of operation is different [34,35,36]. The results of C. incanus antioxidant activity, by country of origin, obtained in this study are shown in Table 3.
The free radical inhibition capacity (expressed as mean %inhibition) was $79\%$ (range 69–$89\%$) in DPPH and $29\%$ (range 6–$69\%$) in ABTS. The results calculated as GAE were 151 mM GAE/g d.w. ( range 133–172) and 3 mM GAE/g d.w. ( range 0.7–7) for DPPH and ABTS, respectively. In the DPPH, products from all regions showed an effect at a similar level of ~$80\%$ inhibition. Larger differences can be seen in the ABTS, where Turkish products showed the highest activity, $38\%$, compared to Greek, $26\%$, and Albanian, $20\%$. In the FRAP test, the results ranged from 110 to 337 mM Fe(II)/g d.w., with an average value of 205 mM Fe(II)/g. The highest reduction activity was observed for samples of Turkish origin with 233 mM Fe(II)/g and slightly lower for Greek with 224 mM Fe(II)/g. The lowest values were obtained for Albanian samples with 175 mM Fe(II)/g. Considering the place of origin, the differences in antioxidant activity of C. incanus teas revealed by ABTS and FRAP were statistically significant. In contrast, no statistically significant differences were observed with the DPPH test. Details of the antioxidant activity in the extracts can be found in Tables S12–S14 and Figure S4.
In our previous work [8], we evaluated the antioxidant activity of C. incanus water infusions prepared mostly from the same products used here. The infusions had more than 2.5 times lower inhibitory activity in DPPH (~$25\%$ inhibition) and slightly lower in ABTS (~$27\%$). As with the extracts, the Turkish products showed the highest inhibitory capacity. The FRAP test also showed a much lower ability of the infusions to reduce iron compounds (134 mM/g d.w.) compared to the extracts. It is noteworthy that water infusions contained more than six times fewer polyphenols than extracts (TPC 55 vs. 350 mg/g d.w.).
Viapiana et al. [ 20], in the FRAP assay, obtained comparable values for $80\%$ hydromethanolic extracts (up to 169 mM Fe2+). Samples of Turkish and Albanian origin showed the highest antioxidant potential compared to samples from Cyprus or of unknown origin. Similarly, the DPPH test for samples from Turkey yielded the highest results (20–97 µM TE/g d.w.) in terms of Trolox equivalents [45]. It is suggested that greater antioxidant activity is strongly related to the total amount of polyphenols, which in turn is influenced by genetic and environmental factors and country of origin [20].
Analyzing the correlation between groups of C. incanus polyphenols and antioxidant potential, we found [1] a strong positive correlation between TFC and FRAP (~0.8) and a moderate correlation with SPP, SET (~0.7) and SF, SPA, TPC (~0.5); [2] moderate, but weaker correlations with TFC, SF, and SPP (~0.5) were observed in ABTS; [3] results with DPPH exhibited only weak correlations except for TFC, where a moderate correlation was noted (~0.6) (Table 4 and Table S15). The above observations are consistent with those previously published for C. incanus water infusions [8]. However, Dimcheva and Karsheva [44], for the twelve Bulgarian C. incanus samples, reached a slightly higher linear correlation of DPPH results with TPC values calculated at 0.667 (R2). This could be related to the different chemical compositions of Bulgarian plant material and the smaller number of samples tested.
An in-depth analysis of the composition of C. incanus products showed that all groups of polyphenols are involved in its antioxidant activity. However, a significant share can be attributed to ellagitannins and flavonoids, which are the main components of this plant material. In summary, among the antioxidant tests used, FRAP best characterized the potential of C. incanus products. The antioxidant activity of rock-rose polyphenols is mainly due to their redox properties, which allow them to act as reducing agents, hydrogen donors, or metal ions chelators [5,6,8,45].
## 3.4. In Vitro α-Glucosidase Inhibitory Activity of C. incanus Extracts and Their Constituents
A common condition in diabetes is postprandial hyperglycemia, which, along with oxidative stress, appears to be a key element in the pathophysiology of its late complications, particularly cardiovascular disease. Therefore, achieving proper glycemic control plays a crucial role in the effective treatment of diabetes and limiting its complications [48,49]. As postprandial hyperglycemia can occur even when glycated hemoglobin (HbA1c) levels are within the standard range, it is emerging to be an important target in diabetes treatment [50]. In addition, plenty of plant-derived compounds and extracts have been found over the past few years to inhibit the activity of digestive enzymes to a considerable extent, with particular regard to α-glucosidase [51,52,53,54].
α-Glucosidase, a digestive enzyme located in the brush border of enterocytes, is capable of hydrolyzing glycosidic bonds in dietary polysaccharides to liberate glucose, subsequent intestinal absorption of which leads to a postprandial increase of blood glucose level. Consequently, its inhibitors would delay the digestion of carbohydrates and reduce the postprandial spike in blood glucose levels along with insulin secretion [55]. Therefore, owing to the utmost importance of glucose level control, the identification of substances able to target α-glucosidase offers a great strategy for treating diabetes. Currently, there are three drugs available that belong to the synthetic inhibitors, i.e., acarbose, voglibose, and miglitol, used in diabetes treatment since the 1990s [56].
In the present study, extracts of C. incanus teas have been examined to assess their ability to inhibit α-glucosidase. Basically, all of the tested extracts in a concentration of 250 µg/mL (drug-extract ratio μg:mL) and under the assay conditions were characterized by an exceptional ability to inhibit α-glucosidase, with the mean value reaching 97.8 ± $2.7\%$, and mean IC50 equal to 128 µg/mL. Further analysis of the selected five extracts (Ci17, Ci43, Ci48, Ci52, Ci53) showed that the inhibition presented by 1 μg/mL extract dilutions exceeded $90\%$, with the exception of the sample Ci52, whose inhibitory activity was noticeably lower. However, after further diluting extracts to a final concentration of 0.5 μg/mL, the lowest inhibition was exhibited by Ci48, and the highest was observed in the case of Ci43. As shown in Figure S1, inhibition of α-glucosidase by the C. incanus extracts occurred in a concentration-dependent manner.
The ability of each pure compound at 500 µg/mL to inhibit α-glucosidase varied (Figure 1). The tested set of compounds contained flavonol aglycones (kaempferol, quercetin, myricetin), flavonol glycosides (myricitrin, myricetin-3-O-galactoside, hyperoside), phenolic acids (ellagic and gallic), and ellagitannins (cistusin, punicalagin, terflavin A). Among the compounds assayed, ellagitannins exhibited the highest activity towards α-glucosidase with nearly $100\%$ inhibition. A considerable inhibitory effect could also be seen in the case of phenolic acids ($81.9\%$ and $82.8\%$ for ellagic and gallic acid, respectively) and tiliroside ($73.6\%$). Moderate values (30–$40\%$ inhibition) were calculated for myricitrin, myricetin-3-O-galactoside, and quercetin. On the other hand, kaempferol and hyperoside presented weaker inhibitory activity ($17.6\%$ and $26\%$ inhibition). Remarkably, all tested polyphenols exhibited stronger anti-glucosidase activity than acarbose assayed in the same concentration ($13.7\%$). Unfortunately, myricetin could not be tested here due to its darkening under test conditions to an intense brown color (most likely due to decomposition), which limited the use of the method.
From our results, it can be noted that the ability to inhibit α-glucosidase by flavonol aglycones increases with the increasing number of hydroxyl groups. Likewise, the same trend can be observed in the case of flavonol glycosides, as hyperoside possesses fewer OH groups than myricitrin and presents significantly lower inhibitory action. The type of sugar forming the glycoside, on the other hand, does not seem to be important (myricitrin vs. myricetin-3-O-galactoside). Meanwhile, the esterification of glycosides was concurrent with the increase of anti-glucosidase activity, as seen in the example of tiliroside, whose effect significantly exceeds the inhibitory properties of the tested glycosides. Also, phenolic acids, represented by gallic and ellagic acids have been determined to be weaker enzyme inhibitors than ellagitannins, which are glucose esters. In this study, three ellagitannins—cistusin, punicalagin, and terflavin A—presented the highest anti-glucosidase activity among C. incanus polyphenols assayed with a comparable level of inhibition.
## 3.5. Mode of α-Glucosidase Inhibition by C. incanus Polyphenols and Extracts
In the next step, we investigated the inhibitory activity of C. incanus constituents, followed by a kinetic study aiming to determine the type of α-glucosidase inhibition shown by the six most potent among the tested polyphenols. Some have been previously demonstrated to possess anti-glucosidase activity, e.g., gallic and ellagic acids [51,52,53]. Abdelli et al. [ 54], in their molecular docking study, demonstrated that gallic acid interacts via numerous H-donor bonds with key amino acid residues located in the active site of the enzyme, reducing its activity. Here, based on the results of kinetic studies, we found that gallic acid inhibits α-glucosidase with an IC50 value equal to 117 µg/mL in a mixed mode of inhibition, which is in agreement with a recent report by Choudhary et al. [ 57]. Another widely studied compound is punicalagin, the main component of pomegranate, which is attributed to anti-diabetic and health-promoting properties [58]. Extensive studies also covered the ability of punicalagin to inhibit α-glucosidase [59,60], and it has been revealed that it occurs in a competitive manner via direct binding to the enzyme. It is suspected to be related to the abundance of -OH groups in its molecule, given that their presence determines the formation of hydrogen bonds with the residues of the α-glucosidase active site and subsequent blocking activity [40]. In addition, tiliroside was previously reported as a potent α-glucosidase inhibitor [61,62].
A kinetic study was conducted using increasing concentrations of p-NPG and varying concentrations of polyphenols or without the presence of any inhibitor. In order to graphically present the kinetics of enzyme inhibition, Lineweaver–Burk plots, also known as double reciprocal plots, were created (Figure S2).
Accordingly, evaluation of inhibition kinetics revealed that patterns presented by acarbose and punicalagin are typical for competitive inhibition type, while the other compounds exhibit features of both competitive and noncompetitive inhibition manifested by a change in both the Michaelis constant (Km) and maximum velocity of reaction (Vmax), which is consistent with the effects of a mixed inhibitor which binds both the free enzyme and the substrate–enzyme complex with different affinities.
According to the results, the most potent inhibitor among the tested compounds was cistusin, with an IC50 value of 0.7 µM (nevertheless, the results presented here were obtained only using in vitro methods; thus, further experiments in appropriate in vivo models will be necessary to verify the proposed indications in clinical practice Table 5), followed by punicalagin and terflavin A (1.1 µM both). Ellagic acid was the fourth inhibitor in the series, which seems relevant because it is a degradation product of ellagitannins (11 µM). In comparison, the half-maximal inhibitory concentration of acarbose, under the assay conditions, equaled 3.3 mM. Several extracts were also subjected to a kinetic study. Their mode of α-glucosidase inhibition according to the calculated parameter change has been determined as mixed, which is comprehensible as they contain a variety of compounds presenting competitive, non-competitive, or mixed inhibition. The concentrations of ellagitannins and flavonoids in C. incanus extracts, determined at 530–3787 μg/mL and 242–458 μg/mL, greatly exceed the IC50 values (μg/mL) necessary for α-glucosidase inhibition.
According to bioavailability studies, ellagitannins (such as punicalagin and punicalin) are not absorbed into the bloodstream due to their size. Instead, after consumption of ellagitannin-rich foods, they are hydrolyzed to ellagic acid and metabolized by the gut microbiota to urolithins. Taking the above into account and considering their long-term persistence in the body, these human metabolites are currently recognized bioactive forms responsible for various health-promoting effects [8,63].
Nevertheless, the results presented here were obtained only using in vitro methods; thus, further experiments in appropriate in vivo models will be necessary to verify the proposed indications in clinical practice.
## 4. Conclusions
Our results show that C. incanus is a rich source of polyphenols (5.5–$23\%$), especially ellagitannins (2.5–$19\%$), among which punicalagin, cistusin, and terflavin A were the main representatives. Flavonoids were the second most abundant group (1.2–$2.3\%$), with myricitrin, myricetin-3-O-galactoside, hyperoside, and tiliroside.
C. incanus teas have shown high antioxidant and α-glucosidase inhibitory potential due to the presence of ellagitannins, flavonoids, and phenolic acids. The most powerful inhibitor was cistusin. Ellagic acid, which is formed abundantly in the intestine by the biotransformation of ellagitannins, also proved to be an important inhibitor. The activity of the extracts against α-glucosidase was significantly superior to acarbose regardless of differences in chemical composition and site of origin (mean IC50 128 µg of plant material per 1 mL of extract). Among the products tested, those of Turkish origin exhibited the highest antioxidant potential and ellagitannin content.
Therefore, the ability to reduce oxidative stress and inhibit polysaccharide metabolism possibly determines the C. incanus hypoglycemic and antidiabetic effects. These properties may also reduce cardiometabolic risk. However, further in vivo studies are needed.
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|
---
title: The Involvement of Endogenous Enkephalins in Glucose Homeostasis †
authors:
- Vanessa Escolero
- Laica Tolentino
- Abdul Bari Muhammad
- Abdul Hamid
- Kabirullah Lutfy
journal: Biomedicines
year: 2023
pmcid: PMC10045905
doi: 10.3390/biomedicines11030671
license: CC BY 4.0
---
# The Involvement of Endogenous Enkephalins in Glucose Homeostasis †
## Abstract
Obesity has nearly tripled since 1975 and is predicted to continue to escalate. The surge in obesity is expected to increase the risk of diabetes type 2, hypertension, coronary artery disease, and stroke. Therefore, it is essential to better understand the mechanisms that regulate energy and glucose homeostasis. The opioid system is implicated in regulating both aspects (hedonic and homeostatic) of food intake. Specifically, in the present study, we investigated the role of endogenous enkephalins in changes in food intake and glucose homeostasis. We used preproenkephalin (ppENK) knockout mice and their wildtype littermates/controls to assess changes in body weight, food intake, and plasma glucose levels when mice were fed a high-fat diet for 16 weeks. Body weight and food intake were measured every week ($$n = 21$$–23 mice per genotype), and at the end of the 16-week exposure period, mice were tested using the oral glucose tolerance test (OGTT, $$n = 9$$ mice per genotype) and insulin tolerance test ($$n = 5$$ mice per genotype). Our results revealed no difference in body weight or food intake between mice of the two genotypes. However, HFD-exposed enkephalin-deficient mice demonstrated impaired OGTT associated with reduced insulin sensitivity compared to their wildtype controls. The impaired insulin sensitivity is possibly due to the development of peripheral insulin resistance. Our results reveal a potential role of enkephalins in the regulation of glucose homeostasis and in the pathophysiology of diabetes type 2.
## 1. Introduction
Worldwide obesity has nearly tripled since 1975, and current trends are expected to continue to rise [1]. As per World Health Organization (WHO) 2022 statistics, more than 1 billion people are obese worldwide [2]. In addition, a recent report from the Center for Disease Control (CDC) estimated that the prevalence of obesity from 2017 to 2020 in the United States was $41.9\%$ among adults and $19.7\%$ among children and adolescents [3]. The increased rates of obesity are driven primarily by the overconsumption of energy-dense foods and an increase in sedentary lifestyles, resulting in a shift in energy balance that leads to excessive fat accumulation. Furthermore, this increases the risks for chronic comorbidities such as diabetes type 2, coronary artery disease, hypertension, stroke, and certain cancers [4]. Given the continued rise in obesity and diabetes type 2 over the past decades, understanding the mechanisms that regulate energy homeostasis and glucose metabolism would be beneficial in developing novel therapeutics to prevent or at least reduce the devastating public health and socioeconomic consequences of obesity and diabetes type 2.
The endogenous opioid system, consisting of opioid peptides (beta-endorphin, dynorphin, and enkephalins) and their corresponding G-protein-coupled receptors, namely, delta (DOP), kappa (KOP), and mu (MOP) opioid receptors [5,6], is heavily implicated in feeding behavior. These opioid peptides and their receptors are highly expressed in brain regions involved in the rewarding and hedonic aspects of feeding, such as the mesolimbic dopaminergic system. In addition, these opioid peptides are localized within the hypothalamus and, thus, are likely involved in modulating homeostatic signals received from the peripheral tissues [7]. Pharmacological studies have shown that opioid receptor agonists stimulate food intake [8,9], while opioid receptor antagonists suppress food intake [8,10,11]. However, the role of each opioid peptide in feeding behavior and, notably, glucose homeostasis is not fully understood.
Previous studies have shown that the administration of enkephalin and enkephalin analogs increases the intake of palatable food [12,13]. Moreover, an increase in enkephalin mRNA was observed in the brain of rats consuming a high-fat diet, reinforcing a positive relationship between a high-fat diet and enkephalin [13]. In addition, a 2015 study showed that mice lacking the ability to produce enkephalin gained less weight when compared to their wildtype controls despite equal amounts of food intake [14].
Enkephalins are found in several peripheral organs, including the pancreas, liver, adipose, skeletal muscle, lungs, heart, as well as in the central and peripheral nervous systems, suggesting the involvement of enkephalins in various biological processes such as feeding behavior [15]. Indeed, enkephalins have been shown to play a functional role in glucose metabolism. For example, one of the first studies conducted in the 1980s demonstrated the presence of opioid receptors in the islets and a low dose of enkephalin-stimulated insulin secretion in vitro [16]. In contrast, in vitro high concentrations of enkephalin inhibited insulin release [16,17], raising the possibility that enkephalins may regulate insulin secretion and glucose homeostasis. Interestingly, delta-opioid-receptor-deficient mice fed a chow diet demonstrated improved glucose tolerance compared to wildtype; however, when challenged with a high-fat diet, the difference between genotypes disappeared [18]. Most recently, enkephalins have been implicated in the beiging process of white adipocytes through the activation of group 2 innate lymphoid cells [19]. However, the role of enkephalins is partially characterized in feeding and glucose homeostasis. Therefore, our study aims to further understand the role of the opioid peptides, enkephalins, in the regulation of body weight and glucose homeostasis.
## 2.1. Animals
We utilized enkephalin knockout mice that were previously generated by Konig and colleagues [20]. Mice lacking the preproenkephalin (ppENK) gene and their wildtype controls were fully backcrossed on a C57BL/6 mouse strain and bred in-house. Pups were weaned on day 21 and genotyped according to our earlier reports [21,22]. In total, there were 21 wildtype and 19 knockout male mice used, but 2 wildtype and 2 knockout mice were sacrificed early in the experiment due to severe dermatitis and excluded from the data analysis. Mice were housed in recyclable plastic cages with bedding and ad libitum access to food and water under a 12 h light/12 h dark cycle in a temperature- and humidity-controlled room. Animal cages were changed to new cages every two weeks. All animal experiments were performed in accordance with the NIH guideline for the care and use of animals in research and approved by the Western University of Health Sciences Institutional Animal Care and Use Committee (Pomona, CA, USA), protocol number R20IACUC024.
## 2.2. Diet
Mice were maintained on a regular laboratory chow diet (Envigo Teklad Global Diets, 2018, $18\%$ protein rodent diet; $24\%$ kcal protein, $18\%$ kcal fat, and $58\%$ kcal carbohydrates; 3.1 kcal/g) for one week. Then, for the remainder of the study, mice were placed on a high-fat diet obtained from Research Diets, Inc ($60\%$ kcal fat, $20\%$ kcal carbohydrates, and $20\%$ kcal protein with blue dye: 5.21 kcal/g formulation D12492).
## 2.3. The Role of Enkephalins in Body Weight and Food Intake
To determine the involvement of endogenous enkephalins in body weight and food intake, we used mice lacking enkephalins and their wildtype littermates/controls that we described previously [21,22]. The experiment was conducted over the course of 17 weeks. Each animal had their body weight and food intake measured in grams (g), once weekly. Initially, animals were isolated and housed one per cage and given a regular diet for the first week to habituate to a single mouse per cage condition. This was performed to enable us to measure the food intake of each mouse accurately and also to avoid fighting in the cage, as this can be stressful and affect food intake and importantly blood glucose. At week 2, they were switched to the high-fat diet (HFD) and remained on the HFD through week 17. The experiment was repeated four times with 4–6 mice per cohort ($$n = 21$$–23 mice per genotype).
## 2.4. The Role of Enkephalins in Glucose Homeostasis
To determine the role of enkephalin in glucose homeostasis, an oral glucose tolerance test (OGTT) was conducted at week 17 in 2 different sets ($$n = 3$$–5 mice per cohort) of mice ($$n = 8$$ mice per genotype). The protocol was according to our earlier report [23]. Mice were fasted overnight starting at 10 p.m., for a total of 11 h with ad libitum access to water during this period. The next morning (9 AM), while the animals were still kept fasted, a small piece of the tail was snipped, and the baseline blood glucose level was measured for each mouse, using a glucometer (Embrace Omnis Blood Glucose Monitoring System/Omnis Health Embrace Blood Glucose Test Strip). Mice were then weighed and administered a 2 g/kg of glucose solution via oral gavage. Blood glucose levels were then measured at 30, 60, 90, 120 min after the glucose challenge. At the end of the experiment, mice were restored with ad libitum access to water and food.
## 2.5. The Role of Enkephalins in Insulin Sensitivity
To determine the role of enkephalins in insulin sensitivity and provide complementary results to the glucose tolerance test, a separate set of mice underwent an intraperitoneal insulin tolerance test (ITT) at week 17. The ITT protocol was according to our earlier report, except we fasted the mice for 6 h to avoid hypoglycemia [23]. Briefly, mice were kept fasted for a total of 6 h with access to ad libitum water. Mice were weighed and then given 0.75 U/kg insulin intraperitoneally (Humulin R 100 U/mL, Eli Lilly and Company). Tail vein blood was used to measure blood glucose levels using a glucometer, as described above. Blood glucose levels were measured before (time 0) and at 15, 30, 45, 60, 90 min after insulin injection. At the end of the experiment, mice were restored with ad libitum access to water and food.
## 2.6. The Role of Enkephalin in Plasma Insulin Levels
Considering changes in plasma insulin level can alter blood glucose levels, we also assessed if the lack of enkephalins would alter plasma insulin levels. Briefly, mice ($$n = 8$$–10 mice per genotype) were euthanatized through decapitation. Whole trunk blood was collected in ethylenediaminetetraacetic acid (EDTA)-containing tubes and centrifuged at 14,000 rpm for 10 min at 4 °C. Plasma samples were collected and stored in a −80 °C freezer until analysis. Mouse insulin levels were measured with a commercially available ELISA kit (Rat/Mouse Insulin ELISA Kit, Cat. # EZRMI-13K). The protocol supplied by the kit manufacturer was followed. Briefly, a 96-well plate pre-coated with monoclonal mouse anti-rat insulin antibodies was loaded with six rat insulin standards, two quality controls, background, and samples. Each sample was diluted 2-fold with the assay buffer according to the manufacturer’s instructions and run in duplicates. The absorbance was read at a wavelength of 450 nm with a plate reader. Based on the standard curves run in duplicate on each plate, plasma insulin concentration was determined with linear regression using GraphPad Prism 9.1.2 software (San Diego, CA, USA).
## 2.7. The Role of Enkephalin in Plasma Leptin Levels
Given that changes in leptin levels can affect plasma glucose levels, we also examined if the lack of enkephalins would alter plasma leptin levels. Mice ($$n = 9$$–11 per genotype) were euthanized, and trunk blood was corrected and prepared, as described above. Mouse leptin levels were measured with a commercially available ELISA kit (Mouse Leptin ELISA Kit, Cat. # EZML-82K). The protocol supplied by the kit manufacturer was followed. Briefly, a 96-well plate pre-coated with pre-tittered antiserum was loaded with six rat insulin standards, two quality controls, background, and samples. Each sample was analyzed in duplicates and diluted 2-fold with assay buffer according to the manufacturer’s instructions. The absorbance was read at a wavelength of 450 nm with a plate reader. Based on the standard curves run in duplicate on each plate, plasma leptin concentration was determined with linear regression using GraphPad Prism 9.1.2 software (San Diego, CA, USA).
## 2.8. Statistical Analysis
Data were expressed as mean ± SEM. Time-course data were analyzed using two-way repeated measures analysis of variance (ANOVA) followed by Fisher’s LSD post hoc multiple comparisons. The area under the curve (AUC) for the OGTT and ITT time-course data was calculated using the trapezoidal rule. Plasma insulin and leptin levels were measured using linear regression analysis. A p-value < 0.05 was considered significant. Statistical analysis and graphing were completed in GraphPad Prism version 9 (San Diego, CA, USA).
## 3.1. Body Wight Measurement in Wildtype and Knockout Mice
We first determined if endogenous enkephalins regulate body weight. Figure 1 shows the raw body weight of male wildtype (ppENK+/+) and knockout (ppENK-/-) animals for the duration of the experiment. The knockout mice displayed a similar pattern in body weight gain as the wildtype mice. A mixed-effects analysis revealed a significant effect for time ($p \leq 0.0001$) but no effect for genotype ($$p \leq 0.9673$$) or genotype x time ($$p \leq 0.9946$$). The percentage change in body weight was also not different between the wildtype and knockout animals (Figure 1b). A mixed-effects analysis revealed a significant effect for time ($p \leq 0.0001$) but not for genotype ($$p \leq 0.0136$$) or genotype x time ($$p \leq 0.1714$$). This result reveals that endogenous enkephalins may not play a significant role in HFD-induced weight gain.
## 3.2. Food Intake Measurement in Wildtype and Knockout Mice
To assess if endogenous enkephalins alter food intake, we used mice lacking enkephalins and their wildtype littermates. Figure 2 shows the daily food intake in grams (g) between wildtype and knockout animals fed the regular and HFD. Week 1 demonstrates the initial measurement of the regular diet (g) followed by the HFD on week 2, as marked on the graph. As can be observed, there was a drop in daily food intake from week 1 to week 2, likely due to the HFD containing higher total calories than the regular diet, leading to reduced consumption. However, there were no significant differences in food intake between wildtype and knockout mice. A mixed-effects analysis revealed a significant effect for time ($p \leq 0.0001$) but no effect for genotype ($$p \leq 1.023$$) or genotype x time ($$p \leq 0.6778$$). This result suggests that endogenous enkephalins may not regulate regular diet or HFD consumption.
## 3.3. Oral Glucose Tolerance Test
To examine the role of enkephalin in glucose homeostasis, an oral glucose tolerance test (OGTT) was conducted in mice lacking enkephalins and their wildtype littermates. The study was repeated twice with $$n = 4$$–5 per cohort ($$n = 9$$ mice per genotype). Briefly, the mice were fasted overnight, and fasted blood glucose levels were measured before administering a glucose solution via oral gavage. Subsequently, a 2 g/kg oral glucose gavage was administered, and blood glucose levels (mg/dL) were measured between wildtype and knockout mice at specific time points for a total of 90 min, as shown in Figure 3. We observed glucose intolerance in ppENK knockout mice throughout all time points after oral gavage compared to wildtype mice. A two-way ANOVA analysis revealed a significant effect for time (F[3, 48] = 57.42, $p \leq 0.0001$), genotype (F[1, 16] = 7.106, $$p \leq 0.0169$$), but no interaction between time x genotype (F[3, 48] = 1.013, $$p \leq 0.3953$$). Fisher’s LSD post hoc test revealed a significant increase at 30 and 60 min in mice lacking enkephalin compared to the wildtype. An unpaired student’s t-test of the AUC also showed a significant ($p \leq 0.02$) increase in blood glucose levels in mice lacking enkephalins compared to their wildtype controls ($t = 2.84$, df = 16; Figure 3b). This rise could be due to changes in basal fasting glucose between wildtype and knockout mice. While repeated measures ANOVA did not reveal any significant difference in basal fasting glucose between mice of the two genotypes, this was a significant difference when basal levels were analyzed by unpaired student’s t-test ($t = 2.41$; df = 16; $p \leq 0.02$). However, this difference was not observed when animals were fasted for 6 h for the ITT (see below). These results suggest that glucose handling in the absence of enkephalins is reduced. However, basal fasting glucose is only affected when the duration of fasting is 12 h.
## 3.4. Insulin Tolerance Test
We conducted an insulin tolerance test to determine whether the impaired glucose tolerance in the knockout than wildtype mice was due to reduced sensitivity to insulin. In total there were seven ppENK wildtype (ppENK+/+) and five knockout (ppENK-/-) male mice; however, two wildtype mice were excluded from the analysis, as these mice were non-responsive to the insulin challenge. The blood glucose levels of these mice remained elevated throughout the experiment compared to the rest. Briefly, mice were kept fasted for 6 h, and fasting blood glucose levels were measured before the intraperitoneal insulin injection. Following a 0.75 U/Kg insulin injection was administered intraperitoneally, blood glucose levels (mg/dL) were measured and compared between wildtype and knockout for 90 min, as shown in Figure 4. We observed knockout mice were glucose-intolerant at all time points following the insulin injection. These findings were consistent with the OGTT results, demonstrating that ppENK-deficient mice have reduced glucose tolerance and are insulin resistant. A two-way ANOVA analysis demonstrated a significant effect of time (F[5, 40] = 11.70, $p \leq 0.0001$), genotype (F[1, 8] = 11.08, $$p \leq 0.0104$$), but no interaction between time x genotype (F[5, 40] = 1.090, $$p \leq 0.3806$$). The Fisher’s LSD test revealed that there was a significant increase in blood glucose at 45 and 90 min in knockout mice compared to their wildtype controls. Analyses of the AUC also revealed a significant ($p \leq 0.01$) increase in blood glucose in mice lacking enkephalins than their wildtype littermates ($t = 3.60$; df = 8; Figure 4b). These results suggest that insulin sensitivity is impaired in mice lacking enkephalins following exposure to HFD for 16 weeks.
## 3.5. Insulin and Leptin Measurement in Wildtype and Knockout Mice
We then investigated if enkephalins could also regulate the level of insulin or leptin, which could explain the changes observed in the OGTT and ITT. We measured the plasma insulin and leptin levels through enzyme-linked immunosorbent assay (ELISA) using a commercially available ELISA kit in wildtype and knockout mice. As shown in Figure 5a, the plasma insulin levels were comparable in wildtype and knockout mice. An unpaired student’s t-test was utilized for statistical analysis, which revealed no significance (t(df = 16) = 0.3155, $$p \leq 0.7565$$). In addition, plasma leptin levels were also measured utilizing a commercially available ELISA kit. As shown in Figure 5b, there were no significant differences in plasma leptin between wildtype and knockout mice. For statistical analysis, an unpaired student’s t-test was used, which demonstrated no significance (t(df = 16) = 0.2194, $$p \leq 0.8288$$). This result shows that enkephalins may not regulate plasma insulin or leptin levels in mice exposed to HFD for 16 weeks.
## 4. Discussion
The regulation of nutrient intake and metabolism is tightly regulated by the interplay of numerous systems [24,25]. In this study, we focused on the opioid system, specifically the role of enkephalins in food intake and glucose homeostasis. As stated earlier, only a handful of studies have examined the role of enkephalins in energy and glucose homeostasis. The findings in this study are consistent with the literature, demonstrating that opioids play a role in glucose and energy homeostasis; specifically, our study suggests that enkephalins are likely involved in glucose homeostasis.
Our results suggest that the deletion of enkephalins does not affect food intake or body weight, as there was no statistically significant difference in raw body weight or the percentage of body weight changes between mice lacking enkephalins and their wildtype controls. Our results contrast that of Mendez et al. [ 2015], which was one of the first studies that demonstrated mice lacking enkephalins were resistant to diet-induced obesity compared to wildtype controls, although there was no difference in food consumption between mice of the two genotypes [14]. Enkephalins are implicated in the beiging of white adipocytes through the activation of uncoupling protein-1 (UCP-1) [19]. However, the different diets and the duration of exposure may explain the discrepancy between our results and that of Mendez and colleagues who utilized a “cafeteria diet” composed of snacks, such as cheese puffs, pretzels, cereal, marshmallows, pepperoni, etc. [ 14]. Age differences should also be investigated as the starting age of animals for our study ranged from 10 to 26 weeks compared to 21 weeks in the former study [14].
Previous studies have shown differences in plasma glucose levels in mice lacking kappa (KOP), mu (MOP), and delta (DOP) compared to their respective wildtype controls [18,26,27]. The current literature demonstrates that MOP knockout mice have enhanced glucose tolerance when fed a high-fat diet, and similar results have been observed in mice lacking DOP or KOP [26,27,28]. However, the role of each opioid peptide in regulating glucose homeostasis is unclear. Thus, we used mice lacking enkephalins and their wildtype littermates/age-matched controls to assess whether basal or oral glucose tolerance tests would be altered in the absence of enkephalins. We also assessed insulin tolerance test in mice lacking enkephalins and their wildtype littermates/controls to assess if the change in plasma glucose observed in the OGGT is a result of changes in insulin sensitivity. Our data show elevated basal blood glucose levels in mice of both genotypes, which is expected based on the literature and consistent with insulin resistance in mice fed a high-fat diet [29,30]. However, enkephalin knockout mice had higher blood glucose in the OGTT and impaired insulin sensitivity in the ITT compared to their wildtype littermates/controls. These findings suggest that enkephalins may offer protection from the negative effects of a dense, high-fat diet on glucose homeostasis and insulin sensitivity in mice. While the 12 h fasting led to a significant difference in basal glucose levels between wildtype and knockout mice, the 6 h fasting was without any effect on basal glucose level (ITT data). It appears that longer fasting may be necessary to yield a difference in basal fasting glucose levels between wildtype and knockout mice, suggesting that enkephalins may regulate basal glucose levels in mice exposed to HFD for 16 weeks and exposed to 12 h of fasting. On the contrary, enkephalin knockout mice fed a chow diet did not show an impaired glucose tolerance test (Figure S1, Supplemental Data), which is consistent with data obtained in DOP knockout mice fed a regular diet, as they did not show an impaired glucose tolerance test compared to their high-fat-diet-fed wildtype counterparts [18].
In addition, our enkephalin knockout mice demonstrated impaired insulin sensitivity during the insulin tolerance test, indicating elevated glucose levels are possibly due to the development of peripheral insulin resistance. It needs to be noted that the rise in glucose could also be due to reduced insulin release from the pancreas. However, the measured plasma insulin levels showed no significant difference between mice of the two genotypes. Additionally, no change in plasma leptin was found between wildtype and knockout mice, but further studies are needed to comprehensively assess the role of enkephalins in plasma insulin and leptin regulation. At the time of blood collection, our mice were not fasted, which likely contributed to the measured results. Additionally, the mice were not challenged with glucose, which allowed us to measure glucose-stimulated insulin secretion. Therefore, repeating this experiment using glucose-stimulated insulin secretion (GSIS) will provide useful information concerning the role of enkephalins in regulating insulin secretion. Regardless, the OGTT and ITT do indicate that these mice are insulin-resistant.
Enkephalins are widely distributed throughout the brain and various organs. Therefore, it is possible that enkephalins influence mechanisms involved in lipid or glucose metabolism within these tissues [7,18]. Specifically, increased gluconeogenesis in the liver contributes to hyperglycemia in diabetes [31]. Under normal conditions, gluconeogenesis is suppressed in the liver when there is elevated blood glucose. However, the dysregulation of key regulatory enzymes, such as glucose-6 phosphatase or phosphoenolpyruvate carboxylase, may contribute to uncontrolled gluconeogenesis in the presence of hyperglycemia or decreased insulin levels; although in our study, we did not observe any difference in insulin levels between the two genotypes, we measured it a few days after the OGTT and ITT. As stated above, there are several studies utilizing both MOP and DOP knockout mice that show elevated liver enzymes, specifically those involved in fatty acid oxidation. The measured difference in these enzymes between knockout and wildtype mice likely influences the phenotype of these mice when fed an HFD [18,27]. Therefore, future studies need to be designed to measure the level and activity of enzymes, such as acetyl-CoA carboxylase (ACC-α) and peroxisome proliferator-activated receptor (PPAR-α). It is also necessary to further investigate the role of adipose tissue, as Brestoff et al. [ 2015] demonstrated that beiging through UCP-1 can be modulated by enkephalins [19]. Thus, future studies are also needed to determine the alteration in brown and white adipose tissues in the presence and absence of enkephalin to comprehensively understand the role of enkephalin in the regulation of glucose homeostasis.
## 5. Conclusions
The opioid system is relatively well known for regulating food consumption and energy homeostasis. In addition to the well-studied opioid receptors, enkephalins may also contribute to energy homeostasis. Our study adds to the current literature by demonstrating that glucose homeostasis is impaired in the absence of enkephalins, and this may be due to reduced insulin sensitivity. However, a great deal of work is needed to better understand the role of enkephalins in this process. We also need to determine the interaction of enkephalins with the neuropeptide systems controlling feeding behavior and glucose homeostasis. Investigating both the central and peripheral effects of enkephalin may reveal how enkephalins alter glucose homeostasis. Considering that we used mice lacking enkephalins throughout the body and throughout their life cycle, additional studies, using pharmacological and molecular biological tools, are needed to temporarily silence the enkephalin gene to have a better understanding of the role of this system in glucose homeostasis.
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|
---
title: 'Estimating individualized treatment effects from randomized controlled trials:
a simulation study to compare risk-based approaches'
authors:
- Alexandros Rekkas
- Peter R. Rijnbeek
- David M. Kent
- Ewout W. Steyerberg
- David van Klaveren
journal: BMC Medical Research Methodology
year: 2023
pmcid: PMC10045909
doi: 10.1186/s12874-023-01889-6
license: CC BY 4.0
---
# Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches
## Abstract
### Background
Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for “personalizing” medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects.
### Methods
We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike’s Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit.
### Results
The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size ($$n = 4$$,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger ($$n = 17$$,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial.
### Conclusions
An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-023-01889-6.
## Introduction
Predictive approaches to heterogeneity of treatment effects (HTE) aim at the development of models predicting either individualized effects or which of two (or more) treatments is better for an individual with regard to a specific outcome of interest [1]. These predictive approaches include both regression and machine learning techniques and are the subject of active research [2–5]. In prior work, we divided regression-based methods for the evaluation of treatment effect heterogeneity in three broader categories: risk modeling, treatment effect modeling and optimal treatment regime methods [6]. Risk modeling methods use only prognostic factors to define patient subgroups, relying on the mathematical dependency between baseline risk and treatment effect [2, 7]. Treatment effect modeling methods use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy. They can be applied in one stage by directly modeling treatment-covariate interactions, in which case penalization of the interaction effects is needed to reduce the effects of overfitting [8], or in two stages that rely on updating working absolute benefit models [9, 10]. Optimal treatment regime methods focus primarily on treatment effect modifiers in order to classify the trial population into those who benefit from treatment and those who do not [11–14].
In a previous simulation study, modeling treatment-covariate interactions often led to poorly calibrated predictions of benefit on the absolute scale (risk difference between treatment arms), compared to risk-modeling methods [15]. In the presence of true treatment-covariate interactions, however, effect modeling methods were better able to separate lower from higher benefit patients [15, 16]. By assuming treatment effect is a function of baseline risk, risk modeling methods impose a restriction on the shape of treatment effect heterogeneity. With smaller sample sizes or limited information on effect modification, risk modeling methods, because of their reduced complexity, can provide a good option for evaluating treatment effect heterogeneity. Conversely, with larger sample sizes and/or a limited set of well-studied strong effect modifiers, treatment effect modeling methods can potentially result in a better bias-variance tradeoff. Therefore, the setting in which treatment effect heterogeneity is evaluated is crucial for the selection of the optimal approach.
Risk modeling methods predict similar treatment benefit for patients with similar baseline outcome risk, i.e. a similar probability of experiencing the outcome of interest in the absence of treatment. These methods are not new and are quite intuitive to practitioners [6]. Often medical guidelines rely on a risk stratified approach to target treatments to different patients. In addition, re-analyses of studies that only looked at overall results using risk stratification often resulted to important insight on how treatment effects varied for different patients. For example, a risk stratified analysis of patients with acute myocardial infarction (MI) based on the Thrombolysis in Myocardial Infarction (TIMI) risk score found no benefit for patients who underwent primary angioplasty compared to fibrinolysis. However, there was a significant benefit for patients with a high TIMI score [17]. Infants at lower risk of bronchopulmonary dysplasia benefit relatively more from vitamin A therapy than infants at higher risk [18]. Finally, higher risk prediabetic patients benefit relatively more from metformin than lower risk patients [19].
Most often, risk-modeling approaches are carried out in two steps: first a risk prediction model is developed externally or internally on the entire RCT population, “blinded” to treatment; then the RCT population is stratified using this prediction model to evaluate risk-based treatment effect variation [7, 20, 21]. This approach identified substantial absolute treatment effect differences between low-risk and high-risk patients in a re-analysis of 32 large trials [22]. However, even though treatment effect estimates at the risk subgroup level may be accurate, these estimates may not apply to individual patients, as homogeneity of treatment effects is assumed within risk strata. With stronger overall treatment effect and larger variability in predicted risks, patients assigned to the same risk subgroup may still differ substantially with regard to their benefits from treatment.
In the current simulation study, we aim to summarize and compare different risk-based models for predicting treatment effects. We simulate different relations between baseline risk and treatment effects and also consider potential harms of treatment. We illustrate the different models by a case study of predicting individualized effects of treatment for acute myocardial infarction in a large RCT.
## Notation
We observe RCT data \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(Z,X,Y\right)$$\end{document}Z,X,Y, where for each patient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Z}_{i}=0,1$$\end{document}Zi=0,1 is the treatment status, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Y}_{i}=0,1$$\end{document}Yi=0,1 is the observed outcome and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{i}$$\end{document}*Xi is* a set of measured covariates. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{{Y}_{i}\left(z\right),$z = 0$,1\}$$\end{document}{Yiz,$z = 0$,1} denote the unobservable potential outcomes. We observe \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Y}_{i}={Z}_{i}{Y}_{i}\left(1\right)+\left(1-{Z}_{i}\right){Y}_{i}\left(0\right)$$\end{document}Yi=ZiYi1+1-ZiYi0. We are interested in predicting the conditional average treatment effect (CATE),\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau \left(x\right)=E\{Y\left(0\right)-Y\left(1\right)|X=x\}$$\end{document}τx=E{Y0-Y1|X=x} Assuming that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(Y\left(0\right),Y\left(1\right)\right)\perp Z|X$$\end{document}Y0,Y1⊥Z|X, as we are in the RCT setting, we can predict CATE from\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{ll}\tau\left(x\right)&=E\{Y\left(0\right)\hspace{0.25em}\vert\hspace{0.25em}X=x\}-E\{Y\left(1\right)\hspace{0.25em}\vert\hspace{0.25em}X=x\}\\&=E\{Y\hspace{0.25em}\vert\hspace{0.25em}X=x,$Z = 0$\}-E\{Y\hspace{0.25em}\vert\hspace{0.25em}X=x,$Z = 1$\}\end{array}$$\end{document}τx=E{Y0|X=x}-E{Y1|X=x}=E{Y|X=x,$Z = 0$}-E{Y|X=x,$Z = 1$}
## Simulation scenarios
We simulated a typical RCT, comparing equally-sized treatment and control arms in terms of a binary outcome. For each patient we generated 8 baseline covariates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1},\dots,{X}_{4}\sim N\left(0,1\right)$$\end{document}X1,⋯,X4∼N0,1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{5},\dots,{X}_{8}\sim B\left(1,0.2\right)$$\end{document}X5,⋯,X8∼B1,0.2. Outcomes in the control arm were generated from Bernoulli variables with true probabilities following a logistic regression model including all baseline covariates, i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left(Y\left(0\right)=1 | X=x\right)={\text{expit}}\left(l{p}_{0}\right)={e}^{l{p}_{0}}/\left(1+{e}^{l{p}_{0}}\right)$$\end{document}PY0=1|X=x=expitlp0=elp$\frac{0}{1}$+elp0, with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l{p}_{0}=l{p}_{0}\left(x\right)={x}^{t}\beta$$\end{document}lp0=lp0x=xtβ. In the base scenarios coefficient values \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β were such, that the control event rate was $20\%$ and the discriminative ability of the true prediction model measured using Harrell’s c-statistic was 0.75. The c-statistic represents the probability that for a randomly selected discordant pair from the sample (patients with different outcomes) the prediction model assigns larger risk to the patient with the worse outcome. For the simulations this was achieved by selecting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}β values such that the true prediction model would achieve a c-statistic of 0.75 in a simulated control arm with 500,000 patients. We achieved a true c-statistic of 0.75 by setting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta ={\left(-2.08,0.49,\dots,0.49\right)}^{t}$$\end{document}β=-2.08,0.49,⋯,0.49t.
Outcomes in the treatment arm were first generated using 3 simple scenarios for a true constant odds ratio (OR): absent (OR = 1), moderate (OR = 0.8) or strong (OR = 0.5) constant relative treatment effect. We then introduced linear, quadratic and non-monotonic deviations from constant treatment effects using:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l{p}_{1}={\gamma }_{0}+{\gamma }_{1}\left(l{p}_{0}-c\right)+{\gamma }_{2}{\left(l{p}_{0}-c\right)}^{2},$$\end{document}lp1=γ0+γ1lp0-c+γ2lp0-c2,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l{p}_{1}$$\end{document}lp1 is the true linear predictor in the treatment arm, so that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left(Y\left(1\right)=1 | X=x\right)={\text{expit}}\left(l{p}_{1}\right)$$\end{document}PY1=1|X=x=expitlp1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma ={\left({\gamma }_{0},{\gamma }_{1},{\gamma }_{2}\right)}^{t}$$\end{document}γ=γ0,γ1,γ2t controls the shape of the evolution of treatment effect as a function of baseline risk (type and strength of deviations from the constant treatment effect setting), while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c$$\end{document}c allows us to shift the proposed shape function to achieve the desired overall event rates. For example, to simulate a constant treatment effect with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{OR}}=0.8$$\end{document}OR=0.8 we would set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma ={\left(\mathrm{log}\left(0.8\right),1,0\right)}^{t}$$\end{document}γ=log0.8,1,0t and \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}$c = 0.$ Finally, we incorporated constant absolute harms for all treated patients, such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left(Y\left(1\right)=1 | X=x\right)={\text{expit}}\left(l{p}_{1}\right)+{\text{harm}}$$\end{document}PY1=1|X=x=expitlp1+harm. The sample size for the base scenarios was set to 4,250 ($80\%$ power to find a statistically significant treatment effect at the $5\%$ significance level, when the true treatment effect is an odds ratio of 0.8). We evaluated the impact of smaller or larger sample sizes of 1,063 and 17,000, respectively. We also evaluated the impact of risk model discriminative ability, adjusting the baseline covariate coefficients, such that the c-statistic of the regression model in the control arm was 0.65 and 0.85, respectively. These settings resulted in a simulation study of 648 scenarios covering the HTE observed in 32 large trials as well as many other potential variations of risk-based treatment effect (Supplement, Sects. 2 and 3) [22]. We analyzed the sensitivity of the results to correlation between baseline characteristics. We first sampled 8 continuous variables \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${W}_{1},\dots,{W}_{8}\sim N\left(0,\Sigma \right)$$\end{document}W1,⋯,W8∼N0,Σ. We then generated four continuous baseline covariates from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1}={W}_{1},\dots,{X}_{4}={W}_{4}$$\end{document}X1=W1,⋯,X4=W4 and four binary covariates with $20\%$ prevalence from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{5}=I\left({W}_{5}>{z}_{0.8}\right),\dots, {X}_{8}=I({W}_{8}>{z}_{0.8})$$\end{document}X5=IW5>z0.8,⋯,X8=I(W8>z0.8), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I$$\end{document}I is the indicator function and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left(U\le 0.8\right)={z}_{0.8}$$\end{document}PU≤0.8=z0.8 for random variable \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U\sim N(\mathrm{0,1})$$\end{document}U∼N[0,1]. The covariance matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Sigma$$\end{document}Σ was such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$cor\left({X}_{i},{X}_{j}\right)=0.5$$\end{document}corXi,Xj=0.5 for any \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i\ne j$$\end{document}i≠j. To ensure that the outcome rate in the untreated subset was $20\%$ and that true prediction c-statistic remained equal to the nominal values of the main simulation analyses, we adjusted the coefficients of the true outcome model. More details on the sensitivity analyses can be found in the Supplement, Sect. 9.
## Individualized risk-based benefit predictions
In each simulation run, we internally developed a prediction model on the entire population, using a logistic regression model with main effects for all baseline covariates and treatment assignment. Individual risk predictions were derived by setting treatment assignment to 0. A more intuitive approach would be to derive the prediction model solely on the control patients. However, this has been shown to lead to biased benefit predictions, because with limited sample size the model will be overfitted to the control arm and induce spurious treatment interactions [15, 23, 24].
We compared different methods for predicting absolute treatment benefit, that is the risk difference between distinct treatment assignments. We use the term absolute treatment benefit to distinguish from relative treatment benefit that relies on the ratio of predicted risk under different treatment assignments.
A stratified HTE method has been suggested as an alternative to traditional subgroup analyses [20, 21]. Patients are stratified into equally-sized risk strata—in this case based on risk quartiles. Absolute treatment effects, within risk strata, expressed as absolute risk differences, are estimated by the difference in event rate between control and treatment arm patients. We considered this approach as a reference, expecting it to perform worse than the other candidates, as its objective is to provide an illustration of HTE rather than to optimize individualized benefit predictions.
Second, we fitted a logistic regression model which assumes constant relative treatment effect (constant odds ratio), that is, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left($Y = 1$ | X=x,Z=z;\widehat{\beta }\right)={\text{expit}}\left({\widehat{lp}}_{0}+{\delta }_{1}z\right)$$\end{document}PY=1|X=x,Z=z;β^=expitlp^0+δ1z. Hence, absolute benefit is predicted from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau \left(x;\widehat{\beta }\right)={\text{expit}}\left({\widehat{lp}}_{0}\right)-{\text{expit}}\left({\widehat{lp}}_{0}+{\delta }_{1}\right)$$\end{document}τx;β^=expitlp^0-expitlp^0+δ1, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\delta }_{1}$$\end{document}δ1 is the log of the assumed constant odds ratio and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\widehat{lp}}_{0}={\widehat{lp}}_{0}\left(x;\widehat{\beta }\right)={x}^{t}\widehat{\beta }$$\end{document}lp^0=lp^0x;β^=xtβ^ the linear predictor of the estimated baseline risk model.
Third, we fitted a logistic regression model including treatment, the risk linear predictor, and their linear interaction, that is, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left($Y = 1$ | X=x,Z=z;\widehat{\beta }\right)={\text{expit}}\left({\delta }_{0}+{\delta }_{1}z+{\delta }_{2}{\widehat{lp}}_{0}+{\delta }_{3}z{\widehat{lp}}_{0}\right)$$\end{document}PY=1|X=x,Z=z;β^=expitδ0+δ1z+δ2lp^0+δ3zlp^0. Absolute benefit is then estimated from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau \left(x;\widehat{\beta }\right)={\text{expit}}\left({\delta }_{0}+{\delta }_{2}{\widehat{lp}}_{0}\right)-{\text{expit}}\left({(\delta }_{0}+{\delta }_{1})+{(\delta }_{2}{+{\delta }_{3})\widehat{lp}}_{0}\right)$$\end{document}τx;β^=expitδ0+δ2lp^0-expit(δ0+δ1)+(δ2+δ3)lp^0. We will refer to this method as the linear interaction approach.
Fourth, we used restricted cubic splines (RCS) to relax the linearity assumption on the effect of the linear predictor [25]. We considered splines with 3 (RCS-3), 4 (RCS-4) and 5 (RCS-5) knots, together with their interaction with treatment, to compare models with different levels of flexibility (Supplement, Sect. 4).
Finally, we considered an adaptive approach using Akaike’s Information Criterion (AIC) for model selection. More specifically, we ranked the constant relative treatment effect model, the linear interaction model, and the RCS models with 3, 4, and 5 knots based on their AIC and selected the one with the lowest value. The extra degrees of freedom were 1 (linear interaction), 2, 3 and 4 (RCS models) for these increasingly complex interactions with the treatment effect.
## Evaluation metrics
We evaluated the predictive accuracy of the considered methods by the root mean squared error (RMSE):\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{RMSE}}=\sqrt{\frac{1}{n}\sum_{$i = 1$}^{n}(\tau \left({\mathbf{x}}_{i}\right)-\widehat{\tau }\left({\mathbf{x}}_{i}\right){)}^{2}}$$\end{document}RMSE=1n∑$i = 1$n(τxi-τ^xi)2 We compared the discriminative ability of the methods under study using c-for-benefit and the integrated calibration index (ICI) for benefit (Supplement, Sect. 6). Since true patient-specific benefit is unobservable, we calculated observed benefit using the following approach: patients in each treatment arm are ranked based on their predicted benefit and then matched 1:1 on predicted benefit across treatment arms. Observed treatment benefit is defined as the difference of observed outcomes between the untreated and the treated patient of each matched patient pair. Since matching may not be perfect, that is, predicted benefits for the patients of the pair may not be equal, pair-specific predicted benefit is defined as the average of predicted benefit within each matched patient pair [26]. Then, the c-for-benefit represents the probability that from two randomly chosen predicted benefit-matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. We evaluated calibration in a similar manner, using the integrated calibration index (ICI) for benefit [27]. The observed benefits are regressed on the predicted benefits using a locally weighted scatterplot smoother (loess). The ICI-for-benefit is the average absolute difference between predicted and smooth observed benefit. Values closer to 0 represent better calibration. For each scenario we performed 500 replications, within which all the considered models were fitted. We simulated a super-population of size 500,000 for each scenario within which we calculated RMSE and discrimination and calibration for benefit of all the models in each replication.
## Empirical illustration
We demonstrated the different methods using 30,510 patients with acute myocardial infarction (MI) included in the GUSTO-I trial. 10,348 patients were randomized to tissue plasminogen activator (tPA) treatment and 20,162 were randomized to streptokinase. The outcome of interest was 30-day mortality (total of 2,128 events), recorded for all patients.
This dataset has been used extensively in prior studies [28, 29]. Therefore, we used the same set of seven covariates that was previously used to fit a logistic regression model (age, Killip class, systolic blood pressure, heart rate, an indicator of previous MI, and the location of MI) along with a binary covariate for treatment indication, to predict 30-day mortality risk (Supplement, Sect. 10). Predicted baseline risk is derived by setting the treatment indicator to 0 for all patients.
We used the derived prognostic index to fit a constant treatment effect, a linear interaction and an RCS-3 model individualizing absolute benefit predictions. Following our simulation results, RCS-4 and RCS-5 models were excluded. Finally, an adaptive approach with the 3 candidate models was applied.
Predicted absolute benefit was derived as the difference of predicted acute MI risk between treatment arms, if all other predictors remained unchanged. All considered methods provided similar fits, predicting increasing absolute benefits for patients with higher baseline risk predictions, and followed the evolution of the stratified estimates closely (Fig. 6). The constant treatment effect model had somewhat lower AIC compared to the linear interaction model (AIC: versus 9,342), equal cross-validated discrimination (c-for-benefit: 0.525), and slightly better cross-validated calibration (ICI-for benefit: 0.010 versus 0.012). In conclusion, although the sample size (30,510 patients; 2,128 events) allowed for flexible modeling approaches, a simpler constant treatment effect model is adequate for predicting absolute 30-day mortality benefits of treatment with tPA in patients with acute MI.Fig. 6Individualized absolute benefit predictions based on baseline risk when using a constant treatment effect approach, a linear interaction approach and RCS smoothing using 3 knots. Risk stratified estimates of absolute benefit are presented within quartiles of baseline risk as reference. $95\%$ confidence bands were generated using 10,000 bootstrap resamples, where the prediction model was refitted in each run to capture the uncertainty in baseline risk predictions. For the risk stratification approach, we also provide $95\%$ confidence intervals for the baseline risk quarter-specific average predicted risk over the 10,000 bootstrap samples
## Simulations
The constant treatment effect approach outperformed other approaches in the base case scenario ($$n = 4$$,250; OR = 0.8; c-statistic = 0.75; no absolute treatment harm) with a true constant treatment effect (median RMSE: constant treatment effect 0.009; linear interaction 0.014; RCS-3 0.018). The linear interaction model was optimal under true linear deviations (median RMSE: constant treatment effect 0.027; linear interaction 0.015; RCS-3 0.018; Fig. 1 panels A-C) and even in the presence of true quadratic deviations (median RMSE: constant treatment effect 0.057; linear interaction 0.020; RCS-3 0.021; Fig. 1 panels A-C) from a constant relative treatment effect. With non-monotonic deviations, RCS-3 slightly outperformed the linear interaction model (median RMSE: linear interaction 0.019; RCS-3 0.018; Fig. 1 panel D). With strong treatment-related harms the results were very similar in most scenarios (Fig. 1 panels A-C). Under non-monotonic deviations the optimal performance of RCS-3 was more pronounced (median RMSE: linear interaction 0.024; RCS-3 0.019; Fig. 1 panel D). A stronger average treatment effect (OR = 0.5) resulted in higher variability of the true treatment effects on the absolute scale (difference in true outcome probabilities between treatment arms) and consequently to larger RMSE for all approaches. When we assumed a stronger relative treatment effect, the relative differences between approaches were similar to the base-case scenario (Supplement, Figure S10).Fig. 1RMSE of the considered methods across 500 replications was calculated from a simulated super-population of size 500,000. The scenario with true constant relative treatment effect (panel A) had a true prediction c-statistic of 0.75 and sample size of 4250. The RMSE is also presented for strong linear (panel B), strong quadratic (panel C), and non-monotonic (panel D) deviations from constant relative treatment effects. Panels on the right side present the true relations between baseline risk (x-axis) and absolute treatment benefit (y-axis). The 2.5, 25, 50, 75, and 97.5 percentiles of the risk distribution are expressed by the boxplot on the top. The 2.5, 25, 50, 75, and 97.5 percentiles of the true benefit distributions are expressed by the boxplots on the side of the right-handside panel The adaptive approach had limited loss of performance in terms of the median RMSE to the best-performing method in each scenario. However, compared to the best-performing approach, its RMSE was more variable in scenarios with linear and non-monotonic deviations, especially when also including moderate or strong treatment-related harms. On closer inspection, we found that this behavior was caused by selecting the constant treatment effect model in a substantial proportion of the replications (Supplement, Figure S3).
Increasing the sample size to 17,000 favored RCS-3 the most (Fig. 2). The difference in performance with the linear interaction approach was more limited in settings with a constant treatment effect (median RMSE: linear interaction 0.007; RCS-3 0.009) and with a true linear interaction (median RMSE: linear interaction 0.008; RCS-3 0.009) and more emphasized in settings with strong quadratic deviations (median RMSE: linear interaction 0.013; RCS-3 0.011) and non-monotonic deviations (median RMSE: linear interaction 0.014; RCS-3 0.010). Due to the large sample size, the RMSE of the adaptive approach was even more similar to the best-performing method, and the constant relative treatment effect model was less often wrongly selected (Supplement, Figure S4).Fig. 2RMSE of the considered methods across 500 replications calculated in simulated samples of size 17,000 rather than 4,250 in Fig. 1. RMSE was calculated on a super-population of size 500,000 Similarly, when we increased the c-statistic of the true prediction model to 0.85 (OR = 0.8 and $$n = 4$$,250), RCS-3 had the lowest RMSE in the case of strong quadratic or non-monotonic deviations and very comparable performance to the – optimal – linear interaction model in the case of strong linear deviations (median RMSE of 0.016 for RCS-3 compared to 0.014 for the linear interaction model; Fig. 3). Similar to the base case scenario the adaptive approach wrongly selected the constant treatment effect model ($23\%$ and $25\%$ of the replications in the strong linear and non-monotonic deviation scenarios without treatment-related harms, respectively), leading to increased variability of the RMSE (Supplement, Figure S5).Fig. 3RMSE of the considered methods across 500 replications calculated in simulated samples 4,250. True prediction c-statistic of 0.85. RMSE was calculated on a super-population of size 500,000 *With a* true constant relative treatment effect, discrimination for benefit was only slightly lower for the linear interaction model, but substantially lower for the non-linear RCS approaches (Fig. 4; panel A). With strong linear or quadratic deviations from a constant relative treatment effect, all methods discriminated quite similarly (Fig. 4 panels B-C). With non-monotonic deviations, the constant effect model had much lower discriminative ability compared to all other methods (median c-for-benefit of 0.500 for the constant effects model, 0.528 for the linear interaction model and 0.530 Fig. 4; panel D). The adaptive approach was unstable in terms of discrimination for benefit, especially with treatment-related harms. With increasing number of RCS knots, we observed decreasing median values and increasing variability of the c-for-benefit in all scenarios. When we increased the sample size to 17,000 we observed similar trends, however the performance of all methods was more stable (Supplement, Figure S6). Finally, when we increased the true prediction c-statistic to 0.85 the adaptive approach was, again, more conservative, especially with non-monotonic deviations and null or moderate treatment-related harms (Supplement, Figure S7).Fig. 4Discrimination for benefit of the considered methods across 500 replications calculated in simulated samples of size 4,250 using the c-statistic for benefit. The c-statistic for benefit represents the probability that from two randomly chosen matched patient pairs with unequal observed benefit, the pair with greater observed benefit also has a higher predicted benefit. True prediction c-statistic of 0.75 In terms of calibration for benefit, the constant effects model outperformed all other models in the scenario with true constant treatment effects, but was miscalibrated for all deviation scenarios (Fig. 5). The linear interaction model showed best or close to best calibration across all scenarios and was only outperformed by RCS-3 in the case of non-monotonic deviations and treatment-related harms (Fig. 5 panel D). The adaptive approach was worse calibrated under strong linear and non-monotonic deviations compared to the linear interaction model and RCS-3. When we increased the sample size to 17,000 (Supplement, Figure S8) or the true prediction c-statistic to 0.85 (Supplement, Figure S9), RCS-3 was somewhat better calibrated than the linear interaction model with strong quadratic deviations. Fig. 5Calibration for benefit of the considered methods across 500 replications calculated in a simulated sample of size 500,000. True prediction c-statistic of 0.75 and sample size of 4,250
Our main conclusions remained unchanged in the sensitivity analyses where correlations between baseline characteristics were introduced (Supplement, Figures S16, S17, and S18).
The results from all individual scenarios can be explored online at https://mi-erasmusmc.shinyapps.io/HteSimulationRCT/. Additionally, all the code for the simulations can be found at https://github.com/mi-erasmusmc/HteSimulationRCT
## Discussion
The linear interaction and the RCS-3 models displayed very good performance under many of the considered simulation scenarios. The linear interaction model was optimal in cases with moderate sample sizes (4.250 patients; ~ 785 events) and moderately performing baseline risk prediction models, that is, it had lower RMSE, was better calibrated for benefit and had better discrimination for benefit, even in scenarios with strong quadratic deviations. In scenarios with true non-monotonic deviations, the linear interaction model was outperformed by RCS-3, especially in the presence of treatment-related harms. Increasing the sample size or the prediction model’s discriminative ability favored RCS-3, especially in scenarios with strong non-linear deviations from a constant treatment effect.
Our simulation results clearly express the trade-off between the advantages of flexibly modeling the relationship between baseline risk and treatment effect and the disadvantages of overfitting this relationship to the sample at hand. With infinite sample size, the more flexible approach (here RCS) will be optimal, but in practice, with limited sample size, parsimonious models may be preferable. Even with the substantial sample size of our base case scenario, the (less flexible) linear interaction model performed better than the (more flexible) RCS approach for most simulation settings. The even less flexible constant treatment effect model, however, was only optimal when the treatment effect was truly constant. Moreover, the assumption of a constant treatment effect may often be too strong [22, 30].
RCS-4 and RCS-5 were too flexible in all considered scenarios, as indicated by higher RMSE, increased variability of discrimination for benefit and worse calibration of benefit predictions. Even with larger sample sizes and strong quadratic or non-monotonic deviations, these more flexible methods did not outperform the simpler RCS-3 approach. Higher flexibility may only be helpful under more extreme patterns of HTE compared to the quadratic deviations considered here. Considering interactions in RCS-3 models as the most complex approach often may be reasonable.
Our results can also be interpreted in terms of bias-variance trade-off. The increasingly complex models considered allow for more degrees of freedom which, in turn, increase the variance of our absolute benefit estimates. However, as was clear in our simulations, this increased complexity did not always result in substantial decrease in bias, especially with lower sample sizes and weaker treatment effects. Consequently, in most scenarios the simpler linear interaction model achieved the best bias-variance balance and outperformed the more complex RCS methods, even in the presence of non-linearity in the true underlying relationship between baseline risk and treatment effect. Conversely, the simpler constant treatment effect model was often heavily biased and, despite its lower variance, was outperformed by the other methods in the majority of the considered scenarios.
Increasing the discriminative ability of the risk model reduced RMSE for all methods. Higher discrimination translates in higher variability of predicted risks, which, in turn, allows the considered methods to better capture absolute treatment benefits. As a consequence, better risk discrimination also led to higher discrimination between those with low or high benefit (as reflected in values of c-for-benefit).
The adaptive approach had adequate median performance, following the “true” model in most scenarios. With smaller sample sizes it tended to miss the treatment-baseline risk interaction and selected simpler models (Supplement Sect. 4). This conservative behavior resulted in increased RMSE variability in these scenarios, especially with true strong linear or non-monotonic deviations. Therefore, with smaller sample sizes the simpler linear interaction model may be a safer choice for predicting absolute benefits, especially in the presence of any suspected treatment-related harms.
A limitation of our simulation study is that we assumed treatment benefit to be a function of baseline risk in the majority of the simulation scenarios, thus ignoring any actual treatment effect modification of individual factors. We attempted to expand our scenarios by considering moderate and strong constant treatment-related harms, applied on the absolute scale, in line with previous work [31]. In a limited set of scenarios with true interactions between treatment assignment and covariates, our conclusions remained unchanged (Supplement, Sect. 8). Even though the average error rates increased for all the considered methods, due to the miss-specification of the outcome model, the linear interaction model had the lowest error rates. RCS-3 had very comparable performance. The constant treatment effect model was often biased, especially with moderate or strong treatment-related harms. Future simulation studies could explore the effect of more extensive deviations from risk-based treatment effects.
We only focused on risk-based methods, using baseline risk as a reference in a two-stage approach to individualizing benefit predictions. However, there is a plethora of different methods, ranging from treatment effect modeling to tree-based approaches available in more recent literature [4, 5, 8, 32–36]. Many of these methods rely on incorporating treatment-covariate interactions when predicting benefits. An important caveat of such approaches is their sensitivity to overfitting, which may exaggerate the magnitude of predicted benefits. This can be mitigated using methods such as cross-validation or regularization to penalize the effect of treatment-covariate interactions. In the presence of a limited set of true strong treatment-covariate interactions and adequate sample size, treatment effect modeling methods may outperform risk modeling methods. However, often treatment effect modifiers are unknown and the available sample size does not allow for the exploration of a large number of interaction effects. In these cases, risk modeling approaches like the ones presented here can provide individualized benefit predictions that improve on the “one-size-fits-all” overall RCT result. In a previous simulation study, a simpler risk modeling approach was consistently better calibrated for benefit compared to more complex treatment effect modelling approaches [15]. Similarly, when SYNTAX score II, a model developed for identifying patients with complex coronary artery disease that benefit more from percutaneous coronary intervention or from coronary artery bypass grafting was redeveloped using fewer treatment-covariate interactions had better external performance compared to its predecessor [37, 38].
Finally, in all our simulation scenarios we assumed all covariates to be statistically independent, the effect of continuous covariates to be linear, and no interaction effects between covariates to be present. This can be viewed as a limitation of our extensive simulation study. However, as all our methods are based on the same fitted risk model, we do not expect these assumptions to significantly influence their relative performance.
In conclusion, the linear interaction approach is a viable option with moderate sample sizes and/or moderately performing risk prediction models, assuming a non-constant relative treatment effect plausible. RCS-3 is a better option with more abundant sample size and when non-monotonic deviations from a constant relative treatment effect and/or substantial treatment-related harms are anticipated. Increasing the complexity of the RCS models by increasing the number of knots does not improve benefit prediction. Using AIC for model selection is attractive with larger sample size.
## Supplementary Information
Additional file 1: Supplement Figure S1-S18 and Table S1-S4.
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|
---
title: 'Increased Renal Medullary NOX-4 in Female but Not Male Mice during the Early
Phase of Type 1 Diabetes: Potential Role of ROS in Upregulation of TGF-β1 and Fibronectin
in Collecting Duct Cells'
authors:
- Felipe Casado-Barragán
- Geraldine Lazcano-Páez
- Paulina E. Larenas
- Monserrat Aguirre-Delgadillo
- Fernanda Olivares-Aravena
- Daniela Witto-Oyarce
- Camila Núñez-Allimant
- Katherin Silva
- Quynh My Nguyen
- Pilar Cárdenas
- Modar Kassan
- Alexis A. Gonzalez
journal: Antioxidants
year: 2023
pmcid: PMC10045926
doi: 10.3390/antiox12030729
license: CC BY 4.0
---
# Increased Renal Medullary NOX-4 in Female but Not Male Mice during the Early Phase of Type 1 Diabetes: Potential Role of ROS in Upregulation of TGF-β1 and Fibronectin in Collecting Duct Cells
## Abstract
Chronic diabetes mellitus (DM) can lead to kidney damage associated with increased reactive oxygen species (ROS), proteinuria, and tubular damage. Altered protein expression levels of transforming growth factor-beta 1 (TGF-β1), fibronectin, and renal NADPH oxidase (NOX-4) are associated with the profibrotic phenotype in renal tubular cells. NOX-4 is one of the primary sources of ROS in the diabetic kidney and responsible for the induction of profibrotic factors in collecting duct (CD) cells. The renal medulla is predominantly composed of CDs; in DM, these CD cells are exposed to high glucose (HG) load. Currently there is no published literature describing the expression of these markers in the renal medulla in male and female mice during the early phase of DM, or the role of NOX-4-induced ROS. Our aim was to evaluate changes in transcripts and protein abundances of TGF-β1, fibronectin, and NOX-4 along with ROS levels in renal medullary tissues from male and female mice during a short period of streptozotocin (STZ)-induced type 1 DM and the effect of HG in cultured CD cells. CF-1 mice were injected with or without a single dose of STZ (200 mg/kg) and euthanized at day 6. STZ females showed higher expression of fibronectin and TGF-β1 when compared to control mice of either gender. Interestingly, STZ female mice showed a >30-fold increase on mRNA levels and a 3-fold increase in protein levels of kidney medullary NOX-4. Both male and female STZ mice showed increased intrarenal ROS. In primary cultures of inner medullary CD cells exposed to HG over 48 h, the expression of TGF-β1, fibronectin, and NOX-4 were augmented. M-1 CD cells exposed to HG showed increased ROS, fibronectin, and TGF-β1; this effect was prevented by NOX-4 inhibition. Our data suggest that at as early as 6 days of STZ-induced DM, the expression of profibrotic markers TGF-β1 and fibronectin increases in renal medullary CD cells. Antioxidants mechanisms in male and female in renal medullary tissues seems to be differentially regulated by the actions of NOX-4.
## 1. Introduction
The epidemic of diabetes mellitus (DM) is progressively worsening worldwide and is a major cause of kidney failure [1]. Although hyperglycemia is often the first symptom of DM, other hallmarks include hyperglycosuria, proteinuria, and other signs of renal damage, including deposition of extracellular matrix and glomerular damage along with tubular fibrosis [2]. These clinical manifestations are primarily observed in the advanced disease. *In* general, the diabetic animal models reported for the study of diabetic disease have been used to analyze the effects of chronic hyperglycemia and glycosuria on renal physiology and on the expression of kidney injury markers related to glomerular damage [3,4].
Among the injury markers observed in chronic DM in experimental animal models, transforming growth factor-beta 1 (TGF-β1), fibronectin, and connective tissue growth factor (CTGF) have been found to be responsible for the profibrotic effects in tubular cells; this enhances collagen deposition and the proliferation of fibroblasts [5,6]. Tubular markers of injury in diabetic disease are associated with proteinuria and albuminuria; however, the progression of tubule-interstitial disease, in addition to glomerular injury in diabetes, is also important and may provide insights into the pathogenesis of diabetic nephropathy beyond the glomerular injury.
Because the renal medullary tissues and the collecting ducts are more likely to be damaged when limited glucose transport in the proximal tubule has been exceeded, their resorptive function is especially affected by the pathological mechanisms activated by hyperglycemia and hyperglycosuria. The exposure to high glucose levels promotes an increase in cytokines levels such as TGF-β1, as well as extracellular matrix formation in the kidney [7]. Tubular and interstitial expression of profibrotic factors thus amplifies the development of fibrogenesis in the renal tubulointerstitium [8]. Advanced glycation also occurs in in the diabetic state, promoting increased reactive oxygen species (ROS) generation. Additionally, it has been reported that the mechanisms underlying hyperglycemia and hyperglycosuria-induced renal complications involve renal NADPH oxidase (NOX-4) [9]. This further increases ROS generation, causing activation of redox-sensitive pathways in DM [10]. In early DM, hyperglycemia and hyperglycosuria may affect redox status in kidney cells; this may favor cell signaling pathways that enhance extracellular matrix and damage in renal tubules [11,12]. Then, increases of ROS in renal epithelial cells may be the mechanism by which renal tubular injury occurs [13,14].
Most of these effects are observed in longstanding hyperglycemia (lasting weeks or longer) and all current data in the literature are primarily focused on male mice. Sex differences in glucose metabolism have been described in animal models of DM [15]. It has been observed that glucose metabolism can be affected by variations in hormone levels through the estrogen cycle. Evidence showed that insulin resistance can be observed in male STZ mice, but not in female STZ mice, after 6 weeks of STZ injection [15,16]. This indicates that glucose metabolism in the diabetic male mice is worse than that observed in female STZ mice, which thereby suggests that estrogen may play an important role in glucose metabolism in STZ-induced DM. We evaluated the physiological parameters and renal medullary expression of TGF-β1, fibronectin, and NOX-4 in male and female mice with streptozotocin (STZ)-induced diabetes mellitus over the course of 6 days following STZ treatment. We also tested the effect of 48 h of high glucose conditions on the expression of profibrotic markers in cultured inner medullary collecting duct cells (IMCD). To further avoid the hormonal and sex influence in cultured cells as potentially confounding variables, we performed additional experiments in M-1 collecting duct cell line to evaluate the role of NOX-4.
## 2.1. Animals and Samples
All methodologies were in accordance with the Animal Care and the Bioethical Committee of the Pontificia Universidad Católica de Valparaiso (code number BIOEPUCV-B 267-2019), under international guidelines and regulations for animal use. Male and female CF-1 mice (12-week-old) were placed under a 12 h light–dark cycle, a temperature of 21 °C, humidity of $50\%$, noise-free conditions, and food and water ad libitum. Male and female mice were divided randomly into two groups: control (saline injection, normoglycemic, $$n = 6$$) and streptozotocin (STZ)-induced (200 mg/kg, single i.p. injection, $$n = 6$$). STZ was injected after a 6 h fast [17]. A diabetic mouse was considered if three consecutive blood glucose readings exceeded 250 mg/dL. At the end of the treatment period, mice were euthanized by cervical dislocation after isoflurane inhalation. A graphical abstract including the protocols of animal and cell cultures is shown in Figure 1.
## 2.2. Blood Glucose Measurements
Blood glucose was directly measured using a ONETOUCH Ultra glucometer (LifeScan, catalog #ZJZ8158JT, Milpitas, CA, USA, reported result range 20–600 mg/dL) and compared with a regular glucometer Prodigy© (Charlotte, NC, USA) demonstrating no differences in plasma glucose measurements. A glucose overload test was performed once at day 5 after STZ treatment. The blood glucose was measured before and after 15, 30, 60, and 120 min of a glucose overload of 2 g/kg BW, i.p. Blood glucose was measured every 15 min.
## 2.3. Sodium Measurement from Plasma and Urine
Sodium was measured from plasma and urine samples collected from metabolic cages on day 6. Sodium was measured using a flame photometer (Instrumentation Laboratory 943, Ramsey, MN, USA).
## 2.4. Blood Creatinine
Blood was collected from the cardiac puncture and centrifuged at 10,000× g for 5 min at 4 °C to collect the plasma. Creatinine was measured using a Creatinine Analyzer 2 (Beckman Coulter, Inc., Fullerton, CA, USA).
## 2.5. Blood Pressure Measurements
The mice were trained (4 days) to the tail-cuff plethysmography protocol, eight to 12 consecutive pulse readings were recorded for each mouse in each set of measurements on day 0, 3, and 6. All data were recorded using a BP-2000 series II Blood Pressure Analysis System (Visitech Systems, Inc.; Apex, NC, USA).
## 2.6. Saline Challenge
On day 5, a saline challenge was performed to evaluate the effect of STZ on Na+ balance. Mice were injected (i.p.) with isotonic saline ($10\%$ of their body weight) and placed in metabolic cages for urine collection.
## 2.7. Assays for Reactive Oxygen Species in Medullary Tissues
Total ROS in renal homogenates and plasma were measured with the Oxiselect™ in vitro ROS/RNS assay kit (Cell Biolabs, Inc. San Diego, CA, USA), following the manufacture’s instruction.
## 2.8. Fibronectin, TGF-β1 and NOX-4 Transcripts Quantitation by Real Time qRT-PCR
The mRNA was extracted from renal medullas (1 mm × 1 mm square tissue) using a RNeasy Mini Kit (Qiagen, Valencia, CA, USA). The RNA was quantified using the nano-drop system. Quantitative real-time RT-PCR (qRT-PCR) was performed using the following primers (5′–3′): TGF-β1: TCGCTTTGTACAACAGCACC (Forward); ACTGCTTCCCGAATGTCTGA (Reverse), gene accession number NM_011577.2; Fibronectin: TCCCGGGCAGAAAGTACATT (Forward), TTCAGGGAGGTTGAGCTCTG (Reverse) gene accession number M18194.1; NOX-4: TGCTCATTTGGCTGTCCCTA (Forward), TGCAGTTGAGGTTCAGGACA (Reverse), gene accession number NM_015760.5. Results were presented as the fold change ratio between the levels of mRNA of the interest gene against β-actin (“housekeeping” gene, gene accession number NM_023063.4) and compared to control group ($$n = 4$$–6). Primers were obtained from IDT Company (https://www.idtdna.com, Newark, NJ, USA).
## 2.9. Immunoblotting Analyses
Inner sections of kidney poles were minced and homogenized in ice-cold RIPA buffer with protease inhibitors (0.15 M NaCl, 50 mM Tris-HCl pH 8.0, $1\%$ NP-40, $0.5\%$ deoxycholate). Total protein content was determined using the BCA Protein Assay Kit (Santa Cruz, CA, USA) according to manufacturer instructions. Protein samples (40 micrograms) were separated on a precast NuPAGE $10\%$ Bis-Tris gel (Novex) at 200 v for 45 min. Blots were blocked at 25 °C and incubated with primary antibodies. Then, the membranes were incubated with the secondary antibodies (1:1000 dilutions) and analyzed by normalization against β-actin bands (used as a housekeeping gene). Fibronectin, TGF-β1, and NOX-4 protein levels were detected using polyclonal rabbit antibody from Santa Cruz, CA, used at 1:200 dilutions. Immunoblots are shown in each figure as representative images. Results are shown as the ratio of each band vs. β-actin (fold change of control). Analysis was conducted by using 4–6 animals per group and 3–5 independent experiments for Western blot analysis (Supplementary Figures S1 and S2). Blots were incubated with chemiluminescent substrate ECL reagent (Perkin Elmer, Waltham, MA, USA) by direct exposure on films that were scanned and analyzed by ImageJ software.
## 2.10. Primary Cultures of Inner Medullary Collecting Duct (IMCD) Cells
IMCD cell cultures were prepared using inner medullary tissues (no glomeruli components). Inner medullary tissues were minced and digested in 10 mL of DMEM-Ham F-12, 20 mg of collagenase B, 7 mg of hyaluronidase, 80 mmol/L of urea, and 130 mmol/L of NaCl and incubated at 37 °C with agitation for 90 min. The pellet was washed in p culture medium without enzymes. The IMCD cell suspension was seeded in 3-mm petri dishes until $70\%$ confluence (3–5 days) and incubated with normal (5 mM) or high (25 mM) glucose conditions. IMCD cells were obtained from female or male mice. Preliminary experiments demonstrated that IMCD cells from male and female showed same profile of expression in response to normal or high glucose (Supplementary Figure S3). Since both IMCD cultures were under no control of sex hormones, we decided to perform the experiments in male IMCD. Blot analysis was conducted by using 4 replicates per group (Supplementary Figure S4).
## 2.11. Immunofluorescence in Kidney Slides and IMCD Cells
For histological studies, a kidney pole was fixed with Bouin’s solution (picric acid, formaldehyde and acetic acid $4\%$) overnight and then subjected to inclusion into paraffin blocks. Kidney poles were cut in 3 μm sections using microtome (HM325, Thermo, Waltham, MA, USA). Kidney slides (3 μm) were fixed and stained with antibodies at 1:100 dilutions and detected with Alexa Fluor 594 conjugated to antirabbit IgG (Invitrogen, Life Science, Co., Waltham, MA, USA). The slides were mounted with ProLong® Gold. Subconfluent IMCD cells were fixed in cold methanol for 20 min, blocked with PBS-Tween ($0.1\%$) plus BSA ($3\%$), stained with Fibronectin, TGF-β1 and NOX-4 at 1:100 dilutions, and detected with Alexa Fluor 488 (Invitrogen Life Science, Co.). Samples were counter-stained with 4,6-diamidino-2-phenylindole (Invitrogen). Omission of the specific primary antibody was used as a negative control. The images were obtained using a Nikon Eclipse-50i (Nikon Eclipse-50i, Minato City, Japan) and NIS-Elements BR version 4.0 from Nikon.
## 2.12. M-1 Cell Culture
M-1 cells (ATCC, VA) cell line possess the phenotype of collecting duct cells [18]. The M-1 cells were cultured as previously described [19]. Cells were harvested after 48 h of treatments with normal or high glucose. NOX-4 inhibitor GKT 137831 was used at 30 μM [20]. The pharmacological inhibitor was added 30 min before high glucose incubations. Controls were performed with vehicle (DMSO, $0.06\%$ vol/vol).
## 2.13. Statistical Analyses
Results are shown as mean ± SEM. The statistical analyses were performed with GraphPad Prism Software Version 6 (GraphPad Software, Inc., La Jolla, CA, USA). Normal distribution of each parameter analyzed was tested by using Shapiro–Wilk. Two-way ANOVA was used to compare the mean differences between groups and divided on variables controls vs. STZ in male and female mice studies and post-test comparisons for two groups using non-paired (one-tailed) t-test.
## 3.1. Physiological Parameters in Male and Female STZ Mice
As shown in Table 1, STZ mice had increased fasting blood glucose (FBG) and reduced body weight at day 6, in a phenotype consistent with diabetic disease. No changes were seen in hematocrit electrolyte balance and serum creatinine. Food and water intake and urine output were increased in both male and female STZ mice when compared to control mice.
To ensure consistent expression of type 1 diabetes mellitus in STZ-induced mice, we performed a tolerance glucose test at day 5. As shown in Figure 2A, the STZ mice were not able to recover control levels of FBG when compared to control animals. Since we previously demonstrated that STZ mice showed Na+ retention [21], control and STZ mice were injected with isotonic saline (i.p. $10\%$ of body weight) and placed in metabolic cages for urine measurements. A slight but not significant trend toward Na+ retention was observed in male and female STZ mice (Figure 2B). Interestingly, proteinuria did not differ between STZ females and control females; however, this parameter was significantly increased in STZ males (Figure 2C). Renal medullary levels of ROS were significantly augmented in female mice when compared to males (Figure 2D).
## 3.2. Expression of TGF-β1, Fibronectin and NOX-4 in Renal Medullary Tissues from Male and Female Mice after 6 Days of Streptozotocin (STZ)-Induced Type 1 Diabetes
After 6 days of STZ administration, NOX-4 mRNA/β-actin mRNA ratio was augmented in female STZ mice when compared to control female mice (39.2 ± 5.1 vs. control 2.5 ± 0.7, $p \leq 0.001$); this increase was not observed in male STZ mice (Figure 3A). Fibronectin mRNA/β-actin mRNA ratios were augmented in female (8.7 ± 0.6 vs. control 2.8 ± 0.6 $p \leq 0.01$) and male STZ mice (5.3 ± 0.4 vs. control 1.6 ± 0.5 $p \leq 0.05$, Figure 3B). Similarly, TGF-β1 mRNA/β-actin mRNA ratios were significantly augmented in STZ females vs. control female mice (3.2 ± 0.2 vs. control 0.8 ± 0.1, $p \leq 0.05$), and male STZ mice (2.0 ± 0.2 vs. control 0.9 ± 0.1, $p \leq 0.05$, Figure 3C).
Next, we evaluated protein abundance of these three profibrotic markers. Similar to what was observed in mRNA levels, NOX-4 was increased in STZ females (2.7 ± 0.1 vs. 1.0 ± 0.1, $p \leq 0.01$) but not in male STZ mice (Figure 4A). We also found significant differences in the abundance of fibronectin protein in STZ females vs. controls (2.7 ± 0.7 vs. 0.9 ± 1, $p \leq 0.05$, Figure 4B) and male STZ mice (1.7 ± 0.4 vs. 0.8 ± 0.1, $p \leq 0.05$, Figure 4B). We found increased levels of TGF-β1 1 in both females (1.8 ± 0.2 vs. 0.9 ± 0.1, $p \leq 0.05$) and males (1.9 ± 0.1 vs. 0.9 ± 0.1, $p \leq 0.05$, Figure 4C). A representative blot is shown in Figure 4D.
By using immunofluorescence, we analyzed renal cortical and medullary staining using fibronectin, TGF-β1, and NOX-4 antibodies. High intensity staining was evident for NOX-4 in STZ female mice. In males, high-intensity staining of NOX-4 was particularly noted in the cortex and in some interstitial cells, as judged by their shape. On the other hand, staining against fibronectin and TGF-β1 occurred primarily in tubular cells (Figure 5).
## 3.3. Expression of TGF-β1, Fibronectin and NOX-4 in Primary Cultured Inner Medullary Collecting Duct Cells Exposed to Normal and High Glucose Conditions
We tested the effect of high glucose during 48 h of incubation with 25 mM of glucose in cultured IMCD. As shown in Figure 5, the mRNA/β-actin mRNA ratios were augmented for fibronectin (1.62 ± 0.89 vs. control 1.00 ± 0.12 $p \leq 0.01$, Figure 6A), TGF-β1 (2.11 ± 0.1 vs. control 1.06 ± 0.03, $p \leq 0.05$, Figure 6B), and NOX-4 (1.63 ± 0.34 vs. control 1.00 ± 0.08, $p \leq 0.05$). An appropriate control for osmolality was evaluated by using 25 mM of mannitol; it showed no differences on the expression of profibrotic genes analyzed (Supplementary Figure S5).
Using the same protocol, we performed an analysis of protein extracts from IMCD lysates after incubations with high glucose. As observed in Figure 7, protein abundance was significantly augmented for fibronectin (1.54 ± 0.12 vs. control 1.00 ± 0.05, $p \leq 0.05$), TGF-β1 (2.25 ± 0.18 vs. control 1.00 ± 0.07, $p \leq 0.05$), and NOX-4 (1.75 ± 0.08 vs. control 1.00 ± 0.08, $p \leq 0.05$).
Next, we performed immunofluorescence studies on IMCD cells using fibronectin, TGF-β1, and NOX-4 antibodies. IMCD cells showed a basal expression of fibronectin that was augmented after 48 h of HG. The TGF-β1 antibody did not show specific immunofluorescence labeling in IMCD cells. NOX-4 labeling intensity was present both at baseline and after 48 h of HG, as observed in Figure 8.
## 3.4. Effect of NOX-4 Inhibition on the mRNA Levels of Fibronectin and TGF-β1 in M-1 Collecting Duct Cell Line Exposed to High Glucose Conditions
To further evaluate the role of NOX-4 in an independent cell culture not influenced by sex, we performed a new protocol by using 30 μM of NOX-4 inhibitor GKT 137831 that was added 48 h before high glucose incubations. Results showed that GKT 137831 partially impairs ROS production and induction of mRNA levels for fibronectin and TGF-β1; this indicates that NOX-4 activity mediates glucose induction of ROS (Figure 9).
## 4. Discussion
In this study we demonstrated that both male and female CF-1 mice with 6 days of STZ-induced hyperglycemia exhibited increased fasting blood glucose, reduction in body weight, increases in food and water intake, and urinary output. All these symptoms are consistent with the clinical manifestation of DM in humans. Interestingly, determinations of Na+ balance among the groups using a Na+ challenge demonstrated a positive Na+ balance on day 6 in male and female STZ mice; this is consistent with previous studies using this model [21]. Because of the impact that positive Na+ balance may have on systolic blood pressure, we performed tail cuff—systolic blood pressure measurements in all groups. The results indicated that STZ treatment at day 6 had no impact on blood pressure in female and male mice. Despite the evident increase in urine proteinuria and the transitory Na+ positive balance ratio observed in STZ males, no impact on systolic blood pressure was observed. This suggests that blood pressure is not affected during the early stages of type I DM. Despite this observation, a direct effect of diabetic disease on blood pressure has been demonstrated in women [22,23,24,25]. By measuring systolic blood pressure using tail-cuff method, Islam et al. found that systolic blood pressure was increased 30 days post STZ injection [26]. More recently, Chandramouli et al., using a protocol of 5 consecutive daily injections of STZ (55 mg/kg), demonstrated that female mice exhibit a heightened susceptibility to diastolic dysfunction and a lower extent of hyperglycemia than male mice [27]. All this evidence may be also related to the peripheral and renal factors resulting in altered volume handling, thereby leading to high blood pressure. This concept is supported by prior studies, which showed paralleled increased levels in intrarenal renin angiotensin system and profibrotic and proinflammatory factors in response to high blood pressure and diabetic models [28,29,30,31].
In the present study, we used a 6-day STZ-induced diabetic model that is consistent with a condition of no gross kidney damage observed in chronic conditions. Longer periods of treatment are associated with renal and tubular damage, including collagen deposition in the glomeruli and tubular structures in rat [32] and mice kidneys after 3 and 5 weeks post-STZ injection, respectively [33]. Even longer treatments using a single STZ dose have shown evidence of tubular necrosis in mice [34]. To examine the impact of STZ treatment on glomerular function, we evaluated plasma creatinine and proteinuria. No significant difference was found in plasma creatinine levels between STZ mice and control mice of either gender. However, we did find a significant increase in urinary proteinuria in STZ males that was not observed in STZ females. This finding may be related to both a mild or incipient glomerular damage due to filtration of low molecular weight proteins and to an impairment of protein reabsorption in proximal tubules and tubulointerstitial disease [35,36].
Long-standing and uncontrolled diabetes is associated with end-organ complications, including diabetic kidney disease. Sex may play a role in the risk of progression to these dreaded complications. For example, women with diabetes have higher mortality rates and higher prevalence of diabetic kidney disease [37,38,39,40]. In contrast, a recent review by Giandalia et al. noted that the risk of developing and worsening diabetic kidney disease is higher in men with DM, while women are at higher risk of glomerular damage. These sex-dependent differences appear to exist in both type 1 and type 2 diabetes mellitus and thereby have implications in the diagnosis and management of DM-induced renal disease. The sex and gender differences in diabetic disease remain to be elucidated; however, hormonal and genetic differences have an impact in the development of renal injury [41].
Advanced glycation products, which promote ROS formation similarly to NOX-4, are present in collecting ducts; there, they are involved in the activation of redox-sensitive pathways in DM [10]. In early DM, hyperglycemia and hyperglycosuria may affect redox status in kidney cells; this may predispose to certain cell signaling pathways such as stimulation of TGF-β1 and extracellular matrix deposition in renal tubules [11,12]. We found that STZ mice showed higher expression of fibronectin and TGF-β1 compared to control mice of both genders. The increased levels of mRNA in STZ mice were more pronounced in females than in males. This finding does not correlate with the significant increases in proteinuria observed in STZ males, which indicates that this effect may be related to glomerular alterations during the early phase and not to damage associated to medullary collecting ducts. On the other hand, STZ female mice showed a near 40-fold increase in mRNA levels and a 3-fold increase in protein levels of kidney medullary NOX-4. Although both male and female STZ mice showed increases in medullary ROS, the increase in female STZ was more evident (Figure 1D). All this evidence was further demonstrated by immunofluorescence in kidney slides showing differences in expression of fibronectin, TGF-β1, and NOX-4 in renal cortex and medullary tissues. The induction of NOX-4 was primarily observed in renal medulla. Similar results were seen for TGF-β1.
Tor further evaluate the role of NOX-4 in ROS production, we performed experiments on M-1 cells lines, as described before [20]. Treatment with NOX-4 inhibitor GKT 137831 in the presence of HG conditions partially impairs ROS production and induction of mRNA levels for fibronectin and TGF-β1. This indicates that glucose-mediated induction of ROS is partially due to NOX-4 activity (Figure 10).
It has been shown that augmented ROS causes the secretion of active TGF-β1 protein complex in the extracellular matrix. Activated TGF-is a paracrine pathway that causes further stimulation of NOX-4 production. TGF-β1 also stimulates the fibrotic process [42]. Additionally, increases in TGF-β1, fibronectin, and NOX-4 (IMCD) were observed after 48 h of high glucose treatment in primary cultures of renal inner medullary collecting duct cells (Figure 7).
By promoting auto-oxidation of glucose to form free radicals, hyperglycemia can lead to microvascular dysfunction. Augmented proteinuria was observed in females and males; however, this was more evident in males. This may reflect a different mechanism of progression in glomerular capillary damage that might be determined by sex. The inhibition of ROS formation may provide a therapeutic strategy to prevent hyperglycemia-related oxidative stress during the early phase of diabetes. Because the inhibition of NOX-4 does not entirely suppress the augmentation of ROS in M-1 cells, it is possible that other mechanisms, such as scavenge defense capacity, may be responsible for the changes observed in renal cells. Then, antioxidants may be beneficial to inhibiting the damaging effects of DM by exerting their effects through variety of mechanisms, such as by inhibiting the formation of ROS and scavenging free radicals. They also promote nitric oxide (NO) production, which may improve endothelial dysfunction in DM. Antioxidants may also decrease vascular NOX-4 activity [43].
## 5. Limitation of the Study
The maximal sustained hyperglycemia is observed at day 6; thus, the mechanisms involved in the adaptative responses during lower intratubular and plasmatic glucose concentrations cannot be evaluated since an extremely brief period makes it difficult to perform physiological assays. In addition, it is possible that glucose handling in other organs may be affected by variations in sex hormones (i.e., through the estrogen cycle). We were unable to demonstrate this effect; however, future studies are proposed to evaluate the influence of female or male hormones in ovariectomized and castrated mice. Indeed, it has been shown that ovariectomized STZ female mice treated with estradiol showed a reduction in hyperglycemia [44]. More importantly, we cannot rule out the fact that the toxic effects of STZ on pancreatic islet β cells may also cause the low-grade inflammation in multiple organs, such as skeletal muscle and liver, causing alteration in pathways related to insulin metabolism [45].
## 6. Conclusions
In conclusion, our STZ-induced type 1 diabetes mellitus models demonstrated that in, as few as 6 days under conditions of hyperglycemia and glucosuria, levels of profibrotic markers TGF-β1 and fibronectin become increased in renal medullary tubular cells. The mechanism of this change involves NOX-4-dependent ROS formation. Of special note, NOX-4 was induced to a greater extent in female mice compared to their counterparts, suggesting that the mechanisms of regulation in IMCD cells are influenced by sex. Although our study did not evaluate the influence of sexual hormones, necessary approaches should be utilized by scientists and clinicians to develop a better understanding of the role of sex hormones in the pathophysiology of diabetic kidney disease, as well as the role of antioxidant pharmacological therapies.
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|
---
title: Genomic Comparative Analysis of Two Multi-Drug Resistance (MDR) Acinetobacter
baumannii Clinical Strains Assigned to International Clonal Lineage II Recovered
Pre- and Post-COVID-19 Pandemic
authors:
- German Matias Traglia
- Fernando Pasteran
- Jenny Escalante
- Brent Nishimura
- Marisel R. Tuttobene
- Tomás Subils
- Maria Rosa Nuñez
- María Gabriela Rivollier
- Alejandra Corso
- Marcelo E. Tolmasky
- Maria Soledad Ramirez
journal: Biology
year: 2023
pmcid: PMC10045941
doi: 10.3390/biology12030358
license: CC BY 4.0
---
# Genomic Comparative Analysis of Two Multi-Drug Resistance (MDR) Acinetobacter baumannii Clinical Strains Assigned to International Clonal Lineage II Recovered Pre- and Post-COVID-19 Pandemic
## Abstract
### Simple Summary
Acinetobacter baumannii is a problematic bacterium that causes hard-to-treat hospital infections worldwide. Multiple cases of A. baumannii/SARS-CoV-2 co-infection were reported during the pandemic. This fact raised the question of whether the strains in those co-infections had or acquired unique genetic traits. This study is a comparative analysis of two strains from the same clonal group, but one was isolated before the pandemic, and the other was isolated from a patient with COVID-19. Their genomes had a high similarity, indicating that they may have derived from a unique background. However, each genome had numerous unique genes that were involved in virulence and resistance to antimicrobials. These differences could result from adaptative evolution to the human body infected with SARS-CoV-2.
### Abstract
Background: After the emergence of COVID-19, numerous cases of A. baumannii/SARS-CoV-2 co-infection were reported. Whether the co-infecting A. baumannii strains have distinctive characteristics remains unknown. Methods and Results: A. baumannii AMA_NO was isolated in 2021 from a patient with COVID-19. AMA166 was isolated from a mini-BAL used on a patient with pneumonia in 2016. Both genomes were similar, but they possessed 337 (AMA_NO) and 93 (AMA166) unique genes that were associated with biofilm formation, flagellar assembly, antibiotic resistance, secretion systems, and other functions. The antibiotic resistance genes were found within mobile genetic elements. While both strains harbored the carbapenemase-coding gene blaOXA-23, only the strain AMA_NO carried blaNDM-1. Representative functions coded for by virulence genes are the synthesis of the outer core of lipooligosaccharide (OCL5), biosynthesis and export of the capsular polysaccharide (KL2 cluster), high-efficiency iron uptake systems (acinetobactin and baumannoferrin), adherence, and quorum sensing. A comparative phylogenetic analysis including 239 additional sequence type (ST) 2 representative genomes showed high similarity to A. baumannii ABBL141. Since the degree of similarity that was observed between A. baumannii AMA_NO and AMA166 is higher than that found among other ST2 strains, we propose that they derive from a unique background based on core-genome phylogeny and comparative genome analysis. Conclusions: Acquisition or shedding of specific genes could increase the ability of A. baumannii to infect patients with COVID-19.
## 1. Introduction
Infections that are caused by Acinetobacter are associated with mortality rates as high as $60\%$ [1,2,3,4]. Multi-drug resistance (MDR) isolates are becoming more common, and many of them include carbapenems as the antibiotics to which they are immune [5,6]. Consequently, the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) have designated A. baumannii as a high-priority pathogen for antibiotic research and development.
A. baumannii causes a variety of infections, such as ventilator-associated pneumonia, bloodstream, and catheter-associated urinary tract infections [7,8]. This bacterium is often part of polymicrobial infections, which are common in hospitalized patients [9,10]. Gram-negative organisms can take advantage of being a component of polymicrobial infections [11,12,13,14]. Co-infections of Acinetobacter with other bacterial, fungi, or viral pathogens complicate the design of successful therapies [15,16,17,18]. Our recent results showed that A. baumannii and *Staphylococcus aureus* can coexist at the site of infection, producing a more aggressive process [19]. A. baumannii can sense and undergo phenotypic changes in response to molecules that are secreted by S. aureus [20]. Other kinds of co-infections are those involving viruses and bacteria, which raise significant medical concerns. Co-infections that are caused by different bacteria and the SARS-CoV-2 virus were widely reported [21,22,23,24,25,26]. A study found that OXA-23-producing carbapenem-resistant sequence type (ST) 2 A. baumannii (ST2-CRAB) isolates were the etiologic agent of outbreaks among COVID-19 patients at the ICU in Tehran, Iran [27]. A. baumannii/SARS-CoV-2 co-infection has not been reported yet in South America. The present study aimed to analyze and compare two ST2 A. baumannii clinical strains from Argentina. One of them was isolated from a patient before the pandemic (AMA166), and the other from a COVID-19 patient (AMA_NO). Extensive genomic analysis suggested that the two strains derive from a common ancestor. The studies also identified genetic determinants that shaped the strains’ antimicrobial resistance profile. The differences in resistance profiles between both strains could result from their adaptative evolution.
## 2.1. Bacterial Isolates
A. baumannii AMA166 was isolated in 2016 (Argentina) from a 64-year-old patient with type 2 diabetes and arterial hypertension. The A. baumannii AMA_NO strain was isolated in 2021 (Argentina) from a 58-year-old patient with COVID-19. Both patients were hospitalized and received colistin as monotherapy. Both strains were cultured in LB medium and were identified using MALDI-TOF MS [28].
## 2.2. Genome Sequencing
Genomic DNA was extracted using the DNeasy Blood and Tissue kit (Qiagen Germantown, MD, USA) following the manufacturer’s instructions. Whole genome sequencing was carried out on the NextSeq 550 Illumina (MiGS sequencing service). Quality control of sequencing was performed using the FASTQ software. De novo assembly and quality assessment were done with the SPAdes and the QUAST software, respectively [29,30]. The Whole Genome Shotgun project has been deposited at GenBank with accession numbers JANKJZ000000000 and JANKKA000000000 for AMA166 and AMA_NO, respectively.
## 2.3. Comparative Genome Analysis
Genome annotation of both strains was performed using the PROKKA software [31]. The ortholog functional assignment was done using EggNOG v2.0 (default parameter) [32]. The taxonomy assignment was performed by ANI% with the seven reference genomes from A. baumannii-calcoaceticus complex using the JSpeciesWS software using the default parameter [33].
The tRNAscan-SE and Infernal software were used for tRNA and ncRNA prediction [34]. The Multilocus sequence typing (MLST) profile was determined using MLST scripts (https://github.com/tseemann/mlst, accessed on 9 February 2023). The antimicrobial resistance genes (ARG) were identified using the BLASTp software and the databases CARD-RGI (e-value < 10−6, Amino Acid Identity > $30\%$, Coverage > $70\%$) [35]. Identification of virulence factors was carried out using BLASTp and the database VFDB (Virulence Factor Database) (e-value < 10−6, Amino Acid Identity > $30\%$, Coverage > $70\%$) [36]. The K and OC loci were identified using the Kaptive software using the default parameters [37]. The high-affinity iron-uptake locus was identified using BLASTp (e-value < 10−6, Amino Acid Identity > $30\%$, Coverage > $70\%$). Nucleotide sequences of each iron-uptake system were taken from Antunes et al. [ 38]. Insertion sequences were determined using BLASTp and the ISFinder database (e-value< 10−6, Amino Acid Identity > $30\%$, Coverage > $70\%$) [39]. The prophages were predicted using the PHASTER Software using the default parameters [40]. The presence of plasmids of different groups was carried out by rep and mob homology analysis (e-value < 10−6) [41].
To determine the core-genome, the initial datasets of the 2938 ST2 genomes were downloaded from NCBI GenBank (January 2022). The core-genome genes were obtained using the Roary software using the default parameters (Table S1) [42]. A total of 722 core genes were concatenated and aligned using the MAFFT software (parameter: --auto) [43]. The core-genome sequences were aligned and clustered by nucleotide identity (nucleotide identity > $98\%$) using the “Decipher” R package [44]. One reference genome of each cluster was selected from a total of 239 clusters (Table S1). All the selected genomes were included in the study to perform the core-genome phylogeny analysis.
The core genome phylogeny analysis was performed applying the maximum likelihood method using the RAxML software using the default parameters [45]. The substitution genetic model was done by JModelTest2 using the default parameters [46]. SNPs were extracted using the snp-sites software using the default parameters [47]. *The* genes that were unique to each genome were extracted from “gene_presence_absence.csv” of Roary output.
The co-linearity analysis was performed using the progressive mauve algorithm with default parameters [48].
## 2.4. RNA Extraction and qRT-PCR Analysis
The A. baumannii AMA_NO strain was cultured in LB broth for 24 h at 37 °C with shaking. RNA extraction was performed using the Direct-zol RNA miniprep Kit (ZYMO research, Irvine, CA, USA). Quantification of RNA was performed using a DeNovix DS-11+ Spectrophotometer. RNA quality was assessed on a $1.5\%$ agarose gel via gel electrophoresis. DNase treatment was performed following the manufacturer’s instructions (Thermo Fisher Scientific, Waltham, MA, USA) and quantified as previously described [49,50]. The absence of DNA was confirmed by PCR amplification of the 16S rDNA gene. Reverse transcription was carried out using the iScript Reverse Transcription Supermix for qRT-PCR (BioRad, Hercules, CA, USA) according to the manufacturer’s instructions. qRT-PCR was performed using iQ™SYBR Green Supermix (BioRad, Hercules, CA, USA) per the manufacturer’s recommendations. Specific oligonucleotides to amplify blaOXA-23 and blaNDM-1 were used. Transcriptional levels of each sample were normalized to the transcriptional level of rpoB. The relative quantification of gene expression was performed using the comparative threshold method 2−ΔCt [51]. The reactions were performed in four technical and three biological replicates, respectively. The statistical analysis (t-test) was performed using GraphPad Prism (GraphPad software, San Diego, CA, USA). A p-value < 0.05 was considered significant.
## 2.5. Antibiotic Susceptibility Assays
The antimicrobial resistance profile was determined by disk diffusion (10 µg ampicillin/sulbactam, 30 µg amikacin, 30 µg cefepime, 10 µg ceftazidime 5 µg ciprofloxacin, 10 µg imipenem, 10 µg gentamicin, 10 µg meropenem, 15 µg tigecycline, 30 µg minocycline, or 10 µg colistin) and minimum inhibition concentration determination according to the Clinical and Laboratory Standards Institute (CLSI) recommendations [52]. The experiments were repeated at least three times for each strain. The results were interpreted with CLSI guidelines, except for colistin and tigecycline, in which cases European Committee on Antimicrobial Susceptibility Testing (EUCAST) and Food and Drug Administration (FDA) recommendations were used, respectively. The CLSI, EUCAST, and FDA publish guidelines for antimicrobial susceptibility testing (AST) that provide recommendations for testing and interpreting the susceptibility of microorganisms to antimicrobial agents. These guidelines include recommendations for standardized methods, quality control procedures, and interpretive criteria for AST [52,53].
## 3.1. Sequencing of AMA166 and AMA_NO. Genomic and Phylogenomic Comparative Analyses
Strains AMA_NO and AMA166, isolated post- and pre-COVID pandemic, were used to study changes at the genomic level to identify gene acquisition that could indicate patterns of adaptative genomic evolution [54,55]. The source of the AMA_NO strain was a patient with COVID-19. The whole genome sequences of both strains were of good quality, and depth was greater than 50X coverage (Table 1). The assembly quality was evaluated using the QUAST software and produced 79 (AMA_NO) and 38 (AMA166) contigs [30]. Both genomes had similar sizes with a difference of 254.427 bp between them. The N50 of AMA_NO and AMA166 were 125.130 and 220.435, respectively. The numbers of tRNAs identified were 61 (AMA_NO) and 60 (AMA 166) (Table 1).
*Mobile* genetic elements, such as insertion sequences (ISs), prophages, and plasmids, play an important role in genome evolution and the dissemination of antimicrobial resistance genes in A. baumannii. ISs were identified using the BLAST algorithm and the ISFinder database [39]. A total of three ISs were common in both bacteria, but strain AMA_NO possessed nine more (Table S2). Also, the disrupted transposases of ISs (as pseudogene) were found in both genomes. There were twelve and two disrupted IS transposases that were found in AMA_NO and AMA166 genomes, respectively (Table S2).
Analysis using the PHASTER online software found four prophages that were common to both strains (Table S2). The PHASTER prediction classification detected one intact category prophage, one incomplete category prophage, and two questionable category prophages in both genomes. Plasmids were not found when searching for known rep and mob genes [41].
A total of 41 and 38 non-coding RNA (ncRNA) regulatory elements were identified in strains AMA_NO and AMA166, respectively (Table 1). The three ncRNA that were present only in strain AMA_NO were ALILL, group-II-D1D4-2, and Intron_gpII (Table S3). ALILL pseudoknot is an RNA element that induces frameshifting. This element was identified through comparative analysis of a class of transposable elements belonging to the IS3 family and is conserved across bacterial species, such as Lactobacillus lactis, Escherichia coli, Acinetobacter species, etc. [ 56,57,58]. Group II introns (group-II-D1D4-2 and Intron_gpII) are a large class of self-catalytic ribozymes and mobile genetic elements that are found within the genes of all three domains of life. Remarkably, AMA_NO contains seven disrupted IS3 family transposase genes (Table S2). Considering these results, we hypothesize that the Group II introns might have had regulatory functions of the IS3-family insertion sequences activity in ancestors of the AMA_NO strain. The presence of incomplete IS3 family sequences could be due to a reduction of the genome and a possible path towards a greater specialization to occupy one or more specific ecological niches.
The MLST profile was determined using the Pasteur and Oxford scheme. AMA_NO and AMA166 belong to the ST2 (Pasteur)/ST208 (Oxford) clone (Clonal Complex 2). With the Oxford scheme, duplication of gdhB was identified, corresponding to alleles 3 and 189 (paralogous genes). Considering Gaiarsa et al. ’s report, the allele 3 was considered, assigned the ST208 as the MLST profile [59]. A core-genome phylogenetic analysis of ST2 (Pasteur) clone showed two main phylogenetic clusters (A and B) (Figure 1). Both strains clustered together in the phylogenetic cluster A with high similarity with the A. baumannii ABBL141 strain isolated in the USA (Figure 1). In the A. baumannii ABLL141, identified as ST208 in the scheme Oxford, the same duplication of gdhB alleles that were found in our strains (AMA_NO and AMA166) is present (Table S1).
A comparison of gene content showed that 3546 genes were shared by both genomes, while 269 and 14 were unique to AMA_NO and AMA166, respectively (Table S3). In addition, co-linearity analysis was performed to evaluate the genome structural variation between AMA_NO and AMA166. There were six local collinear blocks (LCBs), which indicate genomic regions with the highest homology, that were determined between both genomes. LCB 1 was observed inverted and translocated in AMA166, while LCB 4 and 5 were translocated in AMA166 (Figure S1).
To assess the possible biological activity or function of unique genes, functional annotation was performed using the EggNog Mapper v 2.0 software [32]. At least one category was assigned to 237 out of 269 and 10 out of 14 unique genes of AMA_NO and AMA166, respectively (Table S4). The potential functions of the unique genes in AMA166 were transposases, hydrolases, and monoxidases. Some possible functions of the unique genes in AMA_NO were related to virulence, such as folate biosynthesis (folKP, sul1, sul2), pyruvate metabolism (adh, frmA, adhP, pdhD), biofilm formation (impC), and tyrosine metabolism (adh, frmA, adhP) (Table S4). These results indicate that post-pandemic A. baumannii ST2 may have acquired virulence-associated genes, which suggests an increase in pathogenicity and resistance to treatment (Table S4). Similar observations were found in other opportunistic pathogens, such as *Klebsiella pneumoniae* and Aspergillus [60,61].
## 3.2. Antibiotic Resistance, Virulence, and Its Association with Horizontal Genetic Transfer (HGT) Elements
A total of 18 and 10 ARG were identified in strains AMA_NO and AMA166, respectively (Figure 2). The intrinsic β-lactamases blaOXA-66 and blaADC-25 were present in both genomes (Figure 2); according to their genetic environment, they do not seem to be associated with a transposable element. Both strains include a copy of Tn2008, a transposon that contains the blaOXA-23 gene. Identical TnAbaR multidrug resistance genomic islands harboring tetB (tetracycline resistance), strA, and strB (streptomycin resistance) were found in strains AMA_NO and AMA166 genomes (Figure 3). This TnAbaR possesses an identical resistance gene array to those in 907 out of the 6702 A. baumannii genomes deposited in GenBank at the time of this study. When considering only the A. baumannii ST2 clone genomes, the TnAbaR present in strains AMA166 and AMA_NO was found in 202 out of 2938 genomes currently in GenBank (Figure 3). There were three efflux pumps, AdeABC, AdeFGH, AdeIJK, and their regulator systems that were found in both genomes. These efflux pumps are associated with fluorquinolone, carbapenem, cephalosporin, phenicol, macrolide, tetracycline, rifampicin, glycylcycline, and lincosamide resistance [62,63,64]. Also, the ade efflux pumps are associated with biofilm formation, fitness, and pathogenesis [62]. Both genomes contain the class 1 integron with the same gene cassette array, aacC1-orfP-orfQ-aadA1, in the variable region. *This* gene cassette array is present in 167 of the 2938 ST2 A. baumannii genomes in GenBank.
The ARGs blaNDM-1 (carbapenem resistance), floR (florfenicol/chloramphenicol resistance), sul2 (sulfonamide resistance), bleMBL (bleomycin resistance), msrE (macrolide and phenicol resistance), mphE (macrolide resistance), cmlB1 (phenicol resistance), and aphA6 gene (aminoglycoside resistance) were found only in AMA_NO (Figure 2). The aphA6 gene and the ISAba125 are located downstream of blaNDM-1 in the TnAphA6 transposon. The blaNDM-1-TnAphA6 backbone was not found in any ST2 A. baumannii genomes, but it was present in 11 out of 3759 non-ST2 A. baumannii genomes. We analyzed the contig containing the blaNDM-1-TnAphA6 structure (contig 55) and compared it with genomic sequences in the GenBank database. A total of 101 sequences were identified with 98–$100\%$ nucleotide identity and 98–$100\%$ coverage. Most sequences belonged to plasmids (88 sequences), mainly identified in the genus Acinetobacter (49 sequences). However, other plasmid sequences were also identified in other genera, such as Escherichia, Klebsiella, Citrobacter, and Providencia spp. ( Table S5). Consequently, strain AMA_NO, as well as AMA166 are the first strains belonging to the ST2 clone to include the blaNDM-1-TnAphA6 array. The co-existence of blaOXA-23 and blaNDM-1 A. baumannii ST2 genomes occurs in a small percentage of these strains (63 out of 2938 ST2 genomes). Furthermore, only 109 A. baumannii ST2 genomes carry blaNDM-1. The blaOXA-23 gene has a greater representation among this group ($\frac{521}{2938}$ ST2 genomes). Both strains are resistant to ampicillin/sulbactam, cefepime, ceftazidime, ciprofloxacin, gentamicin, imipenem, and meropenem and are susceptible to minocycline, tigecycline, colistin, and amikacin (Table 2).
Virulence factors refer to traits (i.e., gene products) that allow a microorganism to establish itself on or within a host and enhance its potential to cause disease. Several literature reports of genome comparative analyses confirm the multifactorial and combinatorial nature of A. baumannii virulence [65,66,67,68,69,70,71,72]. A total of 46 genes associated with virulence factors were found in AMA_NO and AMA166 (Table S6). A. baumannii produces a capsular polysaccharide (CPS) encoded by a gene cluster that is referred to as the K locus (KL). In contrast, the variable outer oligosaccharide of the lipopolysaccharides is encoded by the OC locus (OCL). CPS is an outer layer that is involved in protection against C3 deposition that occurs mainly in inhibiting macrophage phagocytosis. O antigen is responsible for the resistance of bacteria to complement mediated killing. Both components are essential to the blood passage of bacteria and the development of sepsis, but only CPS is involved in developing pulmonary infections [37,73,74,75]. The OCL5 and KL2 were found in AMA_NO and AMA166, respectively.
The human body has specific and non-specific defenses against infection. Early studies on the latter led to the discovery of the “hypoferremic response,” which consists of a reduction of free iron levels in blood and fluids in response to a bacterial invasion [76]. The consequence of the hypoferremic response is the iron starvation of invading bacteria. Later, it was found that iron is not the only essential element whose availability is reduced to interfere with bacterial growth. This understanding originated the concept of “nutritional immunity,” the group of non-specific defense strategies based on deprivation of the metal ions [77,78]. Bacteria must circumvent these nutritional limitations to establish in the body and cause disease. A common mechanism to overcome the lack of available iron is the biosynthesis of a siderophore that is secreted to the environment and competes with iron with high-affinity iron-binding proteins. Then, the iron-siderophore complexes are recognized by specific bacterial receptors to uptake iron [79,80].
A. baumannii iron uptake systems compete with high-affinity iron-binding host proteins to capture essential iron for the survival and progress of the infection. Antunes et al. identified six iron-uptake systems in A. baumannii. They are coded for by the acinetobactin locus, baumannoferrin locus, fimsbactin locus, Heme uptake cluster 2, Heme uptake cluster 3, and feoABC genes [81]. The acinetobactin locus, baumannoferrin locus, Heme cluster 2, and feoABC genes were found in AMA_NO and AMA166 (Table S6). However, the acinetobactin and baumannoferrin loci were incomplete in both strains and are most probably nonfunctional. In the acinetobactin locus, the genes coding for basE (ACICU_02578) and a hypothetical protein (ACICU_02575) were missing. In the case of the baumannoferrin locus, there was a missing gene that encodes a siderophore synthetase (ACICU_01632). The four iron-uptake systems that were identified in A. baumannii AMA_NO and AMA166 were also found in 545 out of 2938 ST2 genomes (Table S7). The complete acinetobactin and baumannoferrin loci were present in 2273 out of 2938 ($77.36\%$) and 2839 out of 2938 ST2 genomes ($96.63\%$), respectively. The complete sets of other systems were found in different percentages of ST2 genomes (Table S7). While every ST2 genome contains at least one iron uptake system (Table S6), there is heterogeneity in the combinations of systems that are present in each strain. A previous study that included 111 A. baumannii ST2 genomes found that they all harbor incomplete baumannoferrin and acinetobactin loci [82]. This homogeneity can be explained by the small ST2 genomes sample, which might not have been sufficient to represent the iron-uptake cluster variability of the ST2 clone accurately.
It must be noted that only the acinetobactin iron-uptake system has been linked to a high virulence phenotype [83]. The absence of acinetobactin in 665 ST2 strains could be the reason behind the low pathogenicity in some isolates of this clone (Table S6).
## 3.3. Differential Expression of blaNDM-1 and blaOXA-23 in AMA_NO
Carbapenem-resistance in Acinetobacter spp. is usually due to the production of OXA-type carbapenemase and metallo-β-lactamases (MBLs), usually coded for by blaOXA-23-like, blaOXA-58-like, blaOXA-51-like, and the plasmid-mediated blaNDM-1 [84,85].
A recent study reported the co-existence of blaOXA-23 and blaNDM-1 in six isolates with ceftazidime and imipenem MIC values greater than 256 µg/mL and 32 µg/mL [86]. Quantitative RT-PCR (qRT-PCR) assays using total RNA extracted from A. baumannii AMA_NO cells that were cultured in LB showed two-fold higher blaOXA-23 mRNA levels compared to blaNDM-1 (Figure 4). Although other factors may impact the contribution of each gene to the resistance phenotype, the higher expression of blaOXA-23 may reflect a higher contribution to carbapenem resistance. It is also of interest that the blaOXA-23 gene was found in numerous A. baumannii clinical isolates but rarely in other Gram-negative species. Conversely, blaNDM-1 is a promiscuous gene, which could indicate a recent adaptive process. *The* genetic location of this gene can explain NDM global dissemination. Plasmids carrying blaNDM have been described globally, with Klebsiella and *Escherichia as* the prevalent genus harboring them [87]. In Klebsiella pneumoniae, NDM has been reported in a wide variety of different STs worldwide, supporting its broad ability for dissemination [88].
The AMA_NO was isolated from a patient that was also infected with SARS-CoV-2. Bacterial/viral coinfections are not rare and are often synergistic, i.e., they produce enhanced symptomatic manifestations. Numerous pairs of bacteria/viruses that act synergically to cause or enhance infection have been described in the literature. A representative example is the *Streptococcus pneumoniae* and H1N1 influenza virus coinfection, which produce a lethal synergy [89]. The authors of this study observed that the bacterial infection induced a loss of lung repair responses. The fatal outcome was correlated with a loss of airway basal epithelial cells. Interestingly, the S. pneumoniae SirRH two-component system plays a role in the enhancement of bacterial survival inside lung cells that were previously infected with H1N1 [90]. Another mechanism that results in synergism is through the virus neuraminidase, which mediates the removal of sialic acid from the cell surface and facilitates the adhesion of invading bacteria [91]. Another significant observation is that some bacterial strains, when coinfecting with viruses, stimulated genetic recombination between two or more different viruses, acting as a driver of viral evolution. We hypothesize that the interactions between bacterial and viral elements could also result in bacterial acquisition of genes that increase virulence [92]. A future systematic study of bacterial isolates from SARS-CoV-2-infected patients may prove this hypothesis or not.
## 4. Conclusions
A. baumannii AMA_NO and AMA166 appear to belong to one clone. However, differences were identified in the pre- and post-pandemic strains, which could indicate adaptation to the environment found within the COVID patient. The SARS-CoV-2 viral infection could be a driver for evolutionary modifications. Expansion of genomic comparisons is needed to validate or disprove this hypothesis.
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|
---
title: 'The use of angiotensin-converting enzyme inhibitors or angiotensin II receptor
blockers may relate to the survival and walking ability in geriatric patients with
hip fractures: a 1-year follow-up study'
authors:
- Qining Chu
- Liqiang Wang
- Qingbo Chu
journal: BMC Musculoskeletal Disorders
year: 2023
pmcid: PMC10045946
doi: 10.1186/s12891-023-06362-5
license: CC BY 4.0
---
# The use of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers may relate to the survival and walking ability in geriatric patients with hip fractures: a 1-year follow-up study
## Abstract
### Background
Many elder patients with hip fractures also suffered from hypertension. This study aims to explore the relationship between the use of ACEI or ARB and the outcomes of geriatric hip fractures.
### Methods
All the patients were divided into four groups: non-users without hypertension, non-users with hypertension, ACEI users, and ARB users. The outcomes of patients in different groups were compared. LASSO regression and univariable *Cox analysis* were used for variable screening. Then Cox models and Logistics models were established to identify the relationships between the use of RAAS inhibitors and outcomes.
### Results
ACER users ($$p \leq 0.016$$) and ARB users ($$p \leq 0.027$$) had a significantly lower survival probability than the non-users with hypertension. Non-users without hypertension, ACEI users, and ARB users may face lower 6-month and 1-year mortalities and higher 6-month and 1-year free walking rates compared with non-users with hypertension.
### Conclusion
Patients with the use of ACEI or ARB may face a better prognosis of hip fractures.
## Introduction
Hip fractures as one of the most severe diseases have attracted more and more attention from doctors and researchers around the world due to their high mortality and disability rate, especially in older adults [1]. Most hip fractures are always caused by a low-energy injury such as falls and slip directly while the most essential cause of hip fractures is the low bone mass density (BMD) caused by osteoporosis [2]. Facing the increasingly aging society and the high incidence of osteoporosis in older patients, hip fractures have become an intractable problem of public health [3]. Geriatric patients with hip fractures may face multiple complications, such as pneumonia, pressure sores, infections, thrombus, and so on, due to the long stay in bed even after surgery, and these complications may inhibit the recovery of motor abilities and even cause death [4]. It had been reported that many risk factors may relate to the prognosis of geriatric hip fractures, including nutrition status [5], muscle strength [6], oxidative status [7], and so on. Similarly, the use of drugs was also proved to be associated with injury risk and the outcomes of hip fracture [8, 9]. Figuring out the impact of drugs on the prognosis of hip fractures in older patients may provide us with a more advanced strategy to benefit the patients and improve their life qualities.
Angiotensin-Converting Enzyme Inhibitors (ACEI) and Angiotensin II Receptor Blockers (ARB) as two kinds of Renin–angiotensin–aldosterone system (RAAS) inhibitors were widely used for patients with hypertension [10]. Many studies had proved that RASS may play a significant role in both bone metabolism and decrease bone deterioration [11, 12], and the RAAS inhibitors may enhance bone strength and mass and decrease BMD loss through Angiotensin type 1 receptor [13], OPG/ RANKL [14], and ACE2/Ang [1–7] /Mas [15] pathways. Similarly, many population studies reported that the use of ACEI or ARB may reduce the incidence of osteoporotic fractures [16]. From a survey in China, the prevalence of hypertension in Chinese adults aged 18 years and above was $27.9\%$, and the number of patients with hypertension was about 244.5 million [17]. The RAAS inhibitors were one of the most commonly used hypertension drugs [18]. In clinical practice, many older patients with hip fractures may also suffer from hypertension, while to our knowledge, few studies explored the influence of ACEI or ARB on the outcome of older patients with hip fractures. In this study, the older patients who underwent surgeries due to hip fractures were enrolled and divided into four groups according to their use of RAAS inhibitors and diagnosis of hypertension, and the outcomes of patients in different groups were compared to identify the impact of ACEI or ARB on the prognosis of geriatric hip fractures.
## Study design
This study was a retrospective cohort study conducted in Emergency Trauma Center, Nanyang Second People’s Hospital. We declare that this study, as an observational study, was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Nanyang Second People’s Hospital. All the information about patient privacy was well protected, and informed consent was obtained from all patients enrolled in this study. Patients with hip fractures (diagnosed with a femoral neck fracture, intertrochanteric fracture, and subtrochanteric fracture) in our department between January 2014 and January 2021 were extracted from our hospital's electronic medical record system. The inclusion criteria were set as below: a. aged ≥ 50 years; b. underwent hip surgeries due to hip fractures. c. with low-energy injury. The exclusion criteria were: a. with pathological fractures; b. without available data about ACEI or ARB; c. with severe kidney diseases. All the patients were divided into four groups according to their use of RAAS inhibitors and hypertension: non-users without hypertension, non-users with hypertension, ACEI users, and ARB users. All surgeries were conducted by a senior doctor or in his presence and direction. All the patients were followed up for 1 year and the outcomes of patients in four groups were compared. To reduce the bias caused by co-variables, LASSO analysis was used to screen the variables and the multi-variable models including Cox models and Logistics models were established.
## Characteristics of participants and follow-up
Baseline features of patients enrolled in our study were collected and summarized, including age, sex,body mass index (BMI), type of fracture, fracture history, smoking history, alcoholism history, polytrauma, surgical procedures, anesthesia, time from injury to surgery, and so on. The fracture types were summarized as femoral neck fracture and intertrochanteric fracture, and the surgical procedures were divided into internal fixation and arthroplasty. All the total arthroplasty, bipolar and unipolar hemiarthroplasty were defined as arthroplasty. All the patients with polytrauma were not underwent surgeries for secondary injuries. The hospital examination including electrocardiogram, chest radiograph, red blood count (RBC), hemoglobin (Hb), blood glucose (GLU), and albumin (ALB) was also collected. Major comorbidities were summarized to calculate the Charlson comorbidity index (CCI), which may evaluate the pre-operative status of patients conveniently [19]. The diagnosis of hypertension was identified by the record of our electronic medical record system. The medication use of the patients after surgery was obtained by follow-up, and only the patients who used the ACEI or ARB regularly were considered users.
All of the individuals enrolled in our study were followed up for 1 year by telephone. Death was defined as all-cause death, and free walking was identified if the individuals were able to carry out activities of daily living independently. The patients who survived > 1 year were defined as censored data. The primary outcomes were mortality rates at 3 months, 6 months, and 1 year after surgery, and the second outcomes were the free walking abilities at 3 months, 6 months, and 1 year.
## Statistical analysis
Continuous variables were expressed as mean ± standard deviation and categorical variables were presented as count (percent). CCI score was transformed into a binary variable according to the cut-off value of four. Normally distributed variables were analyzed by the ANOVA test, while non-normally distributed variables were evaluated by Kruskal–Wallis test. Categorical data were assessed using Chi-squared tests.
Least Absolute Shrinkage and Selection Operator (LASSO) models were established to identify and screen the possible variables related to the 1-year mortality and 1-year free walking rate of geriatric hip fractures by using R packages “glmnet”. Firstly, the relationships between the coefficients of punishment (λ) and the weight coefficient were calculated and summarized to explore the most suitable λ. Then the λ with the strongest stability was selected by calculating partial likelihood deviance for 1-year mortality and by using the AUC method for 1-year free walking rate. Then the variables included in the LASSO models were summarized and then included in the Cox and Logistics models following.
Kaplan–Meier curves were established and the Log-rank test was used to compare the survival between groups with Bonferroni correction. Then the univariate Cox models were established and those variables that were significant were included in Cox model 1, while variables screened by LASSO models were included in Cox model 2. Similarly, the variables screened by LASSO models were also included in Logistics models to reduce the impact caused by co-variables. Due to the multicollinearity between hypertension and groups, the hypertension variable was excluded in the multi-variable models. All analyses were conducted by using R software version 4.2.2 (R Foundation for Statistical Computing, Vienna, Austria).
## Patient characteristics and outcomes
Finally, 825 patients who underwent hip surgeries due to hip fractures between January 2014 and January 2021 in our hospital met the inclusion and exclusion criteria and were enrolled in our study (Fig. 1). All the patients were divided into four groups according to their use of ACEI and ARB, and their hypertension status: on-users without hypertension ($$n = 361$$), non-users with hypertension ($$n = 123$$), ACEI users ($$n = 209$$), ARB users ($$n = 132$$). Then the baseline features and outcomes were compared between groups and summarized in Table 1. There are significant differences in age ($p \leq 0.001$), fracture history ($$p \leq 0.015$$), hypertension ($p \leq 0.001$), and polytrauma ($$p \leq 0.045$$) in the comparison of baseline data between groups. In the part of outcomes, patients in different groups had significantly different 1-year mortality ($$p \leq 0.005$$), 6-month independent walking rate ($p \leq 0.001$), and 1-year independent walking rate ($p \leq 0.001$). The Kaplan–Meier curves and the Log-rank test showed that the patients in different groups had significantly different survival probabilities ($$p \leq 0.005$$, Fig. 2). To explore the differences between the two groups, a Log-rank test with Bonferroni correction was performed, and as shown in Fig. 2, the non-users with hypertension had a significantly lower survival probability than ACER users ($$p \leq 0.016$$) and ARB users ($$p \leq 0.027$$), while there was no significant difference in non-users without hypertension compared with non-users with hypertension ($$p \leq 0.876$$), ACER users ($$p \leq 0.250$$) and ARB users ($$p \leq 0.314$$), as well as ACEI compared with ARB users ($p \leq 0.999$).Fig. 1Flow chart of our studyTable 1Baseline characteristics of patients grouped by the status of RAAS inhibitors use and hypertensionVariablesnon-users without hypertensionnon-users with hypertensionACEI usersARB users p value($$n = 361$$)($$n = 123$$)($$n = 209$$)($$n = 132$$)Demographic characteristics Sex(female)231 ($64.0\%$)83 ($67.5\%$)146 ($69.9\%$)94 ($71.2\%$)0.344 Age(years)73.11 ± 10.3866.37 ± 11.9676.86 ± 6.9871.58 ± 10.59 < 0.001 BMI (kg/m2)21.84 ± 4.6321.51 ± 3.8021.79 ± 3.8721.72 ± 3.710.938 Fractures history (yes)57 ($15.8\%$)10 ($8.1\%$)27 ($12.9\%$)29 ($22.0\%$)0.015 Smoking history (yes)36 ($10.0\%$)15 ($12.2\%$)23 ($11.0\%$)12 ($9.1\%$)0.845 Alcoholism history(yes)21 ($5.8\%$)5 ($4.1\%$)12 ($5.7\%$)6 ($4.5\%$)0.852Surgery-related variables Location of fracture(femoral neck)189 ($52.4\%$)63 ($51.2\%$)111 ($53.1\%$)60 ($45.5\%$)0.524 Surgical procedures(arthroplasty)169 ($46.8\%$)57 ($46.3\%$)103 ($49.3\%$)54 ($40.9\%$)0.508 Anesthesia (spinal)5 ($1.4\%$)1 ($0.8\%$)0 ($0.0\%$)0 ($0.0\%$)0.197 Time from injury to surgery (Days)4.81 ± 0.954.83 ± 1.034.97 ± 0.934.82 ± 0.840.362 CCI score (> 4)99 ($27.4\%$)20 ($16.3\%$)51 ($24.4\%$)39 ($29.5\%$)0.057 Electrocardiogram (abnormal)226 ($62.6\%$)68 ($55.3\%$)127 ($60.8\%$)68 ($51.5\%$)0.114 Chest radiograph (abnormal)175 ($48.5\%$)61 ($49.6\%$)101 ($48.3\%$)71 ($53.8\%$)0.744 Hypertension(yes)0 ($0.0\%$)123 ($100.0\%$)209 ($100.0\%$)132 ($100.0\%$) < 0.001 Polytrauma(yes)47 ($13.0\%$)15 ($12.2\%$)44 ($21.1\%$)18 ($13.6\%$)0.045Laboratory findings RBC4.48 ± 0.484.38 ± 0.514.41 ± 0.484.46 ± 0.500.188 Hb (g/L)95.87 ± 13.5894.93 ± 13.3294.09 ± 14.4095.91 ± 15.230.471 ALB (g/L)38.39 ± 8.8337.81 ± 9.4137.92 ± 9.2238.34 ± 9.040.838 GLU (mmol/L)6.15 ± 1.386.39 ± 1.416.25 ± 1.356.04 ± 1.420.164Outcomes 3-month mortality15 ($4.2\%$)6 ($4.9\%$)5 ($2.4\%$)3 ($2.3\%$)0.478 6-month mortality32 ($8.9\%$)14 ($11.4\%$)12 ($5.7\%$)7 ($5.3\%$)0.166 1-year mortality71 ($19.7\%$)32 ($26.0\%$)27 ($12.9\%$)16 ($12.1\%$)0.005 3-month independent walking rate84 ($23.3\%$)28 ($22.8\%$)47 ($22.5\%$)31 ($23.5\%$)0.996 6-month independent walking rate168 ($46.5\%$)47 ($38.2\%$)146 ($69.9\%$)94 ($71.2\%$) < 0.001 1-year independent walking rate252 ($69.8\%$)64 ($52.0\%$)177 ($84.7\%$)112 ($84.8\%$) < 0.001Continuous variables were expressed as mean ± standard deviation and categorical variables were presented as count (percent). BMI Body mass index, Hb Hemoglobin, RBC Red blood count, GLU Blood glucose, ALB AlbuminFig. 2Kaplan–*Meier analysis* of one-year survival patients grouped by the status of RAAS inhibitors using and hypertension
## Variable screening
The direct comparison between groups may ignore the influence caused by co-variables such as ages and the bias may affect the conclusion. To identify the relationships between the use of RAAS inhibitors and the prognosis of hip fractures and to reveal the potential co-factors, LASSO models were established for 1-year mortality and 1-year free walking rate. The changes of coefficients and partial likelihood deviance with the increase of λ in LASSO models of 1-year mortality were shown in Fig. 3A and B, respectively. Similarly, the changes of coefficients and AUC were shown in Fig. 3C and D for LASSO models of 1-year free walking rate. Then the LASSO models with the minimum of λ (for 1-year mortality models: λ = 0.00712; for 1-year free walking rate models: λ = 0.01190;) were used to identify the significant variables due to the models’ highest stability. The variables included in the models were listed and summarized in Table 2. Moreover, the univariate Cox regression was also performed to screen the variables (Table 3).Fig. 3LASSO analysis for 1-year mortality and 1-year free walking rate. A The changes of coefficients with the increase of λ in LASSO models of 1-year mortality; B The changes of partial likelihood deviance with the increase of λ in LASSO models of 1-year mortality; C The changes of coefficients with the increase of λ in LASSO models of 1-year free walking rate; D The changes of AUC with the increase of λ in LASSO models of 1-year free walking rate Table 2Variables screened by LASSO analysisVariable1-year mortality1-year independent walkingCoefficientsCoefficientsSex(female)-0.066-Age(years)0.103-0.011Electrocardiogram (abnormal)-0.300-Chest radiograph (abnormal)-0.061-Smoking history (yes)0.032-Surgical procedures(arthroplasty)-0.0790.051Anesthesia (spinal)-0.461-non-users with hypertension (Ref)--non-users without hypertension-0.5120.133ACEI users-1.0680.314ARB users-0.8070.256ALB (g/L)--0.001 ALB AlbuminTable 3Cox analysis of 1-year mortalityVariablesUnivariate ModelsModel 1Model 2HR ($95\%$ CI for HR) p valueHR ($95\%$ CI for HR) p valueHR ($95\%$ CI for HR) p valueDemographic characteristics Sex(female)0.873 (0.622—1.225)0.433--0.848 (0.599—1.200)0.352 Age(years)1.113 (1.090—1.136) < 0.0011.116 (1.095—1.138) < 0.0011.118 (1.097—1.140) < 0.001 BMI (kg/m2)1.003 (0.965—1.043)0.875---- Fractures history (yes)1.085 (0.695—1.693)0.72---- Smoking history (yes)1.190 (0.715—1.967)0.508--1.126 (0.672—1.887)0.652 Alcoholism history(yes)1.036 (0.508—2.114)0.922----Surgery-related variables Location of fracture(femoral neck)1.062 (0.768—1.470)0.715---- Surgical procedures(arthroplasty)0.937 (0.676—1.298)0.694--0.832 (0.595—1.163)0.281 Anesthesia (spinal)0.000 (0.000—Inf)0.993--0.000 (0.000—Inf)0.994 Time from injury to surgery (Days)1.063 (0.893—1.266)0.494---- CCI score (> 4)1.097 (0.760—1.583)0.62---- Electrocardiogram (abnormal)0.775 (0.559—1.074)0.125--0.693 (0.498—0.964)0.03 Chest radiograph (abnormal)0.879 (0.635—1.217)0.437--0.875 (0.628—1.218)0.428 Polytrauma(yes)1.171 (0.761—1.801)0.472----Laboratory findings RBC (10^12/L)1.163 (0.834—1.622)0.373---- Hb (g/L)1.003 (0.991—1.014)0.653---- ALB (g/L)0.994 (0.976—1.012)0.486---- GLU (mmol/L)0.964 (0.856—1.085)0.546---- non-users with hypertension (Ref)------ non-users without hypertension0.734 (0.484—1.115)0.1470.405 (0.266—0.617) < 0.0010.426 (0.279—0.650) < 0.001 ACEI users0.465 (0.278—0.776)0.0030.220 (0.131—0.368) < 0.0010.230 (0.137—0.385) < 0.001 ARB users0.432 (0.237—0.787)0.0060.284 (0.156—0.518) < 0.0010.294 (0.161—0.539) < 0.001 BMI Body mass index, Hb Hemoglobin, RBC Red blood count, GLU Blood glucose, ALB Albumin, HR Hazard ratio, CI Confidence interval. Cox model 1 was adjusted for age and groups, and Cox model 2 was adjusted for age, groups, sex, electrocardiograph, chest radiograph, smoking history, surgical procedures, and anesthesia
## Risk factors
Based on the screened variables, two Cox models were established to identify the relationship between RASS inhibitors and the prognosis of geriatric patients with hip fractures. Cox model 1 was adjusted for age and groups, and Cox model 2 was adjusted for age, groups, sex, electrocardiograph, chest radiograph, smoking history, surgical procedures, and anesthesia. As shown in Table 3, in both Cox model 1 and Cox model 2, the status of RASS inhibitors using and hypertension was the significant factor of 1-year mortalities. In univariate Cox models, compared to non-users with hypertension, ACEI users (HR = 0.456, CI: 0.278—0.776) and ARB users (HR = 0.432, CI: 0.237—0.787) had a lower risk of death, while the non-users without hypertension did not reach significance (HR = 0.734, CI: 0.484—1.115). In Cox model 1 and model 2, the association with ACEI users (Model 1: HR = 0.220, CI: 0.131 – 0.368; Model 2: HR = 0.230, CI: 0.137 – 0.385) and ARB users was sustained (Model 1: HR = 0.284, CI: 0.156 – 0.518; Model 2: HR = 0.294, CI: 0.161 – 0.539), and the non-users without hypertension was also a protective factor in adjusted models (Model 1: HR = 0.405, CI: 0.266 – 0.617; Model 2: HR = 0.426, CI: 0.279 – 0.650).
## Prognostic value
To reduce the bias caused by co-factors and identify the prognostic value of RAAS inhibitors, Logistics models for mortality and free walking rates at 3 months, 6 months, and 1 year were established and adjusted for variables screened in LASSO models (Table 4). As shown in Table 4, non-users without hypertension, ACEI users, and ARB users may face lower 6-month and 1-year mortalities and higher 6-month and 1-year free walking rates compared with non-users with hypertension. Moreover, *Logistics analysis* showed that ACEI users may have lower 3-month mortality compared with non-users with hypertension. Table 4Logistics analysis of mortalities and free walking abilities at 3 months, 6 months, and 1 yearVariables3-month mortality6-month mortality1-year mortalityOR ($95\%$ CI) p valueOR ($95\%$ CI) p valueOR ($95\%$ CI) p valuenon-users with hypertension (Ref) non-users without hypertension0.575 (0.212—1.748)0.2970.470 (0.226—1.006)0.0460.259 (0.136—0.486) < 0.001 ACEI users0.260 (0.069—0.937)0.0380.235 (0.096—0.563)0.0010.112 (0.053—0.227) < 0.001 ARB users0.317 (0.062—1.333)0.130.292 (0.101—0.779)0.0170.154 (0.067—0.338) < 0.001 Variables3-month free walking ability6-month free walking ability1-year free walking abilityOR ($95\%$ CI) p valueOR ($95\%$ CI) p valueOR ($95\%$ CI) p valuenon-users with hypertension (Ref) non-users without hypertension1.072 (0.655—1.793)0.7851.853 (1.192—2.912)0.0074.212 (2.577—6.987) < 0.001 ACEI users1.041 (0.597—1.841)0.8885.808 (3.493—9.807) < 0.00113.205 (7.287—24.587) < 0.001 ARB users1.102 (0.610—1.999)0.7485.393 (3.133—9.456) < 0.00110.156 (5.327—20.121) < 0.001 OR Odds ratio, CI Confidence interval
## Discussion
In this study, the relationships between the use of ACEI and ARB were identified: the use of RAAS inhibitors in geriatric patients who underwent hip surgeries for hip fractures may relate to a better prognosis in both mobility and survival. To reduce the impact of co-variables and to keep the stability of Cox and Logistics models, a procedure of variable screening was conducted by using LASSO analysis. Just as the results of multi-variable regression, besides the use of ACEI and ARB, many variables were associated with the outcomes, such as age an electrocardiogram, and even after the adjustment of multi-variable models, the non-users without hypertension, ACEI users, and ARB users may also have significantly more satisfactory outcomes, which indicated that the status of RASS using and hypertension may be the independent risk factors for the prognosis of geriatric hip fractures.
Age and sex were two of the most significant risk factors for the incidence of hip fractures: the risk of hip fractures increased with age, and women may have a higher incidence of hip fractures compared with men [20, 21]. Undoubtedly, age and sex may also affect the BMD and the prognosis, so in our study, age and sex were included in the multivariable models to reduce the bias caused by them. The all-cause mortality of hip fracture in China was severe: the 1-year mortality of males was $13.69\%$, and the number of females was $14.77\%$ [22]. In a study with a mean follow-up time of 38.9 months, the mortality of patients was $33.80\%$ [23]. The cohorts in the USA showed similar results: a cohort at a level I trauma center with a two years follow-up had a 1-year mortality of $17.4\%$ and a 2-year mortality of $24.0\%$ [24]. Another cohort study conducted at two trauma level I centers and three community hospitals showed that $9.1\%$ of patients died within 90 days and $23.5\%$ within 2 years [25].
The primary outcomes of our study were all-cause death. As we know, the hip fracture itself rarely causes death directly, and usually, the development of complications of hip fractures and aggravation of comorbidity may be the major cause of death in older patients with hip fractures [26]. Hypertension as one of the most common chronic diseases was also proved to be a significant risk factor for geriatric hip fractures, and consistent with previous studies, in our study, the non-users without hypertension have a significantly lower death risk and unable free walking abilities than non-users with hypertension [27, 28]. There might be two major reasons why geriatric patients with hip fractures and hypertension may have high mortality. Firstly, hypertension itself is the most significant risk factor for death from cardiovascular diseases [29]. Moreover, hypertension may affect the survival of hip fractures through its role in bone regeneration and bone metabolism. An animal-based study showed that mice with salt-sensitive hypertension may face the reduction of femur trabecular number and bone volume fraction, and the increased number of osteoclasts and expression of RANK/OPG mRNA [30]. A meta-analysis also suggested that hypertension may relate to the reduction of BMD [31].
The relation and pathway between hypertension and bone metabolism may be achieved via the RAAS [30, 32], and the expression of RAAS components including angiotensin-converting enzyme 2 (ACE2) and Mas receptor in bone tissue, especially in osteoblasts and osteoclasts, was the foundation of the signaling pathway [33]. There also were studies that proved the expression of ACE, angiotensin type 1.receptor (AT1R), and angiotensin type 2 receptor (AT2R) in bone-healing tissue [34]. The excessive activity of the RAAS receptors in bone tissue may lead to bone resorption and inhibit bone formation [35]. Moreover, the RAAS may also affect bone regeneration with other signal factors. RAAS may increase the excretion of calcium and magnesium by interacting with parathyroid hormone (PTH) and then decrease the contents of serum calcium, which may lead to declined bone density and strength [36]. Some population studies also reported a negative relation between RAAS and vitamin D in patients with hypertension [37].
The effects of RAAS inhibitors were also reported in many studies. The mice with bilateral orchiectomy may have a significantly increased trabecular bone area after the treatment of captopril [38]. Similarly, another animal study also proved that imidapril treatment may reduce the decrease of bone density and inhibit the increase in osteoclast activation in rats with ovariectomy [39]. The effect of ACEI on bone metabolism may be achieved via the kinin-kallikrein system: the ACEI may regulate the activity levels of bradykinin [11]. The bradykinin can upregulate the expression of cyclooxygenase 2 and the biosynthesis of cytokine-induced prostaglandin and then contribute to the increase of RANKL and the activity of osteoclasts [40, 41]. In a population study, the patients with ACEI using may have a better BMD than untreated controls in a cohort of African-American elderly men [42]. Moreover, ARB also showed similar results in bone metabolism. It had been reported that ARB may significantly eliminate the levels of osteoclasts differentiation induced by Ang II and improve bone strength and microstructure in animal studies [43, 44]. In population studies, the use of ARB may significantly reduce fracture risk [16]. Recently, a meta-analysis that enrolled more than 360 million individuals indicated that the use of ACEI and ARB may both face a lower risk for fractures compared to nonusers [45]. Consistent with our study, a cohort study conducted in Scotland showed that the use of angiotensin-blocking drugs may associate with a lower risk of hip fractures and death [46].
Our study had several limitations. First, this study as a single-center retrospective study based on a small number of samples may cause bias. Next, the loss of data and follow-up may also reduce the clinical evidence grade. Thirdly, some factors that may affect the prognosis were not included in this study, and the missing co-factors may also affect the outcomes of our patients enrolled in this study. However, the co-variables which may relate to the outcomes significantly, such as ages and sex, had been included in our multi-variable models, and we believed that this may reduce the bias caused by co-variables. Lastly, the dose of RAAS inhibitors was not collected in our study, which may cause some differences in outcomes.
Our study showed that the uses of ACEI and ARB may relate to higher survival and free-walking abilities. Consistent with our study, many studies based on animals had reported the effect of RAAS inhibitors on bone healing and metabolism. We hope more experimental population studies may be conducted to prove our conclusion with a high level of evidence.
## Conclusion
Patients with the use of ACEI or ARB may face a better prognosis of hip fractures.
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|
---
title: 'Association between periodontitis and uric acid levels in blood and oral fluids:
a systematic review and meta-analysis'
authors:
- Lu-wen Ye
- Li Zhao
- Ze-song Mei
- Ying-hong Zhou
- Ting Yu
journal: BMC Oral Health
year: 2023
pmcid: PMC10045947
doi: 10.1186/s12903-023-02900-8
license: CC BY 4.0
---
# Association between periodontitis and uric acid levels in blood and oral fluids: a systematic review and meta-analysis
## Abstract
### Background
Uric acid, a formerly-known antioxidant that has recently been linked to numerous inflammatory diseases as a pro-inflammatory and -oxidative mediator in pathological conditions. It is imperative to reassess the association between periodontitis and uric acid locally and systematically. The aim of this systematic review was to systemically evaluate the association between periodontitis and the uric acid (UA) levels in blood, saliva and gingival crevicular fluid (GCF).
### Methods
Relevant clinical studies up to January 28, 2023 were identified and retrieved from electronic databases including PubMed, Scopus, EMBASE and Web of Science, with periodontitis, uric acid, hyperuricemia and gout as the keywords. The weighted (WMD) or standardized mean difference (SMD) was calculated using fixed- or random-effect models. Methodological heterogeneity was assessed.
### Results
Sixteen eligible observational studies and one RCT were enrolled, which included 1354 patients with periodontitis and 989 controls. Three sample types for UA detection were involved, including blood ($$n = 8$$), saliva ($$n = 9$$) and GCF ($$n = 1$$). Meta-analysis demonstrated an enhanced plasma UA concentration (WMD = 1.00 mg/dL, $95\%$ CI 0.63 to 1.37, $P \leq 0.001$) but a decreased salivary UA level (SMD = -0.95, $95\%$ CI -1.23 to -0.68, $P \leq 0.001$) in periodontitis versus control. Statistical heterogeneity among the plasma- and saliva-tested studies were moderate (I2 = $58.3\%$, $$P \leq 0.066$$) and low (I2 = $33.8\%$, $$P \leq 0.196$$), respectively.
### Conclusions
Within the limitations of the enrolled studies, it seems that there is an association between periodontitis and increased blood UA and decreased salivary UA. ( Registration no. CRD42020172535 in Prospero).
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12903-023-02900-8.
## Introduction
Periodontitis is a multifactorial chronic inflammatory condition caused by an imbalanced interaction between periodontal microbiota and host inflammatory response, which seems to have a bidirectional link with systemic inflammatory diseases [1–3]. The host immune response to periodontal infection can be modified by many genetic and environmental factors, among which gene polymorphisms (e.g., IL1) and chronic non-infectious diseases (e.g., diabetes, obesity, etc.) play a significant role [1, 4, 5]. Severe periodontitis affects $23.6\%$ of the global population [6]. In addition to causing tooth loss and a decline in quality of life, periodontitis imposes an enormous socioeconomic burden [7]. Impaired immune and metabolic response induced by periodontal pathogens is the critical feature in the pathogenesis of periodontitis [8, 9]. However, the underlying mechanisms remain unclear, making early prevention challenging.
Oxidative stress has been considered as an important mechanism in the progression of periodontitis [10]. It is formed when the reactive oxygen species overproduced by immune cells when infections cannot be neutralized by the antioxidant defense system, causing lipid, protein and DNA damage [11]. Historically, uric acid (UA) was regarded as an important radical scavenger among the antioxidant pools. However, its anti-oxidative roles appear to be restricted to hydrophilic environments only [12]. Recent studies have identified hyperuricemia (i.e., blood UA levels > 6.5 mg/dL) as risk factors for a variety of inflammatory conditions such as gout, diabetes, metabolic syndrome and cardiovascular diseases [13–15]. Specifically, UA can exhibit pro-oxidative effects in certain environments. For instance, UA can react with peroxynitrite to form radicals [16, 17]. It can also enhance intracellular superoxide production by elevating nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity [18]. Hence, the current consensus acknowledges that a pathological elevation of UA levels (i.e., hyperuricemia) represents a pro-inflammatory, -oxidative and -osteoclastic state [12].
UA was identified as an anti-oxidative parameter in previous research [19, 20] on periodontitis. Alterations of UA levels in blood have been associated with the presence or severity of periodontitis [21, 22]. Interventions with urate-lowering drugs have shown beneficial effects on animals with periodontitis [23, 24]. However, a re-assessment of their relationship is urgently required because the results of relevant studies are highly contradictory. Firstly, it was discovered that blood UA levels in periodontitis patients were either upregulated, downregulated or unchanged in comparison to controls [25–27]. UA levels in the saliva of periodontitis patients were either reduced or unchanged [28, 29]. Secondly, the presence of periodontitis was associated with an increase in UA levels in the blood but a decrease in saliva UA levels [30]. Thirdly, periodontal treatment increased salivary UA levels but decreased blood UA levels compared to baseline [31]. Integrating the contradictory data would therefore necessitate a systematic review of previous findings. In addition, it would be useful to answer an unresolved question of whether hyperuricemia and periodontitis may be linked [24, 31, 32]. The present systematic review and meta-analysis focuses on the question of whether periodontitis patients have altered UA levels in blood, saliva, and gingival crevicular fluid (GCF) compared to controls.
## Materials and methods
The systematic review and meta-analysis were registered in PROSPERO (no. CRD42020172535) and prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [33]. The present study adhered to the PECO principles: P (population) was participants in systemic health or with gout/hyperuricemia but without systemic complications/comorbidities; E (exposure) was patients with periodontitis; C (comparison) was periodontally healthy controls; and O (outcome) was UA levels in blood/saliva/GCF.
## Eligibility criteria
The studies should be cohort/cross-sectional/case–control study, or randomized/non-randomized controlled trials. For interventional clinical trials, the baseline data before intervention were deemed as information originated from observational studies. Case reports/series, animal/in-vitro studies, narrative/systematic reviews, conference abstracts, editorials, letters and comments were excluded. The following were the selection criteria for full-text analysis.
## Definition of periodontitis and periodontal health control
Plaque-induced periodontal destruction should be measured by periodontal probing or radiographs to diagnose periodontitis. From an epidemiological standpoint, the periodontal healthy control concerned in the present study would include not only clinical periodontal health but also mild (Stage I) or localized periodontitis.
## Inclusion criteria
a) Periodontitis should be clearly defined or stated;b) periodontitis and control groups should have UA levels in blood, saliva or GCF measured;c) participants in systemic health or with gout/hyperuricemia but without systemic complications (e.g., renal dysfunction including abnormal glomerular filtration rate);and d) participants in interventional studies should have baseline/pre-treatment UA levels recorded.
## Exclusion criteria
a) studies without periodontitis patients or control groups;b) female subjects during pregnancy;c) subjects with potential comorbidities shared by hyperuricemia and periodontitis, such as cardiovascular diseases, diabetes, osteoporosis, chronic kidney disease, metabolic syndrome and obesity [31];d) subjects with other potential conditions associated with altered purine/UA metabolism such as inflammatory bowel diseases [34, 35], hyperparathyroidism [36] and vitamin D deficiency [37];e) subjects with significant systemic diseases or conditions such as cancers, liver cirrhosis, organ transplantation, etc.;and f) subjects receiving antibiotics or anti-inflammatory or urate-lowering drugs or have had periodontal treatment in the past three months.
## Information sources and search strategy
Highly sensitive electronic search was conducted in four databases, including PubMed, Scopus, Web of Science and Embase with no language restriction (update to January 28, 2023). The following search model was constructed using Boolean operators. For exposure, “periodontal diseases” and “periodontitis” were used, while “apical periodontitis” was excluded. Regarding UA-tested samples, the keywords “blood”, “serum”, “plasma”, “circulation”, “GCF”, “gingival crevicular fluid” and “saliva” were used. As for outcome, the keywords “UA”, “urate”, “purine”, “hyperuricemia”, “gout” and “antioxidant” were used. The advanced search was based on each database-specific search strategies (see the Supporting Information). The search terms employed were either medical subject headings (MeSH) terms or keywords classified under general category (title/abstract/keywords).
## Study selection
Initial assessment of titles and abstracts was conducted by two independent reviewers (L.Y. and L. Z.), followed by full-text screening of the eligible articles. The disagreement was discussed until consensus was reached or the supervisor arbitrated (T. Y.). During the process, any study that failed to meet the eligibility criteria was excluded and the reason was formally recorded in detail. Inter-examiner agreement for abstract review and full-text screening was assessed using the κ test (κ values > 0.75 and < 0.4 indicated high and low consistency, respectively) [38].
A total of 382 potentially eligible records were found through a highly sensitive electronic search, of which 239 were included for abstract review and 70 for full-text evaluation. Finally, 17 articles were retained for systematic review [22, 25–29, 52–62]. Additionally, the κ value of agreement between the two examiners for the abstract review (κ = 0.81, $95\%$ CI 0.74 to 0.88) and full-text review (κ = 0.87, $95\%$ CI 0.76 to 0.98) demonstrated excellent consistency. Table 1 provided a summary of the characteristics of the 17 included studies. Figure 1 demonstrated the selection procedure for included. The reasons for excluded studies are recorded in Table S1.Table 1Detailed characteristics of included studiesCitationsStudy locationFunding sourceStudy typeDiagnosis of periodontitisStatement on periodontal controlsSample size [P (M/F) vs. C (M/F)]Age (years; P vs. C) †Periodontal parameters (mm, P vs. C) †Sample/Collection methodDetection method for UAUA levels (P vs. C) †Quality assessmentGharbi et al., 2019 [26]Africa (Tunisia)CollegeCase controlAAP criteriaPeriodontal health80 ($\frac{33}{47}$) vs. 50 ($\frac{25}{25}$)42 ± 13.6 vs. 44.8 ± 12.6PPD: 5.3 (2.5–7.5) vs. 1 (0.5–2)Blood/PlasmaEBCM5.22 ± 0.91 vs. 4.13 ± 1.11(mg/dL) ‡NOS:8Banu et al., 2015 [25]Asia (India)UnknownCase controlClinically ≥ 4 teeth in each jaw; PPD ≥ 5 mm; CAL ≥ 4 mm; ≥ $80\%$ BOP + at proximal sites; presence of ABL in ≥ 2 quadrants of the dentition; interproximal ABL ≥ $50\%$Without periodontitis40 ($\frac{14}{26}$) vs. 20 ($\frac{9}{11}$)40–65PPD: 5.55 ± 0.29 vs. 2.28 ± 0.13Blood/PlasmaEBCM5.32 ± 0.95 vs. 4.42 ± 0.68 (mg/dL)NOS:8Mourão et al., 2015 [55]South America (Brazil)UnknownCase controlAAP criteriaPPD < 3 mm in all tooth sites and absence of CAL20 ($\frac{8}{12}$) vs. 20 ($\frac{8}{12}$)54.3 ± 10.02 vs. 50.2 ± 8.79NMBlood/PlasmaEBCM6.45 ± 1.44 vs. 4.77 ± 0.81 (mg/dL)NOS:7Merle, C.L., et al., 2022 [62]Europe (German)UniversityCross-sectionalCPITN > 2CPITN ≤ 232 ($\frac{17}{15}$) vs. 53($\frac{25}{28}$)21.6 ± 3.9 vs. 21.4 ± 3.2NMBlood/PlasmaNM4.76 ± 1.02 vs 4.22 ± 1.25 (mg/dL) ‡AHRQ: 9Narendra et al., 2018 [27]Asia (India)UnknownCross-sectionalAAP criteriaNM78 ($\frac{40}{38}$) vs. 50 ($\frac{33}{17}$)38.34 ± 12.08 vs. 36.56 ± 6.26CAL: 4.60 ± 0.51 vs. 1.49 ± 0.25Blood/SerumEBCM5.10 ± 0.31 vs. 5.11 ± 0.54 (mg/dL)AHRQ:6Sreeram et al., 2015 [60]Asia (India)UnknownCross-sectional ≥ 14 teeth; BOP + at ≥ $30\%$ periodontal sites with PPD = 1–3 mm; BOP + and CAL ≥ 3 mm at ≥ $30\%$ of all sites ≥ 14 teeth; BOP + at < $30\%$ sites with PPD = 1–3 mm; BOP + and CAL ≥ 3 mm at < $30\%$ of all sites150 ($\frac{114}{36}$) vs. 150 ($\frac{120}{30}$)41.0 ± 12.2 vs. 34.2 ± 12.0NMBlood/SerumEBCM4.29 ± 1.15 vs. 4.93 ± 0.87 (mg/dL)AHRQ:7Brotto et al., 2011 [52]South America (Brasil)UniversityCase control ≥ 14 teeth including third molars; at least 4 different teeth had at least one site with PPD = 3–5 mm, and at least 4 others different teeth had at least one site with PPD = 6–10 mm; the proportion of all sites were considered to be AL > 2 mm and PPD > 2 mm ≥ 14 teeth including third molars; BOP + at < $30\%$ sites with PPD = 1–3 mm, only 2 isolated sites with PPD = 4 mm and BOP-, and CAL ≥ 3 mm at < $30\%$ of all sites30 ($\frac{16}{14}$) vs. 30 ($\frac{16}{14}$)46 ± 6 vs. 43 ± 5PPD: 2.25 (1.39–3.62) vs. 1.00 (1.00–1.40) CAL: 2.58 (1.45–4.50) vs. 1.00 (1.00–2.27)Blood/SerumEBCM4.9 ± 2.1 vs. 4.3 ± 1.7 (mg/dL)NOS:6Tsai et al., 2021 [22]Asia (China)National institutionsCross-sectionalLocalized stage II/III periodontitisPeriodontally healthy or stage I periodontitis295($\frac{269}{26}$) vs. 828($\frac{726}{102}$)30.88 ± 5.35 vs. 29.38 ± 5.56PPD: 3.03 ± 0.04 vs.2.92 ± 0.05 CAL: 3.07 ± 0.06 vs. 2.93 ± 0.06Blood/SerumEBCM6.73 ± 1.45 vs. 6.49 ± 1.41 (md/dL)AHRQ:7Sakanaka et al., 2017 [57]Asia (Japan)UniversityCase controlPISA > 215PISA < 21535 vs. 15NMPISA: 490.8 (200.5–1238.5) vs. 199.3 (155.5–252.8)Saliva/RestingGas Chromatography—Mass Spectrometry112.32 ± 66.02 vs. 155.83 ± 82.69 (Intensity)§?NOS:6Novakovic et al., 2014 [56]Europe (Serbia)National institutionsRCT ≥ 3 teeth per quadrant; at least one pocket PPD > 5 mm with BOP + per quadrant; and ABL > $30\%$Periodontally healthy42 ($\frac{28}{14}$) vs. 21 ($\frac{14}{7}$)39.0 ± 11.81 vs. 35.2 ± 7.1PPD: 3.38 ± 0.58 vs. 2.11 ± 1.67; CAL: 3.00 ± 1.00 vs. 0Saliva/RestingEBCM153.93 ± 40.88 vs. 198.43 ± 87.73 (relative level)NOS:6Miricescu et al., 2014 [28]Europe (Romania)National institutionsCross-sectionalGingival inflammation; at least six sites with PPD ≥ 4 mm; and ABL > $30\%$NM25 ($\frac{11}{14}$) vs. 25 ($\frac{5}{20}$)51.26 ± 7.4 vs. 18.66 ± 2.0PPD: 4.41 ± 0.42 vs. 0Saliva/RestingEBCM2.41 ± 0.27 vs. 3.12 ± 0.85 (mg/mg albumin)AHRQ:4Mathur et al., 2013 [54]Asia (India)UnknownCase controlCPITN > 2CPITN ≤ 230 vs. 10NMNMSaliva/RestingEBCM2.34 ± 0.4 vs. 5.19 ± 0.8 (relative level)NOS:4Fatima G et al., 2016 [53]Asia (India)UnknownCase controlPPD = 6 mm or presence of CALNo periodontal pockets as assessed by Williams periodontal probe10 ($\frac{4}{6}$) vs. 10 ($\frac{4}{6}$)46.30 ± 8.62 vs. 27.30 ± 4.37NMSaliva/RestingEBCM3.01 ± 0.68 vs. 5.39 ± 1.49 (mg/dL)NOS:7Sharma et al., 2018 [59]Asia (India)NilCross-sectionalRussell’s periodontal index and a panoramic radiographRussell’s periodontal index, and a panoramic radiograph25 vs. 2534.32 vs. 30.68NMSaliva/RestingEBCM1.95 ± 0.42 vs. 3.72 ± 1.02(mg/dL)AHRQ:7Senouci et al., 2021 [58]Africa (Algeria)UniversityCase controlstage III– IV, grade C periodontitiswithout clinical signs of periodontal disease as measured by PPD or any CAL29 ($\frac{6}{23}$) vs. 28 ($\frac{7}{21}$)24.06 ± 6.09 vs. 24.73 ± 1.38PPD: 7 ± 1.68 vs. 1.7 ± 0.3; CAL: 7.81 ± 1.79 vs. 1.4 ± 0.2Saliva/RestingEBCM1.43 ± 0.93 vs. 2.78 ± 1.60(mg/dL)NOS:8Priya, K.L., et al., 2022 [61]Asia (India)UniversityCase controlStage II/III, grade B periodontitisPPD ≤ 3 mm, without attachment loss or radiographic bone loss20 vs. 2030–65PPD: 5.10 ± 0.26 vs. 1.36 ± 0.27 CAL: 5.47 ± 0.23 vs. 1.35 ± 0.27Saliva/RestingEBCM5.64 ± 4.32 vs. 21.49 ± 10.01(mg/dL)NOS: 6Diab-Ladki et al., 2003 [29]Asia(Lebanon)National institutionsCase controlSevere periodontitis (with tooth mobility, gingival recession and up to one-half of ABL)Apparently healthy gingiva17 vs. 2030–45NMSaliva/StimulatedEBCM2.41 ± 2.32 vs. 2.68 ± 2.46 (mg/dL) ‡§?NOS:2Narendra et al., 2018 [27]Asia (India)UnknownCross-sectionalAAP criteriaNM78 ($\frac{40}{38}$) vs. 50 ($\frac{33}{17}$)38.34 ± 12.08 vs. 36.56 ± 6.26CAL: 4.60 ± 0.51 vs. 1.49 ± 0.25GCF/Paper stripEBCM4.87 ± 0.36 vs. 5.11 ± 0.53 (mg/dL)AHRQ:6The AAP criteria for diagnosis of periodontitis was defined as ≥ 2 interproximal sites with CAL ≥ 3 mm and ≥ 2 interproximal sites with PD ≥ 4 mm (not on the same tooth) or one site with PPD ≥ 5 mm [63, 64]; PISA is a calculated index based on BOP+, CAL and gingival recession to reflect periodontal inflammation. PISA score for an individual tooth = percentage of BOP+ sites × (attachment loss surface area—recession surface area). The PISA scores of total teeth in a mouth were summed to get a total PISA value for a patient [65]. The CPITN is an index with five degrees according to BOP, presence of dental calculus and PPD (0, normal; 1, gingivitis with BOP+; 2, presence of calculus; 3, PPD ≥ 3.5 mm; and 4, PPD ≥ 5.5 mm) [66]. †, the data of periodontal parameters, age and UA levels are presented as means ± standard deviations or medians (interquartile range);‡, For uric acid, 1 mg/dL p;’ = 59.48 µmol/L; §, Standard errors were converted to standard deviations;?, Data of UA in bar charts without numerical display were measured using a digital ruler; AAP American academy of periodontology, ABL Alveolar bone loss, AHRQ Agency for healthcare research and quality, BOP Bleeding on probing, CAL Clinical attachment loss, CPITN Community periodontal index of treatment needs, EBCM Enzymatic colorimetric methods, GCF Gingival crevicular fluid, NM Not mentioned, NOS Newcastle–Ottawa scale, M/F Male/female, P vs. C periodontitis vs. control, PPD Probing pocket depth, PISA Periodontal inflamed surface areaFig. 1PRISMA flow diagram of the selection process
## Quality assessment and quality of evidence
Newcastle–Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) methodology checklist were used to assess the methodological quality of case–control studies and cross-sectional studies, respectively. As for randomized controlled trials, only the baseline data before intervention were collected and the research design was deemed as an observational one. Namely, RCTs were also evaluated by NOS during quality assessment. Quality assessment was conducted by two reviewers independently (L. Y. and L. Z.). Inter-examiner agreement for quality assessment were assessed by the κ test.
The total quality score on the Newcastle–Ottawa Scale ranged from 0 to 9 stars (7 to 9, 5 to 6 and < 5 stars indicated high, moderate and low quality, respectively) [39]. Regarding ARHQ, studies with 8 to 11 points, 4 to 7 points, and 0 to 3 points, respectively, were deemed to be of high, moderate and low quality [40]. Disputes would be discussed with a third reviewer (T. Y.).
The certainty of evidence was evaluated following the Grade of Recommendations Assessment, Development and Evaluation (GRADE) method with GRADEprofiler (v 3.6, the GRADE working group) [41, 42]. The evidence quality of each outcome was rated as high, moderate, low and very low.
## Data extraction
Once these studies were identified, two reviewers independently (L. Y. and L. Z.) extracted the information including bibliometric information (the names, e-mail addresses and institutions of authors, publication date and journals of articles, etc.), characteristics of study design (study place/type, diagnostic criteria of periodontitis/control groups and sample size, etc.), demographics of participants (sex, age, race and smoking habit [43, 44], periodontal parameters (probing pocket depth, gingival/plaque index, bleeding on probing, clinical attachment loss, radiological alveolar bone loss, etc.), collection methods for saliva (resting/stimulated) or blood (plasma/serum) or GCF (paper strip/point), detection methods for UA (enzyme-based colorimetric method or gas chromatography/mass spectrometer, etc.), statistics for UA levels (means and standard deviations (SDs)/errors (SEs)) in periodontitis and control groups. Data extraction forms were cross-checked to verify accuracy and consistency of the extracted data. All data were checked by the third author (T. Y.) and disagreements were resolved by discussion. Three emails were sent to the corresponding authors of the included articles to request the raw data, which include the gender and age of the participants. Unanswered or undelivered emails were regarded as having no response.
## Data conversion and preprocessing
The UA concentrations may be recorded in mg/dL, µmol/L, mmol/L or relative units. The unit reported in this systematic review was mg/dL (1 mg/dL = 59.48 µmol/L) [45]. According to the new classification for periodontal diseases [46], chronic and aggressive periodontitis are no longer distinguished from one another, and the data presented here were compiled in accordance with this classification. The data of UA in periodontitis at different stages or in a single type of sample collected using different methods were also combined based on the means, SDs and sample size of the subgroups. If some studies reported SEs instead of SDs, the former was converted to the latter (SD = SE × \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\surd n$$\end{document}√n) [47]. If the data of UA in a study were shown in bar charts without detailed values, the heights of three repeats were measured using a digital ruler (v1.8.0, Image J, National Institutes of Health, Bethesda, MD, USA) to determine the absolute values.
## Statistical analyses
Statistical analyses were performed using a commercial software (v 14.0, STATA, Stata Corporation, College Station, TX). The UA levels are presented as means ± SDs. The distribution of potential confounding variables (such as gender and age) that may influence the UA levels was compared [48]. Meta-analyses are displayed as forest plots. It would be calculated as weighted mean differences (WMDs) or standardized mean differences (SMDs) for continuous outcomes. Statistical heterogeneity was estimated by Cochrane’s Q test and Higgins’s I2 test (I2 = 0, no heterogeneity; I2 < $50\%$, low heterogeneity; I2 < $75\%$, moderate heterogeneity; I2 > $75\%$, high heterogeneity) [49]. If a statistically significant heterogeneity was found, a random-effect model was used; otherwise, a fixed-effect model was applied. A subgroup analysis was conducted based on the collection methods of testing samples (i.e., serum vs. plasma). $P \leq 0.05$ was considered statistically significant. Publication bias was assessed by Egger’s tests [50]. If publication bias was detected, the Duval and Tweedie trim-and-fill method was used to make adjustments [51]. If statistical heterogeneity was detected, a sensitivity analysis was conducted by excluding each study individually to determine whether the heterogeneity changed significantly.
## General characteristics of included studies
The detailed characteristics of the 17 included studies are presented in Table 1. The 17 identified studies were published between 2003 and 2022. Ten studies were conducted in Asia [22, 25, 27, 29, 53, 54, 57, 59–61], three in Europe [28, 56, 62], two in South America [52, 55] and two in Africa [26, 58]. There were ten case–control studies [25, 26, 29, 52–55, 57, 58, 61], six cross-sectional studies [22, 27, 28, 59, 60, 62] and one RCT [56]. A total of 2343 participants were included (989 patients with periodontitis vs. 1354 healthy controls). The participants generally aged between 18 and 65. The majority of the studies ($\frac{11}{17}$) have a balanced gender distribution between periodontitis and control groups [22, 25–28, 52, 53, 55, 56, 58, 60]. The sample sizes in the studies ranged from 20 to 1123. Three sample types were involved for UA detection including blood (eight studies) [22, 25–27, 52, 55, 60, 62], saliva (nine studies) [28, 29, 53, 54, 56–59, 61] and GCF (one study) [27]. One study involved two sample types, i.e., blood and GCF [27]. Fifteen studies detected the UA levels with enzymatic colorimetric methods [22, 25–29, 52–56, 58–61], while one analyzed the UA content using gas chromatography-mass spectrometry [57]. Most of the studies ($\frac{10}{17}$) were funded by non-profit organizations. Specifically, one study was funded by a college [26], five by universities [52, 57, 58, 61, 62] and four by national institutions [22, 28, 29, 56].
## Definition of periodontitis and control
Regarding the exposure factor, nine studies provided clear but different criteria for defining periodontitis. Among them, three studies used the criteria of American Academy of Periodontology (AAP) in 1999 [26, 27, 55], three used the new classification for periodontal diseases in 2018 [22, 58, 61], and two used Community Periodontal Index of Treatment Needs (CPITN) [54, 62]. In addition, one study distinguished periodontitis from the control group according to the periodontal inflamed surface area (a secondary index calculated from clinical attachment loss, gingival recession and bleeding on probing) [57]. The remaining eight studies were either self-defined ($\frac{7}{8}$) [25, 28, 52, 53, 56, 59, 60] or included only severe periodontitis without clear diagnostic criteria ($\frac{1}{8}$) [29].
In the included studies, the definitions of the controls for periodontitis were also diverse. Six studies claimed clinical periodontal health as controls [25, 26, 29, 55, 56, 61]. Six studies provided clear definitions of controls. Specifically, two studies used CPITN ≤ 2 (0, normal; 1, gingivitis with bleeding on probing; 2, presence of calculus) as controls [54, 62], while one used periodontal inflamed surface area (PISA) < 215 [57]. In addition, two studies allowed the presence of localized periodontitis in the control groups [52, 60], and another one combined healthy periodontium with stage I periodontitis [22]. Three of the remaining studies provided a self-defined statement on controls [53, 58, 59], while two made no mention of controls [27, 28].
Gender and age were two potential confounding variables for the subgroup analysis, which could not be conducted due to missing data. Furthermore, smokers were clearly rejected in the majority of studies ($\frac{13}{17}$) [25–28, 52–54, 56–61]. Three studies failed to specify whether or not they included smokers [29, 55, 62]. Only one study considered body morphology [62], where body mass index was slightly unbalanced, but body weight was comparable between periodontitis and control groups.
## Quality assessment and evidence quality
The two reviewers assessed the risk of bias with a high degree of agreement (κ = 0.85, $95\%$ CI 0.77 to 0.94). The results of the assessment of the risk of bias were shown in Table S2 and S3. Of the ten case–control studies evaluated by NOS, five were of high quality (7 to 8 points) [25, 26, 53, 55, 58], four were of moderate quality (6 points) [52, 57, 61] and two were of low quality (2 and 4 points) (Table S2) [29, 54]. The single RCT assessed by NOS showed moderate quality [56]. The quality of the six cross-sectional studies ranged from moderate (4 to 7 points) [22, 27, 28, 59, 60] to high (9 points) [62] according to the AHRQ checklist (Table S3). The GRADE evidence quality for all outcomes was very low (Table 2).Table 2The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) assessment for the outcomesQuality assessmentNo of patientsEffectQualityNo of studiesDesignRisk of biasInconsistencyIndirectnessImprecisionOther considerationsPeriodontitisPeriodontal healthyRelative ($95\%$ CI)AbsoluteBlood UA levels (Better indicated by lower values)8observational studiesserious1serious2no serious indirectnessno serious imprecisionnone7251201-WMD 0.50 mg/dL higher (0.06 to 0.93 higher)⊕ΟΟΟ VERY LOWSaliva UA levels (Better indicated by lower values)9observational studiesserious1no serious inconsistencyno serious indirectnessno serious imprecisionreporting bias233174-SMD -0.95 lower (-1.23 to -0.68 lower)⊕ΟΟΟ VERY LOW1 The statistical analysis in these studies did not adjust for potential confounding risk factors such as age, gender, smoking condition or body mass index2 Statistical heterogeneity between the studies was high (I2 = $93.5\%$)
## Blood UA levels and periodontitis
Eight studies (725 periodontitis patients vs. 1201 controls) were included to determine whether periodontitis patients have altered UA levels in their blood compared to control [22, 25–27, 52, 55, 60, 62]. Four of them used plasma and the other four used serum as testing samples. Six studies found increased UA levels in blood in periodontitis compared to controls, while one study found a decrease and the remaining studies found no significant change. Because the enrolled studies utilized a universal unit, the UA concentration in blood can be combined. Consequently, the combined study results were presented as WMD. Using a random-effect model, a meta-analysis revealed that patients with periodontitis had a slightly higher UA blood level (WMD = 0.50 mg/dL, $95\%$ CI 0.06 to 0.93, $$P \leq 0.025$$) (Fig. 2). However, the lower limit of the confidence interval was approaching zero. Additionally, the statistical heterogeneity between the studies was substantial (I2 = $93.5\%$, $P \leq 0.001$). The sensitivity analysis failed to identify any discernible effect of individual studies on the pooled risk estimates (Figure S1). Next, subgroup analysis based on blood collection methods was conducted (plasma vs. serum). Compared to controls, periodontitis patients had significantly higher UA levels in plasma (WMD = 1.00 mg/dL, $95\%$ CI 0.63 to 1.37, $P \leq 0.001$) but comparable UA levels in serum (WMD = -0.04 mg/dL, $95\%$ CI -0.47 to 0.39, $$P \leq 0.847$$) (Figure S2). In this instance, statistical heterogeneity was low in the plasma subgroup (I2 = $58.3\%$, $$P \leq 0.066$$), whereas it was high in the serum subgroup (I2 = $91.6\%$, $P \leq 0.001$).Fig. 2Forest plot for comparing the UA levels in blood in periodontitis vs. control. CI, confidence interval; WMD, weighted mean difference.
## Salivary UA levels and periodontitis
The second question is “whether periodontitis patients have altered UA levels in their saliva compared to the control?” Included were nine studies with 233 periodontitis patients and 174 controls [28, 29, 53, 54, 56–59, 61]. In contrast to the content of UA in blood, the content of UA in saliva could not be standardized in the enrolled studies due to the use of various units (five used mg/dL and the other four provided semi-quantitative data). Consequently, the combined study results were presented as SMD. In nine studies, salivary UA levels were consistently lower in patients with periodontitis compared to controls. Meta-analysis utilizing a random-effect model demonstrated that salivary UA content was significantly lower in periodontitis than in controls (SMD = -1.57, $95\%$ CI -2.25 to -0.90, $P \leq 0.001$) (Figure S3). There was considerable heterogeneity between the studies (I2 = $88.0\%$, $P \leq 0.001$). By eliminating four studies, sensitivity analysis (Figure S4) assisted in achieving a low level of heterogeneity (I2 = $33.8\%$, $$P \leq 0.196$$) [29, 54, 59, 61]. In this instance, periodontitis was still associated with a decrease in UA levels in saliva relative to the control (SMD = -0.95, $95\%$ CI -1.23 to -0.68, $P \leq 0.001$), as determined by a fixed-effect model (Fig. 3).Fig. 3Forest plot for comparing the UA levels in saliva in periodontitis vs. control. SMD, standardized mean difference.
## Association of UA in GCF with periodontitis
Only one cross-sectional study (78 vs. 50, periodontitis patients vs. controls) reported UA levels in GCF (Table1) [27]. The study was deemed to be of high quality (5 points) (Table S2). In this study, GCF was collected using paper strips, and UA concentration was determined using enzymatic colorimetric methods. Similar to the findings in saliva, the outcome demonstrated a significant decrease $4.93\%$ in UA levels in periodontitis compared controls [4.87 ± 0.36 vs. 5.11 ± 0.53 (mg/dL), $P \leq 0.001$].
## Publication bias
Publication bias cannot be ruled out given the small number (< 10) of both blood- ($$n = 8$$) and saliva-tested studies ($$n = 9$$). Mandatory analysis with Egger's test showed no publication bias in studies detecting blood UA ($$P \leq 0.118$$, $$n = 8$$), but a potential bias in studies involving salivary UA ($$P \leq 0.012$$, $$n = 9$$).
## Discussion
The present study explored the association between oral/blood UA and periodontitis using systematic review and meta-analysis. We discovered a positive correlation between periodontitis and blood UA content. The increased blood UA might result from accelerated purine degradation in both periodontal tissues and systemic organs. Patients with periodontitis exhibit accelerated purine catabolism and enhanced xanthine oxidoreductase expression in the periodontium [30, 67]. Enhanced secretion of UA has been observed in immune cells stimulated by periodontal pathogens [67, 68], and in the gingiva of mice with periodontitis [9]. Given that ubiquitous xanthine oxidoreductase is sensitive to inflammation and oxidative stress [69], periodontitis-induced low-grade systemic inflammation may accelerate purine catabolism in distant organs. For instance, periodontal infection is associated with increased UA levels in the liver and feces in rodents [70, 71]. However, it should be noted that the present study only supported a positive association between blood UA and periodontitis when normouricemia was present. UA has been considered as a potent antioxidant in blood, especially at a physiological level [12, 72, 73]. Nonetheless, such a theory has not been rigorously examined in the field of periodontology. It is unknown whether elevated UA levels in the circulation or periodontal vessels in a state of normouricemia are beneficial or detrimental for periodontitis.
Almost no direct evidence has investigated the relationship between periodontitis and hyperuricemia or gout. One cross-sectional study identified hyperuricemia as a protecting factor for periodontitis [32]. However, the outcome was measured by questionnaire and only represented a retrospective history of periodontitis. Gouty patients generally have increased abundance of periodontal pathogens (e.g., Prevotella Intermedia) [74]. Given that the highly prevalent hyperuricemia is considered an emerging risk factor for many inflammatory comorbidities of periodontitis (e.g., cardiovascular disease, diabetes, and chronic kidney disease) [75], the association between hyperuricemia and periodontitis would make for an intriguing research topic. Recent research indicates that UA plays a pathological role in periodontitis. For instance, systemic injection of UA aggravated alveolar bone loss in mice with periodontitis [24]. The urate-lowering drug febuxostat alleviated experimental periodontitis induced by molar ligation in rats [23]. Moreover, non-surgical periodontal treatment for periodontitis appeared to reduce urate levels in the circulation [76]. Taken together, pathologically elevated blood UA appears to contribute to the progression of periodontitis.
Interestingly, the change of UA in saliva shows an opposite trend compared to that in blood in periodontitis patients versus controls. UA has been considered as a major antioxidant in saliva, accounting for ~ $70\%$ of the total antioxidant capacity [77]. However, the levels of antioxidants, including UA, in saliva do not appear to correspond to those in blood [19]. A reduced level of salivary UA in periodontitis may be due to either increased consumption or decreased production. Increased consumption of salivary UA may be a result of the enhanced oxidative stress caused by periodontal infection. In the presence of oxidative stress, UA may be oxidized by reactive oxygen species into allantoin in the absence of uricase [78]. Additionally, UA can serve as a substrate for the synthesis of bacterial components [31], a process that may be accelerated by an increase in dental plaque accumulation. Substances that inhibit the purine oxidation activity of the UA-producing enzyme (i.e., xanthine oxidoreductase) may lead to a decrease in salivary UA production. For instance, there is an increase in the demand of nitrate and its metabolites nitrite and nitric oxide in saliva for bactericidal and anti-inflammatory purposes in periodontitis [79]. These substances may competitively inhibit the purine-oxidizing activity of xanthine oxidoreductase, considering that xanthine oxidoreductase also has a nitrate/nitrite reductase activity [80, 81]. In summary, the mechanisms underlying the reduction of salivary UA in periodontitis are unclear, and further investigation is warranted.
The present systematic review included only one article that found a decreased level of UA in GCF in periodontitis patients. The result was consistent with the findings from some studies that were not included in the review [30, 82]. The changes of UA in GCF resembled those in saliva. Different from saliva that comes from salivary glands, GCF is serum transudate in healthy periodontium or from inflammatory exudate during periodontal diseases [83]. In the context of periodontitis with an increased UA content in the blood, a decrease in the exudation of UA from circulation into GCF was unlikely. A more probable scenario, similar to that of saliva, would be that subgingival microbiota could enhance UA consumption. Moreover, it should be noted that some urate transporters (SLC2A9 and SLC22A12) showed increased gene expressions in gingival tissues from periodontitis patients [84], which could be a potential contributor or confounder for an altered UA concentration in periodontal pockets.
There were several potential confounding variables that could have affected the applicability of this study. The data of sex, age and smoking, which have been associated with hyperuricemia or altered tissular UA in the past studies [85–87], were partly missing. We failed to obtain the original data from the corresponding authors in relevant studies. However, the distribution of age and sex was even in most or all of the studies (age, $\frac{6}{11}$; sex, $\frac{11}{11}$). Smoking was unlikely to have a significant impact on the results given that most ($\frac{12}{15}$) of the studies excluded smokers. The data of body mass index (a common cofounding factor), which has been reported to significantly impact blood UA [88, 89], were completely missing. Consequently, it was unclear how much and to what extent body mass index influences the results.
The definition and control of periodontitis could be another source of bias. Periodontitis is frequently defined differently in clinical studies and systematic reviews. It may diminish the comparability of the studies included in the present systematic review. No studies can make a diagnosis without probing depth, clinical attachment loss, or radiographic bone loss, despite the fact that diagnostic thresholds for periodontitis vary. In the majority of the included studies in this review, the diagnosis was based on these periodontal parameters. Specifically, a clinical misdiagnosis regarding whether or not it was periodontitis rarely occurred. Instead, the discrepancies mainly occurred due to the severity and extent of periodontitis. Indeed, the severity of periodontitis appears to be correlated with UA levels in blood or saliva [19, 21, 31, 90]. For instance, patients with severe periodontitis had higher blood UA levels than those with mild or moderate periodontitis [21]. In this context, the meta-analysis tended to homogenize the data, but did not cover up the effect of periodontitis. Another concern involves the definitions of controls. In many studies, the control groups included not only healthy periodontium, but also gingivitis, mild periodontitis and even localized moderate-to-severe periodontitis. This is a common practice in case definitions when conducting clinical research, which could lead to an underestimation of the differences in UA levels between periodontitis and controls. However, the direction of the disparities would not be changed. Future research should utilize a uniform criterion on periodontitis and controls, i.e., the 2018 classifications of periodontal diseases, in order to enhance comparability between included studies.
Methods of sample collection and analysis may be potential confounding variables. Some studies found no difference between plasma and serum UA levels as measured by enzymatic colorimetric methods [91, 92]. As determined by gas chromatography–mass spectrometry in a separated study, the UA concentration in plasma was 1.59 times that in serum [93]. The discrepancy could be attributed to the differences in technological sensitivity [94]. The present study did not calibrate the UA levels in the two types of blood samples because all of the included studies detected the UA content using enzymatic colorimetric methods. Notably, subgroup analysis on studies involving blood UA showed that the statistical heterogeneity was significantly lower in the plasma subgroup than that in serum. A better homogeneity in the plasma subgroup may be partly due to those anticoagulants (e.g., ethylenediamine tetraacetic acid) inhibit xanthine oxidoreductase activity and thus reduce UA production from undesired sources [95], whereas sustained purine degradation may still occur in serum. Another anticoagulant heparin sodium, however, does not appear to affect xanthine oxidoreductase activity [96]. Therefore, plasma with specific anticoagulants may be preferrable to serum for comparing the results of blood UA content across studies. The combined data from studies using plasma would be more reliable from a heterogeneity standpoint than those using serum. In addition, the effect size from studies using plasma was clearly greater than that from all relevant studies using blood regardless of plasma or serum. Another concern would be whether collection methods influence the results of UA levels in saliva. Resting saliva appears to contain more UA than stimulated saliva [77]. The ratios of resting to stimulated salivary UA was found to be approximately 2:1 in both periodontitis patients and healthy controls. In addition, the concluding meta-analysis excluded studies involving stimulated saliva. Thus, the method of saliva collection may not be a significant confounder in the present meta-analysis.
The present systematic review and meta-analysis had some limitations. First, the number and sample size of included studies were limited, particularly those involving saliva and GCF. Even if the 4 studies (all involving saliva) with small sample sizes(i.e., n < 20) were excluded from the meta-analysis [29, 53, 54, 57], the main findings regarding forest plots did not change significantly. The findings should be interpreted with caution until they are confirmed by large-scale studies. Second, the majority of the findings were derived from retrospective studies, which must be confirmed by prospective and interventional studies. Lastly, the raw data for some potential confounding variables (i.e., age, smoking, and body mass index) were unavailable and their effects (especially body mass index) on the results were unknown. It may contribute to statistical heterogeneity in the present meta-analysis. Taken together, high-quality studies, particularly prospective cohort studies and interventional (e.g., periodontal or urate-lowering treatments) studies, are required to elucidate the association between periodontitis and UA in blood and oral fluids.
## Conclusions
Within the limitations of the present study, it might be concluded that:1) Periodontitis appears to be associated with an increased blood UA concentration in the context of normouricemia. It remains to be determined whether periodontitis and hyperuricemia/gout are associated.2) In contrast to the change of UA in the blood, the amount of UA in saliva and GCF seems to be decreased in the presence of periodontitis. The potential mechanisms underlying the reversal of changes require additional investigations. Figure 4 depicts a hypothetical representation of the differences between blood, saliva and GCF thorough time. And 3) The majority of the findings are based on a small number of observational studies with small sample sizes and substantial methodological heterogeneity, which may compromise the reliability of the conclusions. To further validate the findings, high-quality studies, including large-scale prospective cohort studies and interventional studies, are required. Fig. 4A hypothetical illustration of the differences in uric acid levels between blood, saliva and GCF in periodontitis populations. Saliva and GCF purine levels are found to be elevated in hosts with periodontitis. However, uric acid levels of decrease rather than increase, which may be due to an enhanced uric acid consumption by oral/periodontal bacteria and ROS. Purines in saliva and GCF may also be consumed by XOR-like purine-degrading enzymes that are produced by bacteria. By inhibiting XOR activity, increased levels of nitrate and nitrite produced by salivary glands to combat oral microbiota would reduce the production of uric acid in saliva. In periodontal tissues, circulation and systemic organs (e.g., liver and gut), elevated levels of uric acid have been detected in periodontitis patients or animals, which may be the result of accelerated purine degradation and enhanced XOR activity. The XOR activity in circulation may be increased by periodontitis-related systemic inflammation, but inhibited by anticoagulants such as EDTA. Uric acid may be exchanged between periodontal tissues and systemic organs through circulation. EDTA, ethylenediamine tetraacetic acid; GCF, gingival crevicular fluid; NOX, NO3− and NO2−; PDE, purine-degrading enzymes; ROS, reactive oxygen species; XOR, xanthine oxidoreductase.
## Supplementary Information
Additional file 1: Table S1. The characteristics of excluded studies. Table S2. Quality assessment of the included case-control studies with Newcastle-Ottawa Scale. Table S3. Agency for Healthcare Research and Quality for risk of bias assessment of the cross-sectional studies. Figure S1. Sensitivity analysis of the relationship between periodontitis and controls of UA levels in blood. In these studies, no clearly heterogeneous origin could be found. Figure S2. Forest plot comparing UA levels of periodontitis vs. control in plasma/serum subgroups. CI, confidence interval; WMD, weighted mean difference. Figure S3. Forest plot comparing the salivary UA levels of periodontitis vs control before sensitivity analysis. There was a high heterogeneity among the studies (I2 = $88.6\%$, $P \leq 0.001$). Therefore, sensitivity analysis should be performed to find sources of heterogeneity. SMD, standardized mean difference. Figure S4. Sensitivity analysis of the relationship between periodontitis and controls of UA levels in saliva. A sensitivity analysis was performed to explore potential sources of heterogeneity. Statistical heterogeneity was decreased obviously, indicating that they were likely the source of heterogeneity.
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|
---
title: Exploring the Nutritional Composition and Bioactive Compounds in Different
Cocoa Powders
authors:
- María del Carmen Razola-Díaz
- María José Aznar-Ramos
- Vito Verardo
- Sonia Melgar-Locatelli
- Estela Castilla-Ortega
- Celia Rodríguez-Pérez
journal: Antioxidants
year: 2023
pmcid: PMC10045957
doi: 10.3390/antiox12030716
license: CC BY 4.0
---
# Exploring the Nutritional Composition and Bioactive Compounds in Different Cocoa Powders
## Abstract
Cocoa, the main derivative of the seeds of Theobroma cacao L., has been recognized to have several effects on human health including antioxidant and neuro- and cardio-protective effects, among others. These effects have been attributed mainly to its bioactive compounds. In this context, the aim of this work is to evaluate the nutritional composition, bioactive compounds (i.e., phenolic compounds, procyanidins and methylxanthines) and the antioxidant activity of seven different cocoas (alkalized and non-alkalized) from different origins (Peru, Venezuela, Ivory Coast, Dominican Republic, and West Africa). It represents the first stage of a larger project aiming to find high polyphenol cocoa-based nutritional strategies and related biomarkers that may potentiate brain plasticity and cognitive function. Cocoa powders were extracted by ultrasound-assisted technology, and the total phenolic content (TPC) was measured by Folin–Ciocalteu. Methylxanthines (caffeine and theobromine) and procyanidin contents were determined by HPLC-FLD-DAD, and the antioxidant activity was assessed through DPPH, ABTS and FRAP assays. Non-alkalized cocoas showed higher phenolic and procyanidin contents and higher antioxidant activity compared to the alkalized ones. A strongly significant ($p \leq 0.05$) positive correlation between the antioxidant activity and the TPC, especially with the total procyanidin content, but not with methylxanthines was found. In conclusion, the non-alkalized cocoas, especially the one from Peru, were the best candidates in terms of bioactive compounds. The cocoa from Peru had a TPC of 57.4 ± 14.4 mg of gallic acid equivalent/g d.w., 28,575.06 ± 62.37 µg of catechin equivalents/g d.w., and 39.15 ± 2.12 mg/g of methylxanthines. Further studies should be undertaken to evaluate its effect on brain plasticity and cognitive function.
## 1. Introduction
Worldwide cocoa production has increased nearly $18\%$ in the last ten years [1]. The global trends towards health and wellness, low-sugar, vegan, and allergen-free cocoa products have grown steadily. Premiumization, e.g., single origin or natural cocoa, is expected to be a major driver in the cocoa sector. According to recent data, the main cocoa producers are Cote d’Ivoire, Ghana, Brazil, Cameroon, Dominican Republic, Ecuador, Mexico, Nigeria, and Peru [2]. However, in terms of consumption, *Europe is* the main consumer, reaching 1.8 million tons in 2016 [3], with the UK (47 K tons), Germany (41 K tons) and Spain (35 K tons) being the main consumer countries in 2018 and representing $41\%$ of the total cocoa consumption in Europe [4]. Cocoa is derived from the seeds of Theobroma cacao L., an evergreen tree typical of tropical regions. It contains numerous phytochemicals, with polyphenols representing the largest groups of compounds inside the seed; these have been reported to have several biological properties, such as antioxidant, antiapoptotic, anti-inflammatory and anti-cancer [5]. Moreover, cocoa’s effects have been investigated in different health conditions, including heart diseases, dyspepsia, nervous system diseases, blood circulation problems, and many others [5]. The main flavonoids contained in cocoa are procyanidins (flavan-3-ols). Concretely, these compounds are oligomers and polymers of catechin and epicatechin with different degrees of polymerization (DP) [6]. These compounds also have beneficial effects on the brain. They stimulate brain perfusion and allow angiogenesis, neurogenesis and changes in neuron morphology that favor learning and memory. Moreover, flavonoids preserve cognitive abilities and, therefore, they have been highlighted as compounds for reducing the risk of developing Alzheimer’s disease and stroke in humans [7]. In addition, the cocoa seed contains other phenolic compound such as flavonols (quercetin, isoquercetin), flavones (luteolin, apigenin), flavanones (naringenin), anthocyanins and phenolic acids. These compounds are highly related to antioxidant activity [8]. Other interesting compounds present in cocoa are methylxanthines. These alkaloids are non-selective adenosine receptor antagonists and competitive non-selective phosphodiesterase inhibitors [9]. Theobromine is the main one found in cocoa seeds, while caffeine and theophylline are found to a lesser degree [8]. These compounds have been reported to have physiological and psychological effects on humans such as neuroprotection, bronchodilation, diuresis, gastric secretion stimulation and metabolic effects, among others [8].
However, the highly variable content of phenolic compounds in cocoa and cocoa-derived products should be noted. In this regard, the highest content of polyphenols and methylxanthines is found in pure cocoa powder, followed by baking chocolate and dark chocolate, while the lowest polyphenol content is found in so-called white chocolate, which is made from the cocoa butter and barely contains methylxanthines [10,11,12]. Other factors such as the genotype of the cocoa plant—Forastero/Amazónico, Criollo or Trinitario—the region, method of cultivation and the manufacturing processes influence cocoa components significantly [13,14,15]. Moreover, phenolic compounds’ and methylxanthines’ composition could change due to the different cocoas origins and post-harvest processes [16]. Cocoa manufacturing involves fermentation, drying, roasting and Dutch process or alkalization stages. Alkalization is a widely used treatment to produce cocoa powder because it increases its solubility, adjusts its flavor and color, and reduces its astringency, bitterness, and acidic notes. This treatment consists of mixing cocoa with an alkali solution with a determinate temperature and pressure. Thus, depending on the pH, it can be obtained dark natural (pH 5 to 6), light (pH 6 to 7.2), medium (pH 7.2 to 7.6), or strong (pH > 7.6) alkalized cocoas. Alkalization could also produce undesirable nutritional and functional changes. In addition to affecting macronutrients, it can reduce the content of phenolic compounds and methylxanthines [17]. The reduction in these phenolic compounds could be explained by an increase in polyphenol oxidase activity and the oxidation and interaction of polyphenols with polysaccharides, proteins, other polyphenols, Maillard products, and pyrazines and their precursors. The methylxanthines’ reduction could be due to their interaction with alkali agents and their conversion into salts [8,17].
In the context of this research, evidence supports that cocoa consumption can potentiate cognitive function, but the actions of cocoa on the nervous system have been scarcely investigated. Cocoa polyphenols exert fast vasodilatory actions that increase brain perfusion and activate intracellular pathways related to brain plasticity, enhancing the synthesis of neurotrophic factors which can ultimately promote the birth of new neurons in specific regions of the adult brain. In this regard, adult hippocampal neurogenesis (AHN) is a form of neuroplasticity associated with improved learning and memory, emotional regulation, and protection from psychiatric and neurodegenerative diseases [18,19,20]. Nevertheless, the actions of cocoa on AHN are still unknown. For this reason and considering the highly variable phenolic composition of cocoa and cocoa-derived products, the study of cocoa powder’s composition to better understand its health effects is crucial. Thus, the aim of this work was to evaluate the nutritional labelling, phenolic compounds, procyanidins, methylxanthines and antioxidant activity of seven cocoas (alkalized and non-alkalized) from different origins and available on the Spanish market, for selecting the best one in term of bioactive compounds for future neurological in vivo studies.
## 2.1. Reagents
Gallic acid, Trolox, 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and ferric reducing antioxidant power (FRAP) reagents and the pure standards catechin, caffeine and theobromine were acquired from Sigma-Aldrich (St. Louis, MO, USA), while Na2CO3 was purchased from BDH AnalaR (Poole, UK). Ultrapure water was obtained from a Milli-Q system (Millipore, Bedford, MA, USA). Lastly, HPLC-grade water, Folin–Ciocalteu reagent, and other reagents were acquired from Merck KGaA (Darmstadt, Germany).
## 2.2. Samples
Commercially available cocoa powder samples were purchased from Spanish local and specialized cocoa markets. Cocoa powders were far from the best before date of the labelling and were opened for the first time for the analyses. The list of the cocoa powders, indicating their origins and if alkalized or not, is shown in Table 1. The specific nutritional composition of each cocoa powder according to its labelling is shown in Table S1.
## 2.3. Ultrasound Bath Extraction
The extraction of bioactive compounds from cocoa powders was carried out by ultrasound technology, according to previous research, with slight modifications [21,22]. Firstly, the samples were defatted by adding 10 mL of hexane to 1 g of cocoa powder, vortexed 1 min, sonicated in ultrasound bath (Bandelin, Sonorex, RK52, Berlin, Germany) for 5 min, centrifugated at 9000 rpm 5 min and evaporated under nitrogen. This procedure was repeated twice. Then, the extraction was performed by adding 5 mL of a mixture of acetone/water/acetic acid $\frac{70}{29.5}$/0.5; this was vortexed for 2 min, sonicated in an ultrasound bath (Bandelin, Sonorex, RK52, Berlin, Germany) which worked at a frequency of 35 kHz for 5 min, and centrifugated at 9000 rpm for 5 min. The extracting procedure was repeated twice, and the supernatants were collected. These extracts were filtered with regenerated cellulose filters 0.2 µm (Millipore, Bedford, MA, USA) and stored at −18 °C until the analyses.
## 2.4. Determination of Total Phenolic Content by Folin–Ciocalteu
The Folin–Ciocalteu spectrophotometric method was used to determine the total phenolic content (TPC) in cocoa powder samples [23]. Briefly, 100 µL of extract was added of 500 µL of the Folin–Ciocalteu reagent. Then, we added 6 mL of bi-distilled water, and the flask was kept in agitation for a minute. After that, 2 mL of $15\%$ (w/v) Na2CO3 was added, and then the flask was filled up to 10 mL with bi-distilled water. Then, all the flasks were kept in a dark environment for 2 h, and the measurements were carried out at 750 nm and 25 °C with a UV-visible spectrophotometer (Spectrophotometer 300 Array, UV-Vis, single beam, Shimadzu, Duisburg, Germany). Gallic acid was used for the calibration curve from 1 to 1000 µg/g. The analyses were performed in triplicate, and the results are expressed as mg gallic acid equivalents (GAE)/g dry weight (d.w.).
## 2.5. Determination of Flavan-3-Ols by HPLC-FLD
The procyanidin content was determined in the cocoa samples by the methodology previously reported by López-Cobo et al. [ 24]. All the analyses were carried out at 35 °C. The identification of flavan-3-ols was carried out according to the previous HPLC-MS-ESI-TOF analyses and considering the elution of the compounds, because they elute depending on their degree of polymerization; they first elute the monomers and then the different oligomers [25]. To quantify flavan-3-ols, catechin was used as the standard at six concentration levels from 10 to 650 µg/g. In addition, application of the correction factors suggested by Robbins et al. was carried out [25]. Analyses were performed in triplicate and the results are expressed as mg catechin equivalents (CE)/g d.w.
## 2.6. Determination of Methylxanthynes by HPLC-DAD
The determination of caffeine and theobromine was carried out following the procedure described previously by Alañon et al. [ 26]. An Agilent 1200 Series (Agilent Technologies, Palo Alto, CA, USA), equipped with a quaternary pump delivery system, a degasser, an autosampler and a photodiode array detector (DAD) set up at 264 nm were used for the analyses. An Aagilent Zorbax Eclipse XDB-C18 column 5 µm, 150 × 4.6 mm ID (Agilent Technologies, Palo Alto, CA, USA) was used. Mobile phase A, B, C and D consisted of water (A), 200 mM sodium acetate/methanol $\frac{84}{16}$ pH 4.4 (B), methanol (C) and acetonitrile (D). The gradient elution was: $25\%$ B at 0 min, $25\%$ B and $75\%$ C for 3 min, $25\%$ B and $50\%$ D for 10 min and $25\%$ B for 25–40 min. The injection volume was 100 µL, and the flow rate was 1 mL/min. Standard curves of caffeine and theobromine were performed at six concentration levels from 40 to 1250 µg/g for the quantification. The results are expressed as mg/g d.w.
## 2.7. Determination of Antioxidant Activity: DPPH, ABTS and FRAP Assays
The antioxidant activity of the cocoa powders was determined by DPPH, ABTS and FRAP methods, as described in previous research [27,28,29,30]. In all the assays, the calibration curve was made of the standard Trolox, and the results were expressed in mg of Trolox equivalents (TE)/g d.w. Analyses were performed in triplicate and the measurements were carried out using an UV–visible spectrophotometer (Spectrophotometer 300 Array, UV–Vis, single beam, Shimadzu, Duisburg, Germany).
## 2.8. Statistical Analysis
A one-way ANOVA (Tukey’s test) and Pearson’s correlation analyses were performed using the Statistica 7.0 package (StatSoft, Tulsa, OK, USA). Additionally, a correlation heatmap and principal component analysis (PCA) were performed by MetaboAnalyst. The significance level was selected as a $95\%$ confidence interval ($p \leq 0.05$) for all the analyses. However, since the dataset is small, the significance level was extended up to $p \leq 0.1$ ($90\%$ confidence interval) in some cases; this is in agreement with the studies performed by other authors [31,32,33,34,35].
## 3.1. Nutritional Evaluation of the Cocoas
Table 2 summarizes the nutritional value collected from the labelling of cocoa powder samples. The energetic cocoa value ranges from 1249.8 to 1634.4 kJ/100 g. In the study of Jayeola and Oluwadun [36], the energetic value of the different cocoa powders from Nigeria was higher than that of our samples. The range included between 1523.0 and 2104.6 kJ/100 g of product. The energy input of cocoas could be attributed mainly to the fats present in it ($r = 0.8019$, $p \leq 0.05$), carbohydrates and fats being the main elements influencing total energy.
Carbohydrate values ranged from 9 to 28 g/100 g of product, with sugars being in a small proportion, i.e., less than 2 g/100 g cocoa in all the studied samples. The content of carbohydrates in cocoa and cocoa products is highly variable. In fact, different values of carbohydrates have been reported by other authors, i.e., about 50–60 g carbohydrates/100 g cocoa [36,37,38]. A decrease in sugars without changes in total carbohydrates was previously reported in alkalized cocoa products depending on the alkalization conditions [8]. However, no significant differences between the sugar content in the studied alkalized and non-alkalized cocoas were found. According to some authors [39], the sugar content and distribution in cocoas change during the processing. Initially, the characteristic sugars in cocoa beans are fructose and sucrose, but after the fermentation process, it is possible to also find glucose. Moreover, cocoa contains raffinose, stachyose and verbascose in smaller quantities. Finally, after roasting, the main sugars are glucose, galactose and arabinose [39]. It should be noted that many commercialized cocoa powders have added sugars [40]. In this regard, according to the Spanish users and consumer organization (OCU), the sugar content of soluble commercial cocoa products is too high, and it could represent $65\%$ of the product [41]. When discussing dietary sugars (also called total sugars), all mono- and disaccharides that are present in the food, naturally or added, are considered [42]. Their intake could be associated with an increase in glycemic index, hyperglycemia, and type 2 diabetes mellitus (T2DM). Therefore, a high intake of dietary sugars could have undesirable health effects. Nevertheless, the studied samples have no added sugars, and those reported are naturally occurring in the cocoas, making them a healthier alternative to added sugar cocoa powders.
Cocoa powders can be classified according to their fat content. In European legislation, the product obtained by converting into powder cocoa beans which have been cleaned, shelled, and roasted, and which contains more than $20\%$ cocoa butter and not more than $9\%$ water, is called “cocoa powder”, whereas “fat-reduced cocoa powder” contains less than $20\%$ cocoa butter [43]. Accordingly, cocoas 1, 2 and 5 were classified as “cocoa powder” (with fat content from 21 to 23 g/100 g), while cocoas 3, 4, 6 and 7 were classified as “fat-reduced cocoa powder” (with fat content ranging from 11 to 12 g/100 g) (see Table S1 for detailed information about fat composition). Those values agree with the ones previously published [17,21,22,23]. *In* general, fat-reduced cocoa powders showed a major carbohydrates content. Despite this, some authors have reported lower fat values in alkalized cocoa powders (4.93 g/100 g) [44], probably due to the hydrolysis and saponification of triglycerides [8]; in this study, this effect was not observed.
The content of proteins was ≈20 g/100 g of cocoa, except for cocoa 3 (non-alkalized) whose content was 28 g/100 g of cocoa. Petit et al. [ 45] reported protein values between 18.1 to 24.4 g/100 g of cocoa, with this value being higher in samples with lower fat content. In the study of Adeyeye [44], a content of 10.9 g/100 g of alkalized cocoa powder was observed. That diminution of protein content could be due to oxidative destruction by deamination [8]. However, this reduction was not observed in the cocoa powder samples evaluated in this study. This fact could be due to the employed alkalization process. The alkali concentration and thus the pH will lead to the formation of brown compounds in the alkalized cocoa powders through partial protein deamination [46]. Therefore, the employed alkalization process could influence the protein content in cocoa powders.
Finally, the salt content is very close to 0 g/100 g of product; thus, cocoa powder was not characterized as a product with high salt content.
## 3.2. Total Phenolic Content of Cocoas
The total phenolic content (TPC) of the seven cocoa extracts was measured by the Folin–Ciocalteu method. The obtained results (expressed as mg gallic acid equivalent (GAE)/g d.w.) are shown in Figure 1.
As can be seen, the TPC ranged from 9.2 to 57.4 mg GAE/g d.w. Cocoa 7 (West Africa) had the lowest TPC, followed by cocoa 2 (Ivory Coast). Precisely, those two cocoas were submitted to an alkalization process, also known as Dutching, which is usually employed to reduce the cocoa’s bitterness and to darken chocolate with the consequent loss of these compounds. This effect was previously reported by Li et al. [ 17]. They discovered that the content of total polyphenols decreased with the increasing degree of alkalization in cocoa powders. Additionally, they found that the polyphenols in cocoa interact with aroma precursors as well as aroma compounds, and with hydrophobic amino acids during the alkalization process. This can be attributed to the high temperatures and the exposure of the cocoa powders to oxygen during the alkalization process [17]. Within the non-alkalized cocoas, it is also possible to observe significant differences in TPC that could be influenced by the geographical origin, as previously reported [47]. In this regard, the cocoa from Peru (cocoa 4) presented the highest value of TPC (57.4 mg GAE/g d.w). That result is in line with that reported by Ramos-Escudero et al. for *Peruvian cocoa* beans (21.88–33.39 mg GAE/g) [48] and that of Grassia et al. for *Peruvian cocoa* (11.2 mg catechin equivalents/g) [49]. Additionally, the TPC values for non-alkalized cocoas were in line with those reported by Miller et al. [ 12] (from 45.3 to 60.2 mg GAE/g), Samaniego et al. [ 32] (from 33.6–71.7 mg GAE/g) for different cocoa powders, and Kobori et al. [ 50] for a cocoa powder from Ghana (62.3 mg epicatechin equivalents/g). Analyzed cocoa powder 1 from Venezuela had a TPC of 34.4 mg GAE/g d.w; this is in the same range of magnitude as the results reported in cocoas from other regions of south America, with values that ranged from 44–202 mg GAE/g in cocoa beans from Colombia [33,51,52] and from 33.6–71.7 mg GAE/g in cocoa beans from Equator [32].
Figure 2 shows a Pearson correlations heatmap of the cocoa powders’ analyses and nutritional facts. A significant ($p \leq 0.1$) positive correlation between TPC and the carbohydrate content ($r = 0.7306$) was found. In fact, cocoas 3 and 4, which were which outlined higher polyphenol content, were those that had more carbohydrates. Those correlations can be attributed to the fact that a high carbohydrate content with high phenolic content can promote melanoidins’ formation when the product is subjected to heating in the roasting step [53]. Besides, some studies [54] have reported that melanoidins are carbohydrate–phenol structural compounds, thus explaining this positive correlation between carbohydrates and polyphenols in these cocoa powder samples. The same trend has been appreciated previously by other authors in cocoa powders, correlating the polyphenol content and the non-fat cocoa solids [6,55,56].
In addition, a PCA is shown in Figure S1, wherein component 1 and 2 means 99.5 and $0.3\%$, respectively. Here, we can clearly observe the differences between alkalized and non-alkalized cocoa powders.
## 3.3. Procyanidin Content of Cocoas
Table 3 summarizes the total procyanidin content of the cocoa powders, according to their degree of polymerization (DP) as analyzed by HPLC-FLD. In addition, a representative chromatogram of the procyanidin distribution is depicted in Figure 3.
The total flavan-3-ols content ranged between 3299 and 28,575 µg CE/g d.w. in the analyzed cocoas. The alkalized cocoas (cocoa 2 and 7) presented the lowest content in procyanidins, while the non-alkalized cocoas, especially those from Peru (cocoa 4), showed the highest content. Regarding the procyanidin profile, the major flavan-3-ols were those with low DP. Cat + epicat accounted for between 27–$61\%$ of the total content, followed by procyanidin dimer, which accounted for nearly $30\%$ of the total flavan-3-ols in all cocoa samples. Contrarily, polymers with a DP higher than 10 ranged between 3 and $8.5\%$ of the total procyanidin content. González-Barrio et al. reported values of 2.7 mg/g of cat + epicat, them being $16\%$ of the total procyanidins in conventional cocoa powder, as quantified by HPLC-FLD. However, the same authors found around $37\%$ of polymers had a polymerization degree higher than 10 [22]. Similar values of cat + epicat were found in the studied samples, with cocoa 4 (natural, from Peru) being the cocoa that presented the highest content. However, Ramos-Escudero et al. reported values up to 25.22 mg/g of cat + epicat in cocoa beans from Peru [48], and values of 10.9 and 11.5 mg/g were found in beans from Peru and Ghana, respectively [49]. Other studies reported values of 8 and 4 mg/g (monomer and dimer, respectively) in *Colombian cocoa* beans [52], and of 20 and 11 mg/g (monomer and dimer, respectively) in *Equatorian cocoa* beans [32], which are very similar values to those reported here for the natural *Venezuelan cocoa* powder (cocoa 1). In other parts of the cocoa bean, such as the shell, Botella-Martínez et al. obtained epicatechin and catechin as major flavonoids, with values that ranged from 4.56–6.33 and from 2.11–4.56 mg/g, respectively, in cocoa beans from Ghana [57]. Besides, in extracts from cocoa pods, some authors obtained values of 95.4 mg/g for monomers and 7.5 mg/g for dimers [58]. Those results agree with the data presented here, despite the differences in origin.
Interestingly, other authors reported a significant increase in catechin content with alkalization with temperature, time and the alkali NaOH process, but a significant reduction in the epicatechin content of *Forastero cocoa* with 10–$12\%$ fat [59,60]. Similar results, i.e., a $40\%$ increase in catechin of, but a 23–$66\%$ reduction in epicatechin and proanthocyanidins, were reported by Stanley et al. when cocoa powder was alkalized with NaOH (final pH 8.0) at 92 °C for up to 1 h [61]. In the present study, although alkalized cocoas were those with the lowest absolute content of monomers and procyanidins, they had the same tendency regarding proportions. Thus, analyzed alkalized cocoa had a cat + epicat content ≥ $40\%$. In the case of cocoa 7 (alkalized from West Africa), an increment of $54\%$ was found in the monomer content regarding the proportion and distribution. It would mean that the alkalization process probably destroys the linkage between catechin or epicatechin monomers in procyanidins with a higher degree if polymerization.
It has been previously reported that high-molecular-weight polymers of flavan-3-ols are poorly absorbed through the gastrointestinal tract. So, the efficiency of absorption of procyanidins decreased with the increasing degree of polymerization, and only small amounts of intact oligomers of procyanidins might be partially absorbed in the intestinal mucosa [62]. Therefore, compounds with low molecular weight, such as flavan-3-ol monomers and dimers, can achieve higher concentrations in the blood and reach the target organs in the body. According to this, natural cocoa from Peru (cocoa 4) could be the most bioactive cocoa due to its higher content in monomer and dimers compared to the other analyzed cocoas. A dose–response effect has also been described on plasma of cocoa epicatechin around 30–60 min after consumption, and a maximum plasma concentration has been observed 2–3 h after ingestion of flavan-3-ol-rich cocoa products [62]. Most of the bioactivities described for cocoa flavan-3-ols are attributed to their effect on the mitogen-activated protein kinase and phosphoinositide-3-kinase-protein kinase B/protein kinase B molecular signaling pathways. Cocoa procyanidins have been recognized as inhibitors of neuroinflammation; they are preventive against myofibroblasts’ transformation in cardias fibrosis and preventive/therapeutic in life-threatening diseases such as cancer and diabetes. Moreover, cocoa flavanols’ metabolites from catechin and epicatechin have been reported to improve glucose-stimulated insulin. Going further, cocoa epicatechin and its colonic metabolite 3,4-dihydroxyphenylacetic acid have been reported to protect the renal proximal tubular cell against high glucose-induced oxidative stress [63]. Additionally, procyanidin in cocoa powder has been reported to reduce LDL oxidative susceptibility and to have beneficial effects on plasma HDL-cholesterol concentrations in humans [64,65].
Moreover, a significant ($p \leq 0.05$) positive correlation was found between total procyanidin content and total phenolic content ($r = 0.9643$) (Figure 2). This makes sense because in fact, flavan-3-ols are the major polyphenols present in cocoa powders that form part of the total phenolic content determined by Folin–Ciocalteu, so they contribute the most to its total phenolic composition.
## 3.4. Methyxanthines Content of Cocoas
The caffeine and theobromine contents were analyzed by HPLC-DAD. The obtained results, including the theobromine/caffeine (T/C) ratio, are summarized in Table 4.
The total methylxanthine content ranged between 19.4 (natural cocoa from Dominican Republic) and 39.2 mg/g (natural cocoa from Peru), while caffeine and theobromine results ranged from 5.4 to 27.1 and from 8.87 to 15.1 mg/g, respectively. Natural cocoa from West Africa (cocoa 6) was the cocoa with the lowest theobromine content, and natural cocoa from Venezuela (cocoa 1) had the highest. Different trends were found for caffeine content. In this case, natural cocoa from the Dominican Republic (cocoa 5) was the one with the lowest caffeine content, while Peruvian natural cocoa (cocoa 4) had the highest content by some way. Comparing these results with those previously published, Alvarez et al. reported caffeine and theobromine values of 6.1 mg/g and 9.10 mg/g, respectively, with a T/C ratio of 1.5 in Venezuela *Criollo cocoa* beans [16]. In Equatorian cocoas, the theobromine and caffeine content ranged from 1.5 to 2.4 and from 0.2 to 0.4 mg/100 g, respectively [32]. Ramos-Escudero et al. reported values up to 12.95 mg/g of theobromine in *Peruvian cocoa* beans [48]. According to Grassia et al., Ghana’s cocoa beans had a lower caffeine content (0.2 mg/g) than *Peruvian cocoa* beans, but the highest theobromine concentration (10.4 mg/g) [49]. Other authors reported values of 2.9 and 21.5 mg/g of caffeine and theobromine, respectively, in non-alkalized cocoa beans from Ghana, with reductions after the alkalization process [66]. In cocoas from Peru, some authors reported ranges of 57.4–76.0 and 10.6–20.8 mg/g of theobromine and caffeine, respectively, with T/C ratios > 3 in all cases [67]. Similar results have been shown by other authors in cocoas from different regions of South America, with T/C ratios higher than 2 in all cases [33,51,52]. Overall, the present results are in line with those already published. Regarding the T/C ratio, cocoas 4 (natural from Peru), 6 (natural from West Africa) and 7 (alkalized from West Africa) had ratios < 1, while cocoas 1 (natural from Venezuela), 2 (alkalized from Ivory Coast) and 3 (natural from Ivory Coast) had ratio > 1. The only cocoa that presented a ratio higher than 2 was the one from the Dominican Republic (cocoa 5). As can be appreciated, the ratio seems to have origin dependency. Sioriki et al. analyzed alkalized and non-alkalized cocoa powders finding no statistical differences in the theobromine and caffeine content. They reported values of 7.4–8.3 mg/g in theobromine and 2.1–2.2 mg/g in caffeine [59]. In another study, they confirmed that no significant reductions or increments take place in cocoa during alkalization process due to temperature, time or alkali concentration [60]. In this regard, the results found in this study agree with those related to the total methylxanthines and caffeine content, but different results were obtained for theobromine. In this case, a reduction of $33\%$ of theobromine was found from cocoa 6 to cocoa 7; these two are from the same market label, but the first one is alkalized and the second non-alkalized.
Some authors have found that cocoa methylxanthines mediate an increased plasma concentration of (−)-epicatechin metabolites, thus enhancing the effects of cocoa flavanols on cardiovascular function [68]. A moderately strong correlation was found between the total methylxanthine content and the total phenolic ($r = 0.6096$) and procyanidin ($r = 0.6773$) content. However, those correlations were insignificant.
## 3.5. Antioxidant Activity of Cocoas by DPPH, ABST and FRAP
The antioxidant activity of the cocoa powders was measured by three spectrophotometric methods, i.e., DPPH, ABTS and FRAP. The results are shown in Figure 4.
DPPH values ranged from 30.77 to 97.94 mg TE/g d.w. For ABTS, the values were from 73.97 to 267.43 mg TE/g d.w., and for FRAP assay from 28.88 to 98.74 mg TE/g d.w. As expected, and considering the abovementioned discussed data, alkalized cocoas (i.e., cocoa 2 and 7) had the lowest antioxidant activity, while cocoa 4 (natural, from Peru) presented the highest activity, according to the three different assays. Our results agree with those reported by Todorovic et al. [ 35]. They compared alkalized and non-alkalized cocoas in terms of antioxidant activity and reported ranges of 63.4–96.8, 56.9–65.7 and 87.1–113.8 mg TE/g d.w. for DPPH, ABTS and FRAP, respectively, in non-alkalized cocoas extracts, and lower ranges in the alkalized ones (i.e., 32.1–67.9, 28.2–57.0 and 27.6–77.4 mg TE/g d.w. by DPPH, ABTS and FRAP, respectively). Botella-Martínez et al. measured DPPH, ABTS and FRAP antioxidant assays in *Ghanaian cocoa* bean shells, with values of 2.35–5.53, 3.39–11.55, and 3.84–7.62 mg Trolox equivalents/g sample, respectively [57]. In *Malaysian cocoa* powder, some authors reported 47.2, 65.77, $85.8\%$ inhibition by DPPH at concentrations of 5, 10 and 20 mg/mL, respectively [69]. The cocoa powder that had the highest percentage of inhibition for DPPH at 20 mg/mL was the one from Peru (cocoa 4), with $64\%$ of inhibition. Besides, non-alkalized cocoa powders showed >$60\%$ inhibition, and the alkalized cocoa powders showed >$50\%$, in all cases at 20 mg/mL.
The three methods showed significant ($p \leq 0.05$), strong, positive correlation between each other (DDPH vs. ABTS, $r = 0.9284$; DPPH vs. FRAP, $r = 0.9630$; ABTS vs. FRAP, $r = 0.9842$). Furthermore, they showed the highest significant ($p \leq 0.05$) positive correlation with the TPC, followed by the total procyanidin content (r > 0.9). Taking a significance level p of 0.1, a significant positive correlation was also observed between total methylxanthines, and the antioxidant activity measured by DPPH (0.6773) and FRAP ($r = 0.6700$); this is mainly attributable to the caffeine content. Additionally, a light negative correlation was found with the theobromine content. Previously, Kobori et al. reported that in cocoa powder with $80\%$ reduced caffeine, the polyphenol content mas maintained $84\%$, and its antioxidant activity $85\%$, compared to the non-decaffeinated one [50]. So, in accordance with them and with the statistical data, it can be affirmed that the antioxidant activity of the cocoa powders is attributable to its phenolic and procyanidin content.
## 4. Conclusions
Non-alkalized (natural) and alkalized cocoas from Venezuela, the Ivory Coast, Peru, the Dominican Republic, and West Africa have been analyzed in terms of their energy and macronutrients reported in their labelling and their bioactive compounds’ composition. Differences in the nutritional composition of the studied cocoas have been observed. Their fat content allowed the cocoa to be classified as “cocoa powder” or “fat-reduced cocoa powder”. It has been found that fat-reduced cocoa powders have a higher carbohydrate content. Interestingly, no significant differences in carbohydrates, fats, and proteins between alkalized and natural cocoas have been found. In addition, the present results showed high differences in the total phenolic, procyanidins and methylxanthines contents depending on the cacao origin and treatment. In this regard, the alkalization process significantly reduced the concentration of those compounds, excepting the methylxanthine content. The natural *Peruvian cocoa* was the one that showed a significantly higher content of phenolic compounds, total procyanidins and total methylxanthines, and it presented the highest antioxidant activity measured by the three different assays. Meanwhile, alkalized cocoa from West Africa showed the lowest values except for total methylxanthines, with the natural cocoa from Dominical Republic having the lowest methylxanthines concentration. A significant and negative correlation between the fat content and the TPC was found. Contrarily, the TPC was positively associated with the procyanidin concentration. Finally, the antioxidant activity of the cocoa powders was directly related to its phenolic and procyanidin content, but not to the methylxanthines content. It should be highlighted that scarce information regarding the origin, bioactive compound composition or treatment is available on cocoa powder labels, thus, making difficult to compare the different options available in the market and to choose the most suitable to study its potential health effects. This fact, together with the lack of standardized analytical methods focused on measuring the aforementioned cocoa powder compositions, makes this type of research necessary prior to carry out in vivo and clinical studies.
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|
---
title: 'The Role of Type 2 Diabetes in Patient Symptom Attribution, Help-Seeking,
and Attitudes to Investigations for Colorectal Cancer Symptoms: An Online Vignette
Study'
authors:
- Lauren Smith
- Christian Von Wagner
- Aradhna Kaushal
- Meena Rafiq
- Georgios Lyratzopoulos
- Cristina Renzi
journal: Cancers
year: 2023
pmcid: PMC10045970
doi: 10.3390/cancers15061668
license: CC BY 4.0
---
# The Role of Type 2 Diabetes in Patient Symptom Attribution, Help-Seeking, and Attitudes to Investigations for Colorectal Cancer Symptoms: An Online Vignette Study
## Abstract
### Simple Summary
Diabetic individuals have lower cancer awareness and are two-fold more likely than non-diabetics to attribute some red-flag cancer symptoms to medications.
### Abstract
Objectives: Type 2 diabetes is associated with a higher risk of colorectal cancer (CRC) and advanced-stage cancer diagnosis. To help diagnose cancer earlier, this study aimed at examining whether diabetes might influence patient symptom attribution, help-seeking, and willingness to undergo investigations for possible CRC symptoms. Methods: A total of 1307 adults (340 with and 967 without diabetes) completed an online vignette survey. Participants were presented with vignettes describing new-onset red-flag CRC symptoms (rectal bleeding or a change in bowel habits), with or without additional symptoms of diabetic neuropathy. Following the vignettes, participants were asked questions on symptom attribution, intended help-seeking, and attitudes to investigations. Results: Diabetes was associated with greater than two-fold higher odds of attributing changes in bowel habits to medications (OR = 2.48; $95\%$ Cl 1.32–4.66) and of prioritising diabetes-related symptoms over the change in bowel habits during medical encounters. Cancer was rarely mentioned as a possible explanation for the change in bowel habits, especially among diabetic participants ($10\%$ among diabetics versus $16\%$ in nondiabetics; OR = 0.55; $95\%$ CI 0.36–0.85). Among patients with diabetes, those not attending annual check-ups were less likely to seek help for red-flag cancer symptoms (OR = 0.23; $95\%$ Cl 0.10–0.50). Conclusions: Awareness of possible cancer symptoms was low overall. Patients with diabetes could benefit from targeted awareness campaigns emphasising the importance of discussing new symptoms such as changes in bowel habits with their doctor. Specific attention is warranted for individuals not regularly attending healthcare despite their chronic morbidity.
## 1. Introduction
Colorectal cancer (CRC) is the fourth most common cancer in the UK, with the second highest cancer-related mortality. Currently, large proportions of CRCs are diagnosed at an advanced stage ($52\%$) or following emergency presentation ($24\%$) in the UK [1]. A recent international study found that $23\%$ to $36\%$ of CRCs are diagnosed as an emergency [2]. Diagnosing cancer earlier is a key public health target for improving survival [3,4].
Diabetes is associated with an increased risk of developing CRC [5] through complex biological mechanisms related to insulin-like growth factors, insulin resistance, compensatory increased insulin levels, and prolonged hyperglycaemia [6,7]. Moreover, individuals with diabetes have a higher risk of being diagnosed with CRC at an advanced stage [8] and have worse outcomes than nondiabetics [9,10,11,12].
The time it takes for patients to appraise symptoms and seek help is a significant contributor to the overall delay before a cancer is diagnosed [13], but research on how chronic conditions impact this delay is scant [14,15]. When experiencing potential CRC symptoms such as a change in bowel habits, individuals with diabetes may consider alternative explanations, attributing the symptoms to their pre-existing condition or to medications [16,17]. Diabetic neuropathy can cause changes in bowel habits, as can diabetic medications such as metformin, whose possible side effects include constipation and diarrhoea [12]. Competing demands might also contribute to diagnostic delays, especially when the management of diabetes is complex and is prioritised by patients and doctors over the investigation of new symptoms of an as-yet-undiagnosed cancer. It has also been hypothesised that diabetes may facilitate, rather than hinder, timely cancer diagnosis [18]. This is because diabetes, like other chronic conditions, might be associated with frequent healthcare contacts [17,18], leading to opportunities to discuss new symptoms and possibly reducing delays in CRC diagnosis through what is termed a surveillance mechanism or surveillance effect [19].
The aim of the study was to investigate symptom attribution, intended help-seeking, and willingness to undergo investigations for potential CRC symptoms among people living with type 2 diabetes (hereafter, diabetes) compared with individuals without diabetes.
## 2. Methods
We performed an online cross-sectional vignette survey asking participants about the action they would take after reading a vignette describing symptoms such as rectal bleeding or change in bowel habits, with or without additional symptoms of diabetic neuropathy in the feet. The word “cancer” was not mentioned to the study participants in order to mask the study aim and to reduce priming and response bias, similar to previous studies [14].
Vignettes are short, hypothetical scenarios echoing real-life situations [20,21]. They allow the manipulation of symptoms whilst keeping the context and environment constant to explore reactions and intended behaviours [22]. Vignettes have often been used in diagnostic research [23,24] and are particularly useful when investigating complex phenomena such as comorbidity-specific effects [14,23].
The study was approved by UCL Ethics committee (N$\frac{14687}{006}$).
## 2.1. Study Participants
Participants were recruited in August 2021 through Prolific, a survey provider (www.prolific.co, accessed on 28 February 2023). Eligibility criteria included age 50 years or above, resident in the UK, and no cancer diagnosis in the last 5 years. The age of 50+ was chosen due to the increasing prevalence with older age of both type 2 diabetes [25] and CRC [26]. At the time of the study, Prolific had ~5500 participants in the UK aged over 50, of which $62\%$ were aged 50–59 and $64\%$ were female. Participants meeting the eligibility criteria were contacted by email and asked if they would be interested in participating in a study on symptom perception and help-seeking. Participants were compensated £1.25 for completion of the survey, which is based on the questionnaire taking approximately 15 min to complete and a £5 per hour payment. This amount was the standard set by Prolific, who recruited participants from their panel, and is aimed at compensating participants for the time taken to complete the survey.
Quota sampling was used to try to ensure that $50\%$ of the sample were people with diabetes. To facilitate the recruitment of a sufficiently large number of diabetic participants the survey was additionally circulated to a local diabetes group. A total sample of $$n = 2000$$ participants was estimated to provide $80\%$ power to detect a difference of $10\%$ in anticipated help-seeking between people with and without diabetes at a significance level of $p \leq 0.05.$
## 2.2. Vignettes
We developed three vignettes, all of which described symptoms that could be indicative of CRC, and one of which additionally included symptoms of diabetic neuropathy.
Vignette 1 focused on rectal bleeding: When you use the bathroom, you notice blood in your poo (rectal bleeding). Other than this symptom you have noticed no other changes.
Vignette 2 focused on a change in bowel habits: You notice you have had changes in your normal bowel habit (such as looser poo, pooing more often or constipation). Other than this symptom you have noticed no other changes.
Vignette 3 focused on the co-occurrence of diabetic neuropathy and CRC symptoms: You notice you have numbness, tingling and pain in your feet, along with changes in your normal bowel habits (such as looser poo, pooing more often or constipation) and blood in your poo (rectal bleeding). Other than these symptoms you have noticed no other changes.
Changes in bowel habits and rectal bleeding are red-flag CRC symptoms warranting an urgent investigation via the suspected cancer pathway, according to UK NICE guidelines [27]. Symptoms indicating neuropathy in the feet were chosen because these could be concerning to the individual but are different from CRC symptoms, allowing us to examine variations in prioritisation by patients during medical encounters when experiencing multiple symptoms.
The vignettes and study material were co-designed with contributions from patient representatives, clinicians, and researchers. Twenty-two cognitive interviews and a pilot study with 200 participants were performed to ensure the study material was patient-centred and easy to understand [28]. Based on the feedback received, minor changes were made; for example, giving a clearer explanation of some words (e.g., endocrinologist).
Participants were randomly assigned to read either vignette 1 and 2 or vignette 3.
Following the vignettes, participants were asked precoded and open questions on symptom attribution, intended help-seeking, and attitudes to investigations (Supplementary Materials SA). Additionally, participants provided information on their age, sex, ethnicity, educational level, and self-reported history of chronic conditions, using a question and precoded list of conditions adapted from the GP Patient Health Survey [29]. Answers to this question were used to classify participants into those with or without a pre-existing diagnosis of type 2 diabetes. Respondents with diabetes were presented with additional questions on their diabetes management.
## 2.3.1. Symptom Attribution
Symptom attribution was explored using free-text responses, inviting participants to write down anything they thought may be a possible cause of the symptoms. Similar to previous research [14], we used content analysis to code answers as referring to cancer, benign gastrointestinal (GI) conditions, diabetes, or other conditions.
## 2.3.2. Intended Help-Seeking
Intended help-seeking was measured by asking what action people would take. Precoded answers such as “Talk to members of your family” and “Contact the GP” were presented (full list in Supplementary Materials SA), with an additional free-text option. Precoded answers were randomised to avoid order effects. In the analysis stage, answers of “probably would” and “definitely would” were combined into a “would take action” category.
## 2.3.3. Willingness/Attitudes to Undergo Diagnostic Investigations
Participants were asked if they would be willing to have a colonoscopy/sigmoidoscopy and/or stool test (using yes/no response options) following the symptomatic presentation. If participants answered “no,” they were asked to clarify why.
## 2.3.4. Prioritisation of Symptoms
Participants randomised to vignette 3 (co-occurrence of diabetic neuropathy and CRC symptoms) were asked to provide a ranking of the order in which they would mention each symptom to their GP.
## 2.4. Main Explanatory Variables
Having a diagnosis of type 2 diabetes was the main explanatory variable considered. In addition, those with diabetes were asked how often their diabetes is reviewed, with precoded answers of “at least once per year by the GP or by the specialist” or “not being regularly checked by a doctor or nurse”. They were asked what their HbA1c is (either mmol or %) and how they would describe their management of diabetes (“very good”, “good”, “average”, “bad”, or “very bad”; subsequently recoded as “good”, “average”, or “bad”. Based on clinical cut-offs, a HbA1c of 48 mmol/mol indicated a blood sugar level in the diabetic range; 48 and below indicated well controlled diabetes.
Additionally, past faecal occult blood test or colonoscopy/sigmoidoscopy information was collected by asking participants: “Have you ever had a stool sample?” and “Have you ever had a colonoscopy/sigmoidoscopy?”, with pre-coded answers: “no”, “yes, for screening”, and “yes, for symptoms”.
## 2.5. Statistical Analysis
Chi-squared tests were used to compare the characteristics of participants with versus without diabetes. Multivariable logistic regression was used to explore the associations between diabetes and the following outcomes: symptom attribution, intended help-seeking, and willingness to undergo diagnostic investigations. Each outcome was examined in a separate multivariable model, adjusting for variables considered a priori as potential confounders based on the previous literature and on clinical reasoning (including age, sex, ethnicity, previous diagnostic testing, and total number of chronic morbidities).
Additionally, for the subgroup with diabetes, we evaluated the association between diabetes management and intended help-seeking for possible CRC symptoms, combining the participants across all three vignettes.
## 3.1. Sample Characteristics
A total of 1456 participants agreed to take part in the study. Excluding 108 with incomplete responses and 59 with cancer in the last 5 years, a total of 1287 participants were included. Among respondents, 320 had diabetes and 967 were nondiabetics, $60.8\%$ of the sample were female, and $87.3\%$ were from a white ethnic background (Table 1), which is in line with Prolific’s participant characteristics. Diabetic respondents were older and more frequently had 3+ chronic conditions and a history of faecal occult blood test or colonoscopy/sigmoidoscopy (for screening or symptoms). Within the subgroup with diabetes, $16.6\%$ had HbA1c results <48 mmol, $26.3\%$ ≥48 mmol, and $57.2\%$ unknown. Most self-rated their diabetes management as “good” ($56.9\%$), with $10.6\%$ self-rating it as “bad,” and $10.9\%$ did not have their diabetes checked at least once a year (in either primary or secondary care).
## 3.2. Symptom Attribution
The change in bowel habits (vignette 2) was most frequently attributed to dietary changes by both the diabetic and nondiabetic participants, without a significant difference ($30.9\%$ vs. $34.2\%$, respectively). Whilst cancer was the second most frequent symptom attribution overall, participants with diabetes, compared with those without, were less likely to attribute the change in bowel habits to cancer ($9.7\%$, vs. $15.9\%$; adjusted OR: 0.55, $95\%$ CI: 0.36–0.85). Diabetic participants were also less likely to attribute the change in bowel habits to other bowel diseases ($8.1\%$ vs. 12.5 %; adjusted OR: 0.57, $95\%$ Cl: 0.35–0.91) and to more often think that this symptom could be due to medications ($7.5\%$ vs. $2.5\%$; OR: 2.48, $95\%$ Cl: 1.32–4.66) (Figure 1).
No significant differences between participants with and without diabetes were found in the case of rectal bleeding (vignette 1), with the most frequently mentioned symptom attributions being haemorrhoids ($29.7\%$) and cancer ($29.3\%$) (details in Supplementary Materials SB). Multivariable logistic regression odds ratios for symptom attribution for vignettes 1 and 3 are reported in Supplementary Materials SC.
## 3.3. Intended Help-Seeking
The most frequently reported action in case the participants were to experience a change in bowel habits (vignette 2) was “wait and see what happens”, irrespective of diabetes status ($88\%$ in both diabetic and nondiabetic participants). Among participants with diabetes, this was followed by mentioning the symptom to the GP if being seen for something else ($78.5\%$ with diabetes, $70.9\%$ participants without diabetes).
In the case of rectal bleeding (vignette 1) or co-occurrence of CRC symptoms with numbness/pain in the feet (vignette 3), $91\%$ of participants with diabetes would mention these symptoms to the GP if seeing them for something else. Among participants without diabetes the majority would look up these symptoms online ($88.4\%$ for vignette 1, $90.8\%$ for vignette 3).
Compared with those without diabetes, participants with diabetes were more likely to contact the GP to seek help when experiencing rectal bleeding ($41\%$ versus $18\%$, adjusted OR: 1.62, $95\%$ CI: 1.02–2.55) or a change in bowel habits ($27\%$ versus $7\%$ adjusted OR: 1.70, $95\%$ CI: 1.13–2.57) (Table 2). Diabetes was not associated with help-seeking when potential cancer symptoms co-occurred with numbness/pain in the feet (vignette 3), (adjusted OR: 1.28 $95\%$ CI: 0.72–2.28).
In all scenarios, an awareness that the symptom might be linked to cancer was associated with an increased likelihood of seeking help from a GP (rectal bleeding OR: 2.52, $95\%$ CI: 1.76–3.61; change in bowel habits OR: 2.79, $95\%$ CI: 1.92–4.04; co-occurrence of rectal bleeding, change in bowel habits, and numbness/tingling in the feet: OR: 3.90 $95\%$ CI: 2.43–6.25).
## 3.4. Diabetes Condition Management
Among the subgroup of participants with diabetes, we examined the likelihood of seeking help from a GP or a nurse for any of the CRC symptoms described in the vignettes, by self-reported diabetes management. Not having an annual check-up reduced the likelihood of help-seeking (adjusted OR: 0.23; $95\%$ CI: 0.10–0.50), as did self-perceived poor diabetes control (adjusted OR: 0.20; $95\%$ CI: 0.86–0.47) (Table 3). Higher than recommended HbA1c increased the odds of seeking help (adjusted OR: 3.7; $95\%$ CI: 1.36–10.50).
## 3.5. Willingness/Attitudes to Undergo Diagnostic Investigations for Symptoms
Among the total study sample, $98.4\%$ were willing to take a stool test, and $95.6\%$ were willing to have a colonoscopy in case they experienced rectal bleeding or a change in bowel habits, with no differences between those with or without diabetes.
At multivariable analysis there was no significant difference in the likelihood of taking part in diagnostic testing by diabetes status, adjusting for a history of stool test or colonoscopy/sigmoidoscopy, age, sex, ethnicity, and comorbidity number. Having a history of stool test (OR: 0.16; $95\%$ CI: 0.04–0.35) or colonoscopy (OR: 0.28; $95\%$ CI: 0.12–0.63) decreased the likelihood of being willing to have a test in the case of symptoms, compared with never having been tested. Men were less likely to be willing to have a colonoscopy compared with women (OR: 0.37; $95\%$ CI: 0.19–0.72) (details in Supplementary Materials SD).
In those unwilling to undergo investigations, the predominant reason was embarrassment in having a stool test ($19\%$) or a colonoscopy ($26.7\%$) (further details in Supplementary Materials SE).
## 3.6. Patient Prioritisation of Symptoms When Communicating with the GP
When examining symptoms mentioned as the priority during the GP consultation, significant differences were found by diabetes status: a lower proportion of the diabetic participants mentioned rectal bleeding as the first priority compared with the nondiabetic participants ($65.6\%$ versus $77.0\%$, $$p \leq 0.004$$), whilst a higher proportion of the diabetic participants prioritised numbness/pain in the feet ($24.2\%$ versus $13.3\%$, $$p \leq 0.001$$). Change in bowel habits was considered a priority by a minority among both the diabetic and nondiabetic participants ($9.9\%$ versus $10.3\%$, $$p \leq 0.871$$). ( Full result in Supplementary Materials SF)
## 4.1. Main Findings and Comparison with the Literature
Individuals with diabetes compared with those without were less likely to attribute possible CRC symptoms, such as a change in bowel habits, to cancer and more likely to think it might be caused by medications. When seeing their doctor, diabetic individuals were also more likely to prioritise concerns related to their chronic condition rather than discuss typical red-flag CRC symptoms such as a new-onset change in bowel habits. Diabetic individuals not regularly attending healthcare were less likely to seek help if experiencing red-flag cancer symptoms.
Participants did not provide details on specific medicines they considered possibly linked to the change in bowel habits. However, metformin is a cornerstone of diabetes treatment [30,31], and it can lead to changes in bowel habits as a common side effect [12]. This might explain the patients’ interpretation of the symptom as being due to an inconsequential cause rather than cancer, in line with the “alternative explanation” hypothesis. Nonrecognition of cancer alarm symptoms can delay help-seeking [19,32], and comorbidities providing plausible “alternative explanations” have been previously associated with advanced-stage cancer diagnosis [33].
In contrast, having a chronic condition might also lead to more frequent healthcare contacts and opportunities to report possible cancer symptoms to the doctor, in line with the surveillance mechanism [19]. This is supported by the present study, indicating that compared with those without, individuals with diabetes would more likely seek help from a GP, and they would mention a change in bowel habits when attending for other reasons. However, the study shows that this was not the case when cancer symptoms co-occur with diabetic complications. In these circumstances, patients prioritised the symptoms of diabetic neuropathy over the cancer alarm symptoms when communicating with the doctor. This supports the competing demands theory [34] and might at least partially explain why some patients with chronic conditions might have a higher risk of diagnostic delays or advanced stage or emergency cancer diagnosis [33,35] despite increased GP consultations.
Diabetic patients with no annual checks or poor self-management who may have lower levels of primary care use were also found to be significantly less likely to seek help in the current study. This is in line with some previous evidence of an association between poorly controlled diabetes and late-stage cancer [8,36].
Whilst a high percentage of participants were willing to have stool tests and colonoscopies, those who had a history of previous testing were less willing to take part again, even if experiencing red-flag symptoms such as rectal bleeding or changes in bowel habits. Past research found that an all-clear result might lead to a false sense of security and over-reassurance, which can subsequently decrease the likelihood of help-seeking or prompt investigations if the same alarm symptom were to reoccur [37].This may possibly lead to missed opportunities for a prompt diagnosis.
## 4.2. Implications for Research and Practice
Despite being at an increased risk of developing CRC, people with diabetes have a lower likelihood of attributing typical CRC alarm symptoms to cancer and of prioritizing communication of cancer symptoms during a medical consultation. It is important for cancer awareness campaigns [38] to target people with common chronic conditions associated with a higher risk of cancer, promoting earlier symptom recognition and appropriate communication of new symptoms to the doctor. Direct questions and symptom elicitation focusing on red-flag symptoms during medical visits performed for diabetes management could also be useful, possibly adding key CRC screening questions to the Quality and Outcomes Frameworks (QOF). This might ensure it is proactively elicited at annual checks regardless of patient prioritisation. The study also stresses the importance of dedicating specific attention to patients with diabetes who are not likely to attend health care regularly and/or are likely have poor glycaemic control. Additional QOF incentives could be offered for practices able to engage with patients who previously missed diabetic reviews. Patient awareness and understanding of the common side effects of medication such as metformin could also be increased, as well as emphasising the importance of help-seeking if patients experience persistent changes in bowel habits. As part of the medication reviews of patients on metformin, questions regarding possible changes of bowel habits could be introduced.
A recent study reported significantly longer intervals from first symptomatic presentation in primary care to investigations and cancer diagnosis for patients with pre-existing conditions such as diabetes [35]. Future research could explore factors contributing to longer primary care and diagnostic intervals, including whether clinical decision making on referrals for patients presenting with possible cancer symptoms varies by diabetes status.
## 4.3. Strengths and Limitations
The study used online vignettes, a methodology which has an established record in diagnostic research for elucidating the cognitive and attitudinal drivers of behaviour. The methodology allows for the manipulation of conditions in standardised ways and has been shown to be an important methodological tool when it would be impossible to manipulate symptoms, conditions, and comorbidities to investigate the topics using other methods [39,40]. The inclusion of open-ended questions allowed for better understanding of the participant perspective, gaining insights into how they would communicate sensitive issues to their GP. A further strength is a high survey completion ($88\%$).
An inherent limitation of vignette studies is that they examine intended rather than actual behaviour. The symptoms are simulated, and pain, discomfort, and worry about symptoms are not necessarily experienced in the same way as in real life. Whilst intention to act does not automatically equate to a behaviour actually occurring, expressing intentions is an important preliminary step in producing the behaviour.
## 5. Conclusions
The study found that compared with those without, people with diabetes are less likely to attribute red-flag CRC symptoms such as changes in bowel habits to cancer, and they are more likely to attribute them to medications. Individuals with diabetes are also less likely to prioritise the reporting of possible new-onset cancer symptoms over diabetes-related symptoms during medical encounters. Interventions are needed encouraging healthcare visits for individuals not regularly attending healthcare despite their chronic morbidity. The findings can inform cancer awareness campaigns and clinical guidelines, targeting individuals with common conditions such as diabetes who are at higher risk of CRC, in order to help diagnose cancer earlier.
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|
---
title: 'Urinary ACE Phenotyping as a Research and Diagnostic Tool: Identification
of Sex-Dependent ACE Immunoreactivity'
authors:
- Alexander J. Kozuch
- Pavel A. Petukhov
- Miklos Fagyas
- Isolda A. Popova
- Matthew O. Lindeblad
- Alexander P. Bobkov
- Armais A. Kamalov
- Attila Toth
- Steven M. Dudek
- Sergei M. Danilov
journal: Biomedicines
year: 2023
pmcid: PMC10045976
doi: 10.3390/biomedicines11030953
license: CC BY 4.0
---
# Urinary ACE Phenotyping as a Research and Diagnostic Tool: Identification of Sex-Dependent ACE Immunoreactivity
## Abstract
Background: Angiotensin-converting enzyme (ACE) is highly expressed in renal proximal tubules, but ACE activity/levels in the urine are at least 100-fold lower than in the blood. Decreased proximal tubular ACE has been associated with renal tubular damage in both animal models and clinical studies. Because ACE is shed into urine primarily from proximal tubule epithelial cells, its urinary ACE measurement may be useful as an index of tubular damage. Objective and Methodology: We applied our novel approach—ACE phenotyping—to characterize urinary ACE in volunteer subjects. ACE phenotyping includes [1] determination of ACE activity using two substrates (ZPHL and HHL); [2] calculation of the ratio of hydrolysis of the two substrates (ZPHL/HHL ratio); [3] quantification of ACE immunoreactive protein levels; and [4] fine mapping of local ACE conformation with mAbs to ACE. Principal findings: In normal volunteers, urinary ACE activity was 140-fold less than in corresponding plasma/serum samples and did not differ between males and females. However, urinary ACE immunoreactivity (normalized binding of 25 mAbs to different epitopes) was strongly sex-dependent for the several mAbs tested, an observation likely explained by differences in tissue ACE glycosylation/sialylation between males and females. Urinary ACE phenotyping also allowed the identification of ACE outliers. In addition, daily variability of urinary ACE has potential utility as a feedback marker for dieting individuals pursuing weight loss. Conclusions/Significance: Urinary ACE phenotyping is a promising new approach with potential clinical significance to advance precision medicine screening techniques.
## 1. Introduction
Angiotensin I-converting enzyme (ACE, CD143) is a Zn2+ carboxydipeptidase that plays key roles in the regulation of blood pressure and in the development of vascular pathology. ACE is constitutively expressed on the surface of endothelial cells, absorptive epithelial and neuroepithelial cells, and cells of the immune system (macrophages, dendritic cells) [1,2]. Circulating blood ACE originates from endothelial cells [3], primarily lung capillary endothelium [4] by proteolytic cleavage [5]. In healthy individuals, blood ACE levels remain very stable throughout an individual’s lifetime [6], whereas in granulomatous diseases (e.g., sarcoidosis) and Gaucher’s disease, blood ACE activity is significantly increased [7].
Especially high expression of ACE (even higher than in lung endothelial cells) was found within the brush border of the proximal tubular cells of the kidney [8,9,10]. ACE was found in the tubular fluid along the whole nephron but contained significantly different amounts of ACE [11], which was also species-specific [10]. It is also necessary to mention that ACEs in different compartments of tubular fluid are different: in the peripheral blood, in the blood of glomerular efferent or afferent arterioles, and in capillaries ACE originates (sheds) from capillary endothelial ACE, whereas ACE in tubular fluid originates mainly from proximal tubules epithelial cells. Moreover, local ACE conformation from endothelial cells and from epithelial cells is different, which could be distinguished by a set of mAbs to different epitopes of ACE [12,13].
ACE has a high molecular mass (170 kD) and is not permeable during glomerular filtration. Thus, the ACE activity of urine [14,15] likely derives mainly from proximal tubular cells, suggesting the potential usefulness of urinary ACE measurement as an index of tubular damage [16]. The COVID-19 pandemic has provided an additional stimulus for improved detection of renal tubular damage. The kidney is a key organ affected by SARS-CoV-2 [17], and a significant proportion of autopsy specimens have moderate to severe renal tubular damage in fatal COVID-19 infection [18]. However, studies of urinary ACE are limited. This is in part because its activity in urine is very low, the accuracy of the present standard assays is not proven, and no reliability criteria have been specified [19]. In addition, mammalian urine contains naturally occurring angiotensin-converting enzyme inhibitors [15]. Nevertheless, prior studies have demonstrated a threefold increase in urinary ACE in patients with upper urinary tract infections and renal calculi [16] and a three- to fivefold increase in patients with chronic glomerulonephritis and nephrotic syndrome [20]. Elevated ACE excretion into urine was also found in diabetic patients with overt nephropathy [21], and even different fragments of the N domain of ACE were found [22].
We recently developed a novel approach to characterize ACE in tissue and blood—ACE phenotyping—which may be useful for improved characterization of urinary ACE in patients with nephropathy. This ACE phenotyping approach includes only [1] determination of ACE activity (with two substrates (ZPHL and HHL); [2] calculation of a novel kinetic parameter-ratio of hydrolysis of these two substrates (ZPHL/HHL ratio) [23]; [3] quantification of ACE immunoreactive protein levels [24]; and [4] ACE conformation mapping with a unique set of mAbs to ACE—reviewed in [13].
In the first stage of this project, we applied ACE phenotyping to characterize urinary ACE in healthy volunteers. Unexpectedly, we found dramatic differences in immunoreactivity of male and female urinary ACEs that may be explained by sex-specific differences in kidney ACE glycosylation and/or sialylation. In addition, this ACE phenotyping approach was able to identify ACE outliers using only urine. Thus, urinary ACE phenotyping is a promising new approach with potential clinical significance to advance precision medicine screening techniques, specifically as a new research and diagnostic tool to study renal tubular damage.
## 2.1. Chemicals
ACE substrates, benzyloxycarbonyl-L-phenylalanyl-L-histidyl-L-leucine (Z-Phe-His-Leu) and hippuryl-L-histidyl-L-leucine (Hip-His-Leu) were purchased from Bachem Bioscience Inc. (King of Prussia, PA, USA). Other reagents (unless otherwise indicated) were obtained from Sigma-Aldrich (St. Louis, MO, USA). AM-15 and AM-4 ultrafiltration membranes (cut-off Mr 3000 and 100,000, respectively, were from Merck Millipore Ltd. (Cork, Ireland), and dialysis cassettes (cut-off 10,000) were from Thermo Scientific (Rockford, IL, USA).
## 2.2. Antibodies
Antibodies used in this study include a set of 25 mAbs to human ACE, recognizing native conformation of the N and C domains of human ACE [25,26].
## 2.3. Study Participants
The collection of human samples was approved by the Ethics Committee of the University of Debrecen (Hungary) as described in detail previously [27]. All corresponding procedures were carried out in accordance with institutional guidelines and the Code of Ethics of the World Medical Association (Declaration of Helsinki). All volunteers and patients provided written informed consent to have different human tissue samples for ACE characterization.
## 2.4. Urines
Urine was from 11 apparently healthy volunteers (primarily laboratory staff) (5 men, mean age 40.2 ± 20.1 years, and 6 women, mean age 41.0 ± 19.7 years) without any known renal disorder, as judged by a normal diuresis and normal protein content (less than 30 ug/mL). One volunteer (male, 51 y.o.) has had diabetes mellitus (type 2) since 2013 without nephropathy.
Freshly collected morning urine (200 mL) from the volunteers was centrifuged at 2000 g for 3 min (to collect cells shed into the urine). Then, the pH of the supernatant (urine without cell pellet and debris) was measured (the range of the pH was from 5.3 to 6.6) and adjusted to neutral values (7.4–8.0) using 1M Tris-HCl, pH 8.0 or 1.4M NaOH, in order to minimize the action of the proteolytic enzymes), followed by concentrating the samples 30-fold (using filtration of urine by centrifugation at 4000× g) on a filter (Amicon-15, Millipore) with 3 kD pores. Next, 0.5 mL of 30X concentrated urine samples were dialyzed overnight (at +4C) against PBS with 10 μM of Zn2+ (for determination of ACE activity with two substrates), and the remainder was used for immunological characterization (ACE activity precipitation by numerous mAbs to ACE).
In some experiments 30-fold-concentrated urine (5 mL) was further concentrated another 10-fold using AM-4 filters with pores 100 kD, and then the volume (0.5 mL) was restored to the initial level (5 mL). Thus, we decreased the concentration of all proteins in the urine (with molecular weight up to 100 kD) by 10-fold (100–kD depletion experiments).
Tissue processing (lung, heart, and lymph nodes from 9 donors for each tissue) for further determination of ACE activity and immunoreactive ACE protein was performed as described in detail previously [27].
## 2.5. ACE Activity Assay
ACE activity in serum/plasma was measured using a fluorimetric assay with two ACE substrates, 2 mM Z-Phe-His-Leu or 5 mM Hip-His-Leu [28]. Calculation of the ZPHL/HHL ratio [23] was performed by dividing the fluorescence of the reaction product produced by the ACE sample with ZPHL as a substrate by that with HHL as a substrate.
## 2.6. Immunological Characterization of the Blood ACE
Another approach for the quantification of ACE levels, even in the presence of ACE inhibitors, EDTA, or pigments/fluorochromes, is to use an immunoassay (in which ACE initially is captured from biological fluids by antibodies). Several of these immunoassays have been developed, including radioimmunoassay or variations of immunoassays with polyclonal and/or monoclonal antibodies. However, these immunoassays have limited general utility [24].
In order to quantify the amount of immunoreactive ACE protein in the urine, we applied another version of immunoassay, in which native, catalytically active ACE from urine samples was captured by anti-ACE mAbs that recognize conformational epitopes on the surface of ACE molecules. Next, after washing away the unbound ACE (and all components of urine, including possible ACE inhibitors and pigments), precipitated ACE activity was quantified directly in the wells of microtiter plates by fluorometry after adding the substrates ZPHL or HHL [24,28].
Microtiter (96-well) plates (Corning, Corning, NY, USA) were coated with anti-ACE mAbs via goat anti-mouse IgG (Invitrogen, Rockford, IL, USA) bridge and incubated with plasma/serum/lung ACE samples. After washing unbound ACE, the level of ACE immunoreactive protein was quantified as described previously using the strong mAb 9B9 [24,28]. Conformational fingerprinting of ACE was performed as described using a set of mAbs to different epitopes of ACE [25].
## 2.7. Whole Exome Sequencing
Genomic DNA was obtained from cell pellets after centrifugation (3000× g) of urine of tested subjects (#1A and #9Q), or from saliva using a QIAamp DNA Mini Kit (Qiagen, Valencia, CA, USA). Whole exome sequencing (WES) and bioinformatic analysis of the sequencing data were carried out by Novogene (Sacramento, CA, USA).
## 2.8. Computational Analysis of the Dimer Model with Human Serum Albumin (HSA)
The dimer model of somatic ACE was prepared as described previously [29]. The missing portion of the ACE protein between the C and N domains was reconstructed using the Linker Modeler module in a Molecular Operating Environment (MOE, https://www.chemcomp.com/index.htm, accessed on 1 May 2022). Asn residues in the ACE protein were glycosylated with N-acetylglucosamine in MOE. Human Serum Albumin (HSA) X-ray structure 1E78 [30] was downloaded from Protein Databank. The buffer components were removed from the HSA model. Both the ACE dimer and the HSA proteins were subjected to the “structure preparation” procedure using AMBER14EHT forcefield implemented in MOE [31,32]. Hydrogen atoms were added using the Protonate 3D algorithm in MOE.
For protein–protein docking, the ACE dimer and the HSA protein were used as a receptor and a ligand, respectively. The default settings were used for all the docking. The “Patch Analysis” option was used to account for the hydrophobic patch potential. Post-placement refinement was conducted using the “rigid body” option. One of the top models where the HSA protein is close to epitopes 1E10, 5F$\frac{1}{2}$D1, and 1B$\frac{8}{3}$F10 was selected for further analysis in PYMOL [33].
## 2.9. Statistical Analysis
Values of ACE activity with different substrates for each individual, as well as other parameters characterizing the ACE phenotype, were means ±SD from 2 to 5 independent experiments (depending on the individual) with triplicates. Significance was analyzed using the Mann–Whitney test.
## 3.1. ACE Activity in Human Urine Samples
Despite the fact that ACE is highly expressed in renal proximal tubules [10], ACE activity/levels in the urine are 50–300-fold lower than in the blood (depending on the method of measurement [14,19,23,34]). In some studies, ACE activity in control urine was not detected at all [19]. Therefore, enzymatic activity measurement is difficult to perform without a prior concentration of urine and ultrafiltration/dialysis to remove endogenous ACE inhibitors [15,19] and pigments that interfere with the measurement of fluorescence [23].
Therefore, we collected urine samples (200 mL) from 16 volunteers (primarily laboratory staff), centrifuged them (to collect cells shed into the urine), and adjusted their pH to neutral values. They then were concentrated 30-fold (using filtration by centrifugation) on filters with 3 kD pores. Next, the concentrated urine samples were dialyzed overnight (at +4C) against PBS with 10 μM of Zn2+. ACE activity then was measured in $\frac{1}{5}$ diluted samples (to decrease fluorescence quenching) using a fluorimetric assay with two artificial (short) substrates-ZPHL and HHL [23,28].
Urinary ACE activity in our cohort of five apparently healthy males and six females (Figure 1) varied by sixfold for both substrates.
These results are similar to the three- to sixfold differences in urine ACE activity previously reported in early studies in apparently healthy subjects [14,34] and they are more variable than serum ACE activity in multiple previous reports (threefold differences) [6,24,28]. Other prior studies have reported three- to fivefold increases in urinary ACE activity in patients with upper urinary tract infections and renal calculi [19] in patients with chronic glomerulonephritis and nephrotic syndrome [20] and in diabetic patients with nephropathy [21]. Because of this huge range in normal values of urinary ACE activity, and an even greater range in patients with different renal pathologies [34], measurement of urinary ACE activity has been of limited clinical significance and primarily used only for research purposes to characterize differences between various groups of patients.
As in early studies [14,19], we did not find significant differences in urinary ACE activity between men and women (Figure 1A,B). This observation supports the interpretation that urinary ACE is derived mainly from kidney tubular cells, without any urinary contamination from gonadic and prostatic ACE. These data confirm previous conclusions [19]. No sex-specific differences in ACE activity were previously reported in human lung and heart tissues [27] or in human plasma/serum in numerous studies [35,36].
Comparison of the absolute values of ACE activity in the urine samples (Figure 1A,B) with the ACE activity in available plasma from four volunteers (Figure S1A,B) demonstrated that urinary ACE activity is approximately 140-fold less than in the plasma/serum. Because ACE expression in the kidney proximal tubule epithelial cells is very high [8,9,10] and comparable with the level of ACE expression in lung endothelial cells in humans (and even several-fold higher in some species [37]), we may hypothesize that very low ACE activity in the human urine may be explained by [1] a significant decrease in the expression of an unidentified ACE secretase in the proximal tubules epithelial cells in comparison to that in endothelial cells, and/or by [2] the presence of strong inhibitors of ACE shedding precisely in the proximal tubule epithelial cells. Promising candidates for such inhibitors of ACE shedding include lysozyme and bilirubin, which both have very high local concentrations in the proximal tubules due to the effective glomerular filtration and tubular reabsorption of these substances [38,39]. The binding of these compounds to ACE fixes its conformation and prevent excessive ACE shedding [40].
We next calculated an important catalytic parameter of ACE-the ratio of the rates of hydrolysis of two ACE substrates, ZPHL and HHL (ZPHL/HHL ratio [23] and found that this ratio was substantially decreased in urinary sample #9Q (Figure 1C). The two domains of ACE hydrolyze a range of natural and synthetic substrates with different efficiencies [23,41]. Two synthetic substrates, ZPHL and HHL, are used for the determination of ACE activity in laboratories worldwide. These two substrates display some contrasting enzymatic properties: the C domain of human ACE hydrolyzes HHL at a much faster rate (ninefold) than the N domain, whereas ZPHL is hydrolyzed at a similar rate by both domains. As a result, their ratio is characteristic of different forms of ACE [23,41]. The selective inactivation or inhibition of the C-domain in somatic ACE increases this ratio to higher values more characteristic for the N-domain, whereas selective inactivation/inhibition of the N-domain in somatic ACE decreases the ratio toward lower values predicted for the C-domain. The absolute value of the ZPHL/HHL ratio for urinary ACE in subject #9Q is $55\%$ of that for the “gold standard” (urine sample from healthy male, 22 y.o. # 1A—first author of this paper) (Figure 1C) and represents the lowest ZPHL/HHL ratio we have determined in more than 800 tested plasma/serum samples over many years. Therefore, this very low value of the ZPHL/HHL ratio for the urinary ACE in subject #9Q suggests that certain amino acid residues either close to or in the N domain active center of ACE may be altered by a mutation.
Previously, we described another patient with a similar, but less pronounced decrease in the ZPHL/HHL ratio (by 33 %), which carried an ACE mutation in the N domain of ACE-S333W [41]. This parameter, ZPHL/HHL ratio, was increased in the urine of male volunteer #10S, who was taking 10 mg of the ACE inhibitor lisinopril/day (Figure 1C and Figure S1C), as well as in another male volunteer-#14D (Figure S1C). This increase indicates that ACE in concentrated urine is still able to bind some low molecular weight ACE inhibitors, despite the dialysis that occurs during sample processing.
## 3.2. Immunoreactivity of ACE in Human Urine
The levels of ACE immunoreactive protein were first determined in a pilot experiment using a set of mAbs to ACE to analyze urine samples from three females and one male, and the results were absolutely unexpected. Precipitation of urinary ACE (normalized for ACE activity) from all three female subjects was significantly higher than precipitation of urinary ACE from the male subject (55 y.o.) for 4 mAbs to the N domain (out of 8 mAbs tested), and it was significantly lower for all eight tested mAbs to the C domain (Figure S2). To further study this possible sex-specific difference in local ACE conformation, we next compared the pattern of mAbs binding to male and female urinary ACE using the entire set of 25 mAbs to the epitopes on the N and C domains [26] of somatic ACE (Figure 2A,B). The binding of 20 mAbs (out of the 25 tested) to urinary ACE from seven apparently healthy women (from 18 to 69 y.o.) was dramatically different in comparison to the “gold standard” of urinary ACE from the 22 y.o. healthy male (Figure 2A-green bars), confirming the previous results obtained from another male (Figure S2).
In the series of papers from Casarini’s lab, the presence of the N domain fragment(s) of ACE in the concentrated human urine was demonstrated [11], similar to the N domain fragment found naturally in the ileal fluid [42] or after treatment of purified ACE with exogenous proteases [43] or in concentrated urine by endogenous proteases [23]. However, our results with preferential binding of some mAbs to female urinary ACE could not be explained by the presence of N fragment(s) in the female urine, because in that case an increase in mAbs-binding would be seen for all mAbs directed to the epitopes on the N domain, whereas differences in mAbs binding were strongly epitope-specific but not domain-specific—Figure 2A. Moreover, experiments in Figure 2 were performed on urine concentrated 30-fold using a 3 kD filter, thus retaining putative N fragments.
One practical conclusion from Figure 2A is that the binding of several mAbs (3A5, i2H$\frac{5}{2}$D7, 4F4-grey bars) to urinary ACE was not gender-dependent. Therefore, these mAbs could be used for the quantification of urinary ACE levels regardless of gender. In contrast to the observations in female urine, the binding of these 25 mAbs was much more consistent for urinary ACE from five apparently healthy males from 22 to 68 y.o. ( Figure 2B). Of note, the binding of mAbs to urinary ACEs in Figure 2 was normalized by ACE activity precipitation by the strongest mAb 9B9 [26,44,45], which avoids an additional determination of ACE activity in the tested urines, and thus significantly increases accuracy and reproducibility of the repeated determinations. Indeed, inter-individual variations of mAb binding exist (Figure 2B) because ACE mutations have been identified for almost every amino acid residue in ACE molecule (the NCBI SNP database for ACE https://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId = 1636, accessed on 1 May 2022). These mutations may influence some mAb binding in some patients [26,46], but the gender differences in mAb binding to urinary ACE still appear to be very significant (Figure 2A,B).
Especially impressive sex differences in mAbs binding (i.e., in local ACE conformation) were revealed when we compared the binding of mAb 2D1 (which has an epitope on the N domain) and mAb 2H9 (epitope on the C domain of ACE) [26] to urinary ACE from male and female samples (Figure S3A,B). The calculated parameter of 2H$\frac{9}{2}$D1 binding ratio was 10-fold higher for four urinary ACE samples from randomly selected males than for four urinary ACE samples from randomly selected females (Figure S3C). Using only the very limited number of the cells that could be sedimented from urine samples of apparently healthy males and females (including renal tubular epithelial cells that highly express ACE) [47,48], and the correspondingly limited ACE activity in these cell lysates, we demonstrated that differences in the binding of some mAbs to male and female ACEs (i.e., representing the local ACE conformation) are retained for membrane-bound ACE in these cell lysates (Figure S3D–F).
We previously demonstrated that changes in the mAb binding pattern to different ACEs were determined by alterations in ACE glycosylation/sialylation that occur in different organs/tissues—i.e., the concept of ACE conformational fingerprinting [13,49]. Interest in sex-dependent glycosylation of proteins initially derived from observations regarding sexual dimorphism in autoimmunity. The frequency and severity of various infectious diseases are higher in males than in females, suggesting that females may have a stronger immune response. The potential detrimental effect of this sex-dependent phenomenon is that females are more likely to develop many autoimmune diseases, such as systemic lupus erythematosus (SLE), Grave’s disease, Hashimoto’s thyroiditis, multiple sclerosis, rheumatoid arthritis and scleroderma [50]. One initial hypothesis was that differences in IgG glycosylation between males and females may contribute to sex dependence in autoimmune diseases. Later, it was observed that galactosylation of IgG is controlled by estrogen, and IgG-dependent inflammation differs in males and females [51]. Sex-associated differences in N-glycosylation were identified not only in IgG from whole serum, but also in IgG from human salivary glycoproteins [52], in whole murine [53], and human plasma proteins [54] in snake venom [55] and in insects [56]. Sex-specific differences in N glycosylation are also present in individual proteins such as AQP1 [57] and glycodelin [58]. However, the differential glycosylation of glycodelin in human amniotic fluid and in seminal fluid may be an example of tissue-specific glycosylation, rather than sex-specific, a similar effect as in prostate-specific glycosylation of ACE [13,49]. In contrast, the dramatic difference in the pattern of mAb binding (i.e., conformational changes likely reflecting glycosylation) in urinary ACE from males and females (Figure 2A,B and Figure S2) is an example of a pure sex-dependent effect because it occurs on ACEs originating from the same tissue (human kidney). Recently, 1016 N-glycoproteins and 2192 N-glycopeptides were identified in urine by liquid chromatography–mass spectrometry (LC-MS), and their abundance spanned across approximately five orders of magnitude. In females, 175 N-glycoproteins were significantly down-regulated (fold change >4) and 31 significantly up-regulated compared to males [59]. The sex-specific differences in urinary ACE that we identify here are rather qualitative (differences in glycosylation of certain glycosylation sites) and likely not related to differences in total ACE protein abundance.
We also calculated the 2H$\frac{9}{2}$D1 binding ratio as a marker for the putative sex difference in ACE (Figure S3C) from ACE derived from different human tissues (Figure 3). This ratio (which likely indicates the extent of sialylation of ACE at position Asn45) was tissue specific. The highest ratio was found for serum ACE: approximately 20, which reflects the known high extent of sialylation of blood ACE [60]. The lowest ratios (~3) were observed for ACE derived from the lung and lymph nodes, whereas a higher ratio (~6) was present for heart ACE (Figure 3A). This ratio did not differ as dramatically between male and female ACE in these tissues (Figure 3B) as it did for urinary ACE, in which the 2H$\frac{9}{2}$D1 binding ratio was eightfold higher in males than in females (Figure S3).
Nevertheless, some gender differences are evident in these tissues. The 2H$\frac{9}{2}$D1 ratio was significantly lower for ACE in male heart tissue than from female heart tissue, whereas the opposite pattern was observed for serum ACE in which male samples were higher than female samples (Figure 3B). In the 10 male and 12 female sera samples analyzed here, the increased 2H$\frac{9}{2}$D1 binding ratio observed in male sera was statistically significant (Figure 3C). However, the sex differences in these tissues (heart and serum) were less pronounced than for urinary ACE (which represents kidney ACE).
It is not surprising that these dramatic differences in mAb binding to female compared to male urinary ACE are almost completely absent in serum ACE (Figure 2C). Endothelial ACE that is shed from the cell surface into the blood almost immediately passes through the liver, where multiple glycoforms of ACE that are without terminal galactose or sufficient sialic acids are trapped by asialoglycoprotein receptors [61]. In contrast, ACE shed into urine from proximal epithelial cells does not leave the renal tubules [62,63] does not contact any biological filters, and thus retains the huge diversity of its glycoforms. Nevertheless, few mAbs to the N domain (6A$\frac{12}{1}$G12) can distinguish male and female ACE even in plasma (Figure 2C), which may indicate that not only sialylation, but also glycosylation itself may be responsible for differences in the binding of these mAbs.
Since the glycosylation of proteins in general, and ACE in particular (having 17 potential glycosylation sites [64]), is involved in proper protein folding, protein quality control, transport of proteins to specific organelles, and sensitivity to shedding [65,66,67], the sex-specific differences in tissue ACE glycosylation that we have identified may be associated with differential disease involvement. Recently, increased female susceptibility to Alzheimer’s disease was found in the carriers of the novel ACE mutation R1279Q [68] which may be explained by significant differences in the conformation of ACE in male and female tissue ACEs (Figure 2, Figures S2 and S3). Based on our data, the increased female susceptibility to Alzheimer’s disease in the carriers of ACE mutation R1279Q may be due to direct structural differences between male and female brain ACE (i.e., due to differences in glycosylation of male and female ACE), or indirectly due to functional differences in male and female brain ACE caused by differential interactions with putative ACE-binding proteins (or substrates) that result from structurally different male and female ACE in the brains of patients with this mutation. We also can not exclude the possibility that the prevalence of functional pain syndrome in women [69] partially may be explained by gender differences in tissue ACE that we identified, because the pain mediators substance P and bradykinin are substrates for ACE and are effectively cleaved by ACE [70,71]. Therefore, we speculate that subtle sex differences in the local conformation of ACE in brain neurons may contribute to the increased female prevalence of both disease processes.
## 3.3. Characterization of Urinary ACE
To further characterize urinary ACE, we compared the “conformational fingerprint” patterns of binding of 25 mAbs to different epitopes on the N and C domain [26] for our “gold standard” urinary ACE (from male subject #1A-) and human lung ACE purified from a male (Figure 4). The magnitude of the bars shown in Figure 4A,B reflect affinity binding of the individual mAbs to each type of ACE and are not as informative as the urine ACE/lung ACE binding ratio (Figure 4C), which clearly demonstrated significant differences in the local conformation between urinary ACE (in its relatively natural environment, albeit 30X concentrated) and lung ACE. It is readily apparent from this ratio that changes in mAb binding (i.e., in local ACE conformation) between urinary ACE and lung ACE are more pronounced in the C domain. We calculated the mean % change for mAbs to the N domain and the C domain, and the difference was significant ($80\%$ versus $58.3\%$, $$p \leq 0.025$$). Theoretically, differences in mAb binding to urinary ACE compared to pure lung ACE may be due to at least two reasons: [1] differential ACE glycosylation in the kidney (the source of ACE in the urine) compared to the lung; [2] the possible presence of ACE-binding proteins in the concentrated urine sample that may mask (shield) binding of some mAbs to urinary ACE, similar to what we previously proposed in heart tissues [72], in the spleen [73] and found in the blood (lysozyme) [40].
We speculate that a putative ACE-binding protein is present in urine that binds to the C domain to mask the binding of all mAbs to the C domain and also interferes with the binding of some mAbs to their epitopes on the N domain. To explore this hypothesis, we compared the conformational fingerprint of male and female urinary ACE before and after the depletion of proteins with MW less than 100 kD. Initially, male (#1A) and female (#5B) urine samples were concentrated using Amicon filters with 3 kD pores. Then 30X concentrated urine samples were concentrated further (10-fold) using filtration via 100 kD filters and then diluted with PBS to the initial volume. Therefore, the concentration of proteins with MW less than 100 kD was decreased 10-fold. Such depletion dramatically increased the binding of numerous mAbs with epitopes on the N and C domains of ACE to both male and female urinary ACE samples (Figure S4A,B). The changes in binding of those mAbs which were significantly decreased for urinary ACE in comparison to pure lung ACE (Figure 4C) could be attributed to the presence of relatively large ACE–binding proteins in the concentrated urine masking the N domain and the N terminal part of the C domain (the region of the 1E10 epitope). However, the binding of some mAbs (mAbs 9B9 and 5B3 on the N domain and 3F$\frac{10}{4}$C$\frac{12}{5}$G8 on the C domain) did not change (Figure S4A,B), which indicates that their epitopes were not masked by this putative ACE binding protein(s) in the human urine.
Another important functional conclusion can be made from a comparison of the effect of the depletion of 100 kD proteins on mAb binding to male and female urinary ACE (Figure S4C). The effects of 100 kD depletion on the binding of some mAbs were sex-dependent. Although 100 kD depletion affected mAbs 2H4, 2D7, 6H6, and 1E10 binding to male urinary ACE more dramatically, binding of mAbs i1A8, 3G8, 6C8, and 2H9 to female urinary ACE was more affected after 100 kD depletion. Therefore, the structural differences between male and female urinary ACE proteins that we identified (Figure 2, Figure 4A,B and Figure S2) may result in functional differences by the possible differential modulation of ACE function by putative ACE binding proteins that are present in different tissues.
The effects of depletion of 50 kD and 30 kD proteins from concentrated urine were less pronounced (Figure S5), suggesting that human albumin, which constitutes approximately $40\%$ of all proteins in the urine [74], has a greater influence on urinary ACE conformation (and likely function) than putative ACE binding proteins with MW less than 50 or 30 kD. Therefore, it is logical to suggest that albumin, which is a natural inhibitor of ACE in the blood [75,76], also can bind to urinary (i.e., kidney) ACE and influence its function. This may be especially important in the case of proteinuria when the concentration of albumin in the urine is increased up to 100-fold during nephrotic syndrome or glomerulonephritis [77].
The addition of human serum albumin to ACE purified from human lung (diluted in PBS) decreased binding only for C domain epitope mAbs 1E10, 3F10, and 8H1 (Figure S6). Such an effect is similar to the effects of 100–50–30 kD depletion, or the effects of ACE isolation from different human tissues-Figure 4, S3, S4 in [72], and Figure 10 in [46], on mAb binding. When we modeled the possible binding (docking) of human albumin to two-domain ACEs (Figure 5), we observed that such docking is consistent with the dimer model of two-domain ACE [29]. In this docked model, albumin is bound primarily to the C domain (covering epitopes for mAbs 1E10 and 1B$\frac{8}{3}$F10) while also interacting with the N domain near mAbs 5F$\frac{1}{2}$D1 of the other monomer. The model matches well with changes in mAb binding to ACE in the studies cited above.
This model may have pathophysiological significance. Table S1 lists amino acids found at the interface of the ACE protein and albumin. From WES or WGS data obtained from individuals, we may predict whether putative mutations of ACE or albumin in these individuals may alter (such as decrease) the physiological inhibition of ACE by albumin [76] in the blood or kidney.
How should human urine be correctly concentrated? The first goal of this study was to establish the proper approach for the characterization of urinary ACE. Due to the very low levels of ACE in the urine (140-fold less than in serum), we elected to concentrate urine samples by 30-fold using 3 kD filter pores. This procedure increases ACE protein concentration by 30-fold, but small inhibitors present in the urine [15] will be at the same concentration as in unprocessed urine. As a result, the extent of ACE inhibition by these small inhibitors will be 30-fold less in these urine preparations. However, the concentration of albumin will also be increased in this urine by 3 kD pores, and thus it will still inhibit the activity of urinary ACE since albumin is a natural ACE inhibitor [75,76]. Indeed, our data demonstrate that the immunoreactivity of ACE towards various mAbs is different in urine concentrated using pores 3 kD compared to 100 kD (Figure S4). Therefore, to obtain structural information about urinary (kidney) ACE itself (e.g., changes in glycosylation), it is optimal to concentrate urine using filters with 100 kD pores because such an approach will dramatically decrease the effect of albumin (and other putative ACE binding proteins in human urine) on ACE activity or mAb binding to urinary ACE. In contrast, if the goal is to determine the functional parameters of urinary ACE in a more natural environment and/or estimate the effect of proteinuria on ACE functioning, it may be optimal to concentrate urine using a 3 kD pore filter to retain all possible ACE-binding proteins in the urine sample.
We also compared the immunoreactivity of urinary ACE with plasma ACE, as well as the immunoreactivity of ACE in the homogenates of human kidney and lung tissues. The mAb binding pattern observed for urinary ACE revealed substantial increases in binding to female urinary ACE for most N-domain mAbs and substantial decreases in binding for all mAbs to the C domain in comparison to female blood ACE (Figure S7A). This pattern appears very similar to the sex-specific differences in mAb binding to female and male urinary ACE protein (Figure 2A). Because serum ACE protein is much more highly sialylated than lung ACE [60] we may hypothesize that female urinary ACE is less sialylated in comparison to male urinary ACE. As a result, the binding of most mAbs to the N domain of female urinary ACE is much stronger than to the corresponding areas of male urinary ACE protein (Figure 2A,B). However, the observed decrease in binding of all mAbs to the C domain in female urinary ACE protein likely can be attributed to systemic changes in the folding of its less sialylated C domain.
Significant differences in the conformational fingerprints of ACE in kidney homogenates compared to lung homogenates (Figure S7B) may be attributed to differential glycosylation in these two organs of certain sites in the ACE protein. These include Asn45 and Asn117 (localized in the epitopes for mAb 6H6) and Asn731 (epitope for mAb 1B8). It is a reasonable conclusion that dramatic differences in the binding of some mAbs to different types of ACE can be attributed to differential ACE glycosylation because precipitation of various types of ACE was clearly altered by different lectins. Thus, although ACE from human lung homogenates was more effectively precipitated by lectin RCA (which binds to terminal galactose residues of glycoproteins, including ACE [78]) than ACE from kidney homogenates, kidney ACE activity (as well as urinary ACE activity) was not precipitated by lectin MAA (insert in Figure S7B).
Additional support for the hypothesis that sialylation may determine sex differences in urinary ACE conformation came from the characterization of ACE derived from male volunteer #9Q. This subject demonstrated the unusual kinetic property of a lower value of ZPHL/HHL ratio in his urinary ACE (Figure 1C). We performed conformational fingerprinting of his urinary ACE compared to urinary ACE from our gold standard—subject #1A (Figure S8). Precipitation of urinary ACE from #9Q was dramatically different only for mAbs to the N domain (Figure S8A). These are essentially the same mAbs that discriminated between female and male ACE proteins in the urine (Figure 2A). Thus, urinary ACE from male #9Q exhibits similar characteristics as female urinary ACE (Figure S8B–E).
We next compared the precipitation of ACE activity from these two males by lectin RCA and by several mAbs and observed that RCA precipitation from subject #9Q is very similar to that by mAb 2D1 (which essentially does not bind male urinary ACE). Asn45 and Asn117 are in the epitope for mAb 2D1 (and 5F1) [26]. Therefore, we hypothesize that differences in sialylation of Asn45 and Asn117, as revealed by lectin RCA, determine at least in part the sex-specific differences in the conformation of urinary ACE (Figure S8F).
Western blotting for ACE protein from male (AK) and female (BK) subjects was performed with two different mAbs and demonstrated similar apparent molecular weights for male and female urinary ACE (Figure S9). These data support our hypothesis that male/female differences in ACE primarily reflect differences in the number of sialic acids rather than differences in glycan structure.
## 3.4. Urinary ACE as a Marker of Diet Efficiency?
ACE plasma levels are generally stable in individual adults [6], but blood ACE activities are significantly elevated in hyperthyroidism patients [79]. A short fasting period (12 h) did not change blood ACE [80], whereas a low-caloric diet for 8 weeks (which caused $12\%$ of weight loss) was accompanied by an $11\%$ decrease in serum ACE concentration [81]. Intriguingly, serum ACE activity predicted the maintenance of weight loss better than 30 other blood proteins and three steroid hormones that were analyzed [82]. Indeed, it is not realistic to expect that blood ACE could be used as a predictive marker for the maintenance of weight loss in obese patients during dieting. Therefore, attempts were made to estimate changes in urinary ACE during short periods of dieting over 24 h. The authors interpreted their data to indicate that ACE levels in the urine negatively correlated with weight loss following one day of dieting [83].
We estimated the variability of urinary ACE phenotypes in two volunteers during 4 consecutive days of urine sampling. In contrast to blood ACE [6], urinary ACE is highly variable (Figure 6), with the daily differences in ACE activity or ACE protein varying by up to threefold. Therefore, we hypothesize that under these conditions, even short periods of dieting may influence urinary ACE activity or ACE immunoreactive protein. Thus, these urinary ACE parameters may have clinical potential as an early feedback marker on dieting and weight loss for obese individuals [83].
The amount of ACE immunoreactive protein as determined with three mAbs exhibited excellent correlation with ACE activity ($r = 0.861$, mean for three mAbs) for healthy volunteers (#1A). However, this correlation was practically absent (Figure 6) in volunteer #9Q, which demonstrated an abnormal ACE phenotype (Figure 1C and Figure S8). Therefore, it is optimal to determine both ACE activity and immunoreactive protein levels in order to completely characterize urinary ACE in a given individual.
## 3.5. Characterization of an ACE Phenotype Outlier (via Whole Exome Sequencing-WES)
The use of whole genome (WGS) and whole exome (WES) sequence data from multiple subjects can reveal novel mutations predicted to inactivate genes (e.g., premature stop codons), and subsequent exploration of the mechanistic effects and clinical consequences of these inactivating mutations is a valuable approach for advancing our knowledge of gene function. The combination of comprehensive ACE phenotyping as a screening format and whole exome sequencing (in some cases whole genome sequencing) of outliers may help to resolve many unclear issues of ACE biology. Prior screening studies of a large number of individuals and subsequent deep analysis of outliers for different parameters (proteins) already have produced several unexpected discoveries. Among such examples are those lowering LDL levels (PCSK9), decreasing susceptibility to HIV (CCR5), increasing exercise endurance (ACTN3), and increasing sepsis resistance (CASP12)—reviewed in [84].
In the current study, urinary ACE phenotyping of 15 apparently healthy volunteers identified at least one outlier–male subject #9Q with very low values ($50\%$ of mean) of the catalytic parameter of ACE (ZPHL/HHL ratio) (Figure 1C), as well as a “female-type” conformational fingerprint of ACE (Figure S8). The most straightforward and expected reason for these features would be a mutation in one of the ACE gene alleles. Therefore, we submitted the genomic DNA from this subject #9Q (along with DNA from the “gold standard” (subject #1A) as negative control) for WGS. However, no ACE mutations were identified in this individual (Table S2).
The next hypothesis we considered was that this subject may have a mutation in one of the known ACE binding proteins: chaperone BiP (GRP78), ribophorin 1, protein kinase C [85], albumin [76], lysozyme [40]. If such a mutation is present, then the absence or altered binding of these proteins to ACE may alter the local conformation of ACE, leading to changes in the ZPHL/HHL hydrolysis ratio. However, our sequencing data did not detect any Loss-of-Functions (LoF) mutations in any of these ACE-binding proteins.
Therefore, we are left to hypothesize that an important mutation may be present in an unknown ACE-binding protein. As an initial screen for novel ACE-binding proteins, we subtracted from the list of the mutations found in subject #9Q all the stop-codon and indels (with frameshift) mutations, as well as missense mutations with predicted damaging effects (LoF mutations), from a similar list derived from seven subjects whose WES was performed in our lab [24,86] and subject #1A (this study). The ZPHL/HHL ratios were not changed in this comparison group. This subtraction analysis allowed us to significantly decrease the number of possible candidates for novel ACE-binding proteins from 10,000 + to 400+ candidates (Table S3). However, this initial screen did not definitively identify the possible new ACE-binding protein responsible for the abnormal ACE characteristics observed in subject #9Q. Additional subjects with unchanged ZPHL/HHL ratios must be evaluated by WES and subtraction analysis for unequivocal identification of the hypothesized novel ACE binding protein.
## 4. Conclusions
Urinary samples are simple and inexpensive to obtain and represent a promising new approach for the development of precision medicine screening in ACE-related diseases. Here we demonstrate the potential power and clinical utility of urinary ACE phenotyping by defining novel sex-specific differences in urinary ACE structure and function. These variations are likely due to differential sialylation of the ACE protein, and they provide novel insights into the sex differences observed in some ACE-related diseases. Future work will build upon these insights to further define their precision medicine potential.
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|
---
title: Dynamic changes in marker components during the stir-frying of Pharbitidis
Semen, and network analysis of its potential effects on nephritis
authors:
- Yuman Li
- Yuhe Lu
- Yujie Zhu
- Jingchun Yao
- Haibing Hua
- Jinyang Shen
- Xun Gao
- Kunming Qin
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10045986
doi: 10.3389/fphar.2023.1123476
license: CC BY 4.0
---
# Dynamic changes in marker components during the stir-frying of Pharbitidis Semen, and network analysis of its potential effects on nephritis
## Abstract
Introduction: Pharbitidis Semen (PS) has been widely used in traditional Chinese medicine to treat several diseases such as nephritis. PS is usually stir-fried to enhance its therapeutic efficacy before use in clinical practice. However, the changes in phenolic acids during stir-frying and the mechanisms of their therapeutic effects on nephritis are still unclear.
Methods: Here, we studied the processing-induced chemical changes and elucidated the mechanism of PS in the treatment of nephritis. We determined the levels of the 7 phenolic acids in raw PS (RPS) and stir-fried PS (SPS) using high-performance liquid chromatography, analyzed the dynamic compositional changes during stir-frying, and used network analysis and molecular docking to predict and verify compound targets and pathways corresponding to nephritis.
Results: The dynamic changes in the 7 phenolic acids in PS during stir-frying are suggestive of a transesterification reaction. Pathway analysis revealed that the targets of nephritis were mainly enriched in the AGE-RAGE, hypoxia-inducible factor-1, interleukin-17, and tumor necrosis factor signaling pathways among others. Molecular docking results showed that the 7 phenolic acids had good binding ability with the key nephritic targets.
Discussion: The potential pharmaceutical basis, targets, and mechanisms of PS in treating nephritis were explored. Our findings provide a scientific basis for the clinical use of PS in treating nephritis.
## 1 Introduction
Pharbitidis Semen (PS), the dried seeds of Pharbitis nil L.) Choisy or *Pharbitis purpura* L.) Voigt, a plant of the Convolvulaceae family, is a well-known traditional Chinese medicine (TCM) (Liu et al., 2019). More than 211 chemical components of PS have been reported, including phenolic acids, terpenoids, resin glycosides, fatty acids, alkaloids, and volatile oils. These chemical components have significant pharmacological effects, such as increasing the propulsion rate of the large intestine, promoting gastrointestinal peristalsis, treating nephritis, and expelling parasites, as well as anticancer, phlegm-resolving, anti-inflammatory, and immunity-improving effects (Choi et al., 2015; Xiang et al., 2017). Traditionally, PS is processed by plain stir-baking in accordance with “Lei’s Treatise on the Processing of Drugs,” a guideline book on the processing of TCM to yield active medicines (Gao et al., 2022). The process involves placing clean PS in a pot and stir-baking it in a slow fire until the seeds turn slightly brown. However, to the best of our knowledge, the dynamic phytochemical changes occurring in PS during processing have not been investigated. It has been reported that phenolic acids may be the main marker ingredients, which are effective in the treatment of nephritis (Zhou et al., 2011). Specific phenolic acids such as neochlorogenic acid (5-CQA), chlorogenic acid (3-CQA), cryptochlorogenic acid (4-CQA), caffeic acid (CA), isochlorogenic acid B (3,4-DiCQA), isochlorogenic acid A (3,5-DiCQA), and isochlorogenic acid C (4,5-DiCQA), the contents of these phenolic acids change significantly during stir-frying (Li, 2016). However, the dynamic changes that these compounds undergo during stir-frying are unclear.
Nephritis is a common renal disorder and a leading cause of end-stage renal disease. Nephritis can present in protean ways, with general features including proteinuria, hematuria, renal failure, and hypertension. Recent advances in nephritis management indicate that early therapeutic intervention can lead to the improvement in renal function and the long-term preservation of renal function, or slow the progression to end-stage renal failure in many cases. Several studies have reported the advantages of TCM in the treatment of nephritis, among which PS is a commonly used TCM (Li, 2018). However, the key ingredients and mechanism of PS in treating nephritis are still unclear. In recent years, many researchers have conducted pharmacokinetic studies on the main phenolic acids in PS (Li et al., 2020; Yang et al., 2020) and found that the phenolic acids in PS can be absorbed into the systemic circulation and exert pharmacological effects (Li et al., 2021; Li et al., 2022). More importantly, previous studies have also reported the potential of phenolic acids such as CA and 3-CQA in treating nephritis (Sidhoum et al., 2020; Song et al., 2022).
Systems pharmacology is developed based on the cross-disciplines of directional pharmacology, systems biology, and mathematics, and has the characteristics of integrity and synergy in the treatment process using TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Network analysis is an analytical tool that integrates systems biology, multidirectional pharmacology, computational biology, and other emerging concepts and methods. It has been widely used for the functional analysis of drugs (Zhang et al., 2022). Here, we studied the differences in composition during the stir-frying of raw PS (RPS) and stir-fried PS (SPS) using high-performance liquid chromatography (HPLC) and explored their mechanisms of action in treating nephritis using network analysis and molecular docking. Our findings reveal the material basis and potential efficacy of PS in treating nephritis and provide a scientific basis for the clinical use of stir-fried PS in the treatment of nephritis as well as for the development of novel drugs to treat nephritis (Jin et al., 2021). A flowchart of the study process is shown in Figure 1.
**FIGURE 1:** *Flow chart of the study process.*
## 2.1 Reagents and materials
All chemicals and reagents were of analytical grade, and the solvents used for chromatography were of HPLC grade. Methanol (MeOH) and formic acid were purchased from Shanghai Titan Technology Co., Ltd., Chromatographically pure MeOH was obtained from Sigma-Aldrich (Shanghai) Trading Co., Ltd., and purified water was acquired from Hangzhou Wahaha Group Co., Ltd. The standard chemicals included 3-CQA (batch: PS000627), CA (batch: PS010522), 5-CQA (batch: PS000974), 4-CQA (batch: PS001109), 3,5-DiCQA (batch: PS012051), 3,4-DiCQA (batch number: PS012052), and 4,5-DiCQA (batch number: PS001057), which were obtained from Chengdu Pusi Biotechnology Co., Ltd (Lukas et al., 2021). PS samples were purchased from 7 provinces in China in April 2022 and authenticated by Professor Kunming Qin, School of Pharmacy, Jiangsu Ocean University (Table 1). A voucher specimen is deposited at the School of Pharmacy, Jiangsu Ocean University. The structural formulae of the 7 phenolic acids in PS are shown in Figure 2.
## 2.2 Preparation of sample solutions
RPS and SPS samples were crushed in an FW-40 high-speed multifunction pulverizer mill (Shanghai Yiyan Test Equipment Co., Ltd.,). The dried sample (1.0 g) was accurately weighed and dispersed in 50 mL MeOH. After 30 min of ultrasonication, the sample was cooled to room temperature (in 25°C) and filtered. The filtrate was collected and transferred to a 25-mL volumetric flask, and the volume was made up with MeOH. Then, the sample was filtered through a 0.22-µm filter after thorough mixing to obtain the sample solution.
## 2.3 Chromatographic conditions
Samples were analyzed using an analytical SHIMADZU LC-20AD system (Shimadzu Corporation, Japan) equipped with a Kromasil 100-5-C18 column (4.6 × 250 mm, 5 μm) and an SPD-20A ultraviolet detector. The detection wavelength was 325 nm for the 7 phenolic acids. Analytes were eluted at a flow rate of 1 mL/min using a gradient profile at a column temperature of 25°C. The solvent system was composed of $0.1\%$ aqueous formic acid solution A) and MeOH B). Gradient elution was performed as follows: 0–10 min: $10\%$–$20\%$ B), 20–30 min: $20\%$–$30\%$ B), 20–50 min: $30\%$–$50\%$ B), 50–51 min: $50\%$–$10\%$ B), 51–60 min: $10\%$ B). Chromatographic data were processed using a Lab Solutions workstation (Bai et al., 2020).
## 2.4 Network construction
All chemical ingredients were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php). PubChem (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain SDF files with 2D structures of the 7 components, namely 3-CQA, CA, 5-CQA, 4-CQA, 3,5-DiCQA, 3,4-DiCQA, and 4,5-DiCQA. SwissTargetPrediction is an online web-based tool established in 2014 to perform ligand-based target prediction for small bioactive molecules. The SwissTargetPrediction model was trained by fitting multiple logistic regression on various size-related subsets of known actives to weigh 2D and 3D similarity parameters in a so-called Combined-Score. A Combined-Score >0.5 predicts that the molecules are likely to share a common protein target. In reverse screening, the Combined-Score allows to calculate for any query molecule assumed as bioactive and determine the probability to target a given protein. As the 2D and 3D descriptions of molecules are complementary, this dual scoring ligand–based reverse screening showed high performance in predicting the macromolecular targets in various test sets (Daina et al., 2019). The canonical SMILES of the compounds were uploaded into the SwissTargetPrediction database (http://www.swisstargetprediction.ch/), the Herbal Ingredients’ Targets (HIT) Platform 2.0 (http://www.badd-cao.net:2345/), and the HERB Materia Medica Database (http://herb.ac.cn/) to obtain UniProt IDs for predicting targets with the organism Homo sapiens (Ou et al., 2020).
The biological targets related to nephritis were selected from the OMIM (https://omim.org/), TTD (https://idrblab.net/ttd/), and GeneCards (https://www.genecards.org/) databases using “nephritis, glomerulonephritis” as the keywords (Guo et al., 2019). Microsoft Excel was used to input the targets of the 7 phenolic acids and nephritis-associated targets in 2 columns, which were compared to identify the common targets that were potential targets (Xi et al., 2022). The intersection targets were imported into the STRING database (https://string-db.org/), “Multiple proteins” was selected, the species was set to “Homo Sapiens,” and the minimum interaction score was set to medium confidence (0.4) to obtain the protein–protein interaction network diagram. The resulting TSV file was imported into Cytoscape 3.7.1 for visual analysis of the network (Xia et al., 2020).
The Gene Ontology (GO) biological process (BP) was used to further validate that the potential targets were indeed matches for nephritis. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses were performed using the Database for Annotation, and visualization (http://www.bioinformatics.com.cn/) was using p ≤ 0.01 (Lin et al., 2022). Compound–target, disease–target, and target–pathway networks were constructed using Cytoscape 3.7.1 (Bethesda, MD, USA). In these bilateral networks, the nodes represent the compounds, diseases, targets, or signaling pathways, whereas the edges represent their interactions (Cao et al., 2022).
## 2.5 Molecular docking
The 3D structures of phenolic acids were obtained using the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and saved in the SDF format. The PDB database (https://www.rcsb.org/) was used to download the 3D structures of protein macromolecules in the PDB format. PyMoL 3.8.5 was used to remove water and ligands from the protein macromolecules. AutoDockTools 1.5.6 was used to convert macromolecules and compounds from the PDB to the PDBQT format, and the docking box position of protein macromolecular ligands was determined. AutoDock vina 1.1.2 script was used to dock protein macromolecules and compounds in sequence, obtain docking results, and draw molecular docking patterns (Zhou W. et al., 2021).
## 3.1 Determination of the 7 phenolic acids in RPS and SPS
An HPLC method was developed to determine the changes in the phenolic acids in PS during stir-frying (Figure 3). The total chemical content of each batch of PS changed significantly during the stir-fry processing. The level of each compound was analyzed. The levels of 3-CQA, CA, 3,5-DiCQA, and other compounds in most batches of PS were found to decrease after processing, whereas those of 5-CQA, 4-CQA, 3,4-DiCQA, and 4,5-DiCQA increased (Table 2). The total content of phenolic acids in the different batches of PS varied significantly (Figure 4). 3-CQA level was much higher than that of the other compounds, followed by 3,5-DiCQA level. Except for the sample procured from Jiangsu, 3-CQA content in other batches of PS was >$1.07\%$ and 3,5-DiCQA content was >$0.13\%$; samples obtained from the Zhejiang, Sichuan, and Henan provinces were found to contain more phenolic acids in PS, with an average content of $2.13\%$, $1.90\%$, and $1.85\%$, respectively.
**FIGURE 3:** *HPLC chromatograms of the raw sample (A), processed sample (B), and mixed standards (C) at 325 nm. 1.5-CQA; 2.3-CQA; 3.4-CQA; 4. CA; 5.3,4-DiCQA; 6.3,5-DiCQA; 7.4,5-DiCQA.* TABLE_PLACEHOLDER:TABLE 2 **FIGURE 4:** *Total content of phenolic acids in the 14 batches of RPS (A) and SPS (B) samples.*
## 3.2 Dynamic changes changes of the 7 phenolic acids during the stir-frying process of PS
The levels of three compounds, 3-CQA, CA, and 3,5-DiCQA decreased after processing, whereas those of the other four compounds increased significantly after processing. However, as only one processing temperature was used and it was for a short duration, the dynamic change rule of phenolic acid composition during the processing of PS cannot be fully revealed. Therefore, in this study, two processing temperatures were selected and the stir-frying time was extended to explore the change rule and trends of the 7 phenolic acids during stir-frying. The stir-frying temperatures were set at 120°C and 150°C, respectively, and the frying time was divided into 3, 5, 7, 9, 11, and 13 min. Figure 5 presents the images of the representative fried samples.
**FIGURE 5:** *Raw PS sample and various stir-fried PS samples. (A): raw sample; (B) and (C): samples fried at 120°C for 3 min and 13 min; (D) and (E): samples fried at 150°C for 3 min and 13 min.*
The % content of the 7 phenolic acids in the processed products (Tables 3, 4) and the trends in change of each compound during stir-frying are depicted in Figure 6. When heated for 0–13 min at 120 °C, the contents of 5-CQA, 4-CQA, 3,4-DiCQA, and 4,5-DiCQA showed an overall increasing trend, whereas those of 3-CQA, CA, and 3,5-DiCQA showed a generally decreasing trend. When heated for 0–13 min at 150 °C, the contents of 5-CQA, 4-CQA, 3,4-DiCQA, and other compounds increased significantly compared with that in the raw product and during the stir-frying process at 120 °C; the contents of 3-CQA, CA, and 3,5-DiCQA decreased significantly compared with that in the raw product and during stir-fry processing at 120°C. The content of 4,5-DiCQA was the highest when stir-fried for 3 min and decreased with an increase in stir-frying time. As chlorogenic acid, neochlorogenic acid, and cryptochlorogenic acid, as well as isochlorogenic acids A, B, and C are isomers, we speculated that an ester-exchange reaction occurred during the stir-frying process, leading to significant changes in the contents of these six components (Figure 7).
## 3.3.1 Prediction of candidate targets and construction of the “compound-target” network
Nephritis-related genes were obtained by searching the keywords “Nephritis” and “Glomerulonephritis”; 129 disease targets were obtained from the OMIM database, 9 from the TTD database, and 575 from the GeneCards database after the median screening. A total of 641 nephritis-related disease targets were finally obtained after summarizing, deduplication, and union. A Venn diagram was drawn between the targets of the 7 phenolic acids and the targets of nephritis-related diseases, and a total of 33 crossover targets were obtained (Figure 8A). Using SwissTargetPrediction, the species was set to human to obtain the targets of the 7 phenolic acids. They were combined with the targets screened using the HIT 2.0 and HERB Materia Medica databases to remove duplicates and to get new targets, respectively. After summarizing and duplicating the targets of the 7 phenolic acids, 150 compound targets were obtained, which were imported into Cytoscape 3.7.1 together with the compound information to construct a compound–target network diagram (Figure 8B). The network graph has 157 nodes and 324 edges. The yellow diamonds represent the phenolic acids, blue squares represent the relevant targets of the compounds, and edges represent the interactions between the compounds and targets.
**FIGURE 8:** *Network analysis of PS in treating nephritis. (A) Venn diagram of the 7 phenolic acids and targets of nephritis; (B) Compound-target network for the 7 phenolic acids; (C) PPI interaction network diagram; (D) Top 30 enriched GO terms for the biological processes of the potential targets.*
## 3.3.2 Protein-protein interaction network core target screening
The 33 compounds and disease intersection targets that were screened out were imported into the STRING database to generate a protein-protein interaction network map. Nodes represent the target proteins and edges represent the functional correlations between proteins. There are 33 nodes and 255 edges in total, with an average node degree of 15.5 and an average local clustering coefficient of 0.755 (Figure 8C). The interaction relationship between the core target proteins was made more intuitive by importing the TSV file into Cytoscape 3.7.1 for visual analysis. The top 10 were selected as the core targets according to the ranking of degree value, followed by tumor necrosis factor (TNF), vascular endothelial growth factor A (VEGFA), prostaglandin G/H synthase 2 (PTGS2), matrix metalloproteinase 9 (MMP9), toll-like receptor 4 (TLR4), signal transducer and activator of transcription 3 (STAT3), human epidermal growth factor receptor (EGFR), caspase 3 (CASP3), toll-like receptor 2 (TLR2), and peroxisome proliferator–activated receptor gamma (PPARG). It can be seen that the targets TNF, VEGFA, and PTGS2 have larger patterns and darker colors than the other targets. Thus, we speculated that these three targets could be the key targets for the treatment of nephritis by PS.
## 3.3.3 GO biological process and KEGG pathway enrichment analysis
The intersection targets were imported into the Metascape database for GO enrichment analysis, and entries with $p \leq 0.01$ were screened out; there were 510 biological processes, 57 molecular functions, and 38 cellular components. Based on the p-value, the top 10 items with representative biological processes, molecular functions, and cellular compounds were screened out (Tables 5–7), and GO enrichment bar charts were constructed (Figure 8D). Biological processes mainly involved the inflammatory response, regulation of the inflammatory response, lipopolysaccharide response, regulation of interleukin-8 production, and regulation of MAP kinase activity among others. Molecular functions mainly involved protease binding, heme binding, oligosaccharide binding, and lipopolysaccharide binding among others; cell compounds mainly involved membrane rafts, vesicle lumen, cytoplasmic perinuclear region, membrane side, and ficoli-1-rich granule lumen. GO biological process data showed that the 7 phenolic acids may be involved in the regulation of these items to possibly achieve a therapeutic effect in treating nephritis. KEGG pathway enrichment analysis was performed on the intersecting targets by using the Metascape database with $p \leq 0.01$ and an enrichment factor of >1.5. A total of 126 signaling pathways of the 7 phenolic acids were screened and found to interfere with nephritis. The top 15 pathways were filtered from the smallest to the largest p-value (Table 8). Nephritis targets are mainly enriched in the AGE-RAGE, HIF-1, IL-17, and TNF signaling pathways during diabetic complications.
## 3.3.4 Analysis of the “compound-target-signaling pathway” network
The 7 phenolic acids, intersection targets, and results from the KEGG pathway were integrated and processed using Cytoscape 3.7.1 to construct a component-target-signaling pathway network diagram and for visual analysis (Figure 9). There are 55 nodes and 209 edges in the graph; the blue circles represent targets, green hexagons represent the 7 phenolic acids, and yellow diamonds represent the KEGG pathway. It can be seen that CA, 3-CQA, 3,4-DiCQA, and 5-CQA have larger nodes than the other 3 compounds, indicating that the 4 compounds have more nephritis targets and may be the key compounds in treating nephritis. The key targets such as TNF, VEGFA, PTGS2, MMP9, and MAPK1 are directly or indirectly related to the 7 phenolic acids and multiple nephritis-related pathways. Therefore, the treatment of nephritis by these 7 phenolic acids is via multiple targets and pathways.
**FIGURE 9:** *Target-pathway network of the 7 phenolic acids in treating nephritis.*
## 3.4 Molecular docking
Molecular docking was used to validate the results of the above network analysis, screen the network of the 7 phenolic acids with high degree values, and dock them with the targets of action on nephritis. Based on the analysis (Table 9), TNF, VEGFA, PTGS2, MMP9, and MAPK1, which were ranked the highest in degree value, were selected as receptor proteins. It is believed that the prerequisite for the binding of small molecules to protein macromolecules is that the binding energy should be <0; moreover, the lower the binding energy value, the greater the binding possibility. The pairing result with the highest binding affinity was CA and TNF (–3.9 kcal/mol), and the pairing results with the second- and third-highest binding affinities were in the case of 3,5-DiCQA and PTGS2 (–5.4 kcal/mol) and 3-CQA, 5-CQA, 4-CQA, and MAPK1 (–7.0 kcal/mol), which suggested that TNF, PTGS2, and MAPK1 may be the key targets.
**TABLE 9**
| Compounds | Binding energy (kCal/mol) | Binding energy (kCal/mol).1 | Binding energy (kCal/mol).2 | Binding energy (kCal/mol).3 | Binding energy (kCal/mol).4 |
| --- | --- | --- | --- | --- | --- |
| Compounds | TNF | VEGFA | PTGS2 | MMP9 | MAPK1 |
| CA | −3.9 | −6.4 | −6.6 | −7.6 | −5.7 |
| 3-CQA | −4.4 | −8.1 | −7.5 | −9.6 | −7.0 |
| 3,4-DiCQA | −5.4 | −8.7 | −6.4 | −10.0 | −7.1 |
| 5-CQA | −4.2 | −8.1 | −7.6 | −9.0 | −7.0 |
| 4,5-DiCQA | −4.0 | −8.5 | −5.5 | −10.0 | −7.7 |
| 4-CQA | −4.4 | −7.8 | −7.1 | −9.4 | −7.0 |
| 3,5-DiCQA | −4.8 | −8.4 | −5.4 | −9.1 | −7.3 |
The pattern of molecular docking results between some of these components and the target is shown in Figure 10. Molecular docking results showed that the 7 phenolic acids could bind well to the nephritis targets, among which CA, 3,5-DiCQA, 3-CQA, 5-CQA, and 4-CQA had good binding affinity with each target. The docking results between the targets of VEGFA and MMP9 and each component were also good, which may play an essential role in preventing as well as treating nephritis (Liu C. D. et al., 2023; Sanaya et al., 2023). Molecular docking also revealed that 3-CQA had good binding ability with TNF. These results were similar to the findings reported by Liu et al., wherein 3-CQA had good binding ability with TNF (Liu J. H. et al., 2023; Xu et al., 2023). Both theories indicated that 3-CQA exhibited the potential to prevent and treat nephritis. These results suggested that the 7 phenolic acids play an important role in the treatment of nephritis and that these targets are also important target proteins in treating nephritis.
**FIGURE 10:** *Docking mode of the 7 phenolic acids with their key targets.*
## 4 Discussion
Phenolic acids have good antinephritic effects (Ki et al., 2012); network analysis and molecular docking were performed to evaluate the role of the 7 phenolic acids in PS in treating nephritis. The results showed that CA, 3,5-DiCQA, 3-CQA, 5-CQA, and 4-CQA had good binding affinities with key targets and may play a more significant role in the treatment of nephritis. Previous studies have shown that CA can prevent the development of diabetic nephropathy by downregulating miR-636 expression (Salem et al., 2019). 3-CQA alleviates renal ischemia-reperfusion injury by reducing inflammation, reducing myofibroblast expansion, and inducing epithelial cell proliferation (Arfian et al., 2019). The protective effect of 3-CQA against renal injury may be attributed to its antioxidant and anti-inflammatory activities (Feng et al., 2016). 3-CQA, 3,4-DiCQA, and 4,5-DiCQA, identified as anti-inflammatory quality markers using network analysis, were subjected to ultra-high performance liquid chromatography and biological activity verification (Zhou Y. F. et al., 2021). With an increase in 5-CQA concentration, the inhibition rate of the inflammatory factor IL-6 increased correspondingly (Gao et al., 2020). Acanthopanax senticosus extract contains 3-CQA, 3,5-DiCQA, and 4,5-DiCQA, which have strong anti-inflammatory effects (Chen et al., 2021). 4-CQA exerts anti-inflammatory effects by reducing oxidative stress during the inflammatory response (Zhao et al., 2020). Therefore, previous studies have proved that these ingredients can act on relevant targets and pathways, and then exert anti-inflammatory, antioxidant, and other pharmacological effects, further proving the reliability of the results of this study (Zhang, 2021).
The protein interaction analysis network showed that TNF, VEGFA, PTGS2, MMP9, MAPK1, and other proteins were the key targets of the 7 phenolic acids in the treatment of nephritis. TNF-α from the TNF family is involved in the inflammatory response of chronic nephritis, and it is used as a detection index in the acute infectious phase of chronic nephritis (Wu et al., 2010; Yuan et al., 2018). Activation of the mesangial cells in mesangial proliferative glomerulonephritis significantly increases VEGFA expression. PTGS2, also known as cyclooxygenase 2, induces prostaglandin production and plays a key role in regulating inflammatory responses (Jason et al., 2015). High MMP9 expression alters the blood–brain barrier permeability, increasing the possibility of viral infections and susceptibility to inflammatory factors and inflammatory cell infiltration (Qiao et al., 2022). It has been reported that MAPK1 expression significantly increases in model groups of nephritis.
Findings from KEGG enrichment pathway analysis and bubble plots revealed that glomerulonephritis targets were mainly enriched in AGE-RAGE, HIF-1, IL-17, and TNF signaling pathways in diabetic complications. Inhibition of the inflammatory channels activated by the AGE-RAGE signaling pathway can reduce the inflammatory response in diabetic nephropathy, thereby protecting the kidneys (Malik and Kumar, 2022). HIF primarily responds to and mediates the hypoxia response, contributing to the development of renal fibrosis and inflammation during chronic kidney disease (Liu et al., 2017). The proinflammatory cytokine IL-17 is upregulated in endothelial cells during the pathogenesis of acute anti-thy1 glomerulonephritis (Loof et al., 2016). TNF is a cytokine that mediates inflammatory kidney disease, and the exclusive expression of transmembrane TNF exacerbates acute glomerulonephritis (Müller et al., 2019). Based on the above results, it could be concluded that the 7 phenolic acids in PS exerted their effects in treating nephritis through various pathways such as the cellular metabolic pathway, apoptotic pathway, and inflammatory pathway. In addition, many other signaling pathways have been shown; however, their mechanisms of action need to be further explored.
## 5 Conclusion
In this study, the content changes in the 7 phenolic acids in PS during the stir-frying process were determined using HPLC. The 7 phenolic acids of PS could effectively treat nephritis through multiple pathways and multiple targets. The accuracy of the outcomes was further verified using subsequent molecular docking. The results showed that the 7 phenolic acids of PS could regulate the proliferation, metastasis, differentiation, senescence, apoptosis, and other biological processes of inflammatory cells by regulating the expression of related genes in nephritic cells, reflecting the antinephritic effect of PS. These findings illustrate the complexity of the pathological mechanisms of nephritis and the diversity of pharmacological activities of the phenolic acids in PS. However, our research results need to be further corroborated using animal experiments and other relevant studies to ensure their reliability. Overall, our study provides a novel basis for further exploration and subsequent experimental verification of PS in the treatment of nephritis.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
YL generated the main idea, prepared the figures and tables, and wrote the manuscript. YL and YZ performed literature search on pharmacological effects of phenolic acids. JY and HH performed literature search on phenolic acid structures. JS performed the experiments and analyzed the data. XG and KQ performed a critical review of data and literature and edited the manuscript contents and the final content.
## Conflict of interest
JY is employed by Lunan Pharmaceutical Group Co. Ltd, China.
The remaining 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.
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|
---
title: 'Fortification of puffed biscuits with chitin and crayfish shell: Effect on
physicochemical property and starch digestion'
authors:
- Chan Bai
- Jiguo Zhu
- Guangquan Xiong
- Wenqing Wang
- Juguang Wang
- Liang Qiu
- Qingfang Zhang
- Tao Liao
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10045987
doi: 10.3389/fnut.2023.1107488
license: CC BY 4.0
---
# Fortification of puffed biscuits with chitin and crayfish shell: Effect on physicochemical property and starch digestion
## Abstract
Chitin is a polysaccharide and possesses numerous beneficial properties such as nontoxicity, biodegradability and biocompatibility, which draws much attention to its applications in food. Crayfish shell is a source of chitin alongside an antioxidants and a potential source of beneficial dietary fiber. In this study, chitin (CH) and crayfish shell (CS) with different concentrations were used to study their impact on pasting characteristics of flour mixture (wheat flour and glutinous rice flour) and influence on physicochemical and starch digestion property of puffed biscuit. The Rapid Visco-Analyzer results showed that the viscosity of powder mixture was decreased with the ratio of CH and CS increased. CH resulted in lowest peak viscosity and breakdown values of mixed powder. It was indicated that increasing amounts of CH and CS led to significantly reduced moisture content, expansion ratio but raised density of biscuits. CH and CS inhibited starch digestion and promoted a remarkable increase ($P \leq 0.05$) of resistant starch (RS) content. The hydrolysis kinetic analysis suggested a decelerating influence of CH on the hydrolysis content with lower values of equilibrium hydrolysis percentage (C∞) while CS on hydrolysis rate with lower kinetic constant (K). The estimated glycemic index (eGI) of the CH (15-$20\%$) samples were below 55. These results are of great significance in delaying starch digestion and provided a better choice in design of fried puffed snacks for special crowd with chronic diseases such as diabetes, cardiovascular disease, and obesity.
## Introduction
Fried puffed foods are popular with consumers globally mainly due to their unique flavor, crispness, color, attractive taste and affordable cost [1]. The main raw materials of the puffed food is digestible carbohydrates (starches) which are rapidly broken down within the human gastrointestinal tract. Different processing methods lead to different digestive property of starch. The frying process leads to the starch being more susceptible to enzymatic hydrolysis in gastrointestinal, thereby accelerating the starch conversion to sugar and causing a boost of postprandial blood glucose. Long-term hyperglycemia can cause chronic damage to various tissues and contribute to many human health problems, such as diabetes, high glycemic index, heart disease and obesity etc. [ 2]. Over 415 million adults are suffering from diabetes globally and 318 million adults have impaired glucose regulation [3]. Thereby, developing new methods to control the digestion rate of starch and maintain the stability of blood glucose level has become a key technological problem affecting human health. Starch could be classified into rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS) based on its digestibility [4]. Slowly digestible starch (SDS) and resistant starch (RS) can help control and prevent diseases such as diabetes and obesity [5]. Therefore, it is important to design functional foods that can decrease the starch digestion and increase the SDS and RS fraction to maintain the glucose level in the blood. Nowadays, physical methods, enzymatic methods, and chemical method are main methods to inhibit starch digestion. However, problems as consumer safety, environmental pollution and efficiency have limited their application. Therefore, to develop a simple and efficient method to reduce the digestion of starch remains a challenge.
Dietary fiber, consisting of indigestible cellulose, hemicellulose, lignin, chitin, gums etc, are indigestible polysaccharides which is resistant to digestion in the small intestine and fermentation in the large intestine [6]. In recent years, dietary fiber has gained roar attention worldwide as a promising functional food for improving physiological actions and regulating the health conditions of humans due to its potential function in decreasing cholesterol and blood glucose and prevention of some diseases, such as obesity, heart disease and atherosclerosis [7, 8]. It was found that the digestibility, texture, sensory evaluation of starch based puffed snacks could be improved with the addition of dietary fiber as inulin and guar gum [9], etc. The dietary fiber particles dispersed within the starch matrix and crowded around the starch granules to inhibit the starch digestion.
Chitin, a dietary fiber, is a linear chain polymer composed of repeating units of β-(1 → 4)-2-acetamido-2-deowy-β-D-glucose, which has antioxidant, antidiabetic, lipid-lowering and antibacterial effects (10–12). It is safe and non-toxic, with good degradability and biocompatibility. The unique properties of chitin has made it being used amply in food industries and was thought as the third generation of health food. Recently, there has been great interest in using chitin or its derivatives as functional food ingredients [13]. Zhou et al. [ 14] found that nanochitin could be used to retard lipid digestion in foods. Ji et al. [ 15] studied the impact of chitin on the gelatinization and retrogradation behaviors of starch. Chitin was also suggested as a potential novel form of prebiotics [16]. Crayfish (Procambarus clarkii) has become common food source and approximately 100,000 tons of crayfish shells were generated every year, with about $80\%$ of crayfish shells having been discarded as waste [17]. The crayfish shell is an important natural source of chitin, chitosan and carotenoids. By-products like crayfish shell is increasingly explored as alternative food sources, due to its high nutritional value, cost-effective production and low carbon footprint [18]. Consumption of chitin and crayfish shell was also correlated with a reduction in plasma TNF-α levels [16].
Although many efforts have been made to investigate the applications of chitin or chitosan in starch-based products [15], from the nutritional perspective, few studies have addressed the potential impact of chitin and crayfish shell on starch digestive fate in the human alimentary canal. There is still a relatively poor study of the interaction between chitin or crayfish shell with starch in puffed products. The frying process improved digestion of starch in gastrointestinal tract and led to increase in blood glucose levels. We hypothesized that the incorporation of chitin and crayfish shell could reorder the starch during frying process and bind around the starch, thereby inhibiting the digestion of starch by blocking amylase, thus further reduce glycemic index. Understanding the role of chitin and crayfish shell interactions on physicochemical properties and starch digestibility in biscuits may have important healthy benefits.
The purpose of our study was to develop an efficient method to control starch digestion in fried biscuit and made low glycemic index (GI) food by incorporating chitin and crayfish shell into flour mixture. The chitin and crayfish shell inhibited the starch digestion by forming barrier to prevent contact between starch and amylase. Chitin-biscuits (CH-biscuits) and crayfish shell-biscuits (CS-biscuits) containing chitin and crayfish shell at different ratios were prepared. The pasting, physicochemical property and morphology of biscuits was studied by scanning electron microscopy (SEM), texture profile analysis (TPA) and rapid visco analyzer (RVA). The in vitro starch digestibility of biscuits was also investigated. The research are of significance in inhibiting starch digestion and maintaining the health of special crowd with chronic diseases such as diabetes, cardiovascular disease, and obesity.
## Raw materials and chemicals
The chitin (CH) from crayfish shell was purchased from Shanghai Macklin Biochemical Co., Ltd. (Shanghai, China). The crayfish shell (CS) was kindly supplied by Wuhan Liangzihu Aquatic Processing Co. Ltd. (Wuhan, China). The α-amylase from porcine pancreas (4 u/mg) and Glycosylase (100,000 u/ml) were from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). The low gluten wheat flour was purchased from Wuhan Suntyh Food Co., Ltd. and the glutinous rice flour was from Henan Enmiao Food Co., Ltd. Other ingredients including soybean oil, baking soda, yeast, salt and eggs were all available at local supermarket. All chemical reagents were of analytical grade purchased from Sigma-Aldrich (Germany).
The crayfish shells were washed thoroughly with distilled water to remove impurities, dried in a hot air oven at 105°C for 24 h, and pulverized to powder in a ultrafine pulverizer. After pulverizing, the crayfish shell powder was filtered through a 25 μm sieve to obtain a uniform powder and kept in a desiccator until application. Proximate composition of CS was determined in accordance with the methods in AOAC [19] and Ruth et al. [ 20]. The CS contained 14.48 g of protein, 7.69 g of water, 0.2 g of fat, 28 g of chitin and 48.26 g of calcium salt (every 100 g).
## Preparation of samples
The procedure for making the biscuits was followed the method of AACC [21]. Firstly, the flour mixture (wheat flour and glutinous rice flour) were mixed in a ratio of 11:3, to which, different levels 5, 10, 15 and $20\%$ of CH or CS were added, respectively (based on the amount of wheat flour and glutinous rice flour) and mixed well. Then, $0.7\%$ of yeast powder, $3\%$ of salt, $3\%$ of baking soda, $67\%$ of egg and $10\%$ of water were added to the mixture and mixed in a dough mixer for 10 min to form dough. The dough was fermented in an incubator at 37°C for 3 h and then the fermented dough was rolled and cut into 0.6 cm × 0.5 cm × 3 cm biscuit flans. The biscuits were fried in a soybean oil pan at 190 ± 10°C for 50 s and removed to a tray, drained the oil, cooled at ambient temperature, packed in polyethylene bags and stored at 20°C for further analysis.
## Pasting properties
A Rapid Visco Analyzer (RVA-TM, Perten Instruments, Sweden) was used to measure the pasting characteristics of the mixed powder (wheat flour and glutinous rice flour with different content of CH and CS) according to the method of Feng et al. [ 22] with slightly modified. Firstly, the flour mixture slurry ($14\%$, 3 g total weight) was suspended in 25 mL denized water in RVA container. Then the CH or CS (0, 5, 10, 15, and $20\%$ of flour mixture; w/w) were added, respectively. The calculated amounts of CH or CS powder were pre-stirred for 30 s by plastic paddle. The detection procedure was set as: at the beginning of 10 s, the paddle speed was 960 rpm, then the paddle speed slowed down to 160 rpm and kept until the end of the detection. The temperature was set as: held at initial temperature 50°C for 1 min, then heated to 95°C in 4 min, held at 95°C for 25 min, at last cooled to 50°C within 4 min and held at 50°C for 2 min. all the measurements were performed in triplicate and the averages were reported.
## Expansion ration and volume density
The volume of biscuit was determined using a seed displacement method. The bulk density was calculated by dividing its determined volume by the mass of biscuit [23]. The expansion ration was calculated by dividing its volume after puffing by volume before puffing of biscuit [24]. The expression was as follows:
## Moisture content and oil content
The moisture content of the samples was calculated based on the weight difference measured before and after drying the samples in a hot air oven at 105°C for 24 h [25]. The oil content of the samples was obtained as follows: The samples were dried at 105°C to a constant mass, and they were placed into petroleum ether (60–80°C) as solvent to measure the oil absorbed by the samples with a Soxhlet extractor (SOX405 purchased from Hanon Instrument Co., Ltd.). Oil content was expressed as a percentage of total oil content on a dry weight basis. Analysis was subject to three samples.
According to Table 2, The moisture content and oil content were negatively correlated. When $20\%$ of CH and CS were added, moisture content of both group decreased by $1.62\%$, while oil content increased by 2.84 and $5.85\%$ when compared with the control. That was because the strong hydrogen bonds in chitin and between chitin nanowhiskers and starch molecules therefor caused strong hydrophobicity of CH [40], so the moisture in the product cannot be firmly locked during the process of frying. On the other hand, lower moisture content showed higher heat absorption of the food during process. The high temperature during the frying process resulted in oxidation of the products and different types of derivative were produced after the break down of lipid compounds, whose heat transfer efficiency was significantly lower than that of lipid molecules. Chitin has strong antioxidant activity and strong protective effect on lipid oxidation, which could significantly reduce the production of oil derivative, hence improve the heat transfer efficiency of oil and decrease the moisture content [44]. The effect of CS on oil content of biscuits was significantly higher than that of CH, that was because besides of chitin, CS also contained a large amount of proteins. The proteins and protein hydrolysates may lead to reduced surface tension and increased hydrophobicity, which facilitated the entrance of oil, enhancing the oil content of biscuit. Although the crayfish shell and chitin both could increase the oil content when added into biscuits, the oil content of obtained CH or CS-biscuits was < 24 g/100 g, which is less than that in commercial fried (35 g/100 g) and fiber-fortified fried potato snacks (30.6-33.2 g/100 g) [45].
## Water solution index (WSI) and water absorption index (WAI)
Both WSI and WAI were defined by Kowalski et al. [ 26]. 1 g of grated sample was suspended in 15 mL water at room temperature for 30 min, stirred gently during this process and then centrifuged at 3,000 x g for 15 min. The supernatant was then poured into a pan of known weight. WSI refers to the weight of dried solids in the supernatant, expressed as a percentage of the original weight of the sample. WAI refers to the weight of gel obtained by removing the supernatant from the original dried solid of unit weight. Such measurement was subject to three samples. The expressions of WAI and WSI were as follows:
## Textural properties
The textural characteristics of biscuits were measured with the help of a TA-XTPlus Texture analyzer (Stable Micro System, Vienna court, UK) with a P36/R aluminum cylindrical probe. The parameters were set as: pretest speed 1.0 mm/s, test speed 0.5 mm/s, post-test speed 1.0 mm/s, trigger force 5.0 g, and deformation level $75\%$. The harness and springiness was obtained from the force-time curve of the texture profile, respectively.
## Scanning electron microscope
The surface morphology of CH-biscuits and CS-biscuits at different magnification (× 100 and × 30) were observed using a scanning electron microscope (MIRA4, TESCAN Ltd., Bmo, Czech) with an accelerating voltage of 2 kV. The freeze-dried biscuits were adhered to brass slip under argon environment by a gold sputter module in a high vacuum evaporator to form a gold layer with thickness of 20 nm.
## Sensory evaluation
Twenty well-trained assessors (16 females and 4 males, 20–30 years) evaluated the finished CH-based biscuits and CS-based biscuits for their appearance, color, taste, texture and overall acceptability. The trained sensory panel were all from Institute of Agro-Products Processing and Nuclear-Agricultural Technology (Wuhan, China). A nine-point preference scale was used to evaluate the overall acceptability for determining sensory attributes of the above samples (From point 1-9, where 1 is strongly dislike, 5 is acceptable and 9 is strongly like) [27]. The biscuits samples were placed on a panel, and each assessor was asked to observe and taste individually.
The sensory evaluation experiment was conducted according to the guidelines of the Declaration of Helsinkiwas. All work with human subjects performed here was reviewed and approved by the Hubei Academy of Agricultural Sciences Institutional Review Board (IRB). The products tested were safe for consumption.
## In vitro digestion of starch in CH-biscuits and CS-biscuits
The in vitro starch digestion of CH and CS-based biscuits samples was determined based on the modified method of Zhang et al. [ 28]. 3 g of pancreatic α-amylase (14 u/mg)was dissolved in 10 mL sodium acetate buffer (0.1 M, pH = 5.2) and centrifuged at 3000 × g for 5 min. The supernatant was collected and mixed with 1.3 mL of amyloglucosidase (100,000 u/ml) in a beaker. The samples to be tested were prepared so that they all contained the same initial quantity of starch (60.54 g/100 g). The biscuits samples (100 mg, dry basis) were added into 25 mL sodium acetate buffer (0.1 M, pH = 5.2) and then they were equilibrated at 37°C. Then 1.5 mL of enzyme solution (pancreatic α-amylase and amyloglucosidase) were added and incubated for 180 min at 37 °C with continuous agitation (180 rpm). At 5, 10, 20, 30, 60, 90, 120, 150, and 180 min, aliquots (0.1 mL) were removed and mixed with 8.0 mL of $80\%$ ethanol to inhibit the enzymes. Afterward, the mixed solution was centrifuged at 5000 rpm for 20 min, and the glucose content in the supernatant was measured using a 3,5-dinitrosalicylic acid (DNS) method [29]. Rapidly digestible starch (RDS), slowly digestible starch (SDS) and resistant starch (RS) were calculated as follows: Where: G20 and G120 represent the glucose contents produced of hydrolysis within 20 min and 120 min, respectively, and TS is the total starch content of the sample (initial amount of starch). A factor 0.9 indicated the conversion factor of glucose into starch.
The rate of starch digestion kinetics were estimated based on the approach described elsewhere [30]. The starch hydrolysis can be fitted to a first-order eqution, Where: Ct (%) is the percentage of starch digested at t time (0, 5, 10, 20, 30, 60, 90, 120, 150, 180 min); C∞ is the estimated percentage of starch digested at 180 min; K(min–1) is the starch digestion rate constant. The areas under hydrolysis curves (AUC, 0-180 min) were calculated as the integral of the kinetic equation and used to obtain the HI (Hydrolysis Index). The HI was calculated by dividing the AUC of the samples by that of white bread (as a reference). The following formula proposed by Goñi et al. [ 31] was used to calculate the eGI (estimated glycemic index) value.
## Data analysis
All experiments were repeated three times. All data were expressed as the mean ± standard deviation (SD). Experimental data were analyzed using analysis of variance (ANOVA), expressed as the mean value ± SD, and significant differences among means were determined by Duncan’s test. All analyses were performed using IBM SPSS Statistics 25 (SPSS Inc., Chicago, IL). The significance level in all cases was set at $P \leq 0.05.$ All the analysis and data visualizations were conducted on the Origin Pro 2018.
## Pasting properties of base material
The effect of CH and CS on the pasting properties of flour mixture was showed in Figures 1A, B. The pasting curve was often used to describe the swelling and disruption of starch granules in the aqueous phase. CH-flour and CS-flour mixtures were differed in the pasting behavior, which were observed much lower viscosity during heating and cooling. The effect was dependent on the CH (and CS) concentration. As shown in Figure 1A, CS$5\%$ mixed with flour did not have significant impact on the viscosity of flour during the heating step, while the CS10-$20\%$ decreased the viscosity. It suggested that the high concentration of CS played a role in starch swelling in the initial stage of pasting. Table 1 showed the parameters that define the pasting behavior of CH-flour and CS-flour mixtures with different addition ratios of CH or CS (CH or CS/flour mixture = 0, 5, 10, 15, and $20\%$, w/w).
**FIGURE 1:** *Viscosity changes of CS-flour mixtures at different mixing ratios (CS/flour mixture = 0, 5, 10, 15, and $20\%$ w/w) (A) and CH-flour mixtures at different mixing ratios (CH/flour mixture = 0, 5, 10, 15, and $20\%$ w/w) (B).* TABLE_PLACEHOLDER:TABLE 1 Increasing amount of CH and CS caused a significant declining trend of peak viscosity, trough viscosity and final viscosity ($P \leq 0.05$). The addition of CH led to a significant decrease in peak viscosity from 2879 mPa⋅s (control) to 1787 mPa⋅s (CH$20\%$). This could be attributed to the fact that chitin interacted with wheat starch and glutinous rice starch granules through hydrogen bonding, inhibiting the swelling of the granules and leading to lower peak viscosity. The experimental results of Liu et al. [ 32] also showed that polysaccharides could inhibit the expansion of starch granules, leading to the decrease of viscosity. As to CS$20\%$ group, a reduction of peak viscosity about $31.9\%$ when compared with control was observed, which might because of the addition of crayfish shell powder contributing to a dilution of the starch granules concentration in the system thus decreasing the peak viscosity [33]. The final viscosity of CH group was also decreased, which might be due to the interaction between CH and starch amylose molecules. It was indicated that modifications which result from the addition of chitin to a starch system were complex, and these could be ascribed to polymers interactions or phase separation processes. We proposed the chitin be around the starch and form a starch-chitin network structure by hydrogen bonds. The starch-chitin structure was also been reported in other literature [34, 35]. An increase in CH content led to re-crystallization of dispersed amylose chains, which retarded short-term retrogradation of starch and decreased final viscosity [36].
The increasing addition ratio ranging from $5\%$∼$20\%$ of CH or $10\%$ ∼$20\%$ CS both caused a significant decrease of breakdown viscosity ($P \leq 0.05$). The smaller breakdown viscosity indicated that the structure of starch granules during starch gelatinization was more stable and the damage of starch granules by heating and shear force was reduced [32]. As both CH and CS could significantly reduce the breakdown viscosity ($P \leq 0.05$), the addition of CH and CS made the starch granules with more stable structures. System viscosity was mainly affected by three factors: granules swelling degree; granules disruption and the surface interaction especially the system was added with polysaccharide, which would influence the amylose leaching out the swollen granules [37, 38]. High amount of CH or CS could wrap around the starch, inhibit the swelling content of granules and impede the dissipate of leached amylose. Yuris et al. [ 39] have found that wheat starch mixed with mesona chinensis polysaccharide also showed decreased pasting viscosity.
The setback of mixed powder decreased from 1,994 mPa⋅s to 1,372.5 (CH$20\%$) and 1,076.5 mPa⋅s (CS$20\%$) with the content of CH (CS) increased from 0 to $20\%$. Setback viscosity was an index of starch retrogradation, demonstrating the trend of starch paste to retrograde. The decrease in the setback viscosity implied a prevention of short-term retrogradation. It might be explained that –OH groups of CH interacted with the –OH groups of wheat and glutinous starches to form a hydrogen bridge of CH-amylose, thus reducing the association of amylose molecules and inhibiting the amylose rearrangement [32] Qin et al. [ 40] found that the addition of chitin nanowhiskers into starch products could delay the short-term and long-term aging of starch, which was consistent with the results in this paper.
## Expansion ration and density
CH and CS both had significant effects on the puffing degree and density of biscuits ($P \leq 0.05$) (Table 2). The density was inversely correlated with expansion ration of biscuits. When the addition of CH and CS were $20\%$, the puffing degree decreased by 24.14 and $43.84\%$, respectively, while the density increased by $39\%$ and $23.21\%$ when compared with the control. The reason might be that chitin was a rigid material with fibrous nature and high mechanical strength, by which it could cause the low adhesion between starch and CH (and CS) and the weakened interaction between starch molecules [41]. The molten starch stick to the chitin wall and formed a complex wall that hindered the expansion. The rearrangement of starch molecules during frying were affected by CH and the aging time of starch were delayed, resulting in increased density and reduced ability for expansion of puffed products [42]. The results were in agreement with the research found from Jiamjariyatam et al. [ 43]. The puffing degree of CS group was less than that of CH group.
**TABLE 2**
| Sample | Sample.1 | Puffing degree | Density (g/cm3) | Moisture (%) | Oil (%) | WAI (g/kg) | WSI (g/g) | Hardness (kg) | Springiness |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Control | 0% | 2.03 ± 0.03d | 0.56 ± 0.02ab | 2.07 ± 0.06c | 21.34 ± 0.48a | 373.1 ± 3.35e | 8.34 ± 0.17a | 8.83 ± 0.2a | 79.73 ± 0.23g |
| CS | 5% | 1.83 ± 0.05cd | 0.59 ± 0.05abc | 1.23 ± 0.07b | 22.43 ± 0.63ab | 322.32 ± 0.69d | 8.94 ± 0.74ab | 15.27 ± 0.23b | 69.29 ± 1.11ef |
| | 10% | 1.64 ± 0.03abc | 0.58 ± 0.01abc | 0.98 ± 0.07ab | 23.06 ± 0.81ab | 302.97 ± 2.35b | 9.11 ± 0.09ab | 20.52 ± 0.14c | 50.59 ± 0.3b |
| | 15% | 1.61 ± 0.04abc | 0.62 ± 0.02bcd | 0.7 ± 0.04ab | 26.01 ± 0.62cd | 301.17 ± 1.78b | 9.42 ± 0.2ab | 22.51 ± 0.87d | 44.74 ± 0.74a |
| | 20% | 1.41 ± 0.03a | 0.69 ± 0.04de | 0.45 ± 0.09a | 27.19 ± 0.80d | 280.13 ± 1.51a | 11.58 ± 0.46c | 26.51 ± 0.36e | 55.00 ± 0.71c |
| CH | 5% | 1.87 ± 0.18cd | 0.55 ± 0.04a | 0.95 ± 0.05ab | 21.59 ± 2.72a | 366.12 ± 4.44e | 9.93 ± 1.65b | 14.22 ± 0.79b | 69.94 ± 0.54f |
| | 10% | 1.72 ± 0.2bc | 0.62 ± 0.04abcd | 0.67 ± 0.09ab | 21.92 ± 0.26ab | 320.58 ± 2.20cd | 13.13 ± 0.8d | 23.51 ± 0.46d | 68.24 ± 0.59e |
| | 15% | 1.66 ± 0.24abc | 0.64 ± 0.05cd | 0.47 ± 0.01a | 22.18 ± 1.34ab | 315.77 ± 9.85cd | 14.69 ± 0.36e | 27.37 ± 0.7e | 60.71 ± 0.27d |
| | 20% | 1.54 ± 0.12ab | 0.67 ± 0.02d | 0.46 ± 0.03a | 24.18 ± 1.14bc | 313.40 ± 1.77c | 14.92 ± 0.14e | 39.08 ± 0.69f | 60.91 ± 0.93d |
## Water absorption index (WAI) and water soluble index (WSI)
Water absorption index (WAI) is a measure of the water-holding capacity of the starch, cellulose and protein in products after swelling in excessive water [46]. It is the demonstration of the behavior of puffed product during its interaction with water and the degree of the starch conversion [47]. WAI was also used to indirectly evaluate the porosity of material. Water-soluble index (WSI) is a measure of the degradation of starch molecules during processing which increased the amount of soluble polysaccharide. WSI reflects the solubility of product components in water [48]. As shown in Table 2, WAI decreased with the addition of CH and CS, but the WSI was opposite. In $20\%$ CH and $20\%$ CS, WAI decreased by 16 and $24.92\%$, while WSI increased by 78.90 and $38.85\%$ compared with the control, respectively. The increasing CH and CS content led to decreased WAI, which might because CH and CS affected the starch molecular. The availability of hydrophilic group influences the WAI [49]. The addition of CH and CS may decrease the extent of starch gelatinization during frying and cause reduced water absorption. Similarly, Singh et al. [ 50] reported a decrease in WAI with addition of pea grits in extrusion of rice due to the dilution of starch in rice pea blends. WSI increased as the increase of CH and CS content. WSI was a parameter that reflected the degradation suffered by the components of the biscuits. Chitin molecules disrupted continuous structure of the melt in frying and impeded elastic deformation. It could improve the heat transfer efficiency of oil which making the internal temperature of biscuits rise faster, resulting in the increase of water-soluble substances. Kowalski et al. [ 26] also reported a increased in WSI with the raising temperature. The WSI of CH group was significantly higher than that of CS group ($P \leq 0.05$) due to higher chitin quantity.
## Textural properties of biscuits
The textural changes of puffed biscuits fortified with different levels of chitin or crayfish shell are presented in Table 2. The hardness values increased significantly ($P \leq 0.05$) with a corresponding increase in CH or CS concentration. In $20\%$ CH and $20\%$ CS, the hardness reached 39.08 ± 0.69 kg and 26.51 ± 0.36 kg. The hardness value correlated with density, expansion and thickness of cell walls [51]. Puffed biscuits enriched with high level of chitin showed high hardness values, which was because of their compact, hard, not so crunchy nature owing to their low expansion properties. The addition of chitin powder contributed to premature rupture of gas cells which reduced the expansion and the porosity of puffed biscuits [52]. As with CS-biscuits, except for chitin, the harder texture of the biscuits was attributed to the increased protein and calcium content and their interaction during dough development and frying [53]. Texture profile analysis revealed significant differences ($P \leq 0.05$) in springiness characteristics between control and different treatments. A decreasing trend of springiness was observed with increasing CH or CS levels. Low springiness reflects the tendency to crumble upon external forces [54]. CH and CS exerted negligible effects on the hardness and springiness, indicating unacceptable texture with higher levels of CH or CS inclusion in biscuits.
## Surface morphology of biscuits
Scanning electron microscope (SEM) of the cross section of the fried biscuits were showed in Figure 2, from which the organizational structure of the biscuits could be clearly seen. In Figure 2A, the surface of control sample presented a porous spongy structure, with ellipse and thick-shaped pores distributed evenly. As compared to the control, when the addition were $10\%$ and $20\%$ of CS and CH as showed in Figures 2B–E, it could be observed that the number of pores was significantly reduced with irregular shape and uneven distribution, showing an overall less expansion of fried puffed products than control sample. Figures 2B, C showed smaller cell size and thicker cell walls with respect to the Figure 2A, since the CH $10\%$ and CS $10\%$ contained chitin and crayfish shell powder fragments. Chitin fibrils in starch powder resulted in premature rupture of gas cells which reduces porosity and expansion of the functional snacks. The Figures 2C, E indicated that the structure of less expanded products was more closeness and the surface was smoother. According to Figure 2E, in CH $20\%$ group, instead of being puffed and porous, the biscuits split unevenly. It might because that too much addition of CH could result in reduced binding capacity of dough thus decreased the viscoelasticity. At the same time, uneven heating of biscuit interior with CH during frying could also lead to the surface morphology.
**FIGURE 2:** *Scanning electron microscope at different magnification (× 100 and × 30) of CH-biscuits and CS-biscuits with different additions (CH or CS/flour = 0, 10% and 20%); (A) control sample, (B) 10% CS- biscuits, (C) 20% CS-biscuits, (D) 10% CH-biscuits, (E) 20% CH-biscuits; CH indicates chitin; CS indicates crayfish shell.*
## Sensory characteristics of biscuits
The incorporation of either CH or CS at different addition ratios (0, 5, 10, 15 and $20\%$) on judging scores of sensory quality characteristics: appearance, color, taste, texture and overall acceptability of produced biscuits were studied and the results were shown in Figure 3. For CH group, when $10\%$ of CH was added, the appearance, color and taste were highest, scoring 8, 7 and 7, respectively, and the overall acceptability scored top out at 7.5. The CS $10\%$ group was preferred to the control and scored slightly higher than the control especially for color, appearance and overall acceptability, the overall acceptability scoring top out at 8. On the other hand, CH or CS 15∼$20\%$ group exhibited a reduction in judging scores, especially for texture and taste due to less expansion ration the chitin. From the present results for sensory evaluation of the fried biscuits, it could be concluded that the CH and the CS should be incorporated into the biscuits up to the incorporation level of $10\%$ from each.
**FIGURE 3:** *The sensory properties of (A) biscuits with different additions of CS and (B) biscuits with different additions of CH. CS 5, CS 10, CS 15, and CS 20% were CS-biscuits at different addition ratio (CS/flour = 5, 10, 15, and 20%); CH 5, CH 10, CH 15, and CH 20% were CH-biscuits at different addition ratio (CH/flour = 5, 10, 15, and 20%); CH indicates chitin; CS indicates crayfish shell.*
## In vitro digestion studies
The digestion property is an important property of starch-based samples. The RDS, SDS, and RS contents of control, CH-biscuits and CS-biscuits were shown in Figure 4. CH and CS significantly influenced the starch digestible property and the influence was related to the addition ratio of CH and CS. It could be obviously observed that with the increasing of CS, the RS content of CS-biscuit increased, but the RDS and SDS contents decreased significantly. The CH-biscuits showed higher RS and lower SDS content when compared with the CS-biscuits. The CH$20\%$ group exhibited the highest RS ($84.6\%$) content and lowest RDS ($9.92\%$). The possible reason was that CH and CS entangled with starch to form integrated network structure and increased the complexity of starch composition, which caused an increase in RS content [55]. The formed CS or CH-starch structure enhanced the resistant starch content. We proposed that a layer of polysaccharide around the surface of starch granules to prevent amylase from digesting starch. The chitin and crayfish shell around starch had a barrier effect on starch, and this barrier effect increased as the concentration of chitin and crayfish shell increased. Low concentration of chitin formed a weak chitin-starch network, which was insufficient to block amylase. As the concentration of chitin increased, the protective effect of the chitin on starch granules was enhanced. The chitin prevent the binding between enzyme and starch so as to slow down the hydrolysis rate of starch [56]. Wang et al. [ 57] revealed chitin nanowhiskers could bind with pepsin in simulated gastric fluid due to hydrogen bonding and van der Waals forces, leading to the aggregation and alteration of micro-environment of aromatic amino acids in pepsin. Similar phenomenon was also observed by other studies [58]. With the same addition amount, RS of CH group were higher than that of CS group, which indicated that the effect of CH on starch in biscuits was more significant than CS ($P \leq 0.05$).
**FIGURE 4:** *The percentages of RDS, SDS and RS content of biscuits with different addition ratio of CS and CH. RDS, fast digestion starch; SDS, slow digestion starch; RS, resistant starch; CS 5, CS 10, CS 15, and CS 20% were CS-biscuits at different addition ratio (CS/flour = 5, 10, 15, and 20%); CH 5%, CH 10%, CH 15% and CH 20% were CH-biscuits at different addition ratio (CH/flour = 5, 10, 15, and 20%); CH indicates chitin; CS indicates crayfish shell.*
## Kinetics of starch hydrolysis
The digestion of the various biscuits samples (white bread, control, CH $5\%$∼$20\%$ and CS $5\%$∼$20\%$) was measured using an in vitro method based on the same starch content in Figure 5. All the samples followed the same trend for starch hydrolysis, which rose rapidly within 0∼20 min, then increased slowly after 60 min and gradually reached equilibrium after 120 min. The CH-biscuits, which contained different amounts of chitin, displayed the lowest degree of hydrolysis. At the final of digestion, the starch hydrolysis degree of CH-biscuits and CS-biscuits were 19.24 ± $0.60\%$ and 27.53 ± $0.83\%$, respectively, much lower than the control (49.36 ± $1.27\%$). The addition of CH resulted in a decrease in the starch hydrolysis, which could be attributed to the increase of hydrophobicity. Moisture content also played an important role in the hydrolysis of starch. The hydrophobic nature of chitin might limit the availability of water for enzyme substrate reactions, reducing the overall hydrolysis of starch and hydrolysis kinetics. On the other hand, the chitin around the starch granules could limit access of enzymes to starch, leading to the decrease of enzymatic starch hydrolysis. CH-biscuits had significantly lower digestibility than CS-biscuits during the whole digestion period.
**FIGURE 5:** *Impact of CS (A) or CH (B) on the in vitro hydrolysis profiles of starch in CH-biscuits and CS-biscuits at different additions (CS or CH/flour = 5, 10, 15, and 20%); White bread as a reference; Error bars represent standard deviation from the mean of triplicate measurements.*
To further evaluate the effect of CH and CS on digestible properties of starch, the maximum starch hydrolysis degree (C∞), the kinetic constant (K), and the estimated glycemic index (eGI) of each of CH-biscuits and CS-biscuits samples were calculated using a fitting model that assumes first-order kinetics (Eq. 5) to the data (Table 3). The C∞ values were estimated concentration of hydrolyzed starch, which was related to the equilibrium concentration of the digestion. The C∞ values were decreased from 50.67 to 28.5 and $19.04\%$, with the addition of CS or CH from 0 to $20\%$. The K value, which was relevant to the reaction rate of starch hydrolysis, was different between the CH-biscuits and CS-biscuits. The K value of the CS-biscuits ranged from 0.0223 to 0.0177 min–1, which was lower than CH-biscuits samples (from 0.0301 to 0.0211 min–1). The results suggested that the addition of CH decreased the extent of starch digestibility, while the CS decreased the rate of starch digestibility. The nature of chitin determines its physico-chemical behavior and this may affect the rate of digestion of carbohydrates and absorption of sugars in the small intestine. The estimated glycemic index (eGI) of biscuits decreased gradually with the increasing addition of CH and CS, and significant difference ($P \leq 0.05$) was observed between the eGI of the CH and CS groups. It has been reported that eGI above 70 is classified as high blood glucose food, 55 to 69 as middle blood glucose food, and below 55 as low blood glucose food [59]. The $20\%$ CH group had the lowest eGI (52.00 ± 0.48) and could be owned to low eGI food. All the CS-biscuits and CH-biscuits with lesser CH addition were belong to middle blood glucose food. Therefore, the addition of CH and CS could affect the digestion properties of starch in biscuits and reduce the eGI value. CH laid a greater effect on reducing starch digestion in biscuits than CS, resulting in the eGI of CH group lower than CS group. That was because the CH effected in retarding starch digestion and eGI through either formation of a physical barrier or alteration of the starch granule crystal structure, both decreasing the extent of starch digestibility and the rate of starch digestibility. The CS contain protein, calcium, ash, etc. The CS acted as physical barrier which mainly decreased the rate of starch digestibility. Considering that low glycemic food are desirable to generate and moderate postprandial glucose and insulin response, the CH-biscuits with higher content of CH would be advisable.
**TABLE 3**
| Biscuit | Addition amount | C∞ (%) | K (min–1) | HI | eGI |
| --- | --- | --- | --- | --- | --- |
| Control | 0% | 50.67 ± 1.31g | 0.0232 ± 0.0012b | 61.31 ± 2.16g | 73.37 ± 0.20g |
| CS | 5% | 42.62 ± 0.78f | 0.021 ± 0.0007ab | 50.04 ± 1.12f | 67.18 ± 0.62f |
| | 10% | 36.68 ± 0.17e | 0.0223 ± 0.0021ab | 43.87 ± 0.12e | 63.80 ± 0.06e |
| | 15% | 33.30 ± 1.9d | 0.0203 ± 0.0033ab | 38.67 ± 0.78d | 60.94 ± 0.43d |
| | 20% | 28.5 ± 0.71c | 0.0177 ± 0.002a | 31.55 ± 0.84c | 57.03 ± 0.46c |
| CH | 5% | 27.91 ± 1.92c | 0.0301 ± 0.0025c | 36.07 ± 0.79d | 59.51 ± 0.43d |
| | 10% | 26.24 ± 0.60c | 0.0220 ± 0.0014ab | 30.71 ± 0.25c | 56.57 ± 0.13c |
| | 15% | 22.89 ± 0.66b | 0.0212 ± 0.0025ab | 26.96 ± 0.58b | 54.51 ± 0.32b |
| | 20% | 19.04 ± 0.09a | 0.0211 ± 0.0022ab | 22.39 ± 0.87a | 52.00 ± 0.48a |
## Conclusion
The chitin and crayfish shell had significant effects on the physicochemical properties of biscuits and the digestion properties of starch. The moisture content and expansion ratio of the biscuits decreased with the increasing amount of CH and CS, while the oil content and density showed the opposite. In vitro digestion simulation test showed that CH and CS could reduce the percentage of ready digestible starch (RDS) and slow digestible starch (SDS) by 28 and $20\%$ while increasing the resistant starch (RS). Both CH and CS could slow down starch hydrolysis of biscuits. It was indicated that the structure formed by chitin and crayfish shell around the starch surface, which provided protection to the starch granules, prevented the entry of amylase, and slowed starch digestion. The protecting also restricted the swelling of the starch granules. The addition of CH and CS to hinder starch digestion in fried biscuits had healthy consequences, which indicated that CH-biscuits and CS-biscuits were belong to middle eGI food, even some were low eGI food. The CH and CS may help to lower the risk of high blood glucose levels caused by fried biscuits and maintain sensory quality at same time. The research results are of great significance in delaying starch digestion. However, the oil content of the fried biscuits was also a potential danger for health, and future research should identify methods of simultaneously reduce the oil content and the digestibility of starch.
## Data availability statement
The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
CB: conceptualization, verification, draft writing, and project administration. JZ: review and editing and supervision. GX: data curation, validation, and formal analysis. WW: data collection, data interpretation, validation, and language polish. JW: investigation, validation, and data curation. LQ: methodology and validation. QZ: project administration and conceptualization. TL: editing, methodology, resources, and funding acquisition. 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.
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---
title: Rapamycin Treatment Alleviates Chronic GVHD-Induced Lupus Nephritis in Mice
by Recovering IL-2 Production and Regulatory T Cells While Inhibiting Effector T
Cells Activation
authors:
- Jilu Zhang
- Xun Wang
- Renxi Wang
- Guojiang Chen
- Jing Wang
- Jiannan Feng
- Yan Li
- Zuyin Yu
- He Xiao
journal: Biomedicines
year: 2023
pmcid: PMC10045991
doi: 10.3390/biomedicines11030949
license: CC BY 4.0
---
# Rapamycin Treatment Alleviates Chronic GVHD-Induced Lupus Nephritis in Mice by Recovering IL-2 Production and Regulatory T Cells While Inhibiting Effector T Cells Activation
## Abstract
In this study, we test the therapeutic effects of rapamycin in a murine model of SLE-like experimental lupus nephritis induced by chronic graft-versus-host disease (cGVHD). Our results suggest that rapamycin treatment reduced autoantibody production, inhibited T lymphocyte and subsequent B cell activation, and reduced inflammatory cytokine and chemokine production, thereby protecting renal function and alleviating histological lupus nephritis by reducing the occurrence of albuminuria. To explore the potential mechanism of rapamycin’s reduction of kidney damage in mice with lupus nephritis, a series of functional assays were conducted. As expected, rapamycin remarkably inhibited the lymphocytes’ proliferation within the morbid mice. Interestingly, significantly increased proportions of peripheral CD4+FOXP3+ and CD4+CD25high T cells were observed in rapamycin-treated group animals, suggesting an up-regulation of regulatory T cells (Tregs) in the periphery by rapamycin treatment. Furthermore, consistent with the results regarding changes in mRNA abundance in kidney by real-time PCR analysis, intracellular cytokine staining demonstrated that rapamycin treatment remarkably diminished the secretion of Th1 and Th2 cytokines, including IFN-γ, IL-4 and IL-10, in splenocytes of the morbid mice. However, the production of IL-2 from splenocytes in rapamycin-treated mice was significantly higher than in the cells from control group animals. These findings suggest that rapamycin treatment might alleviate systemic lupus erythematosus (SLE)-like experimental lupus nephritis through the recovery of IL-2 production, which promotes the expansion of regulatory T cells while inhibiting effector T cell activation. Our studies demonstrated that, unlike other commonly used immunosuppressants, rapamycin does not appear to interfere with tolerance induction but permits the expansion and suppressive function of Tregs in vivo.
## 1. Introduction
Chronic graft-versus-host disease (cGVHD) is the major late complication following allogeneic transplantation, affecting up to $80\%$ of patients in some series [1]. Compared with acute GVHD, which is more likely to reflect cell apoptosis and necrosis, cGVHD is largely an inflammatory and fibrotic process [1,2,3]. Clinically, cGVHD is characterized by sustained B cell activation and autoantibody production that may lead to renal pathology, chronic liver involvement, and symptoms similar to those of autoimmune disease in allogeneic stem cell transplantation [4,5,6]. Injection of DBA/2 mouse lymphocytes to C57BL/6-DBA/2 F1 hybrids leads to cGVHD characterized by a persistent lymphoid hyperplasia producing hypergammaglobulinemia and a systemic lupus erythematosus (SLE)-like disease [1,7,8] with splenomegaly, B cell expansion, autoantibodies, and severe immune-complex-mediated glomerulonephritis that results in death from lupus-nephritis-induced renal failure [6,9,10,11].
The immunophilin ligand rapamycin, a macrocyclic triene antibiotic produced by Streptomyces hygroscopicus, exhibits potent immunosuppressive properties and is used therapeutically to prevent allograft rejection [12,13,14,15,16,17]. It acts by binding to the mammalian target of rapamycin (mTOR)–FK506 binding protein complex, resulting in the inhibition of mTOR protein kinase activity. It is known that rapamycin affects a variety of cell functions, including cell-cycle control, cell growth, apoptosis, transcription and translation efficiency [18,19,20,21]. In this study, we evaluated the therapeutic effect of rapamycin in a murine model of SLE-like experimental lupus nephritis induced by cGVHD and investigated the potential mechanism by which rapamycin regulates the progression of lupus nephritis.
## 2.1. Induction of Lupus Nephritis in cGVHD Model
Female (C57BL/6-DBA/2J) F1 hybrid mice aged 7 to 8 weeks were used as recipients of female DBA/2J donor lymphocytes. The cell suspension containing the mixture of donor cells from thymus, spleen and lymph nodes was injected intravenously (i.v.) four times within two weeks at intervals of 3 to 4 days. Serum and urine samples were collected weekly. At 12–15 weeks after treatment with rapamycin, all surviving animals were sacrificed for tissue harvesting. All animals were purchased from the Beijing Laboratory Animal Center and kept in specific-pathogen-free facilities.
The care, use and treatment of mice in this study were in strict agreement with the guidelines for the care and use of laboratory animals established by the Institute of Basic Medical Sciences.
## 2.2. Rapamycin Treatment
Mice that received DBA/2J donor lymphocytes were randomized to receive either rapamycin (3 mg/kg, $$n = 13$$) or equal volume of saline as control treatment group ($$n = 13$$) once daily by oral gavage post transplantation, which were successive until the end of the experiments. Oral liquid formulation of rapamycin was purchased from NCPC New Drug Research and Development Co., Ltd. (Amman, Jordan).
## 2.3. Proteinuria
The urine was collected from spontaneous urination when handling the mice. If mice did not urinate, gentle pressure was applied on the mouse bladder. The urine was collected in 1.5 mL Eppendorf tubes and kept on ice until testing [22,23]. Urinary protein concentration was determined by colorimetric analysis using Bradford protein assay kit (ThermoFisher Scientific, Waltham, MA, USA) within 2 h of collection. The onset of proteinuria was determined by stable urine protein concentration of 100 mg/dL or higher [22]. When the mice showed onset of proteinuria, an additional two proteinuria tests were performed in the following 24 h to avoid any false-positive results.
## 2.4. ELISA
The mouse blood was collected by cutting the tail. The serum was collected by centrifuging the blood at 1000–2000× g for 10 min in a refrigerated centrifuge. The standard ELISA assay was used to measure the production of serum autoantibodies 3 weeks after cell injection.
Briefly, 96-well flat-bottom ELISA plates (CorningCostar, Glendale, AZ, USA) were coated with double-stranded DNA (dsDNA) (Sigma-Aldrich, St. Louis, MO, USA) or single-stranded DNA (ssDNA) (Sigma-Aldrich, St. Louis, MO) at 4 °C overnight and then blocked with bovine serum albumin (BSA) (Sigma-Aldrich, St. Louis, MO, USA) before incubation with various dilutions (1:10–1:25) of serum samples from experimental animals. Plates were washed and then incubated with HRP-coupled goat anti-mouse IgG or murine IgG isotype including IgG1 and IgG2a. After incubation with the substrate TMB, the plates were read at O.D. 450 nm.
## 2.5. Histology
Kidney specimens cut from normal mice without cell metastasis and experimental mice were fixed with $10\%$ formaldehyde and then embedded in paraffin. Four-micrometer slices were obtained and stained with hematoxylin and eosin.
## 2.6. qPCR
Quantitative SYBRGreen RT-PCR kit (QIAGEN, Germantown, MD, USA) was used for quantitative PCR of the whole kidney tissue. Each sample was amplified in triplicate, and the relative expression data were determined by normalizing the GAPDH expression measured at the same time in the same sample to calculate the fold change in value using the 2−ΔΔCT method (ABI, step-one, Applied Biosystems, Waltham, MA, USA). The expression level of renal mRNA in normal mice was defined as 1. The primers used are shown in Table 1. Dissociation curve analysis was performed to confirm the specific amplification of each primer for each product.
## 2.7. Immunoblotting
Frozen kidney tissue proteins were prepared by homogenization in M2 lysis buffer, which contained $0.5\%$ NP-40, 250 mM NaCl, 20 mM Tris-HCl (pH 7.6), 3 mM ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA) and 3 mM ethylenediaminetetraacetic acid (EDTA). Before use, 10 µg/mL aprotinin, 1 mM dithiothreitol (DTT), 10 mM p-nitrophenyl phosphate, disodium salt (PNPP) and 1 mM Na3VO4 were freshly added and quantified by Bradford protein assay. The same amounts of samples were separated by $12\%$ sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose membrane. The membrane was incubated with $5\%$ skimmed milk in a heat-sealed bag at room temperature for 1 h, and then incubated with the primary antibody of mouse c-Jun N-terminal kinase (JNK), p-JNK, extracellular-signal-regulated kinases (ERKs), p-ERK, p38 mitogen-activated protein kinases (P38), p-P38 (Cell Signaling, Danvers, MA, USA) or glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (CaliBio, Shenzhen, China) at 4 °C overnight, and gently shaken. Then, we added the second antibody conjugated with relative horseradish peroxidase (HRP), and used the chemiluminescence detection kit (Amersham International, Amersham, UK) to generate the signal.
## 2.8. Flow Cytometry Analysis
Single-cell suspensions of peripheral blood leukocytes or spleen cells were prepared 6–8 weeks after cell transfer. All the flow antibodies and staining kits were purchased from eBiosciences, Inc., San Diego, CA. Cell populations were characterized with the following flow antibodies: CD4 (GK1.5), CD8 (53–6.7), CD69 (H1.2F3), CD44 (IM7), CD62L (MEL-14), TCRβ (H57-597) and ICOS (7E.17G9). Intracellular analysis of FoxP3 (FJK-16s) was performed after fixation and permeabilization, using Fix and Perm reagents. For intracellular cytokine staining, peripheral blood T cells or splenocytes were stimulated in vitro at 37 °C with phorbol myristate acetate (PMA) (50 ng/mL; Sigma) and ionomycin (1 μg/mL; Sigma) in the presence of 1 μg/mL brefeldin A. After 4–5 h culture, cells were collected and stained for surface marker (APC-CD4). Then, cells were washed, fixed and permeabilized according to the manufacturer’s datasheet. PE-conjugated antibodies of cytokines including IFN-γ, IL-2, IL-4 and IL-10 (eBioscience) were then added into the cells in permeabilization buffer for intracellular staining. Last, cells were washed in permeabilization buffer and resuspended in staining buffer for analysis on a flow cytometer. At least 15,000 events were collected and analyzed on a FACSCalibur using CellQuest software (BD Biosciences, San Diego, CA, USA). Isotype matching antibodies were used to define marker gating.
## 2.9. Cell Proliferation by CFSE Analysis
The cells (2 × 106) were labeled with 0.5 μM carboxyfluorescein diacetate succinimidyl ester (CFDA-SE, Molecular Probes, Eugene, OR, USA) and activated with 2 mg/mL anti-CD3 mAb (145-2C11, eBiosciences, Inc., San Diego, CA, USA) and 0.5 mg/mL anti-CD28 mAb (BD Pharmingen, San Diego, CA, USA) in complete culture media, RPMI 1640 (ThermoFisher Scientific, Waltham, MA, USA) containing $10\%$ FBS (ThermoFisher Scientific, Waltham, MA, USA). Intracellular esterases cleave the acetate groups, leading to the fluorescent carboxyfluorescein succinimidyl ester (CFSE). Cell division accompanied by CFSE dilution at 72 h after culture was analyzed by flow cytometry. The proportion of CFSE+ cells proliferating in vitro was calculated as below. The number of cells (events) in each cycle (division: n) was divided by 2 raised to power n to calculate the percentage of original precursor cells from which they arose. The sum of original precursors from division 1 to 6 represents the number of precursor cells that proliferated. The percent of CFSE+ divided cells was calculated by (no. of precursors that proliferated1–6/no. of total precursors0–6) × 100. Percentage of suppression in comparison to proliferation of control group lymphocytes was indicated as 100 × (percentage of divided cells of control group percentage of divided cells of rapamycin group)/(percentage of divided cells of control group).
## 2.10. Statistical Analysis
Data were expressed as mean ± standard deviation (SD). Proteinuria was compared by Chi-square test. For other datasets, Shapiro–Wilk test and homogeneity test of variance were first performed. Datasets meet the standard of normal distribution and homogeneity was further analyzed using Student’s t-test; otherwise, Mann–Whitney U test was selected. In each case, $p \leq 0.05$ was defined as statistically significant, and only significant probabilities are shown.
## 3.1. Rapamycin Treatment Delayed the Onset of Proteinuria, Decreased Serum Autoantibody Levels and Reduced Renal Tissue Damages in Lupus-Nephritis-Bearing Mice
Lupus nephritis is characterized by proteinuria, elevated serum autoantibody levels and renal tissue damages. To investigate the therapeutic effects of rapamycin in lupus nephritis, we used a well-established cGVHD-induced lupus nephritis mouse model by transferring DBA/2J donor lymphocytes in to C57BL/6-DBA/2J F1 hybrid mice. Rapamycin or vehicle control (saline) was administered once daily by oral gavage post transplantation. As shown in Figure 1A, all the control mice had proteinuria at 12 weeks after cell transfer, while only $30.77\%$ of rapamycin-treated mice developed proteinuria. These data suggest that rapamycin treatment significantly delayed the onset of proteinuria. Serum levels of total anti-ssDNA IgG, IgG1 and IgG2a and anti-dsDNA IgG, IgG1 and IgG2a were measured by ELISA 3 weeks after cell injection. As shown in Figure 1B, rapamycin treatment significantly reduced the level of the circulating autoantibodies.
Histological analysis by H&E staining (Figure 2) showed that the kidney tissue of the control group mice showed mesangial hyperplasia, renal tubular dilation with protein cast deposition in the renal tubules, white blood cell infiltration in the perivascular areas at the beginning of proteinuria development and in interstitial areas at the time of disease progression, as well as renal tubular atrophy, crescent formation and glomerular and vascular sclerosis at the end of the disease (Figure 2B). On the contrary, no significant tissue damage was observed in the renal slices of rapamycin-treated animals without albuminuria, including cell infiltration of renal parenchyma and glomerular or interstitial damage (Figure 2C).
All these data suggest that rapamycin treatment ameliorated lupus nephritis in mice.
## 3.2. Rapamycin Treatment Down-Regulated the Expression of Genes Involved in Pathogenesis of Lupus Nephritis and Inflammation-Related Signal Pathways in the Kidney
In order to determine several pathogenic factors involved in the pathogenesis of lupus nephritis in this human-like SLE mouse model, 36 genes, including inflammatory mediators and fibrosis molecular reverse transcripts from renal mRNA, were analyzed by real-time quantitative PCR. Compared with normal animals, 21 genes were up-regulated in nephrotic mice. Then, the effects of rapamycin treatment on the expression of these 21 genes in the kidney were tested. The results shown in Figure 3A demonstrate that rapamycin treatment significantly down-regulated the mRNA expression of these genes, including: 1. chemokines such as B lymphocyte chemoattractant (BLC), regulated on activation, normal T cell expressed and secreted (RANTES), monocyte chemoattractant protein 1 (MCP-1) and interferon gamma-induced protein 10 (IP-10); 2. proinflammatory cytokines such as TNF-α, IL-1β and IL-6; 3. fiber-forming factors such as TGF-β1 and pro (α1) I collagen; 4. cytokines such as IFN-γ, IL-21, IL-4, IL-10 and T cell immunoglobulin domain and mucin domain 3 (Tim-3); and 5. complement-activated components such as C3a, C5a, C3, C5, C3aR and C5aR. Unexpectedly, the level of IL-2 mRNA expression in rapamycin-treated mice was even higher than that in control mice. It is worth noting that we only tested the mRNA levels of these pathogenic factors, and the qPCR results might not accurately correlate with protein levels of these factors. To better understand the mechanism by which rapamycin regulates these pathogenic factors, the protein levels of these factors need to be confirmed in a future study.
Three members of MAPK, extracellular signal-regulated kinase (ERK), p38MAPK and c-Jun NH2-terminal kinase (JNK), have been identified as involved in the pathogenesis of inflammatory response to extracellular signals. Compared with the control group, rapamycin treatment resulted in significantly lower activation of p38, ERK and JNK signals in the kidney of transplanted mice, indicating the inhibitory effect of rapamycin on kidney inflammation (Figure 3B).
## 3.3. Rapamycin Treatment Inhibited Lymphocyte Activation and Proliferation
Rapamycin is a carbocyclic lactone–lactam macrolide antibiotic with strong immunosuppressive properties. To investigate the cellular mechanisms by which rapamycin alleviates the disease symptoms, we explored the effect of rapamycin treatment on lymphocyte activation and proliferation in vivo. The activation/memory phenotype (CD44highCD62low) of CD4+ T lymphocytes in peripheral blood was analyzed by flow cytometry. The results shown in Figure 4A indicate that the percentage of this population in the whole blood of rapamycin-treated mice was about $11.05\%$ ± 0.35, while that in control group mice was about $45.35\%$ ± 7.85, indicating that the activation and migration of CD4 T lymphocytes to target organs was greatly reduced. Spleen CD4 and CD8 T cells from nephrotic mice also mainly exhibited activation/memory phenotype and expressed the early activation marker CD69. Treatment with rapamycin significantly inhibited the activation of CD4 and CD8 T cells in the spleen. This was based on the observation that the percentages of CD4+CD69+ cells and CD8+CD69+ cells in the spleen cells of rapamycin-treated mice were significantly decreased ($3.6\%$ ± 0.4 vs. $7.65\%$ ± 0.64 and $0.97\%$ ± 0.12 vs. $3.25\%$ ± 0.49) compared with the control mice (Figure 4B). In addition, in rapamycin-treated mice, the expression of I-Ab in CD45R/B220+B cells was down-regulated (852.87 ± 23.82 vs. 1267.15 ± 365.50, Figure 4C), indicating that rapamycin treatment inhibited the activation of B cells.
Down-regulation of ICOS expression on splenic T lymphocytes was also observed in rapamycin-treated mice compared to control mice (Figure 4C). To further examine the effects of rapamycin treatment on lymphocyte perforation, peripheral blood lymphocyte cells were isolated from experimental mice and stained with CFSE. The labeled cells were then resuspended in culture medium with anti-CD3 mAb and anti-CD28 mAb. The proportion of CFSE+ cells proliferating at 72 h after culture was analyzed by flow cytometry. The results presented in Figure 4D show that the proliferation of CD4+, CD8+ T cells and total lymphocytes of rapamycin-treated mice was reduced by $65.66\%$, $63.41\%$ and $54.52\%$, respectively, compared to the cells from control mice.
## 3.4. Rapamycin Treatment Inhibited Inflammatory Cytokine Production While Enhancing IL-2 Production in Periphery
Our previous experimental results showed that activated effector T cells, including Th1, Th2 and Th17 cells, were involved in the pathogenesis of this lupus nephritis model with cGVHD, at least at some point in the progression of the disease.
As mentioned above, the kidney mRNA abundance for Th1 type cytokines such as IFN-γ and IL-2 and Th2 pattern such as IL-4 and IL-10 were up-regulated in nephritic mice in contrast to normal mice. This encouraged us to assess the intracellular cytokine production in peripheral lymphocytes. The results demonstrated that, compared with those from normal mice, the effector cells from nephritic mice secreted significantly more IFN-γ, IL-4 as well as IL-10, whereas they secreted a nonsignificantly lower amount of IL-2 (data were not shown). Interestingly, similar to the results obtained from renal mRNA analyses, the frequencies of the circulating IFN-γ-producing (0.75 ± 0.21 vs. 1.8 ± 0.28, $p \leq 0.05$), IL-4-producing (1.2 ± 0.14 vs. 4.4 ± 0.57, $p \leq 0.02$) and IL-10-producing (2.3 ± 0.14 vs. 7.9 ± 0.28, $p \leq 0.01$) effector cells in rapamycin-treated mice were all significantly down-regulated in comparison to those in control mice (Figure 5A–C). Meanwhile, the proportion of the circulating IL-2-producing effector cells in mice with rapamycin treatment was up-regulated compared to those in control group mice (3.05 ± 0.64 vs. 1.05 ± 0.21, $p \leq 0.05$). In summary, rapamycin regulated peripheral cytokine production from T cells. The inflammatory cytokines were inhibited by rapamycin, while the enhancement of IL-2 production indicated that regulatory T cells (Tregs) may be involved in immunosuppression mediated by rapamycin.
## 3.5. Rapamycin Treatment Increased Treg Proportions in Periphery
Given the up-regulation of IL-2 production, we further checked the CD25 (IL-2Rα) expression on lymphocytes by flow cytometry. As shown in Figure 5E, the percentage of CD4+CD25high T cells was significantly increased in mice treated with rapamycin compared to mice from the control group ($54.77\%$ ± 2.84 vs. $26.92\%$ ± 2.94, $p \leq 0.01$). Tregs are a subset of T cells characterized by CD4+CD25high cells that also express forkhead transcription factor FOXP3. They suppress conventional T cells (Tconv) and promote tolerance. There are emerging data suggesting that rapamycin induces suppression by expansion of natural regulatory T cells (nTregs), or at least by the provision of a selective survival advantage. This tempted us to evaluate the significance of Tregs in our model with rapamycin treatment. Proportions of splenic or peripheral blood CD4+FOXP3+ T cells were determined in rapamycin-treated mice as compared with control group animals. The results shown in Figure 5F demonstrate that CD4+FOXP3+ T cells in rapamycin-treated mice were significantly increased compared to the cells from the control group ($6.1\%$ ± 0.28 vs. $3.33\%$ ± 0.78, $p \leq 0.02$). Taken together, these data indicate that rapamycin treatment induced Treg populations which may contribute to the amelioration of lupus nephritis.
## 4. Discussion
The recognition of the foreign major histocompatibility complex (MHC) of the F1 host by parental lymphocytes results in extensive immunopathological changes. These include two main types of GVHD: acute and chronic [24]. The typical characteristics of acute GVHD are gastrointestinal ulcer, liver dysfunction and erythematous skin injury, while the clinical manifestations of chronic GVHD are usually similar to autoimmune diseases such as systemic lupus erythematosus, Sjogren’s syndrome, scleroderma and rheumatoid arthritis [25,26]. cGVHD is the most serious, and is an increasingly common long-term complication of allogeneic stem cell transplantation (alloSCT), affecting up to $80\%$ of patients in some series [27]. Recently, the main means of controlling acute and chronic GVHD is the preventive use of immunosuppressants to reduce or damage the function of donor T cells. However, these treatment strategies often lead to non-specific immunosuppression or other adverse side effects. Although efforts have been made to develop new anti-GVHD treatment methods, including blocking the costimulatory pathway, inducing the tolerance of donor cells, deviation of the recipient immune system towards the Th2 response, and interference with the function or expression of adhesion molecules, cytokines or T cells, cGVHD is still a serious problem. Therefore, it is necessary to better understand the induction and pathogenesis of cGVHD [27,28,29,30,31].
Injecting DBA/2 mouse lymphocytes into C57BL/6-DBA/2F1 hybrid mouse resulted in cGVHD, accompanied by persistent lymphoproliferative, hypergammaglobulinemia and systemic-lupus-erythematosus-like disease [6,9,10,11]. These human-SLE-like animals were mainly characterized by proteinuria, splenomegaly, autoantibody production and immune-complex-mediated glomerulonephritis. With respect to histopathological analysis, when proteinuria occurred in these mice, the deposition of immune complex (IC) and diffuse proliferation were observed in the mesangial regions and leukocytic infiltration in perivascular areas. At the later stage of the disease, ICs confined to the peripheral capillary loops and proliferative glomerulonephritis appeared in endothelial cells and mesangial cells, and interstitial and intraglomerular inflammation and protein deposition in dilated tubules also appeared; the severity increased with the progression of the disease. Eventually, severe interstitial inflammation, tubular necrosis, crescent formation and glomerulosclerosis occurred in the late stage of the disease. By observing the up-expression of activated markers including CD69, ICOS and I-Ab, both T and B lymphocytes were activated in nephritic mice. In addition, many cytokines, chemokines, complements and MAPK pathways involved in the pathogenesis of lupus nephritis were significantly up-regulated in nephritic mice. Most of these inflammatory mediators were present early in the kidneys of nephrotic mice with persistent albuminuria and increased as the disease progressed. In addition, our results show that serum levels of Th1-like IgG2a and Th2-like IgG1 alloantibodies increased 2 weeks after transplantation of the cell mixture prior to nephritis and persisted until onset of albuminuria in this mouse cGVHD model, suggesting an immune disorder in the early stages of SLE-like disease [2]. Consistent with this, cytokine expression of Th1 types (e.g., IFN-γ and IL-2) and Th2 types (e.g., IL-4 and IL-10) increased with disease progression.
Rapamycin, also known as sirolimus, is a macrolide antibiotic with potent immunosuppressive properties and is clinically used for the prevention of acute rejection in renal transplantations and GVHD [32,33,34,35,36]. In renal transplant patients, combinations of rapamycin with cyclosporine (CsA) lower the rate of organ failure. The frequency of biopsy-confirmed acute rejection in the following 6 months was also decreased in patients receiving rapamycin and CsA. Furthermore, rapamycin treatment also decreased frequency of moderate and/or severe histologic grades of rejection as well as the requirement for antilymphocyte antibody treatment [15]. In a recent clinical trial of rapamycin for kidney transplant patients, rapamycin proved to have an acceptable safety profile in the prevention of kidney allograft rejection [32]. In GVHD patients, therapeutic and prophylactic treatment of rapamycin has been reported [33,34]. In a randomized multicenter trial of rapamycin vs. prednisone as initial therapy for standard-risk acute GVHD, rapamycin provided similar overall initial treatment efficacy to prednisone. Patients receiving rapamycin therapy was also associated with reduced steroid exposure, greater immune suppression discontinuation and improved patient-reported quality of life [33]. A most recent study reported a phase 2 trial of GVHD prophylaxis with rapamycin, posttransplant cyclophosphamide (PTCy) and tacrolimus/mycophenolate mofetil (MMF) after peripheral blood haploidentical transplantation. In this study, rapamycin and PTCy/MMF GVHD prophylaxis not only decreased the grade II–IV acute GVHD rates after HLA-haploidentical hematopoietic cell transplantation, but also permitted hematopoietic engraftment and led to low moderate/severe chronic GVHD rates and favorable survival [34,35]. Rapamycin acts by inhibiting mammalian target of rapamycin (mTOR), a key protein kinase necessary for cell cycle progression, thereby suppressing the proliferation and clonal expansion of interleukin-2-stimulated T lymphocytes [18,19,20,21]. Unlike other commonly used immunosuppressants, rapamycin does not appear to interfere with tolerance induction [37,38,39] and permits the in vitro expansion and suppressive function of Tregs [40,41,42]. In vivo studies also demonstrated that rapamycin treatment could enhance TGFβ-dependent FOXP3 expression and generate large-scale human regulatory T cells that suppress disease in a xenogeneic model of GVHD, thus opening the door for using Tregs as a cellular therapy to prevent GVHD, graft rejection and autoimmunity [43].
In this study, we first used an oral liquid formula of rapamycin to treat SLE-like experimental lupus nephritis induced by cGVHD. Our results suggest that rapamycin treatment reduces autoantibody production, inhibits T lymphocytes and subsequent B cell activation, reduces inflammatory cytokine and chemokine production, and thereby protects renal function by manifestation with reduced proteinuria development and attenuated histological lupus nephritis. As expected, rapamycin remarkably inhibited the lymphocytes’ proliferation in the affected mice. Furthermore, consistent with the results for changes of mRNA abundance in kidney by real-time PCR analyses, intracellular cytokine staining demonstrated that rapamycin treatment remarkably diminished the secretion of cytokines from splenocytes, including Th1 type such as IFN-γ and Th2 type such as IL-4 and IL-10. However, the production of IL-2 from splenocytes in rapamycin-treated mice was much higher than that in control group animals (Figure 5A–D). Intriguingly, Tregs were also increased in mice receiving rapamycin. Previous studies have suggested that rapamycin selectively expands the peripheral Tregs, at least providing a selective survival advantage [44,45,46], and IL-2 plays a crucial role in the maintenance of natural immunologic self-tolerance through the generation and maintenance of CD4+CD25+ Tregs [47,48]. Thus, we propose that rapamycin treatment favored the Tregs population and IL-2 augmentation further expanded Tregs in vivo, thus ameliorating lupus nephritis by suppressing activated effector cells. Our study has two limitations regarding the methodology of the proteinuria test. Instead of testing 24 h urine protein, we only tested urine protein concentration at one time point per day, which may not precisely reflect the progression of kidney dysfunction. In addition, we did not examine urine creatinine concentration. The urine protein creatinine ratio has been considered an important parameter for predicting proteinuria [49,50,51], which needs to be included in our further study on lupus nephritis.
In conclusion, our studies demonstrate that, unlike other commonly used immunosuppressants, rapamycin does not appear to interfere with tolerance induction but permits the expansion and suppressive function of Tregs in vivo. Rapamycin treatment alleviated SLE-like experimental lupus nephritis by the recovery of IL-2 production, which modulated the maintenance and expansion of CD4+FOXP3+ Tregs, in turn inhibiting activated effector T cells.
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|
---
title: The Influence of Exercise-Associated Small Extracellular Vesicles on Trophoblasts
In Vitro
authors:
- Shuhiba Mohammad
- Jayonta Bhattacharjee
- Velislava Tzaneva
- Kelly Ann Hutchinson
- Madeeha Shaikh
- Danilo Fernandes da Silva
- Dylan Burger
- Kristi B. Adamo
journal: Biomedicines
year: 2023
pmcid: PMC10045992
doi: 10.3390/biomedicines11030857
license: CC BY 4.0
---
# The Influence of Exercise-Associated Small Extracellular Vesicles on Trophoblasts In Vitro
## Abstract
Exercise induces the release of small extracellular vesicles (sEVs) into circulation that are postulated to mediate tissue cross-talk during exercise. We previously reported that pregnant individuals released greater levels of sEVs into circulation after exercise compared to matched non-pregnant controls, but their biological functions remain unknown. In this study, sEVs isolated from the plasma of healthy pregnant and non-pregnant participants after a single bout of moderate-intensity exercise were evaluated for their impact on trophoblasts in vitro. Exercise-associated sEVs were found localized within the cytoplasm of BeWo choriocarcinoma cells, used to model trophoblasts in vitro. Exposure to exercise-associated sEVs did not significantly alter BeWo cell proliferation, gene expression of angiogenic growth factors VEGF and PLGF, or the release of the hormone human chorionic gonadotropin. The results from this pilot study support that exercise-associated sEVs could interact with trophoblasts in vitro, and warrant further investigation to reveal their potential role in communicating the effects of exercise to the maternal–fetal interface.
## 1. Introduction
Exercise during pregnancy is well known to bestow benefits on both mother and fetus, potentially improving health across two generations. To promote maternal health and as a front-line therapy for mitigating the risk of pregnancy disorders, the 2019 Canadian Guideline for Physical Activity throughout Pregnancy recommends that those without contraindications should engage in at least 150 min of moderate-intensity physical activity per week [1]. Maternal physical activity is associated with decreased risk of pregnancy complications, including preeclampsia, gestational diabetes mellitus, and gestational hypertension [2], and improvement of prenatal depressive symptoms [3] while reducing blood glucose levels [4]. Prenatal physical activity is also associated with beneficial neonatal outcomes such as reduced odds of macrosomia [5] and fat mass at birth [6]. While the advantages are numerous and well established, the biological mechanisms by which these benefits are communicated to the mother and fetus are not well understood.
Exercise has been posited to alter the structure and physiology of the placenta [7], the critical interface between mother and fetus chiefly responsible for maternal–fetal communication. The placenta supports fetal growth and survival by mediating the exchange of gases, nutrients, and waste, while providing endocrine and immune support. As a transient discoid organ anchored to the maternal uterus, the placenta is composed of a heterogeneous population of cells organized in a manner to ensure sufficient maternal–fetal exchange between the juxtaposed maternal and fetal circulatory systems. In the placenta, maternal exercise has been shown to impact oxidative stress [8], nutrient transporters [9,10], and angiogenic growth factors [11,12]. Data from exercise interventions also show beneficial effects on placental growth [13] and volume available for maternal–fetal exchange [14,15]. While it is evident that exercise impacts placental function, the biological mechanisms contributing to these changes are less clear.
The benefits of exercise are hypothesized to be mediated in part by the release of bioactive molecules into circulation after exercise, including myokines (cytokines produced and released by skeletal muscle) [16,17] and small extracellular vesicles (sEVs) [18,19]. sEVs (historically referred to as “exosomes” [20]) are lipid membrane-enclosed particles (~20–120 nm in diameter) secreted by cells into extracellular space and contain bioactive compounds such as nucleic acids (i.e., mRNAs and miRNAs), lipids, and proteins [21,22]. sEVs are theorized to facilitate long-distance intercellular communication by interaction or transfer of their biological cargo from donor to target cells [23,24]. Data from both human and animal studies demonstrate that sEVs are released into circulation after exercise (reviewed by [19]) and contain biological molecules potentially involved in tissue cross-talk in response to exercise [18]. We previously reported that pregnant individuals release greater levels of sEVs into circulation compared to non-pregnant individuals after a single bout of moderate-intensity exercise [25]. The potential biological function and cellular targets of circulating sEVs released in response to maternal exercise are unknown. Since the placenta can release and take up sEVs as a part of maternal–fetal communication [26,27], exercise-associated sEVs could possibly act on the placenta. Therefore, circulating sEVs may represent a potential mechanism involved in improving placental function in the context of maternal exercise.
In this study, we examined in pilot experiments whether exposure to circulating sEVs obtained after acute exercise affected trophoblasts in vitro. Trophoblast cells are specialized epithelial cells that constitute the maternal–fetal interface and are responsible for hormone production and the exchange of gases, nutrients, and waste between mother and fetus. First, we determined whether circulating exercise-associated sEVs from healthy pregnant and non-pregnant individuals could interact with trophoblasts in vitro. Further, we evaluated whether exposure to exercise-associated plasma sEVs could influence metrics of trophoblast biology, including proliferation, expression of angiogenic growth factors known to be altered in the placenta of physically active vs. inactive mothers [11], and secretion of the major pregnancy hormone, human chorionic gonadotropin (b-hCG). Treatment with sEVs from healthy non-pregnant controls was used to determine whether the potential effects on trophoblast biology were associated with pregnancy status or exercise stimulus. In this set of pilot experiments, we hypothesized that exercise-associated sEVs from pregnant individuals would elicit greater effects on trophoblast biology compared to exercise-associated sEVs from non-pregnant controls.
## 2.1. Ethical Approval and Study Participants
Experimental procedures were approved by the University of Ottawa Research Ethics Board (file number: H-06-18-634). All protocols were performed in fulfillment of the guidelines described in the Declaration of Helsinki. Informed written consent was secured from participants after an explanation of the study procedures. Pregnant and non-pregnant individuals were recruited from the *Ottawa area* (ON, Canada) for participation in the study. Healthy individuals without contraindication to exercise between the ages of 18 and 40 were eligible for inclusion, with a self-reported pre- or non-pregnant body mass index (BMI) of 18.5–29.9 kg/m2. Participants were required to be weight-stable (±5 kg) for approximately six months before the study. Pregnant participants needed to be between 13 and 28 weeks gestation carrying a singleton fetus to participate in the study. Those with chronic health conditions including hypertension, diabetes (pre/non-pregnant or gestational diabetes), and untreated thyroid disease and frequent users of tobacco, drugs, or alcohol were excluded from participation.
## 2.2. Acute Exercise Procedure
The acute exercise stimulus followed procedures outlined by Hutchinson et al. [ 2019] [28] and Mohammad et al. [ 2021] [25]. Briefly, participants were requested to refrain from exercise and food for 8 h before the acute exercise session. Participants were provided with a standardized snack, after which resting heart rate was determined as previously described [25,28]. A target range of 40–$59\%$ of heart rate (HR) reserve was used to specify moderate-intensity exercise [29,30], where HR reserve was calculated using the Karvonen equation [31] as described previously [25,28]. The moderate-intensity acute exercise session consisted of a brisk 30 min treadmill walk with continuous HR monitoring using a Polar V800 HR monitor (Polar Electro, Lachine, QC, Canada). A short warm-up (3 min at $2\%$ incline at a speed of 2 miles per hour (mph)) was followed by an incremental phase ($6\%$ incline), where treadmill speed was increased by 0.2 mph every minute until the upper range of moderate-intensity HR reserve ($59\%$) was met. Once this range was met, the participants continued to exercise for 30 min. Blood was collected (10 mL) immediately before (at rest) and after the exercise session from the median cubital vein using potassium EDTA blood collection tubes (#367863; BD Biosciences, Mississauga, ON, Canada). Plasma was promptly processed by centrifugation at 1700× g for 15 min at 4 °C, and samples were stored at −80 °C until further analyses.
## 2.3. sEV Isolation and Labeling
Isolation of sEVs from plasma was performed using differential ultracentrifugation as described previously [25,32,33]. Plasma samples (1.0 mL) were thawed rapidly at 37 °C, then kept on ice for all remaining procedures. Samples were first centrifuged at 20,000× g for 20 min at 4 °C to remove large EVs and apoptotic bodies. The remaining supernatant was centrifuged at a speed of 100,000× g using a Beckman Coulter Optima MAX ultracentrifuge (Beckman Coulter Inc., Brea, CA, USA) equipped with a TLA-55 rotor (Beckman Coulter) for 90 min at 4 °C. The resulting pellet of sEVs was washed with 1.0 mL of 0.1 µm filtered phosphate-buffered saline (PBS) and then centrifuged at 100,000× g as described above. The final residual pellet of sEVs was resuspended in 100 µL of 0.1 µm filtered PBS. Aliquots of 10 µL of this suspension were separated and frozen at −80 °C for further analysis. One 10 µL aliquot was used for protein extraction and quantification to standardize sEV treatment concentrations for subsequent functional assays. To extract protein, 1 µL of 10× radioimmunoprecipitation assay (RIPA) buffer with protease inhibitor cocktail (MilliporeSigma Canada Co., #P8340, Oakville, ON, Canada) was added to a 10 µL sEV aliquot. Samples were sonicated using a bath sonicator for 1 min to achieve EV and protein lysis and subsequently were incubated on ice for 30 min before protein quantification using a DC protein assay (Bio-Rad Laboratories, #5000112, Mississauga, ON, Canada). sEVs from this cohort were previously validated to be the expected size (~100–120 nm) and displayed characteristics confirming the presence of these particles (i.e., expression of classical sEV protein markers TSG-101 and flotillin-1, absence of non-sEV marker calnexin, and intact membrane integrity and characteristic size as determined by transmission electron microscopy [25]. Where indicated, sEVs were fluorescently labeled with PKH26, a lipophilic membrane dye, using the PKH26 Red Fluorescent Cell Linker Kit according to the manufacturer’s instructions (Phanos Technologies, MilliporeSigma Canada Co., #MINI26-1KT). Following the incorporation of the dye, the labeled sEVs were centrifuged at 100,000× g for 90 min at 4 °C, and the resulting sEV pellets were resuspended in 100 µL of 0.1 µm filtered PBS and stored at −80 °C for subsequent sEV internalization and interaction fluorescence assays.
## 2.4. Cell Culture
BeWo choriocarcinoma cells were obtained from the American Type Culture Collection (ATCC CCL-98, Manassas, VA, USA) and grown in Ham’s F-12K (Kaighn’s) medium (Gibco, Thermo Fisher Scientific, #21127022, Waltham, MA, USA) supplemented with $10\%$ fetal bovine serum (FBS) and incubated at 37 °C and $5\%$ CO2 in a humidified environment. For all sEV treatment experiments, cells from passages 7–12 were used in the presence of F-12K medium supplemented with $10\%$ EV-depleted FBS. EV-depletion of undiluted FBS was achieved by ultracentrifugation at 100,000× g for 90 min at 4 °C [34] and retention of the resulting supernatant, confirmed by nanoparticle tracking analysis. Cells were experimentally manipulated 48 h post-seeding.
## 2.5. sEV Localization by Fluorescence Confocal Microscopy
BeWo cells (2.5 × 104) were plated in 8-well chamber slides (ibidi USA, #80826, Fitchburg, WI, USA) and incubated with 2.5 µg/mL of PKH26-labeled sEVs (or PBS control) overnight (16 h) at 37 °C and $5\%$ CO2. The cell culture supernatant was removed, and wells were vigorously washed five times with PBST and then fixed with $10\%$ buffered formalin for 10 min at room temperature. All subsequent steps were conducted at room temperature with three PBST washes between each step unless noted otherwise. Fixative was removed, and then cells were permeabilized using $0.1\%$ Triton X-100 in PBS for 5 min. Next, slides were incubated with phalloidin-iFluor 488 Reagent (1:1000; Abcam Inc., #ab176753, Cambridge, MA, USA) in $1\%$ BSA in PBS for 25 min. Finally, 1 drop of NucBlue Fixed Cell ReadyProbes Reagent (DAPI) (Invitrogen, Thermo Fisher Scientific, #R37606) was added to each well for 5 min. Mounting media (ibidi USA, #500001) were added to each well before imaging using an inverted Zeiss LSM 880 AxioObserver Z1 laser-scanning confocal microscope with Airyscan FAST detector equipped with Zen Black software (version 2.3, Carl Zeiss Microscopy GmbH, Jena, Germany). Images were taken using a 63× oil-immersion objective lens (Carl Zeiss Microscopy GmbH, Plan Apochromat $\frac{63}{1.4}$ NA oil) with optical slices (z-stacks) at a thickness of 0.20 µm. The confocal was equipped with lasers emitting at 405, 488, and 561 nm which were used for the excitation of each fluorophore: DAPI (Ex/Em $\frac{405}{450}$ nm), phalloidin (Ex/Em $\frac{488}{516}$ nm), and PKH26 (Ex/Em $\frac{561}{579}$ nm). Each confocal microscopy image was acquired using the same imaging parameters. Images were subjected to linear unmixing of the measured spectral profiles for each fluorophore (DAPI, phalloidin, and PKH26) using Zen Black software (version 2.3) to account for signal crossover between spectral channels. Representative maximum intensity projections were acquired from a subset of z-stacks corresponding to the middle of the cells. For each condition, a minimum of three random fields of view were selected and examined for sEV localization. To increase the quality of the images for display purposes, lookup tables for the phalloidin channel image were set to “Magenta” and for the PKH26 channel image were set to “Green” using Fiji software (version $\frac{2.3.0}{1.53}$f, U.S. National Institutes of Health, Bethesda, Maryland, USA).
For the localization experiments, a range of PKH26-labeled sEV concentrations was initially tested (1, 2.5, 5, and 10 µg/mL) for visualization by confocal microscopy. The concentration of 2.5 µg/mL of sEVs incubated overnight (16 h) was found to be the exposure with the best signal-to-noise ratio.
## 2.6. Proliferation Assessment by Ki67 Immunostaining
To assess the influence of exercise-associated plasma sEVs on BeWo cell proliferation in vitro, Ki67 immunostaining was used. BeWo cells (2.5 × 104) were seeded onto 8-well chamber slides (ibidi USA, #80826) and incubated with 10 µg/mL sEVs (or PBS control) for 24 h at 37 °C and $5\%$ CO2 in duplicate. Then, the cell culture supernatant was removed and wells were washed three times with PBST. All of the following steps were carried out at room temperature unless stated otherwise. Cells were fixed with $10\%$ formalin for 10 min and then washed three times with PBST. Cells were then permeabilized using $0.1\%$ Triton X-100 in PBS for 5 min followed by three washes with PBST. Cells were blocked for 30 min with BlockAid Blocking Solution (Invitrogen, Thermo Fisher Scientific, #B10710) and then incubated with recombinant anti-Ki67 rabbit monoclonal antibody (SP6) (1:250; Abcam Inc, #ab16667) at 4 °C in PBST overnight. Negative controls omitted the primary antibody. The following day, wells were washed three times with PBST and then incubated with goat AlexaFluor 488 anti-rabbit IgG (H+L) Superclonal recombinant secondary antibody (1:1000; Invitrogen, Thermo Fisher Scientific, #A27034) in PBST for 60 min. Wells were washed three times with PBST and then incubated with 1 drop of NucBlue Fixed Cell ReadyProbes Reagent (DAPI) (Invitrogen, Thermo Fisher Scientific, #R37606) per well for 5 min. Mounting media (ibidi USA, #500001) were added to each well before imaging using a ThermoFisher FL Auto 2 inverted automated epifluorescent microscope equipped with version Auto2 software (Invitrogen, Thermo Fisher Scientific). Each well was divided into four quadrants, with one image taken at 20× magnification per quadrant for a total of four images per well. All experiments were performed in triplicate. A ratio of Ki67 immunostaining intensity to nuclear area was measured using Fiji software (version $\frac{2.3.0}{1.53}$f, U.S. National Institutes of Health).
## 2.7. RNA Isolation and Quantitative Real-Time Polymerase Chain Reaction (qPCR)
BeWo cells were seeded at a density of 1.0 × 105 in 12-well dishes; 48 h later, they were treated with 10 µg/mL plasma sEVs (or PBS control) for 24 h at 37 °C and $5\%$ CO2. Cell culture supernatant was collected and stored at −80 °C for downstream b-hCG analysis described below. Cells were washed twice with cold sterile PBS and then lysed for total RNA isolation using an Illustra RNAspin Mini isolation kit (Cytiva Life Sciences, Fisher Scientific Company, #25050071, Ottawa, ON, Canada) as per the manufacturer’s instructions. Isolated total RNA was eluted in RNase-free water and was analyzed for concentration and purity using spectrophotometry (Take3, Gen5 software version 1.11.5, BioTek Instruments Inc., Winooski, VT, USA). RNA integrity was verified using a $2\%$ agarose gel stained with SYBR Safe DNA gel stain (Invitrogen, Thermo Fisher Scientific, #S33102) and electrophoresis of bromophenol blue-labeled RNA aliquots at 100 V for 30 min in TAE (Tris-acetate-EDTA) buffer. RNA bands were visualized by ultraviolet transillumination using a ChemiDoc XRS+ system (Bio-Rad Laboratories, Mississauga, ON, Canada). Then, 0.5 µg of RNA was reverse transcribed into cDNA using an iScript cDNA synthesis kit (Bio-Rad Laboratories, #1708891) and a Biometra TPersonal Combi Thermocycler (Analytik Jena, Jena, Germany) according to the manufacturer’s protocol, and then stored at −20 °C until further analysis. A total of 25 ng of cDNA was used for quantitative polymerase chain reaction (qPCR) using TaqMan Advanced Master Mix (Applied Biosystems, Thermo Fisher Scientific, #444557). Samples were loaded into a Rotor-Gene 3000 real-time DNA detection system with Rotor-Gene software (version 6.1.93, Corbett Research, Sydney, Australia). The expression of angiogenic growth factors VEGF and PLGF was measured relative to an endogenous control, GAPDH. *Taqman* gene expression assay probes labeled with 6-FAM (6-carboxyfluorescein) fluorescent dye were used for the detection of VEGF (Hs00900055_m1), PLGF (Hs00182176_m1), and GAPDH (Hs02786624_g1). The qPCR reaction was as follows: hold at 50 °C for 2 min, then hold at 95 °C for 2 min, followed by 40 cycles consisting of denaturing at 95 °C for 3 s and hold at 60 °C for 30 s. All qPCR reactions were performed in duplicate with appropriate controls (no cDNA reverse transcriptase cDNA control and no template qPCR control), and all experiments were conducted in triplicate. Corresponding threshold cycle (CT) values were recorded, and relative gene expression was calculated using the 2−ΔΔCT method [35]. Data from the target genes (VEGF and PLGF) were expressed as a ratio to GAPDH gene expression for normalization. Gene expression values from PBS control treatment conditions were considered as control.
## 2.8. b-hCG Assay
Cell culture supernatant collected from the gene expression analyses described above were used to determine the effect of sEV treatment on b-hCG production in BeWo cells after 24 h of exposure using a DRG b-hCG ELISA kit (DRG International, #EIA-1911, Springfield, NJ, USA) as per the manufacturer’s instructions. b-hCG concentration was normalized to mg of total RNA isolated from the corresponding gene expression experiments and is presented in mIU/mL.
## 2.9. Statistical Analysis
All data are presented as mean ± standard deviation (SD) from three independent experiments. All statistical analyses and graphs were generated using GraphPad Prism version 9.3 (GraphPad Software, La Jolla, CA, USA). A one-way ANOVA with Tukey’s post-test (if applicable) was used to compare whether no treatment (PBS control) was different from exposure to exercise-associated sEVs from pregnant and non-pregnant individuals. The homogeneity assumption was confirmed by Levene’s test. Statistical significance was considered when $p \leq 0.05.$
## 3.1. Exercise-Associated sEVs Interact with BeWo Cells
We first examined whether circulating sEVs obtained from pregnant and non-pregnant individuals after a bout of acute moderate-intensity exercise could be found within trophoblast-like cells in vitro. Representative images obtained by confocal microscopy show that PKH26-labeled sEVs from both pregnant and non-pregnant individuals obtained post-exercise localized within the cytoplasm of BeWo cells after overnight incubation (Figure 1).
## 3.2. BeWo Cell Proliferation Was Not Affected upon Exposure to Exercise-Associated sEVs
Immunostaining of the protein Ki67 was used to assess proliferation in BeWo cells after 24 h incubation with exercise-associated sEVs. Ki67 is a widely used marker of cell proliferation and has been used in the examination of trophoblast and BeWo cell proliferation and phenotype [36]. Exposure to sEVs obtained from pregnant participants and non-pregnant controls after exercise did not influence BeWo cell proliferation after 24 h when compared to vehicle treatment (PBS control) (Figure 2).
## 3.3. Exposure to Plasma sEVs Did Not Alter the Gene Expression of Angiogenic Growth Factors in BeWo Cells
We previously reported that maternal exercise was associated with differential expression of angiogenic proteins in the placenta of individuals categorized as physically active vs. inactive during pregnancy [11]. We therefore determined whether exposure to circulating exercise-associated sEVs from pregnant or non-pregnant individuals affected gene expression of VEGF and PLGF in BeWo cells. Gene expression of both VEGF and PLGF did not differ between treatment with PBS control vs. exercise-associated sEVs obtained from both groups ($F = 1.98$, $$p \leq 0.200$$, and $F = 0.726$, $$p \leq 0.513$$, respectively) (Figure 3A,B).
## 3.4. Human Chorionic Gonadotropin Levels Were Not Affected upon Exposure to Exercise-Associated sEVs
The hormone b-hCG is known to be produced and released from BeWo cells in vitro in the process of syncytialization, an essential part of trophoblast differentiation and function [37,38]. We examined the levels of b-hCG in cell media after incubation with exercise-associated plasma sEVs from pregnant and non-pregnant participants. Exposure to exercise-associated sEVs did not result in differing levels of b-hCG in cell media when compared to no treatment (PBS control exposure) ($F = 0.885$, $$p \leq 0.450$$; Figure 3C).
## 4. Discussion
This study aimed to determine whether sEVs released after a single bout of acute moderate-intensity exercise in pregnant individuals and respective non-pregnant controls can be internalized by cells modeling placental trophoblasts in vitro and alter their function. We provide the first evidence that sEVs released after exercise into circulation could interact with trophoblasts in vitro but do not alter traditional indices of trophoblast biology, including proliferation, gene expression of angiogenic markers, or production of the pregnancy hormone b-hCG. sEVs are postulated to be an important delivery mechanism in the adaptive response to exercise on a systemic level [18,19,39]. This study provides preliminary evidence that may facilitate our understanding of how exercise is communicated from the mother to the fetus during pregnancy via the maternal–fetal interface.
Central to the hypothesis that sEVs communicate the benefits of exercise systemically is their uptake into recipient tissues and cells, or surface interactions with activating receptors. Exercise-associated sEVs from both pregnant and non-pregnant individuals were localized within BeWo cells. Few studies have evaluated whether sEVs released into circulation in response to exercise can be taken up by or interact with recipient cells. Notably, Whitham et al. [ 2018] showed that fluorescently labeled EVs from mouse myoblasts could be localized within mouse liver hepatocytes and that cargo of plasma EVs from exercising mice was incorporated into mouse liver hepatocytes [18]. In the context of exercise training and cardiomyocyte injury, PKH26-labeled EVs isolated from the plasma of exercising rats were found to be internalized into cardiomyocytes in vitro after a 6 h incubation period [40]. In a study conducted by Just et al. [ 2020], sEVs released after acute blood flow-restricted resistance exercise in healthy young men were localized within muscle stem cells and fibro-adipogenic progenitor cells in vitro [41]. Our proof-of-concept experiments show that exercise-associated sEVs can also interact with trophoblasts, specialized cells that are in direct contact with maternal blood and constitute the maternal–fetal interface. Since evidence suggests that EV uptake is not a passive process and involves a variety of energy-requiring endocytic pathways (reviewed by [42]), we postulate that exercise-associated sEVs may play a role in modifying trophoblast function in the context of maternal exercise.
Having observed that exercise-associated sEVs could interact with trophoblast-like cells in vitro, we sought to investigate their potential impact on trophoblast physiology. The secretion of b-hCG in cell culture media was investigated as a marker of BeWo cell and trophoblast differentiation. Impaired trophoblast differentiation and fusion are seen in pregnancy pathologies including pre-eclampsia [38], for which risk is mitigated by regular engagement in physical activity during pregnancy [2]. The release of b-hCG was not affected by exposure to exercise-associated sEVs from pregnant or non-pregnant individuals, nor was BeWo cell proliferation. Trophoblasts produce VEGF and PlGF to promote branching and non-branching placental angiogenesis, respectively [43,44]. Given that we previously reported differential expression of angiogenic growth factors VEGF, PlGF, and their respective receptors in term placenta of individuals categorized physically active or inactive [11], we aimed to determine whether gene expression of VEGF or PLGF was affected by exercise-associated sEVs. Regardless of pregnancy status, exposure to exercise-associated sEVs did not produce changes in VEGF or PLGF expression levels in BeWo cells. Our observations differ from data in human umbilical vein endothelial cells (HUVECs), where exposure to plasma sEVs isolated from pregnant and non-pregnant individuals at resting conditions was found to increase endothelial cell migration similarly to VEGF-induced migration [27]. It must be noted that Salomon et al. [ 2014] [27] used a concentration of sEVs that was 10-fold higher than concentrations used to expose trophoblasts in our study (i.e., 100 µg/mL vs. 10 µg/mL EV protein, respectively), and they did not examine gene expression of VEGF.
Relatively few studies to date have determined the physiological impact of exercise-associated sEVs on the biological functions of target cells, and none have been conducted on trophoblasts or cells constituting the maternal–fetal interface. The majority of work on the physiological consequences of exercise-associated sEVs involve animal models, where their interactions have been shown to delay prostate cancer progression [45], provide sustained cardioprotection [40], and elicit beneficial effects in ischemic stroke [46]. In humans, sEVs obtained after blood flow-restricted resistance exercise in healthy men were found to increase the proliferation of fibro-adipogenic progenitor cells [41]. The lack of biological impact on trophoblast biology demonstrated in the present study is likely due to a variety of factors. The bioactivity of sEVs is largely dependent on their diverse biological cargo, which is influenced by the cells of origin. Identifying the contents and origins of exercise-associated sEVs in pregnant and non-pregnant participants was beyond the scope of this preliminary study but represents a critical knowledge gap in this emerging field. Characterization of the biological contents and cellular origins of exercise-associated maternal sEVs will allow for the development of more targeted hypotheses regarding assessments of trophoblastic function. Exercise training status may influence the biological contents of exercise-associated sEVs, as Nair et al. [ 2020] reported that microRNA profiles differed in circulating sEVs obtained from sedentary vs. active older men [47]. In the present study, we were unable to objectively validate the habitual physical activity patterns of the study participants. Future studies should examine whether exercise training or chronic habitual exercise could alter the bioactivity of maternal sEVs, and evaluate their potential impact on trophoblasts. Currently, there is inconsistent evidence to suggest that exercise intensity or modality could affect sEV release and contents [19,48,49,50]. It is unknown whether differing exercise intensities (i.e., moderate vs. vigorous intensity) could impact sEV cargo, and whether a specific intensity or threshold is required to produce functional changes in trophoblasts.
Our study presents some strengths and limitations. As noted by others, caution must be exercised when extrapolating results obtained using transformed immortalized cell lines, including BeWo choriocarcinoma cells, in the modeling of normal trophoblast populations [51,52,53]. In this case, while not an exact simulation of trophoblasts, readily available and accessible BeWo cells provide invaluable insights into trophoblast function [52,53,54]. Future experiments deducing the potential function of sEVs in the context of maternal exercise should employ primary human trophoblasts or trophoblast populations obtained from the derivation and differentiation of trophoblast stem cells [55,56]. Another limitation relates to pitfalls associated with physical-based sEV isolation methods including differential ultracentrifugation. Lipoproteins have been reported to be isolated alongside sEVs using physical-based isolation methods, leading to the contamination of sEV isolate fractions [57,58]. Lipoproteins may also carry bioactive molecules such as miRNAs [59,60], which may have introduced potential confounders in the current preliminary study. Future studies should employ alternative sEV isolation methods (i.e., combinations of density gradient centrifugation and size-exclusion chromatography) to minimize co-isolation of contaminating non-sEV species. Further, co-isolated lipoproteins may compete with sEVs upon labeling with lipid-anchored fluorophores, including PKH26 [61,62,63]. Therefore, future investigations should characterize the extent to which samples may be contaminated with co-isolates that could result in the unintended labeling of non-sEV targets in uptake studies.
A strength of the sEV localization analysis stems from the use of an Airyscan detector in conjunction with confocal microscopy allowing for enhanced detection not available in traditional confocal systems [64]. However, it is important to note that the confocal imaging studies presented here are qualitative in nature. Another strength is the duration and type of exercise selected for the representation of acute exercise during pregnancy. The Canadian evidence-based physical activity guidelines throughout pregnancy recommend individuals engage in a minimum of 150 min of moderate-intensity physical activity to achieve benefits, within a recommended heart rate range based on age [1]. We therefore intentionally designed a physiologically appropriate exercise session for pregnancy, while the majority of studies to date on exercise-associated sEVs involve sustained vigorous-intensity exercise until volitional exhaustion in men (reviewed by [19]). Low sample size was a major limitation of our study, but herein we provide pilot data to support the continued exploration of maternal exercise-associated sEVs and their potential impact on placental function.
## 5. Conclusions
In summary, our preliminary results show that exercise-associated sEVs could be localized within trophoblast-like cells in vitro. Whether they can produce biological changes sufficient to improve trophoblast biology or stimulate other intercellular signaling pathways that may transmit signals to the fetus remain unknown. Since the placenta is primarily responsible for maternal–fetal communication and fetal growth, further investigation is warranted to determine the biological impact and mechanisms linking maternal sEVs to the benefits sustained from engagement in exercise during pregnancy.
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|
---
title: 'Real-World Efficacy of Glucagon-like Peptide-1 (GLP-1) Receptor Agonist, Dulaglutide,
on Metabolic Parameters in Japanese Patients with Type 2 Diabetes: A Retrospective
Longitudinal Study'
authors:
- Hisayuki Katsuyama
- Mariko Hakoshima
- Shohei Umeyama
- Sakura Iida
- Hiroki Adachi
- Hidekatsu Yanai
journal: Biomedicines
year: 2023
pmcid: PMC10046001
doi: 10.3390/biomedicines11030869
license: CC BY 4.0
---
# Real-World Efficacy of Glucagon-like Peptide-1 (GLP-1) Receptor Agonist, Dulaglutide, on Metabolic Parameters in Japanese Patients with Type 2 Diabetes: A Retrospective Longitudinal Study
## Abstract
The glucagon-like peptide-1 receptor agonist (GLP-1RA) dulaglutide has been shown to improve body weight and glycemic control and reduce major cardiovascular (CV) events. In Japan, dulaglutide is used at a fixed dose of 0.75 mg, which is lower than that in Europe and North America. However, the reports of real-world efficacy on metabolic parameters in Japanese patients with type 2 diabetes (T2DM) are limited. This study aimed to examine the real-world efficacy of GLP-1RA dulaglutide on metabolic parameters in Japanese patients with T2DM. We retrospectively selected patients with T2DM who had been prescribed dulaglutide continuously for 12 months or longer between September 2015 and December 2020 and compared metabolic parameters at baseline with the data at 12 months after the start of dulaglutide. One hundred twenty-one patients were enrolled in this study. The 12-month dulaglutide treatment reduced body weight by 1.7 kg and hemoglobin A1c by $1.1\%$. Significant improvements were also observed in serum high-density lipoprotein cholesterol (HDL-C), triglyceride (TG) and non-HDL-C. The change in HbA1c during dulaglutide treatment was significantly correlated with the changes in HDL-C (R = −0.236, $$p \leq 0.013$$), LDL-C ($R = 0.377$, $$p \leq 0.005$$) and non-HDL-C ($R = 0.415$, $p \leq 0.001$). The improvements in HbA1c, HDL-C, TG and non-HDL-C were greater in patients concurrently treated with SGLT2 inhibitors (SGLT2is) at baseline. In conclusion, the treatment with dulaglutide has beneficial effects on multiple CV risk factors in Japanese patients with T2DM.
## 1. Introduction
The prevalence of diabetes is increasing worldwide, and the situation is critical in Asia. It was estimated that 90 million adults in Southeast Asia suffered from diabetes in 2010, and the number was expected to reach 113 million by 2030 [1]. Despite recent developments in pharmacotherapy, the life expectancy of diabetes was shorter than the general population [2].
One of the leading causes of mortality in patients with diabetes is cardiovascular (CV) disease. CV diseases accounted for $14.9\%$ of deaths in Japanese patients with diabetes [2]. Large-scale trials suggested the importance of interventions against CV risk factors such as hyperglycemia, dyslipidemia and obesity to prevent CV diseases in patients with diabetes [3].
Glucagon-like peptide-1 (GLP-1) is a peptide hormone that is secreted by enteroendocrine cells and promotes insulin secretion from pancreatic beta cells. Dulaglutide is one of the human GLP-1 receptor agonists (GLP-1RAs), administered as a once-weekly subcutaneous injection, and is used worldwide for the treatment of type 2 diabetes (T2DM) [4]. Previous reports revealed that dulaglutide improved glycemic control with a lower risk of hypoglycemia and was associated with weight loss [5]. Moreover, in a large-scale randomized placebo-controlled study (REWIMD trial), dulaglutide reduced three-point major adverse CV events in patients with T2DM with a high risk of CV diseases [6].
Although dulaglutide is used at a fixed dose of 0.75 mg in Japan, which is lower than the doses that range from 0.75 mg to 4.5 mg in Western countries, improvements in glycemic control and obesity by dulaglutide were observed in Japanese patients with T2DM in several clinical trials [7,8,9]. However, the number of reports on real-world efficacy in Japanese patients with T2DM is limited. The aim of this study is to examine the effects of dulaglutide on various metabolic conditions such as hyperglycemia, obesity, dyslipidemia, diabetic nephropathy and metabolic-associated fatty liver disease.
## 2.1. Study Population
We selected patients with T2DM who had been prescribed dulaglutide continuously at a fixed dose of 0.75 mg for 12 months or longer between 1 September 2015 and 31 December 2020 by a chart-based analysis. We compared retrospectively the data at baseline with the data at 12 months after the dulaglutide treatment started.
## 2.2. Data Collection
The data on anthropometric measurements, blood tests, urine tests and medications were obtained from medical charts. The anthropometric measurements, such as body weight, height, waist circumference and blood pressure, were conducted according to the clinical standards. Body mass index (BMI) was calculated by body weight in kilograms divided by the square of the height in meters. The results of blood tests included both fasting and postprandial samplings. Plasma glucose was obtained using the hexokinase method. Serum hemoglobin A1c (HbA1c), serum creatinine, serum total cholesterol (TC) and serum triglyceride (TG) were measured by enzymatic assays. Serum low-density lipoprotein cholesterol (LDL-C) and serum HDL-C were determined by a direct method. Serum aspartate aminotransferase (AST), serum alanine aminotransferase (ALT) and serum γ-glutamyl transferase (γGTP) were measured by the Japan Society of Clinical Chemistry transferable method. Urinary albumin was measured by turbidimetric immunoassay, and the albumin-to-creatinine ratio (ACR) was calculated. The estimated glomerular filtration rate (eGFR) was calculated using the following formula: eGFR = 194 × (serum creatinine − 1.094) × (age − 0.287) × (0.739, if female) [10]. Non-HDL-C was calculated as the difference between TC and HDL-C. Hepatic steatosis index (HSI) was calculated as 8 × (ALT/AST) + BMI + (2, if diabetes mellitus) + (2, if female) [11]. NAFLD fibrosis score (NFS) was calculated as −1.675 + 0.037 × age (years) + 0.094 × body mass index (BMI) (kg/m2) + 1.13 × DM (yes = 1, no = 0) + 0.99 × AST/ALT ratio − 0.013 × platelet count (×109/L) − 0.66 × albumin (g/dL) [12]. FIB-4 index was calculated as a marker of hepatic fibrosis, using the following formula: (age × AST)/(platelet counts (× 109/L) × (ALT)$\frac{1}{2}$ [13,14].
## 2.3. Statistical Analysis
Comparison of the variables before and after the dulaglutide treatment was analyzed by the Wilcoxon signed-rank test. Pearson’s simple correlation coefficients were performed to determine the correlations between the parameters. All data are expressed as mean ± SD, and $p \leq 0.05$ was considered to be statistically significant. Statistical analysis was conducted using SPSS version 23 (IBM, US).
## 3.1. Baseline Characteristics of Patients Studied
We found 197 patients who had been first prescribed dulaglutide between 1 September 2015 and 31 December 2020. Among them, 25 patients did not visit our hospital regularly. Five patients died during the observational period due to cancer ($$n = 3$$), cerebral infarction ($$n = 1$$) and pneumonia ($$n = 1$$). In 22 patients, dulaglutide prescription was suspended and switched to other antihyperglycemic agents in 18 patients (insulin 1, other GLP-1RAs 2, dipeptidyl peptidase-4 (DPP-4) inhibitors 13, metformin 1 and SGLT2 inhibitor 1). We also excluded 24 patients due to lack of sufficient data. Thus, we enrolled 121 patients in this study.
The baseline characteristics of the patients are presented in Table 1. Dulaglutide was administered at a dose of 0.75 mg in all patients. The mean age of the patients was 64.7 ± 15.6 years, and the mean BMI was 26.8 ± 5.7 kg/m2. Among the hypoglycemic agents, metformin was mostly used ($47.9\%$), followed by SGLT2 inhibitors ($43.0\%$) and Thiazolidinedione ($30.0\%$). Insulin was used in 28 patients ($23.1\%$). The most prescribed drug for hypertension was calcium channel blockers ($47.9\%$) followed by angiotensin II receptor blockers ($41.3\%$). Of all patients, $54.5\%$ received statins. Antiplatelet drugs were prescribed in 18 patients ($14.9\%$).
## 3.2. Changes in Metabolic Parameters during 12-Month Dulaglutide Parameters
Changes in metabolic parameters at 12 months after the start of dulaglutide are shown in Table 2. HbA1c decreased by $1.1\%$, and the body weight decreased by 1.7 kg during 12-month dulaglutide therapy. Significant improvements were also observed in systolic blood pressure, plasma glucose, serum albumin, HDL-C, TG and non-HDL-C. Serum γGTP and NFS were also improved, whereas HSI and FIB-4 index were not changed during the dulaglutide treatment. There were also no significant changes in eGFR, uric acid and ACR.
Table 3 shows the gender differences in the changes in metabolic parameters during the 12-month dulaglutide treatment. Significant improvements in plasma glucose, HbA1c, γGTP and NFS were observed in both male and female patients. Body weight and BMI were improved significantly in males, and there were the same tendencies in females. Serum TG and non-HDL-C levels decreased only in males, whereas HDL-C levels increased only in females.
## 3.3. Correlations between the Baseline and the Changes in Metabolic Parameters
The significant correlations between the baseline values and the changes during 12 months of dulaglutide treatment were observed in NFS (R = −0.265, $$p \leq 0.007$$), the FIB-4 index (R = −0.325, $p \leq 0.001$) and ACR (R = −0.473, $p \leq 0.001$) (Figure 1). The same tendency was also observed in HSI (R = −0.224, $$p \leq 0.064$$).
## 3.4. Correlations among the Changes in Metabolic Parameters
The correlations between the changes in metabolic parameters during the 12-month dulaglutide treatment are provided in Table 4. The changes in HbA1c were significantly correlated with the changes in HDL-C (R = −0.236, $$p \leq 0.013$$), LDL-C ($R = 0.377$, $$p \leq 0.005$$) and non-HDL-C ($R = 0.415$, $p \leq 0.001$). Nevertheless, the change in BMI was not correlated with the changes in HbA1c, TC, LDL-C and non-HDL-C. The change in HSI was correlated with the change in BMI, but not with the change in NFS or the FIB-4 index.
## 3.5. Subgroup Analysis in Patients with or without Insulin Treatment
We analyzed the changes in metabolic parameters in the subgroups with or without insulin treatments (Table 5), since insulin has anabolic effects and is associated with weight gain [15]. In patients with insulin treatment, significant decreases were observed only in HbA1c, TC, non-HDL-C and NFS, whereas there were significant increases in diastolic blood pressure and uric acid levels. In patients without insulin treatment, there were significant improvements in body weight, BMI, systolic blood pressure, plasma glucose, HbA1c, γGTP, HDL-C, uric acid and NFS.
## 3.6. Subgroup Analysis in Patients with or without SGLT2i Treatment
Since SGLT2 inhibitors were previously reported to reduce body weight and improve glycemia, dyslipidemia and liver function [16,17,18], we divided the patients into two subgroups under the treatments with or without SGLT2 inhibitors and analyzed the changes in metabolic parameters during the 12-month dulaglutide treatment (Table 6). Significant improvements in body weight, plasma glucose, HbA1c and NFS were observed in both groups. TC, HDL-C, TG and non-HDL-C were improved only in the patients with the treatment with SGLT2i. There were no significant changes in HSI, the FIB-4 index and ACR in both groups.
## 4. Discussion
In this study, we examined the real-world efficacy of once-weekly subcutaneous dulaglutide on metabolic parameters in Japanese patients with T2DM. The dulaglutide treatment was associated with improvement in obesity, glycemia and atherogenic lipid profile.
Reductions from baseline in HbA1c and body weight in dulaglutide-treated patients were consistent with previous clinical trials. It was reported that the 52-month dulaglutide treatment with a single oral hypoglycemic agent in Japanese patients with T2DM decreased HbA1c by $1.57\%$ to $1.69\%$ [7]. The other study also showed a reduction in HbA1c by $1.39\%$ after the 52-month dulaglutide monotherapy [8]. In our study, the change in HbA1c during dulaglutide treatment was $1.1\%$, which was slightly smaller compared to such previous clinical trials. The higher age of the patients and multiple concomitant hypoglycemic agents in our study might influence the smaller degree of HbA1c changes.
Asian patients with T2DM were characterized by relatively lower BMI [19]. A previous study clarified that the degree of HbA1c change was greater during GLP-1RA treatment in normal BMI patients compared to obese patients [20]. It was also reported that GLP-1RAs were more effective in lowering HbA1c in Asian patients with T2DM compared to Caucasians [21]. Our results confirmed that dulaglutide effectively reduced HbA1c in Asian patients with T2DM.
GLP-1RAs have beneficial effects not only on glycemia and obesity but also on lipid profiles. Previous randomized controlled studies including REWIND or TODAY trials reported reductions in serum LDL-C and TG levels [6,22,23]. Moreover, a meta-analysis of four trials revealed that GLP-1RAs improved serum LDL-C, TG and HDL-C levels [24]. In our study, dulaglutide significantly increased serum HDL-C levels and decreased TG and non-HDL-C levels. A tendency for a reduction in LDL-C levels was also observed. These results coincided with previous clinical trials. In other several real-world studies, liraglutide or exenatide also showed improvements in carotid intima–media thickness, suggesting the preventive effects of GLP-1RAs against CV diseases [25,26].
Interestingly, the changes in LDL-C, HDL-C and non-HDL-C were significantly associated with the change in HbA1c, which suggests a close association between the improvements of glycemia and lipid profiles by dulaglutide. In an animal study using ApoE knockout mice, both glycemic control and lipid profile were improved but only observed in STZ-induced diabetic model mice and not in non-diabetic mice [27]. The presence of diabetes or insulin resistance elevates the expression and activity of hormone-sensitive lipase in adipose tissue, which catalyzes lipolysis and the release of free fatty acid (FFA). Increased entry of FFA to the liver inhibits the degradation of apoB-100 and elevated production of very-low-density lipoprotein (VLDL). Insulin resistance reduces the activity of lipoprotein lipase (LPL), which, in turn, increases TG-rich lipoproteins such as intermediate-density lipoprotein (IDL) and VLDL and reduces HDL. These are observed as elevated serum TG levels and decreased HDL-C levels in clinical laboratory tests [28]. Improved insulin action by GLP-1RA can play a crucial role in ameliorating both glycemia and dyslipidemia. GLP-1RAs can promote postprandial insulin secretion and inhibit insulin-mediated lipolysis in adipose tissue, resulting in reductions in FFA release in the bloodstream and entry in the liver [29,30]. GLP-1RAs were also reported to improve insulin resistance [31], which elevates the activity of LPL and ameliorates the lipid profile. Indeed, reductions in VLDL-C levels by GLP-1 RA treatment were already reported [32,33]. Reductions in apoB-100 comprising lipoproteins including IDL, VLDL and LDL can be observed as the improvement in non-HDL-C levels [34], which was observed in our study.
Previous studies reported the beneficial effects of GLP-1RAs in the progression of non-alcoholic fatty liver disease (NAFLD) [35,36]. In our study, dulaglutide treatments improved only NFS, but there were no changes in HSI and the FIB-4 index. Nevertheless, the associations between baseline values and changes in HSI, NFS and the FIB-4 index suggested that dulaglutide could ameliorate steatosis and fibrosis in the liver in patients with advanced steatosis and fibrosis at baseline. The lower dose of dulaglutide used in Japan or the limited number of subjects might influence our results.
The use of GLP-1RAs was also associated with reduced ACR and the risk of worsening kidney function [37,38]. In our study, there were no significant changes in ACR in the whole group or even in the group with SGLT2i treatment, despite the renoprotective effects of SGLT2is [39]. Our results might be affected by various factors, such as the lack of a control group, the limited number of available data on ACR, the short period of observation and the large standard deviation of ACR. Further studies with a control group and longer observation will be necessary to examine the effect of dulaglutide on the progression of NAFLD or diabetic kidney diseases.
Previous reports suggested that the addition of dulaglutide to insulin contributed to better glycemia, smaller doses of insulin and smaller weight gain compared to insulin monotherapy [40]. The combination of insulin and GLP-1RAs was also associated with better lipid profiles and lower blood pressure [41]. These reports suggested that GLP-1RAs might offset the adverse effects of insulin treatments. In our study, dulaglutide improved HbA1c and non-HDL-C in patients treated with insulin, but there were no significant changes in body weight and BMI, which might be influenced by the small number of patients with insulin treatments. Direct comparisons with insulin monotherapy will be needed to determine the effects of the combination therapy of insulin and dulaglutide.
SGLT2is have also been reported to improve obesity, glycemia and lipid profiles and were associated with reduced cardiorenal events [16,17,42]. The mechanisms of cardiorenal effects of SGLT2is include attenuation of CV risk factors, amelioration of substrate utilization in myocardial and renal cells, reduction in ventricular and vascular loading through diuresis and natriuresis, and mitigation of atherosclerosis through anti-inflammatory and anti-oxidative effects [42,43]. GLP-1RAs have also been reported to preserve ventricular function and reduce atherosclerosis [44]. These effects of SGLT2is and GLP-1RAs can be complementary in the prevention of CV diseases. Indeed, a meta-analysis revealed that the combination therapy of SGLT2i and GLP-1RA resulted in greater reductions in body weight, HbA1c and LDL-C [45]. Our real-world data confirmed the greater benefits of the combination of SGLT2i and GLP-1RA on glycemia and lipid profiles, suggesting a promising strategy to prevent the progression of CV and renal diseases in high-risk patients [46,47].
Our study has several limitations. First, it was a retrospective study with a limited number of patients in a single center. Second, we cannot exclude possible influences by modifications of the pharmacotherapy. Third, our study lacked information on diabetic durations, co-existing diabetic complications and lifestyle modifications. Fourth, a lack of data might influence the results. Fifth, we could not evaluate metabolites or inflammation markers which might be related to the effects of dulaglutide, since our study was based on real-world data.
## 5. Conclusions
Our findings based on real-world data showed that dulaglutide improved body weight, glycemic control and lipid profiles in Japanese patients with T2DM, suggesting dulaglutide as a promising therapy to control CV risk factors. Moreover, the close associations between the changes in lipid profiles and the change in HbA1c during dulaglutide treatments is intriguing in speculating the presence of common mechanisms of improving glycemia and dyslipidemia by dulaglutide.
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|
---
title: Nicotine Exerts a Stronger Immunosuppressive Effect Than Its Structural Analogs
and Regulates Experimental Colitis in Rats
authors:
- Kohki Okada
- Kano Matsuo
journal: Biomedicines
year: 2023
pmcid: PMC10046003
doi: 10.3390/biomedicines11030922
license: CC BY 4.0
---
# Nicotine Exerts a Stronger Immunosuppressive Effect Than Its Structural Analogs and Regulates Experimental Colitis in Rats
## Abstract
Ulcerative colitis (UC) is an intractable disease that causes persistent colonic inflammation. Numerous studies have reported that smoking can afford clinical benefits in UC. This study aimed to elucidate whether nicotine, the main component in cigarettes, can exert pharmacological effects against experimental UC. To achieve this objective, we compared the effects of nicotine with those of structural nicotine analogs in a UC rodent model (Slc: Wistar rats, male, 9-week-old, and 220–250 g/rat). Nicotine, or a respective structural analog (nornicotine, cotinine, anabasine, myosmine, and anatabine), was administered intraperitoneally daily to rats ($$n = 6$$/group) exhibiting dextran sulfate sodium-induced experimental colitis. Examining the colon tissues of model rats, we compared disease severity, cytokine secretion, and α7 nicotine acetylcholine receptor (nAChR7) expression. We observed that nicotine administration induced weight loss at $2.35\%$ in 10 days. Notably, the reduction in histological severity (score) of UC was more pronounced in rats treated with nicotine (score = 4.83, $$p \leq 0.042$$) than in untreated rats (score = 8.17). Nicotine administration increased nAChR7 expression 6.88-fold ($$p \leq 0.022$$) in inflammatory sites of the colon, mainly by suppressing the production of interleukin (IL)-1β and IL-6. Moreover, the secretion of these cytokines was suppressed in lipopolysaccharide-stimulated rat macrophages (MΦ) treated with nicotine. In conclusion, nicotine better alleviates experimental UC than the examined structural analogs by activating nAChR7 expression and suppressing proinflammatory cytokines in MΦ.
## 1. Introduction
Nicotine is one of the main components of cigarettes and is a known toxin stored in tobacco plants to ward off insect attacks [1,2]. Nicotine is a highly neurotoxic and addictive substance, and nicotine dependence among smokers is considered a major public health challenge. Nicotine ingestion damages the central and peripheral nervous systems, leading to acute and chronic intoxication and often sudden death [3]. Nicotine acts as an agonist of nicotine acetylcholine receptors (nAChRs) in skeletal muscle and brain tissues [4,5]. On binding to these receptors, nicotine promotes the secretion of neurotransmitters (dopamine, adrenaline, and beta-endorphins) [6]. Based on these properties, nicotine may exert therapeutic effects in certain diseases associated with cognitive and behavioral control issues, such as depression and Alzheimer’s disease [7,8]. Conversely, it has been reported that nicotine could aggravate gastrointestinal diseases. In particular, chronic nicotine administration can substantially reduce blood flow to the gastric mucosa while increasing gastric acid secretion, thereby aggravating gastritis and gastric ulcers [9,10,11].
Ulcerative colitis (UC) is an intractable disease that causes persistent inflammation in the colon, particularly in the rectal region [12]. A high prevalence of UC has been reported in Europe and the United States, with the number of patients increasing in Asia, South America, and Africa [13]. Abnormal activity of innate immune cells and disruption of the intestinal microbiota have been associated with the onset and worsening of UC [14] However, the precise underlying mechanisms remain unknown. Although therapeutic protocols capable of comprehensively curing UC remain elusive, immunosuppressive drugs such as tacrolimus and cyclosporine are frequently employed for therapeutic benefits [14]. In particular, suppressing the excessive macrophage (MΦ)-mediated immune response in colon tissues has been effective in UC [15]. Interestingly, numerous clinical and basic research reports have suggested that nicotine intake could alleviate UC [16,17,18]. Of note, nAChRs are abundantly expressed not only in skeletal muscle and brain but also in the colon mucosal epithelium and immune cells, and it has been strongly suggested that α7 nAChR (nAChR7), comprising homopentamers of the α7 subunit, may be associated with UC [5,19]. In contrast, smoking is reported to be a risk factor in Crohn’s disease [20], a disease with a similar pathology to UC, which further complicates the debate over whether nicotine has pharmacological effects.
The underlying mechanism through which nicotine and its receptors participate in the pathogenesis of UC remains unclear. We postulate that the unique chemical structure of nicotine exerts a pharmacological effect on UC. If this assumption is accurate, structural analogs of nicotine could exhibit similar therapeutic effects against UC; however, reports verifying their potential effects remain limited. Moreover, nicotine induces strong dependence and addictive properties, and structural analogs of nicotine could afford UC remission without inducing these disadvantages. In humans, the stress-relieving effects of smoking may lead to remission of UC, although examining the function of nicotine itself appears challenging. Therefore, it is crucial to investigate the functional roles of nicotine in animal models. We previously reported that S100A8 and its recombinant hybrid protein can attenuate the severity of dextran sulfate sodium (DSS)-induced UC [21,22]. Therefore, identifying the chemical structures of nicotine and its analogs that exert anti-inflammatory effects would contribute to the development of novel therapeutic agents for UC.
Based on the above background, we aimed to confirm the effects of nicotine and its five structural analogs on the pathogenesis of experimental UC in rats, as well as perform a detailed comparative analysis of their pharmacological effects.
## 2.1. Ethics Statement
All animal experiments complied with the ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) and were approved by the Animal Experiment Committee of Kyoto Tachibana University (permission number: 21-08).
## 2.2. Animals
Wild-type (WT) Slc: Wistar rats (male, 9-week-old, and weighing 220–250 g/rat) were obtained from Shimizu Laboratory Supplies Co., Ltd. (Kyoto, Japan). Animals were housed for approximately one week prior to the experimentation with free access to diets (MF, Oriental Yeast Co. Ltd., Tokyo, Japan) and water. During the experiments, animals were kept in individual sawdust-lined plastic cages under controlled temperature (22 °C) and humidity ($60\%$) conditions, with day–night cycles regulated by artificial light ($\frac{12}{12}$ h). The content and composition of experimental diets are presented in Figure S1 [23].
## 2.3. Reagents
Nicotine, nornicotine, cotinine, anatabine, and anti-β-actin mouse monoclonal antibody (anti-β-actin IgG) were obtained from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan). Myosmine, anti-nicotinic acetylcholine R alpha 7/CHRNA7 rabbit-poly (anti-nAChR7), and peroxidase (HRP)-labeled anti-rabbit IgG(H+L) goat-poly were obtained from Funakoshi Co., Ltd. (Tokyo, Japan). Anabasine was obtained from Angene International, Ltd. (Nanjing, China). Anti-mouse IgG (goat)-HRP conjugate and goat anti-rabbit IgG H&L (TRITC) were obtained from Abcam (Cambridge, UK). The PRO-PREPTM Protein Extraction Solution (Cell/Tissue) and interleukin (IL)-6 PicoKineTM, IL1-beta (IL-1β), and tumor necrosis factor-alpha (TNF-α) enzyme-linked immunosorbent assay (ELISA) kits were obtained from Cosmo Bio Co., Ltd. (Tokyo, Japan). The DSS salt (molecular weight: 36,000–50,000) was obtained from Wako Pure Chemical Industries, Ltd. (Tokyo, Japan). Clinical thioglycollate medium (E-MC17) was obtained from Eiken Chemical Co., Ltd. (Tokyo, Japan). Escherichia coli-derived lipopolysaccharide (LPS) was obtained from Sigma-Aldrich Co., LLC (Tokyo, Japan). VECTASHIELD mounting medium containing 4′,6-diamidino-2-phenylindole (DAPI) was obtained from Vector Inc. (Burlingame, CA, USA). TRIzol™ reagent, SuperScript ™ II Reverse Transcriptase, PowerUp SYBR Green Master Mix, and all primers were obtained from Thermo Fisher Scientific (Waltham, MA, USA). All other reagents were obtained from Wakenyaku Co., Ltd. (Kyoto, Japan), Nacalai Tesque Co., Ltd. (Kyoto, Japan), and Bio-Rad Laboratories Inc. (Hercules, CA, USA).
## 2.4. Protocol for Animal Experiments
To establish experimental UC model rats (UCR), WT rats were orally administered $3\%$ DSS in distilled water (DW) from their water bottles for 10 days. UCR were divided into six groups (UC + Nico, UC + Nor, UC + Coti, UC + Anaba, UC + Myos, and UC + Anata), with 1.0 mg/kg of each chemical (nicotine, nornicotine, cotinine, anabasine, myosmine, and anatabine) intraperitoneally administered daily ($$n = 6$$). During the experimental period, the body weight of each rat was measured every morning, and disease activity index (DAI) scores were evaluated based on the criteria shown in Table 1 [24]. Considering the control group, normal WT rats were intraperitoneally administered 1.0 mg/kg nicotine daily (Nico group, $$n = 6$$). The dose was determined based on previous reports on the efficacy of nicotine in treating bowel inflammation [25,26]. In addition, one group of UCR was administered an equal volume of saline (UC group, $$n = 6$$). On day 10 of the experiment protocol, blood samples (3 mL/rat) were collected from each rat via intracardiac puncture under anesthesia. In addition, the large colon was quickly removed, and its length was measured. The specimens were fixed in $10\%$ formalin/0.1 M phosphate buffer (pH 7.4) for histological assessments and then embedded in paraffin. The protein and mRNA levels in the residual unfixed tissue were extracted as described below. The experimental animal protocol and animal groups are summarized in Figure 1.
## 2.5. Sample Preparation for the Protein Assay
The large intestines harvested from each experimental animal were weighed independently. Further, 300 mg of each sample was incubated in 0.5 mL of PRO-PREPTM Protein Extraction Solution (Cell/Tissue) for 10 min, followed by centrifugation at 12,000 rpm for 10 min at 4 °C. The resultant supernatants were transferred into 1.5 mL polycarbonate tubes and stored at −80 °C until use.
## 2.6. Sample Preparation for the mRNA Assay
Briefly, 300 mg of each tissue was independently incubated in 1.0 mL of TRIzol™ reagent for 10 min and then centrifuged at 12,000 rpm for 15 min at 4 °C. The mRNA from each sample was extracted following the manufacturer’s protocol and treated with 8 M LiCl to avoid the influence of DSS on RNA reverse transcription. cDNA was synthesized from mRNA using SuperScript™ II reverse transcriptase, as described in the instruction manual.
## 2.7. Microscopic Examination of Harvested Rectal Tissues
We microscopically examined rectal tissues exhibiting severe inflammation in rats with DSS-induced UC. Briefly, 3 µm-thick tissue sections were prepared from the rectal tissues of all rats and stained with hematoxylin and eosin (H&E). Tissue damage was evaluated as histological (HIS) scores and assessed based on the H&E staining, and the extent of damage was scored based on established criteria. The presence and severity of ulcerative lesions, disrupted epithelial structure, damaged crypt architecture, and increased inflammatory cell infiltration were scored on a scale of 0–3 (none = 0; mild = 1; moderate = 2; and severe = 3), which were summed to provide an overall score [27]. The expression of nAChR7 in tissues and MΦ was detected by immunohistochemical staining with diaminobenzidine (DAB) and fluorescent immunochemical staining (FICS), respectively, using anti-nAChR7, as previously described [21]. Microscopic images were obtained using a BIOREVO BZ-9000 microscope (Keyence Co., Ltd., Osaka, Japan).
## 2.8. Western Blotting
The proteins in each fraction were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in the presence of 2-mercaptoethanol, as previously described [28]. The concentration of all polyacrylamide gels was $12.5\%$. After SDS-PAGE, proteins were transferred to nitrocellulose membranes using Trans-Blot Turbo (Bio-Rad Laboratories, Inc.). After blocking with Blocking One (Nacalai Tesque Co., Ltd.), membranes were incubated at 4 °C for 1 h with 2 μg/mL anti-nAChR7 or anti-β-actin IgG. The membranes were then washed thrice for 5 min with 10 mM Tris-HCl buffer (pH 7.4) and $0.9\%$ NaCl (buffer A), twice with buffer A/$0.1\%$ Tween 20, and once with buffer A before incubation with 2 μg/mL HRP-labeled anti-rabbit IgG(H+L) goat poly or anti-mouse IgG (goat)-HRP conjugate at room temperature for 1 h. After the membranes were washed, antibody-bound proteins were detected using a Chemi-DocTM XRS Plus Imaging System and Clarity Western ECL substrate (Bio-Rad Laboratories, Inc.). The original Western blotting data are presented in Figure S2.
## 2.9. ELISA
IL-6, IL-1β, and TNF-α levels in each sample were measured using respective ELISA kits, following the manufacturer’s instructions. The absorbance of the color reaction was measured at a wavelength of 450 nm using a microplate reader (Bio-Rad Laboratories, Inc.). Cytokines in the large intestine were converted to concentrations per gram of the tissue.
## 2.10. Real-Time PCR
Real-time PCR was performed using the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific), as previously described [21]. The primers used were as follows: nAChR7 forward, 5′-ACAATACTTCGCCAGCACCA-3′, nAChR7 reverse, 5′-AAACCATGCACACCAGTTCA-3′ (145 bp); IL-1β forward, 5′-CACCTCTCAAGGAGAGCACAGA-3′, IL-1β reverse, 5′-CACCTCTCAAGGAGAGCACAGA-3′ (81 bp); IL-6 forward, 5′-ATATGTTCTCAGGGAGATCTTGGAA-3′, IL-6 reverse, 5′-GTGCATCATCGCTGTTCATACA-3′ (80 bp); IL-10 forward, 5′-GCCAAGCCTTGTCAGAAATGA-3′, IL-10 reverse, 5′-TTTCTGGGCCATGGTTCCTCT-3′ (75 bp); TNF-α forward, 5′-GTGATCGGTCCCAACAAGGA-3′, TNF-α reverse, 5′-AGGGTCTGGGCCATGGAA-3′ (71 bp); transforming growth factor (TGF)-β forward, 5′-ACCTGCAAGACCATCGACATG-3′, TGF-β reverse, 5′-CGAGCCTTAGTTTGGACAGGAT-3′ (85 bp); and β-act forward, 5′-TGTGTTGTCCCTGTATGCCTCTG-3′, β-act reverse, 5′-ATAGATGGGCACATGGTGGGTG-3′ (85 bp).
## 2.11. Isolation of Peritoneal MΦ from Rats
Peritoneal MΦ were isolated from WT rats as previously described [24]. Briefly, MΦ were induced by intraperitoneal injection of sterilized $4\%$ thioglycollate/DW (10 mL). After three days, MΦ were collected in a plastic tube (50 mL) using 50 mM of sterilized phosphate buffered solution (pH 7.4) and $0.9\%$ NaCl (buffer B). Subsequently, the tube was centrifuged at 3500 rpm for 5 min at 4 °C, and the supernatant was discarded. The cells collected in the tube were suspended in 17 mM Tris-HCl (pH 7.2) containing $0.83\%$ NH4Cl and incubated at 37 °C for 10 min to induce hemolysis of contaminating erythrocytes. After centrifugation, the supernatant was discarded, and pelleted cells were suspended in RPMI-1640 culture medium containing $10\%$ fetal bovine serum (Biological Industries, Kibbutz Beit-Haemek, Israel; medium A). A total of 2 × 106 cells were plated in each well of a 6-well plate, and an appropriate volume of medium A was added. The cells were incubated at 37 °C for 2 h under $5\%$ CO2. After incubation, non-adherent cells in each well were removed by washing three times with buffer B. Adherent cells were maintained in the same medium at 37 °C in $5\%$ CO2 until use.
## 2.12. Stimulation of MΦ
Briefly, MΦ were thoroughly washed with buffer B, followed by stimulation with 1 μg/mL nicotine, its structural analogs, or LPS in medium A for 1 h. After washing thrice with buffer B, the cells were subjected to FICS or collected with 0.2 mL of buffer A in a polycarbonate tube (1.5 mL). Protein and mRNA were extracted from collected cells using PRO-PREPTM Protein Extraction Solution and TRIzol reagent, respectively. The cytokine production capacity of MΦ was analyzed using ELISA and real-time PCR. The intracellular expression of nAChR7 was evaluated via Western blotting, real-time PCR, and FICS.
## 2.13. Statistical Analysis
Pairwise comparisons with the control were performed using non-parametric tests. Significant differences between groups were identified using the Mann–Whitney U test. The Shapiro–Wilk test was used to verify the normality of distribution, and all numerical data in this study were determined to be non-normally distributed. Then, the data are shown as median ± max/min. A p-value of <0.05 was deemed statistically significant.
## 3.1. Changes in Body Weight
During the experimental period, we measured the body weights of rats daily. The weight of rats in the Nico group gradually decreased after day 6, whereas rats in the UC group showed rapid weight loss after day 4 (Figure 2A). Rats in the UC + Nico group exhibited greater weight loss than UC group rats (Figure 2A). Conversely, rats in the UC + Nor group displayed more severe weight loss than any other examined group (Figure 2B).
## 3.2. Changes in Rat Colon Lengths following Nicotine Administration
At the end of the experimental period, rats in the UC group exhibited a visually shorter colon than those in the Nico group; however, colonic atrophy was suppressed in the UC + Nico group (Figure 3A–C). This tendency was also apparent in the mean length of the large intestine among the three groups (Figure 3D).
## 3.3. Changes in Rat Colon Lengths following Treatment with Nicotine Structural Analogs
The colon of rats in the UC + Nor group appeared visually maintained when compared with that of rats in the other four groups (Figure 4A–E). Among the five groups, the mean colon length was the longest in the UC + Nor group and the shortest in the UC + Coti group (Figure 4F).
## 3.4. Comparison of DAI Scores between the Treatment Groups
We evaluated DAI scores based on the criteria listed in Table 1, as previously described [24]. The DAI scores increased in the UC group at Day 10 as physical symptoms worsened, whereas only a moderate change was noted in the UC + Nico group (Table 2 and Figure S3). Compared to the other five groups, the UC + Nico group tended to have lower DAI scores (Table 2 and Figure S3).
## 3.5. Microscopic Observation and Evaluation of Colonic Inflammation
H&E staining was performed to visualize tissue damage and immune cells in the large intestine. Compared with colon tissue derived from the Nico group, the epithelial structure of the colon was damaged in the UC group, with numerous immune cells infiltrating the tissue (Figure 5A,B). Compared with the UC group, the UC + Nico and UC + Nor groups exhibited a preserved mucosal epithelial structure and suppressed immune cell infiltration (Figure 5C,D). Although less severe than that in the UC group, inflammatory findings were documented in harvested colons of the four other groups (Figure 5E–H). HIS scores were assessed based on previously established criteria [27]. Only the UC + Nico group showed significantly suppressed HIS scores when compared with those of the UC group (Table 3).
## 3.6. Evaluation of nAChR7 Expression in the Large Intestine of All Groups
To determine nAChR7 expression in colon tissues, we performed immunohistochemical staining with DAB and Western blotting. Based on immunohistochemical staining, nAChR7 was minimally expressed in the colon of Nico and UC group rats (Figure 6A,B). In contrast, high levels of nAChR7 expression were detected in the colon of UC + Nico and UC + Nor group rats (Figure 6C,D). In addition, the other four groups exhibited some degree of nAChR7 expression (Figure 6E–H). Although we noted some extent of nAChR7 expression in all groups by Western blotting, distinct expression was noted in the UC + Nico group (Figure 6I).
## 3.7. In-Depth Investigation of nAChR7 Expression in the Large Intestines of All Groups
We next performed FICS and real-time PCR to establish the expression of nAChR7 in the large intestines. FICS revealed several nAChR7-positive areas, particularly in the colonic mucosal epithelium of the UC + Nico group when compared with that in other examined groups (Figure 7A–H). In addition, the UC + Nico group exhibited the highest expression of nAChR7 mRNA in colon tissues, followed by the UC + Nor group (Table 4).
## 3.8. Cytokine Secretion in Serum and Colon
IL-1β, IL-6, and TNF-α levels in the serum and colon were measured using ELISA. The serum level of IL-1β was significantly lower in the UC + Nico group than that in the UC group, with no significant differences in the other two cytokines (Figure 8A). Serum IL-1β was decreased in the UC + Nor group when compared with that in the UC group, and no significant changes in the other three cytokines were noted in the other rat groups (Figure 8B). Considering the colon, secretion of IL-1β and IL-6 was notably decreased in the UC + Nico group when compared with that in the UC group (Figure 8C). Considering the other five groups, secretion levels of IL-1β and IL-6 were significantly reduced in the UC + Nor group when compared with those in the UC group (Figure 8D).
## 3.9. Cytokine mRNA Expression in Colon Tissues
We performed real-time PCR to determine the mRNA levels of five cytokines in the colon tissues of all groups. Compared with the UC group, the mRNA levels of IL-1β and TNF-α were significantly downregulated in the UC + Nico group (Figure 9A). The expression levels of IL-1β in the UC + Nor group and IL-6 in the UC + Anaba and UC + Myos groups were significantly decreased, whereas TNF-α expression tended to increase in all five groups (Figure 9B). Regarding anti-inflammatory cytokines, IL-10 expression was significantly upregulated in the three groups, and that of TGF-β was notably increased in the UC + Coti and UC + Myos groups (Figure 9B).
## 3.10. Evaluation of nAChR7 Expression in MΦ
FICS and Western blotting were performed to determine the expression of nAChR7 in MΦ. Based on FICS results, nAChR7 expression was more abundant in nicotine-stimulated MΦ than in other stimulation conditions (Figure 10A–H). Western blotting revealed strong nAChR7 expression in nicotine- or nornicotine-treated MΦ (Figure 10I).
## 3.11. nAChR7 and Cytokine Expression in MΦ
Real-time PCR and ELISA were performed to evaluate the expression of nAChR7 and cytokines in MΦ. Although the mRNA expression of nAChR7 was not significantly increased in LPS-stimulated MΦ, expression levels were significantly elevated in nicotine- and nornicotine-stimulated MΦ (Figure 11A). The secretion of IL-6 and TNF-α was significantly lower in nicotine- or nornicotine-treated MΦ than that in LPS-stimulated MΦ (Figure 11B).
## 3.12. Changes in Cytokine mRNA Expression in MΦ
Based on real-time PCR analysis, MΦ stimulation with nicotine or respective structural analogs alone failed to increase the mRNA expression levels of the five examined cytokines (Figure 12A). LPS-stimulated MΦ exhibited elevated mRNA expression levels of the five cytokines, while cytokine production was mostly reduced upon subsequent treatment with nicotine or its structural analogs (Figure 12B). In particular, the mRNA expression of the three inflammatory cytokines was suppressed in nicotine- or nornicotine-treated MΦ after LPS stimulation (Figure 12B). Interestingly, LPS-stimulated MΦ pretreated with anti-nAChR7 displayed increased IL-1β and IL-6 expression (Figure 12B).
## 4. Discussion
In the present study, we examined the pharmacological effects of nicotine and its structural analogs in a rodent model of experimental UC. Initially, we speculated that nicotine-mediated improvements in inflammation observed in human UC were merely a psychological effect of stress relief. Therefore, we performed animal experiments in rats to assess whether nicotine can induce therapeutic benefits against inflammation. Several previous studies have verified the functional role of nicotine in animal models of UC [18,19]. Herein, we focused on substances with chemical structures similar to that of nicotine, predicting that the structural analogs would likely exert anti-inflammatory activity comparable to that of nicotine. Moreover, identifying structural analogs that are less dependent and addictive than nicotine while exhibiting superior pharmacological effects may be valuable. The present study also contributes to identifying the specific molecular structures that underlie the pharmacological effects mediated by nicotine. Herein, we performed a functional comparison between nicotine and its structural analogs, which will provide useful evidence for pursuing related studies worldwide.
In the present study, data regarding changes in rat body weight had substantial implications. The gradual decrease in body weight of rats administered nicotine daily suggests that nicotine might exert a toxic effect or enhance basal metabolism. Furthermore, substances with a closely related chemical structure to nicotine could be associated with strong basal metabolic activity. Although smoking cessation has long been reported to decrease basal metabolic rate and increase body weight [29], we noted that even short-term nicotine administration could lead to apparent weight suppression. In addition, patients with UC tend to lose weight owing to repeated diarrhea and hemorrhage [12], and the accelerated weight loss caused by nicotine is undesirable in UC. Therefore, regardless of the pharmacological effect of nicotine on UC, excessive dosing should be avoided. The colon diameter of rats tends to atrophy with worsening experimental UC. However, the maintenance of colon length in UC rats, especially those treated with nicotine or nornicotine, indicates the potential of these chemicals as UC therapeutic agents. Given that the loss of colonic folds and mucosal epithelium with strong inflammation leads to the shortening of the large intestine, nicotine and nornicotine may act as anti-inflammatory substances. In contrast, the administration of cotinine, which is produced in the liver as a nicotine metabolite, is unlikely to suppress colon shortening in UC. We speculate that once nicotine is metabolized in the body, its pharmacological effects may be substantially lost. According to previous reports, nicotine treatment of neutrophils inhibited free radicals’ production by up to $90.2\%$, but cotinine treatment of them inhibited free radicals production by only $58.9\%$ [30]. Since nicotine would be superior to cotinine in terms of the free radical’s inhibition, it is likely that the same function was demonstrated in the colon tissue in the experimental colitis of the present study. Furthermore, subtle structural differences between nicotine and cotinine may significantly impact their anti-inflammatory function. We conducted additional experiments to examine the detailed role of these chemicals in UC-mediated inflammation.
Furthermore, in terms of DAI scores, nicotine treatment may exert a better anti-inflammatory effect than the examined analogs. Given that DAI scores increased with body weight loss and considering the weight change results, the score is likely to increase with nicotine administration. However, mild diarrhea was observed in nicotine-treated rats with UC, and the lack of rectal bleeding may have contributed to these results. In nicotine-treated UC rats, H&E-stained colon tissue specimens displayed a preserved mucosal epithelial structure, and the loss of goblet cells seemed to be suppressed. The histological findings also corroborated the pharmacological effect of nicotine. Interestingly, despite nicotine administration in rats with UC, a certain amount of immune cell infiltration was observed in colon tissues, suggesting that nicotine is not actively involved in suppressing immune cell induction or apoptosis. According to previous experiments in which nicotine was administered to mouse models of acute lung injury, nicotine appears to have a particularly potent inhibitory effect on the production of cytokines and chemokines in the inflammatory sites [31]. Therefore, nicotine may suppress the abnormal activity of immune cells or regulate their function to normal. Next, we examined the expression levels and activity of the nicotine-specific receptor, nAChR7. Surprisingly, we revealed poor nAChR7 expression in the colon of WT rats administered nicotine intraperitoneally; however, colonic expression of nAChR7 dramatically increased in nicotine-treated rats with UC. These findings suggest that inflammation triggers the colonic expression of nAChR7 and can impact immune function by promoting nicotine uptake into nAChR7. These findings are mediated via an autocrine activation pathway, suggesting that nicotine uptake by nAChR7 further triggers receptor binding to nicotine. Given the poor nAChR7 expression in UC-induced inflamed colon tissues, administering chemicals such as nicotine, which enhances receptor activity, could effectively treat UC. Tropisetron, a 5-HT3 antagonist, can reportedly increase nAChR7 expression in the colon tissues of DSS-treated rats and alleviate colitis [19]. Thus, nAChR7 activators, including nicotine, could be explored as potential therapeutic agents for UC. Upon nicotine or nornicotine administration, IL-1β showed a notably decreased level in the serum and colon of UC rats, with IL-6 also demonstrating a decreasing tendency, mainly in the colon tissues. IL-1β is known to exert various physiological activities. Most importantly, IL-1β promotes the differentiation and proliferation of helper T cells. In particular, IL-1β and IL-6 stimulate helper T cells to differentiate into Th17 cells, which eventually secrete potent cytokines such as IL-17 and TNF-α, resulting in the deterioration of UC [32]. In addition, IL-1β and IL-6 are closely involved in neutrophil induction at inflammatory sites and B lymphocyte proliferation, indicating that nicotine and nornicotine, which suppress these factors, may exert superior effects as immunosuppressive agents. We found that nicotine treatment suppressed the colonic expression of TNF-α mRNA in UC rats; though mRNA levels of anti-inflammatory cytokines, such as IL-10 and TGF-β, increased under some conditions, they failed to significantly impact the pathogenesis of UC. Collectively, it should be noted that nicotine could suppress inflammatory cytokine secretion via nAChR7 activation in colon tissues, thereby alleviating inflammation in UC.
Although the functions of nicotine and its structural analogs have been estimated by analyzing the pathology and immunocompetence of UC model rats, their direct effects on immune cells have not been thoroughly investigated. Herein, we cultured MΦ extracted from the abdominal cavity of rats and examined the regulation of their immune capacity via direct stimulation with nicotine or its structural analogs. Although LPS is known to activate immune cells, LPS-induced MΦ stimulation did not appear to specifically increase nAChR7 levels when compared with those of unstimulated cells. Furthermore, we found that nAChR7 expression was significantly increased in nicotine- or nornicotine-stimulated MΦ when compared with that in cells stimulated with other structural analogs. nAChR7 is abundantly expressed in skeletal muscle and the nervous system and functions as a regulator of synaptic transmission [5,19]. Although the mechanism of indirect MΦ stimulation via nAChR7 expressed in the nervous system is widely established [33], nAChR7 has been recently detected in MΦ, suggesting the direct regulation of immune functions [5]. Given that nAChR7, which does not exhibit high expression in normal MΦ, is upregulated upon stimulation with ligands such as nicotine and nornicotine, it can be suggested that subsequent immunosuppression may be more potent than when cells are stimulated with the other structural analogs. Inflammatory cytokine production was significantly suppressed in nicotine- or nornicotine-stimulated MΦ when compared with that in LPS-stimulated MΦ and seemed almost equivalent to that in unstimulated MΦ. These results indicate that nicotine- or nornicotine-mediated MΦ stimulation does not induce abnormal activity; however, they may act as mediators. It is widely known that nAChR7 exerts anti-inflammatory effects via suppression of NF-κB nuclear translocation and activation of Jak2/STAT3 in neuroinflammation [34]. If this function is also exerted in the colon tissues of patients with UC, nicotine and nornicotine might be effective immunosuppressive agents. Subsequently, we generated data that reinforced this possibility by examining whether MΦ, once activated by LPS, could be restored to a stable state upon treatment with nicotine or its structural analogs. Nicotine and its structural analogs may contribute to the normalization of MΦ by suppressing the mRNA expression of proinflammatory cytokines. Furthermore, it was quite remarkable that treatment of MΦ with anti-nAChR7, followed by LPS stimulation, markedly enhanced expression levels of IL-1β and TNF-α. Anti-nAChR7-induced inhibition of nAChR7 on MΦ resulted in abnormal MΦ activity, indicating that this receptor and its ligands play a substantial role in modulating the innate immune system. Based on our findings, nicotine is the most effective nAChR7 ligand, superior to other structural analogs. In summary, nicotine appeared to bind tightly to nAChR7, inhibit the secretion of inflammatory cytokines, and promote the steady state of MΦ, thereby alleviating inflammation in UC colon tissues. In addition, nicotine activates Nrf2/HO-1, a regulator of oxidative stress, which may contribute to the suppression of UC severity [34]. Nornicotine also exhibits these immunosuppressive and antioxidant functions, but less than that of nicotine, with the other structural analogs exhibiting even weaker activity.
This study aimed to verify whether the structural analogs of nicotine are effective as therapeutic agents for UC; however, nicotine was found to be the most effective pharmacological agent. Although several reports have assessed nicotine previously, reports on the relationship between its structural analogs and UC remain scarce; therefore, the findings of the present study are of considerable value. In particular, our study provides extremely useful evidence, given that the possible involvement of nornicotine in suppressing UC-related inflammation remains poorly explored. Cotinine, a biomarker of tobacco use, is a nicotine metabolite produced in the liver. Herein, we observed that cotinine exerts limited anti-inflammatory effects against UC, and we predict that maintaining the chemical structure of nicotine as feasible would amplify its pharmacological effect. Elevated plasma cotinine has been reported to be associated with an increased risk of developing UC [35], but it seems likely that other toxic substances in cigarettes are responsible for this. Reports demonstrating the efficacy of anabasine and myosmine in treating UC remain scarce, thereby indicating that small differences in their chemical structure from that of nicotine can lead to large gaps in the therapeutic effect against UC. In contrast, oral administration of anatabine alleviated the symptoms of DSS-induced colitis in mice [36], indicating that such plant-derived alkaloids may function as immunosuppressive agents to a certain extent. Evidence regarding the essential role of structural nicotine analogs in UC remains limited, and additional reports can be expected in the future.
In the present study, we identified a potential clinical application for nicotine. The observed pharmacological effect could be attributed to the unique chemical structure of nicotine, and efficient recombination of this structure could lead to the development of a new therapeutic drug targeting UC. However, this study has some limitations. First, given that the experimental period was only 10 days, long-term administration needs to be assessed in future investigations. Considering UC, medication to maintain remission is crucial; therefore, the advantages and disadvantages of administering nicotine or its structural analogs for a prolonged period need to be comprehensively clarified. Second, we only administered a single chemical concentration (1.0 mg/kg), and dosage-based differences in results were not verified. This dosage follows previous papers in which nicotine has shown therapeutic effects on inflammation [25,26], but it may not be the optimal dosage for other nicotinic structural analogs as well. Experimenting with different concentrations of nicotine or its structural analogs may induce differences in the observed results. Third, it is unclear whether intraperitoneal administration, as in the present study, is the best treatment route for UC. Oral, intravenous, and transrectal administration may exert robust pharmacological effects against UC. Given the number of chemicals examined in the present study, it was necessary to conduct a comprehensive analysis; however, this hindered verification across diverse experimental settings, which needs to be explored in future investigations.
## 5. Conclusions
Comparing the short-term outcomes of nicotine and its structural analogs in UC, nicotine administration appears to be the best choice, followed by nornicotine administration. Treatment with nicotine could alleviate experimental UC by activating nAChR7 expression and suppressing the production of proinflammatory cytokines in MΦ. Clinical application of nicotine is currently difficult because of its addictive and dependent effects on humans. Constructing a recombinant form that overcomes these disadvantages would allow the development of UC drugs with reduced toxicity and enhanced pharmacological effects.
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|
---
title: Predictors and Outcomes of SGLT2 Inhibitor Discontinuation in a Real-World
Population after Hospitalization for Heart Failure
authors:
- Masaki Nakagaito
- Teruhiko Imamura
- Ryuichi Ushijima
- Makiko Nakamura
- Koichiro Kinugawa
journal: Biomedicines
year: 2023
pmcid: PMC10046005
doi: 10.3390/biomedicines11030876
license: CC BY 4.0
---
# Predictors and Outcomes of SGLT2 Inhibitor Discontinuation in a Real-World Population after Hospitalization for Heart Failure
## Abstract
Background: Sodium–glucose cotransporter 2 inhibitors (SGLT2i) reduce mortality and morbidity in patients with heart failure (HF), but are discontinued in some patients. Such patients may not enjoy favorable benefits of SGLT2i therapy. We evaluated the risk factors for SGLT2i discontinuation in a real-world population with HF. Methods: We retrospectively included consecutive patients who were hospitalized for HF and administered SGLT2i during the index hospitalization between February 2016 and September 2021. We assessed the baseline clinical factors associated with post-discharge discontinuation of SGLT2i. Results: This study included a total of 159 patients (median age = 73 years, 57 women). Among baseline characteristics, a lower serum albumin level (odds ratio = 0.23, $95\%$ confidence interval = 0.07–0.76, $$p \leq 0.016$$) and a higher dose of furosemide (odds ratio = 1.02, $95\%$ confidence interval = 1.00–1.05, $$p \leq 0.046$$) were independently associated with the future discontinuation of SGLT2i following index discharge. Patients who terminated SGLT2i ($$n = 19$$) had a higher incidence of HF recurrence or cardiovascular death during the 1-year therapeutic period ($32\%$ versus $11\%$, $$p \leq 0.020$$). Conclusions: Among patients who initiated SGLT2i during hospitalization for HF, lower serum albumin levels and higher doses of loop diuretic at index discharge were associated with the discontinuation of SGLT2i following index discharge. We should pay special attention to patients with such characteristics during the initiation of SGLT2i and during SGLT2i therapy.
## 1. Introduction
To date, sodium–glucose cotransporter 2 inhibitors (SGLT2i) have demonstrated a major therapeutic advance in patients with heart failure (HF). Large-scale randomized controlled trials (RCTs) have shown that they reduce the risk of hospitalization for HF or cardiovascular death in patients with HF with reduced left ventricular ejection fraction (HFrEF) [1,2]. Thus, dapagliflozin and empagliflozin, an example of SGLT2i, have received a class I indication for HFrEF patients in international guidelines [3,4]. The EMPEROR-Preserved trial with empagliflozin and DELIVER trial with dapagliflozin have shown a reduction in HF hospitalization in patients with HF with preserved left ventricular ejection fraction (HFpEF) [5,6]. Furthermore, a reduction in HF hospitalization was reported in patients with HF, independently of the presence of diabetes mellitus (DM). Thus, SGLT2i are prescribed in patients with chronic HF accompanying a wide range of values of left ventricular ejection fraction (LVEF). Recently, several RCTs suggested that SGLT2i might exert a beneficial effect on the cardiovascular outcome even in hospitalized or recently discharged patients with HF, in addition to those with chronic HF [7,8,9].
However, our team previously found that some studies that initiated SGLT2i terminated SGLT2i for a variety of reasons [10,11]. Patients who terminated SGLT2i had worse clinical outcomes compared with those who continued SGLT2i. Thus, we should pay attention to the negative aspect of SGLT2i therapy. Given their pleiotropic effect, several adverse drug reactions including genital and urinary tract infection [12], volume depletion [13], diabetic ketoacidosis (DKA) [14], bone fractures [15], and amputation [16] might be increased.
Previous large-scale RCTs demonstrated that there was no notable excess of serious treatment-emergent adverse events during SGLT2i therapy. Nevertheless, minor adverse effects of SGLT2i treatment are potentially numerous, and real-world data that reflect actual practice should be useful in identifying them. Of note, although clinical implication of early administration of SGLT2i was investigated in several RCTs including the SOLOIST-WHF trial and EMPULSE trial, whether early-administered SGLT2i can be continued over a longer observational period remains uncertain.
An abundant number of comorbidities including HF and other baseline characteristics may affect the extent of SGLT2i treatment and disturb continuation of SGLT2i therapy. Such risk factors associated with the termination of SGLT2i are of great importance for clinicians in determining whether to initiate SGLT2i and managing patients receiving SGLT2i over a long-term therapeutic period. In the present study, we investigated the factors associated with post-discharge discontinuation of SGLT2i, which was initiated during index hospitalization soon after the stabilization of HF.
## 2. Materials and Methods
This was a single-center, retrospective observational study to investigate the baseline factors associated with the discontinuation of SGLT2i, which was initiated during index hospitalization. This study was carried out in accordance with the principles outlined in the Declaration of Helsinki, and the institutional ethics board of Toyama University Hospital approved the study protocol (#R2015154, approval date 11 April 2016). Written informed consent was obtained from all of the participants beforehand.
## 2.1. Study Population
Patients who had been admitted for HF, which was diagnosed according to the Framingham criteria, at our institute between April 2016 and September 2021 were included in this study. Most of the patients had New York Heart Association (NYHA) class III/IV symptoms upon admission. All patients were treated with guideline-directed medical therapy for HF, including renin–angiotensin system inhibitors or angiotensin receptor–neprilysin inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and diuretics, if applicable. Patients who newly received SGLT2i during their index hospitalization immediately following the stabilization of hemodynamics were followed-up for 12 months following index discharge (defined as day 0).
## 2.2. Exclusion Criteria
We excluded the following patients: age < 20 years, end-stage renal failure with estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m2, use of durable left ventricular assist devices, pregnancy or breastfeeding, and current use of SGLT2i during index hospitalization. Patients who stopped taking SGLT2i during index hospitalization were excluded. Adjustment of medical therapy was permitted as a real-world clinical practice. Patients who were lost to follow-up during the 12-month observational period were also excluded. Patients who died due to non-cardiovascular disease were excluded.
## 2.3. Study Design and Data Collection
Baseline characteristics including demographics and laboratory data at index discharge were retrieved. The eGFR was calculated using the guidelines from the Chronic Kidney Disease Epidemiology Collaboration. Plasma B-type natriuretic peptide (BNP) level and eGFR were retrospectively retrieved at the 12-month follow-up. Standard echocardiographic findings during index hospitalization were retrieved. For the present analysis, participants were divided into HFrEF (LVEF < $40\%$), HfmrEF (LVEF 40–$49\%$), and HfpEF (LVEF ≥ $50\%$) groups. We defined DM as patients satisfying glycated hemoglobin (HbA1c) ≥ $6.5\%$ or receiving antidiabetic treatment. Bacterial infections included symptomatic bacteriuria or cases where bacterial cultures from sources such as blood, urine, or stool were positive. When patients died from cardiovascular causes, they were censored at the time of events.
## 2.4. Study Endpoints
The primary outcome was a discontinuation of SGLT2i during the 12 months following their index discharge. Baseline characteristics that would significantly affect the primary outcome were investigated. The secondary outcome was the changes in plasma BNP level and eGFR during the observational period.
## 2.5. Statistical Analyses
Statistical analysis was conducted using JMP® 15.0 (SAS Institute Inc., Cary, NC, USA). The level of significance was set at two-tailed $p \leq 0.050.$ Continuous variables are presented as the median and interquartile range unless specifically stated. Categorical variables are presented as absolute numbers and percentages. The Wilcoxon test was used to compare continuous parameters, and Pearson’s χ2 test was used for comparison of categorical variables.
Univariable and multivariable analyses with logistic regression models were conducted to calculate the adjusted odds ratio to assess the influence of various baseline parameters on the discontinuation of SGLT2i. Variables deemed as significant ($p \leq 0.050$) in the univariate analyses were enrolled in the multivariate analyses. Time-to-event outcomes were evaluated using Kaplan–Meier estimates and the two treatment groups were compared with the use of log-rank test statistics.
## 3.1. Follow-Up and Patient Characteristics
Totally, 187 patients met the inclusion criteria. Of them, 2 died from non-cardiovascular causes, and 26 patients were lost to follow-up during the observational period. Finally, 159 patients were included in this study (Figure 1).
Table 1 lists the baseline characteristics obtained at index discharge. The median age was 73 (64–81) years and $36\%$ of patients were women. NYHA class III/IV symptoms were noted in 36 patients ($23\%$). HFrEF was noted in 62 patients ($39\%$), HFmrEF in 42 patients ($26\%$), and HFpEF in 55 patients ($35\%$). DM was noted in 123 patients ($77\%$) (all of them were type 2). As for the types of SGLT2i, 37 patients received canagliflozin, 82 received dapagliflozin, and 40 received empagliflozin. All patients receiving canagliflozin had a history of DM.
## 3.2. Causes of Primary Outcome
As a primary outcome, a total 19 of patients ($12.0\%$) encountered the discontinuation of SGLT2i. The most common reasons for SGLT2i discontinuation were bacterial infection ($$n = 6$$, $32\%$), followed by patient-reported side effects ($$n = 5$$, $26\%$). The side effects reported by patients included nocturia, anorexia, pruritus, and tiredness. Adjustment for treatment of cardiovascular disease was the third most common reason ($$n = 4$$, $21\%$) (Figure 2).
## 3.3. Prediction of Primary Outcome
Baseline characteristics stratified by achievement of primary endpoint are summarized in Table 1. The level of serum albumin was lower in the discontinuation group than the continuation group. Medications for HF and DM were not statistically different between the two groups. There were no significant differences in the plasma BNP and eGFR levels at baseline between both groups.
Univariable and multivariable analyses demonstrated that serum albumin level (odds ratio = 0.23, $95\%$ confidence interval = 0.07–0.76, $$p \leq 0.016$$) was independently and negatively associated with the discontinuation of SGLT2i, and furosemide dosage (odds ratio = 1.02, $95\%$ = confidence interval 1.00–1.05, $$p \leq 0.046$$) was positively associated with the discontinuation of SGLT2i (Table 2). Prevalence of DM was not associated with the primary outcome.
## 3.4. Impact of SGLT2i Discontinuation on Clinical Outcomes
Patients in the discontinued SGLT2i group had a higher incidence of recurrent HF hospitalization or cardiovascular death ($32\%$ vs. $11\%$, $$p \leq 0.020$$; Figure 3).
The changes in plasma BNP and eGFR were assessed in 117 and 133 patients, respectively, by excluding those who died during the observational period and those whose laboratory data were not retrieved. Changes in plasma BNP level from baseline are shown in Figure 4. Plasma BNP levels decreased in patients with both continuation and discontinuation of SGLT2i from baseline to 12 months. The changes in plasma BNP were not different between the continued SGLT2i group and the discontinued SGLT2i group (−20.5 vs. −96.0 pg/mL, $$p \leq 0.096$$). On the other hand, there were no significant changes in eGFR throughout the 12-month observation period in both groups. The changes in eGFR were not different between the continued SGLT2i group and the discontinued SGLT2i group (+0.65 vs. −0.60 mL/min/1.73 m2, $$p \leq 0.767$$).
## 4. Discussion
We investigated the factors associated with post-discharge discontinuation of SGLT2i in patients with HF. SGLT2i were initiated soon after the stabilization of hemodynamics during index hospitalization for HF. Among baseline characteristics at index discharge, a lower serum albumin level and a higher dose of furosemide were associated with discontinuation of SGLT2i following index discharge. Patients with discontinued SGLT2i had a higher incidence of re-hospitalization for HF or cardiovascular death during the 12-month observational period following index discharge. The changes in plasma BNP and eGFR were not significantly different between the discontinued SGLT2i group and the continued SGLT2i group.
## 4.1. Discontinuation of SGLT2i
In this study, SGLT2i were discontinued for reasons other than death in 19 patients ($12.0\%$). Our discontinuation rate of $12.0\%$ is similar to previously reported rates of 10.5–$23.2\%$ in large-scale RCTs [1,2,5,6]. However, the median follow-up durations of these RCTs (16.0–27.9 months) were longer than ours (12.0 months), and the risk of SGLT2i discontinuation in this study might be higher than in previous RCTs. In the SOLOIST-WHF trial and EMPULSE trial, both of which investigated the clinical implication of early initiation of SGLT2i, the rates of adverse events leading to the discontinuation of SGLT2i were lower than those in the present study ($8.5\%$ and $4.8\%$, respectively) [7,8]. These findings suggest difficulty in continuing SGLT2i in real-world populations.
A possible explanation for the high frequency of SGLT2i discontinuation was that our patients were included in this study soon after an episode of decompensated HF. Our population might be distinct from patients with chronic HF in most of the previous trials, since our patients were at a higher risk of cardiovascular events compared to patients in many of the other trials. Additionally, our population may have a higher risk of SGLT2i withdrawal in the vulnerable period, in which patients may have unstable volume status, renal function, and blood pressure and other HF therapies may require adjustment. Similarly, a subset of the DELIVER trial, which evaluated the response to dapagliflozin in hospitalized or recently discharged patients, reported that serious adverse events were more common in recently hospitalized patients compared with those without episodes of recent hospitalization [17].
## 4.2. Diabetes and SGLT2i Discontinuation
Importantly, prevalence of DM was no longer a predictor of SGLT2i discontinuation in this study. Further, no patients stopped SGLT2i because of major hypoglycemia or DKA. Our observation regarding the safety of SGLT2i is consistent with previous trials and reports that the rate of SGLT2i discontinuation is not significantly different between participants with and without diabetes.
## 4.3. Hypoalbuminemia and SGLT2i Discontinuation
We demonstrated that a lower serum albumin level and a higher dose of furosemide were associated with SGLT2i discontinuation. It is worth noting that both low serum albumin levels and high doses of furosemide at baseline are strongly associated with an increased risk of HF [18,19,20]. In addition, SGLT2i therapy for patients with malnutrition might not be encouraged due to its potential to progress catabolism. Although subsets of the DAPA-HF trial and DELIVER trial demonstrated that dapagliflozin improved cardiovascular outcomes regardless of frailty status [21,22], few studies assessed the efficacy and safety of SGLT2i in malnourished patients. Our finding suggests that caution should be exercised in using SGLT2i in patients with both HF and malnutrition.
## 4.4. High Dose of Loop Diuretics and SGLT2i Discontinuation
High incidence of SGLT2 discontinuation in patients with high doses of furosemide may be associated with adverse events of volume depletion. Although individual RCTs reported that the frequency of adverse events related to volume depletion did not differ between patients with SGLT2i and those without SGLT2i, a meta-analysis indicated that there is an increase in hypovolemia associated with SGLT2i treatment [13]. Moreover, a previous study reported that SGLT2i therapy resulted in a significant augmentation of natriuresis when combined with loop diuretics [23]. Inhibition of compensatory absorption of sodium in the Henle’s loop or distal tubule by loop diuretics in patients receiving SGLT2 may explain the findings of the study.
Therefore, co-administration of SGLT2i and high-dose loop diuretics might act complementarily via double blocking of proximal tubules and Henle’s loop, resulting in rapid volume depletion. This diuretic profile offers significant advantages in the management of volume status in patients with HF, whereas a robust diuretic response may result in excessive fluid loss. In this study, unexpected changes in plasma volume caused by co-administration of SGLT2i and high-dose loop diuretics may have led to SGLT2i discontinuation.
## 4.5. Outcomes of SGLT2i Discontinuation
In our study, patients who discontinued SGLT2i had a higher risk of re-hospitalization for HF or cardiovascular death. This result should be interpreted with caution since this is a retrospective observational study. We found that lower serum albumin levels and higher doses of furosemide were independently associated with SGLT2i discontinuation, and were also associated with an increased risk of HF. Patients who discontinued SGLT2i may be at higher risk of cardiovascular events than those who continued SGLT2i, making it difficult to assess the association between SGLT2i discontinuation and cardiovascular outcome in this study.
On the other hand, we previously reported that patients who discontinued SGLT2i at discharge from hospitalization for HF were more likely to be re-hospitalized for HF than those who continued SGLT2i [11]. Previous findings may support a direct and independent prophylactic effect of SGLT2i on cardiovascular events for post-discharge patients in this study. RCTs demonstrated that SGLT2i improved cardiovascular outcomes, whereas few studies reported the effect of discontinuing SGLT2i on cardiovascular events. Additional research to confirm these findings and to understand the importance of continuing SGLT2i after discharge from hospitalization for HF is needed.
## 4.6. Limitations
This study has several limitations. First, this study was conducted retrospectively in a single center, and both overall sample size and number of patients reaching the primary outcome were small. Given the low event number, the number of potential confounders included in the multivariate analyses was limited. Similarly, limited patients were included in the analysis for secondary outcome. Furthermore, it is not possible to establish a cause–effect relationship given the retrospective observational study design. Second, other HF medications were also adjusted during the observational period in this study. Thus, it is challenging to assess the effect of SGLT2i alone. Lastly, our population received multiple types of SGLT2i in the present study. No RCTs have evaluated the efficacy of canagliflozin in patients with HF. It remains unclear whether such beneficial effects are consistent across individual SGLT2i. However, a recent retrospective cohort study reported that there was no significant difference in the risk of cardiovascular events including HF among patients taking dapagliflozin, canagliflozin, and empagliflozin [24].
## 5. Conclusions
In this study, we found that $12.0\%$ of patients who initiated SGLT2i soon after the stabilization of their HF during index hospitalization terminated SGLT2i therapy within 12 months following index discharge. The rate of discontinuation of SGLT2i is slightly higher than those of previous RCTs. The most common reason for the discontinuation of SGLT2i was bacterial infection. Lower serum albumin levels and higher doses of furosemide at the time of index discharge were independently associated with SGLT2 discontinuation after discharge from hospitalization for HF. Furthermore, SGLT2i discontinuation was associated with an increased risk of re-hospitalization for HF or cardiovascular death. Special attention should be paid when initiating and managing SGLT2i for those with such risk factors.
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|
---
title: Rosiglitazone Mitigates Dexamethasone-Induced Depression in Mice via Modulating
Brain Glucose Metabolism and AMPK/mTOR Signaling Pathway
authors:
- Aisha Alhaddad
- Asmaa Radwan
- Noha A. Mohamed
- Eman T. Mehanna
- Yasser M. Mostafa
- Norhan M. El-Sayed
- Shaimaa A. Fattah
journal: Biomedicines
year: 2023
pmcid: PMC10046017
doi: 10.3390/biomedicines11030860
license: CC BY 4.0
---
# Rosiglitazone Mitigates Dexamethasone-Induced Depression in Mice via Modulating Brain Glucose Metabolism and AMPK/mTOR Signaling Pathway
## Abstract
Major depressive disorder (MDD) is a common, complex disease with poorly understood pathogenesis. Disruption of glucose metabolism is implicated in the pathogenesis of depression. AMP-activated protein kinase (AMPK) has been shown to regulate the activity of several kinases, including pAKT, p38MAPK, and mTOR, which are important signaling pathways in the treatment of depression. This study tested the hypothesis that rosiglitazone (RGZ) has an antidepressant impact on dexamethasone (DEXA)-induced depression by analyzing the function of the pAKT/p38MAPK/mTOR pathway and NGF through regulation of AMPK. MDD-like pathology was induced by subcutaneous administration of DEXA (20 mg/kg) for 21 days in all groups except in the normal control group, which received saline. To investigate the possible mechanism of RGZ, the protein expression of pAMPK, pAKT, p38MAPK, and 4EBP1 as well as the levels of hexokinase, pyruvate kinase, and NGF were assessed in prefrontal cortex and hippocampal samples. The activities of pAMPK and NGF increased after treatment with RGZ. The administration of RGZ also decreased the activity of mTOR as well as downregulating the downstream signaling pathways pAKT, p38MAPK, and 4EBP1. Here, we show that RGZ exerts a potent inhibitory effect on the pAKT/p38MAPK/mTOR/4EBP1 pathway and causes activation of NGF in brain cells. This study has provided sufficient evidence of the potential for RGZ to ameliorate DEXA-induced depression. A new insight has been introduced into the critical role of NGF activation in brain cells in depression. These results suggest that RGZ is a promising antidepressant for the treatment of MDD.
## 1. Introduction
In recent decades, millions of individuals around the globe have been adversely affected by depression, making it one of the most prevalent neuropsychiatric illnesses [1]. The underlying mechanism of depression is still obscure. Patients with depression typically display depressive behavior together with certain pathological signs, including aberrant cytokine release, inflammation, and deficits in cell proliferation and neuroplasticity [2]. Because the central nervous system is an immunologically privileged area, it can produce inflammatory substances that help keep the immune system intact. The development of long-term immunological tolerance leads to a markedly increased secretion of inflammatory components, which are associated with inflammatory exacerbation and, consequently, with the pathogenesis of a wide spectrum of neurological and psychiatric disorders. There is ample evidence that inflammatory cytokines are significantly elevated in the serum of MDD patients [3]. Unpredictable chronic mild stress may cause depressive behaviors in rats, so it is frequently used to model depression in animals [4]. Chronic stress triggers the secretion of corticosterone and glucocorticoids in both humans and animals, and depression occurs with the release of high concentrations of corticosterone [5].
Two important brain regions regarding depression are the prefrontal cortex and the hippocampus. The hippocampus is critical to the brain’s learning and memory processes [6,7]. Previous research has shown that changes in the hippocampal morphology during depression impair its function and memory retrieval [8,9]. The prefrontal cortex has been shown to play an important role in social-emotional processing [10]. Frontal alpha asymmetry showed that major depression disorder (MDD) patients appeared to have increased alpha power in the left frontal hemisphere and decreased alpha power in the right hemisphere compared with control subjects [11]. When a person becomes depressed, he or she leaves the typical waking state and enters a separate global level of consciousness, such as dreaming or having psychedelic experiences [12]. It was suggested that qEEG and functional connectivity methods can be used to predict and characterize patients with disorders of consciousness in terms of their rehabilitative capacity and functional recovery [13].
It is believed that anomalies in brain glucose metabolism have a role in the etiology of depression. Most of the brain’s energy comes from glucose. The optimum neurotransmitter synthesis and function, in particular the production of glutamate and -aminobutyric acid (GABA), as well as the production of enough NADPH, are made possible by glucose metabolism [14]. The frontal brain and hippocampus of depressed mice showed greater glucose/glycogen, and glucose transporter (GLUT1–GLUT3) levels than healthy animals due to the intensity of glucose uptake during depression [15]. A previous study examined the expression of glycolysis and glycogen-related genes in the hippocampus of depressed rats, and the results showed that the mRNA level of Slc2a3 (encoding GLUT3) increased [16]. According to certain hypotheses, the risk of developing mental, metabolic, and cardiovascular disorders is elevated by prolonged stimulation of the hypothalamic-pituitary-adrenal (HPA) axis. In fact, accumulating clinical evidence consistently demonstrates a link between negative cardiovascular events, obesity, type 2 diabetes mellitus (DM), metabolic syndrome, and hypertension [17,18]. Available evidence shows that the principal cause for the coexistence of MDD due to metabolic disturbances is the excessive action of glucocorticoids [19]. Given that there is a significant correlation between depression and metabolic disorders, particularly DM, several studies have investigated the anti-depressive effects of some antidiabetic medications as a trial to develop new therapeutic approaches for the management of depression [20]. Diabetes seems to be a risk factor for the incidence of depression, and a quarter of diabetic subjects show depressive symptoms [21]. Numerous antidiabetic medications, including metformin, thiazolidinedione, and sulfonylureas, have either been shown or are supposed to alleviate depression in such circumstances [22,23,24].
Rosiglitazone (RGZ), a member of the thiazolidinedione group that is frequently prescribed as an insulin-sensitizing drug to alleviate type 2 DM, has been shown to exhibit neuroprotective action in numerous central nervous system disorders [25], including Parkinson’s disease [26], Alzheimer’s disease [27], and stroke [28]. Intriguingly, pioglitazone, which has a similar chemical composition to that of RGZ, has been shown to alleviate depression [29]. Moreover, Patel et al. [ 30] reported that RGZ effectively helped in the management of neurological diseases accompanied by depression-like behavior. However, it is still unclear what processes underlie these effects.
In humans, physiological brain glucose increases in parallel with peripheral hyperglycemia [31]. High glucose, shown to be an independent risk factor for insulin resistance in human neuroblastoma cells, rat primary cortical neurons, and the cerebral cortex of hyperglycemic db/db mice, promotes mitochondrial dysfunction, adenosine monophosphate-activated protein kinase (AMPK) inactivation, and mTOR/AKT activation [32].
Mammalian target of rapamycin (mTORC) is a serine/threonine kinase that is expressed during dendritic development, where mTOR regulates the initiation of new protein translation and subsequent translation [33]. Dysfunction of mTOR is involved in the etiology of different neuropsychiatric disorders, including depression [34]. Recently, it was revealed that the antidepressant effect mediated by the mTOR/AMPK pathway is accomplished by increasing autophagy in chronic unpredictable mild stress mice [35,36]. These findings suggest a possible novel approach for the treatment of depression related to the AMP/ATP ratio and AMPK. Therefore, this study investigated the effect of RGZ on AMPK, mTOR, and their downstream signaling factors adenosine monophosphate-activated protein kinase (pAKT), mitogen-activated protein kinases (p38MAPK), and eukaryotic initiation factor 4E (eIF4E)-binding proteins (4EBP1) in a depressed mouse model.
Olson described the growth factor known as nerve growth factor (NGF) as a neurite outgrowth factor [37]. Studies proved that NGF is implicated in neuronal repair and survival [38,39]. The role of NGF in MDD is still understudied. In the Flinders Sensitive Line (FSL) rat model of depression, electroconvulsive therapy (ECT) increased NGF levels in the hippocampus [40]. Furthermore, subcutaneous NGF injections have been reported to alleviate depression [41].
In the current work, the mechanisms underlying the antidepressant effect of RGZ, such as regulation of AMPK and mTOR, and their downstream signaling pathways of pAKT, p38MAPK, 4EBP1, and NGF, were investigated in a mouse model of depression induced by dexamethasone (DEXA).
## 2. Materials and Methods
In this study, the antidepressant effect of RGZ was investigated in mice that received a subcutaneous dose of DEXA (20 mg/kg) for 21 days to induce MDD-like pathology. The expression of GLUT 1, GLUT 3, pAMPK, pAKT, p38MAPK, and 4EBP1, and the levels of hexokinase, pyruvate kinase, and NGF were examined in the prefrontal cortex and hippocampus tissue samples to explore the potential mechanism of RGZ.
## 2.1. Animals
In the current study, 32 Swiss Albino male mice weighing 25–30 g were used. The sample size was calculated by the resource equation method [42]. Mice were kept in a temperature-controlled environment (22 ± 3 °C) with a regular 12:12 h light:dark cycle, normal food, and unlimited access to water. Prior to the investigation, mice were given at least 5–7 days to acclimate. Throughout the experiment, mice were weighed and regularly monitored for any indications of distress. The experiment protocols were performed in compliance with the Animal Ethics Committee’s guidelines at the Faculty of Pharmacy, Suez Canal University (License number 201609RA1), in accordance with the Canadian Council on Animal Care Guidelines.
## 2.2. Drugs
RGZ was kindly provided by Medical Union Pharmaceuticals (Abou Sultan-Ismailia, Egypt) and was suspended in $1\%$ sodium carboxymethyl cellulose (Na-CMC) to achieve a final concentration of $1\%$. DEXA was acquired from Sigma-Aldrich (St. Louis, MO, USA) and suspended in saline ($0.9\%$ NaCl) containing $0.1\%$ Tween-80 and $0.1\%$ dimethyl sulfoxide (DMSO).
## 2.3. Induction of Depression
To induce depression-like behavior, three experimental groups were injected subcutaneously (s.c.) with DEXA (20 mg/kg) once daily for 21 consecutive days in a volume of 5 mL/kg at casual times during the light phase, whereas the normal control group received only vehicle as previously mentioned [43]. After three weeks, the development of the depressed model was confirmed through exposing mice to the tail suspension test (TST) and forced swimming test (FST). Mice displaying a greater immobility time in FST as well as TST had been considered depressed.
## 2.4. Experimental Design
Mice were randomly allocated to 4 weight-matched groups of eight mice each: Group 1: negative control group received vehicle (saline ($0.9\%$ NaCl) containing $0.1\%$ Tween-80 and $0.1\%$ DMSO); Group 2: positive control untreated “depressed group “ mice received DEXA 20 mg/kg; Group 3 and 4: depressed mice received daily s.c. DEXA 20 mg/kg and per oral (p.o.) doses of RGZ (10 or 30 mg/kg) for 21 days [44,45,46].
Following daily dosage, the TST and the FST were performed at the end of the experiment. Ketamine and xylazine were intraperitoneally injected at doses of 87.5 mg/kg and 12.5 mg/kg, respectively, to euthanize the mice via cervical dislocation. Each mouse’s frontal cortex and hippocampus were dissected, snap-frozen, and kept at −80 °C for subsequent analyses.
## 2.5. Forced Swimming Test and Tail Suspension Test
FST and TST are common methods for evaluating depression-like behavior [47]. The FST and TST were carried out in all experimental groups.
The FST was performed as previously mentioned [48,49]. Individually placed in 2000 mL glass beakers with 10 cm of water at room temperature (25 ± 1 °C), each mouse was given 5 min to swim. “ Duration of immobility” refers to the time interval when mice are non-energetic and show none of the escape-oriented behaviors like swimming, diving, jumping, rearing, or sniffing. The immobility period was recorded during the last 4 min.
The TST was conducted as previously detailed [50]. Mice were allowed to rest for 24 h following FST. Mice were suspended from the edge of a shelf that was 58 cm above a tabletop by adhesive tape that was positioned about 1 cm from the tip of the tail. They were permitted to hang for a total of 6 min, and the duration of immobility was recorded during the test’s last 4 min. The only immobile mice were those that hung passively and motionlessly.
## 2.6. Enzyme-Linked Immunosorbent Assay
The levels of hexokinase and pyruvate kinase in the frontal cortex and hippocampus of mice were measured using Enzyme-Linked Immunosorbent Assay (ELISA) kits (EIAab Science Co., Ltd., Wuhan, China) following the manufacturer’s instructions. Hexokinase and pyruvate kinase concentrations were estimated and expressed as nmole/mg tissue.
NGF levels were assessed using a commercial ELISA kit specific for mice (Cusabio Technology, Houston, TX, USA). Absorbances were measured at 450 nm. NGF levels were expressed as pg/g tissue.
## 2.7. RNA Extraction and Real Time Quantitative PCR (RT-qPCR)
To measure the gene expression of GLUT1 and GLUT3 in the mice’s hippocampus and frontal cortex tissue, RNA was extracted using the SV total RNA isolation kit (Promega, Madison, WI, USA) according to the manufacturer’s instructions. Using a Nanodrop NA-1000 UV/vis spectrophotometer (Thermo Fisher Scientific Inc., Wilmington, DE, USA), RNA purity and concentration were measured and then stored at −80 °C. Messenger RNA (mRNA) transcript levels of GLUT1 and GLUT3 were quantified by real-time PCR using the StepOne Plus™ real-time PCR thermal cycler (Applied Biosystems, Waltham, MA, USA). RT-qPCR was performed using the GoTaq® 1-Step RT-qPCR System (Promega, Madison, WI, USA) as previously described. The primers used are listed in Table 1. The thermal PCR amplification protocol was as follows: 37 °C for 15 min, 10 min at 95 °C, followed by 40 cycles of 95 °C for 10 s, 52 °C for 30 s, and 72 °C for 30 s. *The* generation of specific PCR products was confirmed through dissociation curve analysis. The threshold (Ct) values for each reaction were estimated. All the Ct values of the target genes were normalized to the Ct value of β-actin, which was used as a housekeeping gene.
## 2.8. Western Blotting Analysis
For the detection of pAKT (Ser473), pAMPK-α (Thr172), p38MAPK, 4EBP1, and mTOR, the frontal cortex and the hippocampus were homogenized in an ice-cold RIPA lysis solution supplemented with phosphatase and protease inhibitors to maintain the proteins integrity and phosphorylation. Lysates were centrifuged at 16,000× g for 10 min, and pellets were discarded. The supernatants were kept at −80 °C for subsequent analysis. The Bradford assay was performed to determine the concentrations of protein, as previously detailed [51]. The lysate was combined with an equal volume of 2 × Laemmli sample buffer, heated for 5 min at 95 °C, and then centrifuged for 10 min at 10,000× g. Resulting supernatants were subjected to $12\%$ SDS–polyacrylamide gel electrophoresis. Proteins had been loaded to PVDF membranes using a Bio-Rad Trans-Blot Turbo system (Bio-Rad Laboratories Ltd., Watford, UK). Membranes were then blocked by incubation for 1 h in tris-buffered saline (TBS) containing $0.05\%$ polyoxyethylenesorbitan monolaurate (Tween 20; TBS-T buffer) containing $5\%$ (wt/vol) non-fat dry milk. Rabbit monoclonal anti-pAMPKα (Thr172) (Cat#ab 50081, cell signaling technology, 4228, Danvers, MA, USA), mouse monoclonal anti-pAKT (Ser473) (Cat#ab 4051, cell signaling technology, 4228, Danvers, MA, USA), rabbit monoclonal anti-p38MAPK (Cat#ab 8690, cell signaling technology, 4228, Danvers, MA, USA), rabbit monoclonal anti-4EBP1 (Cat#ab 9644, cell signaling technology, 4228, Danvers, MA, USA) or mouse monoclonal β-actin (Cat# ab3700, cell signaling technology, 4228, Danvers, MA, USA), were used as primary antibodies (1:1000 dilution in TBS-T with $5\%$ non-fat milk) and membranes were incubated overnight at 4 °C. Blots were then subsequently washed in TBS-T and incubated with the HRP-conjugated secondary antibody; Goat Anti-Mouse IgG HRP-linked Antibody (Dako #P0447, Glostrup, Denmark), Goat anti-rabbit IgG HRP-linked antibody (1:5000 dilution, Cat#7074, Cell Signaling, 4228, Danvers, MA, USA). Membranes were incubated for 1 h at room temperature. Signals were visualized by chemiluminescence (ClarityTM Western ECL substrate—BIO-RAD, Hercules, CA, USA cat#170- 5060) in accordance with the manufacturer’s instructions and recorded using a CCD camera-based imager. Densitometry was performed to measure the band intensities using the Image J program (version 1.48, National Institute of Health, Bethesda, MD, USA).
## 2.9. Statistical Analyses
In regard to the current study’s statistical analyses, the SPSS program’s version 24 was used (SPSS Inc., Chicago, IL, USA). Data were expressed as mean ± SD (standard deviation). A one-way analysis of variance (ANOVA) followed by Tukey’s post hoc multiple-comparisons test was applied, and a p-value of <0.05 was considered statistically significant.
## 3.1.1. Forced Swimming Test
The one-way ANOVA analysis showed a significant difference in the mobility time recorded in the FST between the different groups (F3,31 = 15.2, $p \leq 0.001$). As shown in Figure 1A and Table S1, the untreated stressed mice displayed immobility behavior for 168 s, in contrast to 119 s in the normal mice ($p \leq 0.001$). When compared to the untreated group, mice that received DEXA in combination with 10 or 30 mg/kg RGZ showed a substantial decrease in immobility time of $16\%$ ($$p \leq 0.003$$) and $18\%$ ($$p \leq 0.001$$), respectively.
## 3.1.2. Tail Suspension Test
A significant difference in the TST mobility time (F3,31 = 13.4, $p \leq 0.001$) was recorded. Post hoc analysis showed that the duration of immobility of the untreated DEXA group was significantly longer than that of the normal mice (214 and 144 s, respectively; $p \leq 0.001$). Following administration of both dosages of 10 and 30 mg/kg RGZ, immobility time was significantly reduced by $18\%$ ($$p \leq 0.012$$) and $27.6\%$ ($p \leq 0.001$), respectively, when compared with the DEXA group (Figure 1B and Table S1).
## 3.2. Reversal of GLUT1 and GLUT3 Expression Levels in Frontal Cortex and Hippocampus of Mice Post Dexamethasone Administration
Previous data indicated an association between depression and increased glucose metabolism in the hippocampus and frontal cortex [15,52]. In this context, the one-way ANOVA analysis indicated a significant difference in the expression of GLUT1 (F3,31 = 59.3, $p \leq 0.001$) and GLUT3 (F3,31 = 46.8, $p \leq 0.001$) in the different groups.
As shown in Figure 2A,B and Table S2, DEXA was associated with significantly increased cerebral glucose metabolism, as evidenced by a significant 8.3- and 9.6-fold increase in GLUT1 and GLUT3, respectively, compared with the normal control group ($p \leq 0.001$). In comparison to the positive control (untreated) group, there was a significant effect of RGZ (10 and 30 mg/kg for 21 days) on GLUT1, reducing its expression by 4.6- and 5.5-fold, respectively ($p \leq 0.001$) in the frontal brain and hippocampus. Similarly, after administration of 10 or 30 mg/kg RGZ for 21 days, GLUT3 expression was reduced by 5.5- and 6.3-fold, respectively ($p \leq 0.001$).
## 3.3. Reversal of the Quantity of Vital Glycolytic Enzymes in Dexamethasone-Injected Mice Brain
The concentration of the two key glycolytic enzymes, hexokinase and pyruvate kinase, was measured in the hippocampus and frontal cortex of the experimental mice. A one-way ANOVA showed differences in the concentrations of hexokinase (F3,31 = 23.8, $p \leq 0.001$) and pyruvate kinase (F3,31 = 12.3, $p \leq 0.001$) between the different groups. In the frontal cortex and hippocampus, DEXA injection significantly increased the concentration of hexokinase and pyruvate kinase by $58\%$ and $63\%$, respectively, compared with the normal control group ($p \leq 0.001$). Compared with the untreated group, 21-day administration of 10 mg/kg RGZ significantly lowered the concentrations of hexokinase and pyruvate kinase enzymes in the frontal cortex and hippocampus by $32\%$ ($$p \leq 0.015$$) and $57\%$ ($p \leq 0.001$), respectively. When RGZ 30 mg/kg was administered, similar results were observed, with significant decreases in hexokinase and pyruvate kinase enzymes ($32\%$, $$p \leq 0.01$$ and $53\%$, $p \leq 0.001$, respectively). Evidently, administration of 10 and 30 mg/kg RGZ had a considerable impact and restored the pyruvate kinase levels back to their levels in the negative control group (Figure 3A,B and Table S3).
## 3.4. Reversal of mTOR Activity by Regulation of the pAMPK/pAKT/P38MAPK/4EBP1 Pathway in Dexamethazone-Injected Mice Brain
The effect of different RGZ doses on the levels of pAMPK at Thr172, an indicator of low glucose concentration, pAKT at Ser473, p38MAPK at Thr180/Tyr182, 4EBP1, and mTOR was investigated by Western blotting analysis (Figure 4A).
A significant difference in pAMPK levels was detected in the different groups (F3,31 = 70, $p \leq 0.001$). In comparison to the normal control group, DEXA significantly reduced the expression of pAMPK at Thr172 in the hippocampus and frontal brain by $72\%$ ($p \leq 0.001$). Contrarily, administration of RGZ at a dose of 10 or 30 mg/kg for 21 days resulted in a significant increase in pAMPK at Thr172 by $64\%$, $p \leq 0.001$, and $45\%$, $$p \leq 0.001$$, respectively, compared with the untreated group ($p \leq 0.001$) (Figure 4B and Table S4).
Likewise, a significant difference was observed in the expression of pAKT at Ser473 (F3,31 = 29.7, $p \leq 0.001$) and p38MAPK at Thr180/Tyr182 (F3,31 = 38.6, $p \leq 0.001$) between the different groups. A post hoc analysis showed that DEXA significantly increased pAKT at Ser473 and p38MAPK at Thr180/Tyr182 by $83\%$ and $77\%$, respectively ($p \leq 0.001$), as compared with the normal control group. Compared with the positive control (untreated) group, administration of 10 or 30 mg/kg RGZ significantly reduced pAKT at Ser473 by $69\%$ and $71\%$, respectively ($p \leq 0.001$) and p38MAPK at Thr180/Tyr182 by $44\%$ and $47\%$, respectively ($p \leq 0.001$) (Figure 4C,D and Table S4).
A one-way ANOVA analysis showed a significant difference in levels of mTOR (F3,31 = 47, $p \leq 0.001$) and 4EBP1 (F3,31 = 179, $p \leq 0.001$) in the experimental groups. In the untreated DEXA group, mTOR and 4EBP1 showed a marked increase of $77\%$ and $83\%$, respectively ($p \leq 0.001$) compared with the normal control group. In the groups receiving 10 or 30 mg/kg RGZ, post hoc analysis showed a significant decrease in mTOR by $40\%$ and $33\%$, respectively, and in 4EBP1 by $45\%$ and $55\%$, respectively ($p \leq 0.001$) (Figure 4E,F and Table S4).
## 3.5. Reversal of Nerve Growth Factor in the Brain of Dexamethasone-Injected Mice
NGF is an essential factor for neuroregeneration, and NGF levels are significantly lower in major depression [53]. A one-way ANOVA analysis revealed a significant difference in NGF levels between the different groups (F3,31 = 22, $p \leq 0.001$). DEXA administration reduced NGF levels in the frontal cortex and hippocampal tissues by $63\%$ versus the normal group ($p \leq 0.001$). After 21 days of treatment with RGZ 10 mg/kg and 30 mg/kg, these effects had been significantly reversed, as demonstrated by increases in NGF levels of $60\%$ ($p \leq 0.001$) and $48\%$ ($$p \leq 0.001$$), respectively, with nearly normal results obtained by injection of 10 mg/kg RGZ, as compared to untreated mice (Figure 5 and Table S4).
Overall, DEXA administration to mice resulted in increased immobility and depressive behavior, increased GLUT1 and GLUT3 expression, and increased hexokinase and pyruvate kinase levels in the brain. These results were associated with decreased pAMPK expression and increased mTOR, pAKT, p38MAPK, 4EBP1, and NGF expression, which may reduce neuronal autophagy. Administration of both doses of RZG (10 mg/kg or 30 mg/kg) stimulated pAMPK and simultaneously inhibited the pAKT/p38MAPK/mTOR pathway. The results suggest that 10 mg/kg RGZ has a potentiating effect on restoring normal NGF levels.
## 4. Discussion
Treatment of depression is so difficult in large part because there is no specific target that has a long-term antidepressant effect. In addition, many people with depression do not tolerate antidepressants, even at low doses. Patients often try multiple medications to control the symptoms of depression, but unacceptable side effects usually occur. Severe side effects of antidepressants, such as the regularly prescribed selective serotonin reuptake inhibitors (SSRIs), can lead to a potentially fatal condition known as “serotonin syndrome [54]. Here, we investigated the antidepressant effects of RGZ and its mechanism of action.
Depression has been linked to high glucose levels in the frontal lobe and hippocampus [19]. These higher glucose concentrations are most likely caused by increased glucose uptake due to an increased number of glucose transporters [55]. Previously, DEXA-induced depression in male mice was found to increase GLUT1 and glucose extraction in the brain compared to controls [56]. In agreement with previous studies, administration of DEXA at a dose of 20 mg/kg for 21 days was able to induce depression with an increase in the expression of GLUT1 and GLUT3 compared to normal mice. Indeed, DEXA-injected mice showed depression-like behaviors, including weight changes and immobility.
Increased cerebral glucose metabolism has been linked to depression onset, according to earlier research [57]. Neurons in the hippocampus require more energy in reaction to mental or emotional stress. In order to maintain the high ATP production in firing neurons, intracellular glucose and glycolysis are enhanced. Additionally, a prior study found that an increase in glycolytic enzymes causes an increase in glycolysis in the hippocampus and frontal brain of animal depression models [55]. In this regard, the current study demonstrated that untreated depressed rats given DEXA had higher levels of the vital glycolytic enzymes hexokinase and pyruvate kinase compared to normal mice.
Signals indicative of positive energy balance, such as high glucose concentrations and a decreased AMP/ATP ratio, inhibit AMPK [58]. Similarly, our results showed that pAMPK expression was significantly decreased in the brain tissue of mice administered DEXA compared to negative control mice. pAMPK is associated with mediators of cell cycle progression (i.e., p53), protein synthesis and growth (i.e., mTOR); elongation factor 2 kinase (eEF2K), proliferation, p38MAPK, and surviving (i.e., AKT) [59,60,61,62,63].
Animal models of depression have shown symptoms of decreased autophagy. For example, prolonged unpredictable stress decreased the levels of autophagy indicators [35,36]. Autophagy is one of the primary pathways of neurodegeneration in which mTOR and pAMPK interact, and it may have protective effects by degrading toxic, misfolded, or damaged proteins as well as damaged organelles [64]. In a previous study, it was reported that treatment of neuronal cells with Alisertib induced autophagy by increasing AMPK levels and inhibiting mTOR [65]. In response to insufficient cellular energy production, the canonical pAMPK-mTOR pathway (negatively regulated) is activated, leading to autophagy [66]. Additionally, prior research has demonstrated that a drop in glucose levels activates pAMPK, which phosphorylates Raptor, blocking mTORC1 and inducing autophagy [67,68,69]. In this context, our study showed that the induction of stress by DEXA administration was associated with decreased pAMPK protein expression and concomitantly increased mTOR expression, indicating decreased autophagy in the mice’s frontal cortex and hippocampus.
Together with mTOR activity, the AKT/TSC1-TSC2 pathway may control cell division and growth. TSC2 exhibits GTPase activity. TSC2 represses the small GTPase Rheb, which is essential for the activation of mTORC1. TSC2 is phosphorylated by AKT, resulting in the loss of TSC2′s ability to inhibit mTORC1 and activate mTOR. AKT can completely suppress TSC2 and activate mTOR by blocking pAMPK. A prior study also revealed that administration of the pAMPK activator aminoimidazolecarboxamide ribonucleotide (AICAR) led to inactivation of AKT in mice exposed to stress [70]. Moreover, the pathophysiology of depression has been related to AKT, another upstream target of mTOR [71,72]. This suggests that pAMPK activation causes autophagy activation through inhibition of mTOR [70]. Consistent with previous studies, mice stressed by DEXA injection in the current study had significantly higher pAKT protein expression than normal mice, supporting the hypothesis that depression, which is associated with many factors, activates mTOR, leading to a decrease in autophagy.
In addition, active pAMPK was found to decrease mTORC1 activity both directly and indirectly in response to energy stress and to limit amino acid consumption. Active AMPK, as previously mentioned, induces cellular autophagy, which converts excess or defective proteins into amino acids, thus replenishing the cellular amino acid pool [73]. Active AMPK can reduce the quantity of protein produced by phosphorylating and then inhibiting eukaryotic translation elongation factor 2 (eEF2) kinase, an enzyme that regulates protein synthesis. According to recent research, DEXA-stressed mice showed higher expression of the protein 4EBP1 than normal mice, suggesting an accumulation of defective proteins in the brain tissue of depressed mice.
According to previous reports, in response to inflammatory signals, p38MAPK directly and significantly aids in the restoration of autophagy regulation. In response to lipopolysaccharide stimulation in microglia, p38MAPK directly phosphorylates UNC51-like kinase-1 (ULK1), the serine/threonine kinase in the initiation complex of the autophagy cascade. Autophagy flux and level were reduced as a result of p38MAPK’s phosphorylation of ULK1, which also reduced its activity and disturbed its connection with the autophagy-related protein 13 (ATG13) in the autophagy start complex [74]. In this context, we recorded increased protein expression of p38MAPK in depressed DEXA-injected mice compared to normal mice.
Moreover, NGF-mediated autophagy is controlled by the p75NTR/AMPK/mTOR pathway in Schwann cells during Waller’s degeneration [75]. In this regard, the current study found a significantly decreased NGF concentration in the brain tissue of DEXA-injected mice compared with the normal control group, which enhanced the decreased autophagy in the brain tissue of depressed, untreated mice.
In the current research, RGZ was found to reverse depressive behavior in FST and TST. Zhao et al. [ 76] presented RGZ as an antidepressant that ameliorates these depressive behaviors compared with fluoxetine, an effective antidepressant, and activates autophagy by increasing the expression of LKB1 and phosphorylation of AMPK in neurons. However, the effect of RGZ on cerebral glucose metabolism, which may be involved in its antidepressant mechanism, is unknown. One of the most effective autophagy inducers, caloric restriction, has been demonstrated to be an antidepressant in rats and humans [77]. The brain needs a constant supply of glucose from the blood since it cannot store it and is so energy hungry. Whether or not hypoglycemia promotes the expression of GLUT1 and GLUT3 is uncertain and debatable [78]. In addition, a sharp decline in blood sugar levels has the potential to cause the neuroglycopenia symptoms of the “hypoglycemia phenomenon.” Although the precise process is unknown, it may be connected to the downregulation of GLUT1 and GLUT3 [79]. Consistent with previous reports, our findings indicated that the administration of 10 or 30 mg/kg RGZ for 21 days decreased cerebral glucose metabolism, as evidenced by a decrease in the expression of GLUT1 and GLUT3 and the levels of the key glycolytic enzymes hexokinase and pyruvate kinase compared with untreated mice.
A previous study in a model of global cerebral ischemia showed that RGZ had a neuroprotective function by inhibiting autophagic cell death [80]. In harmony, the present study’s findings revealed that RGZ at both doses repressed mTOR but enhanced the pAMPK protein expression in the frontal cortex and hippocampus of the mice as compared to the DEXA group, indicating increased autophagy in RGZ-treated mice. Moreover, our results showed that RGZ could affect autophagy in brain tissue by negatively regulating other upstream targets of mTOR, where RGZ reversed the DEXA-induced induction of pAKT and p38MAPK.
In a previous study, protein synthesis was found to be controlled by two known mTORC1 targets, p70 ribosomal protein S6 kinase (P70S6K) and 4EBPs, which cause elongation and translation initiation, respectively [81]. Our results showed that the low expression of mTOR achieved by RGZ administration was accompanied by downregulation of 4EBP1, suggesting a deficiency of a dysfunctional protein in brain tissue.
Recently, Li et al. [ 75] found that NGF suppresses mTOR activation after nerve injury, which in turn activates autophagy. In the current study, treatment of depressed mice with 10 and 30 mg/kg RGZ increased NGF levels in hippocampal and frontal cortex tissues compared with untreated mice.
The present study does not fully explain the detailed mechanism of RGZ in the animal model of depression. Nevertheless, our findings show that RGZ is a prospective therapeutic approach for MDD, the pathogenesis of which is associated with increased glucose metabolism.
In addition, forthcoming studies are recommended to highlight the effect of RGZ compared with approved pharmacological treatments and to explore safe and effective drugs for the treatment of MDD, such as RGZ.
## 5. Conclusions
Overall, this study concluded that RGZ significantly improved depressive behavior in depressed mice. Based on the positive relationship between depression and glucose metabolism, DEXA administration was found to induce MDD-like pathology through high glucose metabolism associated with increased expression of GLUT1 and GLUT3, along with increased levels of hexokinase and pyruvate kinase in the brain. These findings have been associated with decreased expression of pAMPK and upregulation of mTOR, pAKT, p38MAPK, 4EBP1, and NGF, which in turn may reduce neuronal autophagy. Indeed, the current study provides evidence that RGZ can enhance and maintain protective autophagy in the brain by decreasing the AMP/ATP ratio, activating pAMPK, and inhibiting the pAKT/p38MAPK/mTOR pathway. Our study duly considered the importance of the role of NGF in the neuroprotective effect of RGZ, considering the potentiating effect of 10 mg/kg RGZ in restoring normal NGF levels.
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|
---
title: Evidence of Nrf2/Keap1 Signaling Regulation by Mitochodria-Generated Reactive
Oxygen Species in RGK1 Cells
authors:
- Hiroko P. Indo
- Daisuke Masuda
- Sompong Sriburee
- Hiromu Ito
- Ikuo Nakanishi
- Ken-ichiro Matsumoto
- Samlee Mankhetkorn
- Moragot Chatatikun
- Sirirat Surinkaew
- Lunla Udomwech
- Fumitaka Kawakami
- Takafumi Ichikawa
- Hirofumi Matsui
- Jitbanjong Tangpong
- Hideyuki J. Majima
journal: Biomolecules
year: 2023
pmcid: PMC10046053
doi: 10.3390/biom13030445
license: CC BY 4.0
---
# Evidence of Nrf2/Keap1 Signaling Regulation by Mitochodria-Generated Reactive Oxygen Species in RGK1 Cells
## Abstract
It has been known that reactive oxygen species (ROS) are generated from the mitochondrial electron transport chain (ETC). Majima et al. proved that mitochondrial ROS (mtROS) caused apoptosis for the first time in 1998 (Majima et al. J Biol Chem, 1998). It is speculated that mtROS can move out of the mitochondria and initiate cellular signals in the nucleus. This paper aims to prove this phenomenon by assessing the change in the amount of manganese superoxide dismutase (MnSOD) by MnSOD transfection. Two cell lines of the same genetic background, of which generation of mtROS are different, i.e., the mtROS are more produced in RGK1, than in that of RGM1, were compared to analyze the cellular signals. The results of immunocytochemistry staining showed increase of Nrf2, Keap1, HO-1 and 2, MnSOD, GCL, GST, NQO1, GATA1, GATA3, GATA4, and GATA5 in RGK1 compared to those in RGM1. Transfection of human MnSOD in RGK1 cells showed a decrease of those signal proteins, suggesting mtROS play a role in cellular signals in nucleus.
## 1. Introduction
Oxidative stress contributes to several acute and chronic diseases, including cancer, alcoholic liver disease, Crohn’s disease, rheumatoid arthritis, diabetes, muscular dystrophy, cystic fibrosis, septic shock, premature birth, atherosclerosis, infertility, cataracts, aging, hepatitis, acute respiratory distress syndrome, ischemia, and neuronal degeneration in Parkinson’s and Alzheimer’s disease [1]. The concept of oxidative stress dates back to Sies [2], and oxidative stress and the principles of protection against it have been discussed for normal cells and tumors [1]. In most cancer cells, intracellular energy production is predominantly achieved by glycolysis, followed by lactic acid fermentation in the cytosol, even under aerobic conditions through a phenomenon known as the “Warburg effect” [3,4]. Thus, it appears that the oxidative phosphorylation process is defective in cancer cells. A previous study revealed an increase in the production of reactive oxygen species (ROS) in the mitochondria with an impaired electron transport chain (ETC) and mitochondrial DNA damage [5]. In normal cells, and even cancer cells, oxidative stress is severe, and ROS levels from the mitochondria are expected to increase.
The ETC is composed of over 90 subunits [6]. The emergence of the mitochondrion resulted in increased production of ATP by the ETC in the mitochondrial inner membrane in the presence of oxygen [7]. In the glycolytic pathway, only two ATP molecules are produced, while ETC produces 36; thus, a total of 38 molecules of ATP are produced. The ETC consists of complexes I–IV, ATP synthase, and ANP translocator (Figure 1). The mitochondrion also has DNA, which encodes 13 of these proteins. The mtDNA contains codon sequences different from those of the nuclear DNA. In the mitochondrial ETC, while oxidation/reduction in electrons occurs repeatedly, hydrogen ions are transported into the intermembrane space and finally back to the matrix, and ADP is converted into ATP using the produced energy. The produced ATP is transported into the intermembrane space (Figure 1). This complicated ‘bio-machine’ serves as the largest energy-producing organelle of the cell but is an imperfect machine and thus causes a ‘leakage’ of electrons. This leakage most frequently occurs in complexes I and III. The 2–$3\%$ of electrons leak even in a normal state, and superoxide is considered to be generated from these leaked electrons [1,8,9,10,11]. Oxygen traps the electrons, thus, becoming a superoxide anion (O2•−). It is known that Complex I generates O2•− in the matrix [12], and Complex III generates O2•− into both the matrix and inner membrane space [13]. Coenzyme Q works as both antioxidant [14] and a pro-oxidant [15], and it is known that it produces superoxide [16]. Further, Linnane et al. suggested that coenzyme Q produces superoxide, probably in the intermembrane space, and commits to the transcription of genes and gene regulations [14].
It is known that in chemotherapy, once cancer cells have been exposed to a drug, they become resistant to further treatment [17]. Similarly, after cells are dosed with radiation, they become resistant to further doses [18,19]. These phenomena may explain cells responding to severe oxidative stress and must repeatedly respond subsequently to a series of oxidative insults. We focus on mitochondria to play these roles. Previously we reported that oxidative stress is capable of causing cell death. Majima et al. [ 20] reported the first evidence of a relationship between mitochondrial ROS (mtROS) and apoptosis. Manganese superoxide dismutase (MnSOD) scavenges ETC-generated superoxide molecules. Cells transfected with MnSOD exhibit increased resistance to paraquat [21], tumor necrosis factor [22,23], doxorubicin [23], mitomycin C [23], irradiation [23,24,25,26,27,28], alkaline treatment [20], ischemia–reperfusion injury [29], cigarette smoking toxicity [30], and radiation carcinogenesis [31]. Transgenic mice carrying the human MnSOD (hMnSOD) gene show a reduced severity of hyperbaric oxygen-induced pulmonary damage [32] or doxorubicin-induced myocardial damage [33]. Free radicals generated in mitochondria possibly play roles in all morphological processes of cell death, i.e., apoptosis, necrosis, and autophagy [34,35].
mtROS control cell death; however, mtROS controlling cellular signaling is under debate. ROS formation may also act as a pre-conditioner that helps prevent or ameliorate secondary or subsequent oxidative stress events. This may occur via up-or down-regulation of cell signaling pathways, and some of these processes may be driven, either directly or indirectly, via mitochondrial ROS formation and its downstream consequences. While it is known that ROS can initiate various signal transduction pathways, the role of mitochondrial ROS in initiating signal transductions in the cell cytosol has been the subject of controversy. This subject has been dealt with in several reviews [36,37,38,39,40,41,42,43]. However, all of these papers are hypothetical papers or just ideas. No authors proved that mtROS initiate the signals in the nucleus. The only way to change the amount of mtROS is to change the amount of MnSOD that again scavenges superoxide ions generated from mitochondrial ETC. MtROS can initiate the signals in the nuclei, which can be proved by MnSOD transfection and see a decrease in the signals in nuclei. Therefore, this manuscript aims to transfect MnSOD wherein mtROS production is decreased and to see if the intracellular signaling, such as nuclear factor-erythroid 2-related factor 2 (Nrf2), Kelch-like ECH-associated protein 1 (Keap1), HO-1 and 2, MnSOD, GCL, GST, NQO1, GATA1, GATA3, GATA4, and GATA5 decrease. Whether mtROS initiate cellular signals in nuclei is a fascinating subject, and we will attempt to see if the augmentation of signaling in a nucleus can be suppressed by MnSOD transfection.
We previously established a rat gastric mucosal cell line, RGM1 [44], and a tumorized cell line, RGK1, which was derived from the RGM1 cell line [45] and described how ROS activate cellular signaling and molecules [46]. RGM1 and RGK1 have the same genetic background. In this paper, RGK1 is used because the cells generate more mtROS, and not for the cancer cell line. In normal situations, the augmentation of the generation of mtROS is possible by various stimuli [5]. Focusing on the mtROS, we use RGM1 and RGK1 as models of one producing less mtROS (RGM1) and the other producing more mtROS (RGK1). In this study, using both RGM1 and RGK1 cells, we aim to examine the effects of mtROS on the activation of signal transcription in the nucleus.
## 2.1. Cell Lines and hMnSOD Transfection
RGM1 is a diploid untransformed cell line derived from normal gastric mucosa of wistar rats [44]. The RGK1 cell line established as an N-methyl-N′-nitro-N-nitrosoguanidine-induced mutant of RGM1, can be used as an in vitro model of gastric cancer [45]. A plasmid derived from pCR3.1 (Invitrogen, Waltham, MA, USA) and containing a sense hMnSOD cDNA insert, pCR3.1-Uni, was kindly provided by Dr. Makoto Akashi (National Institute of Radiological Sciences, Chiba, Japan) for this study. The hMnSOD sequence was identical to that in the Kyoto Encyclopedia of Genes and Genomes (locus link ID: 6648; http://www.genome.jp/dbget-bin/www_bget?hsa+6648, accessed on 30 April 2021). RGM1 and RGK1 cells were transfected using Lipofectamine (Invitrogen) according to the manufacturer’s instructions. Briefly, cells were plated 24 h before transfection at $80\%$ confluence in a 60 mm dish. Cells were transiently transfected with 8 µg pCR3.1-Uni in serum-free Dulbecco’s modified *Eagle medium* and Ham’s F12 medium (DMEM/F12; Cosmo Bio, Tokyo, Japan). Controls were transfected with a pCR3.1 vector lacking the hMnSOD cDNA insert. Six hours after transfection, the medium was changed to DMEM/F12 containing $10\%$ fetal bovine serum (JRH Biosciences, Denver, PA, USA). After 24 h, cells were treated with trypsin and plated for use in all subsequent experiments.
## 2.2. Relative Levels of Mitochondrial ROS
HPF (Daiichi Pharmaceutical Co., Tokyo, Japan) is a new fluorescent dye used for the selective detection of hydroxyl radicals that was recently developed by our group [47] and was used to that end in the present study. Hydroxyl radicals are highly reactive oxygen species (hROS). The dynamic range of fluorescence augmentation for such a dye is expected to be broad. Although HPF itself fluoresces to only a small extent, it selectively and dose-dependently yields a strongly fluorescent compound, fluorescein, on reaction with hROS, but not other ROS. Glass-bottomed (35 mm) dishes (MatTek Corp., Ashland, MA, USA) with cell monolayers were prepared for staining with HPF. Twenty-four hours after plating the cells, the cell culture medium was replaced with a modified Hanks’ balanced salt solution containing 10.0 mM HEPES, 1.0 mM MgCl2, 2.0 mM CaCl2, and 8.3 mM glucose, adjusted to pH 7.30 ± 0.05. HPF (10 µM) was added to the cells and incubated for 15 min at 37 °C. Bioimages were obtained using a CSU-10 confocal laser scanning unit (Yokogawa Electric Co., Tokyo, Japan) coupled to an IX90 inverted microscope with a 20x UPlanApo objective lens (Olympus Optical Co., Tokyo, Japan) and a C5810-01 color chilled 3CCD camera (Hamamatsu Photonics, Hamamatsu, Japan). HPF was excited at 488 nm, and emissions were filtered using a 515 nm barrier filter. The intensity of the laser beam, the exposure time of the 3CCD camera, and the gain of the amplifier were set at 500 µW, 1.0 s, and 18 decibels, respectively, to allow quantitative comparisons of the relative fluorescence intensity of the cells between groups. Cells were chosen on a random basis, and a total of over 250 cells were analyzed to detect fluorescence values. Average fluorescence intensity per cell was determined using IPLab Spectrum version 3.0 software (Scanalystics Inc., Fairfax, VA, USA) with some program modification by one of the authors (H.J.M).
## 2.3. Immunofluorescence Staining
Glass-bottomed (35 mm) dishes (MatTek Corp) with cell monolayers were prepared for immunofluorescent staining. The cells were fixed with $4\%$ formaldehyde saline (PBS) solution at 25 °C for 30 min and rinsed twice with PBS; membranes were permeabilized using $95\%$ ethanol with $5\%$ acetic acid for 10 min. After washing twice with PBS, cells were incubated for 30 min in a blocking serum solution ($1\%$ bovine serum albumin in PBS), then for 1 h at room temperature with anti-Nrf2 (rabit polyclonal IgG, SC-13032, Santa Cruz Biotechnology, Dallas, TX, USA), anti-Keap1 (goat polyclonal IgG, SC-15246, Santa Cruz), anti-heme oxygenase (HO)-1 (rabit polyclonal IgG, cat#HC3001, BIOMOL International, Plymouth Meeting, PA, USA), anti-HO-2 (rabit polyclonal IgG, cat#HC3002, BIOMOL), anti-NAD(P)H quinone dehydrogenase 1 (NQO1) (mouse IgG, cat#611426, Novus Biologicals, Englewood, CO, USA), anti-glutamate-cysteine ligase (GCL) (NeoMarkers, Fremont, CA, USA), anti-glutathione S-transferase (GST) (mouse monoclonal IgG, cat#HC3001, Cell Signaling Technology, Danvers, MA, USA), anti-MnSOD (mouse polyclonal IgG, cat #06-984, Millipore, Burlington, MA), anti-GATA1 (mouse monoclonal IgG, SC-266, Santa Cruz Biotechnology, Dallas, TX, USA) [40], anti-GATA3 (mouse monoclonal IgG, SC-268, Santa Cruz Biotechnology, Dallas, TX) [40], anti-GATA4 (goat polyclonal IgG, SC-1237, Santa Cruz Biotechnology, Dallas, TX, USA) [40], and GATA5 (goat polyclonal IgG, SC-7280, Santa Cruz Biotechnology, Dallas, TX, USA) [40], each at a dilution of 1:200. Cells were rinsed twice with $1\%$ bovine serum albumin in PBS and then incubated as appropriate with Alexa-Fluor-488-labeled goat anti-mouse IgG (H+L), Alexa-Fluor-488-labeled donkey anti-goat IgG (heavy and light chains; H+L) or Alexa-Fluor-488-labeled goat anti-rabbit IgG (H+L) (Molecular Probes, Eugene, OR, USA) for 1 h at 25 °C in the dark. Image acquisition and analysis were performed as for HPF, except that the exposure time was 4 s.
## 2.4. SOD Activity Gel Assay
A native gel-based assay for examining SOD activity was carried out according to a previously described method [48] with slight modifications. Cells were sonicated in a 50 mM potassium phosphate buffer (pH 7.8). Cell protein (20 µg/lane) was run on a native riboflavin gel comprising a $5\%$ stacking gel (pH 6.8) and a $12\%$ running gel (pH 8.8) at 4 °C. To visualize SOD activity, gels were first soaked in 2.43 mM nitro blue tetrazolium (Wako Pure Chemical Industries, Osaka, Japan) in deionized water for 20 min and then in 56 nM riboflavin (Wako Pure Chemical Industries, Ltd., Osaka, Japan) with 28 mM N,N,N′,N′-tetramethylethylenediamine (Sigma Aldrich, St. Louis, MO, USA) in a 50 mM potassium phosphate buffer (pH 7.8) for 15 min in the dark. Gels were washed with deionized water and illuminated under fluorescent light until clear zones of SOD activity were evident. The images were recorded and MnSOD bands were quantified using the AlphaImager imaging system (Alpha Innotech, San Leandro, CA, USA). MnSOD activity was assessed by examining the band density. The MnSOD activity of vector-only-transfected control cells was normalized to a value of 1.0 and relative MnSOD activities in other cells were calculated. Results were calculated as the mean of the integrated density from five independent runs.
## 2.5. Isolation of the Total RNA
Total RNA was isolated using ISOGEN from cultured cells as recommended by the manufacturer (*Nippon* gene, Toyama, Japan).
After washing cells by PBS 3 times, 1 mL of ISOGEN was added to cells and collected into 1.5 mL tubes. Two hundred μL of chloroform was added to the samples and vortexed. After centrifugation at 12,000× g for 15 min at 4 °C, the aqueous phase was transferred to new 1.5 mL tubes and 0.5 mL of 2-propanol was added. After incubation for 5 min at 25 °C, centrifugation was performed at 12,000× g for 10 min at 4 °C to precipitate RNA. The pellets were washed with $70\%$ of EtOH and after centrifugation 7500× g for 5 min at 4 °C, pellets were dried briefly and dissolved in TE. The RNA quality was checked by measuring 260 nm absorbance and electrophoresis. All cDNAs were prepared by reverse transcription of 1 μg total RNA using oligo dT[20] primer (0.4 μM/50 μL final volume), and ReverTra Ace (TOYOBO) as recommended by the manufacturer. An equivalent volume of 0.1 μL of cDNA solution was used for quantification of specific cDNA by qRT-PCR.
## 2.6. qRT PCR for hMnSOD and rMnSOD (Control) mRNA Detection
Total RNA was isolated from cells, and cDNA was synthesized as described above. The sequences of primers were compared to those from the available human genome database (https://www.genome.jp/kegg/genes.html, assessed on 31 January 2020) in order to select primers that would produce a single amplification product. The forward and reverse primers are in Table 1.
The qRT-PCR assays were performed on an ABI prism 7000 Sequence Detection System (Applied Biosystems, Foster City, CA, USA) using the QuantiTect SYBR Green PCR Kit (Qiagen, Valencia, CA, USA). The experimental conditions were those recommended by the manufacturer. The resulting cDNA was amplified at the following conditions. Human MnSOD: annealing and elongation at 60 °C for 1 min, followed by 15 s at 95 °C, and 1 min at 60 °C. For rat MnSOD: annealing at 95 °C for 15 s, and elongation at 60 °C for 10 s, followed by 72 °C for 30 s. All PCR assays were performed for 40 cycles. The size of single amplification products was further verified by gel electrophoresis. All data were normalized to an internal standard (glyceraldehyde-3-phosphate dehydrogenase; GAPDH). The triplicate samples were used in an assay of qRT-PCR and repeated three times. The average values were calculated, and the bar was expressed as S.D.
## 2.7. Construction of Antioxidant Response Element (ARE) Reporter Vector
A sequence-encoding ARE was inserted at XhoI and HindIII sites of the pGL4.28[luc2CP/minP/Hygro] vector (Promega, Madison, WI, USA). The inserted sequences were ARE-GST-Ya-F (5′-TCGAGTAGCTTGGAAATGACATTGCTAATGGTGACAAAGCAACTTTA-3′; underlining marks the ARE consensus sequence) andARE-GST-Ya-R(5′-AGCTTAAAGTTGCTTTGTCACCATTAGCAATGTCATTTCCAAGCTAC-3′) [49].
Inserts were annealed in a 1× annealing buffer (30 mM HEPES-KOH, 100 mM potassium acetate, 10 mM magnesium acetate) at 30 °C for 1 h after 90 °C for 1 min. The annealed ARE insert, and pGL4.28[luc2CP/minP/Hygro] vector digested with XhoI and HindIII were ligated using a DNA Ligation Kit, Mighty Mix (TaKaRa, Shiga, Japan). The plasmid was transformed into competent cells, and positive clones were picked. Plasmid for transfection was extracted using a QIAfilter Plasmid Midi Kit (QIAGEN, Valencia, CA, USA).
## 2.8. Dual-Luciferase Reporter Assay
Reporter assays were performed using a Dual Luciferase Reporter Assay System (Promega) and a Fluoroskan Acent FL microplate reader (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol. Cultured cells were lysed in a Passive Lysis Buffer (PLB) after two washes with PBS. Cells in PLB (cell lysis sample) were collected using a Cell Lifter (Fisher Scientific). Cell lysis samples (20 µL) were transferred to a 96-well plate and 100 µL Luciferase Assay Reagent II (LARII) was added to each sample. Firefly luciferase activity was measured for 10 s, then STOP & GLO Reagent (100 µL) was added to the sample and Renilla luciferase activity was measured for 10 s.
## 2.9. Statistical Analysis
Statistical analysis was performed using the Student t-test. The results with p-values < 0.05 were considered to be statistically significant.
## 3.1. Mitochondrial ROS Generation and MnSOD Activity in RGM1 and RGK1 Cells
A dye sensitive to hydroxyl radical, HPF, was used to detect mitochondrial ROS. The dye was loaded for 15 min at 37 °C. and the images were acquired. The results showed that the fluorescence intensity of HPF was higher in RGK1 cells than in RGM1 cells [50] (Figure 2A,B). MnSOD is an enzyme essential for scavenging superoxide in mitochondria. The MnSOD protein was evaluated using a gel-based activity assay and Western blotting. MnSOD activity in RGK1 cells was higher than in RGM1 cells (Figure 2C), in parallel with higher expression of MnSOD protein, as indicated by Western blotting results (Figure 2D). The results confirm that mitochondrial ROS levels are higher in tumor cells.
## 3.2. Expression of Nrf2, Keap1, and Oxidative Stress-Related Proteins in RGM1 and RGK1 Cells
Levels of Nrf2 and Keap1 protein were evaluated using immunohistochemical staining. Nrf2 protein levels in RGK1 cells were significantly higher than in RGM1 cells (Figure 3A), and similar results were obtained for the Keap1 protein (Figure 3B). Comparable results were observed for the oxidative stress-related proteins HO-1 and HO-2 (Figure 3C), MnSOD (Figure 3D), NQO1, GCL, and GST (Figure 4), all of which are downstream products of Nrf2/Keap1 signaling [51].
## 3.3. MnSOD Expression and Enzyme Activity Following Transfection into RGK1 Cells
We transfected the MnSOD gene into RGK1 cells and, using decreasing ROS in mitochondria, tested whether MnSOD, by means of decreasing ROS in mitochondria, also decreases Nrf2/Keap1 signaling. The production of active MnSOD in transfected cells was investigated in cell lysates. Expression of MnSOD mRNA was measured using real-time RT-PCR analysis and expression of MnSOD protein was detected by a gel-based SOD activity assay. mRNA levels for hMnSOD in hMnSOD-transfected cells were significantly higher than in cells transfected with vector alone (Figure 5A). MnSOD activity was also significantly higher in hMnSOD-transfected cells. In contrast, the activity of copper–zinc superoxide dismutase (CuZnSOD) did not differ between cells with vector-alone and hMnSOD-transfected cells (Figure 5B).
## 3.4. Oxidative Stress-Related Protein Expression Following MnSOD Transfection into RGK1 Cells
Levels of oxidative stress-related proteins were evaluated by immunohistochemical staining to determine whether the levels of Keap1, Nrf2, HO-1, and HO-2 proteins decreased following hMnSOD transfection into RGK1 cells. The changes in fluorescence staining in the vector-alone- or hMnSOD-vector-transfected cells, as assessed two days after transfection, indicate that expression of these proteins is suppressed in hMnSOD-vector-transfected cells (Figure 6A,B).
## 3.5. Dual-Luciferase Reporter Assay in RGK1-Transfected ARE Stable Clones with Transient Transfection by MnSOD and Vector cDNA
We used an ARE consensus-based dual luciferase reporter assay to determine the mitochondrial signaling pathway, which activates signals in nucleus outside mitochondria (Figure 7). It is noted that MnSOD locates inside mitochondria and scavenges ROS inside mitochondria. First, we constructed an ARE reporter vector and generated RGK1 cells that stably expressed this construct. Then, we transiently transfected those cells with hMnSOD or vector alone. Two days after the transfection, cell lysate was obtained, and the dual luciferase reporter assay was performed to measure firefly luciferase activity using LARII (Figure 7). Using this assay, if MnSOD transfection decrease the results of ARE luciferase intensity, then prove MnSOD lowered the signals in nucleus. The results showed that hMnSOD-transfected cells had significantly lower luciferase activity compared with cells transfected with vector alone (Figure 8), suggesting that ROS generated in mitochondria can move away from the mitochondrial membrane and activate cellular signals in the cytosol. The initiation of intracellular signaling by mitochondrial ROS can be proven only by MnSOD transfection: MnSOD reduces ROS inside mitochondria (mtROS), and if mtROS activate signals in cytosol or nucleus, the signal must be reduced by the increase in MnSOD amounts. A schematic of Nrf2/Keap1 signal transduction initiated by mitochondrial ROS is shown in Figure 9.
## 4. Discussion
In this paper, we could confirm that hMnSOD lowered the expression of Nrf2, Keap1, HO-1 and 2, MnSOD, GCL, GST, NQO1, probably through Nrf2-Keap1 activation, suggesting mitochondrial ROS regulates the signaling outside mitochondria, in nucleus.
Mitochondria are the major source of intracellular ROS [1,35]. Our group was the first to show that mitochondrial ROS (mtROS) cause apoptosis [20]. Murphy and Hartley previously revealed that mitochondrial dysfunction contributes to the pathology of many common disorders, including neurodegeneration, metabolic disease, heart failure, ischemia–reperfusion injury, and protozoal infections [52].
We recently showed that that mitochondrial ROS can induce the expression of GATA proteins, given that MnSOD transfection, which causes a decrease in superoxide levels, resulted in a reduction in the expression of GATA1, GATA3, GATA4 and GATA5 [46] (Figure S1). It is known that NF-κB controls GATA3 [53]. In cells, signal transduction networks act to maintain homeostasis and prevent major changes in intracellular status including alterations to redox potentials. Among the multiple pathways involved, signal transduction via NF-κB appears to play a key role during inflammation, immunity, development, cell growth, and survival. NF-κB regulates over 100 genes, including those with both antioxidant and pro-oxidant functions [46]. Tumor necrosis factor-α (TNF-α) is a well-established inducer of NF-κB, and induction occurs in a ROS-dependent manner [54]. Although antioxidant TNF-α has been reported to induce NF-κB activation [55], there exists overwhelming evidence for a key role of numerous oxidants in this process, which is postulated to be clinically important in the manifestation of several diseases [56,57,58,59,60,61,62,63,64,65]. The protein NF-κB essential modulator (NEMO, known as an inhibitor of NF-κB kinase subunit gamma, IKK-γ), is a subunit of the IκB kinase complex that activates NF-κB when present in a dimeric (disulfide-bonded) form. The formation of these disulfide bonds involves Cys54 and Cys347, and the treatment of cells with hydrogen peroxide enhances the formation of NEMO dimers. These findings suggest that oxidants can activate the NF-κB-related systems [66]. In our study, lowered mtROS resulted lowered GATAs expression through c less activation by the mtROS. Therefore, lowered mtROS results in the lower activities of both Nrf2-Keap1 signaling axis and NF-κB signaling axis, both the major intracellular signaling pathways.
This study confirms that mtROS activate Nrf2/Keap1 signaling. A schematic figure outlining Nrf2/Keap1 signaling is shown in Figure S2, which indicates that ROS activates Keap1 signaling as a result of Keap1 oxidative modification, consequently Nrf2 unbinds from Keap1. Nrf2/Keap1 signaling has been hypothesized in detail by Kasai et al. [ 67]. Many researchers have hypothesized that superoxide generated in mitochondria diffuses out from the mitochondria and activates cellular signals [46]. This phenomenon can be demonstrated by changing the amounts of mitochondrial superoxide, via a change in MnSOD expression [46]. The findings of this study and our previous work [46] suggest that superoxide generated in mitochondria can activate transduction signals in cytosol (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure S1) and increase ROS concentration, which, in turn, passes one or two membranes and activates GATA, Nrf2/Keap1 and other proteins and pathways.
## 5. Conclusions
In RGK1 cells where ROS production is increased, intracellular signaling, such as, Nrf2, Keap1, HO-1 and 2, MnSOD, GCL, GST, NQO1, GATA1, GATA3, GATA4, and GATA5 increases. This augmentation of signaling can be suppressed by MnSOD transfection. The results suggest that mitochondrial ROS can move out from mitochondria into the cytosol, and regulate various intracellular signals, placing them in a central position in controlling cellular signaling (Figure 9).
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|
---
title: Altered Distribution of Unesterified Cholesterol among Lipoprotein Subfractions
of Patients with Diabetes Mellitus Type 2
authors:
- Livia Noemi Kolb
- Alaa Othman
- Lucia Rohrer
- Jan Krützfeldt
- Arnold von Eckardstein
journal: Biomolecules
year: 2023
pmcid: PMC10046057
doi: 10.3390/biom13030497
license: CC BY 4.0
---
# Altered Distribution of Unesterified Cholesterol among Lipoprotein Subfractions of Patients with Diabetes Mellitus Type 2
## Abstract
Biomarkers are important tools to improve the early detection of patients at high risk for developing diabetes as well as the stratification of diabetic patients towards risks of complications. In addition to clinical variables, we analyzed 155 metabolic parameters in plasma samples of 51 healthy volunteers and 66 patients with diabetes using nuclear magnetic resonance (NMR) spectrometry. Upon elastic net analysis with lasso regression, we confirmed the independent associations of diabetes with branched-chain amino acids and lactate (both positive) as well as linoleic acid in plasma and HDL diameter (both inverse). In addition, we found the presence of diabetes independently associated with lower concentrations of free cholesterol in plasma but higher concentrations of free cholesterol in small HDL. Compared to plasmas of non-diabetic controls, plasmas of diabetic subjects contained lower absolute and relative concentrations of free cholesterol in all LDL and HDL subclasses except small HDL but higher absolute and relative concentrations of free cholesterol in all VLDL subclasses (except very small VLDL). These disbalances may reflect disturbances in the transfer of free cholesterol from VLDL to HDL during lipolysis and in the transfer of cell-derived cholesterol from small HDL via larger HDL to LDL.
## 1. Introduction
Diabetes mellitus type 2 (T2DM) is a leading health burden worldwide due to its high prevalence and high morbidity and mortality. Of the about 350 million patients diagnosed with diabetes in 2015, $80\%$ lived in low-income or middle-income countries, with the highest proportion in Latin America [1]. Until 2035, the prevalence of T2DM is estimated to increase by $70\%$ and $20\%$ in developing and developed countries, respectively [1]. T2DM shortens life expectancy by about six years due to increased risks of atherosclerotic cardiovascular diseases (ASCVD), nephropathy, infections, and various cancers [2]. Moreover, retinopathy and neuropathy reduce the quality of life in many patients with T2DM. The prognosis of patients with diabetes mellitus can be much improved by lifestyle optimization as well as drug treatments that lower blood glucose, blood pressure, and LDL-cholesterol [3,4]. Nevertheless, diabetic complications still occur frequently and limit the quantity and quality of life in patients with diabetes [2]. To improve this situation, personalized treatments may be necessary. To this end, novel diagnostics and biomarkers are needed.
Nuclear magnetic resonance (NMR) spectroscopy allows the comprehensive quantification of metabolites in only 100 to 300 μL of plasma or serum [5]. By applying a high-frequency magnetic field, NMR spectroscopy records protons of molecules as specific resonance spectra. Specific algorithms allow the spectra to be assigned to specific molecules and quantified [6]. A particular strength of NMR spectroscopy is its ability to record different classes of lipids (cholesterol and cholesteryl esters, triglycerides, sphingomyelins, and glycerophospholipids) not only in total plasma but within specific lipoprotein subclasses without any prior fractionation of plasma, for example by ultracentrifugation. Fatty acids can be differentiated for the degree and position of denaturation. In addition, some metabolites such as amino acids and ketone bodies, as well as proteins such as apolipoproteins A-I and B and albumin and glycoprotein acetyls (Gp), are measured [7,8].
We applied this method to a case-control study to answer the following questions: Do biomarkers recorded by NMR spectroscopy of plasma differentiate diabetic subjects from non-diabetic controls? Do any of the NMR measures correlate with glycemic control as reflected by HbA1c?
## 2.1. Patients and Control Subjects
In total, 51 healthy volunteers and 66 patients with diabetes mellitus Type 2 were recruited at the Department of Endocrinology, Diabetology, and Clinical Nutrition and the Clinical Trial Center of the University Hospital Zurich [7]. All patients and controls attended with informed consent. The ‘Kantonale Ethikkommission Zürich’ granted the ethics approval (PB-BASEC_2015−00159). Exclusion criteria included acute illnesses or acute deterioration of a chronic illness, pregnancy, active inflammation (defined by leukocytes > 10 g/L or C-reactive protein > 10 mg/L), and advanced chronic kidney disease with an estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2. Table 1 describes the demographic and clinical features of the two patient groups.
## 2.2. Clinical Laboratory Tests
Glycohemoglobin (HbA1c) was measured with a fully automated High-Performance Liquid Chromatography system (ADAMS A1c from ARKRAY, Kyoto, Japan). Total cholesterol and HDL cholesterol, liver enzyme activities, C-reactive protein (CRP), creatinine, triglycerides, and glucose were quantified by the use of a cobas 8000 analyzer and assays from Roche diagnostics (Rotkreuz, Switzerland). LDL-cholesterol was computed with the Friedewald formula. ApoA-I and apoB were measured by immunonephelometry with the BN ProSpec analyzer and assays from Siemens Healthcare diagnostics (Erlangen, Germany). Lp(a) concentrations were measured by the application of a latex-enhanced immunoturbidimetric assay of Randox Laboratories Ltd. (Crumlin, UK) to a Konelab analyzer (Thermo Fisher, Waltham, MA, USA).
## 2.3. Nuclear Magnetic Resonance (NMR) Spectroscopy of Plasma
NMR spectroscopy of plasma was executed as a service for a fee by Nightingale Health (Helsinki, Finland: https://nightingalehealth.com/, accessed on 10 December 2022). The method records 154 variables, including cholesterol and cholesteryl esters, triglycerides, sphingomyelins, and glycerophospholipids in six subclasses of very low-density lipoproteins (VLDL), intermediate density lipoproteins (IDL), three subclasses of LDL, and four subclasses of HDL. It also allows quantifying numbers and average diameters of lipoproteins, as well as the quantitatively major apolipoproteins apoA-I and apoB. Fatty acids are differentiated into saturated (SAFA), monounsaturated (MUFA), and polyunsaturated fatty acids (PUFA). The latter are further differentiated for omega-3 (FAn3) and omega-6 fatty acids (FAn6). Among the omega-3 fatty acids, marine docosahexaenoic acid (DHA) and plant-derived linoleic acid (LA) are differentiated. Eight amino acids (alanine, glutamine, histidine, isoleucine, leucine, valine, phenylalanine, and tyrosine), three ketone bodies (acetate, acetoacetate, hydroxybutyrate), glucose, lactate, citrate, creatinine, albumin, and glycoprotein acetyls (Gp), a biomarker of inflammation, are also measured [7,8].
## 2.4. Statistics
The statistical evaluation of the data was performed with the software R [9]. Normality distribution was checked with the Shapiro–Wilk test. The parametric t-test and the non-parametric Wilcoxon test were applied for univariate pairwise comparisons of diabetic patients with control subjects for parameters with Gaussian (if Shapiro–Wilk test $p \leq 0.05$) and non-Gaussian frequency distribution (if Shapiro–Wilk test p ≥ 0.05), respectively. Correlations with HbA1c were calculated according to Spearman. p-values were adjusted for multiple testing according to the Benjamin–Hochberg method.
Volcano plots (fold change vs. p-value or coefficients of correlation vs. p-values) were generated using ggplot and ggrepel packages. For a clearer presentation, the variables were split into subgroups (clinical measures, VLDLs, LDLs, IDLs, HDLs, lipids, and non-lipids). ComplexHeatmap and Hmisc packages were used to construct heatmaps and do hierarchical clustering with the complete clustering method and the Spearman clustering distance.
Regularized logistic regression was performed to identify independent variables associated with T2DM. Regularized regression was essential to avoid over-fitting, considering that the dataset encompasses more variables than patients and because of the high degree of correlation between many variables. For that purpose, elastic net regularization was performed using the packages glmnet and c060. The elastic net regularization was performed as described before [7]. In short, concentrations below the level of quantification were imputed by the square root of the minimum value for each variable of the NMR data. Then, the data were log-transformed and scaled around the mean. To identify the best values for the regularization parameters, alpha and lambda values with the lowest root mean squared error (RMSE) were selected after tenfold cross-validation by logistic regression fitting. At alpha =1, the lowest RMSE was observed, so lasso regularization was performed. Lasso (least absolute shrinkage and selection operator) regularization shrinks the regression coefficient by minimizing the sum of their absolute values, thus forcing some coefficients into zero. That is performed to prevent overfitting and as a tool for variable selection. The non-zero regression coefficients after regularization were then calculated as the first output of the regularized logistic regression. Afterwards, stability path analysis was performed (as described earlier [7]). Stability path analysis compensates for some of the stochasticity of regularization in general, thereby providing more robust estimates of the association between independent variables and the T2DM state. Two stability path analyses were performed, with and without correction for multiple testing. The results from all analysis steps are reported.
## 3.1. Univariate Associations with Diabetes
As expected, diabetic patients and healthy volunteers differ significantly in many anthropometric and clinical measures, as well as drug treatment (Table 1). Diabetic subjects were older, had higher BMI and waist circumference, higher heart rate, higher systolic and diastolic blood pressure, higher plasma levels of glucose, triglycerides, creatinine, and C-reactive protein (CRP), higher plasma activities of transaminases, higher HbA1c, hemoglobin, and hematocrit as well as higher blood counts for leukocytes and erythrocytes. Conversely, plasma concentrations of total HDL- and LDL-cholesterol, as well as apoA-I, were significantly lower in diabetic subjects.
Figure 1 shows a volcano plot of the associations of diabetes with these clinical measures as well as 154 features measured by NMR spectroscopy of plasma. All p-values are adjusted for multiple testing. Interestingly, several NMR-measured features are significantly different between patients with T2DM and non-diabetic control subjects—notably, the increase in branched-chain amino acids, alanine, and lactate in plasma samples of T2DM patients. In contrast, we observed a significant decrease in plasma levels of most features related to very large HDL (XL-HDL), large HDL (L-HDL), or related with polyunsaturated fatty acids (linoleic acid, omega-6 fatty acids) and monounsaturated fatty acids. In contrast to most plasma HDL-related parameters, those related to small HDL particles (S-HDL), especially triglycerides in small HDL (S-HDL-TG), were increased rather than decreased in T2DM patients. Similar opposite associations were seen for features related to very small VLDL (except XS-VLDL-TG), which are decreased in patients with diabetes by contrast to features related to small (S-VLDL), medium (M-VLDL), large (L-VLDL), very large VLDL (XL-VLDL), and chylomicrons and extremely large VLDL (XXL-VLDL), which are increased in T2DM patients. In contrast to PUFAs, plasma levels of monounsaturated fatty acids (MUFAs) were higher in diabetics. It is also noteworthy that the inverse association of apoA-I was stronger if it was measured by NMR spectroscopy (marked in red) rather than by the clinical laboratory method (marked in blue).
## 3.2. Univariate Correlations between Variables
Figure 2 and Figure 3 show heatmaps of correlations between all variables in non-diabetic control subjects and patients with diabetes, respectively. The clustering dendrograms on the top and left sides of the heatmaps reflect similarities between features in several clusters. The main findings are as follows:-In both control subjects and diabetic patients, clusters contain several features of one lipoprotein subclass or even several subclasses of one lipoprotein, for example, medium, large, and very large HDL or large, very large, and extremely large VLDL.-One noteworthy exception is triglycerides in small HDL (S-HDL-TG), which is separated from all other HDL parameters both in control subjects (cluster C2 vs. clusters C5 and C7 in Figure 2) and diabetic subjects (cluster D4 vs. cluster D3 in Figure 3).-Moreover, a cluster in control subjects (C4 in Figure 2) and diabetic subjects (D4 in Figure 3) is characterized by triglycerides rather than lipoprotein subclasses.-In the diabetes heatmap (Figure 3), some triglyceride-containing HDLs (M-HDL-TG, XL-HDL-TG, S-HDL-TG, HDL-TG), as well as S-LDL-TG and IDL-TG, are in the VLDL cluster D4.-The diabetes heatmap (Figure 3) contains triglyceride features in clusters otherwise characterized by cholesterol-rich lipoproteins (LDL-TG, M-LDL-TG, L-LDL-TG in cluster D1 and S-LDL-TG, IDL-TG, HDL-TG, XL-HDL-TG in cluster D4).-Features of small HDLs do not cluster with features of the other HDL (medium, large, or very large HDL). Two of them (S-HDL-PL and S-HDL-FC) in the diabetes heatmap (Figure 3) are even linked to the two LDL subclusters, D1 and D2.-VLDLs of different sizes are distributed among two subclusters (D4 and D5 in the diabetes heatmap shown in Figure 3, and C2 and C3 in the control heatmap shown in Figure 2). All features of very small VLDL (except XS-VLDL-TG, which clusters with other VLDL features) and some features of small VLDL (S-VLDL-C in diabetes, S-VLDL-CE in controls and diabetes) cluster with features of LDL in C1, respectively, in D1 and D2 rather than large and very large VLDL.
## 3.3. Multivariate Associations with Diabetes
Next, we tried to identify variables independently associated with the presence of diabetes. Considering the strong correlations between many variables and the fact that the dataset encompasses more variables than patients, we performed regularized logistic regression using elastic net regularization to avoid over-fitting (Figure 4 and Figure 5). After regularization, HbA1c, age, valine, lactate, glucose (measured either with the clinical method or NMR), free cholesterol in small HDL, leucine, isoleucine, waist circumference, and heart rate had positive non-zero regression coefficients (Figure 4A). In contrast, free cholesterol, linoleic acid, and total cholesterol measured by NMR and HDL-diameter had negative non-zero regression coefficients (Figure 4A).
Upon stability path analysis after elastic net regularization, only the associations of T2DM with HbA1c, age, lactate, and free cholesterol remained significant (Figure 4B). However, after correction for multiple testing, only HbA1c remained significantly associated with diabetes (Figure 4C).
## 3.4. Altered Distribution of Free Cholesterol among Lipoprotein Subclasses
The positive association of free cholesterol in small HDL particles with T2DM contrasts with the negative associations of free cholesterol in total plasma and most other HDL-related features with T2DM (Figure 1 and Figure 4A). We compared the associations of free cholesterol (FC), cholesterol ester (CE), and their ratio in all lipoprotein subclasses (Figure 5). The relative content of FC to CE in small LDL and both the absolute content of FC and the content relative to CE in small HDL as well as in all VLDL particles, except FC and CE in very small VLDL and CE in small VLDL, showed positive associations with T2DM. Conversely, both the absolute and relative FC content of medium, large, and very large HDL as well as large LDL and IDL, showed inverse associations with T2DM (Figure 5). Notably, the associations for absolute FC content were stronger than for relative FC content.
## 3.5. Correlations with HbA1c
Figure 6 shows a volcano plot of the correlations between HbA1c and all other variables in the combined cohort of diabetic patients and control subjects. As expected, the strongest correlations were seen for glucose. Other clinical features with significant positive correlations include waist circumference, triglycerides, age, leukocyte number, body weight, and BMI. HDL-cholesterol was inversely correlated. Interestingly, several NMR features have stronger correlations with HbA1c than these established risk factors. They include positive correlations of the branched-chain amino acids (BCAA: isoleucine, leucine, and valine), alanine, lactate, glycoprotein acetyls, and a series of VLDL features such as triglycerides in total, small, or medium VLDL, and VLDL-diameter. Many HDL-related features are inversely correlated with HbA1c, most strongly HDL-diameter. In agreement with this, features related to very large HDL had the strongest inverse correlations, followed by those of large and medium HDL. Several features related to small HDL instead showed positive correlations with HbA1c, namely (in the order of strength) triglycerides in small HDL, free cholesterol in small HDL, the number of small HDL particles, total lipids, and phospholipids in small HDL. In addition, several features related to polyunsaturated fatty acids also showed inverse correlations with HbA1c, namely omega-6 fatty acids, total polyunsaturated fatty acids, and linoleic acid.
Next, we performed elastic net regularized regression and stability path analyses to find variables independently correlated with HbA1c (Figure 7). Figure 7A shows the non-zero regression coefficients after regularization for the parameters with significant correlation. Glucose, leukocytes, age, alanine, and glycoprotein acetyls showed independent positive correlations. In contrast, the concentration of very large HDL particles, eGFR, and the linoleic acid to total fatty acids’ ratio showed independent negative correlations. Upon stability path analysis, only the correlations of HbA1c with glucose, leukocytes, age, alanine, and eGFR remained significant (Figure 7B). Upon correction for multiple testing, only glucose remained significantly correlated with HbA1c (Figure 7C).
## 4. Discussion
Upon maximal adjustment for confounders by elastic net analysis, our comprehensive analysis of clinical and metabolomics data of a case-control study with 117 subjects found diabetes and HbA1c associated and correlated, respectively, with a limited number of variables. We replicated well-established associations of diabetes with HbA1c, glucose, age, and waist circumference and likewise expected correlations of HbA1c with glucose, leukocytes, age, and estimated glomerular filtration rate. Our elastic net analysis also confirmed the independent associations of diabetes with several NMR-spectroscopic phenotypes, namely positively with branched-chain amino acids [10] and lactate [11], as well as inversely with linoleic acid in plasma [12] and HDL diameter [13,14]. HbA1c showed significant and independent correlations with alanine and acetylated glycoproteins (both positive), as well as linoleic acid relative to all fatty acids, and the number of very large HDL particles (inverse).
As a novel yet unreported observation, our elastic-net analysis with lasso regression found the presence of diabetes associated with lower concentrations of free cholesterol in plasma, but higher concentrations of free cholesterol in small HDL. Free cholesterol of small HDL was previously found to be associated with incident diabetes [15]. A closer look at all lipoprotein subfractions revealed decreased contents of free cholesterol in all HDL and LDL subclasses of diabetic subjects except small HDL, but higher concentrations of free cholesterol in all VLDL subclasses except in very small VLDL of diabetic subjects compared to non-diabetic controls. The larger the VLDL particle, the more pronounced the enrichment in free cholesterol.
In plasma, about $80\%$ of cholesterol is esterified and transported in the core of lipoproteins. The unesterified cholesterol, frequently termed free cholesterol, is solved in the phospholipid layer of the lipoprotein surface. AcylCoA-cholesteryl-acyl transferase (ACAT) esterifies free cholesterol in the endoplasmic reticulum of enterocytes or hepatocytes, and the cholesteryl esters are secreted together with chylomicrons and VLDL, respectively [16]. The enrichment of VLDL in free cholesterol may be caused by reduced ACAT activity in diabetes. However, the data from animal experiments are controversial and showed decreased [17,18], increased [19,20,21], or unchanged ACAT activity [18,22] in the liver of diabetic rats or rabbits compared to control animals.
An alternative explanation for the enrichment of VLDL with free cholesterol is its impaired transfer to HDL. Kontush and colleagues previously demonstrated that the lipolysis of triglycerides is accompanied by the transfer of free cholesterol from triglyceride-rich lipoproteins to HDL, possibly as part of surface remnants [23,24]. This transfer is decreased in diabetes, probably as the result of disturbed lipolysis because insulin resistance limits the activity of lipoprotein lipase [23]. Such a disturbance explains both the accumulation of free cholesterol in VLDL and the decreased content of free cholesterol in HDL.
In plasma, lecithin:cholesterol acyltransferase (LCAT) esterifies free cholesterol, mostly in small HDL3, but also in larger HDL2 and even less in LDL [13,25]. The data on LCAT activity in diabetes is controversial. In agreement with the reduced content of free cholesterol in HDL and LDL, some studies reported increased LCAT activity in diabetic patients as compared to controls [26]. However, others found LCAT activity rather decreased or unchanged in diabetes [27]. The discrepancies may result from differences in the method used to determine LCAT activity. In the so-called exogenous substrate method, LCAT activity is determined by LCAT mass concentration, while in the endogenous substrate method, the composition of lipoproteins in plasma is strongly influencing the cholesterol esterification rate.
The opposite alterations in the free cholesterol content of small and larger HDL may reflect the flux and fate of cell-derived cholesterol within these lipoproteins. Small HDL is more effective than larger HDL in inducing cholesterol efflux from cells by the ATP-binding cassette transporter ABCA1. Only a part of cholesterol is immediately esterified in the initial and small HDL acceptor particles. Another part is transferred to larger HDL and then LDL without esterification [28,29,30]. The increased free cholesterol content of small HDL but decreased free cholesterol content of larger HDL and LDL may reflect a disturbed transfer of unesterified cholesterol between these lipoproteins. Interestingly, it was previously suggested that a high bioavailability of free cholesterol in HDL correlates with ASCVD [31]. Our observation of a reduced content of free cholesterol in most HDL particles of diabetic subjects, who are at high risk of ASCVD, rather contrasts this hypothesis. The hypothesis of Pownall and colleagues would not be falsified by our findings if the enrichment of free cholesterol in small HDL particles but deprivation in larger HDL and LDL indicates a block at this early stage of reverse cholesterol transport in diabetic subjects.
The major limitations of our study are the rather small population size, the monocentric design, and the lack of matching. This bears a high risk of confounding. We cannot rule out that our findings, although in line with previous reports, are not specific to diabetes but caused by differences, for example, in diet or drug treatment or ASCVD status, which were not included in our multivariate analysis, or body fat, as waist circumference also remained independently associated with diabetes (Figure 4A). The overrepresentation of males among diabetic subjects may have contributed to the findings, although sex was an independent determinant of neither diabetes nor HbA1c in our multivariate statistical analyses. Further studies are needed to replicate our novel finding on the association of diabetes with alterations in the content of plasma and lipoprotein subclasses in free cholesterol.
In conclusion, our comprehensive analysis of lipoproteins and metabolites using NMR spectroscopy replicated previously described associations of diabetes or correlations of HbA1c with alanine, branched-chain amino acids, lactate, polyunsaturated fatty acids, and sizes of lipoprotein subclasses. As a novel discovery, we found that most HDL particles, as well as LDL, are deprived of free cholesterol. Meanwhile, VLDL, as well as small HDL, are enriched with free cholesterol. This disbalanced distribution of free cholesterol may reflect disturbances in the transfer of free cholesterol from VLDL to HDL during lipolysis and in the transfer of cell-derived cholesterol from small HDL via larger HDL to LDL (see graphical abstract presented as Figure 8). In future research, it will be interesting and of potential clinical importance to unravel the molecular basis and pathogenic consequences of the disbalanced lipoprotein distribution of free cholesterol.
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---
title: Glycation Interferes with the Activity of the Bi-Functional UDP-N-Acetylglucosamine
2-Epimerase/N-Acetyl-mannosamine Kinase (GNE)
authors:
- Vanessa Hagenhaus
- Jacob L. Gorenflos López
- Rebecca Rosenstengel
- Carolin Neu
- Christian P. R. Hackenberger
- Arif Celik
- Klara Weinert
- Mai-Binh Nguyen
- Kaya Bork
- Rüdiger Horstkorte
- Astrid Gesper
journal: Biomolecules
year: 2023
pmcid: PMC10046061
doi: 10.3390/biom13030422
license: CC BY 4.0
---
# Glycation Interferes with the Activity of the Bi-Functional UDP-N-Acetylglucosamine 2-Epimerase/N-Acetyl-mannosamine Kinase (GNE)
## Abstract
Mutations in the gene coding for the bi-functional UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase (GNE), the key enzyme of the sialic acid biosynthesis, are responsible for autosomal-recessive GNE myopathy (GNEM). GNEM is an adult-onset disease with a yet unknown exact pathophysiology. Since the protein appears to work adequately for a certain period of time even though the mutation is already present, other effects appear to influence the onset and progression of the disease. In this study, we want to investigate whether the late onset of GNEM is based on an age-related effect, e.g., the accumulation of post-translational modifications (PTMs). Furthermore, we also want to investigate what effect on the enzyme activity such an accumulation would have. We will particularly focus on glycation, which is a PTM through non-enzymatic reactions between the carbonyl groups (e.g., of methylglyoxal (MGO) or glyoxal (GO)) with amino groups of proteins or other biomolecules. It is already known that the levels of both MGO and GO increase with age. For our investigations, we express each domain of the GNE separately, treat them with one of the glycation agents, and determine their activity. We demonstrate that the enzymatic activity of the N-acetylmannosamine kinase (GNE-kinase domain) decreases dramatically after glycation with MGO or GO—with a remaining activity of $13\%$ ± $5\%$ (5 mM MGO) and $22\%$ ± $4\%$ (5 mM GO). Whereas the activity of the UDP-N-acetylglucosamine 2-epimerase (GNE-epimerase domain) is only slightly reduced after glycation—with a remaining activity of $60\%$ ± $8\%$ (5 mM MGO) and $63\%$ ± $5\%$ (5 mM GO).
## 1. Introduction
GNE myopathy (GNEM; OMIM 605820) is an autosomal-recessive disease, caused by mutations in the gene encoding the bi-functional UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase (GNE), which is the key enzyme of the sialic acid biosynthesis [1,2]. Sialic acids have a plethora of functions; they are important for cellular and molecular recognition [3], they play a role in cell adhesion [4,5] and migration [4,6], and they can be involved in the transport of charged molecules [3]. Until now, more than 200 mutations have been known which lead to GNEM [7,8]. Beside them are certain so-called founder mutations, e.g., M743T (Note: In the original paper the mutation was named M712T [9]. Nevertheless, a change in the nomenclature of GNE-variants led to a rename into M743T [10].), which is a founder mutation in the Middle Eastern population [9], or V603L, which is a founder mutation in Japanese individuals [11,12].
*Another* genetically related disease that is also based on mutations in GNE is sialuria (OMIM 269921) [13,14,15]. GNEM is associated with hyposialylation [16,17,18]—but it should be noted here, however, that the connection between (hypo)sialylation and GNEM has not yet been finally clarified [19,20].
The GNE gene consists of 14 exons, exon A1 and exons 1 to 13 [21]. So far, nine different mRNA splice variants are known, leading to the six already found GNE isoforms 1-6 (two transcripts, NM_001190388.2 and NM_001374798.1, are leading to isoform 3 [22]), and the two predicted isoforms X1 and X2 [23,24]. Furthermore, GNE and its different isoforms are unevenly expressed in different tissues, with high RNA expression in the liver and low RNA expression in skeletal muscle [23,25,26,27].
The two domains of the GNE—the epimerase (E.C. 5.1.3.14; [28,29,30]) and the kinase (E.C. 2.7.1.60; [31,32,33])—are linked and act as a bi-functional enzyme [34,35]. Nevertheless, they also show activities, albeit lower than the wild-type enzyme, when considered individually [36]. One benefit of a bi-functional enzyme with two domains at once is the possibility for substrate channeling, as for example is already known for the formiminotransferase cyclodeaminase (FTCD) [37] or the dihydrofolate reductase-thymidylate synthase [38]. Overall, there are different ways of transferring the substrate between the two active centers of an enzyme, e.g., through a kind of tunnel in the enzyme [39] or via its surface [38]. Although it has not yet been shown for GNE, which is particularly the case since no 3D structure is available for the entire enzyme, substrate channeling could also be a possibility for substrate handover here.
The epimerase domain catalyzes the formation of N-acetylmannosamine (ManNAc) from uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) with a simultaneous cleavage of UDP [29,30] (Figure 1). The so-formed ManNAc is then phosphorylated by the kinase domain whereby ATP is converted to ADP at the same time [32]. Accordingly, the newly formed substance is called ManNAc-6-phosphate. Alternatively, this step can also be accomplished by the N-acetylglucosamine kinase (GlcNAc kinase/also known as NAGK; [40,41]; indicated in orange in Figure 1). The main substrate of this enzyme is usually GlcNAc, as supported, for example, by the corresponding km-values for the two substrates ManNAc and GlcNAc [40,42].
After a few more steps (for a complete overview of all steps see [8,43]), cytidine 5′-monophospho-N-acetylneuraminic acid (CMP-Neu5Ac; [44,45]) is synthesized. CMP-Neu5Ac acts as substrate for sialyltransferases [46] and its bioavailability regulates their expression [47]. Additionally, it feedback-inhibits the activity of the epimerase ([48]; indicated by the red arrow in Figure 1). The allosteric site is located in the epimerase domain between the amino acids 255 and 303 (hGNE1) [14,43,49,50].
The amino acid side chains that are involved in substrate binding or in the formation and stabilization of the active site can be seen in Figure 2Aa,Ab (GNE-epimerase domain), Figure 2Ba,Bb (GNE-kinase domain), and Figure 2Ca,Cb (GlcNAc kinase) [sequences in 2Aa,Ba,Ca: amino acids involved are marked with a diamond ♦]. In the epimerase domain, these are R19, A20, D21, S23, K24, P27, M29, H49, G111, D112, R113, H132, E134, G136, D143, D144, R147, G182, D187, H220, D225, N253, V282, F287, S301, S302, C303, R306, E307 and R321 [43,50,51,52,53]. Please note: Since we show the amino acid sequence of hGNE1, the isoform examined in this study, in Figure 2, we also use the amino acid numbering based on this “old” variant. In the kinase domain, these are D413, R420, G476, R477, T489, N516, D517, G545, E566, H569, C579, C581, C586, and E588 [43,51,52,54]. In the GlcNAc-kinase, these are N36, W38, S76, G77, D107, T127, G128, S129, N130, C131, C143, G145, W146, G147, D152, A156, L201, Y205, A214, C217, R218, S271, V272, K274, and S275 [55,56].
GNEM is a distal myopathy with a worldwide predicted prevalence of 1 to 9 patients per 1 million people [8] that first affects the tibialis anterior, the biceps femoris short head, and the adductor muscles [57,58] leading to certain gait problems and a typical foot-drop [59,60]. After an average of 12 to 15 years after disease onset, most of the patients will be dependent on a wheelchair [61,62]. Other hallmarks of GNEM are the finding of rimmed vacuoles in affected muscles [60,63], an onset in early adulthood [62,64], and a quite slow progression of the disease [8,65]. GNEM-triggered inflammatory changes are not typical features of this disease [60,63,66], although there are confirmed exceptions to this statement [67,68,69].
Muscles/Muscle groups, which are least affected, are the quadriceps [60]—especially the vastus lateralis [57,58]—as well as the muscles in the face [8,59,63] and the deltoid muscle [60]. An influence on the heart and especially on cardiomyocytes is still under debate. While mouse models suggest an influence on cardiac muscle cells [70,71], patient data-based studies do not find direct correlations between GNEM and cardiomyopathy [61,64,72].
One therapeutic approach aims to compensate for potential hyposialylation by adding precursors of individual metabolites of the sialic acid pathway or other heavily sialylated proteins. This approach also led to positive effects in mouse models [8,70,73,74,75,76,77]. Unfortunately, the direct transferability of the results of the animal experiments did not turn out to be that easy [8,78]. Overall, although some trials are quite promising, the disease is not yet curable.
**Figure 2:** *Localization of potential glycation sites in the proteins of interest: the two domains of the human GNE, mRNA variant 2 (hGNE1) and the N-acetylglucosamine kinase. Amino acid sequences of the GNE epimerase (Aa), GNE kinase domain (Ba), and of the N-acetylglucosamine kinase (Ca). Secondary structure elements are highlighted by an orange (sheet) or yellow (helix) background color. Lysines (red), arginines (green) and cysteines (blue) are also color-coded to indicate potential glycation sites. A diamond ♦ over an amino acid indicates whether it is within the active site. Lysines, arginines and cysteines found on the surface of the proteins are represented in Ac, Ad, Bc, Bd, Cc, and Cd. Interactions between the substrate and the active-site amino acids are indicated by green-dashed lines in Ab, Bb and Cb. All visualizations (PDB-IDs: 4ZHT (GNE epimerase domain; 3D structure published in [53]), 2YHW (GNE kinase domain; 3D structure published in [54]), 2CH5 (N-acetylglucosamine-kinase; 3D structure published in [56])) were created with iCn3D [79,80].*
A particular sticking point of this disease, especially in the diagnosis, is the comparatively late onset of the disease. Other muscle disorders as Duchenne muscular dystrophy (DMD), Emery-Dreifuss muscular dystrophy (EDMD), or Facioscapulohumeral muscular dystrophy (FSHD) have an onset in early or late childhood [81]. This suggests that age-related effects may play a role in this disease. One hallmark of molecular aging is glycation. Glycation describes an enzyme-independent process of a post-translational modification (PTM) of proteins; in particular, of the amino acid side chains lysine, which has a primary amino group, arginine with its guanidine group [82], cysteine with its sulfhydryl group [83], or of other substances with a preferably terminal amino group [84].
To simplify the identification of potential glycation sites in the sequences of the GNE-epimerase domain, GNE-kinase domain, and GlcNAc kinase, we used a specific color code for arginines, cysteines, and lysines (Figure 2Aa,Ba,Ca). The same color code was also used in our three-dimensional representations of the enzymes—shown once in the ribbon (Figure 2Ac,Bc,Cc) and once in the molecular surface variant (Figure 2Ad,Bd,Cd). In particular, the amino acids that are part of the active site can strongly affect the activity of an enzyme—e.g., if they are mutated or otherwise modified, as they may be critical for stabilization of the binding site or for interaction with the substrate through hydrogen bonds. The functions of arginines, cysteines, and lysines that are part of the active site are listed in Table 1.
Since it is already known that mutations at some of the positions of these amino acids lead to reduced enzyme activities [50,51,53,55], it seems very likely that glycations of these amino acids would also have an impact on enzyme activity. To complete this picture, it is important to mention that the activity of the GNE can be influenced by phosphorylation [85] and O-GlcNAcylation [86].
The first step in glycation is a condensation between the amino group and a glycation agent, which can be, for example, a certain type of sugar [87], a derivate of sugar [88], di-carbonyls such as glyoxal (GO) or methylglyoxal (MGO) [89,90], or ascorbic acid [91]. A Schiff base adduct is formed, which can be converted into an Amadori product by an Amadori rearrangement [92]. This reaction can be followed by further steps—e.g., dehydration, enolizations, or oxidations [84,87,93]—leading to a multitude of possible advanced glycation endproducts (AGEs) [90,94]. The AGEs formed are also dependent on the glycation agent used ([95]; see also Figure 3A–C). Additionally, the reactivity of different amino acid side chains with different glycation agents varies [96].
The level of AGEs increases steadily over the course of life [97,98,99], which could possibly also have an influence on the symptomatic onset of GNEM. This hypothesis can be supported by the fact that there are already examples in which it has been shown that post-translational modifications caused by MGO lead to a decrease in the activity of an enzyme [100,101].
The aforementioned di-carbonyl substances can occur naturally as a breakdown product of glycolysis, amino acid, and fatty acid degradation [114,115,116]. In addition, they can arise as a byproduct of the glycation process itself via the so-called Namiki pathway [84,117] or by autoxidation of glucose via the Wolff pathway [84,118,119,120]. They are also ingested through food: MGO and GO, e.g., by consuming coffee and wine [121] and Vitamin C through fruits and vegetables [122].
One of the most well-studied AGEs is carboxymethyllysine (CML; Figure 3A), whose formation is based primarily on a reaction between lysine and glyoxal [108,123,124]. Furthermore, most anti-AGE antibodies preferentially recognize CML [125]. In our study, we also used an antibody against CML as a marker for the glycation level of our proteins—the epimerase and the kinase domain of the GNE and the GlcNAc kinase.
In this study, we attempted to find an explanation for the late onset of GNEM by relating it to PTMs, focusing specifically on glycation. Therefore, we investigated the extent of the influence of MGO- or GO-triggered glycation on the activity of the individual domains of the GNE (hGNE1) and on the GlcNAc kinase. For this purpose, all three enzymes were expressed separately from each other, purified, treated with increasing concentrations of one of the glycation agents, and then analyzed for their activity.
Furthermore, we examined the expression of the GNE—as a whole protein—and the GlcNAc kinase in undifferentiated but MGO- or GO-treated C2C12 cells. These cells belong to a murine skeletal muscle cell line. In addition, the effect of the glycation agents on cell viability was investigated by a MTT assay.
## 2.1. Cell Culture and MGO/GO-Treatment
Murine C2C12 myoblasts were cultured in DMEM (Dulbecco’s Modified Eagle’s Medium; 11960044; Gibco/Thermo Fisher Scientific; Waltham, MA, USA) supplemented with $10\%$ FBS (Fetal Bovine Serum; A5256801; Gibco/Thermo Fisher Scientific; Waltham, MA, USA), $1\%$ penicillin-streptomycin (P/S; 10,000 units/mL (P) and 10,000 µg/mL (S); 15140122; Gibco/Thermo Fisher Scientific; Waltham, MA, USA) and $1\%$ L-glutamine (L-Gln; 200 mM; A2916801; Gibco/Thermo Fisher Scientific; Waltham, MA, USA) at 37 °C in a humidified atmosphere with $5\%$ CO2. The C2C12 cell line was kindly provided to us by the Poserns Lab (Martin-Luther-University Halle-Wittenberg, Institute for Physiological Chemistry; Halle (Saale), Germany). Growth medium was changed every 48 h.
To assess the effect of MGO and GO on C2C12 myoblasts, the cells were seeded at a density of 9000 cells/cm2; in a 10 cm (Ø) dish and incubated at 37 °C in a humidified atmosphere with $5\%$ CO2. After one day, the cells were cultured in starvation medium (DMEM + $1\%$ FBS + $1\%$ P/S + $1\%$ L-Gln) with MGO or GO (0.2 mM, 0.5 mM, or 2 mM). Following another day, the cells were divided into two pellets, one for protein isolation and one for RNA isolation.
## 2.2. RNA Extraction and RT-qPCR
Total RNA was isolated using the Quick-RNA Miniprep Kit (R1054; Zymo Research; Irvine, CA, USA). cDNA was synthesized, using 2 µg of total RNA and SuperScript™ II reverse transcriptase (18064022; 2000 units; Thermo Fisher Scientific; Waltham, MA, USA), following the manufacturer’s instructions. RT-qPCR was performed using qPCR SybrMaster (PCR-372S; Jena Bioscience; Jena, Germany) and the CFX Connect™ Real-Time PCR Detection System (1855201; Bio-Rad; Hercules, CA, USA).
The following primers were used: Cq values were normalized to the housekeeping gene RPL26, and relative gene expression was calculated using the ΔΔCq-method.
## 2.3. Protein Isolation from Cell Culture
For protein isolation, cells were washed with PBS and lysed using RIPA buffer containing protease inhibitor cocktail (Sigma Aldrich; St. Louis, MO, USA), 1 mM NaVO4, and 1 mM PMSF. Following 30 min incubation on ice, total protein was isolated by centrifugation at 14,000× g, 4 °C for 5 min and quantified using the Pierce™ BCA Protein Assay Kit (23225; Thermo Fisher Scientific; Waltham, MA, USA).
## 2.4. MTT Assay
C2C12 myoblasts were seeded at a density of 3 × 103 cells in a 96-well cell culture plate and treated with six different concentrations of MGO (0 mM, 0.1 mM, 0.2 mM, 0.5 mM, 0.7 mM, and 1 mM) and GO (0 mM, 0.5 mM, 1 mM, 1.5 mM, 2 mM, and 2.5 mM) for 24 h in DMEM containing $1\%$ FBS. Thiazolylblue-tetrazolimbromide (M5655; Sigma-Aldrich; St. Louis, MO, USA) was added to the cells according to the manufacturer’s instructions and incubated for 4 h.
## 2.5. Protein Expression in E.coli and Purification
The GNE domains of human GNE/MNK were expressed according to a protocol published by the Chen Group [53]. *The* gene encoding the UDP-GlcNAc 2-epimerase domain with an N-terminal His6-tag was bought from Biocat in a pET21a(+) vector. The plasmid was codon optimized to be expressed in *Escherichia coli* BL21 (DE3) cells. The transformed BL21 (DE3) cells were stored until use as glycerol stocks ($5\%$).
Starting from this glycerol stock, an over-day culture was grown in 5 mL LB medium with 100 mg/L ampicillin, for 8 h at 37 °C and 220 rpm. From the over-day culture, an overnight culture was grown in 15 ml LB medium with 100 mg/L ampicillin, at 37 °C and 220 rpm. With 3.5 mL overnight cultures 4 × 500 mL LB medium with 100 mg/L ampicillin in 2 L flasks were inoculated. This was grown at 37 °C and 180 rpm to an OD600 of 1.9. Then all cultures were pooled, and 700 mL were diluted in 1.3 l LB medium with 100 mg/L ampicillin (at 4 °C). This was redistributed to four 2 L flasks (4 × 500 mL), grown to an OD600 of 0.7 and induced with IPTG (50 µM). Subsequently, the cells were kept for 42 h at 8 °C and 120 rpm. Then, they were grown for 24 h at 16 °C and 180 rpm. The cells were harvested by centrifugation with 4000× g for 15 min at 4 °C, and the cell pellet was re-suspended in resuspension buffer (50 mM Tris-HCl, pH 8.0, 500 mM NaCl).
Afterwards, the cells were lysed by using a high shear fluid laboratory homogenizer at 18 kPa (LM10; Microfluidics International Corporation; Newton, MA, USA) and the cell lysate was centrifuged with 20,000× g for 25 min at 4 °C. The clarified supernatant was filtered, and the protein was purified via Ni-NTA chromatography on 2 × 5 mL His60 Ni Superflow column (635657; Takara Bio Inc./Clontech; Kusatsu, Japan) using a Chromatography System (Bio-Rad NGC Discover 10; Bio-Rad Laboratories; Hercules, CA, USA). Resuspension buffer and elution buffer (50 mM Tris-HCl pH 8.0, 500 mM NaCl, 500 mM Imidazole) were used in a linear gradient over 300 mL.
Fractions of the protein peak were pooled and concentrated to 10 mL using a Vivaspin 20 (VS2001; Sartorius; Göttingen, Germany) with a molecular weight cut of 10 kDa. The sample was then buffer exchanged to 50 mM Tris-HCl pH 8.0, 100 mM NaCl, $5\%$ glycerol and 0.2 mM TCEP using a HiPrep $\frac{26}{10}$ Desalting column (GE17-5087-01; Sigma-Aldrich; St. Louis, MO, USA) and the aforementioned chromatography system. Fractions containing the GNE protein were pooled. 20 mL with a concentration of about 7 mg/mL were obtained.
To avoid confusion with the proteins from cell culture, these proteins will be referred to hereinafter as Protein-Ecoli and the proteins from cell culture as Protein-CC.
## 2.6. Glycation of the Proteins (Protein-Ecoli)
In order to investigate the concentration dependence of glycation of methylglyoxal (MGO; $40\%$ in H2O; Sigma Aldrich; St. Louis, MO, USA) and glyoxal (GO; $40\%$ in H2O; Sigma Aldrich; St. Louis, MO, USA), 6 µg each of N-acetylmannosamine kinase and N-acetyl-glucosamine kinase or 4.8 µg of the UDP-N-acetylglucosamine 2 epimerase were mixed with ascending concentrations of the reagents—0.5 mM, 2 mM, and 5 mM—and incubated for 1 h at 37 °C.
## 2.7. Western Blot Analysis (Protein-CC and Protein-Ecoli)
Protein-CC: Equal amounts of protein were mixed with 5× SDS-loading dye (containing 50 mM DTT) and separated on a 4–$12\%$ Tris-Glycin gradient gel (Invitrogen by Thermo Fisher; Waltham, MA, USA).
Protein-Ecoli: The glycated proteins were mixed with 5× SDS-loading dye (containing 50 mM DTT) and separated on a 10–$20\%$ Tris-Glycin gradient gel (XP10205BOX; Invitrogen by ThermoFisher; Waltham, MA, USA).
Proteins were transferred on a nitrocellulose-membrane and stained with Ponceau S as loading control. Membranes were blocked with $5\%$ skimmed milk in TBS-Tween (TBS-T) for 1 h at RT. The membranes were then incubated with the primary antibodies: mouse IgG-anti-carboxymethyl lysine (CML26; dilution: 1:10,000 (Protein-CC) or 1:2000 (Protein-Ecoli); ab125145; abcam; Cambridge, UK) or MG-H1 (1H7G5; dilution: 1:1000; NBP2-62810; Novus Biologicals/Bio-Techne; Minneapolis, MN, USA) overnight at 4 °C. Afterwards, the membranes were washed three times with TBS-T. Each washing step lasts 10 min. Following that, the membranes were incubated with the secondary antibody—goat anti-mouse IgG H&L HRP (dilution 1:10,000; ab6789; abcam; Cambridge, UK)—for 1 h at RT. Thereafter, the membranes were again washed 3 times for 10 min each with TBS-T and the Western blot detection reagent from Amersham (cytiva, Marlborough, MA, USA) was used. The ChemiDoc MP imaging system from Bio-Rad Laboratories (Hercules, CA, USA) was used for visualization. Intensities were quantified by ImageLab Software from Bio-Rad Laboratories (Hercules, CA, USA).
## 2.8. Epimerase Activity Assay (Protein-Ecoli)
An UDP Glo™ Glycosyltransferase Assay (V6961; Promega Corporation; Madison, WI, USA) was performed to determine UDP-N-acetyl-glucosamine 2-epimerase activity. For this purpose, 747 µg epimerase were incubated at 37 °C with different MGO/GO concentrations (0.5 mM, 2 mM, and 5 mM) for 1 h in a volume of 3 µL. The total volume of the reaction mixture of 52.5 µL was prepared, consisting of 5 µM UDP-GlcNAc, $0.05\%$ BSA, and glycated epimerase in buffer. This mix was incubated for 1 h at 37 °C. Afterwards, 25 µL UDP-Detection Reagent™ were added and incubated at room temperature for 1 h. Afterwards, the luminescence was detected by ClarioStar™ (BMG Labtech GmbH, Ortenberg, Germany).
## 2.9. Kinase Activity Assay (Protein-Ecoli)
The N-acetylmannosamine kinase activity and the N-acetylglucosamine kinase activity were determined by a coupled enzyme assay (based on the assays described by [19,126]).
Our enzyme assay used differently glycated GNE-kinase and GlcNAc kinase samples. For this, 3 µg each of the GNE-kinase domain and the GlcNAc kinase were incubated with ascending concentrations of MGO and GO—0.5 mM, 2 mM, and 5 mM—for 1 h at 37 °C. Each of the enzyme samples had a total volume of 2.5 µL.
Afterwards, a reaction mixture with a total volume of 623.5 µL was prepared, consisting of one of each of the different enzyme samples, 62.6 mM Tris, 20.3 mM MgCl2, 9.6 mM ATP, 4.8 mM ManNAc, 4.8 mM phosphoenolpyruvate, 1.4 mM NADH, 6 µL Lactic dehydrogenase (600–1000 U/mL)/Pyruvat kinase solution (900–1400 U/mL) (P0294; Sigma Aldrich; St. Louis, MO, USA), and 7.7 mM sodiumphosphatebuffer.
This reaction mixture was incubated at 37 °C until further use. At time points 0 min and 45 min in the case of the ManNAc kinase, and 0 min and 180 min in the case of the GlcNAc kinase, 25 µL of this mixture was removed and added to 75 µL of an EDTA solution (10 mM). The absorbance at the wavelength of 340 nm reflects the NADH concentration. The ClarioStar™ (BMG Labtech GmbH, Ortenberg, Germany) was used to determine the absorbance.
## 2.10. Structural Comparison
A structural comparison between GNE-kinase and GlcNAc kinase was performed using the VAST+ algorithm (vector alignment search tool; [127,128,129]).
In order to do this, we entered the PDB ID of the GNE-kinase (2YHW) into the search field of the Website of the National Center of Biotechnology Information [130], called up the entry listed under Protein/Structure, and then clicked on the “VAST+” button (similar structures). Afterwards, we entered the PDB ID of the GlcNAc kinase (2CH5) into the search field of the result list.
## 2.11. Statistical Analysis
For statistical analysis of the epimerase and the kinase activity assays, a two-way analysis of variance (ANOVA) was performed (alpha = 0.05; OriginPro 2019 (OriginLab Corporation; Northampton, MA, USA)). One factor (“way”) was the choice of glycation agent—MGO or GO—and the other the concentration used—0.5 mM, 2 mM, or 5 mM. This was followed by a Tukey post hoc-Test. Based on the determined p-values, asterisks were used to classify the significance levels: p ≤ 0.05: *, 0.005 < p ≤ 0.01: **, and p ≤ 0.005: ***.
## 3.1. Expression and Glycation of the UDP-N-Acetylglucosamine 2-Epimerase, the N-Acetylmannosamine kinase, and the N-Acetylglucosamine Kinase
As it is possible that molecular aging is an important component in the progression and onset of GNEM, this aspect will be the focus of the following experiments. Molecular aging was therefore simulated by glycating the proteins/protein domains of interest using different physiological glycation agents. Afterwards, their individual activities were determined by different assays further explained in the next result section.
First, the proteins/protein domains were expressed in *Escherichia coli* BL21 with a 6xHis-tag added to their N-terminal site. This His-tag was used for purification over Ni-NTA-columns (details can be found in the Material and Methods section). Afterwards, the purified proteins were incubated with ascending concentrations of the glycation agents methylglyoxal (MGO) and glyoxal (GO) and the glycation success was verified by western blots using an antibody specific for glycated lysine side chains (anti-CML; see Figure 4, Supplementary Table S1 and the Supplementary Figures S1–S3 (showing the uncut Western Blots)).
With ascending concentrations of MGO and GO, the level of relative glycation normalized to Ponceau increases. Across both GNE domains, the signal of CMLs detected was stronger when using the glycation agent GO than MGO.
Looking at the GlcNAc kinase, it is striking that the amount of CMLs and thus the glycation success seems to be at the same level for both glycation agents. Overall, the proteins/protein domains of interest were successfully glycated by both glycation agents.
## 3.2. Glycation of the GNE-Domains Interferes with Their Enzymatic Activity—Activity of the GlcNAc Kinase Is Not Affected by Glycation
Having shown that the proteins/protein domains of interest were successfully glycated, their activity should now be determined. The aim is to investigate whether glycation can influence enzyme activity, to be able to assess whether the late onset of GNEM might be due to age-related effects.
The first step of the GNE-epimerase activity assay is based on the GNE-epimerase domain reaction, where UDP-N-acetylglucosamine (UDP-GlcNAc) is converted under addition of water to N-acetylmannosamine (ManNAc) and UDP (see Figure 5A). Afterwards, the generated UDP is converted to ATP by adding UDP-Glo-Reagent and light is generated in the form of luminescence (based on the Promega glycosyltransferase assay; details can be found in the Material and Methods section). The amount of light produced was used to back-calculate the UDP concentration. This is possible because the UDP concentration and the amount of light produced are proportionally coupled. The determined UDP concentration was then used as an indicator for enzyme activity. Higher UDP concentrations indicate higher enzyme activities.
The boxplot showing the determined UDP concentrations under the different conditions can be seen in Figure 5B. All boxplot-related data, including mean and standard deviation, can be found in Supplementary Table S2. All samples showed significant differences ($p \leq 0.005$; student’s t-test; all results can be seen in Supplementary Table S5) towards the negative control (see Figure 5B). The negative control consists of water, instead of any enzyme. The positive control consists of the non-glycated enzyme. With the exception of the sample that was treated with 0.5 mM MGO, all other samples showed significant differences towards the positive control ($p \leq 0.05$; student’s t-test), indicating reduced enzyme activities—lower UDP-concentrations—compared to the non-glycated enzyme.
Two-factor ANOVA showed that the concentration of the glycation agent is a significant factor for the activity (p-value: 1.43E-03; see also Supplementary Table S3) and the choice of the glycation agent per se not (p-value: 1.96E-01). The interaction between concentration and glycation agent is again significant (p-value: 3.58E-03), meaning that the result obtained through different concentrations depends on the used glycation agent.
The subsequent Tukey post hoc-Test identified significant differences between the conditions 0.5 mM MGO and 2 mM MGO, between the conditions 0.5 mM MGO and 0.5 mM GO, and between the concentrations 0.5 mM and 5 mM, regardless of the glycation substance used (for the p-values see Supplementary Table S4).
To ensure that the glycation agents do not influence the activity assay itself, the experiment was repeated without the GNE-epimerase domain, but with 0.25 nmol UDP (see Figure 5C). The assay enzymes were exposed to one of the two glycation agents in the same amount as in the normal activity assay (same volume; always assuming the highest concentration used). No influences on the activity of the assay enzymes were found under these conditions (p-value: 5.35E-01 (control and MGO); p-value: 9.07E-01 (control and GO)). This indicates that the reduced UDP concentrations measured with differentially glycated GNE-epimerase domains were a result of a reduced GNE-epimerase domain enzyme activity and not a result of the glycation agents reacting with the assay itself.
In summary, it can be stated that the activity of the GNE-epimerase domain can be influenced by glycation, with a remaining activity of $60\%$ ± $8\%$ after treatment with MGO (5 mM) and $63\%$ ± $5\%$ after treatment with GO (5 mM) compared to the non-glycated protein, which activity was set to $100\%$. The determination of the activity is based on the UDP consumption (see Supplementary Table S2). This also shows that regardless of the glycation agent used, the same level of activity is achieved when comparing the highest glycation conditions.
The activities of the GNE-kinase domain and of the GlcNAc kinase were determined by a coupled enzyme assay ([19,126]; see Figure 6A). This assay is based on the fact that both kinases can phosphorylate ManNAc to ManNAc-6-phosphate, although ManNAc is not the main substrate of GlcNAc kinase [40,42]. The ADP, which remains after the release of the phosphate from the ATP, is then further used in the pyruvate kinase reaction. The product of that reaction, pyruvate, is further converted to lactate. Concomitantly, the coenzyme NADH is converted to NAD+. The decrease in the NADH concentration can be measured by determining the absorbance at 340 nm. The level of NADH consumption per minute can be seen as a measure of enzyme activity. Higher NADH consumption per minute indicates higher enzyme activities.
The determined NADH consumption per minute for the GNE-kinase domain can be seen in Figure 6B and for the GlcNAc kinase in Figure 6C (the boxplot-related data can be found in the Supplementary Tables S6 and S10). The GlcNAc kinase, even non-glycated, shows a lower NADH consumption rate per minute than the GNE-kinase domain—only $17\%$ compared to the GNE-kinase domain.
Concerning the GNE-kinase domain, the positive control—again consisting of non-glycated protein—and the samples treated with the lowest concentrations of the glycation agents showed significant differences towards the negative control ($p \leq 0.05$; student’s t-test; see Figure 6B and Supplementary Table S8). All other samples showed such low NADH consumptions that they no longer show any significant differences from the negative control. With the exception of the treatment with 0.5 mM GO, all samples showed a significant difference from the non-glycated sample (pos. control; $p \leq 0.005$; see also Supplementary Table S8). The overall trend related to the NADH consumption per min is that higher concentrations of the glycation agent leads to lower consumption rates.
The two-factor ANOVA showed that the concentration of the glycation agent and the glycation agent per se are significant factors ($p \leq 0.005$; see also Supplementary Table S7) and the interaction between them is not (p-value: 1.22 × 10−1).
The subsequent Tukey post hoc-Test identified many significant differences between the different conditions, which can all be seen in Figure 6B with the corresponding p-values in Supplementary Table S9.
Concerning the GlcNAc kinase, all samples differ significantly from the negative control ($p \leq 0.01$; see Figure 6C and Supplementary Table S13) and show no significant difference from the positive control. Additionally, all other statistical tests do not reveal any other significant effects (see Supplementary Tables S10 and S11). This is quite surprising, as it states that glycation does not affect the enzymatic activity of this kinase, despite previous results on the GNE-kinase domain suggesting otherwise.
Again, no influence of the glycation agents on the enzymes of the assay could be determined (see Figure 6D; p-value: 4.23 × 10−1 (control and MGO); p-value: 6.52 × 10−1 (control and GO)). We exposed the assay enzymes to the same amounts of the two glycation agents as in the normal activity assay (same volume; always assuming the highest concentration used).
## 3.3. Expression of the UDP-N-Acetylglucosamine 2-Epimerase/N-Acetylmannosamine Kinase, and the N-Acetylglucosamine Kinase in MGO/GO-Treated Undifferentiated C2C12 Cells
We also investigated the effect of MGO and GO on undifferentiated C2C12-cells, a murine skeletal muscle cell line. This should help to obtain a first impression of the effect of glycation on the muscle type that is most affected by the disease. Phase-contrast images of C2C12 cells treated with different concentrations of the glycation agents were then analyzed to find out which concentrations appear to be quite well tolerated. An MTT assay was then performed to determine the effect of the glycation agents on cell viability. Based on the data from the MTT assay, the TC50-value was calculated whenever possible. Finally, the effect of MGO and GO on mRNA expression of GNE and GlcNAc kinase was determined using quantitative PCR (qPCR).
Looking at the phase-contrast images revealed that the cells tolerated higher concentrations of GO than of MGO. Accordingly, we found no morphological abnormalities in the cell cultures at a maximum concentration of 0.2 mM MGO and 0.5 mM GO (see Figure 7A and Supplementary Figure S4 (showing the unprocessed cell culture images)). This can be confirmed by the results of the MTT assay (see Figure 7B; results and p-values can be found in Supplementary Tables S14 and S15), where we determined a TC50-value for MGO of 0.81 mM. With regard to GO, we decided against calculating this value based on the underlying data basis, since we did not exceed the $50\%$ cell viability mark with the concentrations examined. Nevertheless, it is clear that the TC50-value of GO with respect to C2C12 myoblasts must be higher than the TC50-value of MGO.
The western blots showed the same trend as previously observed when examining the proteins/protein domains individually—that with ascending concentrations of MGO and GO, the level of relative glycation increases (see Figure 7D). This also stays true after normalization to Ponceau (see supplementary Figure S5).
The qPCR showed that the mRNA expression of the GlcNAc kinase was significantly reduced after treatment with 0.5 mM GO (p-value: 4.56 × 10−2; see Figure 7C and Supplementary Table S16). The expression of the GNE showed no significant changes independent of the concentration or type of the glycation agent (all p-values can be found in Supplementary Table S17).
Overall, these investigations showed that too high concentrations of glycation agents, especially of MGO, lead to a reduced cell viability. At non-toxic concentrations, GO/MGO-derived glycation products could be successfully detected in the cells/cell lysates; increasing concentrations led to increasing amounts of glycation products. The mRNA expression of GlcNAc kinase in C2C12 cells was affected by the addition of GO. However, the mRNA expression of GNE was not affected by either of the two glycation agents investigated.
## 3.4. Structural Comparison of the UDP-N-Acetylglucosamine 2-Epimerase/N-Acetyl-mannosamine kinase and the N-Acetylglucosamine Kinase
To understand similarities and differences between GNE-kinase and GlcNAc kinase, a structural comparison of both was performed using the VAST+ algorithm (vector alignment search tool; [127,128,129]; see Figure 8). The root mean square deviation (RMSD) of the aligned residues between both kinases was 3.17 Å. Furthermore, a sequence accordance of $21\%$ was found (displayed by white capital letters with a red background in Figure 8A; or by a red color in the three-dimensional image alignment in Figure 8B), and 219 residues could be aligned in 3D space (displayed by blue capital letters in Figure 8A; or by a blue color in Figure 8B). Regions in a gray color (Figure 8B) or with lowercase letters with a gray background represent unaligned amino acids (Figure 8A). In addition, a diamond ♦ marks the amino acid side chains involved in the formation of the active site (Figure 8A).
Three amino acid side chains can be identified whose 3D structure can be aligned against each other, which are identical, and which are part of the respective active sites. An enclosed rectangle with a Roman numeral marks these amino acid side chains in the amino acid sequence in Figure 8A. The amino acid side chain marked by I corresponds to the GNE amino acid at position 476 and to the GlcNAc kinase amino acid at position 77, which was in both cases a glycine. The amino acid side chain marked with III was also a glycine, which was found at position 545 in the GNE and at position 128 in the GlcNAc kinase. Furthermore, this glycine is part of the ATP binding motifs DXGGT and GTG (see yellow rectangles in Figure 8A; [52,131]). The amino acid aspartic acid is located at position II in the sequence. This corresponds to amino acid position 517 in GNE and position 107 in GlcNAc kinase.
The nearly conserved ATP binding motifs in both kinases appear to represent the greatest structural similarity. However, this is not a surprise since both require ATP to transfer the phosphate group to their respective substrate.
The functions, pathogenicity predictions based on polymorphism phenotyping (PolyPhen), statements concerning disease association, genetic locations, and the database of single polymorphisms (dbSNP) IDs [132] of the amino acids at the positions I to III can be found in the table displayed in Figure 8C (everything, except for the function, was based on [133]).
**Figure 8:** *Structural comparison of the GNE-kinase domain (hGNE1) and the GlcNAc-kinase. (A) Amino acid comparison of GNE-kinase domain (orange) and GlcNAc kinase (blue). (B) Three-dimensional structural comparison of the two kinases. Both were based on the vector alignment search tool VAST+ [127,128,129]. A blue color represent amino acids that are aligned in 3D space, a gray color represent unaligned amino acids, and a red color represent identical amino acids. A diamond ♦ over an amino acid indicates whether it is within the active site. The three amino acids, which can be aligned, which are identical, and which are part of the active site are marked by Roman numerals from I to III. The amino acid marked with III—glycine—is part of the ATP-binding motifs DXGGT and GTG [52,131]. All other functions of the marked amino acids can be found in the table in (C). Most of the information given here was based on data found on https://www.uniprot.org, (accessed on 20 December 2022) [133].*
## 4. Discussion and Outlook
All proteins/protein domains of interest—the GNE-epimerase domain, the GNE-kinase domain, and the GlcNAc kinase—could be successfully expressed in E. coli, purified via their 6xHis-tag, and post-translational modified by the two glycation agents methylglyoxal (MGO) and glyoxal (GO). Afterwards, different activity assays were performed to investigate the effect of glycation on the activity of the enzymes. In addition, we also examined the expression of GNE and the GlcNAc-kinase in undifferentiated, MGO/GO-treated C2C12 cells, a murine skeletal muscle cell line.
The choice of a His-tag needs to be discussed, since it seems also possible that under certain conditions histidine side chains could be modified [134,135]. However, it seems that the histidine modifications described in these studies are based on oxidation and not on glycation, and require at least certain amounts of Cu(II) [135], so a His-tag seems like a safe choice from this standpoint. Furthermore, such a tag has already been used successfully in another study in connection with GNE [53]. However, the His-tag itself could also have an impact on the protein and its activity, e.g., due to steric hindrances or due to electrostatic interferences that result in impeded binding of the substrate to the active site [136,137]. Still, one could try introducing removable tags, such as the GST-tag (Glutathione S-Transferase-tag; [138], and see if the purification success stays the same or maybe even improves. Then, His-tag-based influences [139] could be excluded.
The stronger CML signal when examining the GO-treated GNE domains as opposed to the MGO-treated ones can be explained by the fact that a fairly favorable pathway for the formation of CMLs is the reaction between lysine side chains and GO (see Figure 3A; [95,124]). In contrast, the reaction between lysine side chains and MGO leads to the formation of carboxyethyllysines (CELs) (indicated in blue in Figure 3A; [95,109]). It is therefore quite interesting that a putative specific antibody against carboxymethyllysines (CMLs) is also able to detect MGO-derived modifications on our proteins/protein domains of interest (see Figure 4). However, there are already some publications that document cross-reactions of anti-CML antibodies with CELs [140], respectively, with MGO-derived AGEs [86,141]. Further, it seems quite surprising that although the GNE-epimerase domain has almost twice as many lysines as the GNE-kinase domain, the relative glycation normalized to Ponceau of the kinase domain is more than twice as high compared to the epimerase domain and not the other way around. This could perhaps be a first indication of a higher responsiveness/vulnerability of the kinase to glycation.
Based on the elevated MGO/GO-levels in diabetic patients [142,143], it might be interesting to investigate whether there are patients who suffer from both diseases—GNEM and diabetes mellitus (type I) and whether these patients develop the disease earlier (statistically) than patients suffering from GNEM alone.
One-step further, one could also split up all so far known mutations, into the groups: Mutation introduces a new potential glycation site,Mutation has no effect on the number of potential glycation sites (both amino acids lead to a potential glycation site),Mutation deletes a potential glycation site.
This could then be used, for example, to examine whether different MGO/GO blood levels can be detected in the individual groups.
Our preliminary analysis of the mutations listed on UniProt revealed 19 mutations that lead to a reduction in a potential glycation site (see Supplementary Table S18A). These mutations were mainly found in the epimerase domain and $31\%$ of them are predicted to be benign. In addition, five mutations were found in which one amino acid causing a potential glycation site was exchanged for another, resulting in a new potential glycation site (see Supplementary Table S18B). Interestingly, in all five cases, an arginine was exchanged for a cysteine. Again, these mutations were predominantly found in the epimerase domain and are mostly damaging or pathogenic; the one found in the kinase domain was probably benign. Ten mutations were found that added an additional potential glycation site, $20\%$ in the epimerase domain and $80\%$ in the kinase domain. These mutations were mainly malignant ($70\%$), $20\%$ uncertain, and $10\%$ benign. This may lead one to hypothesize that adding a potential glycation site is more likely to result in a malignant mutation—greater impact on the protein—than removing a potential glycation site. However, this needs to be verified further, e.g., in in vitro experiments on mutated versions of the protein. In addition, the conclusions/hypotheses of this preliminary mutation analysis can be linked to the other results in this study. Therefore, it seems fitting that adding an extra glycation site would have such a big impact on the kinase domain, which we have previously shown to be more responsive to glycation than the epimerase domain.
Furthermore, there are genetic mutations within one family that result in different individual risks for disease progression and onset [12,103,144]. Since no genetic differences can be associated with the interindividual variability in the development of the disease, a possible difference in their lifestyles including different/other exposure of GNE to glycation agents can be considered. This could possibly explain the very different courses of the different mutations.
It is already known that mutations in the GNE gene lead to the development of GNEM. Besides the introduction/deletion of glycation sites, mutations can also lead to changes at different structural levels of the protein [50,52] or to alterations in the interaction of substrate and protein [52]. Our experiments should help to see whether age might also affect the onset and progression of GNEM.
Based on our experiments, which showed reduced activities for the glycated GNEM, one could hypothesize that glycation, in addition to the effects that mutations already have on the enzyme activity [50,51], causes the activity to drop to such a level that it can no longer be compensated for (Figure 9). This hypothesis assumes a mutated GNE gene causes a mutated GNE enzyme that appears to work adequately for a certain period of time/the activity of the enzyme is sufficient for this period. Further, we would assume a constant decrease in activity, which is due to an increasing accumulation of PTMs on the enzyme, therefore assuming that these PTMs would also have a negative effect on the activity of the enzyme (proven in this study). This would lead to reaching and exceeding the tilting point, which in turn leads to the onset of the disease.
In the case of one aforementioned diabetes mellitus and GNEM patient, the higher MGO/GO levels could increase the likelihood of reaching the required number of PTMs faster; scheme in Figure 9: the slope of the “activity triangle” increases, causing the tilting point to shift further to the left (Figure 9; towards younger years).
Nevertheless, the whole hypothesis has yet to be verified, for example, by comparing the activity of mutated enzymes with the activity of mutated and glycated enzymes, or by a study that initially deals with whether a glycated GNE can be detected at all—in cell culture and in vivo—to rule out that it is just an artefact that does not exist in reality.
Only the individual domains of the GNE were examined in this study because the purification of the GNE as whole is still quite challenging. One critical point seems to be the removal of chaperone proteins [145]. Furthermore, our approach enables us to better estimate the individual effects of glycation of the two domains of the GNE. The overall effect of glycation on the protein still stays unknown, and it is also uncertain whether all the glycation sites that can be glycated when treated individually, are also accessible in the overall protein in such a way that glycation is also possible at these sites. What remains significant, however, is the fact that glycation of the individual domains leads to reduced enzyme activities; the effect on the GNE-kinase domain seems to be stronger than on the GNE-epimerase domain. In a next step, these experiments could be repeated on the overall protein.
Further, it would also be interesting to investigate the downstream effects of a glycated GNE in subsequent cell culture experiments. For example, one could check whether influences in sialylation are detectable. The result could then be compared with the assumption that hyposialylation play a role in GNEMs pathophysiology [16,17,18].
Furthermore, PTMs such as phosphorylation or O-GlcNAcylation were not considered here, although it has already been shown that these can also have an effect on activity [85,86]. However, O-GlcNAcylation and phosphorylation represent a special type of PTMs; they are enzymatic modifications [146,147]. Therefore, at least in the part of the study where only the protein was treated with the glycation agent, we exclude an influence of these types of PTMs on the activity of the GNE, since the enzymes required for them are missing.
In addition, however, it is quite possible that the glycation reagents react with the amino acid side chains of the protein in a different way than previously described in this study (see, e.g., Figure 3A), thereby generating a different type of PTM. As an example, lysine side chains could also be acylated by α-dicarbonyls as GO leading, e.g., to N6-glycoloyllysine (GALA; [148,149]). The extent to which acylation occurs in our proteins/protein domains of interest and what impact acylation can have on protein activity needs to be investigated in future studies.
Another interesting point was that the activity of the GlcNAc kinase, which, at least from just looking at its reaction schemes, appears to have the potential to stand in for the GNE kinase when needed, was not affected by GO or MGO and so seems to be resistant towards glycation—at least at the concentrations tested.
However, since there are amino acids in the sequence of the GlcNAc kinase that can be glycated (see Figure 2Ca and Table 1) as well as structural similarities to the GNE kinase, other reasons must be found why this kinase appears to be resistant to glycation—under the given conditions. The different glycation resistance found in the two kinases could also just be an artefact due to the individual examination of the GNE kinase domain. Thus, it is possible that the GNE kinase exhibits the same glycation resistance as the GlcNAc kinase when examined as whole enzyme, together with the epimerase domain. A possible steric shielding of glycation sites by the epimerase domain could be a reason for this. All of this shows once again how important it would be to study the protein as a whole.
Another cause for the different behavior of the GlcNAc kinase could be that the investigated substrate ManNAc is not its main substrate [40,42]. It was already shown that the choice and availability of the substrate have an influence on the GlcNAc kinase activity, so only GlcNAc protects the GlcNAc kinase against cysteine modifiers and not ManNAc (MalNEt + GlcNAc: $70\%$ ± $4\%$ (remaining activity) whereas MalNEt + ManNAc: $2\%$ ± $1\%$ (remaining activity); [42]). This suggests that the binding and stabilization of ManNAc in the active site of the GlcNAc kinase differs from GlcNAc, and maybe the interaction partners/amino acids involved are less sensitive towards glycation. Therefore, it might be interesting to repeat the GlcNAc activity assay with GlcNAc as substrate and MGO/GO as the glycation agent so that one can compare the enzyme activity found there with the activity found with ManNAc as substrate.
In the case that the GlcNAc kinase also shows a resistance to glycation with GlcNAc as substrate, it might be interesting to see how this might help in the treatment of GNEM. However, the biggest problem with seeing GlcNAc kinase as a possible replacement/substitute for the GNE-kinase is that the protein is not expressed in human skeletal muscle [150], which would make it impossible to function as a bypass in what are likely to be the most affected cells. On the other hand, reactivating GlcNAc kinase protein expression could be an interesting target in obtaining a functioning bypass for a non-/not-sufficient-working GNE-kinase domain. The RNA is at least expressed in the corresponding muscle cells [150,151]. Having said that, one should not disregard that GlcNAc kinase expression seems to play a role in certain types of human cancer [152].
Furthermore, it might also be interesting to investigate whether glycation can be reversed and thus the activity of the enzyme can be restored or at least increased back to a certain level (the pre-onset level). Fructosamine-3-kinase (FN3K; [153]) is such a protein that is able to de-stabilize fructosamines—glycation products, based on the reaction between glucose and an amino group—through phosphorylation and thus ultimately leads to their cleavage from the protein [154]. Therefore, it would be interesting to see if a correlation between FN3K and GNEM could be found—as it exists, for example, in relation to diabetes mellitus [155,156]. A recent study investigating the effect of ex vivo intravitreal FN3K injections on AGE-based cataracts seems to be quite promising as a potential new treatment of certain types of cataracts [157]. Therefore, it might be good to see if glucose-derived glycation products play a role in GNEM and whether activating the FN3K in muscle cells—maybe it might be possible to inject FN3K-solutions directly into the muscle (i.m.)—would remove these glycations, and so would raise the activity of the GNE back to the pre-onset level.
Based on the aim to reduce or prevent glycations, one could also perform a correlation analysis of enzymes of the glyoxalase system and GNEM. The glyoxalase system is a system that can convert MGO into D-lactic acid [158,159]. Therefore, maybe upregulations of glyoxalase I could help to prevent the GNE from being glycated by MGO, which is a quite ubiquitous molecule in the human body, be it through ingestion through diet, or as byproduct of glycolysis, amino acid, and fatty acid degradation [114,115,116,158]. However, higher expression of glyoxalase I seems to be correlated with psychiatric problems such as anxiety [160,161], something to keep in mind, if such a study should ever be tested as a GNEM treatment.
Altogether, our study showed that glycations with the glycation agents MGO and GO have a negative effect on the activity of the GNE-epimerase and GNE-kinase domain, but not on the GlcNAc kinase. Furthermore, GlcNAc kinase expression can be altered by GO in C2C12 cells, but not by MGO. However, none of the glycation agents tested altered the expression of the GNE.
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|
---
title: 'The Impact of BMI Changes on the Incidence of Glomerular Hematuria in Korean
Adults: A Retrospective Study Based on the NHIS-HEALS Cohort'
authors:
- Yu-Jin Kwon
- Mina Kim
- Hasung Kim
- Jung Eun Lee
journal: Biomedicines
year: 2023
pmcid: PMC10046077
doi: 10.3390/biomedicines11030989
license: CC BY 4.0
---
# The Impact of BMI Changes on the Incidence of Glomerular Hematuria in Korean Adults: A Retrospective Study Based on the NHIS-HEALS Cohort
## Abstract
Obesity and recurrent hematuria are known risk factors for chronic kidney disease. However, there has been controversy on the association between obesity and glomerular hematuria. This study aimed to investigate the association between body mass index (BMI) and weight change and recurrent and persistent hematuria in glomerular disease using a large-scale, population-based Korean cohort. Data were collected from the National Health Insurance Service-National Health Screening Cohort. Cox proportional hazards regression analysis was used to calculate hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for recurrent and persistent hematuria in glomerular disease according to the BMI group. Compared with the BMI 23–25 kg/m2 group, the HR ($95\%$ CI) for incident recurrent and persistent hematuria in glomerular disease was 0.921 (0.831–1.021) in the BMI <23 kg/m2 group, 0.915 (0.823–1.018) in the BMI 25–30 kg/m2 group, and 1.151 (0.907–1.462) in the BMI ≥30 kg/m2 group. Compared with the stable weight group, the HRs ($95\%$ CIs) for incident recurrent and persistent hematuria in glomerular disease were 1.364 (1.029–1.808) and 0.985 (0.733–1.325) in the significant weight loss and gain groups, respectively. Despite adjusting for confounders, this result remained significant. Baseline BMI was not associated with the risk of incident recurrent and persistent hematuria in glomerular disease. Weight loss greater than $10\%$ was associated with the incidence of recurrent and persistent hematuria in glomerular disease. Therefore, maintaining an individual’s weight could help prevent recurrent and persistent hematuria in glomerular disease in middle-aged and older Korean adults.
## 1. Introduction
Hematuria is the presence of red blood cells (RBCs) in the urine. These RBCs originate from the glomeruli, renal tubules, interstitium, and urinary tract [1]. The presence of glomerular hematuria is a marker of glomerular filtration barrier dysfunction or damage and results in RBCs with irregular shapes in the urine [2,3]. The prevalence of glomerular hematuria in the general population ranges from $0.18\%$ to $38.7\%$ [3]. A Korean study using data from Korea’s national health nutrition examination survey reported that $31.8\%$ of participants aged 10 years and older had isolated hematuria [4]. Recent research suggested that glomerular hematuria promotes oxidative stress, inflammation, and kidney injury [3]. Vivante et al. reported that persistent microscopic hematuria in young adults was significantly associated with the incidence of end-stage renal disease (ESRD) over a 22-year follow-up period [5].
IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide and accounts for half of the glomerular diseases diagnosed by renal biopsy conducted after microscopic hematuria is detected via urinary screening programs [6,7]. Although hematuria is not a specific characteristic of IgAN, recurrent and persistent hematuria is one of the typical symptoms.
Several studies have reported that a high body mass index (BMI) induces enlargement and ultrastructural modification of glomeruli [8]. Obesity leads to the development of kidney disease via direct and indirect mechanisms [9]. Adiposity affects the kidney directly by altering adipokines, such as leptin, resistin, and adiponectin, increasing inflammatory cytokines; oxidative stress; insulin resistance; and activating the renin-angiotensin-aldosterone system [10]. Increased renal metabolic demands with increased body weight result in glomerular hyperfiltration, hypertrophy, and focal or segmental glomerulosclerosis [11]. Obesity also indirectly affects kidneys through the development of metabolic syndrome, diabetes, hypertension, and dyslipidemia [12]. Previous studies suggested that reducing body weight could improve the outcomes of kidney diseases [13,14]. A single-center cohort study [15] reported that low BMI is a significant risk factor for kidney disease progression in IgAN. Another study showed that weight loss is associated with worse outcomes in patients with chronic kidney diseases (CKD) [16]. However, some studies did not find any association between BMI and IgAN progression [17,18,19]. Currently, the literature shows inconsistent results on associations between BMI and IgAN progression. Furthermore, the role of body weight changes in the incidence of IgAN or glomerular disease with hematuria is still unclear.
Thus, this study aimed to investigate the association between baseline BMI and BMI changes and the incidence of recurrent and persistent hematuria in glomerular disease.
## 2.1. Data Source
Data were collected from the Korean National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) from 2002 to 2015 [20]. Detailed information about the NHIS-HEALS was previously published [20]. The NHIS-HEALS is the national health screening program of Korea, which is designed to prevent and detect non-communicable diseases and health risk factors. The screening program was conducted at least every 2 years among participants aged between 40 and 79 years from 2002 and followed up through 31 December 2015. The Institutional Review Board (IRB) of Yongin Severance Hospital approved the present study (IRB No: 9-2020-0009), which was conducted according to the guidelines of the Declaration of Helsinki.
## 2.2. Study Population
To diminish any potential effects, participants who underwent health screening in 2002 were excluded. The results on the presence of hematuria by urine dipstick were collected until 31 December 2008. In total, 486,055 participants who underwent their first health screening from 1 January 2003 to 31 December 2008 were included in this study. The follow-up period was from 1 January 2003 to 31 December 2015. Individuals who [1] had proteinuria/hematuria, trace or above, by urine dipstick ($$n = 59$$,902); [2] were diagnosed with recurrent and persistent hematuria in glomerular disease (10th edition of the International Classification of Diseases [ICD-10] codes N02.0–N02.8) ($$n = 231$$); [3] were diagnosed with malignant neoplasia (ICD-10 codes C), CKD, or ESRD (ICD codes N180, N181, N182, N183, N184, N185, N189) ($$n = 16$$,318); and [4] had missing data for age, sex, BMI, or other variables ($$n = 23$$,162) at the time of enrollment were excluded. Finally, 386,422 participants were included. The data management is described in Figure 1.
## 2.3. Definition of Recurrent and Persistent Hematuria
The outcome of this study was recurrent and persistent hematuria in glomerular disease. Data of participants who were diagnosed with recurrent and persistent hematuria as those who were newly diagnosed with ICD codes N02.0–N02.8 from 1 January 2003 to 31 December 2015 were analyzed. These codes are representative codes for IgAN in Korea. The details of the ICD-codes are as follows: N02.0, recurrent and persistent hematuria with minor glomerular abnormality; N02.1, recurrent and persistent hematuria with focal and segmental glomerular lesions; N02.2, recurrent and persistent hematuria with diffuse membranous glomerulonephritis; N02.3, recurrent and persistent hematuria with diffuse mesangial proliferative glomerulonephritis; N02.4, recurrent and persistent hematuria with diffuse endocapillary proliferative glomerulonephritis; N02.5, recurrent and persistent hematuria with diffuse mesangiocapillary glomerulonephritis; N02.6, recurrent and persistent hematuria with dense deposit disease; N02.7, recurrent and persistent hematuria with diffuse crescentic glomerulonephritis; and N02.8, recurrent and persistent hematuria with other morphologic changes.
## 2.4. Classification of BMI at Baseline and Definition of BMI Changes
BMI was calculated as body weight divided by height squared (kg/m2). Baseline BMI was defined as BMI measured at the first health examination from 2003 to 2008. Baseline BMI was categorized into four groups according to the 2018 Korean Society for the Study of Obesity Guidelines: <23 kg/m2, 23 to <25 kg/m2, 25 to <30 kg/m2, and ≥30 kg/m2 [21]. Reference BMI was set at 23–25 kg/m2.
The mean follow-up duration was 6 years. Follow-up BMI was defined as the last checked BMI within 6 years. The BMI change was calculated as the nearest BMI at the end of the follow-up minus the baseline BMI. Patients who had missing follow-up data for BMI within 6 years and those who were diagnosed with recurrent and persistent hematuria in glomerular disease within 6 years were excluded. The flow chart is presented in Supplementary Figure S1. In this analysis, only the incidence of recurrent and persistent hematuria in glomerular disease that occurred after 6 years was considered. The BMI changes during the follow-up period were defined as follows: (follow-up BMI-baseline BMI) ×100/baseline BMI. The BMI changes were categorized into quartiles: <−$3.33\%$ (Q1), −3.33–$0\%$ (Q2), 0–$3.59\%$ (Q3), and ≥$3.59\%$ (Q4).
Moreover, BMI changes were classified into three groups: weight loss (<−$10\%$), stable weight (−$10\%$ to < $10\%$), and weight gain (≥$10\%$) groups. The reference groups were set as 0–$3.59\%$ (Q3) or −$10\%$ to <$10\%$.
## 2.5. Variables
The variables included in the analysis were age, sex, blood pressure (BP), blood glucose level, total cholesterol level, smoking status, alcohol consumption, physical activity, hypertension, diabetes, dyslipidemia, and income status. For these variables, the values measured at the first screening were considered. For the smoking status, the participants were categorized as non-smokers, ex-smokers, or current smokers. Alcohol consumption was categorized into three groups: “rare”, less than twice per month; “sometimes”, twice per month to twice per week; and “often”, more than twice per week. Physical activity level was categorized into three groups: ‘rare’, did not exercise; ‘sometimes’, exercised 1–4 times per week; and “regular”, exercised more than five times per week. Economic status was categorized into three groups based on individual income percentile: “low”, 0–30th percentile; “middle”, 40–70th percentile; and “high”, 80–100th percentile. Hypertension was defined as ICD I10, I11, I12, I13, I15; diabetes, ICD E10–14; and dyslipidemia, ICD E78.
## 2.6. Statistical Analysis
All data are presented as the number of participants (%) or the mean ± standard deviation as appropriate for each variable. To compare the baseline characteristics across the BMI groups, a one-way analysis of variance was used for continuous variables, and the chi-squared test was used for categorical variables. A Cox proportional hazard spline plot was used to assess associations between baseline BMI and BMI changes and the incidence of recurrent and persistent hematuria. The incidence per 1000 person-years was calculated for each group. Cox proportional hazards regression models were used to determine the hazard ratio (HR) and $95\%$ confidence interval (CI) for the incidence of recurrent and persistent hematuria in glomerular disease. An age and sex-adjusted model was set. All multivariable models were adjusted for smoking, alcohol drinking, physical activity, hypertension, diabetes, and dyslipidemia. All p-values were two-sided, and p-values < 0.05 were considered statistically significant. All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R version 3.5.2 (The R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org/ (accessed on 28 February 2022)).
## 3. Results
Table 1 shows the demographic characteristics of the study cohort stratified by BMI category. Those with higher BMI were older, more likely to be women, and had a higher mean systolic BP, diastolic BP, fasting blood glucose, and total cholesterol. Those with lower BMI were more likely to be current smokers, consume more alcohol, and have a higher household income status.
During the mean follow-up of 6 years, there were 2254 incidents of recurrent and persistent hematuria in glomerular disease events. The incidence in the BMI < 23 kg/m2 group was $\frac{0.50}{1000}$ person-years; BMI 23–25 kg/m2, $\frac{0.54}{1000}$ person-years; BMI 25–30 kg/m2, $\frac{0.50}{1000}$ person-years; and BMI ≥ 30 kg/m2, $\frac{0.63}{1000}$ person-years. A multivariable Cox proportional hazards regression model was conducted to determine the relationship between BMI and the incidence of recurrent and persistent hematuria in glomerular disease (Table 2).
Compared to the BMI 23–25 kg/m2 group, the HR ($95\%$ CI) for incident recurrent and persistent hematuria in glomerular disease was 0.921 (0.831–1.021) in the BMI < 23 kg/m2 group; BMI 25–30 kg/m2 group, 0.915 (0.823–1.018); and BMI ≥ 30 kg/m2, 1.151 (0.907–1.462). The model was adjusted for age and sex; however, there were no significant associations between BMI and the incidence of recurrent and persistent hematuria. Furthermore, after adjusting for age, sex, smoking, alcohol drinking, physical activity, hypertension, diabetes, and dyslipidemia, the results remained insignificant. Figure 2A shows a spline curve for incident recurrent and persistent hematuria in glomerular disease according to baseline BMI.
Table 3 shows the independent relationship between BMI change quartiles and incident recurrent and persistent hematuria in glomerular disease, presented as HRs and $95\%$ CIs. Compared to Q3 (0–$3.59\%$), HRs ($95\%$ CI) for incident recurrent and persistent hematuria in glomerular disease were 1.204 (1.017–1.427) in Q1, 1.139 (0.955–1.360) in Q2, and 1.056 (0.887–1.257) in Q4. After adjusting for age, sex, smoking, alcohol drinking, physical activity, hypertension, diabetes, and dyslipidemia, the significant results were attenuated (Q1 (<−$3.33\%$) vs. Q3 (0–$3.59\%$); HR and $95\%$ CI = 1.147 (0.968–1.359). Compared to the stable weight group (−10–$10\%$), the HRs ($95\%$ CI) for incident recurrent and persistent hematuria in glomerular disease were 1.364 (1.029–1.808) and 0.985 (0.733–1.325) in the weight loss (<−$10\%$) and gain groups (≥$10\%$), respectively. The model was adjusted for the same confounders, and a significant association between weight loss and incident recurrent and persistent hematuria was observed. Figure 2B shows a spline curve for incident recurrent and persistent hematuria in glomerular disease according to BMI changes. Greater weight loss was linearly associated with incident recurrent and persistent hematuria in glomerular disease.
## 4. Discussion
Although hematuria has been considered a representative symptom of renal disease, its role in the progression of kidney disease has received less attention than that given to proteinuria [22]. Recent studies have suggested that glomerular hematuria has a pathological role in promoting acute kidney injury (AKI) and progression to CKD through its nephrotoxic action of hemoglobin, heme inflammation, and oxidative stress [22,23]. Several epidemiological studies have reported that persistent hematuria is associated with an increased risk of renal function decline and ESRD during long follow-up periods [5,24,25]. Considering the important clinical implications of hematuria, it is important to investigate the effects of BMI on recurrent and persistent hematuria in glomerular disease. However, there are still conflicting results about the association between BMI and glomerular hematuria. Further large-scale epidemiologic studies are required to clarify the association between BMI and the incidence of recurrent and persistent hematuria in glomerular disease.
This study investigated the association between BMI and recurrent and persistent hematuria in glomerular disease diagnosed with ICD-10 codes recorded directly by clinicians using the large, population-based Korean National Health Insurance Service-National Health Screening Cohort. This is the first study to investigate the null association between BMI and recurrent and persistent hematuria in glomerular disease using a large-scale cohort. Particularly, it was found that weight loss greater than $10\%$ was associated with recurrent and persistent hematuria.
Experimental evidence suggests that obesity has a role in the formation and progression of some glomerular lesions [26,27], but data for human glomerulonephritis are still insufficient. Previous studies have shown that a BMI ≥25 kg/m2 was a risk factor for IgAN progression [28,29]. A study with 43 IgAN patients reported that a BMI ≥25 kg/m2 was a significant predictor of disease progression [28]. Another study with 193 Japanese IgAN patients showed that even a slightly high BMI is a risk factor for disease progression [29]. In a recent cross-sectional study with 537 patients [30], the obese group was significantly associated with higher mesangial matrix expansion scores compared to the normal weight/overweight group ($$p \leq 0.020$$). In a cohort of 331 patients with biopsy-proven IgAN [17], patients with BMI greater than 25 kg/m2 had worse clinical outcomes; however, there was no direct association between BMI and IgA progression. The studies were conducted on patients who had pre-existing IgAN, while this study investigated the effect of BMI on new-onset IgAN in the general population.
Unlike previous studies, this study found that BMI status was not associated with the incidence of recurrent and persistent hematuria in glomerular disease. In the current study, BMI was divided into four groups (<23 kg/m2, 23–25 kg/m2, 25–30 kg/m2, and ≥30 kg/m2). There was no difference in the results when further analysis was conducted by dividing the BMI into various criteria, such as BMI 18.5 kg/m2 (cut-off). One of the possible reasons was the attributes of BMI. Traditionally, BMI is an indicator of obesity, but this study was unable to distinguish between body fat and muscle mass [31]. Additionally, BMI could not represent regional fat distribution. Further, obesity itself may affect the prevalence of glomerulopathy [32], exacerbating IgAN that has already occurred; however, obesity may have no impact on the incidence of hematuria in glomerular disease. More studies are needed using other indicators, such as body fat and waist circumference. In the current study, data on waist circumference were missing, and, therefore, it was excluded from further analysis.
Although BMI has limitations in indicating body components, it is commonly used as a marker in the assessment of nutritional status [33]. Several studies have reported the obesity paradox in populations with chronic heart failure [34], chronic obstructive pulmonary disease [35], and CKD [36,37]. The obesity paradox is likely to be explained by the fact that weight loss and physical frailty are associated with mortality in patients with chronic heart failure, chronic lung disease, and CKD [37]. The Chronic Renal Insufficiency Cohort Study, a multicenter prospective cohort study, showed that weight loss was associated with a $54\%$ higher risk of death after dialysis therapy initiation in the stable weight group [16]. Ouyang et al. [ 15] showed that the incidence of ESRD in the underweight (<18.5 kg/m2) group was higher than that in other BMI groups during a 47.1-month follow-up period. Unintentional weight loss, which might reflect nutritional deficit, has been reported as being associated with the progression of CKD [38]. Similar results were observed in the current study. This study found that the reduced BMI group (Q1 <−$3.33\%$ during the follow-up period) was associated with an increased risk of recurrent and persistent hematuria in glomerular disease. This association was attenuated after adjusting for confounding variables. However, interestingly, significant weight loss (greater than $10\%$ during the follow-up period) was associated with an increased risk of recurrent and persistent hematuria in glomerular disease. The result remained significant even after adjusting for the same confounders.
This study has several limitations. First, the major limitation was that renal biopsy results were unobtainable. Since data from the NHIS-HEALS were used, recurrent and persistent hematuria in glomerular disease was defined using ICD-10 codes (N02.0–N02.8). Therefore, it was not possible to determine whether physicians recorded diagnostic codes based on renal biopsy results. According to a previous study conducted in Korea, among patients with asymptomatic isolated microscopic hematuria, IgAN was the most common cause, followed by idiopathic mesangial proliferative glomerulonephritis, with prevalence rates of $46.9\%$ and $43.1\%$, respectively. IgAN, the most common cause of recurrent hematuria, is usually recorded in young adults during school urine screening tests [7]. However, the participants from NHIS-HEALS were middle-aged and older adults. Therefore, the incidence of IgAN could be lower than that previously reported [39]. Second, diseases commonly associated with recurrent hematuria, such as inflammatory conditions of the urethra, bladder, and prostate and malignancies that developed during the follow-up period, were not completely ruled out. Third, although we found that weight loss was associated with the incidence of recurrent and persistent hematuria, we could not determine the intentionality of weight loss in the cohort study. Unintentional weight loss is an involuntary decline in body weight over time and could reflect chronic diseases, psychological factors, social conditions, and undiagnosed illnesses [40]. In addition, only the association between BMI changes within 6 years and incident recurrent and persistent hematuria in glomerular disease was observed. Annual BMI changes could be a better predictor for incident recurrent and persistent hematuria in glomerular disease. Fourth, adiposity itself, directly and indirectly, affects the kidney through its endocrine activity, such as the production of adiponectin, leptin, and other inflammatory cytokines [5]. However, BMI could not fully reflect the amount of adiposity because the definition of BMI did not distinguish muscle from fat mass. Finally, this study included middle-aged and older Korean adults; therefore, there could have been a selection bias. Therefore, the results from this study cannot be generalized to other races/ethnicities. Further research is required to establish the association between body weight and recurrent and persistent hematuria in glomerular disease by considering the body composition data for young adults and annual changes in BMI. Finally, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers are widely used in hypertension and kidney disease. However, the effect of these drugs could not be determined. Despite these weaknesses, this is the first study to investigate the impact of BMI and BMI changes on recurrent and persistent hematuria in glomerular disease using large-scale, population-based data. Although the results based on renal biopsy could not be determined, ICD-10 codes were used, which the physicians directly coded during the medical care process.
## 5. Conclusions
Baseline BMI was not associated with the incidence of recurrent and persistent hematuria in glomerular disease. Significant weight loss greater than $10\%$ was associated with the incidence of recurrent and persistent hematuria in glomerular disease. Maintaining individuals’ BMI could be a preventive strategy for the incidence of recurrent and persistent hematuria in glomerular disease in middle-aged and older Korean adults. Moreover, studies based on renal biopsy are required to determine these associations.
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|
---
title: Simultaneous and Sensitive Detection of Three Pesticides Using a Functional
Poly(Sulfobetaine Methacrylate)-Coated Paper-Based Colorimetric Sensor
authors:
- Jingyang Zhu
- Xinru Yin
- Weiyi Zhang
- Meilian Chen
- Dongsheng Feng
- Yong Zhao
- Yongheng Zhu
journal: Biosensors
year: 2023
pmcid: PMC10046087
doi: 10.3390/bios13030309
license: CC BY 4.0
---
# Simultaneous and Sensitive Detection of Three Pesticides Using a Functional Poly(Sulfobetaine Methacrylate)-Coated Paper-Based Colorimetric Sensor
## Abstract
Chlorpyrifos (CHL), profenofos (PRO) and cypermethrin (CYP) are widely used in combination to increase crop yields. However, these three pesticides can cause serious harm to human health and do not easily degrade. In this study, a novel visible paper sensor has been prepared successfully and different colorimetric reactions were utilized to detect the three pesticides simultaneously. The sensor was constructed by grafting a zwitterionic polymer onto a cellulose filter (CF) and placing it on a glass surface modified with PDMS. The branch shape was designed to form multiple detection areas, which were modified with specific pesticides and corresponding chromogenic reagents. The as-prepared colorimetric platform exhibited high sensitivity, a short detection time, a good linear response and a low detection limit (LOD) for the three pesticides (chlorpyrifos: $y = 46.801$ − 1.939x, R2 = 0.983, LOD = 0.235 mg/L; profenofos: $y = 40.068$ + 42.5x, R2 = 0.988, LOD = 4.891 mg/L; cypermethrin: $y = 51.993$ + 1.474x, R2 = 0.993, LOD = 4.053 mg/L). The comparison of the results obtained by the proposed paper sensor and those obtained by spectrophotometry further revealed the stability and reliability of the paper sensor. In particular, the color intensity of the interaction between the pesticides and coloring agents could be directly observed by the human eye. The consistency of the colorimetric/optical assay was proven in real target pesticide samples. Thus, this sensing strategy provides a portable, cost-effective, accurate and visualized paper platform, which could be suitable for application in the fruit and vegetable industry for monitoring CHL, PRO and CYP in parallel.
## 1. Introduction
Pesticides are commonly used in the production of vegetables, fruits and grains, because most of them are highly effective and less resistant. However, it cannot be ignored that the widespread use of pesticides could lead to pesticide residues over the maximum residue limit (MRL) according to relevant standards. Excessive pesticide residues in the ecosystem will cause serious pollution to water and soil, thus affecting human safety [1,2,3]. Therefore, it is indispensable to develop a simple and effective method for detecting pesticide residues in agricultural products [4]. In recent years, there were some efficient analytical methods developed to detect pesticide concentrations in food and water, including gas chromatography (GC) [5,6], high-performance liquid chromatography (HPLC) [7] and gas chromatography–tandem mass spectrometry (GC/MS) [8]. Although these analytical technologies display higher sensitivity and accuracy, they still have drawbacks, such as complex operations and expensive equipment, which greatly limit the real-time detection of pesticide residues.
Since the first microfluidic paper-based analytical devices (μPAD) reported by Whitesides [2008] for biosensing, paper devices have exhibited the anticipated performance in point-of-care testing fields [9,10,11]. This method provides tremendous advantages in terms of cost-effectiveness, high throughput and fast response times [12,13]. Meanwhile, it can be integrated with colorimetry to establish a visual device, which facilitates the development of portable devices for in-field food analysis [14,15]. With the development of compound pesticides, the variety of pesticides on a single sample is increasing [16]. For example, chlorpyrifos (CHL), profenofos (PRO) and cypermethrin (CYP), which are frequently used in combination to improve crop resistance in weeds and insect pests for increasing crop yields, should be tested simultaneously [17]. These three pesticides are highly toxic and do not easily degrade. The residues of them on food will cause pollution to the environment and pose threats to the human immune and nervous systems [18,19,20,21]. Due to this concern, maximum residue limits for these three pesticides in various crops have been established in many countries [22]. According to the China National Standard System (GB 2763-2021), the maximum residual limits of chlorpyrifos, profenofos and cypermethrin in fruits and vegetables are 3 mg/kg (<0.235 mg/L), 10 mg/kg (<4.891 mg/L) and 7 mg/kg (<4.053 mg/L), respectively, which are all larger than the detection limits of the sensor. The use of μPAD makes it possible to detect different types of compound pesticide residues, which greatly improves the detection efficiency and reduces the cost of analytes. However, bare paper sensors are susceptible to interference caused by non-specific proteins, microbes and biofilms, leading to limited application in complex real samples. Moreover, the sensitivity and quantification ability of bare paper sensors cannot satisfy the requirements of trace detection [23,24]. Therefore, it is of great importance to address this issue for the better realization of on-site testing with high sensitivity.
In fact, the modification of a cellulose filter (CF) with hydrophilic polymers, such as poly (L-lactic acid) [25] and poly (ethylene glycol) [26,27], has been commonly used to improve the permeability of water and suppress the adhesion of contaminants. Recently, zwitterionic polymers, including poly(sulfobetaine methacrylate) (pSBMA), have been proven to be promising anti-fouling hydrophilic materials owing to their excellent performance, with good biocompatibility and high stability [28,29]. Liu et al. [ 30] developed a functionalized thin film using zwitterionic polymers and silver nanoparticles (AgNPs). The obtained membranes showed excellent performance in bacterial inactivation. Sun et al. [ 31] reported a preparation strategy for novel hybrid ultrafiltration membranes via a polysulfone casting solution of UiO-66-pSBMA in a phase inversion method. The ultrafiltration membranes with UiO-66-pSBMA presented a fast exchange rate between solvent and nonsolvent. Li et al. [ 32] summarized the recent studies on the improved hydrophilicity, antifouling and antibacterial properties of various inorganic/organic surfaces grafted with zwitterionic polymers. According to this review, we found that the cellulose filter (CF) with zwitterionic polymers was rarely studied in the sensor detection field.
In this research, we established an economical pSBMA-functionalized colorimetric multichannel paper-based analytical device for the identification and quantitation of three pesticides in complex samples. By using atom transfer radical polymerization (ATRP), pSBMA was grafted onto the surface of a cellulose filter (CF). The pSBMA-CF possesses the advantages of ultra-low fouling and superior hydrophilicity.
The functionalized pSBMA-μPAD could detect analytes in a complicated matrix without a large number of reagents. When the reaction occurred on the device, the error of detection condition and operation were minimized by matching the absorbance and color relative intensity with the three pesticides’ concentrations. Finally, the pSBMA-μPAD was successfully applied to determine the three target pesticides in real vegetable and fruit samples and the reliability was confirmed by spectrum analysis. The proposed pSBMA-μPAD platform may provide a cost-effective, simple and accurate method for the rapid quantitative detection of pesticide content in food.
## 2.1. Materials and Instruments
Whatman filter paper No. 1 (150-mm diameter) was purchased from Whatman International Ltd. (Shanghai, China). Acetylcholinesterase (AChE, 222 units/mg from electric ell), ninhydrin, 4-aminoantipyrine, acetylthiocholine iodide (ATChI), 5,5-dithiobis(2-nitrobenzoic) acid (DTNB), chlorpyrifos, profenofos, cypermethrin, β-propiolactone ($95\%$), copper(II) bromide (CuBr2, $98\%$), cuprous(I) bromide (CuBr, $98\%$) 2-bromisobutyryl bromide (BIBB, $98\%$), 2,2′-bipyridine (BPY), bromoisobutyryl bromide (C4H6Br2O), 11-hydroxy-1-undecanethiol (C11H24OS), triethylamine (TEA, $99\%$), tetrahydrofuran (THF, HPLC grade) and 3-[dimethyl-[2-(2-methylprop-2-enoyloxy) ethyl] azaniumyl] propane-1-sulfonate (SBMA) were obtained from Sigma-Aldrich (Shanghai, China). Dichloromethane (CH2Cl2), methanol (CH3OH), sodium hydroxide (NaOH), ethanol (CH3CH2OH), acetone (CH3COCH3), isopropanol (CH3CHOHCH3), hydrochloric acid (HCl) and ethyl ether (CH3CH2OCH2CH3) were provided by Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Polydimethylsiloxane (PDMS; Sylgard 184) was purchased from Dow Corning. Ammonium acetate (CH3COONH4) and potassium ferricyanide (K3[Fe(CN)6]) were obtained from Aladdin Industrial Inc. (Shanghai, China). Phosphate-buffered saline (PBS) and bovine serum albumin (BSA) were provided by Solarbio (Beijing, China). All solutions used were compounded by ultrapure water.
The FT-IR measurements were performed on an FT-IR spectrometer (Thermo Scientific, Waltham, MA, USA). All XPS spectra were identified by X-ray photoelectron spectroscopy (XPS, Thermo Scientific EscaLab 250Xi, USA). The UV–vis spectrum and absorbance were measured using a UV–vis absorption spectrophotometer (Shimadzu Corporation, UV-vis-2550, Kyoto, Japan).
## 2.2. Preparation of Standard and Reagent Solution
The stock solutions of 1000 mg/L chlorpyrifos, profenofos and cypermethrin were prepared with methanol, respectively. These solutions were diluted with ultrapure water to obtain the diluents with a series of concentration gradients. ATChI (0.2 g) was dissolved in ultrapure water (100 mL) to prepare ATChI solution ($0.2\%$ w/v). AChE (800 units) was dissolved in PBS (100 mL) to prepare AChE solution (8 units/mL). DTNB (0.08 g) was dissolved in PBS (100 mL) to prepare DTNB solution ($0.08\%$ w/v). Potassium ferricyanide (2.0 g) was dissolved in ultrapure water (100 mL) to prepare potassium ferricyanide solution ($2\%$ w/v). 4-aminoantipyrine (1.5 g) was dissolved in ultrapure water (100 mL) to prepare 4-aminoantipyrine solution ($1.5\%$ w/v). Ninhydrin (3 g) was dissolved in ethyl alcohol (100 mL) to prepare ninhydrin solution ($3\%$ w/v). Ammonium acetate (2.5 g) was dissolved in ultrapure water (100 mL) to prepare ammonium acetate solution ($2.5\%$ w/v).
## 2.3. Fabrication of the pSBMA-μPAD
The hydrophilic and hydrophobic interface was manufactured to prevent the interference of non-target substances and improve the stability of the reaction system. The μPAD was prepared as follows (Figure 1a). ① The preparation of the hydrophobic channel: First, the glass surface was modified with allyltrimethoxysilane, and then solidified with PDMS to compose hydrophobic barriers, and finally operated with a laser cutter to create the designed paper shape on the glass. ② The fabrication of pSBMA-coated cellulose filter (pSBMA-CF): The hydrophilic channel was designed by AutoCAD, and it was mainly composed of a dendritic reaction channel (5-mm diameter) and a straight channel (the width was 4 mm and the length was 6mm). By using an ATRP reaction, the pSBMA was grafted onto the surface of the designed paper. The details of the grafting reaction are presented in Figure S1. ③ After the pSBMA-CF was placed on the patterned PDMS channel, the pSBMA-μPAD assay platform was successfully established. To verify whether pSBMA was successfully grafted onto the surface of CF, the changes in the surface’s elemental composition before and after grafting with pSBMA were observed by XPS and FT-IR.
## 2.4. Colorimetric Analysis
Figure 1b shows the method by which the μPAD was used for the simultaneous detection of the three pesticides. Firstly, the pSBMA-μPAD was pretreated with the corresponding buffer solution and chromogenic reagent and dried for 3 min at room temperature. Then, the pesticide detection was performed by adding 2.0 μL of standard solution or real sample to the test zone. After the reaction, ImageJ software was used to obtain the gray intensity of the image. Image processing and data acquisition are explored and discussed in the Supplementary Materials. A linear curve between the mean relative intensity and the corresponding analyte concentration was established, which was employed for the quantitative analysis of the pesticides in real samples.
## 2.5. Real Sample Testing
To evaluate the performance of the established sensor in practical applications, five vegetables were used as test samples to determine the residual amounts of the three pesticides (CHL, PRO and CPY). Meanwhile, nine vegetables were selected to verify the practicability of the method. According to the China National Standard System (GB 23200.93-2016 and GB/T 5009.110-2003), the sample pretreatment was performed. Briefly, each sample (20.0 g) was homogenized and then extracted with acetonitrile (40 mL). After centrifugation, the supernatant (4 mL) was further filtered with a microporous porosity filter (0.22 μm) to avoid the interference of impurities. Each sample was added with two concentrations (10 mg/L and 20 mg/L) of the three pesticides (CHL, PRO and CPY), and tested with the pSBMA-μPAD at least three times.
## 3.1. XPS Measurements
Figure 2a presents the difference in XPS spectra between the bare-CF and pSBMA-CF. It was found that the bare-CF included the peaks of C 1s (286.4 eV) and O 1s (532.8 eV), which agreed well with the elemental composition of CF. There was no signal of N in the bare-CF, while a new peak at 401.9 eV (corresponding to N 1s) was seen in the XPS spectrum of pSBMA-CF. Another new, weak peak at 167.2 eV was assigned to S 2p (Figure S2), which was attributed to the oxidation of S, such as sulfonate (-SO3−) [33]. After graft copolymerization, the XPS C 1s spectrum of pSBMA-CF (Figure 2b) was curved-fitted into four peaks at 284.5, 285.7, 286.3, and 288.9 eV, owing to C-C/C-H, C-N+, C-S, and O-C=O, respectively [34,35,36,37]. According to the XPS data in Table S1, after functionalization, the signal of carbon was increased, while the signal of oxygen decreased, and small amounts of nitrogen ($3.95\%$, N 1s) and sulfur ($3.62\%$, S 2p) were detected. These results indicated that the pSBMA polymer was successfully grafted onto the CF by the ATRP reaction.
## 3.2. FT-IR Analysis
With respect to the FT-IR spectrum of bare-CF and pSBMA-CF, the bare-CF spectrum showed the characteristic peaks highlighted in Figure 3 in purple (wide stretching vibration of -OH at 3337 cm−1, and skeletal vibration of C-H at 2900 cm−1) [38]. Moreover, the FT-IR spectrum of pSBMA-CF (Figure 3 green) not only possessed characteristic peaks with CF, but also showed absorption peaks consistent with pSBMA (Figure 3 red). Compared with the bare-CF, there were two new peaks at 1482 cm−1 and 1720 cm−1 in the FTIR spectra of the pSBMA-CF, which were attributed to the quaternary ammonium and carbonyl groups in SBMA, respectively. The other two absorption peaks at 1041 cm−1 and 1170 cm−1 were assigned to the S=O symmetric stretch and the S=O asymmetric stretch of the sulfonate groups of SBMA, respectively [39]. In short, the FI-TR results confirmed that pSBMA was successfully grafted with bare-CF via ATRP.
## 3.3. Evaluating Hydrophobicity, Hydrophilicity and Sensing Properties
The same amount of water (2 μL) was dropped onto PDMS-modified glass (hydrophobic area) and pSBMA-modified filter paper (hydrophilic area), respectively, to evaluate the hydrophilic and hydrophobic properties of the platform. Figure 4a shows that the water drop placed in the hydrophobic zone formed its expected arc-shaped surfaces with the glass, while the water placed on the filter paper immediately infiltrated the paper as expected. Moreover, the difference in hydrophilicity between pSBMA-CF and bare-CF was compared, by dropping 2 µL of blue ink onto the paper chip to monitor the ink’s movement over time (Figure 4b). Compared with the unmodified CF, the pSBMA-CF exhibited better directional wettability, and the blue ink filled the whole pSBMA-CF in only 20 s. Finally, chlorpyrifos, profenofos and cypermethrin were used to verify whether the pSBMA-modified CF is beneficial in terms of its sensing ability (Figure 4c). For an independent comparison of each nutrient, images were captured after the same number of standard solutions were added to the reaction zone for 3 min. Compared with the bare-CF, the pSBMA-CF displayed prominent color intensity for each pesticide, indicating that the pSBMA-CF can effectively improve the sensing ability.
## 3.4. Reaction Principle of the Three Pesticides
The principle of chlorpyrifos analysis is depicted in Figure 5a. In the presence of acetylcholine molecules, AChE catalyzed the formation of choline, which reacted with DTNB, resulting in the formation of TNB with a yellow color. The unique inhibitory effect of chlorpyrifos on AChE activity decreased the TCh production, and the yellow intensity was gradually reduced to colorless [40]. The higher the concentration of chlorpyrifos, the stronger the inhibition, and then the weaker the intensity of the yellow signal perceived by the sensor. A schematic of the detection of profenofos is displayed in Figure 5b. Profenofos was first hydrolyzed by NaOH to generate 2-chloro-4-bromophenol, which further reacted with potassium ferricyanide and 4-aminoantipyrine to produce a unique red chelate. Ninhydrin, a well-known color reagent for detecting cyanide, is commonly used to identify and quantify cyanide compounds [41]. A schematic of the cypermethrin experiment is depicted in Figure 5c. Under alkaline conditions, cypermethrin was hydrolyzed to generate cyanide, which further decomposed to free cyanide ions. The cyanide ions then reacted with ninhydrin and ammonium acetate to form a purple compound.
## 3.5. Colorimetric Detection for Chlorpyrifos
The buffer pH, time and the concentration of DTNB, ATChI and AChE on pSBMA-μPAD were optimized to achieve the best color reaction. In order to find the solution environment with the highest acetylcholinesterase activity, the pH value was adjusted from 6 to 10. As can been observed in Figure 6a, the mean relative intensity of μPAD was proven to be the optimal at pH = 8. Moreover, we found that the color of this system started to stabilize after 10 min (Figure S3). As a result, the optimal conditions (pH 8, 10 min) were chosen in subsequent experiments. AChE catalyzes the hydrolysis of ATChI, which was the key to transform DTNB into TNB. As shown in Figure 6b, the mean relative intensity increased with the increasing concentration of AChE, and tended to be stable after 6 μg/mL. According to the previous work, the chromogenic reagent had a great effect on color development. The chromogenic reagent was prepared by mixing different concentrations of ATChI (0–2 mmol/L) and DTNB (0–2 mmol/L) in equal volumes (1:1 ratio). The mean relative intensity of both ATChI and DTNB was positively correlated with the concentration, and tended to be stable when the concentration reached 2 mmol/L (Figure 6c,d). Therefore, ATChI (2 mM) and DTNB (2 mM) were selected to pretreat the reaction region to ensure the accuracy of the subsequent experiment. According to the above optimal conditions (pH 8.0, AChE of 6 μg/mL, ATCh of 2 mM, DTNB of 2mM), the correlation equation $y = 46.801$ − 1.939x (y, mean relative intensity; x, chlorpyrifos concentration, mg/L) between the chlorpyrifos concentration and color intensity in the range of 0.1–16 mg/L was obtained, and the correlation coefficient R2 value reached 0.983 (Figure 7a,b). The limit of detection (LOD, S/$$n = 3$$) for chlorpyrifos was 0.235 mg/L. Concurrently, the comparison experiments were accomplished with spectrophotometry, and all data were acquired and analyzed at the characteristic peak (408nm). Under the optimal experimental conditions, a good linear equation ($y = 2.692$ − 0.058x, R2 = 0.999, LOD = 0.091 mg/L) was obtained (Figure 7c,d). Compared with spectrophotometry, the pSBMA-μPAD greatly improves the convenience and the detection is faster.
## 3.6. Colorimetric Detection for Profenofos
To successfully perform this reaction on pSBMA-μPAD, the optimization of the pH, NaOH, potassium ferricyanide and 4-aminoantipyrine was carried out, respectively. As manifested in Figure 8a, the mean relative intensity was almost unchanged when the pH was 7.0–10.0, but the complexation reaction was obviously inhibited when the pH was over 10.0. Therefore, pH 10.0 was selected as the optimal pH for subsequent experiments. In addition, the mean relative intensity increased and achieved the maximum when the NaOH concentration (Figure 8b) reached $6\%$, the potassium ferricyanide concentration (Figure 8c) reached $2\%$ and the 4-aminoantipyrine concentration (Figure 8d) reached $1.5\%$. Moreover, the unique color signal achieved a stable stage after 10 min of reaction (Figure S4). Hence, all the above optimized conditions were selected to pretreat the detection area. On the pretreated area, a linear relationship over the range of 0.08–2 mM was obtained between the chlorpyrifos concentration and color intensity, with a correlation equation of $y = 40.068$ + 42.5x and a correlation coefficient R2 = 0.988 (Figure 9a,b). A detection limit as low as 4.891 mg/L was achieved. Additionally, the analysis of profenofos was confirmed by spectrophotometry, and all data were captured at a 508 nm characteristic peak. Under the optimal experimental conditions (pH 10.0, NaOH $6\%$, potassium ferricyanide $2\%$, 4-aminoantipyrine $1.5\%$), a good linear equation for profenofos ($y = 0.143$ + 3.16x, R2 = 0.998, LOD = 0.118 mg/L) was obtained (Figure 9c,d). All the above results indicate that the pSBMA-μPAD has great potential value in rapid testing.
## 3.7. Colorimetric Detection for Cypermethrin
The hydrolysis system was composed of NaOH solution and organic solvents, and the organic solvents could increase the extent of hydrolyzation. To ensure the full reaction of this experiment on pSBMA-μPAD, the concentration of NaOH and the type of organic solution were optimized. When the NaOH concentration was 0.25 mol/L, the purple signal was the highest (Figure 10a). Moreover, as shown in Figure 10b, compared with ethanol (Eth), methanol (Met), aether (Aet), isopropanol (Iso) and acetone (Ace) could accelerate the hydrolyzation better and resulted in a stronger purple signal. Subsequently, it was found that the color of this system started to stabilize after 20 min of reaction (Figure S5). These experimental conditions (NaOH 0.25 mol/L, acetone, 20 min) were chosen for subsequent color reagent optimization. As seen in Figure 10c,d, with the increasing amounts of ninhydrin and ammonium acetate, the color intensity increased notably and tended to be stable when ninhydrin reached $3\%$ and ammonium acetate was over $2\%$. Consequently, $3\%$ ninhydrin and $2\%$ ammonium acetate were chosen to prepare the color reagent for further tests. According to the above optimal conditions, a good linear response was acquired at the range of 12.0 to 60.0 mg/L, with a correlation equation of $y = 51.993$ + 1.474x, and the correlation coefficient R2 value reached 0.993 (Figure 11a,b). The detection limit of cypermethrin obtained was as low as 4.053 mg/L. Correspondingly, the absorbance data of cypermethrin were collected from the characteristic peak at 570 nm, and a good linear equation ($y = 0.844$ + 0.113x, R2 = 0.985, LOD = 1.17 mg/L) was established (Figure 11c,d). According to the above results, the pSBMA-μPAD not only has excellent detection performance for cypermethrin, but also displays high consistency with the spectrophotometry results.
## 3.8. Application of the μPAD to Detection in Real Samples
To assess the actual detection performance of pSBMA-μPAD in fruit and vegetable samples, five fresh fruits and vegetables (apple, orange, spinach, tomato, cucumber) were selected as samples. The rapid detection of chlorpyrifos, profenofos and cypermethrin in fruits and vegetables was carried out in the form of adding the target. At the same time, a UV–visible spectrophotometer was used to realize the additional verification of the detection results. As shown in Table 1, Table 2 and Table 3, the relative recoveries of pSBMA-μPAD for CHL, PRO and CYP were 93.5–$105.7\%$, 94.5–$108.3\%$ and 90.9–$108.4\%$, respectively. The results were highly consistent with the results obtained by UV–visible spectrophotometry, indicating that pSBMA-μPAD could successfully detect pesticides in fruits and vegetables. Meanwhile, nine vegetables were selected to verify the practicability of the method. The CHL solution, PRO solution and CYP solution were configured at 0, 0.5, 1 and 2 times the detection limit concentration to treat the actual samples. Among them, 0 and 0.5 times were negative samples, and 1 and 2 times were positive samples, which were added to the detection area for the color reaction. Table 4 shows that the system could clearly distinguish negative and positive samples, and it had high accuracy and practicability.
## 4. Discussion and Conclusions
In summary, we have proposed a method for the efficient detection of three commonly used pesticides in fruits and vegetables using a portable and affordable multiplexed pSBMA-μPAD. On the paper-based detection platform, each pesticide was detected by reacting with its own selective substrates. The concentrations of three pesticides were derived from the color intensity using ImageJ software. After the experimental conditions were optimized, the limits of detection (LOD) of CHL, PRO and CYP were 0.235, 4.891 and 4.053 mg/L using the paper-based colorimetric microfluidic device, respectively. The pSBMA-μPAD constructed through the combination of colorimetric methods and a paper sensor is an important step toward the determination and quantification of trace pesticide residues, which was demonstrated using various samples of vegetables. Thus, this cheap and portable paper chip provides a promising method for the on-site assay of pesticide residues with high sensitivity and selectivity.
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|
---
title: Hyperinsulinemic and Pro-Inflammatory Dietary Patterns and Metabolomic Profiles
Are Associated with Increased Risk of Total and Site-Specific Cancers among Postmenopausal
Women
authors:
- Qi Jin
- Ni Shi
- Dong Hoon Lee
- Kathryn M. Rexrode
- JoAnn E. Manson
- Raji Balasubramanian
- Xuehong Zhang
- Marian L. Neuhouser
- Melissa Lopez-Pentecost
- Cynthia A. Thomson
- Suzanna M. Zick
- Ashley S. Felix
- Daniel G. Stover
- Sagar D. Sardesai
- Ashwini Esnakula
- Xiaokui Mo
- Steven K. Clinton
- Fred K. Tabung
journal: Cancers
year: 2023
pmcid: PMC10046106
doi: 10.3390/cancers15061756
license: CC BY 4.0
---
# Hyperinsulinemic and Pro-Inflammatory Dietary Patterns and Metabolomic Profiles Are Associated with Increased Risk of Total and Site-Specific Cancers among Postmenopausal Women
## Abstract
### Simple Summary
We investigated whether dietary patterns of insulinemia, inflammation and overall dietary quality are associated with the risk of total cancer, site-specific cancers, and pathological subtypes among postmenopausal women. We followed 112,468 women, 50–79 years of age, in the Women’s Health Initiative for a median of 17.8 years, documenting 18,768 incident invasive cancers. A higher overall dietary quality was associated with lower risk of total cancer and colorectal cancer. The potential of the dietary pattern to contribute to higher insulinemia and inflammation was associated with greater risk of total cancer, colorectal cancer and more strongly associated with risk of endometrial cancer and breast cancer (including triple negative breast cancer) than overall dietary quality. Additionally, a higher score of metabolites reflecting higher dietary quality was associated with lower lung cancer risk. Dietary patterns associated with cancer risk, therefore, warrant testing in clinical trials for cancer prevention among postmenopausal women.
### Abstract
We evaluated associations of the Empirical Dietary Index for Hyperinsulinemia (EDIH), Empirical Dietary Inflammatory Pattern (EDIP) and Healthy Eating Index (HEI2015) and their metabolomics profiles with the risk of total and site-specific cancers. We used baseline food frequency questionnaires to calculate dietary scores among 112,468 postmenopausal women in the Women’s Health Initiative. We used multivariable-adjusted Cox regression to estimate hazard ratios (HR) and $95\%$ confidence intervals for cancer risk estimation. Metabolomic profile scores were derived using elastic-net regression with leave-one-out cross validation. In over 17.8 years, 18,768 incident invasive cancers were adjudicated. Higher EDIH and EDIP scores were associated with greater total cancer risk, and higher HEI-2015 with lower risk: HRQ5vsQ1($95\%$ CI): EDIH, 1.10 (1.04–1.15); EDIP, 1.08 (1.02–1.15); HEI-2015, 0.93 (0.89–0.98). The multivariable-adjusted incidence rate difference(Q5vsQ1) for total cancer was: +52 (EDIH), +41 (EDIP) and −49 (HEI-2015) per 100,000 person years. All three indices were associated with colorectal cancer, and EDIH and EDIP with endometrial and breast cancer risk. EDIH was further associated with luminal-B, ER-negative and triple negative breast cancer subtypes. Dietary patterns contributing to hyperinsulinemia and inflammation were associated with greater cancer risk, and higher overall dietary quality, with lower risk. The findings warrant the testing of these dietary patterns in clinical trials for cancer prevention among postmenopausal women.
## 1. Introduction
Hyperinsulinemia and sustained inflammation are two proposed mechanisms driving cancer risk [1,2,3]. Dietary patterns that promote chronic hyperinsulinemia and chronic systemic inflammation may affect the risk of developing cancers and serve as modifiable risk factors for cancer prevention. We developed and validated two empirical hypothesis-oriented dietary indices: Empirical Dietary Index for Hyperinsulinemia (EDIH) and Empirical Dietary Inflammatory Pattern (EDIP), which predict the ability of the diet to contribute to insulin hypersecretion or chronic systemic inflammation, respectively. These dietary patterns are data-driven yet based on specific biological hypotheses relating diet with chronic disease [4,5].
EDIH and EDIP scores have shown stronger associations with cancer risk [6,7,8,9,10,11] than traditional dietary patterns in both men and women [12,13]. For example, dietary patterns including the Mediterranean diet and alternative healthy eating index, and other patterns, have not been consistently associated with cancer risk among women [14]. However, in the Nurses’ Health Study (NHS), higher EDIH was associated with a 22–$47\%$ higher risk of developing digestive system cancers [6,7]. Similarly, higher EDIP was associated with cancer risk among women in the NHS [8,15]. Due to advancing age and higher adiposity, postmenopausal women may represent a higher risk group for cancer related to these underlying mechanisms of malignant progression. However, outside of the NHS, these two dietary patterns have not been investigated in association with cancer risk among women.
Nutritional metabolomics may inform on more specific mechanistic pathways linking diet and cancer. Our metabolomics studies in the Women’s Health Initiative (WHI) suggested that patterns of cholesteryl esters(CEs), phospholipids, acylglycerols and acylcarnitines, may reflect the metabolic impact of insulinemic dietary patterns [16], while metabolites associated with coffee and lipid metabolism may reflect the metabolic potential of an inflammatory dietary pattern [17]. Among these metabolites, some have been evaluated for associations with risk of some cancers [18,19]. Although associations of EDIH and EDIP with cancer risk suggest that hyperinsulinemia and inflammation may broadly underlie these associations, the mechanistic pathways warrant investigation, and metabolomics profiles may provide a link to disease risk. The current study evaluated the etiologic role of EDIH and EDIP in relation to risk of total cancer, site-specific cancers and pathological subtypes, while comparing these associations with an established index of overall dietary quality—Healthy Eating Index-2015 (HEI-2015). We also characterized the plasma metabolomics profiles of each of the three dietary patterns and investigated their associations with cancer risk.
## 2.1. Study Population
The WHI enrolled 161,808 postmenopausal women aged 50–79 years between 1993 and 1998 in the United States. The study design has been described [20]. Briefly, the WHI study consisted of a three-component Clinical Trial (CT, $$n = 68$$,132) and Observational Study (OS, $$n = 93$$,676). The CT included a Dietary Modification trial (DM), two Hormone Therapy trials (HT), and a Calcium and Vitamin D trial (CaD). After the exclusions described in Supplementary Figure S1, for each cancer site, we retained 112,468 women for the total cancer analyses. For the metabolomics aim, we used metabolomics data among 2306 participants from a matched case–control study in the WHI (BAA-24)—the Metabolomics of Coronary Heart Disease in the WHI [21]. The WHI protocol was approved by the institutional review boards at the Clinical Coordinating Center at the Fred Hutchinson Cancer Research Center (Seattle, WA, USA) and at each clinical center and all women signed informed consent. WHI is registered at clinicaltrials.gov as NCT00000611.
## 2.2. Dietary Assessment and Calculation of Dietary Indices
Dietary data from baseline—self-administered food frequency questionnaire (FFQ) representing intake in the preceding three months—were used to calculate the dietary indices [22]. The FFQ scanned data were processed with the University of Minnesota Nutrition Coordinating Center food and nutrient database (version 2005) to derive nutrient intakes [22,23]. The development and validation of the EDIH and EDIP scores have been described [4,5], and components of both scores in the WHI FFQ have been described as well [24]. The HEI-2015 measures adherence to the Dietary Guidelines for Americans (DGA) [25]. Supplementary Table S1 shows the food group components of each index.
## 2.3. Ascertainment of Incident Cancer
Study outcomes included total cancer, invasive breast cancer (overall, ER+, ER-, PR+, PR-, HER2+, HER2-, ER- PR- HER2+, luminal A, luminal B, triple negative, invasive ductal carcinoma, invasive lobular carcinoma), colorectal cancer (colon, proximal colon, distal colon and rectum), non-cancerous intestinal polyps, endometrial cancer (overall, endometrioid, non-endometrioid), ovarian cancer (overall, serous, non-serous) and lung cancer (overall, small-cell, non-small cell). Primarily, CT and OS participants were contacted semi-annually and annually, respectively, to identify cancer diagnoses. Information on cancer incidence was initially verified by medical records and pathology reports and then underwent local and central adjudications by trained physicians [26]. Intestinal polyps were not adjudicated [26]. The definition of each cancer site is included in table footnotes and in Supplementary Table S2.
Time-to-cancer-development was defined as days from enrollment to the return of the follow-up questionnaire in which the event was reported. Participants were followed from enrollment to death, lost to follow-up or to the most recent follow-up (through 1 March 2019), whichever was first.
## 2.4. Metabolomics Profiling and Derivation of Metabolomics Profile Scores for the Dietary Patterns
The metabolomics profiling method used has previously been described [21]. Briefly, plasma metabolites were measured as peak areas using a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics platform at the Broad Institute (Cambridge, MA, USA). The current study included a total of 509 named metabolites. Forty-five metabolites with >$10\%$ missing values were excluded. For 84 metabolites with <$10\%$ missing values, we imputed half the sample minimum value for the metabolite [16]. We transformed all metabolites using rank-based inverse normal transformation to achieve normal distribution of the metabolites [27]. We identified metabolomics profiles for adherence to each dietary pattern using elastic-net regression to regress each dietary index on the 464 metabolites, using a 7:3 training-to-testing dataset ratio and obtained the metabolomics signature using a leave-one-out cross-validation approach to avoid overfitting [28]. Metabolomics profile scores for each dietary pattern were derived from a weighted sum of metabolites selected via a series of elastic-net regressions and the weight for each metabolite was the regression coefficient of the selected model. Furthermore, we grouped metabolites into metabolomics class scores for each dietary index, using the pool of metabolites comprising each dietary index metabolomics profile score. Metabolomics class selection was determined non-empirically using information from the Human Metabolome Database (HMDB) to classify the metabolites into weighted metabolomics class scores.
## 2.5. Statistical Analysis
Each dietary index was adjusted for total energy intake using the residual method [29]. We used Cox proportional hazards regression to estimate hazard ratios (HR) and $95\%$ confidence intervals (CI) of the relative associations of each dietary index and risk of developing total and site-specific cancers using the lowest dietary index quintiles as reference. Covariates included in the Cox models are listed in Supplementary Table S3. The proportional hazard assumption was assessed using Schoenfeld residual method and the time dependent covariate method. Because BMI [30] and type 2 diabetes [24,31] may strongly mediate the association of the dietary patterns and cancer risk, we additionally adjusted for these mediators in separate models.
In addition to the relative risk estimates, we calculated multivariable-adjusted absolute risk (incidence rate) of cancer in quintiles of each dietary index. Using the residual method [29], each dietary index was sequentially adjusted for each of the covariates included in the Cox models, then categorized into quintiles. Incidence rates per 100,000 person years were then calculated in dietary score quintile by dividing the number of cancer cases by the sum of the follow-up time within that dietary score quintile.
Metabolomics profile scores were categorized into tertiles because of the lower sample size and examined in relation to cancer risk (total cancer, colorectal, colon, breast, endometrial, lung cancers and intestinal polyps) using Cox regression and adjusting for the same covariates as in the diet analyses. Furthermore, we used Spearman correlation coefficients to assess correlations between metabolomics classes and dietary index food group components for each dietary pattern, adjusting for BMI, physical activity, and pack years of smoking.
Analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA), and R Studio (2021.09.0) was used for data visualization. Two-sided $p \leq 0.05$ was considered statistically significant, and we further adjusted the nominal p-values for potential false discovery rate (FDR) using the Benjamini–Hochberg procedure.
## 3.1. Participant Characteristics (Table 1)
Over a median of 17.8 years of follow-up, 18,768 incident invasive cancers were diagnosed. Participants who consumed the most hyperinsulinemic or most pro-inflammatory dietary pattern (EDIH/EDIP quintiles 5) or the lowest overall dietary quality per DGA (HEI-2015 quintile 1) were more likely to be Black or Hispanic or Latino, have a higher BMI and report lower physical activity and education levels.
**Table 1**
| Unnamed: 0 | Empirical Dietary Index for Hyperinsulinemic (EDIH) Score a,b | Empirical Dietary Index for Hyperinsulinemic (EDIH) Score a,b.1 | Empirical Dietary Index for Hyperinsulinemic (EDIH) Score a,b.2 | Empirical Dietary Index for Hyperinsulinemic (EDIH) Score a,b.3 | Empirical Dietary Index for Hyperinsulinemic (EDIH) Score a,b.4 | Empirical Dietary Inflammatory Pattern (EDIP) Score a,b | Empirical Dietary Inflammatory Pattern (EDIP) Score a,b.1 | Empirical Dietary Inflammatory Pattern (EDIP) Score a,b.2 | Empirical Dietary Inflammatory Pattern (EDIP) Score a,b.3 | Empirical Dietary Inflammatory Pattern (EDIP) Score a,b.4 | Health Eating Index 2015 (HEI-2015) a,b | Health Eating Index 2015 (HEI-2015) a,b.1 | Health Eating Index 2015 (HEI-2015) a,b.2 | Health Eating Index 2015 (HEI-2015) a,b.3 | Health Eating Index 2015 (HEI-2015) a,b.4 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Characteristic | Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 | Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 | Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 |
| n | 22493 | 22494 | 22494 | 22494 | 22493 | 22493 | 22494 | 22494 | 22494 | 22493 | 22493 | 22493 | 22494 | 22493 | 22493 |
| Race/ethnicity | | | | | | | | | | | | | | | |
| American Indian or Alaskan Native | 0.3 | 0.3 | 0.5 | 0.5 | 0.5 | 0.4 | 0.3 | 0.4 | 0.5 | 0.6 | 0.6 | 0.4 | 0.4 | 0.3 | 0.3 |
| Asian or Pacific Islander | 2.2 | 2.8 | 3.2 | 3.1 | 2.7 | 1.3 | 1.9 | 2.3 | 3.5 | 5.1 | 2.7 | 3.1 | 2.9 | 2.9 | 2.5 |
| Black | 4.0 | 5.1 | 7.1 | 9.5 | 13 | 3.2 | 4.2 | 6.2 | 9.5 | 16 | 11 | 8.9 | 7.4 | 6.0 | 5.5 |
| Hispanic/Latino | 2.5 | 2.9 | 3.4 | 4.1 | 5.0 | 1.5 | 1.8 | 2.6 | 3.9 | 8.3 | 5.4 | 4.5 | 3.5 | 2.6 | 1.8 |
| Other | 1.4 | 1.5 | 1.3 | 1.4 | 1.5 | 1.2 | 1.2 | 1.4 | 1.6 | 1.8 | 1.6 | 1.5 | 1.4 | 1.3 | 1.3 |
| White | 89 | 87 | 84 | 81 | 77 | 92 | 91 | 87 | 81 | 68 | 78 | 81 | 84 | 87 | 88 |
| Age, years | 63 ± 7 | 64 ± 7 | 64 ± 7 | 63 ± 7 | 62 ± 7 | 63 ± 7 | 63 ± 7 | 64 ± 7 | 64 ± 7 | 62 ± 7 | 62 ± 7 | 63 ± 7 | 63 ± 7 | 64 ± 7 | 64 ± 7 |
| BMI, kg/m2 | 26 ± 5 | 26 ± 5 | 27 ± 5 | 28 ± 6 | 30 ± 6 | 27 ± 5 | 27 ± 5 | 27 ± 5 | 28 ± 6 | 29 ± 6 | 29 ± 6 | 28 ± 6 | 27 ± 6 | 27 ± 5 | 26 ± 5 |
| Under/Normal weight (15 ≤ BMI < 25) | 49 | 44 | 39 | 34 | 24 | 44 | 42 | 39 | 36 | 29 | 29 | 33 | 37 | 42 | 49 |
| Overweight (25 ≤ BMI < 30) | 33 | 35 | 35 | 35 | 32 | 35 | 35 | 35 | 34 | 32 | 33 | 34 | 35 | 35 | 33 |
| Obese (BMI ≥ 30) | 18 | 21 | 25 | 31 | 43 | 21 | 23 | 26 | 29 | 39 | 38 | 32 | 27 | 23 | 18 |
| Physical activity, MET-hours/week | 17 ± 16 | 15± 14 | 13 ± 13 | 11 ± 12 | 9 ± 11 | 16 ± 15 | 14 ± 14 | 13 ± 13 | 12 ± 13 | 10 ± 12 | 8 ± 11 | 11 ± 12 | 13 ± 14 | 15 ± 14 | 17 ± 15 |
| Pack years of smoking | 11 ± 18 | 9 ± 17 | 9 ± 17 | 9 ± 18 | 11 ± 19 | 13 ± 20 | 10 ±18 | 9 ± 17 | 8 ± 17 | 8 ± 17 | 12 ± 21 | 10 ± 19 | 9 ± 18 | 9 ± 16 | 8 ±16 |
| Current smoking | 6 | 5 | 6 | 7 | 9 | 8 | 6 | 6 | 6 | 7 | 13 | 8 | 6 | 4 | 3 |
| Aspirin/NSAIDs use | 14 | 14 | 13 | 13 | 13 | 14 | 14 | 14 | 13 | 12 | 13 | 13 | 14 | 13 | 14 |
| Statin use | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 2 |
| Hypercholestrolemia | 12 | 14 | 15 | 15 | 15 | 12 | 13 | 15 | 15 | 16 | 12 | 14 | 14 | 15 | 16 |
| Educational level | | | | | | | | | | | | | | | |
| <high school | 3 | 4 | 5 | 6 | 8 | 3 | 3 | 4 | 6 | 10 | 9 | 6 | 5 | 4 | 2 |
| High school/GED | 45 | 51 | 55 | 59 | 62 | 50 | 52 | 54 | 57 | 58 | 62 | 58 | 54 | 51 | 46 |
| ≥4 years of college | 51 | 45 | 39 | 35 | 29 | 46 | 43 | 41 | 36 | 31 | 28 | 35 | 41 | 44 | 51 |
| Total alcohol intake, alcohol servings/week c | 4.8 ± 7.5 | 2.4 ± 4.2 | 1.9 ± 3.7 | 1.6 ± 3.6 | 1.5 ± 3.6 | 5.3 ± 7.8 | 2.8 ± 4.4 | 1.9 ± 3.6 | 1.3 ± 3.0 | 0.8 ± 2.6 | 1.7 ± 4.0 | 2.2 ± 4.6 | 2.6 ± 5.0 | 2.8 ± 5.2 | 3.0 ± 5.5 |
| Macronutrients, %kcal/d | Macronutrients, %kcal/d | | | | | | | | | | | | | | |
| Carbohydrates | 54 ± 10 | 54 ± 9 | 52 ± 8 | 49 ± 8 | 45 ± 9 | 50 ± 10 | 51 ± 9 | 51 ± 9 | 51 ± 9 | 50 ± 9 | 46 ± 9 | 48 ± 9 | 51 ± 9 | 53 ± 9 | 56 ± 8 |
| Total fat | 28 ± 8 | 29 ± 8 | 31 ± 8 | 34 ± 7 | 38 ± 7 | 30 ± 9 | 31 ± 8 | 32 ± 8 | 33 ± 8 | 34 ± 8 | 38 ± 7 | 35 ± 7 | 32 ± 7 | 29 ± 7 | 26 ± 7 |
| Saturated fat | 9 ± 3 | 10 ± 3 | 10 ± 3 | 11 ± 3 | 13 ± 3 | 10 ± 3 | 11 ± 3 | 10 ± 3 | 11 ± 3 | 11 ± 3 | 14 ± 3 | 12 ± 3 | 11 ± 3 | 9 ± 2 | 8 ± 2 |
| Unsaturated fat | 16 ± 5 | 17 ± 5 | 18 ± 5 | 20 ± 5 | 22 ± 5 | 18 ± 5 | 18 ± 5 | 19 ± 5 | 19 ± 5 | 20 ± 5 | 22 ± 5 | 20 ± 5 | 19 ± 5 | 17 ± 5 | 16 ± 5 |
| Total protein | 16 ± 3 | 17 ± 3 | 17 ± 3 | 17 ± 3 | 17 ± 4 | 17 ± 3 | 17 ± 3 | 17 ± 3 | 17 ± 3 | 17 ± 4 | 16 ± 3 | 17 ± 3 | 17 ± 3 | 17 ± 3 | 18 ± 3 |
| Animal/plant protein ratio | 2 ± 1 | 2 ± 1 | 2 ± 1 | 3 ± 1 | 3 ± 2 | 2 ± 1 | 2 ± 1 | 2 ± 1 | 3 ± 1 | 3 ± 1 | 3 ± 2 | 3 ± 1 | 3 ± 1 | 2 ± 1 | 2 ± 1 |
| Micronutrients, per 1000 kcal | Micronutrients, per 1000 kcal | | | | | | | | | | | | | | |
| Calcium, mg/d | 577 ± 217 | 560 ± 211 | 528 ± 203 | 484 ± 184 | 410 ± 156 | 533 ± 205 | 531 ± 198 | 525 ± 201 | 511 ± 207 | 459 ± 201 | 419 ± 167 | 470 ± 183 | 508 ± 196 | 549 ± 204 | 614 ± 213 |
| Potassium, mg/d | 1860 ± 427 | 1816 ± 412 | 1728 ± 388 | 1599 ± 353 | 1384 ± 319 | 1882 ± 416 | 1781 ± 383 | 1709 ± 376 | 1616 ± 377 | 1397 ± 371 | 1296 ± 295 | 1523 ± 309 | 1688 ± 335 | 1848 ± 360 | 2030 ± 360 |
| Vitamin D, mcg/d | 3 ± 2 | 3 ± 2 | 3 ± 2 | 3 ± 2 | 2 ± 1 | 3 ± 2 | 3 ± 2 | 3 ± 2 | 3 ± 2 | 3 ± 2 | 2 ± 1 | 2 ± 1 | 3 ± 2 | 3 ± 2 | 3 ± 2 |
| Magnesium, mg/d | 180 ± 35 | 175 ± 35 | 167 ± 33 | 154 ± 30 | 134 ± 28 | 177 ± 34 | 170 ± 33 | 164 ± 34 | 158 ± 34 | 141 ± 35 | 126 ± 24 | 147 ± 25 | 162 ± 26 | 177 ± 28 | 197 ± 30 |
| Iron, mg/d | 8 ± 2 | 8 ± 3 | 8 ± 3 | 8 ± 2 | 7 ± 2 | 8 ± 2 | 8 ± 2 | 8 ± 3 | 8 ± 3 | 8 ± 3 | 7 ± 2 | 8 ± 2 | 8 ± 3 | 8 ± 3 | 9 ± 3 |
| Folate, mcg/d | 184 ± 64 | 183 ± 65 | 175 ± 64 | 161 ± 59 | 136 ± 50 | 184 ± 62 | 177 ± 61 | 171 ± 62 | 164 ± 63 | 142 ± 61 | 129 ± 52 | 152 ± 55 | 169 ± 59 | 185 ± 62 | 202 ± 62 |
| Vitamin A, mcg RAE/d | 483 ± 194 | 493 ± 199 | 486 ± 198 | 467 ± 197 | 423 ± 196 | 490 ± 205 | 485 ± 189 | 480 ± 188 | 470 ± 192 | 426 ± 210 | 385 ± 182 | 438 ± 188 | 469 ± 196 | 506 ± 192 | 553 ± 193 |
| Vitamin C, mg/d | 74 ± 40 | 76 ± 41 | 72 ± 39 | 63 ± 35 | 49 ± 28 | 70 ± 38 | 71 ± 38 | 70 ± 39 | 67 ± 38 | 54 ± 34 | 41 ± 26 | 57 ± 31 | 69 ± 36 | 79 ± 38 | 88 ± 38 |
| Vitamin E, IU/d | 6 ± 3 | 6 ± 4 | 6 ± 4 | 6 ± 3 | 5 ± 3 | 6 ± 3 | 6 ± 3 | 6 ± 4 | 6 ± 4 | 5 ± 3 | 5 ± 3 | 6 ± 3 | 6 ± 4 | 6 ± 4 | 7 ± 4 |
## 3.2. Food and Nutrient Profiles of the Dietary Patterns (Supplementary Tables S1 and S4)
There are 27 food groups comprising the EDIH and EDIP and nine are common to both indices, including red meat, processed meat, non-dark (non-fatty) fish, sugar-sweetened beverages (regular sodas), artificially sweetened beverages (diet sodas) and refined grains, contributing higher scores; leafy-green vegetables, wine and coffee contributed lower scores (Supplementary Table S1). Unique to EDIH are French fries, butter, margarine (high scores), whole fruit and whole dairy (low scores). The correlation between EDIH and EDIP was 0.63. In addition to foods, HEI has specific nutrients to reduce, such as saturated fats; therefore, the score only includes low/non-fat dairy foods. Additionally, HEI does not include foods without caloric value such as coffee or diet sodas. HEI-2015 had a correlation of −0.36 with EDIH and −0.26 with EDIP.
Participants consuming a more hyperinsulinemic dietary pattern consumed fewer calories from total carbohydrates and more calories from total fat, saturated fat, added sugar and animal protein. The macronutrient distribution (Supplementary Table S4) for EDIP was similar but with smaller contrasts between high and low EDIP compared to EDIH. Higher overall dietary quality based on higher HEI-2015 was characterized by higher intake of total carbohydrates and lower intake of total fat and saturated fat.
## 3.3. Metabolomics Profile Scores of the Dietary Patterns (Figure 1)
Of the 464 metabolites retained, elastic-net regression selected 93 for EDIH, 88 for EDIP and 67 for HEI-2015. Correlations among the metabolomics profile scores (m---) were mEDIH-mEDIP (0.73), mEDIH-mHEI-2015 (−0.61) and mEDIP-mHEI-2015 (−0.29). In addition, correlations between the metabolomics profile scores and their corresponding dietary scores were: mEDIH-EDIH (0.45), mEDIP-EDIP (0.33), and mHEI-2015-HEI-2015 (0.50). Higher EDIH was associated with higher amino acids and glycerophosphocholines (glyceroPC), and with lower mono/di-carboxylic acids (MCA, DCA), CEs, phosphosphingolipids (phosphoSL), alkaloids, purines, pyridines and pyrimidines (Figure 1). The metabolite classes were similarly associated with the EDIH food group components, e.g., amino acids correlated positively with red/processed meat and French fries, which contribute to higher EDIH, and inversely with fruit, leafy-greens, coffee and wine, which contribute to lower scores. EDIP had a similar pattern of correlations with the metabolomics classes. Alkaloids were strongly positively correlated with coffee, which contributes to lower scores in both EDIH and EDIP (Figure 1A,B). In contrast, alkaloids were not strongly correlated with HEI-2015, which does not include coffee. Higher HEI-2015 was correlated with higher DCA, indoles, benzoic acids, glyceroPE, glyceroPC, phosphoSL, purines, pyrimidines and pyridines and with lower carnitines, amino acids, TAGs and amines. However, saturated fat, a moderation component of HEI-2015 was also associated with higher carboxylic acids, glycerol-PE/PC/SL and lower carnitines and amino acids (Figure 1C).
**Figure 1:** *Correlations between dietary pattern food group components and metabolomics class scores. (A) Correlations between EDIH food group components and EDIH metabolomic class scores. (B), Correlations between EDIP food group components and EDIP metabolomic class scores. (C), Correlations between HEI-2015 food group components and HEI-2015 metabolomics class scores. Values are partial Spearman correlation coefficients adjusted for BMI, physical activity and pack years of smoking. EDIH, empirical dietary index for hyperinsulinemia; EDIP, empirical dietary inflammatory pattern; HEI-2015, healthy eating index-2015; TAG: Triradylcglycerols. MAG: Monoradylglycerols. DAG: Diradylglycerols. GlyceroPS: Glycerophosphoserines. GlyceroPE: Glycerophosphoethanolamines. GlyceroPC: Glycerophosphocholines. CE: Cholesterol esters. PhosphoSL: phosphosphingolipids. MCA: mono-carboxylic acids. DCA: di-carboxylic acids. TCA: Tricarboxylic acids.*
## 3.4. EDIH and Cancer Risk (Table 2, Figure 2)
Women classified in the highest quintile of EDIH had greater risk of total cancer compared to those in the lowest quintile, with a multivariable-adjusted incidence rate difference of 52 per 100,000 person years and corresponding HR ($95\%$ CI) of 1.10 (1.04–1.15). Findings from the categorical analyses were aligned with EDIH modelled as a continuous variable (Table 2). A 1 sd increment in EDIH was associated with higher risk of colorectal cancer, colon cancer and proximal colon cancer but not distal colon or rectal cancer. EDIH was also strongly associated with intestinal polyps. Further, higher EDIH was associated with breast cancer and pathological subtypes including ER-negative, luminal B, invasive lobular carcinoma, and triple negative breast cancer. Higher EDIH was associated with a greater risk of endometrial cancer especially the endometrioid subtype, but not ovarian cancer or lung cancer (Figure 2). The EDIH metabolomics score was generally not significantly associated with cancer risk, but a 1 sd increment in the score was associated with elevated risk of endometrial cancer: HR, 3.58; $95\%$ CI, 0.96–13.29 (Table 3).
## 3.5. EDIP and Cancer Risk (Table 2, Figure 2)
Women in the highest EDIP quintile were at greater risk of total cancer compared to those in the lowest quintile, with a multivariable-adjusted incidence rate difference of 41 per 100,000 person years and corresponding HR ($95\%$ CI) of 1.08 (1.02–1.15) (Table 2). A 1 sd increment in EDIP was associated with higher risk of colorectal cancer, colon cancer and marginally with proximal and distal colon or rectal cancer. EDIP was also associated with intestinal polyps. Higher EDIP was associated with greater risk of overall breast cancer and ER-negative cancer. Furthermore, higher EDIP was associated with greater risk for endometrial cancer, but not ovarian cancer or lung cancer (Figure 2). The EDIP metabolomics score was generally not associated with cancer risk (Table 3).
## 3.6. HEI-2015 and Cancer Risk (Table 2, Figure 2)
Women classified in the highest HEI-2015 quintile were at lower risk of total cancer compared to those in the lowest quintile, with a multivariable-adjusted incidence rate difference of −49 per 100,000 person years and corresponding HR ($95\%$ CI) of 0.93 (0.89–0.98) (Table 2). Higher HEI-2015 was associated with lower risk of colorectal cancer, colon cancer and proximal colon cancer but not distal colon or rectal cancer. HEI-2015 was also strongly associated with intestinal polyps. Unlike EDIH and EDIP, HEI-2015 was not associated with overall breast cancer or its subtypes nor with endometrial or ovarian cancers but was marginally inversely associated with overall lung cancer, though positively associated with small-cell lung cancer. Higher HEI-2015 metabolomics profile score was associated with lower risk for overall lung cancer, HR, 0.46 (0.24–0.90) (Table 3).
## 3.7. Sensitivity Analyses and Subgroup Analyses (Supplementary Tables S5–S8)
Additional adjustment for BMI and type 2 diabetes, major mediators strongly associated with EDIH and EDIP in previous studies, did not materially change the results, though results for endometrial cancer were attenuated and no longer statistically significant (Supplementary Table S5). Findings from subgroup analyses are reported in Supplementary Tables S6 and S7. p values for the interactions of the dietary patterns and the potential effect modifiers were generally not significant. The results for total cancer, breast cancer and endometrial cancer remained robust for EDIH after mutually adjusting for EDIP and HEI-2015 (Supplementary Table S8).
## 4.1. Principal Findings, Strengths and Weaknesses in Relation to Other Studies
The present study employed two empirical hypothesis-oriented dietary indices to investigate associations between diets that contribute to chronic hyperinsulinemia (EDIH) or chronic systemic inflammation (EDIP) and risk of developing total cancer and site-specific cancers among postmenopausal women, while also examining these associations with an established index of overall dietary quality (HEI-2015). While all three dietary indices were significantly associated with risk of total cancer, colorectal cancer and intestinal polyps, EDIH and EDIP were further associated with risk of breast cancer and endometrial cancer. In addition, EDIH was associated with multiple breast cancer subtypes including the more aggressive triple negative breast cancer.
Although a previous study found that higher EDIH was associated with higher total cancer mortality in both men and women [32], most previous studies have examined single cancer sites and reported similar findings to ours. For example, in the NHS (another all-female cohort), EDIH and EDIP were associated with higher risk of colorectal cancer and its anatomic subsites except the rectum [6,8]. EDIH was also associated with higher risk of digestive system cancers and accessory organs [7]. In the WHI, dietary inflammatory potential assessed using a literature-derived nutrient-based dietary inflammatory index (DII) was associated with higher risk of colorectal cancer [33] similar to the current study. Given that DII was calculated with total intake (nutrients plus supplements), findings may not be directly comparable to EDIP, which is exclusively food-based. Though different polyp types were not adjudicated in WHI, the strong associations of EDIH and EDIP with intestinal polyps warrant future studies to test if dietary intakes may inform risk stratification in colorectal cancer screening for improved preventive strategies. Associations between EDIH and EDIP with total cancer and colorectal cancer were consistent with findings for HEI-2015. A study in WHI also found that higher HEI-2010 score was associated with lower colorectal cancer risk [34].
Although EDIH and EDIP were associated with higher risk of overall breast cancer and ER-negative subtype, only EDIH was associated with risk of multiple other breast cancer subtypes, while HEI-2015 showed no association. Only one study has examined EDIH in relation to breast cancer risk, and found higher risk for overall breast cancer with stronger associations for ER- and HER2+ tumors [35]. Two studies in the WHI found no associations between the DII and risk of overall breast cancer/subtypes [36,37]. The HEI and most other dietary indices have not been consistently associated with breast cancer risk [38]. The EDIH and EDIP dietary patterns are more strongly related to obesity and type 2 diabetes than most traditional dietary patterns [24,30,31,39]. Obesity and diabetes drive risk of the same cancers including colorectum, postmenopausal breast, endometrium and ovary, among other sites [3,40]. In the current study, both EDIH and EDIP were strongly associated with endometrial cancer risk, which attenuated after adjusting for obesity as a mediator. Similarly, a study in NHS and NHS-II cohorts applied the EDIH and EDIP scores and found strong associations with endometrial cancer, and showed that BMI mediated $84\%$ and $93\%$ of the associations, respectively [9]. Obesity is linked to inflammation and insulin resistance [41], and a study that analyzed data on 1.2 million women, found that each 10-kg/m2 increment in BMI was associated with a nearly 3-fold increase in endometrial cancer risk [42]. While EDIH has not been studied in relation to ovarian cancer risk, the null association with EDIP was consistent with a study in NHS and NHS-II [43].
The metabolomic profile of HEI-2015 had 23 metabolites that overlapped with the EDIH, and had 17 metabolites overlapped with the EDIP, which may partly explain the higher correlation between the HEI-2015 related metabolomics score is and EDIH-related metabolomics score, compared with the EDIP-related metabolomics score. We observed no associations between the metabolomics profile scores of the three dietary patterns and cancer risk, except for the associations between HEI-2015 score and overall lung cancer. We had 441 cancer cases in the metabolomics sample compared to 18,768 in the overall sample. It is therefore possible that the lack of associations in contrast to the dietary analysis is indicative of the low statistical power in our metabolomics sample. The association observed had wide $95\%$ CIs, reflecting potentially unstable point estimates. Nevertheless, we characterized the correlations of metabolomics classes with the food group components of the dietary scores, which yielded novel and confirmatory findings. In the two previous metabomomics studies [16,17], higher levels of nine CEs, one glycerophosphoserine, trigonelline and eicosapentanoate were associated with lower EDIH score [16]. In the current study, EDIH showed inverse associations with three CEs and one glycerophosphoserine, though using a different method to derive metabolomic profile scores. Higher CE levels were associated with higher intake of wine and fruit and with lower intake of red meat, sugar-sweetened beverages, and processed meat. It is therefore possible that a low EDIH diet may reduce disease risks via CE’s greater efficiency in clearing blood remnants of lipid metabolism [16,44]. We found that higher plasma levels of alkaloids and purines were associated with lower EDIH/EDIP and with higher coffee/tea which contribute to lower EDIH/EDIP scores. Laboratory studies suggest that specific alkaloids can intervene in the insulin signal transduction pathway, and reverse molecular defects that could otherwise lead to insulin resistance and glucose intolerance [45]. These alkaloids may be involved in pathophysiological processes associated with insulin resistance, β-cell failure, oxidative stress and inflammation [45].
## 4.2. Strengths and Weaknesses of the Study
Our study has several strengths. We investigated EDIH, EDIP and HEI-2015 in relation to multiple cancers, and dietary pattern-related metabolomics profiles and their association with cancer risk. We estimated multivariable-adjusted incidence rates in addition to the usual relative risk estimates, providing better clinical and public health context for interpreting the relative risk estimates, e.g., incidence rate among the non-exposed (reference—quintile 1). We adjusted p-values to minimize the potential for false discovery. Potential limitations include using self-reported dietary intake though the FFQ was evaluated for bias and precision [22]. Although the FFQ and metabolites were single measurements, previous studies have shown that diet in adults and plasma metabolites remain stable overtime [46,47]. Though the sample sizes for the cancer risk analyses were large, the number of cancer cases in the metabolomics sample was small, precluding robust associations. Additionally, though we adjusted for numerous potential confounding factors, residual confounding may persist [48,49].
## 4.3. Possible Implications and Conclusions
In summary, our findings suggest that hyperinsulinemic and pro-inflammatory dietary patterns, as well as overall dietary quality, are associated with risk for several cancers among postmenopausal women, supporting further investigation of these dietary patterns in relation to cancer risk in dietary intervention studies to modify cancer risk.
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|
---
title: Pyruvate Dehydrogenase Kinase 4 Deficiency Increases Tumorigenesis in a Murine
Model of Bladder Cancer
authors:
- Benjamin L. Woolbright
- Ganeshkumar Rajendran
- Erika Abbott
- Austin Martin
- Ryan Didde
- Katie Dennis
- Robert A. Harris
- John A. Taylor
journal: Cancers
year: 2023
pmcid: PMC10046149
doi: 10.3390/cancers15061654
license: CC BY 4.0
---
# Pyruvate Dehydrogenase Kinase 4 Deficiency Increases Tumorigenesis in a Murine Model of Bladder Cancer
## Abstract
### Simple Summary
Pyruvate dehydrogenase kinase 4 (PDK4) is a protein that serves as a switch for how the body regulates metabolism. Prior research indicates that blocking the effect of PDK4 with some drugs slows the growth of bladder cancer cells. These experiments relied on cancer cell lines and not tumors grown in mouse models of cancer, which are more closely related to human disease. In a validated mouse model of bladder cancer, mice that did not express PDK4 were found to have larger tumors than mice expressing PDK4 at later points of tumor progression. As tumors became larger, there was a loss of expression of PDK4 in mice that normally expressed it. Human samples with bladder cancer had lower expression of PDK4 than those without bladder cancer. These data indicate that PDK4 may be an unexpected tumor suppressor in bladder cancer.
### Abstract
Pyruvate dehydrogenase kinase 4 (PDK4) is a mitochondrial isozyme in the PDK family (PDK1-4) partially responsible for phosphorylation of pyruvate dehydrogenase (PDH). Phosphorylation of PDH is thought to result in a pro-proliferative shift in metabolism that sustains growth of cancer cells. Previous data from our lab indicate the pan-PDK inhibitor dichloroacetate (DCA) or acute genetic knockdown of PDK4 blocks proliferation of bladder cancer (BCa) cells. The goal of this study was to determine the role of PDK4 in an in vivo BCa model, with the hypothesis that genetic depletion of PDK4 would impair formation of BCa. PDK4−/− or WT animals were exposed to N-Butyl-N-(4-hydroxybutyl) nitrosamine (BBN) for 16 weeks, and tumors were allowed to develop for up to 7 additional weeks. PDK4−/− mice had significantly larger tumors at later time points. When animals were treated with cisplatin, PDK4−/− animals still had larger tumors than WT mice. PDK4 expression was assessed in human tissue and in mice. WT mice lost expression of PDK4 as tumors became muscle-invasive. Similar results were observed in human samples, wherein tumors had less expression of PDK4 than benign tissue. In summary, PDK4 has a complex, multifunctional role in BCa and may represent an underrecognized tumor suppressor.
## 1. Introduction
Bladder cancer (BCa) is a highly morbid disease, particularly in advanced stages [1,2]. Standard-of-care treatment for muscle-invasive disease remains surgical intervention, with added benefit provided by neoadjuvant cisplatin-based chemotherapy [2,3]. Biomarker studies have begun to define which populations of BCa patient might be most likely to benefit from cisplatin-based therapy, but a significant portion of BCa patients receive no benefit currently [4,5]. Novel immunotherapies targeting PD-1:PD-L1 interactions have been approved in BCa and provide important systemic treatment options [2]. However, not all patients respond to immunotherapy, and new therapeutic adjuvants will likely be needed to improve overall response [6]. Currently, there remains a large gap in treatment efficacy for many patients with BCa, particularly those that are cisplatin-ineligible. Adjuvant treatments that improve cisplatin or immune-based therapy and novel treatments are sorely needed [1,2].
Pyruvate dehydrogenase kinase 4 (PDK4) is a mitochondrial enzyme responsible for phosphorylation of pyruvate dehydrogenase (PDH) and one of the four pyruvate dehydrogenase kinase isozymes, PDK1-4 [7,8,9]. PDH converts pyruvate to acetyl-CoA and serves as a major hub in many metabolic processes necessary to generate the metabolic components required for cellular proliferation. Phosphorylation of PDH prevents the conversion of pyruvate to acetyl-CoA [9,10,11]. The inhibition of PDH increases intracellular levels of pyruvate, which cancer cells can then convert to lactate via lactate dehydrogenase to recycle NAD+ and produce ATP via aerobic glycolysis, which can enhance growth rates [9,10,11]. This process has long been of interest to the cancer field, but more recent data indicate that this process likely benefits cells in multiple ways, including through the production of oxidative biomass, in addition to the production of ATP, as the mitochondria of cancer cells are metabolically very active [12,13,14,15]. Inhibition of PDKs as a therapeutic target has been pursued in many different cancers as a means of slowing cancer growth [9,16,17,18,19,20]. The most commonly used inhibitor is dichloroacetate (DCA), a pan-inhibitor of all four PDKs. DCA requires high drug concentrations to be effective in vitro due to its low potency but reduces tumor growth in multiple cancers, including breast, kidney, lung, and liver cancers [7]. DCA has some affinity for all four PDKs, which means it minimally allows for differentiation of the roles of individual PDK enzymes. Other more specific inhibitors that target the PDK:PDH axis, including AZD7545, CPI-613, and VER-246608, have also proven effective against cancer in multiple cell lines [9]. As such, inhibition of PDKs has consistently been proposed as a therapeutic target in cancer.
Recent evidence suggests that PDK4 inhibition may be more complicated than inhibitor studies first indicated. Genetic knockout of PDK4 in hepatocellular carcinoma (HCC) cells resulted in increased proliferation [21,22]. Similarly, PDK4-deficient non-small cell lung carcinoma (NSCLC) cells grew more aggressively, which was proposed to occur through altered regulation of lipid metabolism [23]. Prior data from our lab indicate that BCa cells are responsive to pan-inhibition of PDKs with DCA and that treatment also enhances the effect of cisplatin. Moreover, acute knockdown of PDK4 with siRNA slows BCa growth rates [24]. However, there is currently no understanding of the effect of PDK4 deficiency in orthotopic models of BCa. Given the observed effect of DCA and cisplatin in BCa cell lines, we sought to understand how PDK4-deficient mice would respond to challenge with a BCa-specific carcinogen, N-Butyl-N-(4-hydroxybutyl)nitrosamine (BBN) [25,26]. We hypothesized that PDK4 deficiency would reduce tumor formation and enhance cisplatin efficacy based on these data.
## 2.1. Mice
PDK4−/− animals were a kind gift from Dr. Lisa Zhang and originally produced in the lab of Dr. Robert Harris. PDK4−/− animals were bred homozygously in house, and male mice were used for experimentation due to innate resistance to BBN in female animals. C57BL/6 controls were acquired from Jackson Labs. All experiments were approved by the Kansas University Medical Center IACUC prior to onset of experimentation.
## 2.2. BBN Protocol
Animals were placed on $0.05\%$ N-Butyl-N-(4-hydroxybutyl)nitrosamine (BBN) (TCI America, Portland, OR) in drinking water and provided food and water containing BBN ad libitum for 16 weeks to initiate tumorigenesis. Water containing BBN was provided in opaque bottles to reduce degradation. Cisplatin was dissolved in normal saline via heating at 37 °C for ~5–10 min with mild shaking intermittently until fully dissolved. For experiments with WT and PDK4−/− mice, cisplatin was administered twice weekly at 3 mg/kg i.p. for 3 weeks in total. To assess tumor formation, bladders were bisected along the mid-sagittal plane and then weighed. After weighing, bladders were placed in neutral buffered formalin for future immunohistochemistry. One animal in the 23 w group (WT) was found moribund four days prior to the end of the study but was included in the study due to obvious tumor formation. Chemicals were acquired from Sigma (St. Louis, MO, USA) unless otherwise noted.
## 2.3. Immunohistochemistry
Tissue was formalin-fixed and then embedded in paraffin. Then, 5 µM sections were cut for analysis. Endogenous peroxidase was quenched with a peroxidase suppressor (Thermo, Waltham, MA, USA). Antigen retrieval was performed by submerging tissue in a boiling citrate buffer (pH6) for 10 m. Immunohistochemistry was performed with a Vectastain Elite ABC kit (Vector Labs, Burlingame, CA, USA) as per the manufacturer’s protocol, followed by visualization with 3′,3′-diaminobenzidine. Ki67 antibody was acquired from Cell Signaling (clone D3B5), and PDK4 antibody was acquired from Sigma (HPA056731). Ki67-positive cells were independently quantified in three to four separate fields from each mouse by two separate scientists and then averaged. The H index in human tumors was calculated by evaluating the percentage and degree of positivity in epithelial sections to reduce variability and account for the relatively low degree of epithelium in bladder tissue samples. Any degree of nuclear positivity was considered positive. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining was performed using a Roche in situ cell death detection kit (Roche, Basel, Switzerland). Microscopy was performed on an Olympus IX73 (Tokyo, Japan) using cellSens Standard software version 4.1. H&E staining was carried out using standard methodology. Briefly, tissue was deparaffinized, rehydrated, exposed to hematoxylin and eosin, and serially dehydrated in alcohol and xylene before mounting. Tumor stage was evaluated by a board-certified genitourinary pathologist (KD).
## 2.4. Human Tissue and TCGA Analysis
Data were acquired from either the University of Alabama at Birmingham *Cancer data* analysis portal (UALCAN), Gene Expression Profiling Interactive Analysis (GEPIA), or Xenabrowser [27,28,29,30]. Xenabrowser was used to access values for high-grade samples, in addition to the small number of incidentally acquired low-grade samples in the database, and values for solid normal tissue samples were included in the database as adjacent controls. Samples for which there were no values or no establishment of grade on Xenabrowser were omitted from this analysis. UALCAN and GEPIA were also used to assess normal versus cancer samples. Small differences in overall group numbers are reflective of differences inherent to the way each program analyzes TCGA data. Solid normal tissue represents adjacent solid tissue and not necessarily truly normal tissue. The BCa tumor microarray was acquired commercially from US BIOMAX (Derwood, MA, USA) and represented tumors of varied stage and grade, in additional to normal controls.
## 2.5. qPCR
RNA was isolated using Trizol. QPCR was carried out using TaqMan-based primers and expressed using the delta–delta CT method.
## 2.6. Statistics
Statistics were performed using Sigmaplot. Data were evaluated for normality and then compared via t-test or by either Kruskal Wallis or Mann–Whitney test with post hoc correction. * $p \leq 0.05.$ Data are expressed as mean +/− standard error.
## 3.1. PDK4−/− Animals Have Larger, Higher-Stage Tumors after BBN Treatment at Later Time Points of Tumor Formation
To evaluate the role of PDK4 in BCa development, the BBN-induced BCa model was used. This model has been shown to genetically and phenotypically recapitulate the basal subtype of human BCa and represents a gold-standard model in the field [31,32]. We initially assessed levels of the PDK isozymes in the bladder of PDK4−/− animals to determine whether PDK4 was selectively reduced in PDK4−/− animals. PDK1-3 expression was unchanged in the bladder, whereas PDK4 transcripts were largely undetectable (Figure 1A). To determine how PDK4−/− animals respond to BBN-induced BCa, WT or PDK4−/− mice were treated with $0.05\%$ BBN via drinking water for 16 weeks, followed by 4 additional weeks with normal water (no BBN) for continued tumor formation (20 w total). No difference in tumor weight between WT and PDK4−/− mice was observed after 4 additional weeks of tumor formation time (Figure 1B). Tumor staging via H&E staining was performed by a board-certified pathologist, but again, no difference was observed between WT and PDK4−/− animals (Figure 1C,D). These data indicate that WT and PDK4−/− animals did not demonstrate any difference in early tumor formation.
We next sought to determine whether PDK4 deficiency would alter late-stage tumor formation or progression. Moreover, prior data from our lab indicated that PDK4 inhibition may improve cisplatin-based therapy. To assess this, we again initiated tumor formation with $0.05\%$ BBN for 16 w and assessed tumor levels after 7 additional weeks with or without the presence of 3 mg/kg cisplatin twice weekly in the last 3 weeks. PDK4−/− animals had larger tumors than their WT counterparts after 7 w of additional tumor growth as assessed by bladder weights, which is a surrogate for tumor volume (23 w in total) (Figure 2A). Tumor staging based on H&E staining yielded a significant increase in tumor stage in PDK4−/− animals compared to WT controls (Figure 2B,E). In animals treated with cisplatin, similar results were obtained; PDK4−/− animals, again, had larger tumors than WT controls as assessed by bladder weight (Figure 2C). However, in contrast, no overall difference in tumor stage was observed in cisplatin-treated animals (Figure 2D,F), although the PDK4−/− group had persistent T3 tumors that were absent in the WT group. Overall, these data support the idea that PDK4 acts as an antitumorigenic agent in late-stage tumors but not early-stage tumors.
## 3.2. Increased Proliferative Potential of PDK4−/− Tumors
To assess why PDK4−/− tumors were larger, we initially hypothesized that PDK4−/− tumors may be resistant to apoptosis due to altered metabolism, leading to larger tumor size due to a reduction in cellular turnover. To assess this, TUNEL+ cells were quantified in WT and PDK4−/− animals; however, the overall rate of TUNEL+ cells was relatively low in both WT and PDK4−/− animals, suggesting that this was an unlikely mechanism (Supplementary Figure S1). We next hypothesized that PDK4−/− might lead to a more proliferative phenotype. Proliferation was assessed by immunohistochemistry for Ki67 (Figure 3A,B). Ki67+ cell numbers were higher in PDK4−/− tumors, although not in those treated with cisplatin (Figure 3B,C). Ki67+ cells remained in both WT and PDK4−/− animals, suggesting the continued presence of proliferating cells after treatment with cisplatin. The increase in Ki67+ cells indicates that PDK4−/− tumors were more proliferative.
## 3.3. PDK4 Expression Is Suppressed in Murine and Human BCa
To further understand why PDK4−/− tumors were larger and more proliferative, we next assessed the expression patterns of PDK4 in murine and human tumors. Our lab previously published data demonstrating a significant increase in PDK4 between low-grade and high-grade samples [24]. However, analysis of BCa specimens in TCGA demonstrated a novel trend, as PDK4 expression was suppressed in cancer compared to normal controls (Figure 4A). This was consistent with increased methylation of the PDK4 promoter in TCGA tumor specimens compared to controls, suggesting a potential epigenetic mechanism of PDK4 suppression (Figure 4B). These data were confirmed using the GEPIA database (Figure 4C) [29]. GEPIA analysis including Genotype-Tissue Expression (GTEx)-provided normal bladder tissue further confirmed our findings (data accessed via http://gepia.cancer-pku.cn/index.html, accessed on 25 January 2023). To better understand this difference, TCGA was used to evaluate PDK4 expression in a small group of low-grade tumors that were defined in the cohort. While both low-grade and high-grade cancers exhibited reductions in PDK4, low-grade tumors had a greater reduction than high-grade tumors (Figure 4D). To confirm these data in our mice, we evaluated PDK4 expression in WT animals. Notably, the BBN model does not produce low-grade tumors, and therefore, the only analysis possible was to understand differences in PDK4 expression in BBN-exposed animals with high-grade tumors versus animals exposed to BBN but without tumors. Animals with muscle-invasive (pT2+) tumors consistently had lower expression of PDK4 than animals exposed to BBN but with benign tissue (Figure 5A,B). Murine antibodies for PDK4 were not suitable for IHC, so instead, PDK4 was stained in a human tumor microarray. While the urothelium in normal tissue stained positive for PDK4, it was largely absent in the majority of tumor specimens (Figure 5C) Scoring tumors via H index demonstrated a statistically significant reduction in PDK4 expression in the epithelium compared to WT controls (Figure 5D). These data indicate that PDK4 is largely lost in more advanced tumors in both mice and humans.
In totality, these data indicate that PDK4 likely plays a highly complex role with differential regulation across the spectrum of BCa tumors. Experiments with chronic genetic loss of PDK4 indicate that it may have tumor-suppressive functions, whereas, acute inhibition of all PDKs with agents such as DCA yields a therapeutic effect based on prior experiments [24]. More studies are needed to better elucidate the role of PDK4 and PDKs generally in BCa.
## 4. Discussion
The mitochondrial isozymes PDKs 1-4 are responsible for phosphorylation of PDH and serve as a tightly controlled biological switch to manage cellular energy needs. When PDH is phosphorylated by PDKs, pyruvate is instead metabolized by other enzymes such as lactate dehydrogenase, which produces lactate (aerobic glycolysis), and pyruvate carboxylase, which produces oxaloacetate [7,33]. The original *Warburg hypothesis* suggested that the production of ATP by aerobic glycolysis was a major reason why cancer cells were capable of unlimited proliferation; however, recent data suggest that cancer cells undergo a broad cellular reprogramming of metabolism that allows access to many different metabolically favorable reactions for production of otherwise rate-limiting materials [12,13,14]. Inhibition of PDKs has widely been proposed as a means of slowing cancer proliferation by promoting pyruvate oxidation, which shifts metabolism away from aerobic glycolysis and towards mitochondrial oxidative metabolism [9,14,34]. This both limits production of biomass needed for proliferation and enhances production of mitochondrial ROS, which can be toxic to the cell. Supporting this idea, numerous cancer cell lines are sensitive to PDK inhibition [7,9]. We and others have found that pan-inhibition of all PDKs with DCA can block cellular proliferation. This has alternately been attributed to knockdown of single PDK enzymes or, at times, knockdown of all PDK isozymes [7]. In our own studies and others, acute knockdown of PDK4 with siRNA has been found to limit cellular proliferation in BCa cell lines, although the current study indicates that the biology is likely far more complex in vivo [24,35]. Using the BBN mouse model, we found that PDK4−/− animals had larger bladder tumors than their counterparts and that PDK4 itself is largely lost during tumor formation in both humans and mice. While animals exposed to cisplatin did not have higher stage tumors, they still had larger bladders than their WT counterparts, further confirming the possibility that PDK4−/− animals are more predisposed to tumor progression. The totality of these data indicate that PDK4 specifically may function as a tumor suppressor in BCa, particularly in later-stage tumors. Moreover, our data indicate that the role of PDK4 is likely more complex in vivo than BCa cell lines can accurately model; therefore, understanding the role of PDK4 in BCa will require complicated murine models.
Our initial hypothesis was that PDK4−/− animals would exhibit reduced tumor formation and be more responsive to cisplatin based on prior in vitro data in BCa, in addition to numerous studies demonstrating an antitumorigenic effect of PDK inhibition [7,20,24,34]. The primary outcome of this paper is the surprising finding that PDK4−/− animals had larger tumors, which is indicative of an antitumorigenic effect of PDK4 (Figure 2 and Figure 3). PDK4−/− tumors had higher amounts of Ki67+ cells, which is indicative of a more proliferative phenotype. The underlying mechanism was not defined in this study. Prior work indicated that knockout of PDK4 can cause pro-proliferative changes in tumor cells by enhancing lipogenesis in other cancers [21,22,23]. Knockdown of PDK4 promotes growth of A549 and NCI-H1299 lung cancer cells, whereas overexpression of miR-182 reduces tumor growth via downregulation of PDK4 [23]. This has been proposed to be a result of downstream actions on lipogenesis in tissues with high lipogenic activity [23,36]. Similarly, knockdown of PDK4 increases lipogenesis and has been reported to increase HCC growth rates, with similar findings in prostate cancer [21,22,36]. The dependency/capacity of each tumor on de novo fatty synthesis may be a predictor of whether or not PDK4 acts as a tumor suppressor, although additional studies are needed in this area across a broader range of tumors [36]. As current research indicates that ATP production is not the likely limiting factor for cancer proliferation, the action of PDK4 on metabolism may ultimately reduce metabolic intermediates necessary for proliferation. This study was not able to define an exact mechanism as to why PDK4−/− tumors were larger or were larger exclusively at later time point, but given that BBN-induced tumors increase in stage over time, one possibility is that PDK4 constrains tumors that reach a certain stage due to underlying cellular signaling. Knockout of this protein then alleviates this constraint and allows tumors to grow more quickly. Cell lines do not accurately recapitulate tumor stage or the exact underlying microenvironment; therefore, murine experiments are needed to understand how this occurs. Selectively depleting or overexpressing PDK4 at specific points during tumor formation in mice may allow for a better understanding of how and when PDK4 yields its effects. Because cell line data and murine data on the role of PDK4 have been somewhat discordant in both BCa and other tumors, the most effective way to accomplish this may require the use of conditional knockout animals using a Cre/lox system to selectively deplete PDK4 at different points in tumor formation. This could be further honed to different cellular populations to provide a robust and granular explanation as to which cells are most effected by loss of PDK4 and how this promotes tumor formation.
One area that we did not fully explore mechanistically is the role of PDK4 in tumor inflammation. PDKs can alter metabolism in T cells as in other cell lines. The loss of other PDKs such as PDK1 can control T-cell fate by altering cellular metabolism [37]. While BBN induces some degree of inflammation by itself, inflammation is dramatically increased when a tumor is present [38]. A possible hypothesis is that PDK4 alters the ability of T cells to robustly respond to tumors. Depletion of both PDK2 and PDK4 but not PDK4 alone prevents the conversion of macrophages to the M1 phenotype in other models. If the loss of PDK4 alone is capable of pushing macrophages towards the M2 phenotype in this model, it could be another potential mechanism of reduced immunity [39]. While these studies are outside the scope of this work, understanding how PDK4 loss affects T cells and other immune populations may shed light on why PDK4−/− animals have larger tumors in immunologically intact in vivo models despite opposite result in cell lines.
Non-canonical actions of PDK4 have recently been reported [35]. Multiple proteins other than PDH have been identified as potential substrates of PDK4 [35,40]. This includes a partially defined signaling axis between PDK4-Septin2 (SEPT2)-dynamin-related protein 1 (DRP1), which regulates mitochondrial fission [40]. Altered mitochondrial fission/fusion dynamics have been demonstrated in multiple cancers, with some reports indicating that alteration of fission/fusion may be therapeutically useful [41,42]. If loss of PDK4 prevents normal mitochondrial fission, this may constrain tumor growth by increasing mitochondrial instability, thereby limiting cell proliferation. Because mitochondrial fusion and fission processes are critical to normal biology, the ability to alter/modulate DRP1 activity indirectly through related proteins in the signaling pathway may be therapeutically viable, and therefore understanding the role of PDK4 in fission may be useful in the near future [41]. Currently understood activity of PDK4 on PDH, in addition to inhibitor studies, suggests that PDK4 should function as a positive regulator of tumor formation. The advent of numerous PDK4 client substrates offers the possibility that a significant biological activity remains under investigation and may be highly relevant to future studies, particularly in regard to the ability of PDK4 to suppress tumor formation rather than enhance it. Mechanistic studies investigating the effects of PDK4 on these proteins are needed to better understand how PDK4 suppresses tumor formation.
PDK4 expression was lost in muscle-invasive BCa tumors in murine studies and was consistently repressed in BCa tumors compared to solid normal tissue in analysis of human samples (Figure 4 and Figure 5). In a limited set of human samples present in the TCGA database, PDK4 expression was markedly lower in low-grade tumors compared to high-grade tumors. This is notable because we found that the tumor suppressive effects of PDK4 are most noticeable in later stages of tumor formation in mice (Figure 2). BCa tumors progress through either the CIS muscle-invasive pathway of development or the low-grade pathway of development according to recent literature [43,44]. The BBN model does not produce low-grade tumors, which prohibits analysis of how PDK4 expression in mice alters the low-grade pathway. Because we observed a marked reduction in PDK4 in low-grade samples, PDK4 may also be highly relevant in this pathway, although this would require a separate model to assess. Because BBN is highly proinflammatory and alters the tumor microenvironment, the observed reduction in PDK4 RNA expression in mice may ultimately be related to the presence of inflammatory cells that alter the overall RNA expression profile of the tumor; however, the combination of human and mouse data indicates that the most likely answer remains the direct suppression of PDK4 expression in BCa tumors. TCGA analysis indicates a reciprocal change in PDK4 expression/promoter methylation indicative of an epigenetic silencing mechanism in BCa (Figure 4). Similar observations have been made in HCC and are supported by laboratory studies indicating that arsenic can methylate and reduce the expression of PDK4 [45,46]. Epigenetic silencing of PDK4 has also been reported in other cancers [47]. In addition to epigenetic changes, PDK4 can also be regulated by miRs and undergoes transcriptional regulation in response to many biological stimuli; therefore, multiple mechanisms are plausible [23]. Importantly, through the use of a tumor microarray, we were able to show that PDK4 protein is also reduced in BCa tissue compared to control tissue from a commercially available tumor microarray using human samples. The exact mechanism of this action is undetermined and may involve multiple factors, although given the consistent data around epigenetic suppression, this is a likely contributing factor. Functionally, human studies on HCC indicate that loss of PDK4 is associated with more advanced tumors and worse outcomes, supporting the idea that PDK4 may function as a tumor suppressor [48]. Future studies should delineate what is the precipitating factor for loss of PDK4 that occurs during tumor formation and whether this is functionally related to more aggressive tumor outcomes.
The observation that PDK4 was downregulated in BBN tumors is a surprising finding, given prior in vitro and in vivo results using low-grade and high-grade tumors [24]. The data from our BBN model represent novel findings; however, we were able to corroborate the prior observed changes in low- vs. high-grade samples. The novel finding here is the comparison to solid normal tissue, wherein both late-stage murine tumors and human tumors had reduced PDK4 expression. Our findings are also corroborated by other studies. Fantini et al. previously found that PDK4 was suppressed in established BBN tumors but not animals administered BBN for a briefer period [32]. This supports the idea that the loss of PDK4 is associated with tumor formation and not just the inflammatory phase of BBN or the precancerous phase. The mechanisms that dictate the loss of PDK4 during cancer formation need to be fully evaluated in the context of both a wide range of human samples and in mice that allow for conditional or temporal knockout.
## 5. Conclusions
In conclusion, we report the novel finding that PDK4 may exhibit tumor-suppressor activity in BCa, which suggests a far more complex role for PDK4 during BCa carcinogenesis than previously described. Given the recent identification of alternate substrates of PDK4, understanding how it affects tumor formation both in the context of PDH and in the context of its other substrates is imperative. Future work will aim to understand how both canonical and non-canonical PDK4 activity reduces tumor growth and progression.
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|
---
title: Simultaneous Measurement of Changes in Mitochondrial and Endoplasmic Reticulum
Free Calcium in Pancreatic Beta Cells
authors:
- Sivakumar Jeyarajan
- Irina X Zhang
- Peter Arvan
- Stephen I. Lentz
- Leslie S. Satin
journal: Biosensors
year: 2023
pmcid: PMC10046164
doi: 10.3390/bios13030382
license: CC BY 4.0
---
# Simultaneous Measurement of Changes in Mitochondrial and Endoplasmic Reticulum Free Calcium in Pancreatic Beta Cells
## Abstract
The free calcium (Ca2+) levels in pancreatic beta cell organelles have been the subject of many recent investigations. Under pathophysiological conditions, disturbances in these pools have been linked to altered intracellular communication and cellular dysfunction. To facilitate studies of subcellular Ca2+ signaling in beta cells and, particularly, signaling between the endoplasmic reticulum (ER) and mitochondria, we designed a novel dual Ca2+ sensor which we termed DS-1. DS-1 encodes two stoichiometrically fluorescent proteins within a single plasmid, G-CEPIA-er, targeted to the ER and R-CEPIA3-mt, targeted to mitochondria. Our goal was to simultaneously measure the ER and mitochondrial Ca2+ in cells in real time. The Kds of G-CEPIA-er and R-CEPIA3-mt for Ca2+ are 672 and 3.7 μM, respectively. Confocal imaging of insulin-secreting INS-1 $\frac{832}{13}$ expressing DS-1 confirmed that the green and red fluorophores correctly colocalized with organelle-specific fluorescent markers as predicted. Further, we tested whether DS-1 exhibited the functional properties expected by challenging an INS-1 cell to glucose concentrations or drugs having well-documented effects on the ER and mitochondrial Ca2+ handling. The data obtained were consistent with those seen using other single organelle targeted probes. These results taken together suggest that DS-1 is a promising new approach for investigating Ca2+ signaling within multiple organelles of the cell.
## 1. Introduction
The Ca2+ levels of intracellular organelles are critical for maintaining proper cell function and cellular homeostasis [1,2,3]. These levels are regulated by the concerted interaction of the membrane ATPases that transport Ca2+ at the expense of ATP [4,5,6,7], as well as various Ca2+ binding proteins [8,9,10]. ER function has been of particular interest because of the important role of the ER in maintaining cytosolic Ca2+ levels and for the role of the ER Ca2+ in protein folding within the ER lumen [11,12]. In the mitochondria, which supplies cellular energy to the cell in the form of ATP, Ca2+ regulates pyruvate dehydrogenase, a Krebs cycle enzyme that helps regulate the available cellular energy supply [13,14,15,16].
Ca2+ communication between the ER and the mitochondria is mediated by the MAMS (Mitochondrial Associated Membranes) [17,18,19,20,21], functional contacts between the ER and the mitochondria that can transport Ca2+ and other signals between the two organelles. MAMs are composed of various proteins, including IP3R [22,23] or RyR [24,25] ER Ca2+ channels [26], VDAC [27], GRP75 [28], GRP78 [22] and MCU1 [29,30]. By mediating connections between these organelles, MAMs enable Ca2+ to move from the mitochondrion to the ER. The number and efficiency of MAM contact points have been shown to be modulated during different physiological states [31] or in response to disease processes, including diabetes [13,32,33,34].
To facilitate functional studies of the ER to mitochondrial Ca2+ communication and to better understand the role of MAMs in insulin-secreting pancreatic beta cells [2,28,35], we took advantage of a relatively new family of Ca2+ reporter molecules called CEPIAs [36] These reporters have been shown to be excellent probes for monitoring free Ca2+ in many different types of organelles [37,38,39]. Here we show that two different CEPIAs having distinct spectra and free Ca2+ affinities can be combined in a single plasmid to preserve probe stoichiometry to measure the ER and mitochondrial Ca2+ simultaneously. To accomplish this, we made a plasmid that contained the sequences of two different CEPIAs separated by an intervening T2A peptide sequence. This allowed the two CEPIA Ca2+ sensor sequences to be transcribed into a single mRNA that was, in turn, translated as two proteins due to ribosomal skipping [40]. Successful segregation of the two probes to their respective organelles allowed dual recordings of the ER and mitochondrial Ca2+ to be made simultaneously using live cell imaging in real time.
## 2.1. Cell Culture
Rat insulinoma cells INS-1 $\frac{832}{13}$ cells were cultured in RPMI medium containing 11 mM glucose, $10\%$ fetal bovine serum, 10 mM HEPES (N-2-hydroxyethylpiperazine-N′-2-ethanesulfonic acid), 1 mM sodium pyruvate and 55 µM beta-mercaptoethanol. Penicillin and streptomycin were added to reach a final concentration of 100 units/mL.
## 2.2. Construction of the Dual Sensor
DS-1 was made by fusing two sensors, pCMV G-CEPIA1-er (Addgene plasmid # 58215; a gift from Dr. Masamitsu Iino) and R-CEPIA3-mt (a codon-optimized synthetic gene). Sequences were cloned in frame and separated by the self-cleaving peptide T2A. T2A was used to enable the expression of equimolar concentrations in each of the two sensors [40,41,42,43,44]. To accomplish this, G-CEPIA-er was restriction digested with NotI and XbaI. Subsequently, the genes for T2A and R-CEPIA3-mt were PCR amplified and cloned in frame with G-CEPIA-er using NEBuilder HiFi DNA assembly master mix (New England Biolabs, Ipswich, MA, USA). The construct was then authenticated by Sanger sequencing in the University of Michigan DNA Sequencing Core. The annotated DNA and amino acid sequences are shown in Supplementary Materials Figures S7 and S8.
## 2.3. DS-1 Expression Using Adenovirus
To overcome the characteristic low efficiency of lipofectamine-mediated transfection, DS-1 was packaged in an adenovirus which was then used to infect INS-1 $\frac{832}{13}$ cells. *For* generating the requisite adenovirus, DS-1 was cloned in frame with a CMV promoter into a pACCMV2 adenovirus shuttle vector provided by the University of Michigan Vector Core. INS-1 $\frac{832}{13}$ cells (1 × 105) were seeded onto glass-bottomed 35 mm tissue culture dishes (Fluorodishes; WPI, Sarasota, FL, USA) and cultured overnight. Cells were transduced the next day by incubating the cells for 3 h with AdV containing DS-1 at a viral titer of 0.003 × 109 pfu/mL; the MOI as calculated was 30. Following incubation, the medium containing virus was removed and the cells were washed 2× with fresh medium. Cells were imaged 48 h post transduction. More than $90\%$ of transduced INS-1 cells expressed both DS-1 fluorophores using this protocol.
## 2.4. Solutions Used and Method of Solution Exchange during Live Cell Imaging
For free Ca2+ measurements, standard imaging buffer contained 140 mM NaCl, 3 mM CaCl2, 5 mM KCl, 2 mM MgCl2, 10 mM HEPES and 11 mM glucose. All solutions were made fresh on the day of the experiment by diluting frozen stocks.
Imaging solutions were exchanged manually by pipette during confocal imaging by aspirating off one solution and dispensing the next. A reserve volume of 500 µL was maintained in each Fluoro-dish during these exchanges. To modulate the Ca2+ levels of the two respective organelles, cyclopiazonic acid (CPA; Cayman Chemicals, Ann Arbor, MI, USA), potassium chloride (KCl) or sodium azide (NaN3; Sigma Aldrich, St. Louis, MO, USA) was added to the experimental chamber in standard imaging buffer at 2× of their working concentrations. Similarly, drugs or chemicals were removed from the chamber by repeated exchanges with imaging solution. For the glucose stimulation experiments, imaging solution with and without glucose was applied to the cells.
## 2.5. Microscopy and Imaging
Live cell imaging was carried out using a Nikon A1 laser scanning confocal microscope equipped with NIS Elements software (Nikon Instruments Inc., Melville, NY, USA) to automate both microscope scanning and photomultiplier tube detectors. To compensate for possible drift during image acquisition, the Nikon Perfect Focus System was used. Cells were imaged with a 40×, 1.3 NA Nikon oil immersion objective at 37 °C within a TOKAI HIT environmental chamber (Tokai, Shizuoka, Japan).
To perform live cell Ca2+ imaging, INS-1 $\frac{832}{13}$ cells were transduced with DS-1 for most experiments but also transfected individually with G-CEPIA-er or R-CEPIA3-mt for control experiments. G-CEPIA-er excitation was provided by a 488 nm Argon laser and emission was collected at 500–530 nm, while R-CEPIA3-mt was excited by a 543 nm HeNe laser and emission was collected at 553–618 nm. Images were collected every 10 s. Acquired images were 1024 × 1024 pixels (318.51 µm), and image resolution was 0.31 µm/pixel. With the aforementioned settings, the rate of scanning required for each fluorophore was 2.25 s per frame.
## 2.6. Localization of DS-1 within the Cell
After constructing DS-1 and expressing it in cells, the localization and function of the two distinct fluorophores of the plasmid were evaluated using confocal microscopy as shown in Figure 1. TagBFP-KDEL (Addgene plasmid # 49150) was chosen as an ER marker, and Mito-Tracker deep red (Thermo Scientific, Waltham, MA, USA) was selected to mark mitochondria. Excitation and emission wavelengths of the two markers were as follows: Tag BFP-KDEL:ER marker (Ex 405 nm and Em 425–475 nm) and Mito Tracker deep red (Ex 638 nm and Em 663–738 nm). The specific cell of interest was zoomed at 10× using the previously defined confocal settings to capture colocalization images. The correlation coefficients obtained for each of the respective markers and DS-1 were calculated using the JACoP Plugin [45] of ImageJ [46].
## 2.7. Analysis and Processing of Acquired Live Cell Imaging of Ca2+
After acquisition, images were saved as 8-bit stacked TIFF files and analyzed using ImageJ software as in [47]. Briefly, regions of interest (ROI) were drawn around individual cells, including the nucleus of the cell. As some cells exhibited movement in the scanning field at times while undergoing imaging, we drew ROIs that were larger than the individual cells to ensure that the cell images remained inside the ROIs despite small movements. Subsequently, ROIs were split into their respective red or green channels and a multi-measure prompt option was selected to obtain fluorescent intensity values within the selected cell regions. No masks were applied to fluorescent images. Values of integral density/area vs. time were plotted using GraphPad Prism (GraphPad, San Diego, CA, USA) [11]. Area under the curve (AUC) values for graphed data were calculated using Prism for each treatment condition. AUC values were then normalized and plotted over time. All time series data were plotted relative to their initial values. Data from at least 150 cells were analyzed and are reported in Figure 2. Data from at least 120 cells are reported in Figure 3.
## 2.8. Data Analysis and Statistics
Data were analyzed using GraphPad Prism. Standard error of the mean (S.E.M.) and Student’s t-test (two-tailed paired with criteria of significance: * $p \leq 0.1$; ** $p \leq 0.05$ and *** $p \leq 0.01$) were calculated.
## 3.1. DS-1 Correctly Localized CEPIA-er and CEPIA-mito to Their Targeted Organelles
Figure 1a shows the structure of DS-1 whereby the two respective CEPIA probes, G-CEPIA-er and R-CEPIA-mt, were separated by a T2A sequence. The location of the 5′ and 3′ ends of the construct are indicated, as well as the placement of a stop codon at the 3′ end. A cartoon shown beneath depicts schematically the location in the cell of the Ca2+ components SERCA, IP3 and RyR channels, MCU and NCLX.
To determine whether each sensor was correctly localized to its intended organelle when DS-1 was expressed, we used the blue fluorescent protein ER marker BFP-KDEL [48] and the far-red marker Mitotracker, as in [49]. The green and red channels corresponding to G-CEPIA-er and R-CEPIA-mt are shown together in INS1-$\frac{832}{13}$ cells in Figure 1b. In these cells, both green (ER) and red (mitochondrial) areas can be seen. Figure 1c,d display an enlarged section of the image for the green and red channels. A merger of both channels is displayed in Figure 1e, showing separation of the red and green regions with no overlap between them. This indicates that the ER and mito probes are localized to individual organelles. An additional image supporting this is shown in Figure S1d.
In Figure 1f,i, individual channels showing red vs. green emission are shown, along with either blue-fluorescent protein (BFP) linked to KDEL to mark the ER (Figure 1g) or Mitotracker Red to mark mitochondria (Figure 1j). The merged images and their correlation coefficients, which were determined from the images in the merged panel, are displayed in Figure 1h,k in the far-right columns. Visual inspection combined with the semiquantitative analysis of the confocal images confirmed that the green ER Ca2+ sensor, G-CEPIA-er, and the ER marker, BFP-KDEL, were colocalized, yielding a correlation coefficient of 0.922. The green and blue signals observed were excluded from the nucleus of the cell and appeared to take on a cytoplasmically distributed pattern consistent with that of the ER [36]. Additionally, the red mitochondrial Ca2+ sensor, R-CEPIA3-mt, colocalized with the mitochondrial standard (correlation coefficient of 0.764). The red- and magenta-colored organelles showed a Mitotracker pattern with areas of staining showing small spherical- or oval-shaped tubules. Both the morphology we observed and the colocalization of the two CEPIAs with the standard organelle markers strongly indicated that the sensors were correctly localized to the organelles of interest.
## 3.2. DS-1 Expressing Cells Responded Appropriately to Drugs Targeting ER or Mitochondrial Ca2+ Pools
When observed using confocal microscopy and standard imaging buffer with 11 mM of glucose, INS1-$\frac{832}{13}$ cells exhibited their characteristic oval-spheroid shape, with some elongated extensions apparent in individual cells. The cells clearly contained red and green fluorescence, although the patterns and intensities of the green and red signals within the cells were heterogenous (Figure 2a). While some individual cells showed more green fluorescence than red, in others, red fluorescence was prominent, or both colors were visible, including regions of red/green overlap, visible as patches of yellow. In these, signals from the ER and the mitochondrial Ca2+ pools appeared to overlap strongly. The variability we observed in the subcellular fluorescence could reflect differences in the respective trafficking of the two CEPIAs to their respective target organelles or could reflect differences in the Ca2+ concentrations of the corresponding mitochondrial or ER Ca2+ pools.
To monitor functional changes in the ER and mitochondrial Ca2+, live cell imaging was performed. Images were collected every 10 s, and cells were exposed to an imaging buffer (as above) and then the imaging buffer, which contained agents that have been previously shown to alter either the ER or mitochondrial Ca2+. The image stack acquired is shown as Movie S1 in the Supplementary Materials. Still images taken of a representative field of cells corresponding to each treatment condition are shown in Figure 2a–g. The treatments tested are listed at the top of each panel. The time courses of individual G-CEPIA-er and R-CEPIA3-mt traces are shown in Figures S4 and S5.
The mean (+/−sd) time course of the ER Ca2+ that was observed in response to various treatments carried out over more than 40 min is shown in Figure 2h, while the corresponding time course for the mitochondrial Ca2+ collected simultaneously is shown in Figure 2j. Analysis of changes in the ER and mitochondrial signals (as normalized AUC, see Methods) is shown in Figure 2i,k, respectively. The statistical summary was obtained from three independent experiments, and asterisks denote a significance level at * $p \leq 0.1$; ** $p \leq 0.05$ and *** $p \leq 0.01.$
Following an initial period in the control solution exposure to 25 μM CPA, a reversible inhibitor of ER SERCA activity [50] promptly reduced the ER Ca2+, as evidenced by a marked reduction in the level of green fluorescence while red fluorescence attributed to mitochondrial Ca2+ was still clearly visible (Figure 2b). CPA removal resulted in increased green fluorescence, consistent with the restoration of the ER Ca2+, as pumping was resumed (Figure 2c). Interestingly, the decrease in green fluorescence seen upon CPA addition corresponded to an enhanced level of red fluorescence as well (Figure 2b). Quantification of the fluorescence values obtained in Figure 2j confirmed there was a transient increase in red fluorescence that was restored to its basal level within a few minutes.
To determine whether raising the cytosolic Ca2+ in turn affected the ER and mitochondrial Ca2+, we added 30 mM KCl to the INS1-$\frac{832}{13}$ cells to depolarize them to open the voltage-gated Ca2+ channels (VDAC) [51]. As shown in Figure 2d,h–k, membrane depolarization caused a modest increase in ER Ca2+ but a much larger increase in mitochondrial Ca2+. Washing KCl reduced the Ca2+ levels of each organelle pool (Figure 2e).
As the Ca2+ pumping and K(ATP) channel closure are both metabolically regulated processes in beta cells [52,53], we added sodium azide to the cells. Azide lowers ATP/ADP in the cytosol by inhibiting complex III (cytochrome oxidase) of the mitochondrial electron transport chain [54]. We thus predicted that azide would block the mitochondrial Ca2+ uptake and reduce the ER Ca2+ by lowering ATP/ADP. As can be seen in Figure 2f–k, azide addition promptly reduced green and red fluorescence, consistent with an action to lower both the ER and mitochondrial Ca2+ levels. After removing the azide, both red and green fluorescence levels were recovered (Figure 2g).
## 3.3. DS-1 Fluorescence Responded to Changes in Glucose Concentration
To test how DS-1 and its respective G-CEPIA-er and R-CEPIA-mt signals responded to changes in glucose, INS-1 $\frac{832}{13}$ cells expressing DS-1 were bathed in glucose-free imaging solution and were then exposed to a solution containing 11 mM of glucose. Raising glucose concentration causes a rise in cytosolic Ca2+ due to the K(ATP) channel closure [55]. Increasing the glucose concentration resulted in a sustained rise in the ER and mitochondrial Ca2+, as shown in Figure 3a,c. The integrated responses of DS-1 to glucose were statistically significant, as shown in Figure 3b,d for both the ER and mitochondrial Ca2+ (obtained from three independent experiments). Specifically, INS-1 cells exhibited either single free Ca2+ transients or sustained oscillations atop a small plateau. Traces of a few individual cells showing these heterogenous responses are shown in Figure S6. However, only 20–$30\%$ of our cells showed consistent responses to rises in glucose, likely because of the heterogenous nature of INS1-$\frac{832}{13}$ cells [56,57,58,59,60], which can exhibit quite variable responses to glucose. We also noted that in some INS1-$\frac{832}{13}$ cells, the mitochondria exhibited robust Ca2+ oscillations, as can be seen in Figure 3c’s lower panel. These have been reported by other investigators using different approaches to measure mitochondrial Ca2+ [61,62,63]. In our study, mitochondrial oscillation was typically observed following a pronounced rise in ER Ca2+.
## 4. Discussion
We herein describe a novel dual Ca2+ sensor that we designed by fusing two genetically engineered Ca2+ probes, one targeted specifically to the ER (G-CEPIA-er) [36] and the other to the mitochondria (R-CEPIA3-mt) [64]. Using this new tool allowed us to measure Ca2+ simultaneously in both organelles and within the same cells. *The* gene construct was transduced into insulin-secreting INS1-$\frac{832}{13}$ cells using standard adenovirus-based methodology. We validated that DS-1 and its resultant ER and mitochondrial Ca2+ reporters were correctly targeted both by morphological criteria and by colocalization with standard organellar markers.
It has been shown previously that two or three genetically encoded Ca2+ indicators (GECIs) [36,65,66] can be expressed in the same cell using co-transfections [67,68] or sequential transductions [69,70,71,72,73]. This allows two different Ca2+ sensors to be expressed within different organelles, similar to our current study. However, the chance that each target cell receives the same ratio of the two genetically engineered genes is statistically low; selecting cells expressing probes with similar ratios in a mixed population is time-consuming and requires that certain assumptions are made. In contrast, an advantage of our single plasmid approach is that it ensures that the stoichiometry of the two different Ca2+ probes is fixed and both are expressed in the same cell. Additionally, multiple rounds of sequential transduction are avoided in our approach. DS-1 was developed to enable us to simultaneously study two organellar Ca2+ pools within the same cell and with the same stoichiometry after a single transduction. By targeting specific cellular compartments, DS-1 also avoids the limitations inherent to small fluorescent dyes, such as fura-2, which only report the Ca2+ levels of the cytosol and which cannot be targeted to the interior of specific organelles.
The functional properties of DS-1 were validated using pharmacological agents that target specific organellar Ca2+ pools. As expected, the ER Ca2+ decreased following CPA exposure and increased following CPA removal. KCl-mediated plasma membrane depolarization caused a modest increase in the ER Ca2+ but a large increase in the mitochondrial Ca2+. Briefly treating the cells with azide decreased both the ER and mitochondrial Ca2+, which returned to their initial levels after the azide removal. It is known that azide action is reversible [74,75].
We also used DS-1-expressing INS-$\frac{832}{13}$ cells to test the glucose responsiveness of the ER and mitochondria. Glucose enters the beta cell, and its metabolism subsequently leads to the closure of the K(ATP) channels [76]. This activates the voltage-dependent Ca2+ channels to increase cytosolic Ca2+ [51]. This, in turn, results in an increase in both the ER and mitochondrial Ca2+ (Figure 3a,c) [77]. This finding is generally consistent with earlier reports of the ER Ca2+ studied by use of the FRET probe, D4ER and mitochondrial Ca2+ measurements made using mitopericam [62,78], mitochondrially targeted aequorin [61,79,80] or other probes [81].
To the best of our knowledge, this is the first report showing that the ER and mitochondrial free Ca2+ can be measured simultaneously with a single plasmid in pancreatic beta cells. The approach is promising, in our view, because the overall strategy we employed to make DS-1 can be used to make other dual or even triple Ca2+ probes or probes for other metabolites of interest such as ATP [82], which are targeted to various organelles. The endless varieties of possibilities are exciting and may well lead to many new discoveries.
An obvious limitation of CEPIA-based constructs is, that being intensitometric probes, they only report relative signal levels rather than ratios. Ratiometric probes can be more sensitive to changes in levels of the analyte and may be less susceptible to changes in the optical path length or variations in probe concentration [83,84]. In addition, ratiometric probes can be more readily calibrated to absolute levels of the species being detected. We emphasize that the general dual-sensor/multi-sensor method developed for this study can be extended to ratiometric probes, as well as to study a variety of cell types.
## 5. Conclusions
In summary, we have developed a novel means to simultaneously monitor the Ca2+ levels of distinct intracellular compartments in pancreatic beta cells. Future work will incorporate additional compartments and different fluorophores for real-time imaging of metabolism in beta cells and other cell types.
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|
---
title: Impact of Hormone Replacement Therapy on Risk of Ovarian Cancer in Postmenopausal
Women with De Novo Endometriosis or a History of Endometriosis
authors:
- Hee Joong Lee
- Banghyun Lee
- Hangseok Choi
- Taehee Kim
- Yejeong Kim
- Yong Beom Kim
journal: Cancers
year: 2023
pmcid: PMC10046182
doi: 10.3390/cancers15061708
license: CC BY 4.0
---
# Impact of Hormone Replacement Therapy on Risk of Ovarian Cancer in Postmenopausal Women with De Novo Endometriosis or a History of Endometriosis
## Abstract
### Simple Summary
Endometriosis is a risk factor for ovarian cancer. Meta and pooled analyses have shown hormone replacement therapy (HRT) is significantly associated with an increased risk of ovarian cancer. A combination of estrogen and progesterone is currently recommended to improve menopausal symptoms in women with a history of endometriosis. However, the effect of HRT on the malignant transformation of postmenopausal endometriosis remains unclear. Therefore, this study investigated the impact of HRT on ovarian cancer occurrence in 20,608 postmenopausal women with de novo endometriosis or a history of endometriosis using the nationwide cohort study. With the exception of HRT using estrogen alone, HRT did not increase the risk of ovarian cancer in postmenopausal women with de novo endometriosis or a history of endometriosis. HRT, but not estrogen alone, can be used to improve the quality of life in symptomatic women with postmenopausal endometriosis.
### Abstract
The effect of hormone replacement therapy (HRT) on the malignant transformation of postmenopausal endometriosis remains unclear. This study aimed to investigate the impact of HRT on ovarian cancer occurrence in postmenopausal women with de novo endometriosis or a history of endometriosis. A total of 10,304 women that received HRT (the HRT group) and 10,304 that did not (the control group) were selected by 1:1 matching those that met the study criteria. Incidences of ovarian cancer ($0.3\%$ in the HRT group and $0.5\%$ in the control group) and cumulative incidence rates of ovarian cancer were similar in the two groups. The overall mean duration of HRT was 1.4 ± 2.2 years, but the duration of HRT in women with ovarian cancer was 2.2 ± 2.9 years. After adjusting for co-variables, receipt of HRT, duration of HRT, combined use of estrogen and progesterone, and tibolone were not found to be risk factors for ovarian cancer. However, the use of estrogen alone was found to be a significant risk factor for ovarian cancer (HR 2.898; $95\%$ CI 1.251–6.715; $$p \leq 0.013$$). With the exception of HRT using estrogen alone, HRT did not increase the risk of ovarian cancer in postmenopausal women with a history of endometriosis or de novo endometriosis.
## 1. Introduction
Endometriosis is an estrogen-dependent benign disease considered resolved after menopause [1]. However, endometriosis affects 6–$10\%$ of premenopausal women and 2–$4\%$ of postmenopausal women [2]. Endometriotic lesions have a risk of around $1\%$ for malignant transformation [2,3,4], and after menopause, they may undergo recurrence or malignant transformation [1,5,6].
Endometriosis is a risk factor for epithelial ovarian cancer (EOC), which accounts for about $90\%$ of ovarian cancer cases, especially clear-cell and endometrioid subtypes [7,8]. Ovarian cancer is the most common cause of death among women with gynecologic cancer [9]. Women with ovarian cancer have a median age at diagnosis of 57–63 years [10], and therefore, many women are diagnosed with ovarian cancer after menopause. Meta and pooled analyses have shown hormone replacement therapy (HRT) is significantly associated with an increased risk of ovarian cancer [11,12]. Furthermore, the risk of ovarian cancer is reported to be higher for current-or-recent users and serous and endometrioid subtypes as well as to increase with HRT duration (especially for durations of ≥10 years) [11,12].
It has been proposed that HRT may reactivate endometriotic lesions and stimulate malignant transformation in women with a history of endometriosis [1,13,14,15,16,17]. However, only a few small studies, namely one randomized controlled trial (RCT) and two retrospective cohort studies, have compared endometriosis recurrence with respect to HRT in postmenopausal women with a history of endometriosis [18,19,20]. Therefore, due to the lack of high-quality evidence, the absolute risks of endometriosis recurrence and malignant transformation cannot be determined, and the impact of HRT use on these risks is unknown [1].
Low-quality evidence suggests that in women with a history of endometriosis, HRT may be used to improve menopausal symptoms [13,21]. However, estrogen alone appears to have a higher risk of endometriosis recurrence and malignant transformation of endometriomas than estrogen and progesterone in combination [1,13,22], and tibolone seems to be safer than estrogen with or without progesterone [23]. Based on available results, combined estrogen and progesterone is currently recommended in women with a history of endometriosis [13,21], though tibolone may be considered [13].
Currently, the effect of HRT on the malignant transformation of recurrent or de novo postmenopausal endometriosis remains unclear. This retrospective, nationwide study was undertaken to determine the impact of HRT on ovarian cancer occurrence in postmenopausal women with a history of endometriosis or de novo endometriosis using Korean Health Insurance Review and Assessment Service (HIRA) data.
## 2. Materials and Methods
South Korea has a universal health coverage system, known as National Health Insurance, that covers ~$98\%$ of the Korean population [24]. HIRA claims data include ~23 million women per year [24]. In the present study, we used the claims data of women with diagnostic codes for endometriosis first registered in HIRA between 1 January 2007 and 31 December 2020. The study was approved by the Institutional Review Board of The Catholic Medical Center at the Catholic University of Korea (No. UC21ZESI0126) on 24 September 2021. The requirement for informed consent was waived because the HIRA dataset uses anonymous identification codes to protect personal information as required by the Korean Bioethics and Safety Act.
The codes used to select eligible patients were based on the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), Health Insurance Medical Care Expenses (2017 and 2018 versions), and HIRA Drug Ingredients Codes. Women with endometriosis were defined to have diagnostic codes for endometriosis (ICD-10: N80X) with surgery codes within 60 days before or after an initial diagnostic code. The following exclusion criteria were applied: women ≤49 years old at last clinic visit; women with N80X codes registered between 1 January 2007 and 31 December 2007 (washout period); women diagnosed with ovarian cancer before the date of the first diagnostic code for endometriosis. Women that received HRT (the HRT group) and those that did not (the control group) were identified via 1:1 matching according to age at last clinic visit. Women diagnosed with ovarian cancer before receiving HRT and those diagnosed within one year of HRT receipt were excluded (Figure 1). In addition, women diagnosed with ovarian cancer before menopause or within one year after menopause were excluded from the control group. In this study, in the HRT group, the date of menopause was defined as the date of HRT commencement, whereas in the control group, the date of menopause was defined by matching with the HRT group. Thus, women diagnosed with ovarian cancer in the HRT or control groups before HRT or menopause or within one year after HRT or menopause were excluded (Figure 1).
Women with ovarian cancer were defined to have one or more diagnostic codes for ovarian cancer (ICD-10: C56x) and V193; the V code is a special code for women with any ICD-10 cancer code in South Korea and was established by the Korean Ministry of Health and Welfare in 2008. Women in the HRT group received HRT prescriptions for ≥28 days, whereas women in the control group did not receive a prescription for HRT. Low socioeconomic status (SES) was defined as the use of Medicaid as National Health Insurance. Charlson Comorbidity Indices (CCIs) were calculated using data obtained between 365 days and 1 day before last clinic visit, as described by Quan [25]. Surgery was defined using surgery codes for fulguration, ovarian cystectomy, bilateral or unilateral salpingo-oophorectomy (BSO or USO), or hysterectomy and a concurrent diagnostic code for endometriosis, or surgery codes for fulguration, ovarian cystectomy, BSO, or USO or hysterectomy within 60 days before or after the date of the diagnostic code for endometriosis. Hormone therapy was defined as prescription codes for hormone therapy (combined estrogen and progesterone, estrogen alone, or tibolone).
## Statistical Analyses
The analysis was conducted using R version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria), and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) was used to explore and modify big data [26,27]. In the HRT and control groups, women with the same ages at their last clinic visits were considered homogeneous, and thus, the two groups were 1:1 matched for age. The independent t-test was used to analyze continuous variables, and logistic regression analysis, adjusted or not for confounding factors, was used to identify associations between the independent risk factors in each group. Kaplan–Meier curves were constructed using the log-rank test to examine the cumulative incidence rates of ovarian cancer. Trend analysis was conducted using the chi-squared test for trends in proportions, and associations between variables and ovarian cancer were identified via Cox Proportional Hazard *Regression analysis* with or without adjustment for confounders. Two-tailed tests were used throughout. The mean imputation method was used to account for missing values, and p values of <0.05 were considered statistically significant.
## 3. Results
Initially, the data of 115,552 women with a diagnostic code for endometriosis first registered by HIRA between 2007 and 2020 were extracted. Of these, 20,608 women met the study eligibility criteria (Figure 1), and 10,304 ($50.0\%$) were assigned to the HRT group and 10,304 ($50.0\%$) to the control group.
## 3.1. Characteristics of Women with Endometriosis According to Receipt of HRT
The baseline characteristics of the 20,608 study subjects are shown in Table 1. Mean age at last clinic visit in both groups was 55.0 ± 4.6 years, and ages ranged between 50–90 years (Figure S1). Age at endometriosis diagnosis was lower in the HRT group than in the control group (48.9 ± 5.8 vs. 49.6 ± 5.7 years, $p \leq 0.001$), and ages ranged between 37–89 years (Figure S2). The duration of HRT was 1.4 ± 2.2 years and ranged between 1–11 years (Figure S3). The rate of HRT use before endometriosis diagnosis was $26.0\%$, and the rate of HRT use after endometriosis diagnosis was $74.0\%$.
## 3.2. Incidences and Characteristics of Ovarian Cancer According to Receipt of HRT in Women with Endometriosis
The incidences of ovarian cancer were very low ($0.3\%$ in the HRT group and $0.5\%$ in the control group). Receipt of HRT was not associated with the occurrence of ovarian cancer (Table 2). Cumulative incidence rates of ovarian cancer were similar in the two groups (Figure 2), and the incidences of ovarian cancer did not change with time in the HRT and control groups ($$p \leq 0.799$$ and 0.183, respectively).
Times between endometriosis diagnosis and ovarian cancer diagnosis were similar in the two groups (1.9 ± 2.9 vs. 1.7 ± 2.7 years) (Table 2) and ranged between 1–11 years (Figure S4). Mean time from HRT commencement to ovarian cancer diagnosis was 5.7 ± 3.4 years (Table 2) and ranged between 2 and 11 years (Figure S5). Duration of HRT in women with ovarian cancer was 2.2 ± 2.9 years (Table 2) and ranged between 1 and 11 years (Figure S6). Mean ages at ovarian cancer diagnosis were similar in the HRT and control groups (55.4 ± 8.1 vs. 56.1 ± 7.0 years, respectively) (Table 2) and ranged between 42 and 76 years (Figure S7).
## 3.3. Risk Factors of Ovarian Cancer According to Receipt of HRT in Women with Endometriosis
Multivariate analysis adjusted for potential confounders showed that the risk of ovarian cancer increased significantly with age at endometriosis diagnosis in the control group (HR 1.066; $95\%$ CI 1.022–1.111; $$p \leq 0.003$$) but not in the HRT group. In both groups, the risk of ovarian cancer increased significantly with respect to time after endometriosis diagnosis (the HRT group: HR 1.385; $95\%$ CI 1.210–1.585; $p \leq 0.001$) (the control group: HR 1.216; $95\%$ CI 1.099–1.347; $$p \leq 0.001$$) and the number of surgeries for endometriosis (for the HRT group, HR 5.206, $95\%$ CI 1.631–16.612, $$p \leq 0.005$$, and for the control group, HR 7.605, $95\%$ CI 3.230–17.905, $p \leq 0.001$). However, the risk of ovarian cancer was significantly reduced via hysterectomy for disease except ovarian cancer, in both groups (for the HRT group, HR 0.111, $95\%$ CI 0.036–0.344, $$p \leq 0.001$$, and for the control group, HR 0.031, $95\%$ CI 0.007–0.137, $p \leq 0.001$) (Table 3).
Receipt of HRT, HRT duration, receipt of combined estrogen and progesterone, and receipt of tibolone were not found to be risk factors for ovarian cancer. However, the use of estrogen alone was found to be a significant risk factor for ovarian cancer (HR 2.898; $95\%$ CI 1.251–6.715; $$p \leq 0.013$$) (Table 3).
## 4. Discussion
In the present study, the incidences of ovarian cancer were very low and similar in postmenopausal women with a history of endometriosis or de novo endometriosis administered or not administered HRT, and cumulative incidence rates of ovarian cancer were similar in these two groups. Moreover, incidences of ovarian cancer did not change with time in either group. Multivariate analysis adjusted for co-variables showed the use of HRT was not a risk factor of ovarian cancer and that the duration of HRT, use of combined estrogen and progesterone, and use of tibolone were not risk factors of ovarian cancer. However, the use of estrogen alone was found to increase the risk of ovarian cancer significantly.
Age at menopause for Korean women is ~50 years [28]. Therefore, women aged ≤49 years at their last clinic visit were excluded from the present study. HRT is usually prescribed to perimenopausal or postmenopausal women to ameliorate menopausal symptoms. In the present study, based on HRT use before or after endometriosis diagnosis, $26.0\%$ of the women with HRT may have had de novo postmenopausal endometriosis and $74.0\%$ have a history of endometriosis.
Endometriosis and HRT are associated with an increased risk of ovarian cancer [7,8,11,12]. Therefore, we expected that women with endometriosis who received HRT would have a higher risk of ovarian cancer than those that did not receive HRT. However, HRT did not increase the risk of ovarian cancer or influence cumulative incidence rates. Moreover, HRT did not change the incidence of ovarian cancer with respect to time. The incidence of ovarian cancer determined in the present study concurs with that found in a previous large-scale cross-sectional study ($$n = 4331$$; $0.2\%$) [29].
In previous meta and pooled analyses, the risk of ovarian cancer was found to increase significantly with HRT duration [11,12], but receipt of estrogen alone for 1 to <10 years in women >50 was not associated with ovarian cancer risk [12]. We found HRT duration was not a risk factor for ovarian cancer in postmenopausal women with a history of endometriosis or de novo endometriosis. We attribute this difference to the short duration of HRT. Furthermore, the duration of HRT was longer in women with ovarian cancer than in all women that underwent HRT (2.2 ± 2.9 vs. 1.4 ± 2.2 years). Furthermore, in our study, women developed ovarian cancer ~3.5 years after stopping HRT.
Previous studies and the ESHRE guideline recommend that in women with a history of endometriosis, combined estrogen and progesterone may be used to treat postmenopausal symptoms or until at least the age of natural menopause after surgical menopause [13,21]. However, estrogen alone is not recommended because of an elevated risk of malignant transformation [13,21]. In a small RCT ($$n = 21$$), tibolone resulted in a non-significantly better moderate pelvic pain rate than transdermal estradiol with or without progesterone ($9\%$ vs. $40\%$) after treatment for one year in women with residual pelvic endometriosis [23], and as a result, tibolone was recommended as a safe hormonal treatment for postmenopausal women with a history of endometriosis [13,23]. Likewise, in our study, neither tibolone nor combined estrogen and progesterone were risk factors for ovarian cancer. On the other hand, estrogen alone was identified as a risk factor for ovarian cancer. Thus, the present study shows that unopposed estrogen increases the risk of malignant transformation of endometriotic foci, which supports previous studies and the ESHRE guideline [1,13,21].
In a nationwide cohort study ($$n = 45$$,790), the incidence of ovarian cancer increased with time after endometriosis diagnosis (standardized incidence ratios for ovarian cancer were 1.51 at 1–4 years and 1.78 at 5–9 years post-diagnosis) [8]. Interestingly, in our study, women that received HRT underwent more surgeries for endometriosis, and regardless of HRT use, the risk of ovarian cancer increased with the number of surgeries, which suggests the risk increased with time after endometriosis diagnosis.
The CNGOF/HAS clinical practice guidelines mention that concomitant hysterectomy during an endometriotic lesion resection might reduce recurrence rates as compared with the endometriotic lesion resection alone [30]. In the present study, women in the HRT group received ovarian cystectomy, BSO, or USO for endometriosis more frequently than women without HRT but underwent fewer hysterectomies for endometriosis or diseases not including ovarian cancer. However, regardless of HRT use, hysterectomy for disease except ovarian cancer reduced the risk of ovarian cancer, which suggests hysterectomy reduced the risk of endometriosis recurrence.
This nationwide, population-based cohort study is the first to investigate the impact of HRT on the incidence of ovarian cancer in postmenopausal women with a history of endometriosis or de novo endometriosis. Limitations of this study are mainly related to the use of claims data. First, diagnostic and prescription codes were used to define diseases and treatments and medical records were not reviewed, and thus, a small number of women with incorrect codes may have been misclassified. Second, we were not able to determine the times of menopause in either study group because the HIRA dataset did not provide this information. Therefore, we defined the date at menopause in both groups as the date when women in the HRT group first received HRT. Third, we included women first diagnosed with endometriosis from 1 January 2007 using the HIRA dataset. However, it is possible women diagnosed with endometriosis before 2007 and subsequently suffered recurrence were included. Furthermore, we were unable to precisely determine when women were diagnosed with endometriosis.
## 5. Conclusions
This retrospective, nationwide study based on HIRA claims data shows that, excepting estrogen alone, HRT does not increase the risk of ovarian cancer in postmenopausal women with a history of endometriosis or de novo endometriosis. Our results indicate HRT, but not estrogen alone, can be used to improve the quality of life in symptomatic postmenopausal women with a history of endometriosis or de novo endometriosis. We recommend that a large-scale RCT be undertaken to confirm our results.
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|
---
title: Exploratory Evaluation of Neopterin and Chitotriosidase as Potential Circulating
Biomarkers for Colorectal Cancer
authors:
- Andra Ciocan
- Răzvan A. Ciocan
- Nadim Al Hajjar
- Andreea M. Benea
- Stanca L. Pandrea
- Cristina S. Cătană
- Cristina Drugan
- Valentin C. Oprea
- Dan S. Dîrzu
- Sorana D. Bolboacă
journal: Biomedicines
year: 2023
pmcid: PMC10046191
doi: 10.3390/biomedicines11030894
license: CC BY 4.0
---
# Exploratory Evaluation of Neopterin and Chitotriosidase as Potential Circulating Biomarkers for Colorectal Cancer
## Abstract
Chronic inflammation is demonstrated to play a direct role in carcinogenesis. Our exploratory study aimed to assess the potential added value of two inflammation biomarkers, chitotriosidase and neopterin, in follow-up evaluation of patients with colorectal cancer (CRC). An observational exploratory study was conducted. Patients with CRC and matched controls (1:1, age, sex, and living environment) were evaluated. The patients with CRC (CRC group) and controls were assessed at baseline (before surgical intervention for patients with CRC). Patients with CRC were also evaluated at 1-year follow-up. Significantly more patients with blood group A ($54.5\%$ vs. $25.0\%$) and smokers ($50.0\%$ vs. $22.7\%$) were in the CRC group. The serum values of chitotriosidase and neopterin were higher in CRC patients than in controls, but only neopterin reached the conventional level of statistical significance (p-value = 0.015). The circulating chitotriosidase and neopterin values decreased significantly at 1-year follow-up (p-value < 0.0001). Patients with higher N- and M-stage showed statistically significant higher levels of chitotriosidase and neopterin at baseline and 1-year follow-up (p-values < 0.03). Circulating chitotriosidase levels also showed statistically significant differences regarding baseline and 1-year follow-up on patients with CRC and different differentiation grades (p-values < 0.02). The circulating levels of neopterin significantly decreased at 1-year follow-up, indicating its potential as a prognostic marker. The circulating values of chitotriosidase and neopterin exhibit significant differences in patients with than without recurrences. Our results support further evaluation of chitotriosidase and neopterin as prognostic markers in patients with CRC.
## 1. Introduction
Colorectal cancer (CRC) is the third most common type of cancer worldwide, 3rd most common in men, and 2nd most in women [1]. Colorectal cancer was responsible in 2020 for $12.7\%$ of all new cancer diagnoses in 27 European Countries and $12.4\%$ of all deaths due to cancer [1].
Tumor development and tumor progression are associated with chronic inflammation, impaired immunity, and cellular activation [2,3]. Moreover, in a mutual and continuous exchange of information, tumor cells are exposed to microenvironment transformation [4]. The presence of lymphocyte cells is noticed at the microscopic level, with tumor-associated macrophage cells as a significant component of tumor infiltrates [5]. Tumor-associated macrophage cells are involved in tumor progression by stimulating angiogenesis, tumor proliferation, invasion, and metastasis [6]. Colonization of the gut with proinflammatory bacterial strains, interpreted as dysbiosis, promotes chronic inflammation. Furthermore, the interaction between the intestinal microbiome and the immune system acts as aggressive elements on the gut mucosa, thus increasing the risk of dysplasia and consecutive carcinogenesis [7,8,9].
Neopterin is a direct product of the immune system activation, stimulated by the T-cell’s release of interferon-γ, which also induces indoleamine 2,3-dioxygenase, an enzyme involved in the catabolism of tryptophan to kynurenine [10]. Neopterin is the oxidized form of 7,8-dihydroneopterin, a catabolite of the purine nucleotide guanosine triphosphate—GPT [11]. The effects of neopterin are not yet fully elucidated. Still, studies indicated a link between neopterin and oxidative stress, with the formation of reactive oxygen species [12] known to play an essential role in the initiation, proliferation, and development of cancer cells by maintaining their survival [13]. The serum values of neopterin showed limited performance as a marker for CRC compared to CEA (CarcinoEmbryonic Antigen), TPA (tissue plasminogen activator), and CA $\frac{19}{9}$ (cancer antigen 19-9) [14]. However, patients with CRC showed significantly elevated neopterin levels (median = 20.2 nmol/L, IQR = [14.2 to 27.2], where IQR is the interquartile range) than controls (median = 19.6 nmol/L, IQR = [15.4 to 24.2]; p-value < 0.001) [15]. Furthermore, Zuo et al. [ 16] reported that subjects with elevated serum neopterin levels are at higher risk of developing colorectal cancer (hazard ratio = 1.09, $95\%$ CI = [1.03 to 1.16], $$p \leq 0.007$$; value adjusted for age, sex, body mass index, smoking status, and renal function).
Chitotriosidase, an enzyme produced by polymorphonuclear neutrophils and mature macrophages in their late differentiation state [17], is encoded by the CHIT1 gene on chromosome 1q32.1 [18]. It is a well-conserved enzyme that catalyzes the hydrolysis of chitin and chitin-like substrates [19], aiding the destruction of chitotriose-walled pathogens and, thus, promoting innate immunity [20]. The enzyme is closely related to macrophage and neutrophilic activation. Chitotriosidase serum levels had higher values in patients with critical limb ischemia [21], diabetes mellitus [22], lysosomal storage disorders [23], overweight and obesity in children [24], breast or prostate cancer [20], and pulmonary diseases [25,26]. Patients with CRC had higher levels of chitinase (median = 21.13 ng/μL, IQR = (17.35–26.16)) than healthy controls (median = 17.21 ng/μL, IQR = (15.39–21.27); $p \leq 0.0001$) [27]. Furthermore, metastasis was associated with higher chitinase levels in colorectal cancer patients. Kawada et al. [ 28] reported significantly (p-value < 0.05) higher plasma levels of CHI3L1 (chitinase 3-like 1), another member of the family of chitinases, in patients with CRC ($$n = 31$$) than in controls ($$n = 12$$), the higher values being associated with TNM (T = tumor, N = nodes, and M = metastases) stage III/IV [28].
We hypothesize that circulating levels of chitotriosidase and neopterin, as biomarkers of inflammation, might change in patients with colorectal cancer after surgery, followed by oncological treatment or not. The objectives of the current study were to assess: (i) the variability of circulating chitotriosidase and neopterin in patients with CRC as compared to controls; (ii) the changes of circulating values of chitotriosidase and neopterin after standard treatment in patients with CRC; (iii) the association of chitotriosidase and neopterin plasma levels with current CRC markers.
## 2. Materials and Methods
The study was conducted according to the ethical principles of the Declaration of Helsinki. All patients signed informed written consent at inclusion in the study after an appropriate presentation of study objectives and highlighting the volunteer participation and the right to withdraw without consequences upon the medical care.
## 2.1. Study Design
An exploratory observational study was conducted at Third Surgical Clinic, “Prof. Dr. Octavian Fodor” Regional Institute of Gastroenterology and Hepatology Cluj-Napoca, Romania. Two groups of subjects were evaluated: the CRC group, which included patients with a diagnosis of CRC based on a colonoscopy with a positive adenocarcinoma diagnosis at biopsy, and the control group (C group), which included matched (1:1) CRC-free subjects (Figure 1). The stage of CRC was established according to contrast-enhanced Computer Tomography (CT) scan, Contrast Enhanced Ultrasound (CEUS), or Magnetic Resonance Imaging (MRI) [29].
## 2.2. Demographic and Clinical Characteristics
Age, sex, location, and lifestyle (smoking status and alcohol consumption) data were collected as demographic variables. Data regarding body mass index (BMI) and blood type, known risk factors for CRC [30,31], were used to characterize the subjects included in the study.
## 2.3. Measurement of Circulating Biomarkers
Peripheral venous blood samples were collected from participants at the inclusion, and the circulating markers were dosed (Figure 1). Blood samples were centrifuged within less than 45 min from the collection to obtain 1.5 mL of serum, which was later frozen and stored at −80 °C. The biochemical dosages of the inflammation markers (chitotriosidase and neopterin) took place using the Human Neopterin ELISA kit Fine Test® (EH3413, Wuhan Fine Biotech Co., Wuhan, China) and Human CHIT1 (Chitotriosidase-1) ELISA kit Elabscience® (E-EL-H5620, Elabscience Biotechnology Inc, Houston, TX, USA), according to the manufacturer’s instructions.
To quantify C-Reactive Protein (CRP), particles with a polystyrene core and a hydrophilic shell are employed to connect anti-CRP antibodies covalently. A diluted test sample solution is combined with latex particles coated with monoclonal anti-CRP antibodies from mice. The CRP present in the test sample will combine with latex particles to produce an antigen-antibody complex. Light scattering, determined by a nephelometric technique after six minutes, is proportional to the sample’s analyte concentration. A blank subtraction is executed automatically, and the CRP concentrations are calculated using a calibration curve. Signal data reduction is conducted using a logit-log function for the stored calibration curve. For quantitative CRP determination, these experiments were done using a Behring Nephelometer.
The concentration of CarcinoEmbryonic Antigen (CEA) was measured using a semi-quantitative fluorescence approach. Establishing a CEA Standard Curve CEA antigen samples of varying concentrations were sequentially added and treated with tagged primary antibodies and fluorescence-labeled secondary antibodies. The microfluidic device was spun in a horizontal centrifuge for 150 s at 2500 rpm. Simultaneously, fluorescence pictures were captured using a microscope with an exposure period of 3.5 s. ImageJ software was used to build a standard curve between the concentration of CEA and the matching fluorescence intensity from the fluorescence pictures. Bovine serum albumin (BSA) blocked bead–antibody complexes, labeled primary antibodies, and fluorescence-labeled secondary antibodies were introduced to the microfluidic centrifuge system and incubated at room temperature for 2 h. Then, clinical blood samples obtained from healthy and cancer patients were then put into the centrifuge chip and spun for 2.5 min at 2500 rpm. After acquiring fluorescence pictures using a fluorescence microscope and processing them with the ImageJ program, the standard concentration curve was used to calculate the experimental CEA concentrations.
Measurements of CRP (C-Reactive Protein) and CEA (CarcinoEmbryonic Antigen) were made with COBAS PRO C 503/E801 (Roche Diagnostics International Ltd., Rotkreuz, Switzerland).
Circulating markers of patients with CRC were measured at the 1-year follow-up visit. Furthermore, the events that occurred between the surgery and the 1-year follow-up were collected: adjuvant chemotherapy/radiation/biological treatment, tumor recurrence, newly diagnosed metastasis, and vital status (dead/alive).
## 2.4. Statistical Methods
Qualitative raw data were reported as numbers and ratios or percentages, and the differences between groups were tested with the Chi-squared or Fisher’s exact test according to expected frequencies. The distribution of quantitative data was tested with the Shapiro-Wilk test and reported as mean (standard deviation) whenever p-value > 0.05, or median IQR (interquartile range, defined as [Q1 to Q3], where Q1 is the value of the first quartile and Q3 is the value of the third quartile). The differences between groups (CRC group vs. C group) on quantitative data were tested with a Student t-test for data that proved to follow the normal distribution; otherwise, the Mann-Whitney test was used. The Kruskal-Wallis test was used to compare more than two sub-groups at once (e.g., T-stage, G grade, etc.) followed by post-hoc analysis whenever statistical significance was observed and more than three subjects per sub-group were encountered. Wilcoxon matched pairs test was applied to compare the baseline with follow-up quantitative data in the CRC group.
The exploratory statistical analysis was run with the TIBCO Statistica program (v. 13.5, StatSoft Inc, Tusla, OK, USA) at a significance level of $5\%$.
## 3. Results
Fifty-nine patients with CRC were eligible, and 44 patients were evaluated. Fifteen patients were lost from postoperative follow-up and thus were excluded from the study.
## 3.1. Colorectal Cancer Group vs. Control Group
Forty-four patients with CRC and an equal number of cancer-free subjects aged between 31 and 86 years were evaluated.
Most evaluated subjects were men (29, $65.9\%$), and a small number of participants were from rural areas (16 subjects, $36.4\%$) in each group. The groups were similar regarding age, declared alcohol consumption, and body mass index (BMI) (Table 1). The colorectal cancer group contains significantly more smokers and patients with blood type AII (Table 1).
Twenty-two subjects in the CRC group were smokers, out of which 5 ($\frac{5}{22}$) were light smokers, 8 ($\frac{8}{22}$) were moderate smokers, and 9 ($\frac{9}{22}$) were heavy smokers. Ten subjects in the C group were smokers: 1 ($\frac{1}{10}$) light smoker, 3 ($\frac{3}{10}$) moderate smokers, and 6 ($\frac{6}{10}$) heavy smokers. No significant association was observed between smoking status (light, moderate, or heavy smokers) and the groups (Fisher’s exact test: p-value = 0.6495).
The values of CRP and neopterin were significantly higher in the CRC group than in the C group (Table 2 and Figure 2).
The circulating value of chitotriosidase and neopterin showed no significant differences when smokers were compared with non-smokers, neither in the CRC group (Mann-Whitney test: p-value = 0.9626 for chitotriosidase and 0.6221 for neopterin) nor in C group (Mann-Whitney test: p-value = 0.9219 for chitotriosidase and 0.2686 for neopterin).
## 3.2. Patients with Colorectal Cancer: Pre- and Postoperative Comparison
Most tumors in the colorectal cancer group were moderately differentiated grade G2 and metastasis-free (Table 3).
A total of two patients from the colorectal cancer group (T4 stage) died after discharge during the postoperative follow-up. Therefore, the number of patients in the follow-up comparison was 42.
The values of CEA significantly reduced at follow-up in patients without recurrent metastasis (Table 4). The serum values of CRP at 1-year follow-up were not statistically significant different from the baseline (Table 4).
Thirty-two out of forty-two patients ($76.2\%$) received adjuvant chemotherapy. Twenty-five patients received neoadjuvant radiotherapy ($56.8\%$), 28 received chemotherapy ($63.6\%$), and six patients ($13.9\%$) received biological treatment. The hospitalization stays ranged from 6 to 24 days (median = 9, IQR = [7 to 11]). Tumor recurrence was observed in 8 patients ($19\%$), and metastasis in evolution was observed in 10 patients ($23.8\%$).
The values of chitotriosidase and neopterin decreased significantly at follow-up (Figure 3). The circulating values of neopterin remain significantly reduced at 1-year follow-up after excluding extreme values (Wilcoxon matched pairs test: p-value < 0.0001).
Neopterin proved to be sensitive in distinguishing between tumor stages at baseline, N-stage, and M-stage, both baseline and follow-up (Table 5). Similar differences were also observed for chitotriosidase; this marker was also significantly associated with the differentiation grade (Table 5).
The variation of chitotriosidase and neopterin and significant differences within sub-groups are illustrated in Figure 4 and Figure 5.
No statistically significant differences were observed when baseline values were compared with 1-year follow-up values neither for chitotriosidase nor for neopterin in patients with and without neoadjuvant radiotherapy (Mann-Whitney tests: p-values > 0.18) or chemotherapy (Mann-Whitney tests: p-values > 0.28). Patients with biological neoadjuvant therapy exhibit, at the baseline measurements, significantly higher values of chitotriosidase (with 6.7 ng/mL [6.5 to 7.5], $$n = 6$$ vs. without 2.5 ng/mL [1.3 to 3.6], $$n = 37$$, Mann-Whitney test: p-value = 0.0002) and neopterin (with 9.6 ng/mL [9.5 to 9.6], $$n = 6$$ vs. without 2.6 ng/mL [2.2 to 7.8], $$n = 37$$, Mann-Whitney test: p-value = 0.0001) than those without biological neoadjuvant therapy.
Patients with tumor recurrence at 1-year follow-up showed statistically significant elevated follow-up chitotriosidase values (with 4.3 ng/mL [4.1 to 4.7], $$n = 8$$ vs. without 1.8 ng/mL [0.9 to 4], $$n = 34$$; Mann-Whitney test: p-value = 0.0282) and neopterin (with 3.1 ng/mL [2.3 to 6.2], $$n = 8$$ vs. without 1.6 ng/mL [1.3 to 2], $$n = 34$$, Mann-Whitney test: p-value = 0.0003) than those without recurrence.
A monotonic association has been identified between circulating values of chitotriosidase and CEA at the 1-year follow-up, with a value of 0.30 for Spearman’s correlation coefficient (ρ) (p-value = 0.0496). The association between the two at baseline reached a tendency to statistical significance (ρ = 0.27, p-value = 0.0780).
## 4. Discussion
Our study showed increased circulating levels of chitotriosidase and neopterin in patients with colorectal cancer compared to cancer-free subjects. Still, the difference reached statistical significance only for neopterin. Chitotriosidase and neopterin showed high and proportional levels in patients with CRC with advanced stages and with the presence of metastasis. At the 1-year follow-up, a statistically significant decrease in neopterin circulating levels was observed, indicating its potential as a prognostic marker.
## 4.1. Patients with Colorectal Cancer versus Controls
The subjects included in our study were between 31 and 86 years old, with a higher preponderance of men ($65.9\%$) as already reported in the scientific literature [32]. The median age of patients with CRC in our study was 63 years. Studies have shown an increasing trend in incidence towards young ages, between 20 and 40 years, compared to previous reports [33,34], probably due to the screening programs.
In our sample, half of the patients in the CRC group were smokers, almost twice that in the control group (Table 1). The association between smoking and CRC is already known, with a statistically significant pool relative risk of 1.18 (IQR = 1.11 to 1.25) reported by Botteriet al. [ 35]. Smoking was associated with a poor prognosis in colorectal cancer (pooled estimated relative risk of cancer mortality equal to 1.25, IQR = 1.14 to 1.37) [35]. Quitting smoking improves CRC-specific survival (HR ≥ 10 years = 0.76; $95\%$ CI = [0.67 to 0.85], HR is the hazard ratio) [36].
Obesity and a sedentary lifestyle are associated with the onset of colorectal cancer [37]. In our CRC group, more than half of the patients were obese and had A blood type followed by 0 blood type (Table 1). Similar results were previously published in the scientific literature, with CRC reported more frequently among subjects with A blood type [38], similar to gastric cancer [39].
The value of C-reactive protein was higher in the CRC group than in the control group (p-value < 0.0001), a result similar to what Holm et al. reported [40]. The patients with CRC had higher neopterin and chitotriosidase levels than the controls, but only neopterin reached the significance threshold (Figure 2). Elevated levels of circulating neopterin in patients with CRC compared to controls were previously reported [11,41]. The neopterin circulating levels in our study look higher (Table 2, Figure 2) than the values reported by Hacisevki et al. [ 41] (CRC group: 4.20 ± 0.68 ng/mL, $$n = 40$$ vs. control group: 1.57 ± 0.13 ng/mL, $$n = 25$$ in the controls). Circulating chitotriosidase shows similar values in CRC patients than controls (Table 2, Figure 2). Opposite to our results, Song et al. [ 42] reported higher chitinase values in patients with CRC than in controls in the Chinese population. The characteristics of the evaluated population could explain the differences. In Romania, at least one lipid abnormality is reported as $67.1\%$, and the prevalence of low HDL-cholesterol is $47.8\%$ ($95\%$CI = [46.3 to $49.2\%$]) [42]. High neopterin circulating levels have been reported as associated with low HDL-cholesterol levels (high-density lipoprotein) [43].
In our study, circulating values of chitotriosidase and neopterin proved similar in smokers and non-smokers regardless of the group (CRC or C group). As previously reported, smoking is not a confounder [21,22]. In neoplastic patients, the inflammation effect induced by exposure to nicotine could be masked by tumor-associated chronic inflammation.
## 4.2. Changes of Evaluate Biomarkers at 1-Year Follow-Up in Patients with Colorectal Cancer
Chitotriosidase and neopterin circulating values decreased at 1-year follow-up (p-value < 0.002, Figure 3), showing the effect of tumor removal on these markers. The decreased levels of these biomarkers could also be explained by the systemic treatment ($76.2\%$ of the patients have undergone adjuvant chemotherapy and one patient, radiotherapy), which, combined with surgery, is expected to improve the survival rates. However, $19\%$ of our patients with CRC exhibited tumor recurrence, and $23.8\%$ had metastases (mainly liver, peritoneal, or bone). Li et al. [ 18] reported on the Chinese population based on SNP analysis that variants rs61745299 and rs35920428 in the CHIT1 gene that encode enzyme chitotriosidase are associated with the risk of CRC.
The circulating values of CEA and CRP are associated with TNM stages, differentiation grade, presence of metastasis, and complications (Table 4). The changes in CEA and CRP circulating values 1 year after surgery (Table 4) suggest a decrease in tumor burden after surgery [44]. Similarly, circulating values of chitotriosidase and neopterin were significantly altered in association with tumor status at baseline and 1-year follow-up (Table 5, Figure 4 and Figure 5). Until now, studies have focused only on the difference between specific gene mutations and tumor status [45]. Thus, the results of our study shed light on possible, more cost-effective, and faster ways to predict the evolution of the tumor in patients with colorectal cancer.
Our results show that the postoperative trend of circulating chitotriosidase levels follows the carcinoembryonic antigen in a statistically significant monotonic moderate but statistically significant association. This result suggests the possible usefulness of this biomarker in evaluating disease progression, with increased levels compared to baseline in the presence of recurrence or metastasis. Moreover, this association implies that chitotriosidase could be used as a substitute biomarker in CEA non-secreting adenocarcinomas.
## 4.3. Study Limitations and Further Research
Several limitations of our study must be listed. The main limitations are related to the applied study design, which takes the causality analysis out of the discussion. In our study, the higher percentage of smokers in the CRC group compared to the control group is reflected in the inflammatory status of patients with CRC and the levels of evaluated biomarkers. So, the circulating levels of chitotriosidase and neopterin could not be solely attributed to colorectal cancer, similar to other tumor markers used in daily healthcare practice. However, the changes in the dynamic of the circulating levels of chitotriosidase and neopterin (pre- and post-surgery) support the possible use of these serum markers in the postoperative follow-up of patients with colorectal cancer. Furthermore, the number of evaluated patients is small, and the number of patients lost from observation is higher than expected and estimated. The overlap of our study with the COVID-19 pandemic, which limited access of patients with neoplasms to hospitals, could explain the high percentage of patients lost from follow-up. According to these limitations, the generalizability of the results is not possible. However, considering lipid profile and therapeutic schemas, our results support an extensive evaluation of chitotriosidase and neopterin circulating values as pre- and post-surgery markers. More comprehensive and controlled studies are needed to appropriately link the assessment of these biomarkers with patients’ and tumors’ characteristics. Furthermore, evaluating tissue chitotriosidase and neopterin levels could bring more insights into the effectiveness of these markers in assessing patients with colorectal cancer.
## 5. Conclusions
Our study showed the value of circulating chitotriosidase and neopterin levels in distinguishing T-, N-, and M-stage before surgical treatment, with higher performances of chitotriosidase regarding differentiation grade. We observed an association between tumor recurrence or metastasis and high levels of circulating neopterin and chitotriosidase, with a counterbalance of significantly lower levels in patients with good evolution after surgery. The postoperative trend of circulating chitotriosidase levels follows the carcinoembryonic antigen in a statistically significant monotonic moderate but statistically significant association. This association supports further evaluation of chitotriosidase as a substitute biomarker in CEA non-secreting adenocarcinomas.
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|
---
title: Identification and Analysis of Necroptosis-Related Genes in COPD by Bioinformatics
and Experimental Verification
authors:
- Yingxi Wang
- Xin Su
- Yan Yin
- Qiuyue Wang
journal: Biomolecules
year: 2023
pmcid: PMC10046193
doi: 10.3390/biom13030482
license: CC BY 4.0
---
# Identification and Analysis of Necroptosis-Related Genes in COPD by Bioinformatics and Experimental Verification
## Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex progressive inflammatory disease. Necroptosis is a newly identified type of programmed cell death. However, the role of necroptosis in COPD is unclear. This study aimed to identify necroptosis-related genes in COPD and explore the roles of necroptosis and immune infiltration through bioinformatics. The analysis identified 49 differentially expressed necroptosis-related genes that were primarily engaged in inflammatory immune response pathways. The infiltration of CD8+ T cells and M2 macrophages in COPD lung tissue was relatively reduced, whereas that of M0 macrophages was increased. We identified 10 necroptosis-related hub genes significantly associated with infiltrated immune cells. Furthermore, 7 hub genes, CASP8, IL1B, RIPK1, MLKL, XIAP, TNFRSF1A, and CFLAR, were validated using an external dataset and experimental mice. CFLAR was considered to have the best COPD-diagnosing capability. TF and miRNA interactions with common hub genes were identified. Several related potentially therapeutic molecules for COPD were also identified. The present findings suggest that necroptosis occurs in COPD pathogenesis and is correlated with immune cell infiltration, which indicates that necroptosis may participate in the development of COPD by interacting with the immune response.
## 1. Introduction
Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways, alveoli, and microvessels characterized by persistent respiratory symptoms and incompletely reversible airflow limitation [1,2]. Tobacco smoking and exposure to indoor air pollution (including biomass combustion), ambient air pollution, and occupational pollutants have been reported as leading risk factors in most settings [3]. The main pulmonary pathologies of COPD include chronic bronchitis, airway remodeling, and emphysema. COPD is the third most common cause of death worldwide [4]. Still, the current treatments are limited to controlling symptoms and reducing exacerbations and exhibit an inability to repair defective tissues and modify the course of the disease. Therefore, an understanding of the pathogenesis of COPD is imperative for guiding clinical diagnosis and treatment and achieving improved clinical efficacy.
Necroptosis is a newly recognized genetically regulated form of necrotic cell death that integrates some features of necrosis and apoptosis [5,6]. The necroptosis pathway is induced by a variety of intracellular signals and regulated by receptor-interacting protein kinases 1 and 3 (RIPK1 and RIPK3) and mixed-lineage kinase domain-like pseudokinase (MLKL), which form a regulatory necrosome complex [5,7,8]. Phosphorylated MLKL facilitates the formation of membrane-disrupting pores, ultimately leading to necrotic death and the release of proinflammatory intracellular contents. Accumulating evidence has implicated necroptosis in the pathogenesis of immune system disorders, inflammatory diseases, and cancer [9,10,11,12]. Some findings have indicated the roles of necroptosis and its regulatory proteins in COPD [13,14,15,16]. For example, necroptosis is induced in human COPD and mice after smoke exposure, and genetic or pharmacologic inhibition can attenuate cigarette smoke (CS)-induced airway inflammation, airway remodeling, and emphysema. However, the necroptosis-related genes (NRGs) in COPD remain largely unknown and need to be further explored.
In the present study, we systematically analyzed the differential expression profiles of NRGs between normal and COPD tissues using the microarray dataset GSE38974. Moreover, the potential functional mechanism and hub genes associated with necroptosis were explored, and the relationship between necroptosis and infiltrating immune cells was examined. We further externally verified the hub NRGs using another sequencing dataset (GSE57148) and experimental animals and evaluated the diagnostic value of the hub genes. We also constructed a transcription factor (TF)–miRNA coregulatory network for the verified NRGs and identified candidate drug molecules. The bioinformatics analysis of this study was conducted according to Figure 1.
## 2.1. Selection of NRGs
A profile of 159 human necroptosis genes was collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.kegg.jp/kegg/pathway.html, accessed on 11 June 2022) [17]. Additionally, 99 genes associated with necroptosis were acquired from GeneCards (https://www.genecards.org/, accessed on 11 June 2022) based on a relevance score > 1.0 [18]. The two gene profiles were combined to obtain 232 NRGs. Table S1 in the Supplementary Materials shows further details.
## 2.2. Data Acquisition
The normalized expression matrix of the microarray dataset GSE38974 contained 23 COPD and 9 normal lung tissue samples and was obtained with the GPL4133 platform (Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F). This dataset was used to screen differentially expressed NRGs. *The* gene expression levels in the high-throughput sequencing dataset GSE57148, which included 98 COPD and 91 normal lung tissue samples and was based on the GPL11154 platform (Illumina HiSeq 20009, Homo sapiens), were normalized using transcripts per million (TPM). The PCA plot was generated using the “ggplot2” package of R software (version 4.2.1) [19]. All data used in the study are publicly accessible in the GEO database (http://www.ncbi.nlm.nih.gov/geo/, accessed on 9 June 2022) [20]. Detailed information on the datasets is shown in Table 1.
## 2.3. Differentially Expressed NRGs
The package “limma” of R software was used to identify differentially expressed NRGs [21], which met the following criteria: false discovery rate < 0.05 and |log2 (fold-change)| > 0.58. Volcano plots, heatmaps, and box plots were generated with the “ggplot2” and “pheatmap” packages [19].
## 2.4. Functional Enrichment Analysis
Gene Ontology (GO) functional enrichment analysis, which included the biological process (BP), cellular component (CC), and molecular function (MF) categories, and KEGG pathway analysis were performed with the R packages “clusterProfiler” and “GOplot” [19,22]. The package “org.Hs.eg.db” was applied to convert the probe IDs [23]. The Z scores were calculated using the “GOplot” package by integrating the expression levels [24]. A Z score greater than 0 indicates positive regulation, and a Z score lower than zero indicates negative regulation.
## 2.5. Protein–Protein Interaction (PPI) Analysis and Hub Gene Identification
The online STRING database (https://string-db.org/, accessed on 11 June 2022) and Cytoscape software (version 3.8.1) were used to analyze the interactions among the differentially expressed NRGs [25,26]. The top 10 potential hub genes were identified based on the cytoHubba degree algorithm [27]. The plugin MCODE was used for cluster analysis of the PPI network [28]. We combined the top 10 potential hub genes and the genes involved in the most significant module to identify the overlapping genes, which were ultimately regarded as hub genes related to necroptosis. The correlations of the hub genes were analyzed using the Spearman correlation in the “corrplot” package, and differences with $p \leq 0.05$ were considered statistically significant [29].
## 2.6. Evaluation of Immune Cell Infiltration
CIBERSORT is widely used to calculate the abundance of immune cells in the microenvironment [30]. The LM22 signature was downloaded from the CIBERSORTx website (https://cibersortx.stanford.edu/, accessed on 11 June 2022). In this study, we utilized CIBERSORT-based deconvolution combined with LM22 to measure the relative proportion of 22 types of immune subpopulations in lung samples in GSE38974. The infiltrating immune cell composition in each sample was visualized using the “ggplot” package, and 22 immune cell subtypes in the COPD and normal groups were compared using the packages “ggpubr” and “cowplot” [31]. The “corrplot” package was used to analyze the correlations among differentially infiltrated immune cells. A Spearman correlation analysis of the hub genes and infiltrating immune cells was performed using the “ggstatsplot” package [32].
## 2.7. Validation of Necroptosis-Related Hub Genes in Other Datasets
The expression levels of the necroptosis-related hub genes were extracted from the independent external validation dataset GSE57148, and the difference between COPD and normal lung tissues was calculated and visualized with the packages “ggpurb” and “ggplot2”, with $p \leq 0.05$ considered to indicate statistical significance.
## 2.8. Animal Model of Cigarette Smoke (CS)-Induced Emphysema
Twelve male C57BL/6J mice (8–10 weeks, 18–20 g) were purchased from Changsheng Biotechnology Company (Liaoning, China). The mice were randomly divided into 2 groups: [1] the normal control group ($$n = 6$$), and [2] the CS group ($$n = 6$$). Based on previous studies [33,34], the mice in the CS group underwent whole-body exposure to smoke from 20 Marlboro cigarettes (Philip Morris Companies, 0.8 mg of nicotine, 10 mg of CO, and 10 mg of tar per cigarette) for 40 min in a HOPE-MED 8050 inhalation exposure system (HOPE company, Tianjin, China) twice a day and 6 days per week, whereas the mice in the normal control group were exposed to normal air. After 12 weeks, immediately following sacrifice, the left lung was inflated with $10\%$ paraformaldehyde at a constant pressure of 25 cm H2O, and the right lung tissues were removed and stored at −80 °C. The fixed lungs were embedded in paraffin. The design and protocol of the animal experiments were approved by the Animal Care and Use Committee of the China Medical University.
## 2.9. Lung Morphometric Analysis
Paraffin sections (4 μm) were stained with hematoxylin and eosin (H&E) according to conventional protocols. The morphology of the lung tissues was assessed with respect to emphysema changes based on the mean linear intercept (MLI) and mean alveolar number (MAN) at 100× magnification, as previously described [35,36].
## 2.10. Cell Death Assessment
Terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling (TUNEL) was performed with a TUNEL Assay Kit-HRP-DAB (Abcam, Cambridge, UK) following the manufacturer’s protocol. TUNEL staining can detect both apoptosis and necrosis, including necroptosis [37]. The ratio of TUNEL-positive cells to total cells was measured in a population of more than 3000 parenchymal cells of each lung sample in each group.
## 2.11. Immunohistochemical Staining
Immunohistochemical staining was conducted according to the manufacturer’s instructions. The primary antibodies were as follows: rabbit anti-MLKL (1:2000 dilution, Biorbyt, Cambridge, UK), rabbit polyclonal anti-RIPK1 (1:1000 dilution, Abcam, Cambridge, UK), and rabbit polyclonal anti-RIPK3 (1:500 dilution, Abcam, Cambridge, UK). The mean optical density of positive cells was determined using ImageJ software (version 1.53e) based on the optical density of stained positive cells normalized to the total area of cells in each view at 400× magnification.
## 2.12. Quantitative Reverse Transcription Polymerase Chain Reaction
Gene expression analysis was performed as previously described [38]. Total RNA was extracted from lung tissue using RNAiso Plus (Takara, Kusatsu, Japan). An Evo M-MLV RT Kit with gDNA Clean for qPCR II (Accurate Biotech, Changsha, China) was used to remove the mixed genomic DNA from RNA, and complementary DNA (cDNA) was obtained by reverse transcription of the mRNA. The cDNA was subjected to a real-time quantitative polymerase chain reaction (PCR) with a Roche 480 LightCycler (Roche, Basel, Switzerland) and an SYBR Green Premix Pro Taq HS qPCR Kit (Accurate Biotech, Changsha, China). According to the manufacturer’s instructions, the PCR settings were as follows: initial denaturation for 30 s at 95 °C, followed by 40 cycles of 5 s at 95 °C and 40 s at 59 °C. Relative quantification was performed with the 2−ΔΔCt method based on endogenous control (GAPDH). The primers were synthesized by Takara Biotechnology Company (Dalian, China); Table S2 in the Supplementary Materials details all the primers used in this study.
## 2.13. Receiver Operating Characteristic (ROC) Curve Analysis
ROC curve analysis was performed using the “pROC” and “ggplot2” packages [39]. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic efficacy of the necroptosis-related hub genes. The AUC combines sensitivity and specificity to validate the intrinsic efficacy of diagnostic markers [40]. If the AUC is greater than 0.5, the closer the AUC is to 1, the better the diagnostic effect is. In our study, an AUC greater than 0.7 was considered the ideal diagnostic value.
## 2.14. Construction of a TF–miRNA Coregulatory Network
The RegNetwork repository (https://regnetworkweb.org/, accessed on 14 July 2022) aids the detection of miRNAs and regulatory TFs that regulate differentially expressed genes of interest at the posttranscriptional and transcriptional levels [41]. We collected TF–miRNA coregulatory interactions from this repository using the validated necroptosis-related hub genes. A TF–miRNA coregulatory network was then visualized using NetworkAnalyst (https://www.networkanalyst.ca/, accessed on 14 July 2022), which helps researchers easily navigate complex datasets to identify biological features and functions and thus reach an effective biological hypothesis [42]. The TF–miRNA coregulatory network reflected the miRNA and TF interactions with common hub gene targets and may thus help explain the regulation of the expression of NRGs.
## 2.15. Potential Therapeutic Drug Prediction
The DSigDB database in Enrichr (https://maayanlab.cloud/Enrichr/, accessed on 14 July 2022) is a drug prediction database that can be used to select candidate drugs that potentially target certain genes [43]. These drugs may be therapeutic agents for COPD that act by modulating necroptosis. The PubChem database (https://pubchem.ncbi.nlm.nih.gov/, accessed on 14 July 2022) was used to retrieve the molecular structures of the drugs [44].
## 2.16. Statistical Analysis
R software was used to calculate the significance of the differential expression of NRGs by the Wilcoxon rank sum test. Gene expression in experimental animal samples was statistically analyzed with the Student’s t-test using GraphPad Prism (version 8.0.1, GraphPad Software). The results were depicted as the means ± SEMs, and $p \leq 0.05$ was considered to indicate significance.
## 3.1. Differential Expression of NRGs
The PCA results showed a good clustering degree between the two groups in GSE38974 (Supplementary Materials, Figure S1). Differential expression analysis of NRGs was performed using 23 COPD lung tissue samples and 9 normal lung tissue samples from GSE38974 (Figure 2A). The results identified 49 differentially expressed NRGs, including 32 upregulated (Figure 2B) and 17 downregulated genes (Figure 2C). Their differential expression patterns in COPD and normal lung tissues are shown in Figure S2 in the Supplementary Materials.
## 3.2. Enrichment Analysis of Differentially Expressed NRGs and Mechanism Exploration
To explore the functional annotations of the 49 NRGs, GO and KEGG enrichment analyses were performed (Supplementary Materials, Tables S2 and S3). As shown in Figure 3A, programmed necrotic cell death, regulation of cytokine-mediated signaling pathway, extrinsic apoptotic signaling via death domain receptors, regulation of response to cytokine stimulus, and necrotic cell death were significantly enriched biological functions. The greatest number of NRGs were involved in regulating the innate immune response. Moreover, the KEGG enrichment analysis revealed that 49 biomarkers associated with necroptosis were significantly correlated with necroptosis, the NOD-like receptor signaling pathway, influenza A, apoptosis, and measles (Figure 3B). In addition to being correlated with cell death, the differentially expressed NRGs were mainly related to the regulatory functions of innate immune responses, regulation of I-kappaB kinase/NF-kappaB signaling, and immune-related pathways, such as the TNF signaling pathway and IL-17 signaling pathway. This finding suggested an interaction between NRGs and the immune system. The absolute value of the Z score represents the probability of regulation. The significantly enriched terms shown in Figure S3 in the Supplementary Materials are all likely to be positively regulated by the 49 differentially expressed NRGs.
## 3.3. PPI Network Construction and Correlation Analyses of Necroptosis-Related Hub Genes
A PPI network was established using STRING and analyzed with Cytoscape (Figure 4A). The top 10 hub genes (CASP8, IL1B, HSP90AA1, RIPK1, MLKL, IKBKB, XIAP, TNFRSF1A, FADD, and CFLAR) were identified (Figure 4B). The key PPI module revealed a significant cluster composed of 12 nodes and 60 edges, including the genes RIPK3, CFLAR, TNFRSF1A, CASP8, HSP90AA1, IL1B, XIAP, MLKL, TNFAIP3, FADD, SHARPIN, and IKBKB (Figure 4C). The intersection of the results from the two analyses identified 10 genes as the final set of predicted hub genes related to necroptosis (Figure 4D). The heatmap revealed a certain degree of interaction among the expression levels of the 10 hub genes (Figure 4E).
## 3.4. Immune Infiltration Profiling
According to the aforementioned functional enrichment analysis, we found that NRGs in COPD seemed to show some connection with human immunity regulation. The GSE38974 dataset was selected for the analysis of immune cell infiltration. Figure 5A shows the general distribution of immune cells in individuals; specifically, M2 and M0 macrophages, resting memory CD4+ T cells, and CD8+ T cells accounted for the majority of all infiltrating immune cells. Compared with that in normal lung tissue, the infiltration of CD8+ T cells, activated natural killer (NK) cells, M2 macrophages, and resting mast cells in COPD lung tissue was relatively reduced, whereas that of M0 macrophages was enhanced (Figure 5B). The correlations among different infiltrating immune cells are shown in Figure 5C. CD8+ T cells, M2 macrophages, and resting mast cells were positively correlated, and CD8+ T cells were negatively correlated with M0 macrophages and eosinophils. Activated NK cells were negatively correlated with M0 macrophages, M2 macrophages, and monocytes, whereas resting dendritic cells were positively correlated with CD8+ cells, M2 macrophages, and resting mast cells. The correlations between necroptosis-related hub genes and infiltrating immune cells are shown in Figure 6. Most of the hub genes were positively correlated with M0 macrophage infiltration but inversely correlated with M2 macrophage and CD8+ T-cell infiltration.
## 3.5. Validation of Necroptosis-Related Hub Genes and Immune Infiltration
The PCA plot of the high-throughput sequencing dataset GSE57148 is shown in Figure S4 in the Supplementary Materials. The GSE57148 dataset was used to verify the expression levels of the abovementioned 10 necroptosis-related hub genes (Figure 7A). Among these genes, HSP90AA1 and IKBKB exhibited expression trends opposite to those in GSE38974, and no significant difference in FADD expression was detected. The reason for this finding may be sample heterogeneity. Figure 7B shows the immune infiltration information, indicating that M2 macrophage and resting NK-cell infiltration was attenuated in COPD samples and that neutrophil infiltration was enhanced.
An emphysema model was established by subjecting the mice in the CS group to whole-body smoke exposure for 12 weeks. H&E staining of mouse lung tissues showed that the CS group exhibited a significantly decreased MAN and an increased MLI compared with the normal control group, which indicated that the emphysema animal model was successfully established (Figure 8A–C). Previous studies have confirmed that CS can induce necroptosis of lung tissue in COPD and experimental emphysema [14,15,16]. We performed TUNEL staining to assess cell death and found an increase in the number of positive cells in the lungs of the mice in the CS group (Figure 8D,E). Immunohistochemical staining analysis showed that the expression levels of RIPK1, RIPK3, and MLKL in alveolar epithelial cells in the CS group were higher than those in the normal control group (Figure 9A–D), indicating that necroptosis was enhanced in the lungs of mice with emphysema. The mRNA expression levels of 7 NRGs, CASP8, IL1B, RIPK1, MLKL, XIAP, TNFRSF1A, and CFLAR, were verified again by qRT–PCR (Figure 9E). The results were very similar to those obtained from the bioinformatics analysis. These results further suggest that CASP8, IL1B, RIPK1, MLKL, XIAP, TNFRSF1A, and CFLAR are potential biomarkers of necroptosis in COPD.
## 3.6. Diagnostic Value of Necroptosis-Related Hub Genes in COPD
The diagnostic efficacy of necroptosis-related hub genes for COPD in GSE38974 is shown in Figure S5 in the Supplementary Materials, and all the candidate genes possess a high diagnostic value. The GSE57148 dataset was used to validate the diagnostic efficacy of the biomarkers for COPD. The diagnostic values of these genes were as follows (all greater than 0.5): CASP8, AUC = 0.730; IL1B, AUC = 0.653; HSP90AA1, AUC = 0.773; RIPK1, AUC = 0.664; MLKL, AUC = 0.623; IKBKB, AUC = 0.599; XIAP, AUC = 0.743; TNFRSF1A, AUC = 0.664; FADD, AUC = 0.544; and CFLAR, AUC = 0.852 (Figure 10A,B). Seven validated hub genes CASP8, IL1B, RIPK1, MLKL, XIAP, TNFRSF1A, and CFLAR were fitted into one variable, and the AUC was 0.874, demonstrating a favorable diagnostic performance in predicting COPD (Figure 10C).
## 3.7. TF–miRNA Coregulatory Network
The interactions of TFs and miRNAs with the 7 confirmed NRGs are depicted in a TF–miRNA coregulatory network (Figure 11), which may provide clues to the regulation of NRG expression. The coregulatory network consists of 168 nodes and 199 edges and involves 56 TFs and 105 miRNAs.
## 3.8. Molecular Identification of Candidate Drugs
Drug molecules targeting the 7 validated NRGs were searched in the DSigDB database. The top 10 predicted potential drugs according to the combined scores are shown in Table S3 in the Supplementary Materials. The molecular structures of the top 5 candidate drugs, dehydroxymethylepoxyquinomicin (DHMEQ), anacardic acid, 1′-acetoxychavicol acetate, pregna-4,17[20]-diene-3,16-dione, and lonafarnib, were retrieved from the PubChem database (Figure 12).
## 4. Discussion
Necroptosis has been implicated in the pathogenesis of several human pulmonary diseases, such as acute respiratory distress syndrome, COVID-19, asthma, and idiopathic pulmonary fibrosis [45]. However, COPD is a heterogeneous and complex progressive inflammatory disease, and studies on the role and molecular mechanisms of necroptosis in COPD are just beginning. This study constitutes the first bioinformatics analysis of NRGs in the pathogenesis of human COPD.
In this study, we first obtained 49 differentially expressed NRGs (32 upregulated and 17 downregulated genes) by analyzing the gene expression profiles of lung tissues from COPD patients and normal controls, which indicated that NRGs are indeed involved in the pathogenesis and progression of COPD. To understand the function and role of the differentially expressed NRGs, we performed enrichment analyses. We found that the biological process mainly involved programmed necrotic cell death, regulation of cytokine-mediated signaling pathway, extrinsic apoptotic signaling pathway via death domain receptors, regulation of response to cytokine stimulus, and necrotic cell death. Abnormal cell death is closely related to the development of emphysema in COPD [14,46,47,48,49] and involves various forms, such as apoptosis, necrosis, and programmed cell necrosis. Apoptosis was once considered the only regulated cell death mechanism, and increased numbers of apoptotic alveolar, bronchiolar, and endothelial cells have been observed in lung tissue from patients with COPD [50,51]. Necroptosis is a form of genetically encoded necrotic cell death involving rupture of the plasma membrane and therefore is a strong inducer of inflammation. It has been demonstrated that the release of damage-related molecular patterns (DAMPs) from CS-induced necroptosis triggers the production of proinflammatory cytokines [52]. Furthermore, a GO enrichment analysis indicated that several NRGs were involved in regulating innate immune response. A KEGG enrichment analysis revealed that differentially expressed NRGs were primarily engaged in inflammatory immune response pathways, including the NOD-like receptor signaling pathway, tumor necrosis factor (TNF) signaling pathway, and IL-17 signaling pathway. Ample evidence shows that TNF can induce necroptosis [53,54], and several necroptosis effectors have been reported to engage in crosstalk with the NLRP3 inflammasome to induce its activation [55,56,57]. A recent study found that theaflavin-3,3′-digallate attenuated emphysema in mice by suppressing necroptosis and significantly decreased the TNF-α and IL-1β levels [58]. Interleukin 17 (IL-17), mainly secreted by T-helper (Th) 17 cells, plays a vital role in autoimmune diseases. Jing Xiong et al. proposed that the B lymphocyte RANKL pathway is involved in IL-17A-dependent lymphoid neogenesis in COPD [59]. A recent study also confirmed that airway epithelium-derived IL-17A can amplify inflammation and increase mucus production in COPD pathogenesis in an autocrine manner [60]. These findings provide new insights into the function of necroptosis in COPD.
Adaptive and innate immune responses to risk factors contribute to the immunopathology of COPD [61]. We calculated the abundance of 22 immune cells based on the microarray profiles of normal and COPD lung tissue samples and obtained a comprehensive view of the immune infiltration status. In our study, a significant increase in infiltrating macrophages was found in COPD lung tissues, and a significant increase in the M0 macrophage numbers and a significant decrease in the M2 macrophage numbers were observed. This finding contradicts those reported by Erica Bazzan [62]. We hypothesize that the increase in M0 macrophages in COPD represents an enhanced reserve capacity in preparation for further polarization and that the decrease in the number of M2 macrophages implies that the anti-inflammatory and repair capacity is weakened in COPD. We also found that CD8+ T lymphocyte infiltration was reduced in COPD but not significantly altered in COPD lung tissues in the validation dataset GSE57148. Interestingly, regarding CD8+ T-cell expression in COPD, previous studies have not yielded consistent conclusions. Eapen et al. observed fewer CD8+ T cells in the large airways of smokers with and without COPD [63]. Forsslund et al. also reported a lower percentage of CD8+ T lymphocytes in the peripheral blood of smokers with and without COPD [64]. However, other reports have mentioned increased quantities of CD8+ T lymphocytes in COPD [65,66,67]. It has been demonstrated that the heterogeneity of macrophages and the activation of T cells are dependent on external stimuli and the microenvironment [68]. Several factors, including smoking status, severity of COPD, acute exacerbations, and corticosteroid use, may contribute to different conclusions [69,70,71]. Additionally, in the validation dataset, the degree of neutrophil infiltration was enhanced in lung tissue samples from COPD patients, which has been extensively demonstrated in previous studies [72,73].
The PPI analysis demonstrated that the proteins encoded by the 49 differentially expressed NRGs interacted and identified 10 necroptosis-related hub genes, including CASP8, IL1B, HSP90AA1, RIPK1, MLKL, IKBKB, XIAP, TNFRSF1A, FADD, and CFLAR. Previous studies have reported that some of these hub genes affect immune responses by regulating necroptosis. For example, XIAP mediates TNFα-induced neutrophil necroptosis [74], and CFLAR plays a critical role in autophagy, necroptosis, and apoptosis in T lymphocytes [75]. Our study demonstrated substantial correlations between necroptosis and immune cell infiltration in COPD, and some were consistent with those identified in previous studies. Notably, RIPK1, MLKL, XIAP, and CFLAR were significantly positively correlated with M0 macrophages but negatively correlated with M2 macrophages. Neutrophil infiltration was positively correlated with RIPK1, MLKL, and XIAP, and monocyte infiltration was positively correlated with CFLAR. These results imply that necroptosis drives the development of COPD by regulating the immune response, which provides a hypothesis and basis for further research. We also evaluated and validated the diagnostic efficacy of these 10 hub genes in the external dataset, and their AUCs were all greater than 0.5. Among these genes, CFLAR, which had an AUC greater than 0.80, is considered to have the best capability to diagnose COPD with excellent specificity and sensitivity. The combination of the 7 validated hub genes showed better discrimination (AUC = 0.874) than each gene alone.
TFs are modular proteins that regulate gene transcription by binding with target genes, and miRNAs can silence target gene expression through mRNA degradation or translational inhibition [76]. TFs and miRNAs can jointly regulate common target gene expression and play critical roles in multiple biological processes. The necroptosis-related hub genes were validated using an independent external dataset and animal experiments to achieve improved accuracy, and 7 genes were ultimately screened. A TF–miRNA coregulatory network was constructed to explore the upstream regulatory biomolecules of the 7 necroptosis-related hub genes, and 105 miRNAs and 56 TFs were identified. Among the most interactive TFs, TP53 exhibited the highest degree value of 5. A previous study confirmed that sirtuin 3-induced necroptosis in small-cell lung cancer is associated with the expression of mutant p53 [77]. Furthermore, the current study highlights the predicted potential drugs targeting the 7 validated necroptosis-related hub genes. DHMEQ, a novel NF-kappaB inhibitor, may exert therapeutic effects on allergic inflammation and airway remodeling in asthmatic mice [78]. However, the application of DHMEQ in COPD treatment has not yet been studied. The therapeutic effects of 1′-acetoxychavicol acetate on pulmonary inflammatory diseases also suggest that this drug may be useful for COPD [79,80]. The predicted potential drugs should be considered for further verification by chemical experiments to shed light on new therapeutic strategies for preventing COPD progression.
This study has some limitations. First, we performed only a preliminary exploration of the correlation between NRG expression and immune cell infiltration. The complex regulatory mechanisms and interactions between necroptosis and immune cell infiltration have not been specifically studied in depth. Second, quite a few samples are included in GSE38974, and the datasets used in this study do not provide extensive information on clinical characteristics or prognostic information. Although the GSE38974 dataset has been used alone in many published studies [24,81,82,83], the results may be subject to minor errors. To facilitate our future research on COPD, we are collecting our own clinical samples and relevant clinical information. Third, lung macrophages remain poorly understood in COPD. We only investigated macrophage polarization according to M1 and M2 classifications in the study. However, based on the available evidence, changes in M1 and M2 macrophages did not yield consistent conclusions, supporting the idea that classical M1 or M2 phenotypes are insufficient to explain lung macrophage differentiation and dyshomeostasis in COPD. A comprehensive study of macrophage and monocyte immunophenotyping will be needed to develop more accurate biomarkers in the future. Moreover, we only predicted the coregulatory networks of upstream TFs and miRNAs as well as potential therapeutic agents. These predictions were based on bioinformatics methods, and subsequent in vivo and in vitro confirmatory experiments are thus needed.
## 5. Conclusions
Bioinformatics analyses of necroptosis in COPD have rarely been reported. Our analysis revealed that the expression levels of NRGs significantly differed between COPD and normal lung tissues. Moreover, this study not only obtained insights into the landscape of immune cells associated with COPD and their correlation with NRGs but also identified effective diagnostic biomarkers for COPD. Furthermore, we validated 7 necroptosis-related hub genes of COPD. The interactions of TF and miRNA with their common NRGs and potential new therapeutic drugs for medical interventions were then identified.
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|
---
title: Impact of Gender on Insomnia
authors:
- Jolijn Boer
- Nadya Höhle
- Lisa Rosenblum
- Ingo Fietze
journal: Brain Sciences
year: 2023
pmcid: PMC10046211
doi: 10.3390/brainsci13030480
license: CC BY 4.0
---
# Impact of Gender on Insomnia
## Abstract
There is a distinct preponderance of female insomniacs when compared to male insomniacs. The aim of this study was to examine possible gender differences in the causes for insomnia, and the phenotypes of insomnia, and to investigate whether gender-specific insomnia diagnosis and treatment could be relevant in clinical practice. Data were collected from 121 insomniac patients by a medical specialist in the framework of normal clinical practice in Germany. The data consist of the patient’s medical history and various sleep-related patient questionnaires. Data from both genders were tested for independence using chi-square tests and Mann–Whitney U tests. We found a correlation between the gender of the patient and insomnia phenotypes in several aspects: concomitant lipometabolic disorders, diabetes mellitus, and high BMIs are more common in male insomniacs ($p \leq 0.05$). Frequency of insomnia occurrence in certain age groups, insomnia severity, distribution of SOI (sleep onset insomnia), SMI (sleep maintenance insomnia) and combined SOI + SMI, sleep duration, the time needed to seek medical consultation, trying out sleep-inducing drugs/techniques and the trigger, etiology and familial predisposition of the insomniac disorder were independent of the patient’s gender. We would like to re-evaluate the results with a larger number of patients in a further study.
## 1. Introduction
In healthcare, gender-specific medicine considers both physiological and biological variances (sex differences) as well as sociocultural disparities (gender differences) regarding pathogenesis, diagnosis, and treatment in healthcare [1,2,3]. Gender also has an influence on health patterns, psychosocial patterns, sociodynamics patterns, and access to healthcare [3]. The specialized approach of gender-specific medicine is a possibility to overcome these barriers and increase therapy adherence and therapy success. One example could be to implement female-only treatment settings to provide a secure surrounding for females and to focus on female-specific issues, such as co-occurring disorders, family responsibilities, and parenting. These could also offer childcare services for women with children who cannot find time for treatment [4,5,6]. Gender-specific treatment services are already common for patients with substance abuse disorders [7]. However, they are not so common for other diseases, such as type 2 diabetes, and here experts see the opportunity for gender-specific treatment to increase quality of life and survival [8]. Just like for type 2 diabetes, there is currently no gender-specific treatment for insomnia as of yet.
The prevalence of insomnia is high and varies greatly [9,10,11]. Remission rates for chronic insomnia are low [12]. Online surveys conducted between May and September 2020 suggest that the prevalence increased during and due to the COVID-19 pandemic: In Europe, $8.2\%$ to $25.6\%$ of all respondents were found to have a probable insomnia disorder [13,14]. This increase could already be detected at the beginning of the COVID-19 pandemic in February 2020, especially among females [15]. However, not only did the COVID-19 pandemic have an impact on prevalence, it also affected the management of insomnia [16].
Insomnia increases the risk of heart attacks, heart failure, high blood pressure, anxiety disorders, and suicide, just to name a few [17,18,19,20,21,22,23,24]. Consequently, this sleep disorder results in high direct and indirect, social and economic costs, especially when left untreated [25]. This includes healthcare utilization, deadweight loss from inefficiencies in taxation/social assistance, medication, therapy, sick leave, and early retirement [25,26]. A study from 2010 found that sleep disorders, including insomnia, narcolepsy, sleep apnea, and hypersomnia, resulted in costs of around EUR 790 per patient a year in Europe [27].
Phenotyping insomnia patients in general is still under development. So far, common classification criteria for insomnia include total sleep duration, insomnia severity, and psychological stress [28,29]. A total sleep duration of <6 h identifies more pronounced insomnia, increased psychological impairment, and a possible genetic predisposition. This subtype may benefit less from cognitive behavioral therapy than insomnia patients with longer sleep duration, where the cause is less likely to be genetic and more likely to be psychological [28]. Insomnia severity can be well assessed with the validated Insomnia Severity Index (ISI) questionnaire, which allows the classification of insomnia into mild, moderate, and severe [30]. Daytime sleep, stability of insomnia, and better sleep in unfamiliar environments are also interesting for the classification of insomnia. However, the clinical benefit of these classifications has yet to be identified [31].
It is known that there is a distinct preponderance of female insomniacs when compared to male insomniacs [32]. A possible explanation for this finding can be based on differences in sex hormones [33,34,35]. Another reasonable explanation is the higher prevalence of depression and anxiety disorders in women [36,37,38], which are high-risk factors for insomnia [39,40,41]. A study on Chinese insomniacs found that unemployment is correlated to insomnia in men and marital status is correlated to insomniac disorders in women [42]. Another Chinese cohort of 9851 subjects indicated that being divorced or widowed was only correlated to insomnia in women [39]. A cross-sectional study in Taiwan suggests that no or irregular exercise, using sleeping pills, and suffering from Restless Legs Syndrome were correlated to insomnia in males. Poor appetite has been shown to be relevant in female insomniacs [43].
For the first time, we will examine differences in the causes for insomnia and the phenotypes of insomnia when comparing genders in a German population. Additionally, we will examine whether gender-specific insomnia diagnosis and treatment could be relevant in clinical practice.
## 2.1. Participants and Recruitment
We collected data from 121 patients who were already diagnosed with insomnia (difficulties initiating sleep, maintaining sleep, or early morning awakening) according to the ICSD-3 criteria. Recruitment took place at the Interdisciplinary Center of Sleep Medicine at Charité—University Medicine Berlin, Germany. Patients over the age of 18 were included in the study. Another requirement for participation was a signed informed consent on the use of their pseudonymized data for research purposes. Patients with paradoxical insomnia were excluded from the study. The study was conducted in compliance with the declaration of Helsinki, and the study was approved by the Ethics Committee of the Charité—University Medicine Berlin, Germany (Research Ethics Committee Reference Number: EA$\frac{4}{204}$/22).
## 2.2. Procedure
The data were collected by a medical specialist in the framework of normal clinical practice after the insomnia diagnosis was confirmed. This consisted of collecting the medical history of the patients as well as various self-report questionnaires: Insomnia Severity Index (ISI), Beck Depression Inventory (BDI-II), Epworth Sleepiness Scale (ESS), Restless Legs Syndrome-Diagnostic Index (RLS-DI) and STOP-BANG questionnaire. Categorized results from the ISI questionnaire were used for the analysis: 0–7 points = normal finding; 8–14 points = mild insomnia; 15–21 points = moderate insomnia and >22 points = severe insomnia [30]. The BDI-II scores were also categorized into groups for the analysis: minimal (BDI-score 0–13), mild (BDI-score 14–19), moderate (BDI-score 20–28), and severe depression (BDI-score 29–63) [44]. All data were pseudonymized and entered into a database (Excel Version 16.70 and SPSS Version 28.0.1.0).
## 2.3. Statistics
Statistical analysis was carried out in SPSS (IBM SPSS Statistics 28.0.1.0). We used chi-square tests to test the independence of two variables, with Cramer’s V being the measure of the effect size of the test result. Parametric unpaired t-tests were used to examine whether the means of two independent measurement groups differed from one another and the Mann–Whitney U test was used as a non-parametric alternative to the t-test if the criteria of normal distribution were not met by the data. In cases where individual questions were omitted, we excluded these patients from the analysis of the according question and adjusted the number of patients to the variable. This leads to the varying number of patients “N” in the evaluation.
## 3.1. Patient Characteristics
Out of the 121 patients in our study cohort, $58.7\%$ ($$n = 71$$) were female. The average age was 54.7 ± 15.1 years (range: 21–85). Nine patients were under 30 years old. More than half of the patients ($64.5\%$) had a combined SOI (sleep onset insomnia) and SMI (sleep maintenance insomnia) and had been suffering from insomnia for 9.6 ± 9.9 years until they consulted a sleep specialist. One-third ($33.6\%$) of the study population was concomitantly suffering from a type of cardiovascular disease, including arterial hypertension, cardiac arrhythmia, coronary artery disease, or myocardial infarction. All collected patient characteristics are shown in Table 1.
## 3.2.1. Age
There was no significant correlation between the occurrence of insomnia in certain age groups (21–40 years, 41–60 years, >60 years) and the gender of the patient (χ2 [2] = 2.77, $$p \leq 0.25$$, $V = 0.15$).
## 3.2.2. Sleep Parameters
A chi-square test for independence was performed to investigate the relationship between gender and the insomnia severity. There was no statistically significant connection between the mentioned parameters (χ2 [3] = 3.27, $$p \leq 0.35$$, $V = 0.17$). The chi-square test was repeated with merged subgroups (no or mild insomnia versus moderate to severe insomnia) to eliminate the interference factor of too low cell frequencies. However, the final result was still not significant: χ2 [1] = 0.27, $$p \leq 0.61$$, $V = 0.05.$
The ISI score was compared between males and females and showed no significant difference (mean ISI score female: 18.7; mean ISI score man: 18.0; unpaired t-test t [112] = 0.78, $$p \leq 0.44$$) (see Figure 1).
While more women were affected by SOI and more men by SMI, combined SOI and SMI was equally common in both genders (see Figure 2). A chi-square test between the variables indicates no statistical significance: χ2 [2] = 3.43, $$p \leq 0.18$$, $V = 0.17.$
There was also no significant difference in sleep duration (during the week) between genders (Mann–Whitney U test: $U = 1436.50$, $Z = 0.72$, $$p \leq 0.47$$).
## 3.2.3. Time Needed to Seek Medical Consultation
On average it took the patients 9.6 ± 9.9 years to seek medical consultation. The difference between genders was analyzed with a Mann–Whitney U test. The test was not significant: $U = 1286.50$, Z = −1.22, $$p \leq 0.22.$$
## 3.2.4. Previous Sleep-Inducing Drugs/Techniques
There was no significant difference between genders in treatment attempts, that had been made before the patients came to the sleep medicine outpatient clinic (see Figure 3).
The individual results of the chi-square tests with the respective dependent variables are displayed in Table 2.
## 3.2.5. Preexisting Conditions
Multiple chi-square tests were calculated to examine the gender-specific distribution of concomitant diseases (see Table 3). The test shows a significant correlation between the gender of the patient and the frequency of occurrence of diabetes mellitus (type 2) and lipometabolic disorder in insomnia patients (χ2 [1] = 4.34, $$p \leq 0.04$$, $V = 0.20$ and χ2 [1] = 4.62, $$p \leq 0.03$$, $V = 0.20$, respectively). Lipometabolic disorders and diabetes mellitus are more common in men suffering from insomnia (see Figure 4). An unpaired t-test for the variables gender and BMI was carried out with regard to the more frequent occurrence of lipometabolic disorders in the male insomnia population. This became significant with a p-value < 0.05: t [114] = −2.87, $$p \leq 0.01.$$
Patients were grouped into different categories according to their BDI scores (minimal = BDI score 0–13, mild = BDI score 14–19, moderate = BDI score 20–28 and severe depression = BDI score 29–63) in order to investigate whether concomitantly suffering from depression is more common in female insomniacs. For the comparison of frequencies of minimal, mild, moderate and severe depression between genders, the chi-square test indicated no statistical significance i.e., correlation: χ2[1] = 0.75, $$p \leq 0.39.$$
## 3.2.6. Etiology of the Insomniac Disorder
The known triggers of the insomniac disorder in males and females did not differ significantly (χ2[1] = 0.00, $$p \leq 0.98$$). A possible correlation between gender and an idiopathic etiology of insomnia was examined using a chi-square test. This also showed no significant result: χ2 [1] = 1.41, $$p \leq 0.24.$$ A total of $44.2\%$ of the study population reported a familial predisposition to sleep disorders. A chi-square test showed no statistical significance between males and females (χ2[1] = 0.09, $$p \leq 0.76$$).
## 4. Discussion
In this study on insomnia, patients were predominantly female ($59.0\%$ vs. $41.0\%$), mirroring the results of previous studies [31,32,45,46]. The majority of patients ($81.6\%$) were suffering from moderate insomnia (ISI score: 15–21 points) [30]. The mean BDI-II, ESS, STOP-Bang, and RLS-DI scores of 12.7 points, 6.4 points, 2 points, and 1 point can be translated into no indication for depression, slightly increased daytime sleepiness, low risk for obstructive sleep apnea, and a mild restless legs syndrome [47,48,49,50].
No statistically relevant connection could be made between the frequency of occurrence of insomnia in certain age groups when comparing genders. In contrast to our results, a study from France examining elderly insomniacs shows that women tend to be younger [51]. On the other hand, a registry of 2144 adults in Spain found there was a steady deterioration of the sleep quality in women with increasing age, whereas this worsening was not as constant in men. The age range of this registry was, however, somewhat higher and narrower than in our study (43–71 years vs. 21–85 years) [52].
The insomnia severity also seemed to not be related to gender (ISI score 18.0 ± 4.8 in males vs. 18.7 ± 4.4 in females, $$p \leq 0.44$$). This is consistent with a cross-sectional study in Korea, where the ISI score comparing 260 male and female insomniacs showed no statistically significant difference (14.0 ± 4.3 vs. 14.6 ± 4.4, $$p \leq 0.199$$) [53].
The influence of gender on subjective sleep duration was not significant in our study. This finding is supported by the results of the Spanish registry, where this correlation also showed no significance [52].
In our study, the distribution of SOL, SMI, and combined SOI + SMI also seems to be independent of the gender of the patient. This result is consistent with a study of 260 Korean insomniacs [53]. It does not, however, comply with the results of an online survey conducted in Norway, a study from France, or a Taiwanese cross-sectional study, which found that SOL alone was more common in women than in men [43,45,51].
Our results show that gender is also not related to how long insomnia symptoms persist or which drug treatment attempts are made before a sleep specialist is seen. On average it took 9.6 ± 9.9 years until our patients went to see a sleep specialist and most ($72.0\%$) had previously tried herbal medicines. A study on French elderly insomniacs shows that women tend to use sleep-inducing drugs (including benzodiazepines, nonbenzodiazepines, antihistamines, and herbal medicines) slightly more often than men [51].
Around $40\%$ of our study population could name a trigger for their insomniac disorder, the most common reasons being psychological and/or familial (separation or death of a loved one, stress, and children). Even though females tend to suffer more from stressful life events than men [54], our study showed that the trigger of the insomniac disorder is independent of gender. The hypothetical influence of gender on the etiology and familial predisposition of insomnia can also be rejected from our analysis.
Our findings suggest that there is no significant connection between insomnia comorbid with depression and the gender of the patient. This contradicts the expectation, in that depression is more common in females in general [36,37,55,56,57]. On average, the BMI of the total population was within the normal weight range of 24 ± 5 kg/m2 [58]. In our study, diabetes mellitus ($$p \leq 0.04$$), lipometabolic disorders ($$p \leq 0.03$$), and obesity (higher BMI) ($$p \leq 0.01$$) as concomitant diseases of insomnia are more common in men. A French study of elderly insomniacs supports the statement that men with insomnia tend to have a higher BMI and, in contrast to our findings, it shows that women are more likely to concomitantly suffer from depression [51].
The aim of the study was to examine whether there are differences in the phenotype of insomnia when comparing genders. The literature shows contradictory results. We would like to encourage scientists to explore this hypothesis further to complement the phenotyping of insomnia and, within this framework, personalize treatment and possibly establish gender-specific insomnia diagnostics and treatment.
## Limitations
The study was intended to discover what impact gender has on insomnia. Only very few aspects were found showing a correlation between the gender of the patient and insomnia phenotypes. However, it is important to note that even though many possible factors were analyzed (reaching from triggers for insomnia, sleep duration, and insomnia severity to the family predisposition) many other factors such as alcohol, drug use, nightmare recall, menopause, employment, social conditions, and children were not taken into account. It is also important to consider differences in sleep homeostasis and circadian rhythms between the genders. For example, studies suggest that women have higher basal sleep pressures [59,60] and that their intrinsic circadian period is significantly shorter [61]. This could play an important role in the development of personalized treatment for insomniacs.
Another limitation of the study is the rather small sample size. As a result, the frequencies of the chi-square test were often too low, making the interpretation of the result less reliable. In addition, certain questions were often left out by the patients, e.g., questions regarding depression as a pre-existing condition.
## 5. Conclusions
Overall, our cohort showed that the gender of the patient only has a minor influence on the phenotype of insomnia. We are currently continuing our data collection and would like to re-evaluate the results with a larger number of patients. Further studies should also investigate the role of other moderators such as age, origin, fitness, nutrition, medical comorbidities, and medications to better understand the pathophysiology and thus the phenotype of insomnia.
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|
---
title: Laser-Cutted Epidermal Microfluidic Patch with Capillary Bursting Valves for
Chronological Capture, Storage, and Colorimetric Sensing of Sweat
authors:
- Yuxin He
- Lei Wei
- Wenjie Xu
- Huaping Wu
- Aiping Liu
journal: Biosensors
year: 2023
pmcid: PMC10046219
doi: 10.3390/bios13030372
license: CC BY 4.0
---
# Laser-Cutted Epidermal Microfluidic Patch with Capillary Bursting Valves for Chronological Capture, Storage, and Colorimetric Sensing of Sweat
## Abstract
Flexible wearable microfluidic devices show great feasibility and potential development in the collection and analysis of sweat due to their convenience and non-invasive characteristics in health-level feedback and disease prediction. However, the traditional production process of microfluidic patches relies on resource-intensive laboratory and high-cost facilities. In this paper, a low-cost laser-cutting technology is proposed to fabricate epidermal microfluidic patches for the collection, storage and colorimetric analysis of sweat. Two different types of capillary bursting valves are designed and integrated into microchannel layers to produce two-stage bursting pressure for the reliable routing of sweat into microreservoirs in sequential fashion, avoiding the mixing of old and new sweat. Additionally, an enzyme-based reagent is embedded into the microreservoirs to quantify the glucose level in sweat by using colorimetric methods, demonstrating a high detection sensitivity at the glucose concentration from 0.1 mM to 1 mM in sweat and an excellent anti-interference performance that prevents interference from substances probably existent in sweat. In vitro and on-body experiments demonstrate the validity of the low-cost, laser-cut epidermal microfluidic patch for the chronological analysis of sweat glucose concentration and its potential application in the monitoring of human physiological information.
## 1. Introduction
Nowadays, blood examination is widely used in clinical practice due to its advantage of high detection accuracy. At the same time, because of its puncturing characteristics, professional persons and specialized collection/analysis instruments are necessary. Patients who need a long-term blood collection go through both psychological and physiological stress during repetitive puncturing. Meanwhile, a large number of biomarkers, such as glucose, lactate, urea, sodium, potassium and proteins, also exist in human sweat [1,2]. Compared with blood collection, sweat collection is more convenient and non-invasive; thus, it is expected to be an ideal method for health information monitoring, especially for patients with diabetes with daily sampling requirements [3]. Many studies have found that the concentrations of biomarkers in sweat are highly correlated with those in blood. Although the change in sweat glucose concentration has a short lag compared with that in blood glucose concentration, sweat glucose can still reflect the health status and the change trend of human blood glucose [4]. It is hoped that sweat sensing can be introduced into home medical treatments to achieve the early warning and diagnosis of diseases. Recently, many methods have been proposed for sweat collection to obtain meaningful information about the physiological state of the body [5,6]. Thereinto, microfluidic chips integrated with microsensors are the ideal platforms to perform the monitoring of individual health conditions based on sweat analyses, where sweat can be collected, stored and analyzed in situ [7]. Sweat glands naturally secrete sweat after an increase in body heat or a rise in ambient temperature, and the osmotic pressure between plasma and the epidermis pushes the secreted sweat toward the skin surface, where it enters the analysis area of microfluidic chips [8,9]. However, there is an inevitable mixing between measured sweat and that to be measured on the skin surface, which greatly affects the measured accuracy of biomarkers due to biomarkers’ metamorphosis with time [10]. In order to avoid this problem, the chronological collection strategy based on microfluidic patches has been proposed as an effective way to realize the precise control of sweat collection, storage, in situ detection and outflow along a well-designed path under the assistance of multifunctional valves [11]. For example, Choi et al. introduced an approach for guiding sweat flow in a sequential manner in a skin-mounted microfluidic device via carefully designed capillary bursting valves (CBVs) [12]. Kim et al. used a valving technology to control the flow of sweat from an inlet to an isolated reservoir in a well-defined sequence [13]. Kim et al. fabricated a microfluidic system with microchannels, reservoirs, valves and other components in low-modulus elastomers [14]. However, these elaborate microchannels and valves integrated into microfluidic patches are usually well prepared by using soft-lithography technology via silicon molds, and the entire production process relies on resource-intensive laboratory and high-cost facilities [15]. Therefore, a flexible microfluidic patch with an ingenious design and a simplified preparation method is both imperative and challenging for convenient sweat collection and sensing for patients with diabetes with daily sampling requirements.
In this study, we fabricate a microfluidic patch by utilizing a low-cost, simple laser-cutting manufacturing technique using double-sided tape and transparent polyimide (PI) films. CBVs with an adjustable bursting pressure (BP) are also designed and achieved within microchannels by using this laser-cutting method for the chronological capture and storage of sweat to avoid the mixing of the sweat tested and that to be tested (Figure 1). Compared with conventional photolithography, the laser-cutting method just needs one step to make programmable microchannels and CBVs without predesigned templates, and it does not need an ultra-clean room or other expensive experimental conditions. Moreover, the glucose concentration in sweat detected in chronological order makes it possible to monitor glycemic elevation in a timely manner. The color markers in the microreservoir of the microfluidic patch have a sensitive color reaction to glucose at different concentrations, presenting a high sensitivity at the glucose concentration from 0.1 mM to 1 mM in sweat and an excellent anti-interference performance regarding alcohol, urea, chloride and lactic acid, which are probably existent in sweat; this is carried out by contrasting the R value in the RGB color model with a standard one. This indicates the application potential of the proposed microfluidic patch in the field of sweat management and the physiological information monitoring of humans.
## 2.1. Design of Microfluidic Patch
The whole microfluidic patch consists of six layers and has four microchambers for sweat storage (Figure 1). Two different types of CBVs are designed on two microchannel layers (namely, the sweat flow layer and the spiracle layer) to achieve chronological collection. CBV#1 in the sweat flow layer is proposed to provide the first-stage BP to control the flow and collection of liquid in the four microreservoirs in sequence (Figure 1 and Figure 2a). The second-stage BP is provided by CBV#2 in the spiracle layer. Its function is to balance the pressure inside and outside of the microreservoirs to ensure that, when liquid flows in, the air in the microreservoirs can be moved out smoothly, and the liquid is retained in the microreservoirs without leakage (Figure 2b). When the secreted sweat enters the patch from the inlet (Figure 1), the microchannels can transfer the sweat from the skin surface to the microreservoirs with parallel CBV#2 to release air. When the liquid is stationary in the microchannel, the additional pressure from the difference between the inside and outside flexure liquid levels satisfies Young’s equation (Figure 3a,b) [16]:[1]ΔPe=PA−PO=−2σcosθAw+cosθVh where PA is the pressure from the inside flexure liquid level, PO is the pressure from the outside flexure liquid level, and σ is the coefficient of the surface tension [17]. θA and θV represent the contact angle of the liquid with the side wall and the top/bottom walls, respectively. w and h are the width and the height of the microchannel, respectively. As the liquid flows into CBV#1, the width of the microchannel spreads out suddenly, and the three-phase contact line of the liquid stops immediately [18]. Until the contact angle increases from θA to θA+β (Figure 3c), the liquid can enter through CBV#1 [19]. The BP of the liquid at the entrance is [16] [2]ΔPrec=PA−PO=−2σcosθIw+cosθVh, θI=minθA+β,180, Thus, the liquid flow pressure increases at CBV#1. The expectation of shunting in different branches can be achieved. Additionally, because CBV#2 has a significantly smaller size in height and width than CBV#1 (Figure 2), the BP of the former is much larger than that of the latter, which makes sweat burst out of CBV#1 and advance to the next microreservoir instead of leaking out from CBV#2.
## 2.2. Preparation of Microfluidic Patch
For the typical preparation of the microfluidic patch, the spiracle layer with CBV#2 was first fabricated by cutting a transparent PI film (50 μm in thickness) using a laser-cutting system (ultraviolet laser marking machine-3 W, Shenzhen Chaoyue laser Intelligent Equipment Co., Ltd. of China, Shenzhen, China) under 3 W power 3 times (Figure 4a). The laser wavelength was 355 nm; the pulse width was 1 μs; and the laser cutting rates were set to 10 mm/s, 30 mm/s, 40 mm/s and 50 mm/s. Then, a monolayer of double-sided tape (3M 93015LE-300LSE) was cut into a predetermined shape by using the laser-cutting technology, and this operation was repeated three times to form a sweat flow layer with enough height for sweat inflow. After laminating the well-fabricated spiracle layer onto the sweat flow layer, the sample was encapsulated with a single-sided sticky biaxially oriented polypropylene (BOPP) film as the top layer.
During the alignment of the different layers, the double-sided tape (0.15 mm) was first fixed on an operating table to ensure that its position remained unchanged in the carving process. After carving the first layer of tape, the second layer and third layer of double-sided tape were covered for in situ engraving. The position of the double-sided tape did not change during the carving process to ensure that each layer of the structure was aligned. After preparing the sweat flow layer, the spiracle layer (PI film) was manually aligned and adhered to the sweat flow layer with the help of an optical microscope. Because the transparent PI film had a good shape recovery ability, it did not produce deformation during preparation. Moreover, the total thickness of the microfluidic patch was about 0.85 mm.
For the sensing module in the microfluidic patch, we chose the colorimetric method to detect the glucose concentration in sweat. The colorimetric method is based on the color response of the testing sample to a reactant, or a substrate can usually be used to ensure the type of analyte and content or concentration by comparing the color before and after the reaction. Pre-prepared color markers (indicator plates) were put into the four microchambers in the sweat flow layer and encapsulated with a transparent PI film with a hole of 5 mm as the bottom layer. To compensate for the color error caused by varying light intensity, a standard color marker was pasted onto the surface of the top layer. Finally, the microfluidic patch was easily stuck onto skin via double-sided tape with a hole of 5 mm as the adhesive layer.
## 2.3. Experimental Characterization
The morphologies of the CBVs and microchannels in the spiracle layer and sweat flow layer were observed using an optical microscope, a scanning electron microscope (SEM, S-4800 Carl Zeiss SMT Pte. Ltd., Guangzhou, China) and a laser microscope (VK-X110, KEYENCE, Shanghai, China). For the chronological collection test of liquid in the microfluidic patch (in vitro experiment), red dye was uniformly injected into the microchannels with an injection pump (LSP01-3A, Baoding Lange constant flow pump Co., Ltd., China, Baoding, China) at an injection rate of 1 μm/mm. For an in situ experiment, the patch was attached to the forearm of a volunteer exposed to a high temperature while cycling to collect the sweat produced during the exercise.
The glucose concentration in the sweat was examined by using the glucose oxidase (GOD) coupling method. For a colorimetric analysis, a phenol reagent (POD) was mixed with GOD (1:1) to make a GOD-POD solution. Dust-free paper was cut into circular patches with a radius of 2.8 mm, ensuring that the dust-free paper could be embedded into the microreservoir. The dust-free paper was kept soaked in 4 °C GOD-POD solution for 3 h with light avoidance, and the surface was air-dried to form color markers. The color marker could maintain stability at 4 °C for at least 20 days.
The largest color block was selected from the website of Color Hunter, and the software of Color Picker was used to obtain the RGB values of the largest color block; these values were compared with the values of a standard color card to indicate the glucose concentration in the sweat. Each set of colors was taken six times to reduce the error. Subsequently, we prepared different concentrations of the glucose solution and injected them into the microfluidic patch at a uniform speed (1 μm/mm) with an injection pump, resulting in the color change of the patch. In order to investigate the influence of the light of the detection environment on the color of the patch, we placed the patch under three lights with different light intensities (2.5 W white light, 5 W white light and 3 W yellow light) and determined the color value with a color picker and compared it with the standard color card. For selectivity and anti-interference performance tests of the microfluidic patch, a 0.3 mM glucose solution, with the addition of chloride, lactic acid, alcohol and urea, was injected into the patch, and the color values were collected.
For an in situ experiment of sweat glucose detection, we attached the patches to the forearms and chests of two volunteers exposed to a high temperature of 30 °C. At higher temperatures, the volunteers were more likely to sweat while cycling, allowing for sweat to be collected more quickly. All tests of the sweat glucose concentration for each volunteer were carried out by pasting six patches onto the test areas, and the standard deviation was calculated for statistical analyses. During the on-body tests of the sweat glucose concentration for each volunteer, we also measured blood glucose with a commercial blood glucose meter (Yuwell 590, Shanghai, China).
## 3.1. Morphology of Microfluidic Patch
Figure 4b shows the assembled microfluidic patch, which presents a good flexibility and has a thickness of less than 1 mm (Figure 4c,d), indicating its compatibility with human skin and application potential in the field of sweat management and physiological information monitoring in situ.
To obtain the smallest channel size using laser-cutting technology, we adjusted the preset path of the laser to change the width of the channel. We made separate 0.6 mm, 0.4 mm, 0.3 mm, 0.25 mm and 0.2 mm wide channels in the double-sided tape and observed the morphology of the channels by using an optical microscope. When the width of the channel was 0.2 mm, the channel edges had obvious defects (Figure S1a). Similarly, we made separate 0.4 mm, 0.3 mm, 0.25 mm, 0.2 mm, and 0.15 mm wide channels in the transparent PI film, and a well-defined channel was obtained when the width was no less than 0.25 mm (Figure S1b). Therefore, 0.25 mm was the smallest channel size that could be obtained by using laser-cutting technology. When liquid flows into the microchannels in the sweat flow layer, the roughness of the inner walls of the microchannels will influence the sweat flow process. Therefore, we adopted different laser cutting rates (10 mm/s, 30 mm/s, 40 mm/s and 50 mm/s; the total processing time was similar) to obtain microchannels with different roughness. As shown in Figure 5, when the cutting rate increased, the smoothness of the inner wall decreased (from 28.375 μm to 16.372 μm, 8.995 μm and 7.463 μm) because slower laser processing can cause more burnt inner walls [20,21]. Therefore, the optimized parameter of laser treatment for obtaining smooth inner walls in the microchannels was 50 mm/s. The detection results of sweat collection indicate that the roughness of the microchannel inner wall would have a certain effect on the collection time of sweat (Figure S2).
## 3.2. Chronological Collection of Liquid in the Microfluidic Patch
In order to realize the chronological collection of liquid in the microfluidic patch, two different types of CBVs (CBV#1 and CBV#2) are designed. Then, red dye is uniformly injected into the microchannels with an injection pump at an injection rate of 1 μm/mm. When the red dye liquid enters the microreservoir, it flows into the next set of microreservoirs after filling the secondary branch, as shown in Figure 6a, indicating the effectiveness of CBV#1. However, in some cases, the liquid preferentially fills one side of the microreservoir, and sweat blocks the spiracle valve (CBV#2), as shown in Figure 6b, so the residual air in the microreservoir cannot be vented out; therefore, sweat will not fill the microreservoir. To solve this problem, we design three parallel spiracle valves (CBV#2), ensuring that the air can be vented out thoroughly, as shown in Figure 6c.
To verify the effect of the CBVs (CBV#1 and CBV#2) in the microfluidic patch for sweat collection, we injected red dye into the patch via an injection pump at an injection rate of 1 μm/mm (Video S1). Figure 7a shows a simulation diagram of sweat flowing into the four microreservoirs successively under the BP of CBV#1. As shown in Figure 7b, the time it takes for the dye to fill the entire patch is about 9 min and 22 s, and it fills each microreservoir in sequence for about 2 min. Therefore, our results indicate that CBV#1 can control the flow of solution in sequence, and CBV#2 can ensure that the liquid does not leak out.
## 3.3. Colorimetric Measure for Glucose Concentration in Sweat
The colorimetric method is selected to detect the glucose concentration in sweat via an enzyme-based reagent [22]. Catalyzed by GOD, glucose is oxidized to gluconic acid, which couples with oxygen to produce H2O2. Then, the produced H2O2 oxidizes the colorless o-dianisidine to generate red-colored oxidized o-dianisidine. The presence of glucose brings out the red color of the GOD-peroxidase-o-dianisidine system [23]. Therefore, the amount of H2O2 generated in the initial reaction is directly proportional to the glucose concentration [24]. The color markers consisting of GOD and POD in the microreservoir have a sensitive color reaction to H2O2 with different concentrations, presenting an obvious color change related to glucose concentration. Figure 8a shows images of color markers with different concentrations of the glucose solution dropped onto them, presenting large differences in the color value. This indicates that the color markers can distinguish and determine the concentration of glucose in sweat with a color picker. When the color markers with different concentrations of the glucose solution (0.1–1 mM) are placed under three different kinds of light (2.5 W white light, 5 W white light and 3 W yellow light), the RGB value of the color response to glucose is found to be linear with the glucose concentration for these three different kinds of light (Figure 8b–d). The linear fitting of their RGB values shows that the R value has a high fitting degree (Figure 8e), which can be used as the final reference curve for the colorimetric result [25,26]. We also study the selectivity of the color markers for glucose by performing four sets of contrast tests in which chloride, lactic acid, alcohol and urea solutions with the same concentrations (0.3 mM) were added to a 0.3 mM glucose solution. The RGB value is almost constant (Figure 8f), proving that the color marker is highly selective for glucose, and it can effectively prevent interference from other biomarkers in sweat.
For an in situ experiment, we attached the patch to the forearm of a volunteer exposed to a high temperature of 30 °C (Figure 9a,b). When they cycle, sweat flows from the center hole and into the microreservoirs. After about 5 min, the sweat fills the first microreservoir and makes the color change (Figure 9c). The time it takes for the sweat to fill each microreservoir is shown in Figure 9c, with the total time being about 21 min and 22 s. The RGB values of the color markers in the four microreservoirs are obtained by using a color picker, and they are contrasted with the standard color card in the center. Referring to the fitting line in Figure 8e, the glucose concentration in the volunteer’s sweat is determined to be about 0.19 mM after 0–12 min of cycling, and it decreases to 0.10 mM after 15–21 min of cycling (Figure 9c) due to the glucose loss caused by movement. This result is consistent with that reported in previous work [23].
When we attach two sets of patches to the forearm and chest of one volunteer (Volunteer 1), the sweat collection from the forearm (black line) is slower than that from the chest (red line) due to the lower sweat rate of the forearm (Figure S3a). However, the glucose concentrations are essentially the same at the same times. This indicates that, for the same individual, different parts of the skin would not affect the function of the patch. For different individuals with different densities of sweat glands, for example, wider or thinner arms, and hairy or hairless skin, the sweat rate has individual variation, but the change trend of sweat glucose is basically the same. As shown in Figure S3, usually, hairy skin (with more sweat glands) has a higher rate of sweat collection than hairless skin. Although sweat collection is faster on the chest than on the forearm, the glucose concentrations are the same basically at the same times; that is, the sweat rate does not affect the glucose concentration. Additionally, because the valves in the patch are not in direct contact with the skin, body hair will not affect the air flow into the patch, and the liquid circulation is normally not blocked. So, hairy or hairless state does not affect the detection result. During the on-body tests of the sweat glucose concentration for each volunteer, we also measured blood glucose with a commercial blood glucose meter (Yuwell 590). The change trend of blood glucose detected by using the commercial blood glucose meter is basically the same as that obtained by using our microfluidic patch (Figure S3). There is no inconvenience or restriction for the volunteer during exercise, no discomfort or irritation on their skin surface and no reduced adhesion or fluid leakage during collection, indicating the favourable usability of the microfluidic patch for the detection of glucose concentration in sweat.
Of course, there are still some limitations to our microfluidic patch. Firstly, compared with soft-lithography technology, laser-cutting technology has a lower fineness. Through the structural design of the CBVs, the effective collection and transportation of sweat can be achieved. Secondly, the inner wall of the microchannel obtained by using the laser-cutting method is rough to a certain extent, which has a certain effect on the collection time of sweat but almost has no effect on the detection of the sweat glucose concentration (Figure S2). Finally, different light intensities may affect the measurement result of RGB values, although we minimized the error by setting a standard color card. Multiple measurements are sensible to determine the true concentration.
## 4. Conclusions
In summary, we proposed a microfluidic patch fabricated using a low-cost, simple laser-cutting manufacturing technique. The microfluidic patch has the functions of collecting, storing and detecting sweat in chronological order via the manipulation of different types of CBVs. Moreover, the microfluidic patch can detect the glucose concentration in sweat effectively with a high selectivity and an excellent anti-interference ability, indicating its application potential in sweat management and the physiological information monitoring of humans.
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|
---
title: Transcriptional and Epigenetic Alterations in the Progression of Non-Alcoholic
Fatty Liver Disease and Biomarkers Helping to Diagnose Non-Alcoholic Steatohepatitis
authors:
- Yalan Zhu
- He Zhang
- Pengjun Jiang
- Chengxia Xie
- Yao Luo
- Jie Chen
journal: Biomedicines
year: 2023
pmcid: PMC10046227
doi: 10.3390/biomedicines11030970
license: CC BY 4.0
---
# Transcriptional and Epigenetic Alterations in the Progression of Non-Alcoholic Fatty Liver Disease and Biomarkers Helping to Diagnose Non-Alcoholic Steatohepatitis
## Abstract
Non-alcoholic fatty liver disease (NAFLD) encompasses a broad spectrum of conditions from simple steatosis (non-alcoholic fatty liver (NAFL)) to non-alcoholic steatohepatitis (NASH), and its global prevalence continues to rise. NASH, the progressive form of NAFLD, has higher risks of liver and non-liver related adverse outcomes compared with those patients with NAFL alone. Therefore, the present study aimed to explore the mechanisms in the progression of NAFLD and to develop a model to diagnose NASH based on the transcriptome and epigenome. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) among the three groups (normal, NAFL, and NASH) were identified, and the functional analysis revealed that the development of NAFLD was primarily related to the oxidoreductase-related activity, PPAR signaling pathway, tight junction, and pathogenic *Escherichia coli* infection. The logistic regression (LR) model, consisting of ApoF, THOP1, and BICC1, outperformed the other five models. With the highest AUC (0.8819, $95\%$CI: 0.8128–0.9511) and a sensitivity of $97.87\%$, as well as a specificity of $64.71\%$, the LR model was determined as the diagnostic model, which can differentiate NASH from NAFL. In conclusion, several potential mechanisms were screened out based on the transcriptome and epigenome, and a diagnostic model was built to help patient stratification for NAFLD populations.
## 1. Introduction
Non-alcoholic fatty liver disease (NAFLD) is now recognized as the most common cause of chronic liver disease, with a global prevalence of $25\%$ [1]. It encompasses a broad spectrum of conditions, from simple steatosis (non-alcoholic fatty liver (NAFL)) to non-alcoholic steatohepatitis (NASH), which is characterized by necroinflammation and faster fibrosis progression than NAFL. Because of its high prevalence, NAFLD is now the most rapidly growing cause of liver-related mortality and morbidity worldwide [2]. Furthermore, there is currently no approved specific therapy for NAFLD, and lifestyle changes such as exercise and dietary modifications remain the mainstream treatment for NAFLD [3]. Compared with NAFL, patients with NASH have an increased risk of adverse hepatic outcomes such as cirrhosis, liver failure, and hepatocellular carcinoma, and also carry a higher risk of non-liver adverse outcomes including cardiovascular diseases and type 2 diabetes mellitus (T2DM) [4,5]. Thus, the differentiation of NASH from NAFL is a key issue for patients with NAFLD [6]. To date, liver biopsy remains the gold standard to differentiate NASH from NAFL. The histopathologic features of NASH generally include the presence of liver steatosis, inflammation, hepatocellular injury, and different degrees of fibrosis [7]. However, these features of NASH are not manifested in equivalent proportions in every biopsy, and no single feature by itself is diagnostic, making the diagnosis difficult at times [8]. Furthermore, its well-known limitations such as the dependence on pathologist experiences and the discrepancy between pathologists have not been solved either. Other methods, either relying on a “physical” approach based on the measurement of liver stiffness or a “biological” approach based on the quantification of biomarkers in the serum samples have been developed for NASH diagnosis, but none have been widely accepted yet in routine practice due to their limited sensitivity or specificity [6]. Therefore, novel objective molecular biomarkers are urgently needed to assist in standardizing and improving the diagnosis of NASH.
Epigenetics is characterized by heritable and reversible changes in gene expression, and it affects the phenotype by regulating gene transcription, without changing the primary DNA sequence [9]. DNA methylation, as the most common epigenetic modification, is closely related to the transcriptional regulation of genes and maintains the stability of the genome [10]. The function of DNA methylation seems to vary with different genomic contexts, but the gene expression level is usually inversely associated with the DNA methylation level [11]. The inverse association between methylation and transcription has been previously reported in NAFLD studies. Tissue repair genes, such as fibroblast growth factor receptor 2 (FGFR2) and caspase 1 (CASP1), were hypomethylated and high-expressed, whereas a gene in one-carbon metabolism, methionine adenosyl methyltransferase 1A (MAT1A), which generates SAM, was hypermethylated and low-expressed in liver biopsies from patients with advanced NAFLD [12]. In recent years, an ever-increasing number of high-throughput omics technologies including transcriptomics and epigenomics have been developed to explore the pathogenesis of NAFLD, as well as the establishment of diagnostic biomarkers [13]. *Differential* gene expression and significant alterations in DNA methylation are observed in the progression of NAFLD, including genes involved in glucose, lipid, or acetyl-coenzyme A (CoA) metabolism; insulin-like signaling; cellular division; immune function; mitochondrial function; and so on [13]. Several potential diagnostic biomarkers such as the circulating micro-RNAs and the altered DNA methylation sites in peripheral blood leukocytes have also been reported [14,15]. However, most previous studies have merely focused on either gene expression or methylation data, and few diagnostic biomarkers were based on multi-omics.
In the present study, we systematically analyzed the gene expression and DNA methylation data in the occurrence and development of NAFLD. The molecular mechanisms and functional pathways in the progression of NAFLD have been explored in transcriptional and epigenetic aspects, respectively. Using different methods, six diagnostic models based on both abnormally methylated and differentially expressed genes between NAFL and NASH were constructed and compared. The workflow diagram is shown in Figure 1. Our study would further assist in understanding the pathogenesis and patient stratification of NAFLD.
## 2.1. Data Source and Data Processing
*The* gene expression data and the corresponding DNA methylation data of NAFLD patients were obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/, accessed on 15 March 2022), and four datasets were included (Table 1). For the RNA transcriptome data, a total of 126 patients (28 normal, 49 NAFL, and 49 NASH) were retrieved from GSE48452 and GSE31803. DNA methylation data (Infinium Human Methylation 450 k) were downloaded from GSE48325 and GSE49542, including 118 patients (34 normal, 45 NAFL, and 39 NASH). Strict criteria were adopted when selecting the samples: patients without a clear histological diagnosis were excluded, and patients who received additional clinical treatments such as bariatric surgery were also excluded.
Considering the impact of different data processing methods, the raw cell files of the gene expression data were downloaded and processed with the R package oligo v.1.58.0 in the same way. Meanwhile, the R package ChAMP v.2.24.0 was used for the methylation analysis. The β value, ranging from 0 (unmethylated) to 1 (fully-methylated), was selected to represent the methylation level of each probe. In addition, the R package impute v.1.68.0, with its K-nearest neighbor (KNN) imputation procedure, was applied to impute the missing values in all datasets. The batch effects between different datasets were also adjusted by the ComBat function in the R package sva v.3.42.0, which allows users to adjust for batch effects in datasets using an empirical Bayes framework.
## 2.2. Identification of DEGs and DMGs in the Progression of NAFLD
To search for critical genes for NAFLD development, we identified differentially expressed genes (DEGs) and differentially methylated genes (DMGs) among the three groups (normal, NAFL, and NASH), seperately. The R package limma v. 3.50.0 was applied to screen DEGs, while DMGs were detected by the ChAMP.DMP function. To improve the accuracy, significant cut-off values of false discovery rate (FDR) < 0.01 and |log2 fold change (FC)| > 0.5 were used to identify DEGs, and FDR < 0.01 and |log2 fold change (FC)| > 0.1 were utilized to determine DMGs.
## 2.3. Functional Enrichment Analysis
For the sake of exploring the possible mechanisms involved in the progression of NAFLD, we conducted the following analyses with the R package clusterProfiler v.4.2.2. Gene ontology analysis (GO) was used for annotating DEGs and DMGs, and Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to perform the pathway enrichment analysis. FDR < 0.05 was set as the threshold value. Moreover, the search tool for the Retrieval of Interacting Genes (STRING) database (version 11.5) was used to evaluate the protein-protein interaction (PPI) information. The interaction score was set at 0.7. MCODE was conducted to screen modules of the PPI network with the degree cutoff, k-core, node score cutoff, and max depth set at 2, 2, 0.2, and 100, respectively, in Cytoscape software (version 3.9.1).
## 2.4. Construction and Validation of the Diagnostic Model
The timely identification of high-risk individuals plays a vital role in clinical practice, so we shifted focus to the construction of the NASH diagnostic model. To provide a robust model, we combined gene expression and methylation data. Genes that were reversely correlated (hypomethylated-high expressed or hypermethylated-low expressed) were identified based on DEGs and DMGs, and were regarded as the input variables in the succeeding study. To avoid overfitting and to simplify the model, least absolute shrinkage and selection operator (LASSO) regression was initially utilized to filter the variables. The key parameter λ was determined by ten-fold cross validation and λ_1se was selected in this study. In addition to the LASSO regression, another five popular methods, including logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN), were further conducted to construct the diagnostic model. All of the models were realized by the corresponding R packages: glmnet v.4.1-3, randomForest v.4.7-1.1, e1071 v.1.7-9, xgboost v.1.6.0.1, and kknn v.1.3.1. The hyperparameters of these methods were determined by grid search or cross validation based on the R package caret v.6.0-90. For each model, the Youden index was calculated to determine the optimal cut-off values. Area under curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were adopted to evaluate the diagnostic ability of the model. The external dataset, GSE167523 (51 NAFL and 47 NASH), was used as the testing cohort to further assess the diagnostic ability of the above models.
## 2.5. Development and Assessment of the Nomogram
The R package rms v.6.2-0 was applied to build a nomogram to visualize the final model. Furthermore, calibration curve and decision curve analysis (DCA) were employed to weigh the calibration and clinical applicability of the nomogram.
## 2.6. Validation of the Expression Pattern of the Model Genes in the Testing Cohort
The expression pattern of the model genes was further verified in the testing dataset. The Wilcoxon rank-sum test was used to identify the differential expression levels of the model genes between the NASH and NAFL groups. A two-sided $p \leq 0.05$ was considered statistically significant.
## 2.7. Development of NAFL and NASH in Mice
Wild-type male C57BL/6J mice (8 weeks of age) were acquired from Beijing HFK Bio-Technology. The mice were housed in a specific pathogen free environment with a stationary temperature at 22 ± 1 °C on a 12 h dark/light cycle. After 1 week of adaptive feeding, the mice were randomly divided into two groups: high-fat diet (HFD, $60\%$ calories from fat purchased from Research Diet, USA) only and HFD with CCl4 (Sigma-Aldrich, Saint Louis, MO, USA, 289116). CCl4 at a dose of 0.2 μL (0.32 μg)/g of body weight was injected intra-peritoneally once per week, starting simultaneously with the diet administration. After 10 weeks of treatment, liver tissues were collected and processed for histological analysis. NAFLD activity score (NAS) and disease stage were evaluated by an expert pathologist according to the NASH CRN scoring system (Table S1). Pictures were taken from representative areas showing steatosis, lobular inflammation, and hepatocyte ballooning, in consultation with the pathologist (Figure S1). All of the animal experiments were approved by the Animal Care and Use Committee of West China Hospital, Sichuan University.
## 2.8. Exploration of the Model Genes Expression Levels in Mice Using qRT-PCR
The total RNA was extracted from the liver tissues of the mice using the Trizol reagent (Thermofisher, Singapore, Cat No. 15596026) following the manufacturer’s instructions. The cDNA reverse transcription kit (Accurate Biology, Cat No. AG11711) was used to reverse transcribe RNA, and the SYBR Green Premix Pro Taq HS qPCR kit (Accurate Biology, Cat No. AG11701) was utilized to amplify the resulting cDNA. The samples were detected using an ABI 7500 Real-Time PCR System. The 2(−∆∆Ct) method was adopted to calculate the expression of the genes relative to the housekeeping gene β-Actin. The primers used for qRT-PCR are shown in Table S2.
## 3.1. Identification of DEGs and DMGs in the Progression of NAFLD
For the gene expression data, the DEGs were regularly observed during the whole progression of NAFLD: 45 upregulated genes and 52 downregulated genes were identified in the normal vs. NAFL group, respectively; 135 upregulated genes and 23 downregulated genes were identified in the NAFL vs. NASH group, respectively; and 78 upregulated genes and 31 downregulated genes were identified in the normal vs. NASH group, respectively (Table S3). In contrast, the DMGs were mainly observed in the latter period of NAFLD: no significant difference was observed in the normal vs. NAFL group; 650 hypermethylated genes and 377 hypomethylated genes were identified in the NAFL vs. NASH group, respectively; 195 hypermethylated genes and 99 hypomethylated genes were identified in the normal vs. NASH group, respectively (Table S4). The volcano plots show the distribution of DEGs and DMGs (Figure 2a–f).
## 3.2. Functional Enrichment Analysis of DEGs
Regarding DEGs, the GO analysis showed that a total of 3, 13, and 24 GO terms were obtained in the normal vs. NAFL group, the NAFL vs. NASH group, and the normal vs. NASH group, respectively (Table S5). Of note, one overlapping GO term (oxidoreductase activity, acting on paired donors, with the incorporation or reduction of molecular oxygen) was identified between the normal vs. NAFL group and the normal vs. NASH group, and nine overlapping GO terms (extracellular matrix structural constituent, extracellular matrix structural constituent conferring tensile strength, platelet-derived growth factor binding, glycosaminoglycan binding, etc.) were observed between the NAFL vs. NASH group and the normal vs. NASH group (Figure 2g). In addition, the KEGG pathway analysis found 2, 6, and 3 pathways significantly enriched in the normal vs. NAFL group, the NAFL vs. NASH group, and the normal vs. NASH group (Table S6). Similarly, one overlapping pathway (PPAR signaling pathway) enriched in both the normal vs. NAFL group and the normal vs. NASH group, and one overlapping pathway (ECM-receptor interaction) was identified between the NAFL vs. NASH group and the normal vs. NASH group (Figure 2h). The PPI network map of the DEGs had 107 nodes and 64 edges, and then the network was imported into the Cytoscape software to perform module analysis, in which the DEGs were constructed into four modules (Figure S2a,b).
## 3.3. Functional Enrichment Analysis of DMGs
Meanwhile, a total of 32 GO terms (22 GO terms in the NAFL vs. NASH group and 10 GO terms in the normal vs. NASH group) and 15 KEGG pathways (10 pathways in the NAFL vs. NASH group and 5 pathways in the normal vs. NASH group) were obtained for the DMGs (Table S7). Both the GO and KEGG results were highly overlapped between the two groups. The GO terms are mainly related to “GTPase regulator activity”, “GTPase activator activity”, “nucleoside-triphosphatase regulator activity”, and “guanyl-nucleotide exchange factor activity”, and the KEGG results indicate that DMGs were mainly involved in “tight junction”, “regulation of actin cytoskeleton”, “pathogenic *Escherichia coli* infection”, and “chemical carcinogenesis-DNA adducts” (Figure 2i,j). The PPI network map of DMGs had 288 nodes and 124 edges, and the DMGs were constructed into six modules (Figure S2c,d).
## 3.4. Construction and Validation of the Diagnostic Model
The diagnostic model was generated based on combined gene expression and methylation data. LASSO regression was initially applied to filter the variables, and another five methods were used to build the diagnostic model. The detailed hyperparameters are summarized in Table S8.
Based on DEGs and DMGs, a total of 21 genes (16 hypomethylated-high expressed genes and 5 hypermethylated-low expressed genes) were identified, and four genes (THOP1, ApoF, BICC1, and CCDC146) were selected as the final input variables by LASSO regression (Figure S2e,f). In the training cohort, all six methods exhibited an extraordinary diagnostic performance (Table 2). The highest AUC was found in the RF model and XGBoost model (AUC = 1.0000, $95\%$CI: 1.0000–1.0000), followed by the KNN model (AUC = 0.9842, $95\%$CI: 0.9677–1.0000), the LR model (AUC = 0.9792, $95\%$CI: 0.9575–1.0000), the SVM model (AUC = 0.9775, $95\%$CI: 0.9556–0.9994), and the LASSO model (AUC = 0.9733, $95\%$CI: 0.9476–0.9991). A reduction in performance was inevitable and was considered acceptable in the testing cohort, which contained heterogeneous patient data. Despite all of this, the LR model still reached an AUC of 0.8819 ($95\%$CI: 0.8128–0.9511), higher than the five other models (SVM: AUC = 0.8623, $95\%$CI: 0.7868–0.9378; KNN: AUC = 0.8502, $95\%$CI: 0.7707–0.9298; RF: AUC = 0.8454, $95\%$CI: 0.7695–0.9214; XGBoost: AUC = 0.8256, $95\%$CI: 0.7455–0.9058; LASSO: AUC = 0.8052, $95\%$CI: 0.7192–0.8911) (Figure 3f and Figure S3a–e). Therefore, the LR model was eventually determined as the optimal model for diagnosing NASH and was taken into further study.
## 3.5. Development and Assessment of the Nomogram
*Three* genes (ApoF, THOP1, and BICC1) were screened out as the model genes by the LR model ($p \leq 0.05$). Diagnostic scores were calculated using the following formula. logit (P = NASH) = 0.5470 − (1.5909 × THOP1 expression level) − (1.3167 × ApoF expression level) + (3.9034 × BICC1 expression level) Based on the LR model, a nomogram was built to predict the risk score of individual patients, and the three model genes were used as parameters in the nomogram (Figure 3c). The predicted NASH probability was compared to the actual NASH probability in the calibration curve, and a high level of consistency was observed (Figure 3d). Moreover, the DCA curve showed a net benefit across the whole range of threshold probabilities, indicating that the nomogram was feasible to make beneficial clinical decisions (Figure 3e).
## 3.6. Validation of the Expression Pattern of the Model Genes in the Testing Cohort
In the training cohort of NASH patients, significantly high DNA methylation and low expression levels were noted for ApoF and THOP1, while a low DNA methylation and high expression level was observed for BICC1 ($p \leq 0.001$) (Figure 4e–j). To further validate the expression pattern of the three model genes, these genes were selected from the GSE167523 testing cohort. As shown in Figure 4k–m, ApoF and THOP1 exhibited a significantly lower expression in the NASH group than in the NAFL group, whereas BICC1 exhibited a higher expression in the NASH group than in the NAFL group ($p \leq 0.001$). Altogether, the consistent results between different cohorts demonstrated that the expression levels of the three genes were reliable and useful for constructing the diagnostic model.
## 3.7. Exploration of the Expression Pattern of the Model Genes in the Mouse Model
The model genes selected by the LR model might help in improving the understanding of the disease pathogenesis. Thus, we preliminarily explored the expression pattern of the three genes in the mouse model. Our results indicate that the NAFL group exhibited a higher THOP1 expression level than the NASH group ($p \leq 0.05$), which was consistent with the results we obtained from the human cohorts (Figure 5). However, no meaningful findings were observed for the other two genes. The original data are provided in Table S9.
## 4. Discussion
NAFLD is the hepatic manifestation of the metabolic syndrome, representing a substantial health and economic burden worldwide. NASH, the progressive form of NAFLD, may culminate into cirrhosis and hepatocellular carcinoma, and is presently a leading cause of liver transplant [16]. Because of the poor prognosis of NASH, novel biomarkers or methods are urgently needed to improve the diagnosis of NASH. Meanwhile, the mechanisms underlying the development of NAFLD remain unknown. In this study, we systematically analyzed DEGs and DMGs in the progression of NAFLD. Functional enrichment analysis was conducted based on DEGs and DMGs. Several pathways were screened out, indicating the potential mechanisms involved in the development of NAFLD. Subsequently, based on DEGs, DMGs, and machine learning methods, six models that differentiate NASH from NAFL were constructed and compared. The LR model outperformed other models and was determined as the final diagnostic model.
NAFLD is a multifactorial disease, and exploring the molecular mechanisms and the functional pathways based on the accumulating transcriptional and epigenetic alterations might facilitate the understanding of NAFLD development. In the aspect of DEGs, the functional enrichment results indicated that oxidoreductase-related activity and the PPAR signaling pathway might be involved in the whole progression of NAFLD, while extracellular-matrix-related activity might only participate in the latter period of NAFLD, the transition from NAFL to NASH. Oxidative stress is defined as an imbalance between the production of reactive oxygen species (ROS) and the scavenging capacity of the antioxidant system. Previous studies have suggested a central role of oxidative stress in the transition from NAFL to NASH mediated by increased ROS production, which could lead to the reprogramming of hepatic lipid metabolism, changes in insulin sensitivity, and modulation of inflammation by interacting with innate immune signaling [17]. PPARs are a group of nuclear regulatory factors that provide fine tuning for key elements of glucose and fat metabolism and regulate inflammatory cell activation and fibrotic processes, all of which determine NASH progression [18]. Currently, several PPAR agonists such as PPARα agonist Wy14643, and PPARβ/δ agonists GW501516, GW0742, and MBX-8025 have been reported to be used in the treatment of NAFLD in experimental and clinical studies and for developing dual and pan-PPAR agonists, and might have a broader and more efficacious therapeutic potential in the future [19]. In addition, the extracellular matrix is a multi-molecule complex structure composed of collagen, elastin fibers, and structural glycoproteins, and has proven to be closely related to progressive fibrosis and inflammation in NASH [20]. Of note, oxidative stress and inflammation can lead to the excessive production of extracellular matrix in liver diseases, which is consistent with our enriched results in the continuous periods of NAFLD [21].
For DMGs, the functional analysis revealed that the development of NAFLD was primarily related to tight junction, pathogenic *Escherichia coli* infection, and GTPase-related activity. Tight junctions are intercellular adhesion complexes in the epithelia and endothelia that control paracellular permeability, playing a vital role in architecture and homeostasis in the liver [22,23]. The disruption of tight junctions can impair intestinal permeability, and the subsequent increased gut microbial translocation can lead to the inflammatory pathway involved in NASH development [24]. Previous studies have demonstrated that NAFLD contains a disease-specific gut microbiome, and alteration of the gut microbiota plays a significant role in its progression to NASH and cirrhosis [25,26]. An increase in *Escherichia coli* was observed in patients with advanced NASH fibrosis, which was consistent with our enrichment results [27]. Furthermore, the translocation of intestinal E. coli NF73-1 into the liver was found to be responsible for the high hepatic M1 population in a mice model, which further aggravated liver injury, leading to disturbance of the hepatic triglyceride metabolism and, eventually, NAFLD progression [28]. GTPases are conserved regulators of cell motility, polarity, adhesion, cytoskeletal organization, proliferation, and apoptosis, but the role of GTPases in the NAFLD progression remains unclear [29].
NAFLD is an umbrella term that comprises a continuum of liver abnormalities, ranging from NAFL to NASH [30]. NASH is defined as a more serious stage of NAFLD and has higher risks of liver and non-liver related adverse outcomes compared with those patients with NAFL alone [31]. Thus, the prompt and accurate diagnosis of NASH is of extreme significance in clinical practice. Several biomarkers have been developed in the past decades, but their performances vary across studies. The plasma cytokeratin 18 (CK18) fragment level, a marker of hepatocyte apoptosis, has been extensively evaluated in steatohepatitis, while its limited sensitivity of $58\%$ (51–$65\%$) makes it inadequate as a screening test for staging NASH [32,33]. Serum metabolomics has identified pyroglutamate as a diagnostic biomarker for NASH, with a sensitivity and specificity of $72\%$ and $85\%$, respectively [34]. The intrahepatic thrombospondin 2 (THBS2) expression level has shown an AUROC of 0.915 for diagnosing NASH [35]. Six differentially methylated CpG sites in peripheral blood leukocytes can be potentially used as diagnostic biomarkers for differentiating NASH from NAFL, with AUCs ranging from 0.689 to 0.882 [36]. However, most previous studies have been merely based on a single omics platform. In this study, we adopted six machine learning methods, including LASSO, LR, RF, SVM, XGBoost, and KNN, to build a diagnostic model based on inversely related methylation-transcription genes. The LR model, consisting of ApoF, THOP1, and BICC1, saw the highest AUC (0.8819, $95\%$CI: 0.8128–0.9511). Although the LR model exhibited a moderate specificity of $64.71\%$, it could distinguish NASH from NAFL with a sensitivity of $97.87\%$. Furthermore, the integration of multi-omics data can avoid the randomness of single omics data and improve the diagnostic capacity of disease phenotypes, and hence aid in patient stratification [37,38].
In addition to the stable diagnostic ability, the LR model generated based on methylation-transcription data also possessed the following traits. DNA methylation alterations are highly reversible and change in response to environmental and lifestyle experiences such as diet, obesity, and physical activities [12]. Likewise, studies have indicated that NASH may regress to NAFL after treatment. Weight loss has the strongest association with histologic improvement in NASH, and the methylation patterns of NASH are also altered after bariatric surgery [31,39]. As a result, the LR model could monitor the dynamic disease conditions in real time in terms of the similar reversibility, either during the disease transition or after treatments. Moreover, the LR model also possesses a potential early predictive capacity, as DNA methylation alterations are inheritable and can be transferred to the next generation. A previous study has shown that a high-fat and high-cholesterol Western diet (WD)-induced maternal hypercholesterolemia increases the male offspring risk for NAFLD and metabolic diseases, which is related to the decreased ApoB gene expression regulated by DNA hypermethylation [40].
Furthermore, the underlying mechanism for the progression of NAFLD is complex and multifactorial, and the model genes selected by the LR model might help in improving the understanding of disease pathogenesis. Thus, we explored the expression pattern of the three genes in the mouse model. Apolipoprotein F (ApoF) is a minor apolipoprotein mainly involved in cholesterol transportation by inhibiting cholesteryl ester transfer protein (CETP) activity with LDL [41,42]. *The* gene BicC family RNA-binding protein 1 (BICC1) encodes an RNA-binding protein that was firstly identified in Drosophila melanogaster and was later shown to have important roles in vertebrate development and embryogenesis [43]. Unfortunately, no meaningful results were obtained for the two genes in the mouse model. We speculated that the main reason was the species difference; ApoF and BICC1 may not have equally important functions in the disease progression in mice. In this study, the gene ApoF was expressed at lower levels in NASH patients compared with NAFL patients. Similarly, Liu et al. found that hepatic ApoF mRNA levels were decreased by high fat, cholesterol-enriched diets, and ApoF was subject to negative regulation by agonist-activated LXR or PPARα nuclear receptors binding to a regulatory element ~1900 bases 5′ to the ApoF promoter [44]. The concentration of protein ApoF in serum has also been quantified to decrease across NAFLD stages in a previous study, which was consistent with our study [45]. In addition, recent studies have revealed that BICC1 might be involved in the immune response in the tumor microenvironment by affecting immune cells, especially macrophages, and the overexpression of BICC1 was closely related to the poor prognosis in tumors such as gastric cancer and oral cancer, as well as multiple functional pathways such as focal adhesion and ECM-receptor interaction, which were also enriched in our study [46,47]. Thus, the similar higher expression pattern of BICC1 in NASH patients suggests its potential role in the immune response in NAFLD development.
Unlike ApoF and BICC1, a similar different expression pattern of THOP1 across species was observed, suggesting its potential key role in the development of NAFLD. Thimet oligopeptidase (THOP1) is a metallopeptidase widely distributed in mammalian tissues, initially purified from the soluble fraction of rat brain homogenates in 1983 [48]. Apart from the role in major histocompatibility class I (MHC-I) antigen presentation, THOP1 was recently reported to be involved in energy metabolism regulation [49]. Gewehr et al. found that the THOP1 null (THOP1−/−) mice gained $75\%$ less body weight and showed neither insulin resistance nor non-alcoholic fatty liver steatosis (NAFLS) when compared with wild-type (WT) mice after 24 weeks of being fed a hyperlipidic diet (HD), and also observed a higher adipose tissue adrenergic-stimulated lipolysis in THOP1−/− mice [50]. Furthermore, specific genes and microRNAs involved in obesity and adipogenesis were differentially modulated in the liver and adipose tissue of THOP1−/− mice. An increased expression level of PPAR-γ, which was also enriched in our study, was observed in THOP1−/− mice fed the HD when compared with either THOP1−/− mice fed a standard diet (SD) or WT mice fed the HD [50]. Altogether, previous studies have suggested that THOP1 could be a therapeutic target for controlling obesity and associated diseases such as insulin resistance and NAFLD.
Potential limitations of the present study should be noted. The biological mechanisms of THOP1 in the progression of NAFLD remain to be explored. In addition, the study was based on research data from the public database, which might induce selection bias. Thus, a multicenter and large-scale study should be conducted to further validate our findings.
## 5. Conclusions
In conclusion, we systematically explored the potential mechanisms and pathways in the progression of NAFLD by tracing the flowing information in both the transcriptome and epigenome, and a diagnostic model based on the combination of gene expression and methylation data was built to differentiate NASH from NAFL. To the best of our knowledge, this is the first diagnostic model that employed both transcriptional and epigenetic data that can provide a robust diagnostic ability. Further studies remain to be implemented to explore the pathogenesis of NAFLD and refine patient stratification to benefit NAFLD populations.
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|
---
title: Single Nucleotide Polymorphisms of the RAC1 Gene as Novel Susceptibility Markers
for Neuropathy and Microvascular Complications in Type 2 Diabetes
authors:
- Iuliia Azarova
- Elena Klyosova
- Alexey Polonikov
journal: Biomedicines
year: 2023
pmcid: PMC10046239
doi: 10.3390/biomedicines11030981
license: CC BY 4.0
---
# Single Nucleotide Polymorphisms of the RAC1 Gene as Novel Susceptibility Markers for Neuropathy and Microvascular Complications in Type 2 Diabetes
## Abstract
Single nucleotide polymorphisms (SNP) in the RAC1 (Rac family small GTPase 1) gene have recently been linked to type 2 diabetes (T2D) and hyperglycemia due to their contribution to impaired redox homeostasis. The present study was designed to determine whether the common SNPs of the RAC1 gene are associated with diabetic complications such as neuropathy (DN), retinopathy (DR), nephropathy, angiopathy of the lower extremities (DA), and diabetic foot syndrome. A total of 1470 DNA samples from T2D patients were genotyped for six common SNPs by the MassArray Analyzer-4 system. The genotype rs7784465-T/C of RAC1 was associated with an increased risk of DR ($$p \leq 0.016$$) and DA ($$p \leq 0.03$$) in males, as well as with DR in females ($$p \leq 0.01$$). Furthermore, the SNP rs836478 showed an association with DR ($$p \leq 0.005$$) and DN ($$p \leq 0.025$$) in males, whereas the SNP rs10238136 was associated with DA in females ($$p \leq 0.002$$). In total, three RAC1 haplotypes showed significant associations (FDR < 0.05) with T2D complications in a sex-specific manner. The study’s findings demonstrate, for the first time, that the RAC1 gene’s polymorphisms represent novel and sex-specific markers of neuropathy and microvascular complications in type 2 diabetes, and that the gene could be a new target for the pharmacological inhibition of oxidative stress as a means of preventing diabetic complications.
## 1. Introduction
Over 500 million people worldwide are affected by type 2 diabetes (T2D), which, along with obesity, is the second most frequent endocrinological disease [1]. About 7.8 million people in the Russian Federation have diabetes, with T2D accounting for the majority of cases [1]. Type 2 diabetes is associated with a multitude of disorders in lipid, protein, nucleotide metabolism, and redox homeostasis, leading to long-term complications that made T2D the ninth cause of mortality in 2020 [2]. In particular, impaired redox homeostasis is thought to be an important pathological condition underlying oxidative stress that contributes to the initiation and progression of type 2 diabetes [3,4,5]. Hyperglycemia, an increase in reactive oxygen species (ROS) production with cytosolic NADPH oxidase, and a deficiency in key antioxidants such as reduced glutathione (GSH) are considered to be the major damaging factors that are responsible for the structural and functional alterations in the retina, kidneys, nerves, and vessels in diabetics that ultimately lead to complications such as diabetic retinopathy (DR), diabetic nephropathy (DF), diabetic neuropathy (DN), diabetic angiopathy of the lower extremities (DA), and diabetic foot syndrome (DFS) [6,7,8,9].
The NADPH oxidase (NOX) enzyme is primarily responsible for the generation of superoxide anion radicals, which accumulate excessively in the cell and cause oxidative stress [10]. *Once* generated, these superoxides are rapidly dismutated into hydrogen peroxide, either spontaneously or via superoxide dismutase [11]. Other ROS are generated through the reactions of the superoxide radical with nitric oxide to form peroxynitrite, the peroxidase-catalyzed formation of hypochlorous acid from hydrogen peroxide, and the iron-catalyzed Fenton’s reaction which produces hydroxyl radicals [12,13]. Notably, increased ROS generation was found to interact with the proteins of the insulin signaling pathway, contributing to insulin resistance [14,15] and triggering the dysfunction and apoptosis of pancreatic beta-cells [16].
The NOX enzyme represents a multi-subunit complex consisting of several proteins, among which small GTPases, such as RAC1 and RAC2, are known to activate the holoenzyme [17]. A total of two experimental studies [18,19] have shown that the transcriptional activation of the RAC1 gene in diabetic mice has been found to contribute to mitochondrial damage and retinopathy, suggesting at least a causal role of this gene in diabetic complications. We have recently observed that the single nucleotide polymorphisms (SNP) of the RAC1 gene are tightly associated with impaired redox homeostasis, an increased risk of type 2 diabetes, and hyperglycemia [20]. Pursuing further interests in the role of this gene within T2D, the purpose of the present study was to investigate whether the polymorphisms of the RAC1 gene contribute to the development of common diabetic complications, such as retinopathy, nephropathy, neuropathy, angiopathy of the lower extremities, and diabetic foot syndrome.
## 2.1. Study Participants and Diagnosis of Type 2 Diabetes
The Regional Ethics Review Committee of Kursk State Medical University gave its approval to the study protocol, which complied with the ethical standards of the Declaration of Helsinki. Before being enrolled in the study, each subject provided their written informed consent. A total of 1470 patients with type 2 diabetes were included in the study. Most of the study participants were Russians from the Kursk region (central Russia). All were patients that had been admitted to the Endocrinology Division of the Kursk Emergency Hospital between November 2016 and October 2019. The following WHO criteria [21,22] were used to verify the T2D diagnosis: a fasting blood glucose (FBG) level of ≥7.0 mmol/L, a random blood glucose level of ≥11.1 mmol/L, and/or a glycated hemoglobin (HbA1c) level of ≥$6.5\%$. The criteria for the inclusion of T2D patients in the study were: [1] a physician-verified diagnosis of T2D, confirmed by clinical, laboratory, and instrumental investigations; [2] an age of over 35 years old; and [3] written informed consent to participate in the study. The criteria for excluding patients from the study were the following: [1] an age of less than 35 years; [2] an absence of written informed consent to participate in the study; and [3] advanced-stage diabetes or the decompensation of diabetes, diabetic coma, immune-mediated or idiopathic type 1 diabetes, gestational diabetes, MODY types of diabetes, diseases of the exocrine pancreas, such as pancreatitis, pancreatic trauma, or pancreatectomy, pancreatic tumors, hereditary diseases affecting the pancreas, or any other endocrine disorders. All of the study’s participants completed a questionnaire [23] on the risk factors of type 2 diabetes.
## 2.2. Genetic Analysis
Fasted venous blood samples were collected from all the study participants, and the genomic DNA was purified by a spin column QIAamp Blood Mini Kit with the use of a robotic workstation QiaCube (QIAGEN, Germany). In total, six commonly tagged SNPs of the RAC1 gene, such as rs4724800, rs7784465, rs10951982, rs10238136, rs836478, and rs9374, were selected for the study, as described previously [20]. The SNP genotyping was performed using MALDI-TOF mass spectrometry with the MassArray-4 System (Agena Bioscience Inc., San Diego, CA, USA). The primer sequences that were used for the genotyping are available upon request. The genotyping analysis was performed blindly, with regard to the case–control status to ensure quality control. Repeat genotyping was performed on approximately $10\%$ of the samples, randomly selected from the T2D group, and the repeatability test yielded a $100\%$ concordance rate.
## 2.3. Biochemical Analysis
All the biochemical investigations were performed using fasted whole blood samples that were collected in standard sterile tubes with lithium heparin, and immediately centrifuged at 3500 rpm, according to the manufacturer’s instructions (Cell Biolabs, San Diego, CA, USA; Abcam, Waltham, MA, USA). The plasma samples were aliquoted and stored at −80 °C until their further use. For the determination of oxidized glutathione (GSSG), the plasma was immediately deproteinized with trichloroacetic acid. The plasma hydrogen peroxide levels were assessed in 489 T2D patients, whereas the GSSG levels were measured in 258 diabetics that were recruited at the final study phase (between March 2019 and October 2019). The GSSG levels were determined by a fluorometric assay protocol (GSH/GSSG Ratio Detection Assay Kit II, Abcam, Waltham, MA, USA) that used a proprietary, non-fluorescent, water-soluble dye that became strongly fluorescent upon reacting with GSH. The levels of ROS were quantified by fluorometric assay using the OxiSelect™ In Vitro ROS/RNS Assay Kit (Cell Biolabs, San Diego, CA, USA), which employed a proprietary quenched fluorogenic probe, dichlorodihydrofluorescin DiOxyQ (DCFH-DiOxyQ), which is a specific ROS/RNS probe. It was first primed with a quench removal reagent and subsequently stabilized in a highly reactive DCFH form. In this reactive state, the ROS and RNS species react with the DCFH, which is rapidly oxidized to the highly fluorescent 2’,7’-dichlorodihydrofluorescein. The standard curve of H2O2 was used to quantify the ROS concentrations in the plasma samples. Absorbance at 405 nm and fluorescence at 480 nm excitation/530 nm emission were measured on a microplate reader Varioscan Flash (Thermo Fisher Scientific, Waltham, MA, USA). The concentrations of glycated hemoglobin, the fasting blood glucose in blood plasma were determined with the use of a semi-automatic biochemical analyzer Clima MC-15 (Ral *Tecnica para* el Laboratorio, S.A., Barcelona, Spain) and the reagents produced by DIAKON-DS, Moscow, (Russia). These biochemical and genetic analyses were performed at the Research Institute for Genetic and Molecular Epidemiology of Kursk State Medical University, Kursk (Russia).
## 2.4. Statistical and Bioinformatics Analysis
Statistical power was estimated using the genetic association study power calculator (http://csg.sph.umich.edu/abecasis/gas_power_calculator/, accessed on 12 June 2016). Based on the sample size of 1470 people with T2D, a sub-group association analysis of the RAC1 polymorphisms with diabetic complications could detect a genotype relative risk of 1.25–1.50, assuming a 79.1–$90.0\%$ power and a $5\%$ type I error (0.05). The chi-square test was used to compare the genotype frequencies in T2D patients to the values predicted by the Hardy–*Weinberg equilibrium* assumption. The association between the RAC1 gene polymorphisms and diabetic combinations was evaluated by a multiple logistic regression analysis, with the calculation of odds ratios (OR) and $95\%$ confidence intervals ($95\%$CI) adjusted for age, sex, and body mass index (BMI). The associations were analyzed using the SNPStats software [24]. A codominant model was used to present the results in tables. A p-value of ≤0.05 was selected as statistically significant. To control for the multiple testing of the SNP-phenotype associations, the calculated p-values were adjusted by the false discovery rate (FDR). A Q-value of ≤0.05 was considered statistically significant to interpret the genotype–phenotype associations [25].
The Kolmogorov–Smirnov test was used to determine the normality of the biochemical parameters. Age and BMI were expressed as means with standard deviations and compared between the groups by the Student’s t-test. The non-normally distributed traits (glycated hemoglobin, fasting blood glucose, hydrogen peroxide, and total glutathione) were expressed as medians with the first and third quartiles (Q1–Q3) and compared between the groups with the Kruskal–Wallis test. These statistical calculations were performed using the STATISTICA for Windows v13.0 package (TIBCO, Palo Alto, CA, USA).
## 3.1. Demographic, Clinical and Laboratory Characteristics of Patients
The demographic, clinical, and laboratory characteristics of the study patients are shown in Table 1. The majority of the T2D patients had diabetic neuropathy ($92.3\%$) and diabetic retinopathy ($71.2\%$). Other T2D complications included diabetic angiopathy of the lower extremities ($65.9\%$), diabetic nephropathy ($38.4\%$), diabetic foot syndrome ($7.6\%$), and coronary artery disease ($32.5\%$). The patients with the above complications had a significantly longer duration of T2D ($$p \leq 0.001$$). As can be seen from Table 1, there were no regularities or trends in the quantitative parameters of redox homeostasis, such as glutathione or hydrogen peroxide, regardless of the type of diabetes complication.
## 3.2. Association of RAC1 Gene Polymorphisms with Diabetic Retinopathy
The frequency of the minor allele rs7784465-C was significantly higher in the patients with DR within the entire group (OR 1.35, $95\%$CI 1.09–1.68, $$p \leq 0.006$$) and in males (OR 1.52, $95\%$CI 1.05–2.21, $$p \leq 0.032$$) after a sex-stratified analysis. The alternative allele rs836478-T was associated with DR in the entire group of patients with DR (OR 1.34, $95\%$CI 1.14–1.59, $$p \leq 0.0005$$), in males (OR 1.51, $95\%$CI 1.15–1.99, $$p \leq 0.003$$), and in females (OR 1.27, $95\%$CI 1.02–1.57, $$p \leq 0.03$$). The genotype frequencies of the studied SNPs in diabetics with and without DR are shown in Table 2.
The genotypes rs7784465-T/C and rs836478-T/T were associated with the risk of diabetic retinopathy in the entire group of diabetics. A sex-stratified association analysis showed that the polymorphisms rs7784465 and rs836478 were associated with an increased risk for DR in males, whereas in females, no difference in the genotype frequencies for these SNPs was seen between the patients with and without DR. The estimated frequencies of the RAC1 haplotypes in T2D patients with and without DR are shown in Table 3.
The frequency of the haplotypes H2 rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478T-rs9374G and H3 rs4724800G-rs7784465T-rs10951982A-rs10238136A-rs836478T-rs9374A was significantly higher in the patients with DR. A sex-stratified analysis showed a much stronger association of the haplotype H2 rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478T-rs9374G with DR in diabetic males (OR 2.32, 95CI 1.46–3.67, $$p \leq 0.0004$$).
## 3.3. Polymorphisms of the RAC1 Gene and Diabetic Nephropathy
The minor allele rs836478-T was associated with DNF exclusively in males (OR 1.43, $95\%$ CI 1.05–1.96, $$p \leq 0.025$$). The genotype frequencies for the studied SNPs in the diabetics with and without DNF are shown in Table 4.
As can be seen from Table 4, the polymorphism rs836478 was associated with the risk of DNF in males in the codominant model. However, the rs836478-C/T-T/T genotypes of RAC1 were found to be associated with an increased risk of diabetic nephropathy in male diabetics (OR 1.84, $95\%$ CI 1.06–3.19, $$p \leq 0.024$$) in the dominant model. The other SNPs of the RAC1 gene showed no significant associations with a DNF risk. A haplotype analysis (Supplementary Table S1) revealed that none of the RAC1 haplotypes were associated with diabetic nephropathy.
## 3.4. RAC1 Gene Polymorphisms and the Risk of Diabetic Neuropathy
The frequencies of the minor alleles rs7784465-C (OR 1.80, $95\%$ CI 1.17–2.75, $$p \leq 0.007$$) and rs836478-T (OR 1.35, $95\%$ CI 1.02–1.80, $$p \leq 0.037$$) were significantly higher in the patients with DN compared to the patients without DN. The allele rs7784465-C was also associated with DN in females (OR 2.02, $95\%$ CI 1.08–3.76, $$p \leq 0.028$$). The genotype frequencies of the RAC1 gene polymorphisms among the T2D patients with and without diabetic neuropathy are given in Table 5.
The genotype rs7784465-T/C of RAC1 was associated with an increased risk of DN in the entire group of T2D patients and diabetic females. As can be seen from Table 6, the haplotype H2 rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478T-rs9374G and the minor alleles rs7784465-C and rs836478-T were associated with an increased risk of DN in both diabetic males and females. Interestingly, the haplotype H5 rs4724800G-rs7784465T-rs10951982G-rs10238136A-rs836478C-rs9374G showed an association with an increased risk of DN only in males. Meanwhile, the haplotype H7 rs4724800A-rs7784465T-rs10951982G-rs10238136T-rs836478T-rs9374G possessed a protective effect against the DN risk in diabetic females.
## 3.5. The Link between RAC1 Gene Polymorphisms to Diabetic Angiopathy of the Lower Extremities and Diabetic Foot Syndrome
The minor allele rs10238136-T was found to be associated with diabetic angiopathy of the lower extremities in females (OR 3.47, $95\%$CI 1.42–8.46, $$p \leq 0.004$$). The genotype frequencies for the RAC1 gene polymorphisms among the T2D patients with and without diabetic angiopathy of the lower extremities are given in Table 7.
The genotype rs10238136-A/T was associated with an increased risk of DA in the entire group and in diabetic females, whereas the genotype rs7784465-T/C was associated with DA only in males. Meanwhile, the haplotype H7 rs4724800A-rs7784465T-rs10951982G-rs10238136T-rs836478T-rs9374G (Table 8) showed an association with an increased risk of DA in females.
As can be seen from Supplementary Table S2, none of the studied SNPs of the RAC1 gene showed an association with the risk of diabetic foot syndrome. However, the minor allele rs10238136-T (OR 3.67, $95\%$CI 1.48–9.10, $$p \leq 0.016$$) and haplotype H6 rs4724800A-rs7784465T-rs10951982G-rs10238136T-rs836478T-rs9374G (Table 9) were associated with diabetic foot syndrome in males.
## 3.6. The Link between RAC1 Gene Haplotypes and Plasma Parameters of Redox Homeostasis
An analysis of the relationship between the genetic and biochemical parameters of redox homeostasis (Supplementary Table S3) revealed an association of the haplotype rs4724800A-rs7784465T-rs10951982G-rs10238136A-rs836478T-rs9374G with increased levels of ROS in the plasma of diabetics with DR (Diff = 1.02, $95\%$ CI 0.18–1.85, $$p \leq 0.017$$), DNF (Diff = 1.14, $95\%$ CI 0.26–2.02, $$p \leq 0.011$$) and DN (Diff = 0.90, $95\%$ CI 0.25–1.55, $$p \leq 0.0069$$). Moreover, DNF patients carrying the same RAC1 haplotype had a lower concentration of total plasma glutathione (Diff = −1.72, $95\%$ CI −3.01–−0.44, $$p \leq 0.0095$$) compared with the carriers of the reference haplotype rs4724800A-rs7784465T-rs10951982G-rs10238136A-rs836478C-rs9374G.
The haplotype rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478C-rs9374G was associated with higher ROS levels in patients with DNF (Diff = 6.82, $95\%$ CI 4.90–8.73, $p \leq 0.0001$), DN (Diff = 5.73, $95\%$ CI 4.44–7.01, $p \leq 0.0001$), and DA (Diff = 7.23, $95\%$ CI 5.83–8.62, $p \leq 0.0001$). In patients with DFS, the haplotype rs4724800G-rs7784465C-rs10951982A-rs10238136A-rs836478T-rs9374A was associated with increased ROS (Diff = 4.10, $95\%$ CI 2.52–5.68, $p \leq 0.0001$), whereas the carriers of the haplotypes rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478T-rs9374G (Diff = 1.09, $95\%$ CI 0.29–1.90, $$p \leq 0.012$$) and rs4724800G-rs7784465T-rs10951982A-rs10238136A-rs836478T-rs9374A (Diff = 1.03, $95\%$ CI 0.08–1.98, $$p \leq 0.039$$) had higher levels of total glutathione in their blood plasma (Supplementary Table S3).
## 4.1. Summary of the Study Findings
The present study found, for the first time, that the polymorphisms of the gene encoding Rac family small GTPase 1 (RAC1) in type 2 diabetes are associated with complications such as diabetic retinopathy, neuropathy, and angiopathy of the lower extremities. However, the observed associations were sex-specific. In particular, the genotype rs7784465-T/C was associated with an increased risk of retinopathy and angiopathy of the lower extremities in males, as well as diabetic neuropathy in females. Furthermore, the polymorphism rs836478 of RAC1 was linked to diabetic retinopathy and nephropathy in males, whereas the polymorphism rs10238136 was linked to diabetic angiopathy in females. Figure 1 depicts the structure of the RAC1 gene, the genomic position of the SNPs, and the haplotype structure of the gene, as well as summarizing the overall research findings. The RAC1 haplotypes were found to be associated with DR in males and with DN in females. Furthermore, the RAC1 haplotype rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478T-rs9374G showed an association with DR in males and DN regardless of sex. In addition, the haplotype rs4724800A-rs7784465T-rs10951982G-rs10238136T-rs836478T-rs9374G was associated with a 4-fold risk of DA in females and DFS in males. The haplotype rs4724800A-rs7784465T-rs10951982G-rs10238136A-rs836478T-rs9374G showed an association with the increased plasma levels of ROS in diabetics with DR, DNF, and DN. The patients with DNF who carried the above haplotype had lower concentrations of total plasma glutathione. Moreover, the haplotype rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478C-rs9374G was associated with higher ROS levels in patients with DNF, DN, and DA. The haplotype rs4724800G-rs7784465C-rs10951982A-rs10238136A-rs836478T-rs9374A was correlated with the increased ROS in patients with DFS, whereas the haplotypes rs4724800A-rs7784465C-rs10951982G-rs10238136A-rs836478T-rs9374G and rs4724800G-rs7784465T-rs10951982A-rs10238136A-rs836478T-rs9374A were correlated with the increased levels of total glutathione in the plasma. A functional annotation of the studied SNPs [20] showed that the minor alleles rs7784465-C, rs10951982-A, rs10238136-T, rs836478-T, and rs9374-A were associated with the increased expression of the RAC1 gene in various tissues and might be binding sites for transcription factors (TF). For instance, an analysis of the TF-binding affinity of the rs836478 polymorphism (which was associated with DNF) with the atSNP tool [26] (http://atsnp.biostat.wisc.edu/search, accessed on 2 November 2020) has shown that the minor allele rs836478-T was predicted to create binding sites for 34 TFs, including FOXC1, FOXD1, PBX1, GATA3, and POU3F3, which are enriched with GO terms that are related to the development of the nephron epithelium and renal tubules, as assessed by the STRING database [27] (https://string-db.org/, accessed on 14 December 2022).
Although many hypotheses have been proposed to explain the molecular pathways underlying diabetic complications, it is widely accepted that glutathione deficiency, the increased production of reactive oxygen species, and its resulting oxidative stress are the major pathological processes responsible for the development of diabetic complications [28,29,30,31,32].
## 4.2. Diabetic Retinopathy
Diabetic retinopathy is one of the most common complications of diabetes mellitus and is a major global contributor to vision loss and blindness [33,34]. According to a meta-analysis of large, population-based studies, the prevalence and progression of diabetic retinopathy have been linked to the serum levels of HbA1c, total cholesterol, and blood pressure, but only in about $10\%$ of patients with type 2 diabetes [35], suggesting that other factors exist that explain the development of diabetic retinopathy in the majority of diabetics. Numerous studies [18,29,31,36] have shown that oxidative stress plays a key role in the onset of diabetic retinopathy. RAC1 is required for NADPH oxidase 2, an enzyme that generates reactive oxygen species. The transcriptional activity of the RAC1 gene may be regulated through epigenetic mechanisms. In particular, Kowluru and co-workers observed that the histone mark H3K9me3 at the Rac1 promoter assists with active DNA methylation-hydroxymethylation reactions, activating *Rac1* gene transcription in diabetic mice [19]. Cells that were exposed to high glucose concentrations were found to exhibit increased signaling in the chain Rac1–Nox2–ROS, increased levels of Rac1 transcripts, and increased 5-hydroxymethylcytosine levels at the gene promoter [37]. ROS overproduction has been shown to speed up the loss of capillary cells and to cause retinal neurodegeneration through mitochondrial damage, whereas the inhibition of ROS production was found to inhibit caspase-3-mediated neuronal apoptosis and to prevent vision loss [38,39].
## 4.3. Diabetic Nephropathy
Diabetic nephropathy is a clinical syndrome that is characterized by persistent albuminuria and a progressive decline in renal function [40]. DNF is thought to be the most common cause of end-stage renal disease, affecting $20\%$ to $50\%$ of people with diabetes. The mechanisms of DNF are very complex, and despite decades of intensive research, the pathogenesis of this complication in type 2 diabetes is still not fully understood [41,42]. Numerous pathways, processes, molecules, and conditions, such as oxidative stress, the renin-angiotensin-aldosterone system, mitogen-activated protein kinases, the formation of advanced glycosylation end products (AGE), connective tissue growth factor, transforming growth factor beta-1 (TGF-β), and inflammatory cytokines, are known to contribute to the onset and progression of DNF [43,44,45]. The pathways and mediators that are involved in kidney damage in type 2 diabetes share a lot of overlaps. For instance, it has been discovered that oxidative stress damages the kidneys through the activation of the renin-angiotensin-aldosterone system, whereas angiotensin-II itself is capable of causing renal injury through oxidative stress [42]. Another example is NADPH oxidase stimulating the production of TGF-β, which stimulates the production of ROS via NADPH oxidase activation [41]. The experimental observation that the inhibition of oxidative stress improved a renal feature associated with streptozotocin-induced DNF has highlighted the role of oxidative stress in the induction and progression of DNF [46,47]. Meanwhile, oxidative stress can damage cells indirectly by activating other pathological pathways which damage the renal cells through unknown mechanisms [48]. Metabolic and hemodynamic alterations in the kidneys are also linked to oxidative stress, and both have additive detrimental effects on the organ [49].
The direct and indirect mechanisms by which oxidative stress causes kidney damage in diabetes have been proposed. ROS were found to cause direct damage to podocytes, mesangial cells, and endothelial cells, leading to proteinuria and tubule-interstitial fibrosis [50]. The mechanism of this damage was argued to be that the glomerulus, the filtering unit of the kidney, is more sensitive to oxidative injury than the other parts of the nephron [51]. Hyperglycemia is known to induce ROS production and oxidative damage to DNA, lipids, and proteins [52]. Chronic hyperglycemia can cause oxidative stress by increasing angiotensin-II levels, activating protein kinases, and increasing TGF-β expression [53]. For instance, increased angiotensin-II levels induce ROS production in the kidneys through the activation of NADPH oxidase [54]. It is observed that the ROS-associated activation of TGF-β causes the excessive remodeling of the extracellular matrix in the mesangium and promotes fibrotic processes in the kidneys [55]. As mentioned above, the increased production of ROS via NADPH oxidase in diabetes is attributed to the activation of the NF-κB pathway, which also promotes the transcriptional activation of the genes encoding inflammatory cytokines, thereby contributing to kidney injury and leading to renal fibrosis and a decline in renal function [56,57,58]. The activation of the α and β isoforms of protein kinases C is also known to induce oxidative damage to the kidneys through the increased production of NADPH-dependent superoxide anion radicals [59]. There are many other redox-sensitive signal transduction pathways, such as c-Jun N-terminal kinase (JNK), p38 MAP kinase, and the transcription factor activator protein 1 (AP-1), determining a vicious cycle between inflammation and oxidative stress [60,61].
We have established, for the first time, an association between the minor allele rs836478-T and an increased risk of diabetic nephropathy. According to the GTEx portal (https://gtexportal.org, accessed on 24 February 2023), the RAC1 gene is expressed at a relatively high level in the kidneys, suggesting an important role of the Rac family small GTPase 1 in this organ. There have been no studies on humans or animals that have investigated the expression level of the RAC1 gene in diabetic nephropathy, but there are studies that have investigated other NOX enzymes. In particular, an increased NOX-4 expression in renal cells was discovered in streptozotocin-induced diabetic rats [62], and subsequent studies have argued that up-regulated NOX-4 is the primary source of the increased ROS production in the kidneys that contributes to renal fibrosis and DNF [63]. Both the deletion and the inhibition of the NOX4 and NOX1 genes have been shown to be renoprotective [64]. Finally, Ying and co-workers have recently observed that the binding of RAC1 to the pyrin domain containing 3 (NLRP3) activates the NLRP3 inflammasome in the kidney and accelerates the pathological processes underlying diabetic nephropathy [65]. The above studies clearly demonstrate the importance of RAC1-mediated oxidative stress for the development of diabetic nephropathy.
## 4.4. Diabetic Angiopathy of Lower Extremities
Diabetic angiopathy of the lower extremities is a change in the structure of the vessels of the legs in patients with diabetes mellitus, in the form of a decrease in the elasticity of the vascular wall and its thickening, leading to the narrowing of the lumen or the complete obliteration of the arteries. Increased oxidative stress is implicated in the pathogenesis of the various vascular complications of diabetes, including in diabetic angiopathy of the lower extremities [66,67,68]. It is well-known that abnormal endothelial-dependent vasodilation in diabetic patients is at least partially attributed to the reactive oxygen species that are primarily generated by up-regulated NOXs and downregulated endothelial nitric oxide synthase [69,70]. The increase in ROS levels and the decrease in nitric oxide are known to cause irreversible damage to the vascular endothelial cells through apoptosis [68]. The increased expression of NOX subunits, such as p22phox, p47phox, and p67phox, and the associated increased production of vascular superoxide anion radicals have been identified in diabetic subjects [71].
## 4.5. Diabetic Neuropathy
Diabetic neuropathy is a unique neurodegenerative disorder of the peripheral nervous system that preferentially targets sensory axons, autonomic axons, and later, to a lesser extent, motor axons [72]. The peripheral neurons that supply the feet are the longest cells in the body and require a properly functioning vascular supply, mitochondria, and glucose and lipid metabolism [73]. The duration of the diabetes and the plasma levels of the HBA1c are considered to be major predictors of diabetic neuropathy [74]. We revealed an association of the genotype rs7784465-T/C of the RAC1 gene with an increased risk of diabetic neuropathy in females. Female sex was found to be a risk factor for painful diabetic neuropathy, which is consistent with our findings [75]. The overproduction of superoxide anions has even been implicated in diabetic microvascular complications [76]. ROS production inhibits the GAPDH enzyme (glyceraldehyde-3-phosphate dehydrogenase) activity, causing upstream glycolytic metabolites to be diverted into the molecular pathways of glucose overutilization [77]. It is known that ROS production overwhelms the endogenous antioxidant defense in diabetic peripheral neuropathy, impairing the neural blood flow, nerve conduction, neurotrophic support, and neuronal mitochondrial function [78,79]. Hyperglycemia-induced oxidative and/or nitrosative stress causes DNA damage and the subsequent hyperactivation of poly(ADP-ribose) polymerases (PARP), which are the enzymes involved in DNA repair, cellular proliferation, and programmed cell death [80]. Overactivated PARPs consume NAD+, slowing glycolysis and impairing ATP function, as well as inhibiting GAPDH. PARP activation also promotes the formation of excess amounts of the superoxide anions and peroxynitrites that are associated with endothelial dysfunction, decreased nerve blood flow, neuronal energy deficit, a loss of nerve fiber density, and nerve conduction slowing [81,82].
## 4.6. Diabetic Foot Syndrome
Diabetic foot syndrome is a long-term complication of type 2 diabetes that is caused by a combination of vascular and neurological deterioration [83]. Epidemiological studies have shown that neuropathy is responsible for about $50\%$ of the cases of diabetic foot syndrome [84]. Our study revealed that the RAC1 haplotype rs4724800A-rs7784465T-rs10951982G-rs10238136T-rs836478T-rs9374G was associated with a four-fold risk of DFS in males. The study of Rossboth S. and co-workers found a positive association of DFS with the male sex [85]. The pathogenesis of DFS has been linked to a variety of conditions, including oxidative stress, the malfunction of polyol and inositol metabolism, increased Na/K-ATPase activity, endoneural microvascular deficits and ischemia, defective axonal transport, and the non-enzymatic glycosylation of proteins in peripheral neurons [86,87].
The study has some limitations. Because the sample size of the patients with a specific diabetic complication was relatively small, the statistical power of the association analysis that was performed in the subgroups was decreased. A limited number of patients undergoing biochemical investigations of their redox homeostasis did not allow for the obtainment of more reliable estimates of the effects of the studied SNPs on these parameters in subgroups with particular diabetic complications. This limitation made it difficult to interpret the revealed associations between the RAC1 haplotypes and the plasma levels of the ROS and total glutathione. Furthermore, there may be other unexplored confounding variables in the diabetics that contribute to the development of diabetic complications.
## 5. Conclusions
The present study demonstrated, for the first time, that the genetic variants in the RAC1 gene represent novel susceptibility markers for diabetic retinopathy, nephropathy, angiopathy of the lower extremities, and neuropathy, with the potential to influence the risk of diabetic complications through perturbations in redox homeostasis. The sexual dimorphism of the associations between the RAC1 gene polymorphisms and the risk of diabetic retinopathy, particularly in men, appears to be due to the male sex itself being a known risk factor for this complication [85,88]. The mechanisms underlying the sex-specific associations of these genetic polymorphisms with a susceptibility for common diseases are a hallmark of research and continue to pique the interest of scientists [89,90]. The associations of the RAC1 gene haplotypes with the elevated concentrations of reactive oxygen species in patients with diabetic retinopathy, nephropathy, neuropathy, angiopathy, and diabetic foot syndrome may be intermediate damaging factors underlying the development of microvascular and nerve tissue diabetic complications. Because this is the first study to look into the role of the RAC1 gene polymorphisms in diabetic complications, there are no comparable studies to compare our findings to. Further studies into other populations of the world are required to validate these associations between the polymorphisms of the RAC1 gene and diabetic complications. However, our findings can already be applied to the development of new pharmacological agents that inhibit the RAC1 expression in specific tissues and thus reduce the ROS production.
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---
title: Interactions of SNPs in Folate Metabolism Related Genes on Prostate Cancer
Aggressiveness in European Americans and African Americans
authors:
- Hui-Yi Lin
- Susan E. Steck
- Indrani Sarkar
- Elizabeth T. H. Fontham
- Alan Diekman
- Lora J. Rogers
- Calvin T. Ratliff
- Jeannette T. Bensen
- James L. Mohler
- L. Joseph Su
journal: Cancers
year: 2023
pmcid: PMC10046243
doi: 10.3390/cancers15061699
license: CC BY 4.0
---
# Interactions of SNPs in Folate Metabolism Related Genes on Prostate Cancer Aggressiveness in European Americans and African Americans
## Abstract
### Simple Summary
Prostate cancer (PCa) is a complex disease. Identifying inherited genetic variants or single nucleotide polymorphisms (SNPs) for predicting PCa aggressiveness is essential for improving PCa clinical outcomes. However, the interactions of folate-related SNPs associated with PCa aggressiveness are understudied. The study’s objective is to evaluate interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). None of the 11 individual polymorphisms were significant for EAs and AAs. For the EA PCa patients, the two SNP–SNP interaction pairs in MTHFR-MTHFD1 and MTHFR-SLC4A5 were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. These findings can provide valuable information for precision intervention and medicine of PCa aggressiveness.
### Abstract
Background: Studies showed that folate and related single nucleotide polymorphisms (SNPs) could predict prostate cancer (PCa) risk. However, little is known about the interactions of folate-related SNPs associated with PCa aggressiveness. The study’s objective is to evaluate SNP–SNP interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. Methods: We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). Both individual SNP effects and pairwise SNP–SNP interactions were analyzed. Results: None of the 11 individual polymorphisms were significant for EAs and AAs. Three SNP–SNP interaction pairs can predict PCa aggressiveness with a medium to large effect size. For the EA PCa patients, the interaction between rs1801133 (MTHFR) and rs2236225 (MTHFD1), and rs1801131 (MTHFR) and rs7587117 (SLC4A5) were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. Conclusions: These SNP–SNP interactions in the folate metabolism-related genes have a larger impact than SNP individual effects on tumor aggressiveness for EA and AA PCa patients. These findings can provide valuable information for potential biological mechanisms of PCa aggressiveness.
## 1. Introduction
Prostate cancer (PCa) is the most common incident cancer and the second leading cause of cancer death ($11\%$) among American men [1]. PCa is a complex and heterogeneous disease. In the majority of cases, PCa is an indolent disease, although approximately $30\%$ of PCa are aggressive with a high risk of progressing to lethal metastatic disease [2]. In addition, racial disparity in PCa incidence and mortality has been observed. African Americans (AAs) suffer a disproportionate burden of PCa, with 2.3 times higher PCa mortality rates and more aggressive PCa compared to European Americans (EAs) [3,4]. Therefore, identifying modifiable risk factors and genetic markers for aggressive PCa, particularly among AAs, who are at higher risk of virulent disease and have been underrepresented in research, is imperative for reducing the burden of this disease. Folate, a potentially modifiable factor for PCa, is a water-soluble B vitamin involved in DNA synthesis and repair and regulation of gene expression through DNA methylation as a methyl donor. The effect of folate on carcinogenesis is complex and depends on timing, dose, and type of cancer [5]. Several studies have shown that folate is significantly associated with PCa risk, but some did not verify this association [6,7,8,9,10,11,12,13,14,15,16,17]. A study showed that serum folate was positively associated with PCa risk, and PCa patients had a 10 nmol/L increase in serum folate compared with the controls in a population of African descent [18]. In addition, a recent meta-analysis based on seven studies showed that a high serum folate level was associated with increased PCa risk (odds ratio [OR] = 1.43) [19]. Another meta-analysis of six clinical trials found that PCa risk was significantly increased with folic acid supplementation (rate ratio [RR] = 1.24) [6]. In contrast, two other meta-analyses did not find an association between folic acid supplementation and PCa risk [20,21]. However, the number of studies evaluating folate and PCa aggressiveness is very limited.
The variability in unmetabolized serum folic acid is likely affected by genetic polymorphisms because it was not explained entirely by dietary intake [22]. It is well recognized that polymorphisms in folate pathway genes can modify folate levels and risk of cancers (such as colon cancer), such as dihydrofolate reductase (DHFR) and methylenetetrahydrofolate reductase (MTHFR) [23,24,25]. DHFR is the only enzyme involved in reducing folic acid and converting it into tetrahydrofolate [26]. The 19-bp deletion polymorphism in the DHFR gene could predict higher plasma concentrations of unmetabolized folic acid [27]. Previous studies have addressed the effect of MTHFR gene polymorphisms on PCa risk, but the results are inconsistent [28,29,30,31,32,33,34,35,36,37]. In addition, folate can affect one-carbon metabolism, which supports several physiological processes, including biosynthesis, amino acid homeostasis, epigenetic maintenance, and redox defense [38]. One-carbon metabolism genes have also been shown to impact DNA repair and gene methylation and are related to several cancers, including breast, colorectal, and liver [25,39,40]. The relationships between genes involved in one-carbon metabolism and PCa risk have also been investigated but less extensively, and these studies also produced conflicting results [11,30,41,42].
*Identifying* genetic markers for predicting PCa aggressiveness is imperative for improving PCa outcomes, especially for AAs at greater risk of high aggressive PCa. Most PCa genetic association studies have been conducted on men with European ancestry. The results from single nucleotide polymorphism (SNP) studies among EAs are challenging to apply to AA populations, where genomic variation may differ in types and frequencies. For example, the frequency of polymorphisms in DHFR and MTHFR genes differs by race, and associations between polymorphisms and circulating folate levels also vary by race [43,44,45,46,47,48]. In addition, some SNPs in one-carbon metabolism genes are significantly associated with high-grade PCa in white and black men, but these associations differ by race [49]. Furthermore, we are interested in two more genes: LGALS3 and SLC4A5. Galectin-3 (LGALS3, also called GAL3) is commonly overexpressed by cancer cells and promotes cancer progression and metastasis for several cancers, such as PCa, breast cancer, and colon cancer [50]. SLC4A5 is a member of the Na+ driven bicarbonate transporter (NDBT) family, whose expression levels are associated with hypoxia (low oxygen) [51]. The hypoxia tumor microenvironment has been shown to be associated with PCa aggressiveness [52]. The interactions of these SNPs associated with PCa aggressiveness are understudied. Therefore, the objective of this study was to evaluate whether interactions among the DHFR 19-bp deletion polymorphism and SNPs in genes in the folate metabolism pathway (MTR, MTRR, and MTHFR), one-carbon metabolism pathway (MTHFD1, MTHFR, MTHFS), two PCa-related genes (SLC4A5 and LGALS3) can predict PCa aggressiveness in EAs and AAs.
## 2.1. Study Population
We included a total of 1294 PCa patients (690 EAs and 604 AAs) from the population-based North Carolina and Louisiana Prostate Cancer cohort (PCaP) [53]. In this study, the EA and AA groups are based on self-reported race. The PCaP cohort recruited men with the first diagnosis of histologically confirmed adenocarcinoma of the prostate who resided in the North Carolina and Louisiana study areas during 2004–2009. PCa patients were eligible to participate if they self-reported being EA or AA, were between 40 and 79 years old at diagnosis, could complete the study interview in English, did not live in an institution (nursing home), were not cognitively impaired, were not in a severely debilitated physical state, and were not under the influence of alcohol, severely medicated, or apparently psychotic at the time of interview. PCa aggressiveness is defined by a combination of Gleason score, clinical stage, and prostate-specific antigen (PSA) level at diagnosis as: [1] high aggressive (Gleason score ≥ 8 or PSA >20 ng/mL, or Gleason score ≥ 7 and clinical stage T3–T4); [2] low aggressive (Gleason score < 7 and stage T1–T2 and PSA < 10 ng/mL), and [3] intermediate aggressive PCa (all others). In order to reduce the potential misclassification of disease aggressiveness status, 1338 PCa patients (717 EAs and 621 AAs) with SNP data diagnosed with highly aggressive and low aggressive PCa were considered. Among them, 1294 patients who had genetic ancestry information were included in this study. *The* genetic ancestry proportions of European ancestry and African ancestry for each participant were estimated based on the fifty ancestry informative markers. The details of genetic ancestry were reported previously [54].
## 2.2. Genotyping
Genotyping of the DHFR 19-bp deletion (del) polymorphism, MTR, MTRR, MTHFR, MTHFD1, MTHFR, MTHFS, SLC4A5, and LGALS3 was conducted at the Winthrop P. Rockefeller Cancer Institute at the University of Arkansas for Medical Sciences. The DHFR 19-bp deletion (del)/insertion (ins) polymorphism was analyzed with the TaqMan SNP genotyping assays (Thermo Fisher Scientific, Waltham, MA, USA) on the 7900HT Fast Real-Time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). Primers and probe mix were available as premade and validated TaqMan genotyping assays, and all PCR reactions were carried out with the TaqMan Genotyping Master Mix. Briefly, reactions were heated to 95 °C for 10 min and subjected to 40 cycles of amplification at 95 °C for 10 s and 60 °C for 1 min. PCR amplification was followed by allelic discrimination plate reading and analysis. For quality control, blinded repeats of approximately $5\%$ of samples were included. All SNPs had a call rate greater than $95\%$.
## 2.3. Statistical Analyses
Participants’ age and study site status by EAs and AAs were summarized using descriptive statistics. The age distribution by race was compared using the t-test, and the study site by race was tested using the chi-square test. All analyses were performed separately for EAs and AAs. The linkage disequilibrium (LD) status among the SNPs on the same chromosome was tested using r2. For each SNP, three different inheritance modes (additive, dominant, and recessive) based on the minor allele were evaluated. For testing SNP individual effects associated with PCa aggressiveness (high vs. low aggressiveness), logistic regression was applied. For each SNP, the best model with the smallest p-value among the three inheritance modes was selected. Additionally, we evaluated SNP–SNP interactions among the DHFR 19-bp polymorphism and 10 selected SNPs from the seven target genes associated with PCa aggressiveness. For SNP–SNP interaction analyses, we tested a total of 55 SNP/polymorphism pairs among the candidate polymorphisms. The SNP–SNP interaction analyses were performed using the logistic-model-based SNP interaction pattern identifier (SIPI) approach [55]. All models were adjusted for age, study site, and genetic ancestry.
SIPI tests 45 biologically meaningful interaction patterns for SNP–SNP interactions for each pair by considering three key features, which reflect the 3 parts of the SIPI model labels. As shown in Figure 1, the 1st part is the SNP’s inheritance modes (additive, dominant, and recessive), the 2nd part is model structure (hierarchical and non-hierarchical interaction models), and the 3rd part is risk direction (original and reverse). Based on two SNPs, there are 9 genotype combinations. The conventional approach for testing 2-way SNP–SNP interactions is the full or hierarchical interaction model with 2 SNPs with the additive inherited mode (coding as 0, 1, and 2) and their interaction. Using this full model to detect SNP–SNP interactions tends to lead to false negatives because it only tested one complicated interaction pattern. For importing detection accuracy, SIPI intensively searches 45 interaction patterns/models. As shown in Figure 1, there are 9 possible models by considering model structure and risk direction for each combination of inheritance mode. SIPI considers 4 model structures: the full interaction model (‘Full,’ both main effects plus interaction), the models with one main effect and interaction (M1_int or M2_int), models with only an interaction (such as int_oo), and 2 risk directions with the original (‘o’) direction based on the number of minor alleles and reverse (‘r’) direction. By integrating 5 combinations of inheritance modes (Part 1), there are a total of 45 (=5 × 9) SNP–SNP interaction patterns (such as DD_Full, DD_M1_int_o1, DD_M1_int_r1, DD_M2_int_o2, DD_M2_int_r2, DD_int_oo, DD_int_or, DD_int_ro, and DD_int_rr for the dominant-dominant mode). With this design, SIPI can combine genotype sub-groups with a similar risk profile or a small size for enhancing prediction power. The details are described previously [55]. For each SNP pair, the interaction pattern with the lowest Bayesian information criterion (BIC) value among the 45 testing patterns was selected. For multiple comparison justification, the Bonferroni correction criterion was p-value < 0.0045 (=$\frac{0.05}{11}$) for individual SNPs and p-value < 0.0009 (=$\frac{0.05}{55}$ pairs) for SNP–SNP interactions. However, it is well-known that Bonferroni correction is conservative. Thus, we applied the bootstrap internal validation method for the top SNP pairs with a $p \leq 0.05$ for selecting the promising pairs. The bootstrap method, a resampling technique, has been used in SNP association studies to reduce false positive findings [56]. In this bootstrapping, 500 samples are repeatedly drawn from the original data. In each bootstrap sample, a significant result was defined based on whether a SNP pair followed the 3pRule approach, which is the modified significance criterion by considering the p-values of its 2 SNPs’ individual effects (p-value of interaction pair (p-pair) < 0.01, p-pair< p-SNP1, and p-pair < p-SNP2). The percentage of the significance for each SNP pair based on 500 bootstrap samples was calculated. The significant SNP pairs were defined as a p-pair < 0.05 with a significance percentage greater than $65\%$ out of the 500 bootstrap samples. The R SIPI package version 1.22, which can be accessed at https://github.com/LinHuiyi/SIPI, was applied to detect individual SNP effects and SNP–SNP interactions [55].
## 3. Results
For the 690 EA PCa patients, the mean age was 64.0 years (standard deviation [SD] = 7.7), and $53.8\%$ were from the Louisiana site. For the 604 AA PCa patients, the mean age was 61.8 years (SD = 7.8), and $55.5\%$ were from the Louisiana site. As shown in Table 1, $21.4\%$ of EAs and $30.6\%$ of AAs had high aggressive PCa, and the study site distribution was similar in both race groups ($$p \leq 0.541$$). There was high consistency between self-reported race and genetic ancestry status. The mean ancestry proportion of European ancestry was $96.7\%$ (median = $98.8\%$) for self-reported EAs, and the mean ancestry proportion of African ancestry was $90.6\%$ (median = $97.7\%$) for self-reported AAs. All of the target genes are protein-coding genes. The details of these genes are listed in Table 2. We tested linkage disequilibrium (LD) for SNPs on the same chromosome. There are 4 SNPs (rs2274976, rs1801131, rs1801133, and rs1805087) on chromosome 1 and 3 SNPs on chromosome 14 (rs4644, rs4652, and rs2236225). For EAs, only rs4644 and rs4652 were strong LD (r2 = 0.86), and others on the same chromosome had weak LD (r2 < 0.3). For AAs, all SNPs on the same chromosome had weak LD (r2 < 0.2).
Details regarding the DHFR 19-bp polymorphism and 10 selected SNPs from theseven genes for EAs and AAs are listed in Table 3. The minor alleles of the nine SNPs are consistent for EAs and AAs, except rs4652 in LGALS3. For rs4652, the ‘C’ allele was a minor allele (minor allele frequency [MAF] = 0.42) for EAs but was a major allele for AAs (‘C’ allele frequency = 0.84). Most minor allele frequencies for the 10 SNPs differed by race. Using rs1801133 in MTHFR as an example, the MAF of the ‘A’ allele was $33\%$ for EAs and $13\%$ for AAs. As shown in Table S1, all genotype distributions for the 10 SNPs and the del/ins status for DHFR 19-bp polymorphism were significantly different by race. For rs10380 in MTRR, the TT genotype was only $1.5\%$ for EAs but was $11.3\%$ for AAs (race difference, $$p \leq 1.7$$ × 10−45). For rs4652 in LGALS3, the CC genotype was $19.2\%$ for EAs but $72.4\%$ for AAs (race difference, $$p \leq 1.2$$ × 10−87). The DHFR 19-bp del/del genotype prevalence was $17.7\%$ for EA and $31.0\%$ for AA PCa patients (race difference, $$p \leq 2.5$$ × 10−9). The individual effects of the selected SNPs/polymorphism associated with PCa aggressiveness by considering three inheritance modes for EAs and AAs are shown in Table 3. Among the 11 polymorphisms, none of them were significantly associated with PCa aggressiveness for both EAs and AAs (all p-values > 0.05).
The top SNP–SNP interaction pairs with a $p \leq 0.05$ associated with PCa aggressiveness for EAs and AAs are shown in Table 4 and Table 5, respectively. None of the SNP pairs in EAs and AAs reached the Bonferroni correction criterion (all $p \leq 0.0009$). Among them, three pairs (two pairs for EAs and one pair for AAs) were selected based on the bootstrap approach with >$65\%$ times of significance out of 500 bootstrap samples. For EAs, the SNP pairs rs1801133- rs2236225 in MTHFR and MTHFD1 ($$p \leq 0.009$$, $68.8\%$ significance) and rs1801131- rs7587117 in MTHFR and SLC4A5 ($$p \leq 0.018$$, $69\%$ significance) were significantly associated with PCa aggressiveness (Table 4). The interaction between MTHFR rs1801133 and MTHFD1 rs2236225 was significantly associated with PCa aggressiveness with a pattern of DR_int_or, an original-dominant and reverse–recessive interaction-only model (Figure 2A). This pattern indicated that EA PCa patients with the ‘GA/AA + CC/CT’ genotype in rs1801133 and rs2236225, respectively, suggested a lower risk of developing aggressive PCa (OR = 0.59, $$p \leq 0.009$$) compared to those with other genotypes in this SNP pair. As shown in Table 4 and Figure 2B, the interaction between rs1801131 MTHFR and rs7587117 in SLC4A5 was associated with PCa aggressiveness ($$p \leq 0.018$$). The SIPI selected the RR_int_oo pattern, an interaction-only model with an original-recessive mode for both SNPs. As shown in Figure 2B, this RR_int_oo pattern indicated that the EA PCa patients with the CC+ CC genotype combination of rs1801131-rs7587117 had a higher risk of aggressive PCa compared to those with other genotypes in this SNP pair (OR = 6.8, $$p \leq 0.018$$). For the EA PCa patients, the top two high-risk groups of PCa aggressiveness were the CC+ CC genotype of rs1801131- rs7587117 ($57\%$) and the AA+TT genotype of rs1801133- rs2236225 ($44\%$), while the overall PCa aggressiveness prevalence was $21\%$.
For SNP–SNP interaction analyses for AAs (Table 5), there was one SNP pair significantly associated with PCa aggressiveness (p-value = 0.012, $65.4\%$ bootstrap significance) out of the seven pairs with a $p \leq 0.05.$ This SNP pair was the interaction of DHFR-19bp polymorphism and rs4652 in LGALS3 with an interaction pattern of DD_int_ro, an interaction-only model with a reverse-dominant for DHFR-19bp polymorphism and original-dominant mode for rs4652. As shown in Figure 2C, AA PCa patients with the del/del status in the DHFR-19bp polymorphism and the rs4652 CA or AA genotypes had a lower risk of PCa aggressiveness (OR = 0.37, $$p \leq 0.012$$). The PCa aggressiveness prevalence for AA PCa patients with DHFR-19bp del/del and CA/AA in rs4652 was 14–$15\%$ compared with the overall PCa aggressiveness prevalence of $30\%$. For the AA PCa patients, the high-risk group of PCa aggressiveness was the del/ins+ AA genotype of DHFR-19bp-rs4652 ($70\%$), while the overall PCa aggressiveness prevalence was $30\%$.
## 4. Discussion
We identified three SNP–SNP interaction pairs significantly associated with PCa aggressiveness: rs1801133 (MTHFR)-rs2236225 (MTHFD1) and rs1801131 (MTHFR)-rs7587117 (SLC4A5) for EAs and DHFR-19bp-rs4652 (LGALS3) for AAs. However, none of the individual effects of the DHFR 19-bp polymorphism and 10 target SNPs associated with PCa aggressiveness were significant. To our knowledge, the three SNP–SNP interaction pairs for PCa aggressiveness have not been reported. However, SNPs in some genes involved in these SNP pairs associated with PCa outcomes have been reported. The SNP of rs1801133 in MTHFR is related to PCa risk [57]. Another MTHFR SNP (rs9651118) has been reported to be associated with PCa recurrence with and without adjusting for known risk factors [58]. Another study with most Caucasians did not find associations between rs1801131 and rs1801133 in MTHFR and rs2236225 in MTHFD1 with PCa risk, localized, and advanced PCa [30].
For interactions, SNP interactions between MTHFR and MTHFD1 related to other clinical outcomes have been reported [59,60]. The SNPs between MTHFR and MTHFD1 are associated with anterior encephalocele, a rare congenital anomaly of the central nervous system related to genetic defects in folate metabolism [59]. In this study, we also found SNPs in LGALS3 and SLC4A5 interacted with folate-related genes associated with PCa aggressiveness. LGALS3 expression is associated with PCa progression and is a suggested PCa prognostic marker and therapeutic target [61]. In addition, galectin-3 is a proteolytic substrate for the serine protease PSA [62]. The non-synonymous SNPs rs4644 and rs4652 generate histidine-to-proline and threonine-to-proline polymorphisms in the galectin-3 protein at amino acids 64 and 98, respectively [63]. Proline introduces a Phi angle, creating a bend in the protein’s secondary structure [64]. These alterations in secondary structure may alter the function of the galectin-3 protein at the molecular level and contribute to increased PCa aggressiveness. LGALS3, expressed in human prostate intraepithelial neoplasia lesions and metastatic lymph nodes, is a crucial molecule and a potential therapeutic target in PCa progression and metastasis [65]. Furthermore, increased dairy consumption is associated with PCa progression [66,67]. The galectin-3 protein binds to galactose containing glycans. Thus, increased plasma galactose from the diet may impact the function of circulating galectin-3. In addition, studies showed that LGALS3 expression [68,69] and SLC4A5 expression [51] affected oxidative stress in animal experiments. Oxidative stress does appear to downregulate expression of the SCL4A5, which would be expected to disrupt pH regulation [70]. Moreover, the link between oxidative stress and folate deficiency in animal experiments also has been reported [71,72]. These support the potential biological links of our identified SNP–SNP interactions.
For racial differences, most of the top SNP–SNP interaction pairs associated with PCa for EAs and AAs were different (Table 4 and Table 5), and racial differences of all 10 SNPs in the folate-related genes and the DHFR 19-bp deletion polymorphism were significant. The EA and AA groups’ MAF status for the folate-related SNPs tested in our study is very similar to the results in the NCBI SNP database [73]. In our study, the MAF of the two SNPs (rs1801131 and rs1801133) in MTHFR were higher in EA than in AA PCa patients. Similar racial differences of these MTHFR SNPs between EAs and AAs were also observed in a large-scale study with both gender groups based on the US national survey [43]. For DHFR 19-bp deletion polymorphism, AA PCa patients had more del/del genotype in DHFR 19-bp deletion polymorphism than EA PCa patients ($31.0\%$ vs. $17.7\%$, $p \leq 0.0001$) in this study. It has been shown that the DHFR 19-bp deletion polymorphism is common with del/del genotype frequencies ranging from $10.5\%$ to $48\%$ in different populations [47].
## 5. Conclusions
In summary, we identified three novel interactions of SNPs or polymorphisms in the folate metabolism pathway, one-carbon metabolism pathway (MTHFR, MTHFD1, and DHFR), SLC4A5, and LGALS3 associated with PCa aggressiveness, although the individual effects of these SNPs were not significant. To our knowledge, this paper is the first to assess SNP–SNP interactions in folate-related pathways associated with PCa aggressiveness. Our study demonstrated that SNP–SNP interaction findings could provide better prediction than individual SNP effects in the folate-related pathways. The strengths of this study are the inclusion of two race groups (EAs and AAs) and the application of a powerful statistical approach for SNP–SNP interaction analyses. The limitation is a lack of external validation due to a relatively small sample size, so the bootstrap internal validation approach was applied to reduce false positivity. Thus, future large-scale studies are warranted to verify these findings and elucidate the biological mechanism of the identified SNP–SNP interactions. The SNP-SNP interactions discovered in this study may lead to further understanding of the mechanistic pathways regarding interactions of these genes and the discovery of future therapeutic options for PCa.
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|
---
title: Photodynamic Activation of Cholecystokinin 1 Receptor Is Conserved in Mammalian
and Avian Pancreatic Acini
authors:
- Jie Wang
- Zong Jie Cui
journal: Biomedicines
year: 2023
pmcid: PMC10046250
doi: 10.3390/biomedicines11030885
license: CC BY 4.0
---
# Photodynamic Activation of Cholecystokinin 1 Receptor Is Conserved in Mammalian and Avian Pancreatic Acini
## Abstract
Cholecystokinin 1 receptor (CCK1R) is the only G protein coupled receptor that is activated in type II photodynamic action, but whether this is a property common to both mammalian and avian species is not known. In this work, pancreatic acini were isolated from the rat, mouse, and Peking duck, and photodynamic CCK1R activation was examined. Isolated pancreatic acini were exposed to photosensitizer sulphonated aluminum phthalocyanine (SALPC) and photodynamic action elicited by a brief light-emitting diode (LED 675 nm) pulse (1.5 min); photodynamic CCK1R activation was assessed by Fura-2 fluorescent calcium imaging. Photodynamic action was found to induce persistent calcium oscillations in rat, mouse, and Peking duck pancreatic acini, with the sensitivity order of mouse > rat > Peking duck. Photodynamically-activated CCK1R could be inhibited reversibly by CCK1R antagonist devazepide (1 μM); photodynamic CCK1R activation was blocked by pre-incubation with 1O2 quencher Trolox C (300 µM). The sensitivity of photodynamic CCK1R activation was correlated with the increasing size of the disordered region in intracellular loop 3. These data suggest that photodynamic CCK1R activation is conserved in both mammalian and avian species, as evidenced by the presence of the photodynamic activation motif “YFM” in transmembrane domain 3.
## 1. Introduction
The minimal functional unit of all organisms is the individual cell, which executes specific functions via functional proteins such as G protein coupled receptors (GPCR), ion channels, transporters, enzymes, and others [1]. Conventional receptor pharmacology is based on non-covalent, ligand-specific modulation of resting receptor activity, including both orthosteric and allosteric ligand regulations [2,3,4]. In recent years, ligand-independent receptor regulation, especially of GPCR, has emerged prominently, such as quenching of receptor activation by neutrophil respiratory burst [5], by neutrophil extracellular trap components (such as extracellular histones) [6], by hydrophobic bile acids [7], and by cholesterol density in the plasma membrane [8,9]. Most remarkably, the cholecystokinin 1 receptor (CCK1R), a rhodopsin-like or class A GPCR family member, is permanently activated in photodynamic action [10,11,12,13,14,15].
CCK1R is unique among GPCR of all classes in that it is activated permanently in a type II photodynamic action (photon-to-photosensitizer energy-transfer process involving photon-absorbing macromolecular photosensitizer, light of wavelength at maximal absorption by photosensitizer, and ground state oxygen) by the excited delta singlet oxygen (Δ1O2) [13,14,15]. The initial hint for this unique pharmacological property of CCK1R might have been obtained from photo-affinity labeling experiments performed by the Jamieson laboratory at Yale [16]. The photoaffinity probe 2-nitro-5-azidobenzoyl-glycine-CCK-8 (NAB-Gly-CCK-8) was found, after multiple cycles of ultraviolet A (UVA) photolysis (>320 nm), to irreversibly elicit amylase secretion in the isolated guinea pig pancreatic acini [16]. It has been subsequently reported that photodynamic action, with either Rose Bengal [17] or with sulphonated aluminum phthalocyanine (SALPC) [18] as the photosensitizer, stimulated amylase secretion from freshly isolated rat pancreatic acini, when the pancreatic acinar cell plasma membrane remained completely intact [17,18]. In perifused isolated rat pancreatic acini, photodynamic action with SALPC or gadolinium porphyrin-like macrocycle B (PLMGdB) as a photosensitizer was found to elicit persistent cytosolic calcium oscillations, which highly resembled stimulation with picomolar CCK (namely, physiological concentrations), with the exception that the calcium oscillations could not be washed out after photodynamic action, i.e., calcium oscillations persisted [10,11]. Photodynamically induced persistent calcium oscillations were blocked specifically by CCK1R antagonist FK480 in isolated rat pancreatic acini, confirming that rat CCK1R was activated in photodynamic action with SALPC as the photosensitizer [12].
Other than in normal, physiologically functioning pancreatic acini, CCK1R in rat pancreatic acinar tumor cell line AR4-2J was also found to be photodynamically activated, with genetically encoded protein photosensitizer KillerRed [13]. In fact, all genetically encoded protein photosensitizers (GEPP) reported in the literature were found to photodynamically activate CCK1R in AR4-2J cells [19], with either an external light source or with bioluminescence emitted from simultaneously expressed NanoLuc to drive photodynamic action [19,20].
Interestingly, it was found that the human CCK1R, when heterologously expressed in HEK-293 cells (i.e., outside of the microenvironment of pancreatic acinar cell plasma membrane), was activated permanently in photodynamic action, with SALPC as the photosensitizer [13]. Protein photosensitizer KillerRed or miniSOG tagged either to the N- or C-terminus of the human CCK1R directly, or with a (GSG)n linker of determined length ($$n = 4$$, 8) in between the protein photosensitizer miniSOG and N-terminus of CCK1R, was able to photodynamically activate in-frame human CCK1R [14]. Photodynamic CCK1R activation was found to be critically dependent upon the third transmembrane domain (TM3) of human CCK1R. Most remarkably, TM3 of CCK1R, when transplanted to the muscarinic acetylcholine 3 receptor (M3R), could confer upon M3R the property of photodynamic activation, which was absent in the native M3R [12,15]. Photodynamic CCK1R activation was found to be accompanied by a dimer-to-monomer transition of the CCK1R protein molecule, which was partially purified from isolated rat pancreatic acini [21], a monomerization process similar to CCK-stimulated rat CCK1R dimer-to-monomer transition, as monitored by bioluminescence resonance energy transfer (BRET) in between CCK1R protein molecules expressed in COS-1 cells [22].
It is now established that CCK1R could be readily activated in photodynamic action with varied photosensitizers including both chemical photosensitizers such as SALPC and genetically encoded protein photosensitizers such as KillerRed and miniSOG [13,19]. However, does this property of photodynamic activation of human and rat CCK1R extend to CCK1R in other species?
Therefore, in the present work, photodynamic CCK1R activation was compared in freshly isolated rat, mouse, and Peking duck pancreatic acini. It was found that CCK1R could also be photodynamically activated in mouse and Peking duck pancreatic acini, with the sensitivity order of mouse > rat > Peking duck. A detailed comparison of rat, mouse, and Peking duck CCK1R sequence and structure revealed a high level of similarities, including conservation of the photodynamic activation motif of “YFM”; further, the size of the disordered region in the third intracellular loop (ICL3) of CCK1R could be correlated to the sensitivity of their photodynamic activation.
## 2.1. Materials
Sulfated cholecystokinin octapeptide (CCK, #1166) and CCK1R antagonist devazepide (#2304) were from Tocris Cookson (Bristol, UK). Minimum essential medium (MEM) amino acid mixture (50×, #11130051) was from Thermo Scientific (Shanghai, China). Fura-2 AM (#21020) was from AAT Bioquest (Sunnyvale, CA, USA). Goat anti-human CCK1R polyclonal primary antibody (#ab77269) (against synthetic peptide of human CCK1R242−257) and tetramethylrhodamine isothiocyanate (TRITC)-conjugated donkey anti-goat secondary antibody (#ab6738) were from Abcam (Cambridge, UK). Cell-Tak (#354241) was from BD Bioscience (Bedford, MA, USA). Sulfonated aluminum phthalocyanine (SALPC, #AlPcS 834) was from Frontier Scientific (West Logan, UT, USA). ( ±)-6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox C, #238813) and soybean trypsin inhibitor (#T9128) were from Sigma (St. Louis, MO, USA). Collagenase P (#11213873001) was from Roche (Mannheim, Germany). 4-(2-Hydroxyethyl) piperazine-1-ethanesulfonic acid and N-(2-Hydroxyethyl) piperazine-N′-(2-ethanesulfonic acid) (HEPES, #391338) were from Merck (Darmstadt, Germany). Restriction enzymes (EcoRI, #1611 and XhoI, # 1635) were from Takara (Beijing, China). HiPure Total RNA Plus Mini Kit (#R4121-02) was from Magen (Guangzhou, China). The 2×Taq Master Mix (#P112-01) was from Vazyme (Nanjing, China). GoScript Reverse Transcription Kit (#238813) was from Promega (Shanghai, China). GoldView Molecular Markers (GV-2) was from SaiBaiSheng (Beijing, China).
## 2.2. Isolation of Rat, Mouse, and Peking Duck Pancreatic Acini
Rat pancreatic acini were isolated from male Sprague-Dawley rats (250–450 g in body weight) by collagenase P digestion in Krebs buffer, as reported [10,12]. The pancreata were excised, infiltrated with collagenase P-containing buffer (0.2 g·L−1, 10 mL) before digestion in a shaking water bath (37 °C, 120 cycles per min) for 10 min, followed by an additional 20 min digestion in fresh collagenase P-containing buffer. The digested tissue was dispersed with a plastic pipette, filtered through a nylon mesh (150 mesh), layered onto buffer containing $3.8\%$ BSA, then acini were allowed to gravitate to the bottom of the 10 mL test tube. The rat pancreatic acini pellet obtained were then re-suspended, centrifuged (20 g, 1 min), and washed three times.
Krebs buffer used for collagenase P digestion had the following composition (in mM): NaCl 118, KCl 4.7, CaCl2 2.5, MgCl2 1.13, NaH2PO4 1.0, D-glucose 5.5, HEPES 10, L-glutamine 2.0, BSA 20 g·L−1, MEM amino acid mixture (50×) $2\%$, and soybean trypsin inhibitor 0.1 g·L−1. Buffer pH was adjusted to 7.4 with NaOH 4 M and oxygenated with O2 $100\%$. L-glutamine, BSA, amino acid mixture, and soybean trypsin inhibitor were omitted when buffer was used for perifusion and calcium imaging. Pancreatic acini perifusion was performed at a rate of 1 mL.min−1.
Mouse pancreatic acini were isolated similarly by collagenase P (0.2 g.L−1, 10 mL) digestion, from male Kunming white mice (body weight 20–35 g). The pancreas was excised and injected with collagenase P-containing buffer for digestion in a shaking water bath sequentially for 10 and 15 min. The digested pancreatic mince was dispersed, filtered (150 mesh), passed through buffer containing $3.8\%$ BSA, centrifuged (20 g, 1 min), and washed three times.
Peking duck pancreatic acini were similarly isolated by collagenase P digestion from Peking duck (Anas platyrhynchos domestica) (body weight 250–450 g) [23]. The spleen lobe of the pancreas was excised, infiltrated with collagenase P-containing buffer (0.4 g·L−1, 10 mL), and sliced with a razor blade, before sequential collagenase P digestion (10, 15 min sequentially). Digested pancreatic tissue was dispersed, filtered (150 mesh), and passed through buffer containing $3.8\%$ BSA. The Peking duck pancreatic acini obtained were centrifuged (35 g, 1 min) and washed three times.
The above isolated rat, mouse, and Peking duck pancreatic acini were incubated in Krebs buffer for 30 min for recovery before experimentation (CCK stimulation, photodynamic action, immunocytochemistry, or for total RNA extraction for CCK1R gene cloning from the Peking duck pancreatic acini).
Rats, mice, and Peking ducks used for pancreatic acini isolation were maintained under a natural light/dark cycle, with commercial food and tap water fed ad libitum, and killed by CO2 asphyxia. The experimental protocol was approved by Animal Use and Ethics Committee, College for Life Sciences, Beijing Normal University (CLS-EAW-2017-015).
## 2.3. The Cloning of CCK1R Gene from Peking Duck Pancreatic Acini
Total RNA was extracted from freshly isolated Peking duck pancreatic acini with HiPure Total RNA Plus Mini Kit as per the manufacturer’s instructions. RNA concentration was determined in a Nanodrop2000 spectrometer (Thermo Fisher Scientific, Wilmington, DE, USA). mRNA was reverse-transcribed (RT) with a GoScript Reverse Transcription Kit to obtain cDNA. To a polymerase chain reaction (PCR) tube, Oligo (dT) 1 µL and RNA 1 µg were added, denaturation was performed at 70 °C for 5 min. It was then cooled on ice for 5 min before the addition of the following: reaction buffer (×5) 5 µL, MgCl2 2.5 µL, RNAase inhibitor 0.5 µL, M-MLV reverse transcriptase 1.25 µL, and dNTP (10 mM) 1.25 µL, topped up with diethylpyrocarbonate (DEPC)-treated water to 25 µL. Reverse transcription was performed at two temperatures sequentially: 40 °C for 60 min and then 70 °C for 15 min to obtain cDNA. PCR reactions were then performed in a solution composed of 2 µL cDNA template, 15 µL 2×Taq Master Mix, and primers (forward and reverse) 1 µL, topped up with diethylpyrocarbonate (DEPC)-treated water to 30 µL. PCR was carried out: initial denaturation at 95 °C for 5 min, followed by 35 cycles at 95 °C for 30 s, 60 °C for 30 s, and 72 °C for 1.5 min, followed by one cycle at 72 °C for 5 min for final prolongation. The PCR primers based on the CCK1R sequence of mallard duck (Anas platyrhynchos) were as follows: forward primer 5′-ATGGTTAAAGAGCTTACTTC-3′, reverse 5′-TCAGGGGGGTGCAGAGATATGT-3′. RT-PCR products were run on $1\%$ agarose gel with $0.01\%$ GoldView added, 120 V, 40 min before imaging.
After PCR product verification by agarose gel electrophoresis, target bands in the agarose gel were cut off and collected, and PCR product was recovered with TIANgel Midi Purification Kit (TIANGEN, #DP209-02) as described in the users’ instructions manual.
The recovered PCR DNA product was ligated to T vector (pMD19): DNA product 5 µL, T vector 1 µL, and solution I 5 µL were mixed and placed in a metal bath (16 °C) overnight. Positive ligation products were detected on agarose gel.
The ligation products were transformed into E. coli (Top10 competent cells). TOP10 cells (50 µL) from refrigerator (−80 °C) were thawed on ice, incubated sequentially on ice with ligation products (30 min), in a water bath (42 °C) for 45 s, on ice for 3 min. Sterile LB medium (450 µL) was added and shake incubated (37 °C) for 50 min. The bacteria suspension (100 µL) was evenly placed onto LB plates (Φ9 cm) containing Amp+ and incubated (>12 h) before resistant strains appeared. Colonies were picked and cultured in liquid LB + Amp+ medium (>12 h). The resistant E. coli suspension was used for PCR verification. PCR experiments were performed as described above, but with annealing at 59 ℃. Boiled bacteria (200 µL) were used as a DNA template. Universal primers of M13-47 (TGTAAAACGAC-GGCCAGT) and RV-M (CAGGAAACAGCTATGACC) were used as forward and reverse primers, respectively. PCR products were run on agarose gel, and bacteria suspension showing target DNA stripe was sent for DNA sequencing (RuiBiotech, Beijing, China).
DNAman 8.0 was used to concatenate the forward and reverse sequences to obtain the complete sequence. An ORF finder was used for gene sequence discovery, and TMHMM was used for putative protein transmembrane domain prediction.
The predicted Peking duck (Anas platyrhynchos domestica) CCK1R gene sequence was appended with restriction enzyme (EcoRI/XhoI) cut site and protective bases, primers were re-designed, and PCR amplification was carried out. PCR reaction was re-run as above, but at an annealing temperature of 64 °C. PCR primers used were as follows: forward primer 5′-CCGGAATTCATGGAAATAGTTGAAG-3′ and reverse primer 5′-GCTCTAGATCAGGGGGGTGCAGAT-3′. Agarose gel verification, product recovery, T-vector ligation, transformation, amplification on resistant medium, PCR verification, and sequencing were performed. The verified sequence was uploaded to the NCBI database (accession number MN250295.1).
## 2.4. Immunocytochemistry
The freshly isolated rat, mouse, and Peking duck pancreatic acini were fixed in $4\%$ paraformaldehyde in phosphate buffered saline (PBS, 10 min) after attachment to Cell-Tak-coated cover-slips. Fixed pancreatic acini were permeabilized in $0.2\%$ Triton X-100 (in PBS) for 15 min. Non-specific binding was blocked in $3\%$ BSA (in PBS) before incubation with goat anti-human CCK1R primary antibody (dilution 1:100) in a wet chamber at 4 °C overnight, followed by incubation with TRITC-conjugated donkey anti-goat secondary antibody (dilution 1:100) for 30 min. The attached pancreatic acini were washed in PBS before incubation with primary antibody, and thereafter in PBS containing $0.2\%$ Triton X-100 and $2\%$ Tween-20. Cover-slips were placed on glass slides, sealed, and stored at 4 °C in the dark before confocal imaging (Zeiss LSM 510 META) under objective 63×/1.40 oil, with λex 543 nm.
## 2.5. Photodynamic Treatment
Rat, mouse, or Peking duck pancreatic acini, attached to the bottom Cell-Tak-coated cover-slip of a Sykes–Moore perfusion chamber, were perifused in Krebs buffer, but without glutamine, BSA, amino acid mixture or trypsin inhibitor. SALPC (1 or 2 μM) was added to the perfusion buffer and LED 675 nm light (LAMPLIC, Shenzhen, China) was applied as indicated in the calcium tracings at a power density of 40, 50, or 60 mW·cm−2 for 1.5 min. LED light power density was measured at the level of attached acini in the Sykes–Moore chamber with a power meter (IL1700, Intl. Light Inc., Newburyport, MA, USA).
The optimized photodynamic intensities as indicated above were determined in preliminary experiments, where different photodynamic intensities were used in isolated rat, mouse, and Peking duck pancreatic acini. For light irradiation of SALPC-treated pancreatic acini, red light (>580 nm) from a halogen cold light source (Hoya-Schott, HL100R, Tokyo, Japan), red LED 650 nm, and red LED 675 nm (LAMPLIC, Shenzhen, China) were all tested. Data obtained in preliminary experiments are not shown. Data in Results were obtained with LED 675 nm.
## 2.6. Calcium Imaging
The freshly isolated rat, mouse, or Peking duck pancreatic acini were loaded with Fura-2 AM (10 µM) in a shaking water-bath (37 °C, 30 min, 50 cycles per min). Fura-2-loaded pancreatic acini were attached to the bottom cover-slip (coated with Cell-Tak, 1.7 g·L−1, 3 µL on each cover-slip) of Sykes–Moore perfusion chambers.
The perfusion chamber was placed on the platform of a Nikon NE 3000 inverted fluorescence microscope connected to a calcium imaging device (Photon Technology International, PTI, Edison, NJ, USA) with alternating excitations at 340 nm/380 nm (PTI monochromator DeltaRam X). Emission (emitter 510 ± 40 nm) was detected with a CCD (NEO-5.5-CL-3, Andor/Oxford Instruments, Belfast, UK). Fluorescence ratios F340/F380 indicative of cytosolic calcium concentration were plotted against time, as reported previously [14,15,19,20,24].
## 2.7. Statistical Analysis
All data are presented as mean ± SEM (standard error of means). A Student’s T-test was performed, and a statistically significant difference at $p \leq 0.05$ is indicated by an asterisk (*).
## 3.1. Photodynamic CCK1R Activation in Rat Pancreatic Acini
Rat pancreatic acini were fixed for immunocytochemistry, which revealed basolateral plasma membrane localization of CCK1R (Figure 1A). The freshly isolated rat pancreatic acini were perifused and CCK was added to the perifusion buffer. CCK 3 pM was without any effect (Figure 1Ba), CCK 10, 30 pM induced regular calcium oscillations (Figure 1Bb,Bc), whereas CCK 100 pM elicited a phasic increase followed by sparse calcium spikes upon washout (Figure 1Bd). CCK 300 pM induced phasic increases, followed by a plateau after wash out (Figure 1Be). Sequential CCK concentrations (3, 10, 30, 100, and 300 pM) demonstrated a similar dose response relationship (Figure 1Bf). Continued CCK 30 pM triggered regular calcium oscillations over an extended period (70 min, Figure 1Bg). Statistical analysis revealed monophasic dose–response curves, with single (S) or sequential multiple (M) doses (Figure 1Bh).
After CCK-induced calcium spikes were washed out, the addition of photosensitizer sulphonated aluminum phthalocyanine (SALPC) 1 µM alone had no effect (Figure 1Ca). However, if after sequential CCK 30 pM/SALPC 1 µM doses, a brief red LED light pulse (675 nm, 60 mW·cm−2, 1.5 min) was applied, persistent calcium oscillations appeared, which lasted to the end of the experiment (Figure 1Cb). If photodynamic action (SALPC 1 µM; light 675 nm, 60 mW·cm−2, 1.5 min) was triggered in the presence of 1O2 quencher Trolox C 300 µM, the LED light pulse no longer induced any calcium increases (Figure 1Cc). Statistical analysis of data from multiple experiments as demonstrated in Figure 1Ca–Cc confirmed that SALPC alone had no effect (Figure 1Ca), photodynamic action (SALPC 1 µM; LED 675 nm, 60 mW·cm−2, 1.5 min) induced calcium increases (Figure 1Cb), and those calcium increases were blocked if photodynamic action (SALPC 1 µM; LED 675 nm, 60 mW·cm−2, 1.5 min) was triggered in the presence of Trolox C (Figure 1Cd, $p \leq 0.05$). SALPC photodynamic action thus triggered calcium oscillations in rat pancreatic acini, which were blocked by the simultaneous presence of Trolox C.
Photodynamic action (SALPC 1 µM; light 675 nm, 60 mW·cm−2, 1.5 min)-triggered calcium oscillations persisted after LED light irradiation. Such persistent calcium oscillations were blocked reversibly by devazepide 3 nM (Figure 1Ce). The reversible inhibition was statistically significant (Figure 1Cf, $p \leq 0.05$).
While calcium oscillations induced by CCK 30 pM were inhibited by devazepide 3 nM (Figure 1Cg), 1O2 quencher Trolox C 300 µM had no effect (Figure 1Ch). Statistical analysis confirmed devazepide inhibition, but the lack of effect of Trolox C (Figure 1Ci).
These experiments with rat pancreatic acini were repeated with mouse and Peking duck pancreatic acini for comparison.
## 3.2. Photodynamic CCK1R Activation in Mouse Pancreatic Acini
The experimental data obtained from mouse pancreatic acini are presented as Figure S1 in the Supplementary Materials.
## 3.3. Photodynamic CCK1R Activation in Peking Duck Pancreatic Acini
Peking duck (Anas platyrhynchos domestica) ducklings (250–450 g) are suitable to isolate pancreatic acini from the spleen lobe. Immunocytochemistry confirmed basolateral CCK1R distribution in Peking duck pancreatic acini (Figure 2A). In perifused Peking duck pancreatic acini, CCK 0.03 nM (i.e., 30 pM) had no effect (Figure 2Ba); CCK 0.1, 0.3, and 1 nM induced calcium spikes of increasing frequency (Figure 2Bb–Bd); and CCK 3 nM elicited calcium oscillations superimposed on a plateau (Figure 2Be). Sequential addition of CCK 0.03, 0.1, 0.3, 1, 3, and 10 nM induced dose-dependent calcium responses (Figure 2Bf). Extended perifusion with CCK 100 pM triggered regular calcium oscillations (Figure 2Bg). Similar dose–response (Figure 2Bh) was seen with CCK added to different pancreatic acini (Figure 2Ba–Be) or added sequentially to the same pancreatic acini (Figure 2Bf).
After the calcium oscillations induced by CCK 300 pM were washed out, the addition of photosensitizer SALPC 2 µM alone had no effect (Figure 2Ca). Following sequential CCK 300 pM/SALPC 2 µM, red LED light (675 nm, 40 mW·cm−2, 1.5 min) triggered robust fresh calcium spikes (Figure 2Cb). However, if photodynamic action (SALPC 2 µM; LED 675 nm, 40 mW·cm−2, 1.5 min) was repeated in the presence of Trolox C 300 µM, LED light irradiation no longer induced any calcium increases (Figure 2Cc). Statistical analysis confirmed blockade of photodynamic action (SALPC 2 µM; LED 675 nm, 40 mW·cm−2, 1.5 min) by Trolox C 300 µM ($p \leq 0.05$, Figure 2Cd).
SALPC photodynamic action (SALPC 2 μM; LED 675 nm, 40 mW.cm−2, 1.5 min)-triggered calcium oscillations in Peking duck pancreatic acini were inhibited reversibly by CCK1R antagonist devazepide 1 µM (Figure 2Ce). Statistical analysis of data from multiple repeat experiments, as shown in Figure 2Ce, corroborated that devazepide inhibition was significant ($p \leq 0.05$, Figure 2Cf). In parallel control experiments, it was observed that CCK 300 pM-induced calcium oscillations were inhibited by devazepide 1 µM (Figure 2Cg), but Trolox C 300 μM has no effect (Figure 2Ch), and devazepide inhibition was statistically significant ($p \leq 0.05$, Figure 2Ci).
Basolaterally localized Peking duck pancreatic acinar cell CCK1R was thus photodynamically activated, likely via oxidative reactions of singlet oxygen.
## 3.4. Comparison of Photodynamic Activation of Rat, Mouse, and Peking Duck Pancreatic Acinar Cell CCK1R
Picomolar CCK (10, 30 pM) induced regular calcium oscillations in rat and mouse pancreatic acini (Figure 1Bb,Bc,Bg and Figure S1Bb), but high picomolar to nanomolar CCK (0.1, 0.3, 1 nM) was required to induce regular calcium oscillations in Peking duck pancreatic acini (Figure 2Bb–Bd,Bg). The CCK concentration needed to induce regular calcium oscillations, as defined in the literature [25,26], was 30 pM, 10 pM, and 300 pM in rat, mouse, and Peking pancreatic acini, respectively, with a concentration ratio of 3:1:30 (Table 1).
Photodynamic action triggered robust calcium oscillations in rat, mouse, and Peking duck pancreatic acini (Figure 1Cb, Figure 2Cb and Figure S1Cb) (Table 1). The rat:mouse:Peking duck photodynamic intensity ratio was 60:50:80 μM × mW·cm−2 (1.2:1:1.6). The ratio of agonist (CCK) concentration (3:1:30) was more wide-ranging than photodynamic intensity (1.2:1.0:1.6). It may be noted here that, for photodynamic activation, lower than optimal photodynamic intensity induced no or only sparse calcium spikes and higher intensities induced a calcium plateau (data not shown).
Although some difference is noted in both agonist-stimulated and photodynamic activation of rat, mouse, and Peking duck pancreatic acinar cell CCK1R, it is now confirmed that mouse and Peking duck pancreatic acinar cell CCK1R could be photodynamically activated, as rat pancreatic acinar cell CCK1R (this work and [12]) and human CCK1R ectopically expressed in CHO and HEK293 cell lines could be [13,14,15,19,20,21].
## 3.5. Structural Basis of Photodynamic Activation of Rat, Mouse, and Peking Duck CCK1R
At first glance, varied efficacy of CCK-stimulated activation of rat, mouse, and Peking duck pancreatic acini might be due to peptide sequence variation in CCK. A comparison of rat (NP_036961.1), mouse (XP_017168605.1), and mallard duck (XP_038031411.1) (a close relative to Peking duck) and human (NP_000720.1) prepro-CCK peptide showed a sequence homology of $78\%$, $80\%$, and $53\%$, respectively (Figure 3). The intermediate–long human CCK-33 peptide showed a sequence homology with rat, mouse, and mallard duck CCK-33 of $91\%$, $91\%$, and $67\%$, respectively (Figure 3). The human, rat, mouse, and duck sulphated CCK-8 octapeptide (used in this work), however, are completely identical (Figure 3). The CCK concentration difference to activate CCK1R in rat, mouse, and Peking duck pancreatic acini (3:1:30) to induce regular calcium oscillations, as defined in [25,26] (Figure 1, Figure 2 and Figure S1; Table 1), must rest with the target receptor CCK1R, instead of the sulphated CCK octapeptide.
To compare the complete protein sequence of rat, mouse, Peking duck CCK1R, we first need to obtain the Peking duck CCK1R protein sequence, which was not present in any database previously, but a CCK1R sequence of the related mallard duck was found (XP_005019307.1).
A DNA fragment of 1433 bp was cloned by RT-PCR from isolated Peking duck pancreatic acini, with primers derived from the mallard duck (Anas platyrhynchos) CCK1R gene (XP_005019307.1) (Figure 4A). Target band was recovered and attached to T-vector. Ligation products were transformed into E. coli (Top10) and resistant strains screened on an ampicillin-resistant LB plate. Resistant strains were expanded in LB liquid medium + ampicillin for DNA sequencing. The sequence obtained (Sangon Biotech, Beijing, China) was analyzed with Open Reading Frame (ORF) Finder (www.ncbi.nlm.nih.gov/gorf/gorf.html, accessed on 19 November 2022). Ten ORFs were identified from the cloned gene sequence (1433 bp): five in the forward strand (1–96, 178–282, 1150–1248, 956–1168, 147–1433) and five in the reverse strand (1207–959, 769–689, 454–224, 199–119, 996–823) (Figure 4B). The ORFs were subject to TMHMM2.0 (www.cbs.dtu.dk/services/TMHMM/, accessed on 19 November 2022) analysis, to predict transmembrane topology; only 147–1433 was found to encode a seven-transmembrane-domain protein (Figure 4C). This ORF5 (147–1433, 1287 bp) is thus concluded to be the CCK1R sequence of Peking duck (Anas platyrhynchos domestica) pancreatic acini. New primers for RT-PCR were designed from ORF 147-1433 to amplify the Peking duck CCK1R gene (Figure 4D), which was then registered at the NCBI database (MN250295.1).
Probably unsurprisingly, the deduced Peking duck CCK1R protein sequence was found to be completely identical to mallard duck CCK1R, both being 428 residues long, thus only the Peking duck sequence is shown in Figure 5 for inter-species protein sequence comparison. Although the Peking duck CCK1R protein sequence was found to be identical to the reported mallard duck CCK1R protein sequence, the coding sequences of these two were found to differ slightly. For CCK1R residues Asp17, Leu46, Val128, Ala161, Ser262, and Leu384, the codons in the Peking duck sequence were GAC, CTA, GTC, GCG, AGT, and CTT, but in mallard duck, the corresponding codons were GAT, CTG, GTA, GCC, AGC, and CTG. Note the single nucleotide difference in each codon.
The human, rat, mouse, and Peking duck CCK1R protein sequences were then compared. The rat (NP_036820.1), mouse (AAH20534.1), and Peking duck CCK1R (MN250295.1 for nucleotide sequence, QIJ58473.1 for protein sequence) showed a sequence homology of $91\%$, $91\%$, and $75\%$), respectively, with the human CCK1R (NP_000721.1) respectively. In the transmembrane domains 1–7 (TM1–7), the sequence homology increased to $98\%$, $97\%$, and $89\%$, respectively (Figure 5A). The phylogenetic tree shows the relationship of human, rat, mouse, and Peking duck CCK1R protein sequences (Figure 5B).
Early point mutation experiments with human CCK1R identified residues important for CCK recognition, binding, and CCK1R receptor activation (W$\frac{39}{1.30}$, Q$\frac{40}{1.31}$, C$\frac{94}{2.57}$, F107/ECL1, M$\frac{121}{3.32}$, V$\frac{125}{3.36}$, M195/ECL2, R197/ECL2, F$\frac{218}{5.47}$, W$\frac{326}{6.48}$, I$\frac{329}{6.51}$, F$\frac{330}{6.52}$, N$\frac{333}{6.55}$, R$\frac{336}{6.58}$, I$\frac{352}{7.35}$, L$\frac{356}{7.39}$, Y$\frac{360}{7.43}$, N$\frac{366}{7.49}$) [27,28,29] (Figure 5A; Table 2). The examination of aligned CCK1R sequences (Figure 5A) found that most of these residues in human CCK1R are conserved in rat and mouse CCK1R, but W$\frac{39}{1.30}$, Q$\frac{40}{1.31}$, and L$\frac{356}{7.39}$ in human CCK1R were found to be changed to L$\frac{39}{1.30}$, H$\frac{40}{1.31}$, and H$\frac{358}{7.39}$ in Peking duck CCK1R (Figure 5A; Table 2). This might account, at least partially, for the decreased CCK affinity in Peking duck pancreatic acinar cell CCK1R (Figure 1, Figure 2 and Figure S1; Table 1), as W$\frac{39}{1.30}$ and Q$\frac{40}{1.31}$ mutations each decreased CCK binding affinity 10-fold, while L$\frac{356}{7.39}$ mutation decreased CCK binding affinity 8-fold [28]. Figure 5Aligned protein sequence of human, rat, mouse, and Peking duck CCK1R. (A) Human (NP_000721.1), rat (NP_036820.1), mouse (AAH20534.1), and Peking duck sequences (MN250295.1 for nulceotide sequence, QIJ58473.1 for protein sequence) are aligned by Mega 7.0 using clustalW. TM1–7 are outlined by pink rectangles. Asterisks (*) indicate key residues: grey, CCK-8 recognition; brown, CCK-8 binding; black, CCK1R activation; red, G protein-binding. Black circles indicate Ballesteros–Weinstein [30] reference residues N1.50, D2.50, R3.50, W4.50, P5.50, P6.50, and P7.50. ( B) Phylogenetic tree generated from Mega 7.0 via the maximum likelihood method for 1000 calculations. Red numbers, confidence. Black numbers, evolutionary distance.
The point mutation experiments mentioned above were corroborated by the more recently solved complex structure of human CCK1R bound to sulphated CCK octapeptide. In the human CCK1R structure, a CCK-binding pocket was identified (composed of K105/ECL1, F107/ECL1, T$\frac{118}{3.29}$, M$\frac{121}{3.32}$, Y$\frac{176}{4.60}$, F185/ECL2, M195/ECL2, C196/ECL2, R197/ECL2, H$\frac{207}{5.39}$, N$\frac{333}{6.55}$, R$\frac{336}{6.58}$, A343/ECL3, E344/ECL3, L347/ECL3, S348/ECL3, I$\frac{352}{7.35}$, and Y$\frac{360}{7.43}$) [31] (Table 2). Again, these residues are largely conserved in rat and mouse CCK1R (Figure 5A; Table 2). Although E344/ECL3 in human CCK1R remained the same in rat (E360/ECL3) and mouse (E352/ECL3) CCK1R, the corresponding Peking duck residue was D346/ECL3 instead (Figure 5A; Table 2). In addition, the corresponding H$\frac{207}{5.39}$ in human CCK1R was found to be Q$\frac{222}{5.39}$ in rat, Q$\frac{207}{5.39}$ in mouse, but Y$\frac{210}{5.39}$ in Peking duck CCK1R (Figure 5A; Table 2). This double mutation (human E344/ECL3, rat E360/ECL3, mouse E352/ECL3, to Peking duck D346/ECL3; human H$\frac{207}{5.39}$, rat Q$\frac{222}{5.39}$, mouse Q$\frac{207}{5.39}$, to Peking duck Y$\frac{210}{5.39}$) (Table 2) in the CCK-binding pocket will contribute to the decreased CCK sensitivity of Peking duck CCK1R (Figure 1, Figure 2, and Figure S1; Table 1), although it is not known at the present time why the mouse CCK1R was more sensitive to CCK stimulation than the rat CCK1R (Table 1).
It is known that agonist binding to class A members of GPCR triggers a series of layered transitions (from extracellular to intracellular) of receptor protein structure conformation [32]. More specifically, for CCK binding to human CCK1R, these layered changes were also defined, involving these bracketed residues (V$\frac{62}{1.53}$, D$\frac{87}{2.50}$, L$\frac{133}{3.43}$, I$\frac{136}{3.46}$, R$\frac{139}{3.50}$, K$\frac{308}{6.30}$, I$\frac{318}{6.40}$, V$\frac{319}{6.41}$, F$\frac{322}{6.44}$, W$\frac{326}{6.48}$, F$\frac{330}{6.52}$, S$\frac{362}{7.45}$, N$\frac{366}{7.49}$, Y$\frac{370}{7.53}$, and F$\frac{377}{8.50}$), with eventual binding and activation of Gq protein [33]. Remarkably, residues involved in layered activation signal transitions are completely identical among human, rat, mouse, and Peking duck CCK1R (Figure 5A, Table 2).
Activated human CCK1R in dynamics modeling was found to bind Gq by hydrophobic interactions of cytosolic facing residues (I$\frac{143}{3.54}$, I296/ICL3, V$\frac{311}{6.33}$, and L$\frac{315}{6.37}$) [31], by salt bridge-formation (R$\frac{376}{8.49}$) [31], and by hydrogen bonding of cytosolic cavity-forming residues (R$\frac{139}{3.50}$, L147/ICL2, V151/ICL2, and Q153/ICL2) [34]. Most of these residues are identical in human (R$\frac{139}{3.50}$, I$\frac{143}{3.54}$, L147/ICL2, V151/ICL2, Q153/ICL2, V$\frac{311}{6.33}$, L$\frac{315}{6.37}$, and R$\frac{376}{8.49}$), rat (R$\frac{154}{3.50}$, I$\frac{158}{3.54}$, L162/ICL2, V166/ICL2, Q168/ICL2, V$\frac{327}{6.33}$, L$\frac{331}{6.37}$, and R$\frac{392}{8.49}$), mouse (R$\frac{139}{3.50}$, I$\frac{143}{3.54}$, L147/ICL2, V151/ICL2, Q153/ICL2, V$\frac{319}{6.33}$, L$\frac{323}{6.37}$, and R$\frac{384}{8.49}$), and Peking duck (R$\frac{142}{3.50}$, I$\frac{146}{3.54}$, L150/ICL2, V154/ICL2, Q156/ICL2, V$\frac{313}{6.33}$, L$\frac{317}{6.37}$, and R$\frac{378}{8.49}$) CCK1R, but human I296/ICL3 (hydrophobic index 4.5), rat I312/ICL3, and mouse I304/ICL3 were found to correspond to Peking duck residue A298/ICL3 (hydrophobic index of 1.8) (Figure 5A). This is likely correlated to decreased efficiency in Gq coupling, either with CCK stimulated and photodynamic activation of Peking duck pancreatic acinar cell CCK1R (Figure 1, Figure 2 and Figure S1; Table 1), because it is known that I296/ICL3 (hydrophobic index 4.5) mutation to G (hydrophobicity −0.4) significantly weakens human CCK1R coupling to Gq [31].
To summarize, residues to recognize and bind sulphated CCK octapeptide are mostly conserved in human, rat, mouse, and Peking duck CCK1R, but with the following exceptions: human W$\frac{39}{1.30}$, Q$\frac{40}{1.31}$, H$\frac{207}{5.39}$ (rat Q$\frac{222}{5.39}$, mouse Q$\frac{207}{5.39}$), E344/ECL3, and L$\frac{356}{7.39}$ are replaced in Peking duck by L$\frac{39}{1.30}$, H$\frac{40}{1.31}$, Y$\frac{210}{5.39}$, D346/ECL3, and H$\frac{358}{7.39}$, respectively (Table 2). Residues involved in transmembrane conduction of CCK1R layered conformational changes are completely identical in human, rat, mouse, and Peking duck CCK1R (Table 2). Residues involved in transduction of activation information from CCK1R to Gq protein are mostly conserved, but the human residue I296/ICL3 (rat I312/ICL3 and mouse I304/ICL3) is replaced by A298/ICL3 in Peking duck CCK1R (Table 2). The variation in residues for CCK recognition, binding, and G protein coupling, but no difference for layered transmembrane (transmembrane domains) conduction of receptor activation signaling, could account for reduced efficacy of CCK stimulated CCK1R activation in isolated Peking duck pancreatic acini when compared with rat and mouse pancreatic acini (Figure 1, Figure 2 and Figure S1; Table 1).
Met (M), Trp (W), Cys (C), His (H), and Tyr (Y) residues are recognized targets of 1O2 oxidation in type II photodynamic action [35,36]. Out of the key residues in CCK-stimulated CCK1R activation described above, the following 10 are susceptible to 1O2 oxidation: C$\frac{94}{2.57}$, M$\frac{121}{3.32}$, Y$\frac{141}{3.51}$, Y$\frac{176}{4.60}$, M195/ECL2, C196/ECL2, H$\frac{207}{5.39}$, W$\frac{326}{6.48}$, Y$\frac{360}{7.43}$, and Y$\frac{370}{7.53.}$ Of these residues, Y$\frac{141}{3.51}$ and Y$\frac{370}{7.53}$ in motifs of E3.49R3.50Y3.51 and N7.49P7.50xxY7.53, respectively, are known to be involved in G protein binding in all class A GPCR [32], Y$\frac{370}{7.53}$ is also involved in the layered transmembrane signal conduction [33] (Figure 5A; Table 2). Out of these 10 residues, 9 are completely identical in human, rat, mouse, and Peking duck CCK1R; but human H$\frac{207}{5.39}$ is changed to rat Q$\frac{222}{5.39}$, mouse Q207.5.39, and Peking duck Y$\frac{210}{5.39}$ (Figure 5A, Table 2). This might account for the conserved pharmacological property of photodynamic activation of rat, mouse, and Peking duck pancreatic acinar cell CCK1R (Figure 1, Figure 2 and Figure S1; Table 1). The variation in the potency of photodynamic activation of rat, mouse, and Peking duck CCK1R (mouse > rat > Peking duck) might be related to point mutation at position5.39 located in the CCK octapeptide-binding pocket (human H$\frac{207}{5.39}$, rat Q$\frac{222}{5.39}$, mouse Q$\frac{207}{5.39}$, and Peking duck Y$\frac{210}{5.39}$). The change in Gq protein-binding residue of human I296/ICL3 (rat I312/ICL3, mouse I304/ICL3, to Peking duck A298/ICL3 (hydrophobic index of 1.8) (Figure 5A, Table 2) could be accountable too, but isoleucine is not susceptible to 1O2 oxidation.
The human CCK1R structure (PDB, 7F8X) [31] was used as a template to draw and compare with rat, mouse, and Peking duck CCK1R structure (Figure 6A). Other than transmembrane domains 1–7 (TM1–7), these four structures also share a similar short extracellular loop 1 (ECL1), bipartite β sheet-containing ECL2, horizontally oriented α helix-containing ECL3, short intracellular loop 1 (ICL1), twisted ICL2, a rather extensive ICL3, and the horizontal-oriented short α helix 8 at the C terminal (Figure 6A).
The human, rat, mouse, and Peking duck CCK1R structures overlap completely in the TM1–7, ECL1–3, and ICL1,2 (Figure 6B). Note, for example, complete overlap of the two β-sheets in ECL2, as well as the horizontal α helix in ECL3 (Figure 6B). Diversion or variation, however, could be noted especially in ICL3; progressively smaller loop (mouse > rat > Peking duck) in the secondary-structure-free or disordered portion of ICL3; the size of this region is similar in human and mouse, but seemingly with different orientations (bottom left part in Figure 6C).
Of potential importance for photodynamic activation of human CCK1R, as noted before [14], 1O2-susceptible residues Met$\frac{121}{3.32}$ and Met195/ECL2 are shown in Figure 7. The sulfur–aromatic interactions (with Y7.43 + W6.48 and F185, respectively) could all be observed, but showed very little variation in bond length in human, rat, mouse, and Peking duck CCK1R: Met3.32-Y7.43, 5.329, 5.328, 5.335, 5.450; Met3.32-W6.48, 7.083, 7.075, 7.083, 7.076; M193-F185, 4.136, 4.129, 4.133, 4.138 Å (Figure 7).
## 4. Discussion
In the present work, photodynamic action with photosensitizer SALPC and red LED light was found to trigger persistent cytosolic calcium oscillations in the perifused rat, mouse, and Peking duck pancreatic acini, with a sensitivity order of mouse > rat > Peking duck. The photodynamically induced calcium oscillations were inhibited reversibly by CCK1R antagonist devazepide. Photodynamic action no longer induced any calcium increases after preincubation with 1O2 quencher Trolox C. Examination of predicted rat, mouse, and Peking duck CCK1R structures revealed a secondary-structure-free region in intracellular loop 3 (ICL3); the size of this region was correlated to the sensitivity to photodynamic activation of CCK1R: mouse > rat > Peking duck. The photodynamic activation motif YFM identified previously in human CCK1R was found to be conserved in rat, mouse, and Peking duck CCK1R.
## 4.1. Evolution of CCK-Stimulated Activation of CCK1R
Immunocytochemistry localized CCK1R to the basolateral plasma membrane in rat, mouse, and Peking duck pancreatic acini (Figure 1, Figure 2 and Figure S1). This subcellular localization is consistent with previous works carried out with rat and mouse pancreatic acini [37,38]. The anti-peptide antibody used in the present work was raised against human CCK1R242−257 (FEASQKKSAKERKPST) (https://www.abcam.com/cck1-r-antibody-ab77269.html, accessed 2 January 2023), which is within ICL3 (Figure 5). Although the human, rat, mouse, and Peking duck sequences are not identical in this small segment of 16 residues, the sequence homology is reasonably high (Figure 5). This intracellularly targeted antibody could tolerate variation in this region of ICL3 to recognize both mammalian and avian CCK1R (Figure 1, Figure 2 and Figure S1).
CCK stimulation of isolated rat, mouse, and Peking duck pancreatic acini (at 30, 10, and 300 pM, respectively) triggered regular calcium oscillations (Figure 1, Figure 2 and Figure S1). The CCK concentration needed in Peking duck pancreatic acini was much higher than in rat and mouse pancreatic acini (Figure 1, Figure 2 and Figure S1), with the sensitivity order of mouse > rat > Peking duck (Table 1). These data are consistent with previous reports [12,23,39]. Although Peking duck pancreatic acini were less sensitive to CCK stimulation, the receptor could be sensitized to be responsive to low picomolar CCK after treatment with cAMP-mobilizing secretagogues, such as the pituitary adenylate cyclase-activating peptide (PACAP) [23].
Although the complete preproCCK peptide sequence shows marked variation in human, rat, mouse, and mallard duck, the sulfated CCK octapeptide, which is used in most works, as in the present study, is completely identical (DYMGWMDF) (Figure 3). Therefore, this difference in CCK efficacy to activate CCK1R in rat, mouse, and Peking duck pancreatic acini must rest with the CCK1R receptor protein themselves.
To compare the protein sequence of human, rat, mouse, and Peking duck CCK1R, we cloned for the first time the CCK1R gene from Peking duck pancreatic acini. The CCK1R gene sequence of the mallard duck (Anas platyrhynchos) was used to design primers for RT-PCR amplification from Peking duck (Anas platyrhynchos domestica) mRNA (Figure 4). The cloned Peking duck CCK1R gene was uploaded to the NCBI database (MN250295.1) (Figure 4). The inferred Peking duck CCK1R protein sequence (QIJ58473.1) was found to be completely identical to mallard duck CCK1R, but the coding sequences were not identical; the last digit in the codon triplets showed variation for residues Asp17, Leu46, Val128, Ala161, Ser262, and Leu384.
The comparison of human, rat, mouse, and Peking duck CCK1R protein sequences (Figure 5, Table 2) revealed that, of residues important in CCK-recognition, binding, and receptor activation [27,28,29], most are identical. However, human W$\frac{39}{1.30}$, Q$\frac{40}{1.31}$, and L$\frac{356}{7.39}$; rat W$\frac{54}{1.30}$, Q$\frac{55}{1.31}$, and L$\frac{372}{7.39}$; and mouse W$\frac{39}{1.30}$, Q$\frac{40}{1.31}$, and L$\frac{364}{7.39}$ were found to be changed to L$\frac{42}{1.30}$, H$\frac{43}{1.31}$, and H$\frac{358}{7.39}$ in Peking duck CCK1R (Figure 5; Table 2). Because, in human CCK1R, W$\frac{39}{1.30}$, Q$\frac{40}{1.31}$, and L$\frac{356}{7.39}$ are CCK octapeptide-binding residues, and their mutations could decrease CCK-binding affinity 10-, 10-, and 8-fold, respectively [28]; this may be the reason why Peking duck CCK1R was less sensitive than either rat or mouse CCK1R (Figure 1, Figure 2 and Figure S1; Table 1).
Of CCK-binding-pocket-forming residues revealed by the solved complex structure of CCK-bound human CCK1R [31], human E344/ECL3, rat E360/ECL3, and mouse E352/ECL3 changed to Peking duck D346/ECL3, while human H$\frac{207}{5.39}$, rat Q$\frac{222}{5.39}$, and mouse Q$\frac{207}{5.39}$ changed to Peking duck Y$\frac{210}{5.39}$ (Figure 5; Table 2). This double mutation should also be correlated to decreased sensitivity of Peking duck CCK1R to CCK stimulation. However, the reason why mouse CCK1R is more sensitive than rat CCK1R remains to be explained (Figure 1, Figure 2 and Figure S1; Table 1).
The layered structural transitions (from extracellular to intracellular) of GPCR [32] were confirmed for human CCK1R [33]; the relevant residues involved are completely identical in human, rat, mouse, and Peking duck CCK1R (Figure 5, Table 2).
Of residues involved in Gq binding, most are identical in human, rat, mouse, and Peking duck CCK1R. The human I296/ICL3, rat I312/ICL3, and mouse I304/ICL3 residues were found to be changed to Peking duck residue A298/ICL3 (Figure 5, Table 2). As I296/ICL3 point mutation to G296/ICL3 was previously found to decrease human CCK1R binding to Gq [31], this could be the reason why Peking duck pancreatic acinar cell CCK1R should show decreased sensitivity to both CCK stimulated and photodynamic activation when compared with rat and mouse CCK1R (Figure 1, Figure 2 and Figure S1; Table 1).
## 4.2. Conserved Photodynamic Activation of CCK1R
Of the 20 amino acids made up of most proteins, only Met (M), Trp (W), Cys (C), His (H), and Tyr (Y) are susceptible to photodynamic 1O2 oxidation [35,36]. Out of the residues playing a key role in CCK-stimulated human CCK1R activation (Figure 5), only 10 are susceptible to 1O2 oxidation: C$\frac{94}{2.57}$, M$\frac{121}{3.32}$, Y$\frac{141}{3.51}$, Y$\frac{176}{4.60}$, M195/ECL2, C196/ECL2, H$\frac{207}{5.39}$, W$\frac{326}{6.48}$, Y$\frac{360}{7.43}$, and Y$\frac{370}{7.53.}$ Of these 10 residues, 9 are completely identical in human, rat, mouse, and Peking duck CCK1R. This might be the reason why human [13], rat [10,11,12,19,20], mouse, and Peking duck CCK1R (this work; Figure 1, Figure 2 and Figure S1; Table 1) could all be photodynamically activated. The remaining critical residue (1 out of 10) in human CCK1R, H$\frac{207}{5.39}$, is changed to rat Q$\frac{222}{5.39}$, mouse Q207.5.39, and Peking duck Y$\frac{210}{5.39}$ (Figure 5, Table 2). This might partially account for the variation in sensitivity to photodynamic activation of rat, mouse, and Peking duck pancreatic acinar cell CCK1R, with the order of potency of mouse > rat > Peking duck (Figure 1, Figure 2 and Figure S1; Table 1). Note that both human H$\frac{207}{5.39}$ and Peking duck Y$\frac{210}{5.39}$ are ready targets for 1O2 oxidation, but Q5.39 (rat Q$\frac{222}{5.39}$ and mouse Q207.5.39) is often used in point mutation studies as an equivalent to oxidized Met [40,41]. The difference in Gq-binding I296/ICL3 (human I296/ICL3, rat I312/ICL3, and mouse I304/ICL3, but Peking duck A298/ICL3) (Figure 5, Table 2) might also account for this variation, but neither isoleucine nor alanine are susceptible to 1O2 oxidation [35,36].
Chemical photosensitizer SALPC was used in this work for photodynamic CCK1R activation in rat, mouse, and Peking duck pancreatic acini (Figure 1, Figure 2 and Figure S1). The mammalian and avian CCK1R receptors could also potentially be photodynamically activated with the genetically encoded protein photosensitizers, as confirmed with both human and rat CCK1R [13,14,15,19,20]. It may be noted here that SALPC is a widely used photosensitizer [24,42] with a high 1O2 quantum yield [43,44].
Both CCK-stimulated and photodynamic activation of rat, mouse, and Peking duck CCK1R were confirmed by reversible inhibition by CCK1R antagonist devazepide (Figure 1, Figure 2 and Figure S1). The potential involvement of 1O2 in photodynamic CCK1R activation in rat, mouse, and Peking duck pancreatic acini was corroborated by complete inhibition by 1O2 quencher Trolox C (Figure 1, Figure 2 and Figure S1), which has been widely used to confirm the involvement of 1O2 in photodynamic modulation [45,46,47].
The human, rat, mouse, and Peking duck CCK1R structures are highly similar in all regions, including TM1–7, ECL1–3, ICL1,2, and α helix 8; most diversity was seen in the extensive ICL3. These four CCK1R structures superimposed to a very high degree, especially in TM1–7, ECL1–3, and ICL1,2 (Figure 6). The highly similar structures likely underlie the conserved pharmacological property of photodynamic activation (Figure 1, Figure 2 and Figure S1; Table 1). The variation in ICL3 among rat, mouse, and Peking duck CCK1R (Figure 6), together with other differences (single point changes in critical residues; see above), could explain the ordered sensitivity to photodynamic activation (Table 1). Here, the progressively smaller loop in the disordered region of ICL3 was found (mouse > rat > Peking duck); the human loop was of similar size to the mouse, but with a different orientation (Figure 6C). To corroborate whether this decreasing order in size in the disordered region has any relevance in terms of liquid phase transition or phase condensation, and thus to Gq coupling efficiency and the order of sensitivity of CCK-stimulated and photodynamic CCK1R activation in rat, mouse, and Peking duck CCK1R, more experimentations will be needed in the future. Any solved rat, mouse, and Peking duck structures and their complex with Gq, under identical conditions to the human structure, will be most helpful.
## 4.3. Better Conservation of Photodynamic Activation of CCK1R
In photodynamic activation of pancreatic acinar cell CCK1R, although triggering rather similar cytosolic calcium oscillations as agonist CCK-stimulated CCK1R activation, greater variation in efficacy was seen in agonist-stimulated than photodynamic activation (Table 1) among rat, mouse, and Peking duck CCK1R (ratio ranging from 1 to 30 and from 1 to 1.6, respectively) (Table 1). Therefore, photodynamic activation is probably more conserved than agonist-stimulated CCK1R activation.
This difference between agonist-stimulated and photodynamic activation of CCK1R was also noted in previous studies with human CCK1R tagged with N-terminal protein photosensitizer miniSOG [15]. Of TM1–7 in human CCK1R, TM3 was especially important for receptor activation by both agonist stimulation and photodynamic activation. For Met$\frac{121}{3.32}$ in TM3 of human CCK1R, if mutated in miniSOG-tagged CCK1R (miniSOG-hCCK1R) to miniSOG-CCK1RM121A and miniSOG-CCK1RM121Q, EC5 values (a value of 5 in integrated calcium spike areas, per 10 min) increased from 21 pM to 150 and 760 pM respectively, with a decrease in CCK efficacy of 7 and 36 times, respectively [15]. In contrast, photodynamic activation with LED light irradiation (450 nm, 85 mW·cm−2, 1.5 min) decreased from $100\%$ for wild type miniSOG-CCK1R, to $55\%$ and $73\%$ for miniSOG-CCK1RM121A and miniSOG-CCK1RM121Q, respectively, a decrease of less than one half (at $55\%$) or about a quarter (at $73\%$), respectively [15]. Therefore, for the human CCK1R, point mutations at Met$\frac{121}{3.32}$ affected agonist stimulation-induced receptor activation much more than photodynamic activation. This is rather similar to the difference between agonist-stimulated and photodynamic activation of CCK1R in rat, mouse, and Peking duck pancreatic acini.
Although agonist-stimulated CCK1R activation is likely more susceptible to receptor sequence variations than photodynamic CCK1R activation, both modes of receptor activation are likely to go through rather similar pathways of conformational changes, leading to Gq activation and elicitation of calcium oscillations. Protein photosenstizer miniSOG was tagged to human CCK1R at N-terminal, either directly, or via a linker of (GSG)n with different n numbers ($$n = 0$$, 4, 8, 12, ∞). The extended length of the linker between miniSOG and N-terminus of CCK1R in miniSOG-(GSG)12-CCK1R resulted in diminished miniSOG photodynamical CCK1R activation in comparison with miniSOG-CCK1R, miniSOG-(GSG)4-CCK1R, or miniSOG-(GSG)8-CCK1R [14], possibly because the longer linker of (GSG)12 affords more steric hindrance for both CCK-stimulated and miniSOG photodynamic activation of CCK1R, confirming that photodynamic human CCK1R activation may follow the same spatial conformational changes as CCK agonist activation of human CCK1R [14].
These previous works together with the present work suggest that photodynamic activation of CCK1R is better conserved than CCK-stimulated CCK1R activation. Then, it is interesting to note the photodynamic activation motif “YFM” we have identified previously [15].
## 4.4. Conserved Photodynamic Activation Motif “YFM” and Met-Aromatic Interactions
As mentioned above, TM3 is a critical pharmacophore for the photodynamic activation of human CCK1R, and the Y$\frac{119}{3.30}$F$\frac{120}{3.31}$Met$\frac{121}{3.32}$ motif is likely important for CCK1R activation by photodynamic 1O2 oxidation [15]. Examination of human, rat, mouse, and Peking duck CCK1R sequences confirmed the presence of the Y3.30F3.31M3.32 motif in all of them (Figure 5). Although the exact significance of this motif is not yet completely understood, Met residues have been noted for their regulation of both protein structure and function via sulfur–aromatic interactions [48,49]. Such interactions are present in human, rat, mouse, and Peking duck CCK1R; 1O2-susceptible Met$\frac{121}{3.32}$ interacts with aromatic Y7.43 + W6.48 and Met195/ECL2 with aromatic F185, respectively (Figure 7). Note the very similar bond length (Met3.32-Y7.43: 5.329, 5.328, 5.335, 5.450; Met3.32-W6.48: 7.083, 7.075, 7.083, 7.076; M195-F185, 4.136, 4.129, 4.133, 4.138 Å) in human, rat, mouse, and Peking duck CCK1R (Figure 7). These Met–aromatic interactions will impact photodynamic oxidative CCK1R activation and their conservation in mammalian and avian pancreatic acinar cell CCK1R.
## 5. Conclusions and Perspectives
In conclusion, CCK1R is photodynamically activated in both mammalian and avian pancreatic acini, with the sensitivity order of mouse > rat > Peking duck. This sensitivity order is correlated to the decreasing size (mouse > rat > Peking duck) of the disordered portion of ICL3. Photodynamic CCK1R activation is better conserved in mammalian and avian species than CCK-stimulated CCK1R activation (Figure 8).
We have limited our present work to the isolated pancreatic acini. It is possible that CCK1R would show similar photodynamic activation in other expressing tissues, organs and cell types. In situ photodynamic CCK1R activation and their effect on organ/tissue function and, by extension, on overall organism neural and humoral signaling, would be interesting to study. Whether photodynamic CCK1R or CCK1-like receptor activation is limited to mammalian and avian species, or rather extended to other vertebrates or even to invertebrates, will need to be studied in the future. The clinical or medical implications of photodynamic CCK1R activation will be an important field of future studies.
After the submission of this work, a preprint has appeared that also stressed the importance of ICL3 in G protein coupling efficiency [50].
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|
---
title: Role of Altered Metabolism of Triglyceride-Rich Lipoprotein Particles in the
Development of Vascular Dysfunction in Systemic Lupus Erythematosus
authors:
- Ágnes Diószegi
- Hajnalka Lőrincz
- Eszter Kaáli
- Pál Soltész
- Bianka Perge
- Éva Varga
- Mariann Harangi
- Tünde Tarr
journal: Biomolecules
year: 2023
pmcid: PMC10046254
doi: 10.3390/biom13030401
license: CC BY 4.0
---
# Role of Altered Metabolism of Triglyceride-Rich Lipoprotein Particles in the Development of Vascular Dysfunction in Systemic Lupus Erythematosus
## Abstract
Background: Impaired lipid metabolism contributes to accelerated inflammatory responses in addition to promoting the formation of atherosclerosis in systemic lupus erythematosus (SLE). We aimed to evaluate the lipid profile, inflammatory markers, and vascular diagnostic tests in active SLE patients to clarify the association between dyslipidemia and early vascular damage. Patients and Methods: 51 clinically active SLE patients and 41 age- and gender-matched control subjects were enrolled in the study. Lipoprotein subfractions were detected by Lipoprint. Brachial artery flow-mediated dilation and common carotid intima-media thickness were detected by ultrasonography. Arterial stiffness indicated by augmentation index (Aix) and pulse wave velocity was measured by arteriography. Results: We found significantly higher Aix, higher VLDL ratio, plasma triglyceride, ApoB100, and small HDL, as well as lower HDL-C, large HDL, and ApoA1 in patients with SLE. There was a significant positive correlation of Aix with triglyceride, VLDL, IDL-C, IDL-B, and LDL1. A backward stepwise multiple regression analysis showed IDL-C subfraction to be the best predictor of Aix. Conclusions: Our results indicate that in young patients with SLE, triglyceride-rich lipoproteins influence vascular function detected by Aix. These parameters may be assessed and integrated into the management plan for screening cardiovascular risk in patients with SLE.
## 1. Introduction
Systemic lupus erythematosus (SLE) is a chronic, multiorgan, systemic autoimmune disorder that predominantly affects women. The most common cause of death in SLE patients affected by the disease for more than five years is cardiovascular disease (CVD). A recent meta-analysis found an increased risk of stroke, myocardial infarction, CVD, and hypertension in patients, with more than a two-fold increase in relative risk (stroke: 2.51; myocardial infarction: 2.92; cardiovascular disease: 2.24; and hypertension: 2.70) [1]. Beside several nontraditional disease-specific factors such as systemic inflammation, antiphospholipid antibodies; and corticosteroid use, increased prevalence of traditional cardiovascular risk factors including hypertension, obesity, age, diabetes, and dyslipidemia also contribute to high cardiovascular event rates among patients with SLE [2,3]. Indeed, SLE is now considered to be an independent risk factor for the development of atherosclerosis [3,4].
In patients with SLE, Atta et al. discovered the prevalence of dyslipidemia to be more than $70\%$ [5]. This dyslipidemia is characterized by elevated plasma triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B100 (ApoB100), as well as decreased plasma concentrations of high-density lipoprotein cholesterol (HDL-C) resulting in a proatherogenic lipid profile [6,7]. Previous studies reported this characteristic “lupus pattern” of lipoproteins in SLE usually occurring in the active phases of the disease [8,9,10]. Furthermore, the LDL particle size in SLE is significantly smaller than in controls [11]. Most lipid abnormalities observed in SLE might be explained by an accumulation of TG-rich lipoproteins, namely chylomicrons and very low-density lipoprotein (VLDL) particles. In their catabolism, both lipoproteins undergo degradation by lipoprotein lipase (LPL) [12]. In patients with SLE, a low LPL-activity results in the accumulation of chylomicrons and VLDLs, leading to high plasma TG and low HDL-C [13]. Beside LPL, the apolipoprotein C3–angiopoietin-like protein 4 axis is also disrupted in SLE, resulting in significantly lower ApoC3, and significantly higher ANGPLT4. Both of these are particularly important as regulators of triglyceride transport and are novel therapeutic targets [14].
To assess atherosclerosis and vasculopathy in SLE, different non-invasive, ultrasound-based imaging techniques are used. While common carotid intima-media thickness (IMT) is an early indicator of generalized atherosclerosis, brachial artery flow-mediated dilation (FMD) based on B-mode ultrasound assesses endothelium-dependent vasodilation. Furthermore, vascular stiffness is reflected by pulse-wave velocity (PWV) and augmentation index (Aix) [15]. In women with SLE, Cypiene et al. found that PWV and Aix were significantly higher, while FMD was not different from controls [16]. In adolescents with SLE, Boros et al. examined arterial stiffness and found that central PWV and characteristic impedance were elevated, while IMT, FMD, and myocardial perfusion were in the normal range [17]. However, others have detected early endothelial dysfunction indicated by low FMD in SLE [18,19,20]. In a previous study, SLE has been associated with increased arterial stiffness and higher IMT [21], while another indicated that plasma TG is an independent predictor of carotid atherosclerosis in women with SLE [22].
In summary, although several previous studies reported data on dyslipidemia and vascular diagnostic tests, their results are controversial and the link between dyslipidemia and vascular impairment in SLE has not been fully explored. Therefore, we aimed to evaluate the lipid and lipoprotein subfraction profile, inflammatory markers, and vascular diagnostic tests including arterial stiffness indicated by Aix, PWV, IMT, and FMD in active SLE to clarify the association between dyslipidemia and early vascular damage.
We hypothesized that lipid abnormalities, especially higher concentrations of TG-rich lipoproteins, may be associated with the development of vascular dysfunction detected by the above-mentioned non-invasive vascular diagnostic tests in patients with active SLE.
## 2.1. Patient Enrollment
51 clinically active SLE patients (44 females and 7 males), who are treated at the Division of Clinical Immunology, Department of Internal Medicine, University of Debrecen, and 41 age- and gender-matched control subjects (36 females and 5 males) were enrolled in the study. Patients fulfilled the 2019 EULAR/ACR Classification Criteria for Systemic Lupus Erythematosus [23]. SLE Disease Activity Index (SLEDAI) was also calculated to stratify the activity of SLE (0 = no activity, 1–5 = mild, 6–10 = moderate, 11–19 = high, and 20 ≥ very high activity, respectively). Exclusion criteria were active lupus nephritis, pregnancy, and malignant disease. Neither the study population, nor the control group had any previous major cardiovascular event (acute myocardial infarction, ischemic stroke, or significant carotid artery stenosis). Clinical phenotypes and concomitant diseases are summarized in Supplementary Table S1.
Patients gave informed written consent. The laboratory was approved by the National Public Health and Medical Officer Service (approval number: 094025024). The study was approved by the Regional Ethics Committee of the University of Debrecen (DE RKEB/IKEB 4775-2017, date obtained: 3 April 2020) and the Medical Research Council (ETT TUKEB 34952-$\frac{1}{2017}$/EKU, date obtained: 30 June 2017).
## 2.2. Sample Collection and Biochemical Measurements
All venous blood samples were drawn after a 12 h fasting. Routine laboratory parameters, including high-sensitivity C-reactive protein (hsCRP), total cholesterol, TG, HDL-C, LDL-C, apolipoprotein A1 (ApoA1), and ApoB100 levels were determined from fresh sera with Cobas c600 analyzers (Roche Ltd., Mannheim, Germany) from the same vendor. Anti-double-stranded deoxyribonucleic acid (dsDNA) (Orgentec, Mainz, Germany), anti-beta-2-glycoprotein I (B2GPI) (Orgentec, Germany), anticardiolipin (aCL) (Orgentec, Mainz, Germany), anti-Sm/RNP (Hycor Biomedical, Garden Grove, CA, USA), anti-Sjögren’s-syndrome-related antigen A (SSA), and anti-Sjögren’s-syndrome-related antigen B (SSB) autoantibodies (Hycor Biomedical, Garden Grove, CA, USA) were determined by enzyme-linked immunosorbent assays (ELISA). Complement 3 and 4 (C3 and C4) were measured by nephelometric methods (Siemens AG, Munich, Germany). An in-house hemolytic immunoassay was used for measuring CH50, which is a functional test for classical pathway complement activity. The commercially available methods were used following the manufacturers’ protocols. All laboratory measurements have taken place at the Department of Laboratory Medicine, University of Debrecen.
## 2.3. Interleukine-6 (IL-6) Measurement
Serum IL-6 was determined with a commercially available quantitative sandwich enzyme immunoassay technique (R&D Systems, Abington, United Kingdom). Values are expressed as pg/mL with 1.7–$4.4\%$ and 2.0–$3.7\%$ intra- and inter-assay precision, respectively.
## 2.4. Lipoprotein Subfraction Analyses
Lipid electrophoreses were performed with a Lipoprint system for the analyses of LDL and HDL lipoprotein subfractions (Quantimetrix Corporation, Redondo Beach, CA, USA). 25 µL serum was added to the polyacrylamide gel tubes along with Sudan Black positive 300 and 200 µL loading gel solution, respectively. Tubes were photopolymerized at 21–23 °C for 30 min and then electrophoretized using a constant of 3 mA/tube in an electrophoresis chamber. Each electrophoresis chamber involved a quality control (Liposure Serum Lipoprotein Control, Quantimetrix Corporation, Redondo Beach, CA, USA). Subfraction bands were scanned with an ArtixScan M1 digital scanner (Microtek International Inc., CA, USA) and Lipoware software (Quantimetrix Corporation, Redondo Beach, CA, USA) was used for analyses.
During LDL subfraction analysis, up to seven LDL subfractions were distributed based on their size between the VLDL and HDL peaks. Mid-C, Mid-B, and Mid-A mainly corresponded with intermediate density lipoprotein (IDL) particles (IDL-C, IDL-B, and IDL-A). Proportion of large LDL (large LDL %) was defined as the sum of the percentage of LDL1 and LDL2, whereas proportion of small LDL (small-dense LDL %) was defined as the sum of LDL3–LDL7. Cholesterol content of each LDL subfraction was calculated by multiplying the relative area under the curve (AUC) by total plasma cholesterol of the sample using Lipoware.
During HDL subfraction analysis, 10 HDL subfractions were distributed based on their size between the LDL and albumin peaks. The three major classes were calculated as the sum of HDL1–HDL3 (large HDL), HDL4–HDL7 (intermediate HDL), and HDL8–HDL10 (small HDL). Cholesterol content of each HDL subfraction was calculated by multiplying the relative AUC by total HDL-C concentration of the sample using Lipoware.
## 2.5. Flow-Mediated Vasodilation of the Brachial Artery (FMD)
Examinations were performed under standardized conditions, after 8 h of fasting and after an 18 h cessation of smoking, coffee, and tea. No vasoactive drugs were allowed 24 h prior to measurements. Tests were performed on the right brachial artery with a high-resolution duplex ultrasound using a 5–10 MHz linear transducer (Phillips HD11XE; Tampa, FL, USA). ECG was recorded during examination. We obtained a longitudinal cross-sectional image of the brachial artery 4–7 cm proximally from the cubital fossa. Arterial diameter of at least five standard points were detected. Measurements were electrocardiogram-gated and performed synchronized with R-waves [24,25,26]. After the cuff was placed on the forearm, it was inflated for 4.5 min, maintaining a 50 mmHg suprasystolic pressure above the baseline, then with a sudden release of the cuff we triggered a reactive hyperemia. Changes in the arterial diameter were detected 60 s after releasing the cuff. Mean FMD was calculated using three subsequent measurements. FMD was expressed in percentages indicating the change of diameter triggered by reactive hyperemia compared to resting diameter.
## 2.6. Determination of Carotid Intima/Media Ratio (IMT)
Measurement of IMT was also performed with duplex ultrasound using a 5–10 MHz linear transducer (Phillips, HD-11XE; Tampa, FL, USA). We examined both carotid arteries. Before taking measurements we screened the common, internal, and external carotid arteries for plaques. If no plaque formation was detectable measurements were performed 1 cm below the carotid bulb. IMT was defined by determining the distance between the first (lumen–intima border) and second (media–adventitia border) echogenic line visible in the carotid artery. We performed 10 measurements on both sides, then the average value on each side, and then the mean IMT was calculated. Results were presented in centimeters.
## 2.7. Analysis of Stiffness Parameters
Examination was conducted under standardized circumstances after resting for 10 min. Determination of Aix and PWV was performed with an Arteriograph system (TensioMed Kft., Budapest, Hungary). The method of measurement is based on the principle that myocardial contraction produces a pulse wave in the aorta which is reflected from the aortic bifurcation. As a result, a second (reflected) wave can be observed during the systole (late systolic peak). The morphology of the second wave depends on the stiffness of the common carotid artery and on peripheral resistance which determines the amplitude of the wave. Return/reflexion time (RT S35) is calculated as the difference between the first and the second systolic wave, when cuff pressure on the brachial artery exceeds systolic pressure by at least 35 mmHg [27,28,29]. Aix can be calculated as the difference between the early and late systolic peak pressure, divided by the late systolic peak pressure. To estimate the distance between the aortic arch and bifurcation, we measured the distance between the jugular fossa and the symphysis, then used the distance to calculate PWV as the ratio of the jugulo–symphyseal distance and RT S35. Dimension of the calculated ratio is m/s.
## 2.8. Statistical Methods
Statistical analyses were performed using the Statistica 13.5.0.17 software (TIBCO Software Inc. Palo Alto, CA, USA) and GraphPad Prism 6.01 (GraphPad Prism Software Inc., San Diego, CA, USA). The Kolmogorov–Smirnov test was used to determine the normality of data. Student’s unpaired t-test and the Mann–Whitney u-test were performed to describe the difference between continuous variables. The Chi-squared test was used to analyze the difference between binominal variables. Data were expressed as mean ± SD or median (upper quartile–lower quartile) in case of normal and non-normal distribution, respectively. Pearson’s correlation was used to analyze the relationship between continuous variables. A multiple regression analysis (backward stepwise method) was performed to determine the best independent predictor of accelerated atherosclerosis. p ≤ 0.05 probability values were considered statistically significant.
## 3. Results
Anthropometric and SLE-related clinical data alongside inflammatory markers and the results of imaging techniques are summarized in Table 1. Compared to controls, patients with SLE had significantly higher serum CRP and IL-6. A significantly lower Aix ratio was detected in patients compared to controls; however, there were no significant differences in IMT, FMD, and PWV between patients and controls (Table 1).
SLE patients had significantly higher plasma TG and ApoB100 concentrations, with lower HDL-C and ApoA1 compared to the control group. Higher total IDL, IDL-B, and IDL-C subfractions were found in SLE. There were no significant differences in LDL subfractions. Significantly lower concentrations of large, intermediate, and small HDL subfractions were found in patients with SLE compared to controls (Table 2).
Aix positively correlated with VLDL ($r = 0.31$, $$p \leq 0.04$$), IDL-C ($r = 0.41$, $$p \leq 0.006$$), and IDL-B subfractions ($r = 0.29$; $$p \leq 0.05$$) in subjects with SLE (Figure 1a–c). Marginally significant associations were found between Aix and LDL1 ($r = 0.29$, $$p \leq 0.059$$) (Figure 1d), TG ($r = 0.27$, $$p \leq 0.078$$), total cholesterol ($r = 0.30$, $$p \leq 0.058$$), and ApoB100 ($r = 0.29$, $$p \leq 0.057$$) in the patient group. Similar to Aix, PWV showed positive correlations with the concentrations of VLDL ($r = 0.41$, $$p \leq 0.007$$), IDL-C ($r = 0.4$, $$p \leq 0.004$$), IDL-B ($r = 0.35$, $$p \leq 0.02$$) and LDL-1 ($r = 0.31$, $$p \leq 0.04$$) subfractions as well as with TG ($r = 0.31$, $$p \leq 0.04$$) in SLE.
There were significant negative correlations between hsCRP (r = −0.4; $$p \leq 0.006$$), TG (r = −0.36; $$p \leq 0.02$$), LDL-C (r = −0.31; $$p \leq 0.03$$), ApoB100 (r = −0.34; $$p \leq 0.02$$), VLDL (r = −0.36; $$p \leq 0.01$$), LDL-2 subfraction (r = −0.32; $$p \leq 0.03$$) and FMD in patients (Figure 2).
IMT showed significant negative correlation with C4 (r = −0.4; $$p \leq 0.005$$) in patients (Figure 3a). Patients with SLE were divided into two groups based on SLEDAI; subjects with mild and moderate disease activity comprised the low (SLEDAI = 0–10), and subjects with high and very high activity comprised the high disease activity group (SLEDAI < 11). C4 was significantly higher in the low SLEDAI group (0.165 vs. 0.103 g/L; $$p \leq 0.03$$); while ApoB100 and IMT was significantly lower in the low SLEDAI group (0.82 vs. 1.03 g/L; $$p \leq 0.05$$ and 0.0478 vs. 0.0554 cm; $$p \leq 0.05$$, respectively) (Figure 3b–d).
Multiple regression analysis showed that IDL-C was an independent predictor of Aix (β = 0.399; $$p \leq 0.0009$$). The model included age, TG, total cholesterol, VLDL, IDL-C, IDL-B, and LDL1.
We could not find significant correlations between the results of vascular diagnostic test (PWV, Aix, IMT, and FMD) and lipid or inflammatory parameters in the control population.
## 4. Discussion
While LDL-C is an established major causal factor of atherosclerotic cardiovascular disease, the causality between triglyceride-rich lipoproteins, their remnants, and cardiovascular diseases is unclear [30]. Indeed, remnant particles, especially in the small VLDL and IDL range, with at least $30\%$ cholesterol by weight, may contain up to four-fold more cholesterol molecules than an LDL particle. VLDL and remnants are also enriched in ApoE and ApoC3, both implicated in binding and retention in the artery wall [31]. These factors enhance remnant cholesterol deposition in the plaque. Moreover, unlike native LDL, which may exit the subendothelial space almost as rapidly as it enters, remnants efflux very slowly compared to their rate of entry with increased opportunity for internalization by macrophages and foam cell formation [32]. Triglyceride-rich lipoproteins affect the autophosphorylation of focal adhesion kinase and its downstream signaling pathway, phosphatidylinositol 3-kinase/protein kinase B (Akt), causing the inactivation of nitrogen-monoxide (NO) synthase (NOS), and decreased endothelial synthesis of NO. Furthermore, accumulation of triglyceride-rich lipoproteins in the plasma is associated with higher asymmetric dimethyl arginine (ADMA), an endogenous inhibitor of NOS. Moreover, these lipoproteins induce endothelin-1 (ET-1) release in humans, which induces vasoconstriction by increasing the tone of vascular smooth muscle cells, increases the proliferation of these cells, promoting thrombosis, oxidative stress, and inflammation. In addition, plasma triglyceride-rich lipoproteins increase plasma viscosity and favor a procoagulant state via increased platelet aggregation [33].
While epidemiological studies in humans suggest that remnant lipoproteins (IDL and smaller VLDL) are predictors of the severity or progression of atherosclerosis [34], their causal role in the enhanced atherogenesis of patients with SLE has not been clarified. A previous study reported that atherogenic lipoproteins, including small VLDL subsets, were associated with increased disease activity, and the ApoB100:ApoA1 ratio correlated positively with SLEDAI disease activity score in juvenile SLE [35]. We found higher serum ApoB100 in patients with high/very high SLEDAI compared to those with mild/moderate disease activity indicating the role of inflammatory processes in disturbed lipoprotein metabolism. Moreover, we also detected higher VLDL and smaller IDL subfractions in our young patients with clinically active SLE.
Although *Aix is* probably not a commonly mentioned arterial stiffness parameter, in our study Aix was the only vascular diagnostic parameter that showed significant alteration in our young patients with SLE. Its significant positive correlations with VLDL, IDL-B, and IDL-C may also support the atherogenic potential of these triglyceride-rich lipoproteins in the early phase of atherosclerosis. Multiple regression analysis also showed that IDL-C was an independent predictor of Aix, which also highlights the probable importance of triglyceride-rich lipoproteins in the pathomechanism of SLE-associated vascular complications. In a former study, Aix turned out to be related to organ damage measured by Systemic Lupus International Collaborative Clinics (SLICC) index in young women diagnosed with SLE without significant organ damage [16]. In another study, higher Aix was reported in a middle-aged SLE cohort, and Aix correlated with IMT in the common carotid artery, common femoral artery, and internal carotid artery [36]. A recent meta-analysis comprising 49 studies also showed increased Aix in patients with SLE compared to healthy controls [37], which may also underline the importance of measuring Aix.
While IMT and stiffness are relatively stable, FMD may be influenced by many confounding factors [15]. We found that IMT was higher in patients with high/very high disease activity compared to those with mild/moderate, which demonstrates the crucial role of inflammation in atherosclerotic remodeling of the arterial wall even in younger ages. Although we did not find significant differences in FMD values between patient with SLE and controls or between patients with high or low disease activity, significant negative correlations were found between serum hsCRP, TG, LDL-C, ApoB100, VLDL, LDL2, and FMD. These results are in line with the above-mentioned multicausal characteristics of FMD.
A previous study demonstrated that increased carotid IMT in both the right and left carotid arteries and increased PWV in the left carotid artery of patients with SLE correlated positively with the increase in LDL subfraction L5 percentage (representing electronegative, small-dense LDL) determined by a fast protein liquid chromatography system [38]. Despite the larger patient population, we could not find any significant correlations between PWV and LDL subfractions, probably due to the different method used for LDL subfraction analysis and the lower age of our patients.
Like other previous studies, we could not find any significant correlations between vascular index and lipid markers in the control population, probably because of the lack of vascular damage in these young, mostly female subjects [20,38]. The lack of associations between the results of vascular diagnostic tests (PWV, Aix, IMT, and FMD) and lipid or inflammatory parameters in the control population may also indicate that vascular abnormalities found in patients with SLE are disease-specific and due to the SLE-associated dyslipidemia and inflammation.
Beside LDL-C reduction, 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase inhibitor statins exert pleiotropic effects to modulate various components of the immune system; hence, they might be of benefit in SLE by inhibiting immune activation in the arterial wall and attenuating disease activity [39]. However, some former uncontrolled studies reported a few cases of lupus-like syndrome, autoimmune hepatitis, skin lesions similar to subacute lupus, and dermatomyositis related to statin administration [40]. Furthermore, the effect of statins on triglyceride-rich lipoproteins is limited. Taken together, statin administration might not be the optimal lipid-lowering strategy in SLE. On the other hand, well-known and novel agents lowering triglyceride-rich lipoproteins are currently being tested for the prevention of atherosclerotic cardiovascular disease in large clinical trials, including REDUCE-IT, STRENGTH, and PROMINENT using purified omega-3 fatty acid and selective peroxisome proliferator-activated receptor α modulator pemafibrate, with inconclusive results. However, even more studies appeared on the horizon aiming to inhibit apolipoprotein C3 and angiopoietin-related protein 3 and 5 [41]. Promising results demonstrating substantial lowering of triglyceride-rich lipoproteins have already been reported after administrating some of these drugs in development; thus, it is possible that therapies aiming at lowering triglyceride-rich lipoproteins could become increasingly available in the clinical setting [41], and, based on our results, SLE-associated dyslipidemia should be considered as a potential new indication.
Some limitations of our study should also be mentioned. The relatively small number of SLE patients may reduce the power of our study. The low number of male patients does not allow the investigation of gender differences. It must be noted that cardiovascular diseases occur independently of SLE. Since our research groups (both SLE patients and healthy controls) were young, and mostly females, the risk of any cardiovascular events is very low. Therefore, vascular abnormalities identified by vascular diagnostic tests are likely the consequences of SLE-associated dyslipidemia and systemic inflammation. Furthermore, long-term follow-up could help us clarify the role of triglyceride-rich lipoproteins in the atherosclerotic processes associated with SLE. Still, these results underline the potential importance of studying the detailed lipoprotein profile including triglyceride-rich lipoproteins and the measurement of arterial stiffness parameters in this special patient population.
## 5. Conclusions
This is the first clinical study evaluating the lipid and lipoprotein subfraction profile, inflammatory markers, and various vascular diagnostic tests including arterial stiffness, IMT, and FMD simultaneously in young, clinically active SLE patients. Our results demonstrate the possible effect of triglyceride-rich lipoproteins on vascular function in young SLE patients and underline the importance of further studies to clarify its long-term consequences. Based on our results measurement of Aix may be used for the early detection of SLE-associated vascular abnormalities. Given the high risk of cardiovascular morbidity and mortality in young patients with SLE, it is advisable to routinely assess risk factors including triglyceride-rich lipoproteins and arterial stiffness parameters and to integrate appropriate preventive measures into our patients’ complex management plans.
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|
---
title: Proposal and Definition of an Intelligent Clinical Decision Support System
Applied to the Screening and Early Diagnosis of Breast Cancer
authors:
- Manuel Casal-Guisande
- Antía Álvarez-Pazó
- Jorge Cerqueiro-Pequeño
- José-Benito Bouza-Rodríguez
- Gustavo Peláez-Lourido
- Alberto Comesaña-Campos
journal: Cancers
year: 2023
pmcid: PMC10046257
doi: 10.3390/cancers15061711
license: CC BY 4.0
---
# Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer
## Abstract
### Simple Summary
Designing systems that optimize the process of evaluating mammogram images with the goal of improving the diagnostic process of breast cancer is an active field of research due to the large health and social impact of this disease. This paper presents a new intelligent clinical decision support system that, through the concurrence of inferential models, allows the definition of various risk metrics for patients. Those metrics are weighted and combined into a Global Risk value to be finally corrected by means of an empirical weighting factor derived from the BI-RADS analysis and condition associated with the patient’s mammogram images. The validation results have shown meaningful disease detection rates within the study group used, which makes it possible to estimate the potential for a diagnostic use of the developed system.
### Abstract
Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global *Risk is* obtained, which after interpretation can be used to establish the patient’s status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis.
## 1. Introduction
Breast cancer is currently the most frequently diagnosed tumor pathology worldwide, accounting for one in eight cases of cancer among the overall population. One in four cases of oncologic pathology in women correspond to breast cancer [1,2,3], being the cause of $15.5\%$ of overall cancer deaths by the year 2020 [2]. Due to the high impact of this type of cancer, in the last century, a major effort has been made to implement early detection strategies that, along with early diagnosis as well as effective treatment, have significantly improved the diagnosis of patients and lowered the associated mortality rates [3]. These early detection strategies, usually carried out through population screening programs, are generally based on the performance of periodic mammograms in high-risk groups, mainly women over 40 years of age. The aim is to detect, as soon as possible, any signs that may indicate the possible presence of the pathology, with the subsequent referral of suspected cases for further testing in order to confirm or refute the suspicion and establish a diagnosis. In this regard, for the evaluation of mammograms, the Breast Imaging Reporting and Data System (BI-RADS) [4] is standardly applied, providing the medical team with a common lexicon for the description of the findings observed in the images and facilitating the categorization of the mammogram through the definition of seven levels. These range from no suspiciousness to absolute certainty that the subject has a case of breast cancer, thus facilitating the task of describing and classifying the findings. Nevertheless, the interpretation of mammograms is not trite, and depending on the training and experience of the medical team in charge of evaluation, the results may show a certain degree of variability and subjectivity [5,6,7]. In some cases, this may imply the performance or omission of extra tests to confirm or rule out a potential case of breast cancer, with all the possible disadvantages that this may entail for the patient.
In this context, within the healthcare field, it is essential to have mechanisms and tools available to provide support in the arduous and challenging clinical decision-making processes. These tools, usually supported by artificial intelligence techniques, are currently a working reality, with multiple and diverse proposals existing in the current literature, mostly integrated into clinical decision support systems [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]. With regard to breast cancer, many proposals make use of symbolic inference models [21,25], although with the rise of the connectionist paradigm, in recent years applications have been developed that use large and complex neural network models for the detection of these tumors [26,27,28,29,30,31,32]. They have enabled the management of hospital resources to be improved upon, thereby increasing the quality of the services provided while reducing the high costs associated with them. Along these lines, this paper proposes an adaptation, development and proof of concept of the work presented by the authors in Casal-Guisande et al. [ 2022] [25]. In that publication, the authors outlined the concept of a breast cancer diagnostic system based on the implementation of a cascade of expert systems that collected and formalized the patient’s medical information. Their output was processed to find covariance factors that would form a new knowledge base to train a statistical classifier upon which the final diagnostic prediction could be based. The sequential flow of information that was proposed reduced the uncertainty related to both processing and interaction, although it diminished the inferential counter position. In other words, the statistical inference was set off after the symbolic inferential process, whose outputs were the grounds for the definition of the classifier’s starting data. Therefore, there is a conditioning of the symbolic part to the statistical part that, in a sense, limits the diversification of knowledge and, above all, limits the interpretation of the correlation patterns that the machine learning algorithm can obtain.
The improved system proposed in this paper is built upon the aforementioned one and aims not only to improve upon the results published at the time, but also redesign the flow of data and knowledge in order to increase the diversifying effect of the inferential models. To this end, an intelligent system was implemented consisting of two blocks that work concurrently [12,13,17,25,26,27]. The first block is based on the use of expert systems employing fuzzy inference engines, focused on the processing of data related to the findings present in mammograms, i.e., masses, calcifications, asymmetries and distortions of the architecture. The second block, based on the use of a classification machine learning algorithm, is focused on the joint processing of data from mammograms, with the exception of BI-RADS, as well as general patient data. Through these concurrent processes it is able to obtain a series of risk indicators, the Symbolic Risks and the Statistical Risk, respectively. These indicators are then aggregated to obtain a percentage indicator of Global Risk, representative of the likelihood of developing breast cancer. This might then be corrected and its value weighted according to the BI-RADS level assigned by the medical team, thus determining a Corrected Global Risk indicator that, after adequate interpretation and evaluation, allows for the generation of warnings regarding the patient’s condition as well as provides recommendations. The simultaneous deployment of the inferential systems enables their outputs to be contrasted and merged, while the development of analytical models, as will be discussed later, can effectively reduce the uncertainty associated with this type of structure.
This document is organized in five sections. Section 2 presents the conceptual design of the proposed intelligent system, outlining the different stages and data flows. It is then followed by a thorough description of the software implementation of the intelligent system in addition to commenting on the previous results of its proof of concept. In Section 3, a practical case of application of the proposed intelligent system is presented so as to illustrate its performance. Section 4 discusses the architecture of the proposed system, followed by Section 5, which presents the conclusions obtained along with potential development lines.
## 2.1.1. Database Description
In order to conduct this research, a database belonging to the School of Medicine and Public Health of the University of Wisconsin-Madison was used containing information on 130 patients. Out of the 130 patients within the dataset, 21 were diagnosed with breast cancer after comprehensive testing. All patients in this study underwent confirmatory biopsies that, in each case, either validated or ruled out the presence of cancer from a histopathological point of view. This dataset was comprised of general patient information (age, family and personal history of cancer), commonly available on electronic health records, as well as mammography profiling via BI-RADS terminology [28]. It also included the associated BI-RADS category.
A summarized overview of the main characteristics of the population used can be found in Table 1. A further detailed description of the data considered is presented in Table 2 and Table 3.
## 2.1.2. Conceptual System Design
A flowchart for the intelligent clinical decision support system for breast cancer risk evaluation is shown in Figure 1.
## Gathering and Interpretation of Patient Information
The first stage in the proposed intelligent system is restricted to the collection of general patient data, typically available in electronic health records (for more information see Table 2) as well as the interpretation and annotation of the findings found in mammograms following the BI-RADS© terminology [28] by a medical specialist, detailed in Table 3. Together with each of the descriptors, a corresponding field associated with the nature of the data is added, differentiating between numerical and categorical data.
## Data Processing
Once the data presented in Stage 1 have been compiled and arranged, they are then processed by the proposed intelligent system, firstly employing a series of expert systems and a classification machine learning algorithm arranged in two sub-stages that work concurrently [12,13,17,25,26,27], 2.1.a and 2.1.b. Through these two sub-steps, it is possible to determine a series of risk indicators related to the risk of developing breast cancer. These same indicators are then aggregated in Step 2.2, which determines a Global Risk index that combines them and represents the risk of a patient suffering from breast cancer. The different sub-stages are detailed below:Stage 2.1.a—Determination of Symbolic Risks: once the information regarding the findings in the mammogram, introduced in Stage 1, has been gathered, the processing is carried out using a series of expert systems that work concurrently [12,13,17,25,26,27] which are based on Mamdani-type fuzzy inference systems [29,30,31]. Each of these expert systems is assigned the processing of the data subsets associated with the different findings (masses, calcifications, asymmetries and distortions) in order to obtain a series of risk indicators, the Symbolic Risks (R1, R2 and R3), with values ranging between 0 and 100, each of them related to the risk of developing breast cancer. Stage 2.1.b—Statistical Risk determination: In parallel to Stage 2.1.a, Stage 2.1.b carries out the processing of all the collected data, both those in Table 2 and Table 3, excluding the BI-RADS category determined by the expert, by means of a machine learning classification algorithm [32]. According to the nature and quality of these data, they may be subjected to a normalization process with a possible synthetic scaling of the sample [25]. The algorithm training is based on the dataset introduced in Section 2.1.1, where each case is labeled as either “cancer” or “non-cancer”. This allocation is indisputable within the study group since all the patients underwent a biopsy and their real condition is known. This considerably reduces the epistemological and interaction uncertainty of the training data itself. Once the model has been trained, a new patient’s data are presented, so that the model may determine a percentage indicator of risk at the output, the so-called Statistical Risk (Rs), ranging from 0 to 100, which is intended to indicate the risk that the patient may suffer from breast cancer. Stage 2.2—Risk aggregation and Global Risk determination: Having obtained the Symbolic Risks (R1, R2 and R3) as well as the Statistical Risk (Rs), they are then aggregated by means of the expression shown in Equation [1], which allows for the calculation of the Global Risk (RG). Said expression is based on the product of the weighted sum of the Symbolic Risks and the decimal logarithm of the Statistical Risk. The first term, the weighted sum, provides a measure of risk that brings together the different Symbolic Risk indicators according to the potential importance given by the medical team to each of the groups of findings (masses, calcifications and asymmetries, and distortions of the architecture). Meanwhile, the second term has a multiplicative effect, increasing the level of risk previously obtained in the event that the patient under analysis presents a similar pattern to that of a patient with breast cancer within the sample with which the statistical model was constructed. Note that in the event that any of the groups of findings used to calculate the Symbolic *Risks is* absent in the case under study, meaning that any of the risk indicators is null, an equitable weight redistribution will be performed among the rest of the findings. Alternatively, a new weight redistribution proposed by the medical team will be considered. Furthermore, it is also worth mentioning that the Global Risk value ranges between zero and one hundred; in case of a higher value, despite the multiplicative effect of the logarithm term, its maximum value shall be one hundred. [ 1]RG=ω1·R1+ω2·R2+ω3·R3·log10Rs
## Global Risk Correction
As discussed in the previous stage, a series of risks were calculated and aggregated, resulting in the Global Risk being determined. Following this, during this stage, the Global Risk could be corrected by taking into account the value of the BI-RADS index proposed by the medical team according to the analysis of the mammograms. This Global Risk, as has been stated, is a measure of the patient’s potential risk of developing breast cancer, computed by means of statistical categorical data as well as assorted symbolic variables related to findings from the mammograms. While it is an effective metric, it is preferable to use the internationally validated BI-RADS indicator, previously presented. Currently, this indicator is the standard for tumor assessment in breast cancer, being the main diagnostic criterion. Nevertheless, the interpretation of the index itself, and the allocation of an index to a potential tumor lesion, requires considerable expertise and accuracy on the part of the medical team. Both knowledge and experience in the use of the BI-RADS scale are required, which makes its widespread use difficult in medical teams with little practice in its use or in the diagnostic interpretation. Still, as has been stated, its use continues to be crucial in the staging of breast cancer and should therefore be valued and accepted as a fundamental diagnostic vector. This study, in not including the BI-RADS index when calculating the Global Risk, aims towards the formalization of knowledge, both in its statistical and symbolic aspects, to be precise. Thus, the diagnostic process would not depend on the determination of highly specific knowledge, but rather on a more general formalization of the data relating to the patient’s health. This would allow a plausible and reliable risk level to be obtained, with less epistemic uncertainty associated with the application of the BI-RADS index. However, the influence of the BI-RADS cannot be ignored under any circumstances; hence, it is proposed that once the Global Risk has been determined, it should be weighted in an orderly manner by the BI-RADS index that the medical team associates with the possible cancerous tumor that the patient may suffer from. To achieve this, a weighted order algorithm was adapted in which, depending on the BI-RADS index obtained, factors that rescale the Global Risk are established according to the risk itself. This allows us, on the one hand, to limit the uncertainty of the index and, on the other, to reaffirm the implicit closeness that should exist between the Global Risk and the BI-RADS index. Equation [2] presents the expression for the calculation of the Corrected Global Risk (RG’), where *Fp is* the weighting factor. [ 2]RG’=RG·Fp Table 4 presents a proposal of the Global Risk weighting factors according to the order of the BI-RADS index assigned to the patient. The determination of the factors was derived from an empirical analysis of the correlation and causality chains existing between the presence of cancer, the BI-RADS index and the Global Risk value determined. The aim was to obtain an analytical relationship that would allow the Global Risk value to be modified as a function of the BI-RADS index, thereby maximizing its diagnostic accuracy. With this, the aim is not only to improve the accuracy of the prediction, but also to bring said prediction closer to the standard medical interpretation derived from the BI-RADS index value, often determinant in the confirmation of a suspected disease case. Precisely due to this, the weighting factor does not contemplate any statistical inferential process, since it exclusively involves an analytical multiplication or division factor associated with what the BI-RADS index means and represents. It is important to note that despite the application of this weighting, the absolute maximum value of the Corrected Global Risk equals one hundred.
## Generation of Warnings and Decision Making
Once the Corrected Global Risk (RG’) is determined, its evaluation is passed on, thereby setting up a series of statuses and recommendations related to each patient:Healthy case: Refer the patient for routine review;Dubious case: Reconsider the patient’s case, consider performing other tests as well as summoning the patient for a new visit in a period of time to be specified;Potential breast cancer case: Perform confirmatory tests.
The evaluation is a simple suggestion of classification that obeys the need to make an explicit diagnostic decision. Although it is supported by the Corrected Global Risk, it could equally be supported by the Global Risk as long as the medical team does not consider the correction necessary.
## 2.2. Implementation of the System
This section deals with the implementation of the intelligent clinical decision support system for breast cancer diagnosis, describing the proposed software application in detail and commenting on the previous results derived from its proof of concept. The system was developed following the recommendations of Hevner et al. [ 33,34], which would allow for integration into hospital information systems, if needed.
MATLAB© software (R2021b, MathWorks©, Natick, MA, USA) was used to carry out the implementation. Table 5 shows a list of the different Toolboxes used.
In addition to these tools, it was necessary to use Python (version 3.9.12) as an auxiliary tool to provide support for the data augmentation process by means of the SMOTE-NC algorithm from the imbalanced-learn library [38].
Figure 2 depicts a screenshot of the home screen of the developed software application. The Stage #1 box corresponds to the area of the application used to enter the starting data, i.e., the patient’s general data and those related to the findings coming from the mammography interpretation by the specialists. The Stage #2 box corresponds to the area of the application used to calculate the risks. This represents Stage 2 of the methodology presented, with three different boxes: the Symbolic Reasoning box, relating to Stage 2.1.a, in which the Symbolic Risks associated with the groups of findings present in the mammograms are determined, the Statistical Reasoning box relating to Stage 2.1.b, associated with the calculation of the Statistical Risk, and finally the Global Risk box, relating to Stage 2.2, in which the aggregation of the Symbolic Risks and Statistical *Risk is* carried out, yielding the Global Risk. Then, in the Stage #3 box, the Global *Risk is* corrected, taking into account the value of the BI-RADS indicator. Finally, the Stage #4 box presents the panel related to Stage 4 of the intelligent system, which deals with the generation of warnings and decision making.
## 2.2.1. Data Collection
Once the data of each of the patients being studied by the system are submitted, they must be input into the software through the available fields in the Stage #1 box in Figure 2. There are two areas: one relating to general patient information in the “Other data” section, and the other to information regarding the findings observed in the mammograms, as well as the BI-RADS indicator established by the medical team after the evaluation of each case, which must be entered in the “Mammogram” section. This information should be typed into the forms with caution, avoiding errors or omissions that could lead to an inflated printout, thus increasing the uncertainty of the system.
## 2.2.2. Data Processing
After inputting the data into the application, they are processed by the proposed intelligent system. In the application there is a panel, the so-called Stage #2, where the different calculation blocks are located. In line with what was discussed in Section 2.1.2, this processing consists of two main blocks that work concurrently [12,13,17,25,26,27]. The first has a series of expert systems that also work concurrently [12,13,17,25,26,27], focused on the determination of the Symbolic Risks, while the second deploys a classification machine learning algorithm, focused on the determination of the Statistical Risk. Following this, both the Symbolic Risks and the Statistical Risk are aggregated, determining the Global Risk.
Next, the definition associated with each of the blocks is described, as well as the calculation of the previously mentioned risk indicators.
## Determination of the Symbolic Risks
As mentioned before, a number of concurrent expert systems are used to determine the Symbolic Risks [12,13,17,25,26,27]. These systems are in charge of processing the different groups of data relating to the findings present in mammograms (masses, calcifications and asymmetries, and distortion of the architecture), already presented in Table 3.
In this paper, Mamdani-type fuzzy inference engines [29,30,31,39] are employed, similar to those used in the first level of the cascade in the work of Casal-Guisande et al. [ 25] and others [12,13,17,26,27].
Table 6 summarizes the antecedents and consequents of each of the deployed expert systems. Regarding the membership functions, we complied with the recommendations of Ross [39], choosing the use of normal, convex and symmetric functions. As for the antecedents, singleton membership functions were used, while in the case of the consequents, those related to Symbolic Risks, triangular membership functions between 0 and 100 were used. The choice to use singleton functions for the antecedents was due to the nature of the data represented, as they point to a single value within a category, such as the shape of a mass within the typified ones. On the contrary, the consequents are represented by triangular functions since their variables can take membership values between zero and one, but there is only a single value that presents maximum membership. The risks, intended here as the consequent variables, were defined by assuming that there is a single value within their measurement scale that represents the highest risk, in line with the latest work published by the authors in this field [25].
A general summary of the overall configuration of the expert systems’ inference engine is presented in Table 7.
Once the symbolic inference process is carried out, three risk indicators are obtained, R1, R2 and R3, each one of them related to the risk of developing breast cancer according to the group of findings associated with their respective calculation. The greater the value of each of them, the greater the risk of developing breast cancer.
## Determination of the Statistical Risk
Concurrently [12,13,17,25,26,27] to the determination of the Symbolic Risks, the determination of the Statistical *Risk is* carried out through the use of a classification machine learning algorithm. This model is based on the dataset presented in Section 2.1.1, with the exception of the BI-RADS indicator established by the medical team. Most of the data are categorical [40,41], as can be seen in Table 2 and Table 3. The only exception is age, which is normalized using the Min-Max normalization method, the expression of which is shown in Equation [3]. [ 3]t’=ti−mintmaxt−mint Upon revision of the data set distribution, a significant imbalance between the “cancer” and “non-cancer” classes is apparent, which may affect the performance of the classifier and its subsequent generalization. In light of these circumstances, and following the usual trend in the development of decision support tools for medical diagnostic environments, it was decided to use controlled data augmentation techniques, through which the results of the binary classifiers are improved [25,42]. In this paper, the use of SMOTE-NC, a variant of the synthetic minority over-sampling technique (SMOTE) [42,43], is employed. This approach allows for the augmentation of datasets in which numerical and categorical variables exist simultaneously once all of them have been transformed [25]. The data augmentation strategy adopted involves adding patients until there were 200 cases of each class (cancer and non-cancer) using a strategy with a number of neighbors $k = 5$ [25].
By doing so, a coherent and representative dataset was assembled to be used for the training and tuning of the classification machine learning algorithms. Several tests were performed using the MATLAB© Classification Learner app, with a k-fold cross validation strategy [44], with $k = 5.$ After carrying out different tests, and relying on the use of ROC curves [25], it was found that the bagged tree algorithm was the one that demonstrated the best results. Regardless, it is worth noting that any other machine learning classification approach might be a valid alternative as long as it provides better results, in terms of the interpretation of the ROC curves, than those obtained with the current approach. Figure 3 shows the ROC validation curve of the bagged trees algorithm for the “cancer” class, showing a very high AUC [25] value of 0.98.
After defining the model, the classifier returns a risk indicator, the so-called Statistical Risk, corresponding to the risk of a new patient developing breast cancer. It ranges from zero to one, although for the sake of convenience it is scaled between zero and one hundred. The greater the Statistical Risk value, the greater the risk that the patient is suffering from breast cancer.
## Determination of the Global Risk
Having determined the Symbolic Risk as well as the Statistical Risk, their aggregation is carried out using Equation [1], as presented in the conceptual description of the system. This results in a new risk indicator that groups together the previous risks, known as Global Risk. Its value lies between zero and one hundred, representing, similarly to previous cases, the risk of suffering breast cancer. However, in this instance, the indicator is aggregated, bringing together the different findings and general data of the patient under different perspectives, both symbolic and statistical.
## Determination of the Corrected Global Risk
As mentioned earlier, in this study, the BI-RADS indicator was not included in the calculation of the risk indicators. Despite this, it is not reasonable to ignore its influence, so once the Global *Risk is* determined, it is weighted according to the BI-RADS index allocated to the case by the medical team. The objective of this correction does not lie in improving the real predictive capacity of the system, an issue that is implicitly covered in the determination of the Global Risk, but in bringing the usual medical decision process based on the BI-RADS standard closer to the decision itself. Since it is common practice to refer patients based mainly on the values of this index, the system adopts this influence and generates a corrected risk value that is close to said practice, even though it might differ from the real prediction provided by the system. In this sense, a series of empirical expressions were defined that make it possible to correct the predictions to bring them closer to the suspicion signs inherent to BI-RADS. Table 4 shows the correlation between the BI-RADS level and the expression used to determine the Corrected Global Risk indicator.
## 2.2.3. Generation of Warnings and Decision Making
Once the patient’s data are processed and the Global Risk indicator is determined and corrected in accordance with the BI-RADS level assigned to the case, an assessment of the existing risk is carried out with the aim to establish the patient’s status in order to provide recommendations that will facilitate the patient’s diagnosis. In this sense, three possible states are considered, which are summarized in Table 8, together with the established risk thresholds. It should be underlined that these risk criteria may be subject to revision in the future depending on the results of the clinical validation stage, as well as on the criteria of the medical specialists.
It is worth to emphasize that the risk levels established here are just illustrative and obey only the need to carry out an explicit diagnosis and the potential application of the Global Risk correction. As already mentioned, the system has classification capabilities represented by the Global Risk that, in principle, would not require risk thresholds, since it classifies patients into two classes: those who could suffer from cancer and those who could not. However, by incorporating a correction based on the BI-RADS index, this classification is vaguer to approach and adapt to the criteria of the diagnostic medical team. For this reason, it is considered convenient to keep a suggestion of risk levels, which is much closer to the interpretation of the doctor than to the inferential classification of the system.
## 2.2.4. Analysis of Results
After the implementation of the system and taking as a reference the collected and conveniently labeled dataset, a proof of concept [45] was carried out to demonstrate the correct operation of the software prototype developed, as well as estimate its capabilities and diagnostic success. The system can be understood as a binary classifier [46,47] that predicts the assignment of classes, two in this case: “cancer” or “non-cancer”, to the patients included in the study. Likewise, this prediction must be reflected, first in the calculation of the Global Risk, and second in the determination of the Corrected Global Risk, since in reality both risks start from different premises. As already mentioned, the former is determined from the patient’s data through an inferential process, while the latter consists of an analytical approximation to the usual medical practice based on the BI-RADS index. To measure the efficiency and performance of this classifier [46,47] the standard measures of sensitivity (a metric for the ability to detect the disease in patients actually suffering from the disease) and specificity (a metric for the ability to not detect the disease in patients actually not suffering from the disease), were used. Furthermore, a global precision metric was added that measures the general performance of the classifier, in this case through the Matthews correlation coefficient (Mcc) [48,49,50]. On the other hand, additional and complementary metrics to the usual ones were incorporated, such as the false negative rate (a metric for those cases incorrectly classified as not suffering from the disease), and the false positive rate (a metric for those cases that the classifier incorrectly identifies as suffering from the disease). Other values, such as the sensitivity per lesion (a metric for the fraction of correctly identified tumors) were, in this case, integrated into the metrics described above. All the previous values are collected in Table 9.
From the analysis of the results shown in that table, it is possible to draw different conclusions. Analyzing the values obtained in the calculation of the Global Risk on the study dataset, high values of sensitivity ($90.5\%$) and specificity ($89.81\%$) are observed, which also results in high values of the Matthews correlation coefficient (0.7). Taking these data, it is reasonable to conclude that the system presents, in this proof of concept, unique and differentiating predictive capabilities supported by the concurrence of inferences. The ability to model knowledge through symbolic models, while incorporating the results of statistical inference as well, undoubtedly increases its performance as a classifier.
On the other hand, considering the metrics derived from the Corrected Global Risk, sensitivity values of $100\%$ and specificity of $60.19\%$ are observed with a Matthews correlation coefficient value of 0.44. It is clear that the predictive capabilities of the classifier decreased. However, in the same way, its cause is also evident: as mentioned before, the correction was not intended to improve the results over the real prediction, but to bring them closer to the usual medical and diagnostic practice. In other words, the goal is that the system can behave in a standardized and recognizable way for the healthcare team that uses it. Thus, the classification strongly obeys the BI-RADS index and, based on it, supports subsequent decisions, which would guarantee $100\%$ success in the detection of the disease, even assuming an increase in the number of patients undergoing unnecessary confirmatory tests. The data obtained give a clear reflection of what has been said.
Therefore, the intelligent system has two characteristics that are differentiated and very useful in diagnosis. On the one hand, it has enhanced predictive capabilities with excellent results on the study dataset. On the other hand, the system can adapt these results to the usual behavior of medical teams in the diagnosis of breast cancer and maximize the detection of all suspected cases. The potential of use and diagnosis of the system are thus highlighted and verified in the proof of concept, which thus fulfills the main objective of its realization.
Despite all this, it should be noted that those are only preliminary results derived from the proof-of-concept analysis on the study dataset. It is foreseeable, and reasonable, that the incorporation of new data, unrelated to the reliability and low uncertainty of the data used, decreases the predictive capacity of the system, although the correction strategy can always guarantee detection.
## 3. Results
In this section, the intent is not to validate the proposed intelligent system, but rather to present a practical case study of its performance to illustrate its potential use in the clinical setting as well as its ease of use. As commented in the previous section, the scope of our work only spans the proof-of-concept stage, so this example, derived from it, just aims to show the application workflow using data from a new patient. Thus, it is of note that the patient’s data analyzed in this scenario were not involved in the statistical model training process.
## 3.1. Data Collection
A summary of the findings in the mammography, as well as the general data of the patient, are presented in Table 10. With the intention of comparing the results of the model with the real clinical picture presented by this patient, it is essential to bear in mind that this was a patient who, after diagnostic tests, was found to have breast cancer.
Having fed the data into the app, it was then processed by the intelligent system.
## 3.2. Data Processing
Once the data were submitted to the application, they were processed by the proposed intelligent decision support system. Firstly, the Symbolic Risk indicators and Statistical Risk indicator were determined. As for the Symbolic Risks, values of $89.97\%$, $99.98\%$ and $0\%$ were obtained for R1, R2 and R3, respectively. As for Statistical Risk, it had a value of $25.61\%$.
These risks were then aggregated by means of Equation [1]. It was assumed that all the Symbolic Risks were equally influential. However, since the third risk had a null value, the weights associated with each Symbolic Risk were as follows: ω1=ω2=0.5, ω3=0. Equation [4] shows the numerical calculation of the Global Risk, which in this case, because of its upper bounded value, presented a value of 100. [ 4]RG=0.5·89.97+0.5·99.98+0·log1025.61 ≥100→ RG=100 A screenshot of the Stage #2 panel in the application is shown in Figure 4, in which the obtained risk values are displayed.
## 3.3. Global Risk Correction
Having obtained the Global Risk, in Stage #3, it was corrected by applying a weighting according to the BI-RADS level assigned by a specialist. Since the BI-RADS level assigned was 4B and the Global Risk already was the maximum value, the Corrected Global Risk value was identical, with a value of 100. The obtained Corrected Global Risk value can be seen in the Stage #3 box in Figure 4.
## 3.4. Warning Generation and Decision Making
Lastly, the risk assessment was carried out. A value higher than the second threshold was found, generating a status of maximum alert, as can be seen in the Stage #4 box in Figure 4. A recommendation was made to the medical team to carry out tests to verify the potential diagnosis. As this example shows, the correction confirmed the suspicion derived from the inferential process that, without any doubt, placed the patient at high risk of suffering from breast cancer. In this case, the correction based on BI-RADS could even be considered unnecessary, but it was carried out in order to consider and adapt the system to the team’s standard diagnostic criteria.
The system’s recommendation was consistent with the verified patient’s condition.
## 4. Discussion
Breast cancer is currently the world’s major diagnosed tumor disease, overtaking lung cancer to be one of the leading causes of death among women. The early detection of this pathology is crucial in reducing its associated impact by means of population screening programs. These consist of mammography screening of high-risk groups in order to detect potential cases and treat them as quickly as possible if needed, thus improving diagnoses and reducing the mortality rates associated with the disease. When interpreting and assessing mammograms, it is standard practice to use the BI-RADS system, establishing a common lexicon as well as a series of levels for categorizing the patient’s lesion, in an attempt to represent the likelihood that a malignant case of cancer is present. Despite this, the interpretation and evaluation process of mammography findings involves a certain degree of variability associated with the experience and training of the medical team in charge of their interpretation. This directly influences the diagnosis and the next steps to be taken regarding each patient.
In this paper, a new intelligent system for clinical decision support in breast cancer is presented as a result of the evolution of the architecture of the intelligent system proposed by the authors in Casal-Guisande et al. [ 25]. The aim is to adapt it to the particular needs of medical teams in order to facilitate its future validation and improve its performance. The knowledge bases were adapted to the medical team, optimizing the information flows and customizing the system to their particular needs.
Breast cancer diagnosis is a multi-variable problem, in which a series of variables are used to determine whether or not a patient is suffering from the condition. The conventional approach usually employed in these cases, which is the most common in terms of state-of-the-art knowledge, relies on the use of single inferential approaches, either statistical or symbolic. In line with the latest works by the authors [17,25,27], it has become increasingly common and convenient to jointly use inferential approaches of a heterogeneous nature, both statistical and symbolic. In this case, this is carried out through the joint use of a series of fuzzy inferential engines and a machine learning algorithm for classification, representing the diagnostic procedure of breast cancer from different but complementary approaches that seek to represent the same reality. However, from the point of view of the architecture of the proposed intelligent system, and in contrast to the previous work proposed by the authors in Casal-Guisande et al. [ 25], the use of the symbolic and statistical inferential engines is not sequential, but concurrent. This allows the simultaneous determination of a series of risk indicators associated with the different blocks, the Symbolic Risks and the Statistical Risk, which are associated with the risk of suffering from breast cancer.
This new approach uses the knowledge needed for calculation through two parallel and concurrent inferential processes, which represents a considerable departure from the use of such knowledge into a single inferential process, the outputs of which could feed further processes. An individual analysis of the different risks could allow the medical team to gauge the risk of a patient developing cancer. However, the individual inferential capability of the different engines deployed is the only one that would be given preference. Nevertheless, in this paper, thanks to the concurrence of the inferential engines, an approach that gives priority to the joint use of the engines can be opted for by using an empirical expression, namely Equation [1], to facilitate the joint interpretation of the engines through a risk indicator, the Global Risk. This indicator is based on the weighted sum of the Symbolic Risks, each one representing the main groups of findings present in the mammography (masses and calcifications, as well as asymmetries and distortion of the architecture), which allows a medical team to fine-tune the significance and contribution of each of the findings groups towards the aggregated risk, represented by the Global Risk indicator. The ability to make this adjustment is a remarkable feature, since it allows customizing and tailoring the use of the system to suit each medical team, making it more versatile.
Moreover, Statistical Risk has a multiplicative effect, increasing the existing risk level (obtained through the weighted sum of the Symbolic Risks), thereby increasing the hazard level in suspicious cases. With this approach, the different types of risk can be aggregated in a simple and efficient way, incorporating an additional layer to the risk layer associated with the reasoning, thus increasing the risk level in the case that a pattern similar to that of the cancer cases used to train the classification machine learning algorithm is detected.
Undoubtedly, one of the greatest advantages of these inferential models is their almost unlimited capabilities to incorporate knowledge into their knowledge base, expressly formalized or simply in the form of a dataset. This knowledge can always be expanded and used in the prediction once it has been properly analyzed.
Notably, the BI-RADS level assigned in this study by the medical team to each patient does not have a direct influence on the inferential processes. In contrast to the previous work proposed by the authors, it is not an input to the inferential processes. In this case, this assigned BI-RADS level allows weighting the Global Risk level obtained by means of the use of an analytical function, thus making possible to reduce or increase the risk level obtained in accordance with the opinion of the medical team.
Hence, the proposed approach is not only a reframing of the already published breast cancer diagnostic process, but an entirely novel conceptualization of it. The shift from a sequential flow of knowledge to a concurring flow is a remarkable breakthrough. Whereas the use of a sequence of inference processes guarantees a preservation of information, adopting a concurrent sequence of inference, fostered by the same knowledge, provides a notable diversifying effect in addition to the aforementioned preservation of information. Regardless of the inferential model used, its prediction results should be unique when it aims at a diagnosis given the same initial input. Fluctuation will always imply an unbounded increase in the uncertainty of the process. Whereas in the sequential approach this could be corrected by factorial techniques, a concurrent approach requires interpretation of the outputs obtained from the inferential processes.
Said interpretation, which is not free of variability, should be carried out, at least initially, through analytical models that adjust the correspondence between their actual values and the predictions. Indeed, the presence of analytical solutions reduces the influence of the generalist artificial intelligence approach by simplifying, if not linearizing, a deductive process. Yet, in this case, the aim is not to find an analytical expression for the prediction, but rather to look for one that will allow the predictions to be combined. Subsequent empirical analysis aims to limit the uncertainty of variability by using simple mathematical relationships, which may seem somewhat perplexing. The use of probabilistic models, Bayesian classifiers, or management of this uncertainty through fuzzy logics, while effective in slightly improving the robustness of the expressions, would not represent a substantial change, as it would be impossible to find realistic relationships between the degrees of certainty of the explanatory variables, in this case the risks, and the explained variable, here the presence or absence of cancer.
Perhaps once a significant number of triads of variables and labels are gathered, annotated and classified, it might be possible to carry out multivariate analyses and find implied causal relationships. However, in a concept proposal such as the one presented in this paper, this question is hardly feasible. Without doubt, this is a novelty, although it is also a weakness (perhaps the most representative of all) of the current formulation.
All the circumstances discussed so far constitute a clear departure from the previous work proposed by the authors, while also representing a first in this field of study.
In addition to all the above, the conceptualization of the proposed intelligent system, supported by the use of symbolic inferential approaches (through the use of expert systems) and statistical inferential approaches (through the deployment of a classification machine learning algorithm), allows for the improvement and optimization of decision-making processes. In this case, this knowledge usually resides in specialized medical teams, so this app facilitates achieving a common diagnostic process by medical teams with different training and experience. Likewise, the intelligent system carries out an implicit control and management of the uncertainty present in the diagnostic process, both in its epistemological and random aspects, as well as considers ambiguity and interaction [51,52]. It does so through the joint use of the aforementioned symbolic and statistical approaches, a trend that is becoming pervasive in the field of intelligent systems.
Besides all the advantages and matters discussed so far, the use of an intelligent system such as the one proposed in this work presents important improvements in the diagnostic and healthcare fields. Determining a percentile risk indicator for breast cancer is a very valuable metric, helping the medical team with the arduous task associated with the early diagnosis of a possible case of breast cancer. This in itself reduces, as far as possible, the false positive and false negative rates during the screening stage, decreasing the associated costs and improving upon the quality of the services rendered. However, although this reduction in false classifications is transcendental in order to avoid unnecessary tests to patients, with the consequent relief not only in their state of mind but also in costs and medical processes, the incorporation of the Global Risk correction endows the intelligent system with a unique empirical approach. This means that, although having a diagnostic method with high precision and success is the main motivation of intelligent clinical decision support systems, by incorporating analytical and empirical approaches into their architecture, we succeed in bringing their capabilities closer to the usual medical practice. With this, and whenever the healthcare team considers it necessary, they can maximize detection, sacrificing diagnostic accuracy even in those cases where the system clearly classifies a patient as healthy but their BI-RADS index raises some suspicion. The corrective approach does not improve the classifier, but increases its trust on the part of the team, who can see their own criteria reflected in the system’s predictions, even when these, for example, are excessively conservative. The system could, allegedly, produce minor discrepancies, especially in doubtful cases, based on this correction, although it would always allow the medical team to observe the real prediction, compare, learn and reason based on it.
On the other hand, for the proposed system, in addition to the difficulties previously expressed and related to the reduction of diversification associated with the use of the empirical corrective approach, there are other limitations that must be commented. Obviously, the first one is associated with the formalization of knowledge and the generation of the knowledge base required by expert systems. This can become a significant handicap when it comes to generating the set of declarative rules necessary to activate the inferential mechanisms of the fuzzy logic used. Along with this, it should be noted that the concurrent counterpoint of symbolic inference and statistical inference, although less dependent on the express formalization of knowledge, implies that predictions cannot be explained and, therefore, has no plausible chain of rationality. The combination of inferential models, even hybridized, can mean an extraordinary improvement in the accuracy of diagnostic classifiers, but it is mainly the explicability of reasoning through the formalization of existing knowledge that should be considered.
## 5. Conclusions
In this paper, a novel intelligent system to support the breast cancer diagnostic process has been presented. For this purpose, the system employs, both jointly and concurrently, symbolic inference approaches (through the deployment of expert systems) and statistical inference approaches (through a statistical classifier), by means of which it is possible to determine a percentage risk metric related to the risk of suffering from breast cancer. The novelty of this system, besides the joint use of symbolic and statistical inferential approaches, lies in the way these are combined. This is achieved by means of an empirical expression that accommodates the nature of the data, as well as the possible and subsequent correction of the risk indicator using a weighting according to the BI-RADS level assigned, which allows the opinion of the medical team to be taken into account in the recommendation generated by the system.
The intelligent system was implemented in a software tool, developed based on data from the School of Medicine and Public Health of the University of Wisconsin-Madison, demonstrating its usefulness through a practical case, highlighting its simplicity and potential for further application once it is validated. In this regard, the system is currently undergoing adaptation and maturation phases in order to validated in the near future in clinical settings in order to establish its validity and reliability.
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|
---
title: Electroacupuncture Alleviates Depressive-like Behavior by Modulating the Expression
of P2X7/NLRP3/IL-1β of Prefrontal Cortex and Liver in Rats Exposed to Chronic Unpredictable
Mild Stress
authors:
- Fang Pang
- Yunhao Yang
- Siqin Huang
- Zhixue Yang
- Zhengwei Zhu
- Dongmei Liao
- Xiao Guo
- Min Zhou
- Yi Li
- Chenglin Tang
journal: Brain Sciences
year: 2023
pmcid: PMC10046261
doi: 10.3390/brainsci13030436
license: CC BY 4.0
---
# Electroacupuncture Alleviates Depressive-like Behavior by Modulating the Expression of P2X7/NLRP3/IL-1β of Prefrontal Cortex and Liver in Rats Exposed to Chronic Unpredictable Mild Stress
## Abstract
Depression is a complex clinical disorder associated with poor outcomes. Electroacupuncture (EA) has been demonstrated to have an important role in both clinical and pre-clinical depression investigations. Evidence has suggested that the P2X7 receptor (P2X7R), NLRP3, and IL-1β play an important role in depressive disorder. Our study is aimed at exploring the role of EA in alleviating depression-like behaviors in rats. We therefore investigated the effects of EA on the prefrontal cortex and liver of rats subjected to chronic unpredictable mild stress (CUMS) through behavior tests, transmission electron microscopy, Nissl staining, HE staining, immunohistochemistry and Western blotting. Five weeks after exposure to CUMS, Sprague-Dawley (SD) rats showed depression-like behavior. Three weeks after treatment with brilliant blue G (BBG) or EA, depressive symptoms were significantly improved. Liver cells and microglia showed regular morphology and orderly arrangement in the BBG and EA groups compared with the CUMS group. Here we show that EA downregulated P2X7R/NLRP3/IL-1β expression and relieved depression-like behavior. In summary, our findings demonstrated the efficacy of EA in alleviating depression-like behaviors induced by CUMS in rats. This suggests that EA may serve as an adjunctive therapy in clinical practice, and that P2X7R may be a promising target for EA intervention on the liver–brain axis in treatment of depression.
## 1. Introduction
Depressive disorder is a crippling condition that substantially affects psychosocial functioning and reduces life quality [1]. It involves both emotional and physiological components and can cause significant distress and impact daily functioning. Diagnosis requires the presence of symptoms such as persistent low mood or anhedonia. Emotion dysregulation is considered a crucial aspect of depression, as emotions have the ability to influence behavior [2]. Thus, it is essential to comprehend the neural mechanisms behind the impaired inhibitory control, which is prevalent in several psychopathologies and mood disorders, including depression, anxiety, and fear conditioning [3,4]. Emotion regulation has been identified as a central process in both the research and treatment of depression [5].
The intricate interplay between emotions and the brain is facilitated by a number of neural systems spanning from the brainstem to the prefrontal cortex (PFC). The PFC, as a significant nerve center of thinking and behavior regulation in the brain, involves the regulation of emotions, and mediation of cognitive processes, such as the formation of intentions, goal-directed behavior, and attentional control [6], and has emerged as one of regions most consistently impaired in major depressive disorder [7]. The impairment manifests as over or hypo-activation in affective and cognitive tasks requiring emotional or stress regulation, or cognitive control [8]. Recent studies have provided evidence supporting a strong association between mitochondria and the metabolism of kynurenine (KYN). Additionally, it has been observed that malfunction of mitochondria, as well as activation of the tryptophan (Trp)-KYN system, are contributing factors in the development of neuropsychiatric conditions such as depression [9]. Despite extensive research into the neural circuits underlying depression, both in animal models and in human patients, the exact mechanism remains a topic of debate due to the highly heterogeneous nature of depression in terms of its phenomenology, etiology, and pathophysiology [10]. Advances in neuroimaging technology have nonetheless provided valuable insights into the neuroanatomical brain circuits associated with mood disorders [11]. Common themes have emerged, including alterations in volume [12], gray matter density [13] and activity levels across a network of regions including PFC, hippocampus, and amygdala. PFC plays a crucial role in acquisition of fear learning through interactions with the amygdala, hippocampus, and other key neural structures, collectively forming the neural network of fear conditioning [7,14,15,16,17]. Additionally, the relationship between depression and neuroinflammation has been widely recognized [18], as increased microglia activation has been observed in depression-related brain regions, including the PFC [19].
The connection between chronic liver disease and depression has been well established for a long time [20]. As a metabolic disorder, nonalcoholic fatty liver disease and depression share several risk factors, especially chronic systemic inflammation [21,22]. The incidence of depression is three to four times more common in patients with chronic hepatitis than in the general population [23]. Extrahepatic clinical manifestations of chronic hepatitis are commonly associated with the onset of depression—for example, amplified somatic symptoms, exacerbation of functional impairment, and even reductions in treatment adherence and health-related quality of life [24]. Systemic inflammation is pivotal in liver disease, and it is also well documented in patients with depression. Depressive symptoms, behavior, and inflammation are changed by peripheral cytokine signals such as IL-1β via peripheral immune cell-to-brain signaling, notably the activation of macrophages and microglia [25].
In recent years, the activation of microglia and macrophages is mediated by purinergic signaling, which is via the membrane-bound adenosine triphosphate (ATP) receptor, such as the P2X7R [26,27,28]. P2X7R [29] is most commonly associated with activating inflammatory mechanisms in several inflammatory diseases such as liver injury and depression. Furthermore, P2X7R is a critical player in activating the NLRP3 inflammasome, which in turn acts as a signal for the release of IL-1β [30,31]. Interestingly, NLRP3 inflammasome is the pathogenesis of both chronic liver disease and depression [22,32,33]. Based on previous observations [34,35], BBG is a selective and non-competitive P2X7R antagonist with high blood–brain barrier permeability. BBG, meanwhile, is a derivative of a widely used food additive, more than 1 million pounds of which are consumed yearly in the United States [36].
Few researchers have addressed the problem of pathological changes in the liver in patients with depression. The current research focus is not only on the role of EA in the PFC of depressive-like rats but also on pathological changes in the liver. Furthermore, EA may be engaged in the liver–brain axis via P2X7R affecting rats with depressive-like behavior.
In 1996, the World Health Organization added depression to acupuncture indication. Electroacupuncture (EA) combines traditional acupuncture with modern scientific techniques to generate a stable output pattern that overcomes individual differences between therapists. Several existing meta-analyses support EA’s safety and significant clinical efficacy in alleviating depressive symptoms. In addition, patients with depression prefer complementary therapy to drugs when a previous drug treatment has been invalid. Considering the above, our hypotheses are as follows: 1. the physiological function of the liver also alters with depressive-like behavior exposed by CUMS; 2. this alteration is related to the simultaneous activation of P2X7R/NLRP3/IL-1β expression in the prefrontal cortex and the liver; 3. EA alleviates depressive-like behavior and physiological changes in the liver and prefrontal cortex by inhibiting P2X7R/NLRP3/IL-1β expression.
## 2.1. Animals and Group Allocation
Rat husbandry and animal strategies were carried out in accordance with the recommendations and protocols authorized by the Institutional Animal Care and Use Committee of Chongqing Medical University. Male SD rats weighing 150–180 g (purchased from the Experimental Animal Center of Chongqing Medical University, Chongqing, China) were used in all experiments. These rats were housed in a temperature-controlled (22 ± 2 °C) and light-controlled (12:12-h light:dark cycle) room and provided with free admission to food and water except on the experimental days. Before the experiment started, the rats were habituated to the experimental conditions for one week. There were control, CUMS, BBG, and EA groups. We used the CUMS [37,38,39] method to establish depressive model rats. First, the rats were randomly separated into two groups: the control group ($$n = 10$$) and the model group ($$n = 80$$). The two groups of rats were not housed in the same room. During the modeling period, all except the control group were subjected to CUMS for 5 weeks. Because not all rats developed MDD after CUMS, we allocated more rats to the model group. Moreover, the MDD model rats were randomly divided into the CUMS group ($$n = 10$$), BBG group ($$n = 10$$), and EA group ($$n = 10$$). The rats received saline (vehicle for BBG, $0.01\%$ in saline; Sigma) except for the BBG group to which was applied intraperitoneal injections of BBG ((Sigma Aldrich, St. Louis, MO, USA) 50 mg/kg/d) at 9:30 a.m. on 6 consecutive days per week for 3 weeks [40,41] (Figure 1A).
## 2.2. CUMS Procedure
Eighty SD rats in the model group were housed in 80 cages. They were exposed to one of 7 mild stressors daily in a random sequence for 5 weeks [42]. The following stressors were used in the experiment (Table 1).
## 2.3. EA Group
The rats were lightly restrained by hand to minimize stress during EA treatment. Acupuncture needles were inserted bilaterally at the Baihui (GV20), Yintang (GV29), and Ganshu (BL18) acupoints [43,44] to a depth of 5 mm. EA treatment caused slightly visible muscle twitching around the area of insertion. Electrical stimuli were delivered for 20 min at 2 Hz using an Hwato SDZ-III electronic acupuncture treatment instrument. EA treatments were performed from 9:00 a.m.–11:30 a.m. on 6 consecutive days per week for 3 weeks.
## 2.4. Behavioral Tests
The body weight [45] and sucrose preference were tested 9 times from 0th to 8th week; open field test and forced swimming test were carried out only once at the end of the 8th week.
## 2.4.1. Sucrose Preference Test
Sucrose preference test (SPT) [46] was performed on 4 consecutive days, and included a two-day sugar water adaptation phase, a one-day water deprivation phase, and a one-day test phase. On the first two days (at 8 a.m.), every rat was once simultaneously introduced to two bottles containing 100 mL of either $1\%$ sucrose solution or tap water. The test phase was separated into two parts. At 8 a.m., two bottles were given to each rat, and then after 12 h, the location of the bottles was changed. The rats had free access to food during the test. The test was repeated 8 times with training in between.
## 2.4.2. Open Field Test
The open field test (OFT) was performed in a 100 cm × 100 cm × 40 cm black plexiglass box with a black floor as previously described [47]. At the beginning of the test, the rats were individually placed in the same location in the corner of the testing box facing the wall. The amount of time they spent in the center zone, the percent of time spent resting, and their average speed of movement in the box were recorded for 5 min, with the data analyzed by means of the SMART video tracking system. After every animal was tested, the equipment used was cleaned with $75\%$ alcohol to eliminate olfactory cues.
## 2.4.3. Forced Swimming Test
In the forced swimming test (FST) [48,49], the rats were gently placed individually in a 20 cm diameter glass cylinder filled with 23 ± 1 °C water to a depth of 40 cm for 6 min. Data were recorded from the third minute to the end of the trial. Immobility and struggling behavior during the 4 min swimming session were recorded and subsequently analyzed using the SMART video tracking system. Struggling was described as multiple actions of the rat’s forepaws that broke the water, and immobility was used to describe a rat who floated without struggling, solely making those movements essential to preserving the head above the water.
## 2.5. Sample Collection
After the end of the behavior tests, rats from each group were fasted for 24 h. After isoflurane inhalation anesthesia, rats were fixed on the operating table, the chest was opened to expose the heart. A perfusion needle was obliquely inserted into the aorta along the apex of the heart, and the right atrial appendage was open and perfused with 0.09 mol/L PBS solution. When the liquid being pumped from the right atrial appendage became clear, the brain was removed on ice, and the prefrontal cortex and right lobe of the liver were separated. The right brain tissue and right liver lobe of 3 rats from each group were fixed with $4\%$ paraformaldehyde. The remaining prefrontal cortex and liver tissues were quickly placed in a liquid nitrogen tank and then stored in a −80 °C freezer. Three rats from each group were randomly selected, and perfusion with $1\%$ glutaraldehyde solution was performed until the limbs and tail of the rats were stiff. After decapitation, the brains were removed, and the right prefrontal cortex was trimmed to obtain an approximately 1 × 1 × 1 mm sample, and fixed in $2.5\%$ glutaraldehyde at 4 °C.
## 2.6. Transmission Electron Microscopy
The prefrontal cortices were rinsed with 0.1 mol/L phosphoric acid solution 3 times and fixed with $1\%$ osmic acid solution 3 times, then dehydrated in graded alcohol and acetone solutions at 4 °C, and again with $100\%$ acetone at room temperature. After embedding, the tissues were cured in the oven. The tissues were sectioned with an ultrathin slicer at 70 nm thickness and stained with $3\%$ uranium acetate and lead citrate to observe microglia in the prefrontal cortex.
## 2.7. HE Staining
The right lobe of the liver was fixed with $4\%$ paraformaldehyde for 24–48 h, and was then dehydrated in gradient ethanol solutions, embedded in wax, sliced into paraffin sections with a thickness of 5 μm, dewaxed in water, stained with hematoxylin and eosin successively, and sealed. Ten fields were randomly selected from each section, and the liver morphology was observed under a light microscope.
## 2.8. Nissl Staining
Paraffin-embedded tissues were cut at a thickness of approximately 5 μm. After drying, the slides were dewaxed with xylene, dehydrated in gradient ethanol solutions, stained in toluidine blue solution at 56 °C for 20 min, rinsed with distilled water for 5 min, bathed in xylene until transparent for 10 min, sealed with neutral gum, and dried in a ventilated place. Ten fields were observed from each group under an optical microscope, and the results were analyzed by a researcher who did not know the grouping information.
## 2.9. Immunohistochemistry
Paraffin-embedded tissues were cut at a thickness of approximately 4 μm. The sections were dried, dewaxed with xylene, dehydrated in gradient ethanol solutions, and washed with distilled water. Next, the sections were cooled, washed with PBS, incubated with $3\%$ hydrogen peroxide solution for 25 min, washed with PBS, and blocked with $3\%$ BSA at room temperature for 30 min. Paraffin sections of the prefrontal cortex were incubated with an Iba1 (1:1000) primary antibody, and paraffin sections of the liver were incubated with a CD68 (1:500) primary antibody, overnight at 4 °C. The sections were incubated with secondary antibody at room temperature for 50 min after washing with PBS. DAB color development was performed under a microscope after washing in PBS. When the staining was obvious, the sections were washed with tap water, and color development was terminated. After dehydration with anhydrous ethanol and clearing with xylene, the sections were sealed with neutral gum. At least 10 visual fields were randomly observed under a microscope with a 10× lens, and brown CD68- or Iba1-positive cells were observed in the cytoplasm. The optical density of the staining was analyzed with Image Plus software.
## 2.10. Western Blotting
Western blotting was used to measure the expression of P2X7, NLRP3, pro-caspase-1, cleaved-caspase-1, pro-IL-1β, cleaved-IL-1β, and ASC in the prefrontal cortex and liver. Twenty-four hours after the end of the behavioral experiment, the rats were anaesthetized by intraperitoneal injection of pentobarbital sodium., and PBS was perfused through the apex of the heart. The bilateral prefrontal cortex, hippocampus and right lobe of the liver were completely removed. Total protein was extracted from the prefrontal cortex and liver with a tissue protein extraction kit (lysate:phenylmethylsulfonylfluoride = 99:1). The protein concentration was determined by the BCA method, the concentration of each sample was adjusted, and the samples were denatured for 10 min. A total of 25 μg of each protein sample was separated by $12\%$ polyacrylamide gel electrophoresis, and then transferred onto a 0.45 μm or 0.2 μm nitrocellulose membrane. The membrane was incubated with a rabbit anti-NLRP3 monoclonal antibody (3:1000), rabbit anti-P2X7 monoclonal antibody (1:1000), rabbit anti Caspase-1 monoclonal antibody (1:500), rabbit anti-ASC monoclonal antibody (1:1000), or rabbit anti-IL-1β monoclonal antibody (1:1000) overnight at 4 °C, 3 times in TBST for 10 min each the next day, incubated with secondary antibody (goat anti-rabbit antibody, 1:10,000), for 1 h at room temperature, rinsed 3 times with TBST for 10 min each, then developed with chemiluminescence reagent and imaged using an imaging system. Software was used for absorbance analysis.
## 3. Statistical Analyses
All experiments were conducted in a randomized manner. The data sets were analyzed for normality and homogeneity of variance, and parametric post hoc statistical analysis was performed to confirm a priori power calculations. GraphPad Prism 8.0 software (GraphPad, Inc., La Jolla, CA, USA) and SPSS 25.0 (IBM, Armonk, NY, USA) were used for analysis, and $p \leq 0.05$ was considered significant. The data are reported as the mean ± SD. The data of body weight and SPT data were tested by repeated-measures two-way ANOVA, and the OFT, SPT, immunohistochemistry, and Western blotting data were analyzed by one-way ANOVA. Tukey’s post-hoc test was used to compare the different groups.
## 4.1. Behavior
The depressive and anxiety-like behaviors—including those in the SPT, FST, and OFT—and body weight of the different groups were examined, to explore the effects of EA on the CUMS rat model. The SPT and body weight measurements were performed nine times, and the FST and OFT were performed only once before execution.
## 4.1.1. Sucrose Preference and Body Weight
As shown in Figure 1B and Table 2, SPT was not different at baseline. Significant differences in the changes of SPT were found among groups from the 1st week to the 8th week. The repeated-measures analysis of variance showed a statistically significant effect of group ($F = 5538.038$, $p \leq 0.01$, η2 = 0.998), a statistically significant effect of time points ($F = 1970.66$, $p \leq 0.01$, η2 = 0.982), and a statistically significant interaction between time points and group ($F = 309.616$, $p \leq 0.01$, η2 = 0.963).
At the end of the 3rd week of CUMS exposure, the SPT of the CUMS group, BBG group and EA group was decreased by approximately $30\%$. At the 4th and 5th weeks, the SPT of the CUMS group, BBG group and EA group did not decrease further. At the 6th week, i.e., the week after BBG or EA treatment for 1 week, the SPT of the BBG group was significantly higher than that of the EA group ($p \leq 0.05$), but there was no difference in SPT between the EA group and the CUMS group ($p \leq 0.05$). At the 8th week, the SPT of the two groups was markedly higher than that of the CUMS group ($p \leq 0.01$), and the SPT of the BBG group was clearly greater than that of the EA group ($p \leq 0.01$). Rats in the CUMS group, BBG group, and EA group displayed decreased sensitivity to reward stimulation and pleasure when they were exposed to CUMS. However, both the BBG and EA exhibited a significant reversal of the decreased sucrose consumption compared to the CUMS group at the 7th week and 8th week of the experiment, as demonstrated by statistically significant results.
The body weight of rats in each group increased, but the body weight gain of the control group was significantly faster than that of the other three groups. Rats exposed to CUMS were observed to have decreased food intake, and showed signs of reduced activity, fur shedding, and reduced luster.
The results of repeated-measures analysis of variance showed a statistically significant effect of group ($F = 79.915$, $p \leq 0.01$, η2 = 0.869), a statistically significant effect of time points ($F = 2750.444$, $p \leq 0.01$, η2 = 0.987), and a statistically significant interaction between time points and group ($F = 14.080$, $p \leq 0.01$, η2 = 0.540). After 5 weeks of CUMS exposure, the weight gain of the rats in the CUMS group, BBG group and EA group was slow, but there was no difference among the groups ($p \leq 0.05$). After BBG or EA treatment for 1 week, there was no difference in body weight between the rats in the CUMS group, BBG group and EA group, this being significantly lower than the control group ($p \leq 0.01$). After BBG and EA treatment for 3 weeks, the body weight of the BBG group and EA group was significantly higher than that of the CUMS group ($p \leq 0.01$), but still significantly lower than that of the control group ($p \leq 0.01$), and there was no difference in body weight between the two groups ($p \leq 0.05$), as shown in Figure 1C and Table 3.
## 4.1.2. OFT and FST
One-way ANOVA showed a significant effect of CUMS on the performance in the FST and OFT (Figure 1, Table 4).
Rats among different groups exhibited significant differences in FST which tests for behavioral despair, including immobility time and total distance. The immobility time and total distance of the CUMS group was significantly greater compared with that of the control group ($p \leq 0.01$). Compared with the CUMS group, the immobility time and total distance travelled of the BBG group and EA group were significantly lower ($p \leq 0.01$, $p \leq 0.01$). Of note, there was no significant difference between the BBG group and EA group ($p \leq 0.05$), as shown in Figure 1D,E and Table 4.
Results of the OFT showed that compared with the CON group, rats in the CUMS group exhibited significant differences in locomotor activity (percent of time resting, percent of time in center zone, and average speed) ($p \leq 0.01$). Compared with the CUMS group, the activity ability of the rats in the BBG group and EA group to adapt to a new environment was significantly increased ($p \leq 0.01$, $p \leq 0.01$, $p \leq 0.01$, $p \leq 0.01$, $p \leq 0.01$, $p \leq 0.01$). There was no difference between BBG and EA treatment in the percentage of resting time ($p \leq 0.05$); however, percent of time spent in the center zone and average speed of the rats in the two groups were notably elevated following the BBG and EA treatment ($p \leq 0.05$, $p \leq 0.01$), as shown in Figure 1F–H and Table 4.
## 4.2.1. Microglial Morphology of PFC
The ultrastructure of the blood–brain barrier and microglial morphology were observed by transmission electron microscopy. In the control group, the blood–brain barrier was intact, microglia were non-oval-shaped, and there was no edema around the cells and blood vessels. In the model group, the blood–brain barrier was loosely arranged, the microglia were oval-shaped, and edema could be seen around the cells and blood vessels. Cell and vascular edema were slightly alleviated in the BBG group and EA group compared with the CUMS group, and microglia were oval shaped in the BBG group and EA group (Figure 2A).
## 4.2.2. Nissl Staining of PFC
Nissl staining showed that in the control group, neurons exhibited a normal morphology, there were a large number of Nissl bodies, and neurons showed a uniform distribution and dark blue staining. In the CUMS group, the shape of neuronal cells was irregular, the number of Nissl bodies was decreased, neurons were lightly stained, and some Nissl bodies were unevenly distributed. The morphology of neurons in the BBG group and EA group was irregular. Image collection and Nissl body counting were performed by investigators blinded to the group information. The number of Nissl bodies in the CUMS group was significantly lower than that in the control group ($p \leq 0.01$), and the number of Nissl bodies in the BBG and EA groups was higher than in the control group ($p \leq 0.01$, $p \leq 0.05$). However, there was no significant difference in the number of Nissl bodies between the BBG group and EA group ($p \leq 0.05$). These results are shown in Figure 2B,D and Table 5.
## 4.2.3. Iba1 Expression in PFC
The results showed that, compared with that in the control group, the expression of Iba1-positive cells in the prefrontal cortex of the CUMS group was increased ($p \leq 0.05$). Compared with that in the CUMS group, the expression of Iba1-positive cells in the BBG group and the EA group was decreased ($p \leq 0.05$). These results are shown in Figure 2C,E and Table 5.
## 4.2.4. Effects of EA on the Expression of P2X7R, NLRP3, and IL-1β Related Protein in PFC
In the prefrontal cortex, the protein expression of P2X7R, pro-caspase-1, cleaved-caspase-1, and ASC were significantly increased in the CUMS group compared with those in the control group ($p \leq 0.01$), and the protein expression of NLRP3, pro-IL-1β, and cleaved-IL-1β was increased in the CUMS group ($p \leq 0.05$). Compared with that in the CUMS group, the protein expression of P2X7R in the BBG group was significantly decreased ($p \leq 0.01$), and the protein expression of NLRP3, pro-caspase-1, cleaved-caspase-1, pro-IL-1β, and cleaved-IL-1β in the BBG group was decreased ($p \leq 0.05$). The protein expression of NLRP3, P2X7R, pro-caspase-1, pro-IL-1β, and cleaved-IL-1β were decreased in the EA group ($p \leq 0.05$), but the protein expression of cleaved-caspase-1 and ASC did not change significantly ($p \leq 0.05$). These results can be seen in Figure 3A–H and Table 6.
## 4.3.1. HE Staining of Liver
In the control group, liver structure was normal, hepatic lobules were intact, and hepatocytes were of the same size and arranged neatly. There were obvious pathological changes in liver structure in rats in the CUMS group, as hepatocytes were hypertrophic, their arrangement was disorderly, and inflammatory cell infiltration was observed. Liver structure in the BBG group and EA group was basically normal, although there was inflammatory cell infiltration (Figure 4A).
## 4.3.2. CD68 Expression in Right Liver Lobe
Immunohistochemistry showed that, compared with that in the control group, the expression level of CD68 in the liver tissue of the CUMS group was significantly increased ($p \leq 0.01$), suggesting that macrophage infiltration was increased. Compared with that in the CUMS group, the expression level of CD68 in liver tissue of the EA group and the BBG group was significantly lower, suggesting that macrophage infiltration was decreased ($p \leq 0.05$) as shown in Figure 4B,C and Table 5.
## 4.3.3. Effects of EA on the Expression of P2X7R, NLRP3, and IL-1β Related Protein in Liver
In the liver, the protein expression of NLRP3, P2X7, pro-caspase-1, and ASC were significantly increased in the CUMS group ($p \leq 0.01$), and the protein expression of cleaved-caspase-1, pro-IL-1β, and cleaved-IL-1β were increased in the CUMS group compared with the control group ($p \leq 0.05$). Compared with that in the CUMS group, the protein expression of NLRP3, P2X7, pro-caspase-1, leaved-caspase-1, pro-IL-1β, cleaved-IL-1β, and ASC in the BBG group was decreased ($p \leq 0.05$). The protein expression of NLRP3, P2X7, pro-caspase-1, and cleaved-IL-1β were decreased in the EA group ($p \leq 0.05$), but there was no difference in the protein expression of cleaved-caspase-1, or ASC ($p \leq 0.05$). There was no significant difference in NLRP3, P2X7, pro-caspase-1, cleaved-caspase-1, pro-IL-1β, cleaved-IL-1β or ASC protein expression between the BBG group and the EA group ($p \leq 0.05$) as shown in Figure 3I–P and Table 7.
There was no significant difference in NLRP3, P2X7, pro-caspase-1, cleaved-caspase-1, pro-IL-1β, cleaved-IL-1β or ASC protein expression between the BBG group and the EA group in PFC and liver ($p \leq 0.05$). The results showed that EA inhibited the expression of inflammatory factors in the prefrontal cortex and liver of rats exposed to CUMS.
## 5. Discussion
Previous research has verified that the procedures used to induce CUMS in rats not only lead to depression-like emotions and behaviors but also accurately simulate the pathological process of depression in a rat model [42,50]. In our study, we aimed to validate this CUMS model and further investigate the potential mechanisms of the antidepressant effect of EA in the PFC and liver. Our findings revealed that CUMS induced depression- and anxiety-like behaviors and caused central and peripheral inflammation. Moreover, our results indicated that EA effectively alleviated these depression- and anxiety-like behaviors, suppressed the expression of P2X7R/NLRP3/IL-1β, reduced the excessive activation of microglia in the PFC and macrophages in the liver, decreased the release of IL-1β, and regulated central and peripheral inflammation. Although few studies have explored the effects of depressive-like models on rat liver cell morphology, our study provides new evidence for the antidepressant effect of EA and offers potential avenues for further exploration in the treatment of depression.
In this study, changes in behavioral assessment at different time points were observed to evaluate the states of nutrition and anhedonia [51,52]. At the start of the study, the four groups at week 0 before intervention showed consistent baseline values. After exposure to CUMS, a significant difference in behavior was observed compared to the control group. The body weight of the CUMS, BBG, and EA groups increased slowly over time, with a significant difference from the CON group by the 8th week. Although a previous study [53] has shown that obesogenic diets can cause depression- and anxiety-like behaviors in rodents, little research has explored the relationship between depression, anxiety and weight loss. Our study indicates that EA effectively regulated the weight loss of CUMS rats and had a positive therapeutic effect. Therefore, we will focus on further indicators to understand the specific mechanisms of the changes in the body weight in future experiments. In previous studies, the SPT was used to evaluate the depression-like behavior in rats through anhedonia, while the FST evaluated the depression-like behavior in rats through despair. Our data showed that exposure to CUMS induced depression-like behavior in the SPT and FST, indicated by decreased sucrose preference and increased immobility time, which was reversed by EA and BBG at the 8th week, suggesting that EA alleviated the CUMS-induced depression-like behavior. Anxiety is a common symptom associated with depression [54]. The OFT is widely used to measure anxiety behavior. Our results indicated that EA alleviated anxiety behavior, such as central zone exploratory behavior.
Previous studies have primarily centered on the role of hippocampal neurons in the development of depression-like behavior in rats and patients with depression [55,56]. The PFC is known to play a critical role in regulating and modifying emotion [57]. Our study and other previous studies have demonstrated that CUMS exposure induces inflammatory injury in the prefrontal cortex [50,58]. The effect of acupuncture to improve PFC function in conditions such as Parkinson’s disease, pain and depression has been well documented [59,60]. Our results are consistent with previous findings [61,62] that indicate EA can effectively inhibit the overactivation of microglia, downregulate P2X7R/NLRP3/IL-1β expression, and reduce IL-1β release in the PFC. Similarly, administration of BBG intraperitoneally produced similar results. This provides basic experimental evidence for exploring the impact of EA on emotion-related neural circuitry.
Chronic liver disease and depression appear to share common risk factors and signaling pathways [63]. Depression is considered a metabolic disease related to liver function, and it has been shown that apolipoprotein B, very-low-density lipoprotein cholesterol, triglycerides, unsaturated fatty acids, tyrosine and abnormal metabolism are related to depression [64,65]. It is often accompanied by liver disease, and can further affect liver function, and even aggravate liver disease [66]. It is widely acknowledged that liver steatosis, characterized by the accumulation of fat in liver cells, often leads to a proinflammatory environment and activates microglial cells [67]. Moreover, it has been suggested that liver steatosis can also trigger a systemic hyperinflammatory state, which can result in damage to the PFC—a phenomenon frequently observed in depression [68]. Our results showed that the morphology of liver cells was significantly altered in CUMS-induced depression in rats and that the increase in the expression of CD68, upregulation of P2X7R/NLRP3/IL-1β expression, and increase in the release of proinflammatory cytokine IL-1β were all improved by EA as well as the intraperitoneal injection of BBG. At present, there are no clinically available drugs that target liver macrophages; thus, these data provide a new idea and strategy for the treatment of depression and liver diseases through targeting liver macrophages. Considering the relationship between liver and PFC function and the occurrence and development of depression, the liver–brain inflammation axis may become a new target for diagnosing and treating depression.
There is growing evidence that stress, both psychological and physical, can activate immune and inflammation processes, which can contribute to the development of depressive symptoms. It is well-established that stress activates microglia, which is a hallmark of neuroinflammation in the central nervous system [27,69]. P2X7R is mainly expressed in microglia [62,70]. P2X7R is the primary driver of inflammation, and the secretion of several proinflammatory cytokines and chemokines depends on the activation of P2X7R by large amounts of ATP released from damaged CNS cells [71]. In particular, P2X7-induced NLRP3 activation has been widely studied in innate myeloid cells (monocytes, macrophages and dendritic cells). Several signaling pathways downstream of P2X7 receptor activation are associated with the induction of NLRP3 inflammasomes [72]. NLRP3 inflammasomes are considered important mediators of depression [73]. NLRP3 inflammasome activation is the pathogenesis of both chronic liver disease and depression. As previously noted, our study and other previous studies have shown that CUMS exposure induces inflammatory injury in both prefrontal cortices [8,50]. In various CUMS-induced models, electroacupuncture has shown a good anti-inflammatory effect [74,75]. Therefore, P2X7R may be a potential target for EA in treating depression.
EA, an integration of traditional Chinese medicine and electronic therapy, has demonstrated efficacy in the treatment of depression and amelioration of depressive symptoms. Based on Chinese medicine theory, “liver controlling dispersion”, EA at Baihui (GV20), Yintang (GV29), and Ganshu (BL18) have positive effects on behavior, the prefrontal cortex, and liver cell function in CUMS-induced depression-like behavior in rats. Baihui (GV20) and Yintang (GV29) are the core acupoints according to the latest research, which is based on data mining technology, on the acupoint characteristics in the treatment of depression by modern acupuncture [76,77]. However, single acupuncture at either GV20 or GV29 fails to alleviate the state of depression [78]. The brain (referred to in traditional Chinese medicine theory as “the spirit’s house”) has the function of regulating memory, feelings, and emotions which is consistent with the theoretical understanding of the role of the cerebral cortex in regulating the human spirit and thinking in modern medicine. The Shu acu points, which are located on the back, are the regions where the qi of the viscera is infused and are chiefly used to treat disorders of related viscera. Liver dysfunction is the main reason for depression according to TCM. According to clinical evidence [79], we selected the Ganshu (BL18) acupoint. The results of the present study have identified that CUMS-induced depression-like behaviors and that EA at GV20, GV29, and BL18 exhibited antidepressant- and antianxiety-like effects.
Our experiment confirmed our hypothesis, but there are some limitations. The amygdala, hippocampus, thalamus and prefrontal cortex play a synergistic role in cognitive function, learning, memory, emotion, and other functions. However, we did not study in detail the relationship between the hippocampus and prefrontal cortex in depression. As technology continues to advance, the use of brain imaging techniques and real-time brain function recording has become increasingly prevalent in assessing the effects of different therapies on brain function in individuals with depression. This objective evidence provides greater validation for the effectiveness of electroacupuncture (EA) in the treatment of depression. Further investigation is required to determine the extent to which EA can improve depression-related neural circuits through the application of advanced neuroimaging methods, and to evaluate whether EA can enhance liver metabolism and mitigate metabolic changes in CUMS-induced depression-like behavior in rats. While the current findings are derived in a laboratory setting, additional randomized controlled trials will be necessary to establish clinical efficacy.
## 6. Conclusions
In conclusion, the results of the study provide evidence to support the hypothesis that rats displaying CUMS-induced depression-like behavior exhibit damage to the prefrontal cortex and liver, characterized by an inflammatory state triggered by microglia and macrophages. The antidepressant effect of EA may be achieved through its ability to modulate inflammation by downregulating the expression of P2X7R/NLRP3/IL-1β and reducing the release of IL-1β. This study highlights the potential role of EA in alleviating depression symptoms associated with CUMS and provides new experimental evidence for the use of EA as an add-on therapy in the treatment of depression and the co-occurrence of chronic liver disease and depression.
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|
---
title: Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic
Basis of Type 2 Diabetes
authors:
- Aditya Saxena
- Nitish Mathur
- Pooja Pathak
- Pradeep Tiwari
- Sandeep Kumar Mathur
journal: Biomolecules
year: 2023
pmcid: PMC10046262
doi: 10.3390/biom13030432
license: CC BY 4.0
---
# Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes
## Abstract
Insulin resistance (IR) is considered the precursor and the key pathophysiological mechanism of type 2 diabetes (T2D) and metabolic syndrome (MetS). However, the pathways that IR shares with T2D are not clearly understood. Meta-analysis of multiple DNA microarray datasets could provide a robust set of metagenes identified across multiple studies. These metagenes would likely include a subset of genes (key metagenes) shared by both IR and T2D, and possibly responsible for the transition between them. In this study, we attempted to find these key metagenes using a feature selection method, LASSO, and then used the expression profiles of these genes to train five machine learning models: LASSO, SVM, XGBoost, Random Forest, and ANN. Among them, ANN performed well, with an area under the curve (AUC) > $95\%$. It also demonstrated fairly good performance in differentiating diabetics from normal glucose tolerant (NGT) persons in the test dataset, with $73\%$ accuracy across 64 human adipose tissue samples. Furthermore, these core metagenes were also enriched in diabetes-associated terms and were found in previous genome-wide association studies of T2D and its associated glycemic traits HOMA-IR and HOMA-B. Therefore, this metagenome deserves further investigation with regard to the cardinal molecular pathological defects/pathways underlying both IR and T2D.
## 1. Introduction
The global prevalence of insulin resistance (IR) has been estimated to range from 15.5 to $46.5\%$ among adults [1]. It has been identified as a central pathophysiological factor of several endocrine–metabolic disorders, such as type 2 diabetes (T2D), high blood pressure, dyslipidemias, polycystic ovary syndrome, metabolic syndrome (MetS), cardiovascular diseases, etc. More significantly, it precedes and could lead to T2D, which now affects over 422 million people globally and accounts for 1.5 million deaths annually [2].
T2D is the most frequent subtype of diabetes and it is characterized by alterations of blood glucose levels due to varying combinations of IR and a relative deficiency of insulin secretion by the pancreatic β cells. If untreated, T2D may escalate to various microvascular and macrovascular complications such as retinopathy, diabetic kidney disease, peripheral neuropathy, atherosclerotic vascular disease, etc. Its underlying pathogenesis is complex and includes an approximately 40–$70\%$ contribution of genetic factors [3,4,5]. The majority of the genetic risk loci, however, have been found to decrease insulin secretion rather than its action [6].
IR is considered to be the major underlying pathophysiological defect in both obesity and early-stage T2D. It is also the major mechanistic link between them. This pathway is supposed to be driven by “adiposopathy” or “sick fat”, characterized by adipocyte hypertrophy and infiltration of pro-inflammatory cells (such as M1 macrophages and dendritic cells, etc.) into the expanding adipose tissue. The resulting heightened free-fatty-acid release, pro-inflammatory cytokine flux within the pathological adipose tissue, and ectopic fat deposition set the stage for heightened IR and lipotoxicity-mediated impairment in pancreatic β cells, together leading to overt T2D.
However, healthy obese individuals without complications of IR or T2D are frequently observed, indicating that the pathophysiology of IR and its transition to T2D is much more complicated than was earlier thought. Furthermore, the monogenetic disorder lipodystrophy, which is characterized by high IR status despite the substantial absence of adipose tissue, makes the pathophysiology of IR more elusive. At the molecular level, there might be some core set(s) of genes and gene-driven pathways that are shared by both IR and diabetes. *These* genes and pathways might also be responsible for the transition of IR to T2D. Therefore, identification of these genes would enable us not only to devise further clinical strategies to mitigate these diseases, but also to predict the transition from IR to T2D.
High-throughput gene expression methods such as DNA microarray and RNA-seq have proven useful in deciphering the pathophysiological processes occurring at the molecular level in various disease states. A huge number of gene expression datasets are now available in public databases like the Gene Expression Omnibus (GEO) and Array Express, which are routinely mined for the extraction of biological insights to further fundamental and clinical understandings. Problems associated with these studies include their low sample sizes due to ethical and other constraints, and the simultaneous measurements of tens of thousands of genes in these experiments. This high-dimensional nature makes the findings of these studies difficult to generalize to other related studies, which is a prerequisite for their clinical utility. One solution to this problem is the statistical meta-analysis of multiple gene expression datasets across related studies, which provides a robust set of metagenes that could be more confidently exploited for diagnostic and therapeutic purposes [7].
Various microarray meta-analysis studies have been conducted to decipher the molecular pathophysiology of IR and T2D. For example, Jung et al. [ 2018] conducted a meta-analysis of seven IR microarray studies and obtained drug signatures for two antidiabetic medicines, metformin and thiazolidinediones [8]. They also validated their signatures through cross-species analysis. Saxena et al. ( 2017–18) conducted combined system-level meta-analysis of IR and T2D datasets and highlighted adiposopathy—the inflammation of adipose tissue—as a central mechanism that leads to T2D [9,10]. However, one problem with large scale meta-analyses is that they yield a long list of metagenes which are difficult to interpret biologically, and it is practically difficult to exploit them further for clinical intervention due to the pleiotropic nature of the majority of genes in a genome.
In recent years, due to the availability of high volumes of clinical and molecular data, machine learning (ML) methods (also including deep learning methods) have begun to gain wide acceptance in the development of data-driven solutions to biomedical problems. These methods provide various mathematical and statistical models, which are provided with labeled datasets for supervised learning to enable them to predict the label or class of new data instances. Additionally, they can also be utilized for feature selection by identifying features or parameters in training datasets, which can significantly contribute to the models’ predictions. Machine learning has been used to develop predictive models for T2D using clinical features [11,12,13,14]; however, its use to predict T2D from gene-level features is relatively limited. Furthermore, the development of machine learning models based on IR gene expression could allow prognostication of the onset of T2D, and could be considered a novel approach with prospective clinical utility in T2D treatment.
It this study, we first attempted to obtain a robust IR metagene signature through meta-analysis of multiple microarray gene expression studies comprising samples of NGT and IR individuals.
Gene expression datasets are high-dimensional in nature and contain expression measurements of tens of thousands of genes across a limited number of samples. ML models based on these datasets may suffer problems with overfitting, which limits their predictive capability. To alleviate this problem, we first reduced the number of metagenes using an ML method—Least Absolute Shrinkage and Selection Operator (LASSO)—and obtained a core set of metagenes, hereafter termed key metagenes. These key metagenes were subsequently used to develop five baseline machine learning models based on LASSO, Support Vector Machine (SVM), eXtream Gradient Boosting (XGBoost), Random Forest (RF), and Artificial Neural Network (ANN).
We explored the potential of metagenes and key metagenes to explain the causation of T2D using GSEA-based enrichment analysis and disease-domain-specific tools, respectively. We also checked whether these key metagenes might represent the genetic basis of T2D, based on their presence in T2D and other glycemic traits identified in genome-wide association studies (GWASs). In addition to this direct evidence supporting the robustness of our ML models, we also checked for the presence of upstream transcription factors of these key metagenes in the GWASs as indirect evidence for the involvement of these genes in the genetic basis of T2D.
Our best-performing ML model demonstrated high prediction power in both cross-validation and test datasets. To sum up, the findings of present study endorse the use of our ML model in predicting the occurrence of T2D in IR individuals on the basis of the transcription signature of key metagenes. This ML model deserves further investigation of potential applications of these key metagenes in the prediction of transition from NGT to T2D in IR individuals, and of potential drug targets for the prevention and treatment of T2D.
## 2.1. Selection of Microarray Datasets and Meta-Analysis
The GEO database at the National Centre for Biotechnology Information was searched for gene expression studies including NGT and IR human tissue samples. As various insulin-responsive organs and tissues are involved in IR development, including pancreas, liver, skeletal muscle, kidneys, brain, small intestine, adipose tissue (subcutaneous and visceral), and peripheral blood mononuclear cells (PBMCs), we selected a total of nine gene expression datasets which profiled gene expression levels in these tissues (Table 1).
The GEOquery package [15] from R Bioconductor was used to download series matrix files for each selected study. Meta-analysis was carried out using the web-based tool Network Analyst [16]. To adjust for the batch effect among datasets, the ComBat function in the SVA R package was used. To derive meta-signatures, Fisher’s method was used on gene-level log-transformed p-values after adjustment of batch size. Metagenes were selected based on p-values < 0.05.
## 2.2. Enrichment Analysis of Metagenes
Computational validation of IR metagenes was conducted using gene set enrichment analysis [17] methods to assess whether these genes were enriched in phenotype-relevant biological processes. The GSEAPreranked method in GSEA allows the submission of metagenes ranked by their Z-scores. Enrichment analysis of metagenes was conducted using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) collections of the Molecular Signatures Database (MSigDB) [18] to biologically validate the obtained metasignature; the results of the enrichment analyses were later visualized using EnrichmentMap [19] in the Cytoscape [20] environment.
## 2.3. Selection of Key Metagenes and Their Biological Validation for T2D
The batch-effect-adjusted combined expression matrix was then filtered against the metagenes to obtain the IR meta-expression matrix. This matrix was subsequently used as the input for the ML method LASSO, which also performs feature selection and has been widely used in various biomedical studies to reduce numbers of genes [21,22,23,24]. The reduced set of metagenes obtained from LASSO was termed the “key metagenes”. As GO terms are not disease-specific, in order to explore the implications of these key metagenes in the disease domain, we used two tools—GLAD4U (Gene List Automatically Derived For You) [25] and DisGeNET [26]—to compile up-to-date gene–disease association information using text mining.
## 2.4. GWAS Evidence for Involvement of Key Metagenes in T2D
In order to further validate the roles of the 72 identified genes in diabetes, we checked the Type 2 Diabetes Knowledge Portal (https://t2d.hugeamp.org/; accesed on 23 November 2022) which houses genes that have been found to be associated with type 2 diabetes and other glycemic traits such as HOMA-IR and HOMA-B across multiple GWASs. The p-values for associations with phenotypes were calculated using the MAGMA (Multi-marker Analysis of GenoMic Annotation) method. In addition to finding GWAS-based direct associations of key metagenes, we also obtained a list of upstream regulators (transcription factors) of these genes using the Expression-to-Kinase (X2K) [27] webserver, which infers upstream regulatory networks from the signatures of differentially expressed genes through a combination of transcription factor enrichment analysis, protein–protein interaction network expansion, and kinase enrichment analysis.
## 2.5. Construction of Machine Learning Models and Their Performance Evaluation
The IR meta-expression matrix was further filtered for the key metagenes and the resulting matrix was then used to train five baseline ML models. SVM has been used to classify gene expression data for many years [28,29]; however, its use as a predictive model in the bioinformatics community has also begun to gain momentum in recent times [30]. XGBoost and RF are ensemble-based learning methods that assimilate multiple tree models to build a robust learner model. These methods have been used for the prediction and classification of gene expression data [31,32]. Deep learning models, particularly ANN, have also been used extensively in bioinformatics [33,34,35,36,37,38,39]. Details of the ML methods and their tuning parameters are presented in the Supplementary File.
The GEO dataset GSE64577, comprising 64 human adipose tissue samples, was used as a test dataset to check the performance of the ML models. These samples were drawn from a Mexican-American population under the Veterans Administration Genetic Epidemiology Study (VAGES) [40] and included 38 T2D and 26 NGT individuals as per their fasting plasma glucose level (T2D > 100 mg/dL).
We used five-fold cross-validation and the performance of the ML models was evaluated using the following parameters: accuracy (A), precession (p), recall (r), sensitivity (Sen), specificity (Spe), F1 score, and Matthews correlation coefficient (MCC) from the confusion matrix for both the validation and the test dataset. These values were calculated using the indicators TP (true positive), FP (false positive), FN (false negative), and TN (true negative). A=TP+TNTP+TN+FP+FN p=TPTP+FP r=TPTP+FN F1=2×p×rp+r MCC=TP∗TN−FP∗FNTP+FPTP+TNTN+FPTN+FN where TP is the number of IR/T2D individuals predicted to be IR/T2D by the model; FN is the number of IR/T2D individuals incorrectly predicted to be NGT by the model; TN is the number of NGT individuals predicted to be NGT by the model; and FP is the number of NGT individuals incorrectly predicted to be IR/T2D by the model.
## 3. Results
NetworkAnalyst identified an IR metasignature of 2574 genes ($p \leq 0.01$), containing 1488 upregulated (Z-score > 0) and 1086 downregulated (Z-score < 0) genes. The derived IR meta-expression matrix for these metagenes had 367 expression datasets consisting of 182 NGT and 185 IR samples.
GSEA-based functional analysis of the IR datasets showed enrichment in various GO terms such as “RNA metabolism”, “chromosome”, “transmembrane transport”, and “ubiquitin protein activity” (Figure 1a). The majority of these processes were found to be downregulated, likely due to weak insulin signaling in peripheral tissues in the setting of insulin resistance. Enriched KEGG pathways were “oxidative phosphorylation”, “type 2 diabetes”, and “ribosome” (Figure 1b). Enrichment of these pathways therefore provides biological validation of the IR signature.
A total of 72 key metagenes were obtained from the meta-signature through LASSO-based feature selection. GLAD4U analysis showed robust enrichment of various diabetic terms (Figure 2) such as “diabetes mellitus”, “diabetes mellitus type 2”, “diabetes mellitus type 2 and obesity”, “diabetic ketoacidosis”, etc. ( marked red). Various cardiovascular complication terms were also found, such as “ventricular premature complexes”, “shortened QT interval”, “ventricular fibrillation”, etc. ( marked blue). Traditionally, diabetes mellitus is considered to be a disorder of glucose metabolism, whereas the roles of the disorders of lipid metabolism and adipose tissue dysfunction in the pathogenesis of atherosclerotic vascular disease are well known. However, this classical picture is changing rapidly due to appreciation of the fact that IR driven by adipose tissue dysfunction induces beta-cell dysfunction both directly via its exhaustion and indirectly via lipotoxicity. Therefore, adipose tissue also plays a major role in the pathogenesis of T2D.
Additionally, cardiovascular diseases and phenotypes and terms related to renal and hepatic complications such as kidney diseases, fatty liver, carcinoma hepatocellular, urogenital neoplasm, and urination disorder were also found to be enriched. Six of the key metagenes—INSR, MAP3K5, NDUFB8, NDUFS1, SDHB, and UQCRC2—are involved in nonalcoholic fatty liver disease [41] (hsa04932), which is caused by a defect in insulin suppression of free fatty acid (FAA) disposal due to the induction of insulin resistance. Furthermore, a reduction in nitric oxide production due to IR has been reported to inhibit bladder smooth muscle cell growth and consequently lead to urination disorder [42].
DisGeNET analysis highlighted that 27 of the IR key metagenes (~$37\%$)—ALDH6A1, APOB, ARID5B, ATP2B2, ATXN1, B2M, CAT, CFB, CHI3L1, COL8A1, EIF2AK2, FHL2, GATM, HDAC4, HIPK3, HOMER1, IGFBP5, INSR, MAP3K5, MMP9, NDUFS1, RASSF7, REV3L, SLC19A2, UROD, WAS, and ZEB1—were found to be associated with T2D or its related variants such as alloxan, autoimmune, monogenic, neonatal, ketosis-prone, sudden-onset, insipidus, fibrocalculous pancreatic, phosphate, gestational, post-transplant, lipoatrophic, streptozotocin, brittle, and maturity-onset diabetes, among others (Figure 3).
To assess the pathway-based functional assignment for these genes, we selected the human collection of WikiPathways and found enrichment of the following 15 pathways ($p \leq 0.05$), including “insulin signaling”, which has a conspicuous role in the IR phenotype (Table 2).
The pathway “FTO obesity variant mechanism” is relevant to the T2D phenotype as SNPs in the Fat mass and Obesity-associated (FTO) gene have been found to be associated with adiposity and risk of obesity in multiple populations [43]. Due to involvement of FTO in energy homeostasis, it could link IR to T2D. Disturbance in the “folate metabolism” pathway has been reported to induce glucose and lipid metabolism disorders in animal studies [44], suggesting its involvement in IR. The pathway “selenium micronutrient network” also influences adipocyte physiology and modifies the risk of developing T2D. In one study, high Se and hs-CRP concentrations were found to be associated with high HbA1c levels in various BMI groups [45]. Another pathway, “vitamin B12 metabolism”, is also relevant as high prevalence of low B12 levels have been shown in European ($27\%$) and South Indian ($32\%$) patients with type 2 diabetes (T2D) [46]. Angiopoietin like 8 (ANGPTL8) is involved in the regulation of lipid metabolic processes and triglyceride homeostasis, so the term “Angiopoietin Like Protein 8 regulatory pathway” is also relevant to IR pathophysiology. AGE/RAGE signaling increases oxidative stress to promote diabetes-mediated vascular calcification through activation of Nox-1 and decreased expression of SOD-1. Different pharmacological interventions are underway to regulate the AGE/RAGE pathway to decrease the severity of this diabetic complication [47]. Enriched pathways therefore showed the biological validity of the key metagenes.
Key metagenes were further checked against T2D GWASs to ascertain their implications in the genetic architecture of the disease. *Seventeen* genes were mapped to genetic loci associated with T2D in GWASs. *These* genes were CFB, PRSS3, ARID5B, REV3L, CROCC, HOMER1, NDUFB8, DDX17, DCAF8, EIF2AK2, ZEB1, ATF6B, UROD, MAP3K5, MMP9, STK24, and WNT4; three genes with HOMA-IR-related loci, MATK, FHL2, and PHKB; and two with HOMA-B-related loci, HS3ST1 and ALDH6A1. *The* gene INSR has been found to show association with all the three traits.
The human genome consists of 24,500 protein-coding genes, of which 4049 have been identified to have T2D GWAS signals ($p \leq 0.05$), so the probability of picking a T2D gene by chance would be 0.17 (i.e., $\frac{4049}{24}$,500).
To check the statistical robustness of our key metagenes, we estimated the probability of finding 17 T2D genes by chance across 72 key metagenes using a binomial test ($$n = 72$$, $x = 17$, $$p \leq 0.17$$) and obtained a low probability ($$p \leq 0.03$$), and thus concluded that our key metagene set was nonrandomly enriched with T2D genes.
We carried out further analysis by mapping of the regulatory elements of the key metagenome using the X2K webserver. The webserver analyzed the key metagenome of IR and reported 83 transcription factors (including 2 key metagenes). Interestingly, 42 of these transcription factors were found to be associated with T2D and its related traits in GWASs. In other words, we could provide in silico evidence of the mapping of key metagenome genes to T2D and related loci identified in GWASs and shed light on their potential role in the genome-to-phenome trajectory of T2D. Therefore, our ML model based on expression profiles of these 72 key metagenes could be considered highly robust for predicting diabetes.
All five machine learning models demonstrated good predictive capabilities upon five-fold cross-validation. Detailed information of the various evaluation indicators is shown in Table 3.
The classification accuracy was visualized using receiver operator characteristic (ROC) plots for all the models and all showed an area under the curve (AUC) > $75\%$, with ANN returning the maximum AUC of $95\%$ (Figure 4).
The best-performing model, ANN, was subsequently evaluated for its performance on the test dataset. Out of 64 samples (38 T2D: 26 NGT), it predicted 28 samples as true positive, 21 samples as true negative, 10 samples as false negative, and 5 samples as false positive, and showed $73\%$ sensitivity, $80\%$ specificity, and an overall F1 score of 0.78 (overall accuracy $76\%$). Despite the high biological noise in the gene expression data, differences among microarray platforms, and also marked differences in the molecular pathologies of IR and T2D due to chronic hyperglycemia in the latter condition, our IR-based ML model achieved fairly good accuracy in differentiating individuals with T2D from non-T2D people.
## 4. Discussion
In this study, we developed a machine learning model that could differentiate an individual with T2D from a NGT individual on the basis of expression of a key metagenome of IR in insulin-responsive tissues, particularly adipose tissue, with almost 70 to 75 percent accuracy in a cross-sectional study. This key metagenome was created via the meta-analysis of publicly available databases of transcription profiles of various insulin-responsive tissues obtained from IR non-T2D individuals with diverse ethnic backgrounds. *The* genes in this key metagenome enrich several known pathways of not only diabetes mellitus, but also cardiovascular disease, specifically cardiac arrhythmias, kidney diseases, fatty liver, carcinoma hepatocellular, urogenital neoplasm, urination disorder, etc. *The* genetic loci regulating the expressions of most of these genes are mapped to genomic regions identified in GWASs as associated with diabetes and related traits. Therefore, these findings suggest that the key metagenome of insulin resistance (IR) also plays a functional role in the genetic susceptibility to T2D via the enrichment of several known pathophysiological molecular pathways of T2D in metabolically active tissues. As the IR key metagenome can be mapped to GWAS-identified genetic loci of diabetes, this metagenome is unlikely to be merely an acquired pathophysiological adaptation to impaired insulin signal transduction; rather, it is a primary genetic defect.
There are several other implications of these findings. For example, T2D and IR are complex traits and their genetic susceptibility is generally believed to be omnigenic in nature. However, in this study, out of almost 30,000 genes in the human genome, only 72 genes were found to be of functional significance at the transcriptome level to the manifestation of genetic susceptibility to T2D via the known molecular pathways of IR. However, a limitation of the present study is that the source of this cross-sectional meta-analysis was data from publicly available depositories, and the mapping and functional roles were imputed in silico. Though we have estimated that our metagenes are associated with T2D GWAS signals, due to the nature of our study being cross-sectional, these gene expression results might not be free from reverse causation. Therefore, there is a need for validation of the role of this metagenome through deciphering of the genome-to-phenome trajectory of diabetes using replication studies in different insulin-responsive tissues.
If this metagenome proves to be a gold standard transcriptomic signature of IR in diabetes, then it would also deserve further inquiry regarding clinical applications like diagnostic molecular markers and drug target discovery. However, the most important and challenging question is as follows: How should these metagenes obtained from insulin-responsive tissues in research settings be measured/assessed in clinical practice? Therefore, from the clinical point of view, there is a need to identify not only the clinical, biochemical, and radiological parameters that show associations with the key metagenome, but also the gene sequence polymorphisms and the circulatory biomarkers associated with this metagenome.
IR and beta-cell dysfunction are two major pathophysiological defects in T2D. IR has traditionally been considered to be caused by environmental factors, whereas the genetic susceptibility to develop T2D possibly plays a major role in beta-cell failure. However, identification of the key metagenome of IR, its imputed mapping to T2D GWAS loci, and functional enrichment of known T2D pathways and validation in adipose tissues of T2D individuals suggests a role of genetic factors in driving IR. Additionally, this metagenome is derived from meta-analysis of several insulin-responsive tissue transcription profiles. Therefore, it points towards a common molecular thread shared by most metabolically active insulin-responsive tissues and it could serve as “the cardinal molecular pathology” of IR, T2D, and MetS. However, this concept deserves further investigation.
Another important finding of this study is that the genes in the key metagenome of IR enrich both diabetes- and cardiac-arrhythmia-related pathways. Therefore, they shed light on a common functional genomic defect underlying these two diseases. The relationship between diabetes and cardiac arrhythmias is complex and it is not yet fully understood. However, relationships between diabetes and arrhythmias have been reported [48]. There are several potential mechanisms of arrhythmia in diabetes, such as increased blood glucose levels, glucose fluctuation, hypoglycemia, autonomic dysfunction, alterations in the architecture of the heart including fibrosis, fat deposition, hypertrophy, etc. In addition, the finding of a key metagenome linking IR with cardiac arrhythmias in the present study points towards another novel molecular mechanism shared by these two diseases.
## 5. Conclusions
In conclusion, a machine learning model trained with the key metagenome of IR can differentiate individuals with T2D from NGT with moderate accuracy on the basis of transcription profiles of adipose and other insulin-responsive tissues. The mapping of this metagenome to GWASs identified loci of diabetes, and the enrichment of known molecular pathways of diabetes suggest a primary role of this metagenome in the pathogenesis of diabetes rather than merely a pathophysiological response to impaired insulin signaling. Therefore, this metagenome deserves further investigation in terms of the cardinal molecular pathological defects/pathways underlying both IR and diabetes.
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|
---
title: Microfluidic Electrochemical Glucose Biosensor with In Situ Enzyme Immobilization
authors:
- Nina Lokar
- Borut Pečar
- Matej Možek
- Danilo Vrtačnik
journal: Biosensors
year: 2023
pmcid: PMC10046266
doi: 10.3390/bios13030364
license: CC BY 4.0
---
# Microfluidic Electrochemical Glucose Biosensor with In Situ Enzyme Immobilization
## Abstract
The development and characterization of a microfluidic electrochemical glucose biosensor are presented herein. The transducer part is based on thin-film metal electrodes on a glass substrate. The biological recognition element of the biosensor is the pyrroloquinoline quinone–glucose dehydrogenase (PQQ-GdhB) enzyme, selectively in situ immobilized via microcontact printing of a mixed self-assembling monolayer (SAM) on a gold working electrode, while the microfluidic part of the device comprises microchannel and microfluidic connections formed in a polydimethylsiloxane (PDMS) elastomer. The electrode properties throughout all steps of biosensor construction and the biosensor response to glucose concentration and analyte flow rate were characterized by cyclic voltammetry and chronoamperometry. A measurement range of up to 10 mM in glucose concentration with a linear range up to 200 μM was determined. A detection limit of 30 µM in glucose concentration was obtained. Respective biosensor sensitivities of 0.79 nA/µM/mm2 and 0.61 nA/µM/mm2 were estimated with and without a flow at 20 µL/min. The developed approach of in situ enzyme immobilization can find a wide number of applications in the development of microfluidic biosensors, offering a path towards continuous and time-independent detection.
## 1. Introduction
The synergy of biosensors, electrochemistry and microfluidics has been signaling a new era in science and technology in recent years [1]. One example of this overlap is an electrochemical biosensor on a chip [2]. Compared to other analytical methods, the advantages of biosensors are on-site detection, real-time detection and ease of use. Biosensors are also useful for preliminary analyses before more complex and expensive analyses are performed. They also enable simultaneous detection of multiple analytes [3]. Recently, portable smartphone-based readers have been developed to monitor analytes in biofluids [4,5,6,7,8]. These also have many applications, with special emphasis on wearable biosensors [9,10]. Due to the diversity of biosensors, which comprises a variety of combinations of bio-recognition elements and transducer elements, a wide range of biosensor applications is possible [1]. Optical biosensors are among the most common types of biosensors [11,12,13]. One example of these is based on localized surface plasmon resonance phenomenon for the real-time detection of biomolecules [14,15,16,17]. In addition, reliable, practical and relatively inexpensive electrochemical biosensors have been developed [3,18]. In terms of the biological recognition element, electrochemical biosensors are of particular interest for the integration of enzymes that are highly selective for specific analytes [1]. Currently used commercial biosensors are still large in size, slow in operation, expensive and labor-intensive; thus, it is difficult to automate, integrate and miniaturize existing conventional devices for multiple applications. There are numerous strategies to design and fabricate miniaturized biosensors. One of them is microfluidics, which has been widely explored [3,19,20,21,22]. Integrating a biosensor into a microfluidic chip brings miniaturization with extremely low sample consumption and continuous measurement of the analyte. The microfluidic approach also enables self-calibration with an internal analyte standard and the development of automated analyte control with a feedback loop based on real-time measurements of the analyte in the bioprocess. There is also the possibility of automatic selection or exchange between sensory elements [3,18].
Microfluidic biosensors are used to improve quality of life in various fields such as environmental monitoring [23,24], medicine [25,26], the food industry [27] and security and defense and drug discovery [19]. Some examples of microfluidic electrochemical enzyme biosensors are those for the measurement of cholesterol [28,29], thyroid drugs [30] and adenosine-5’-triphosphate [31].
In this paper, we present the development and characterization of a microfluidic electrochemical pyrroloquinoline quinone–glucose dehydrogenase (PQQ-GdhB) biosensor, which has the potential for continuous glucose monitoring in biopharmaceutical process control. Compared to other glucose-oxidizing enzymes, PQQ-GdhB has a long list of advantageous properties. PQQ-GdhB displays one of the highest catalytic efficiencies while remaining highly selective for glucose. In contrast to fungal glucose oxidase, the reaction mechanism with PQQ-GdhB does not depend on oxygen levels in the system and does not generate reactive byproducts such as H2O2. The PQQ cofactor is tightly bound with the GdhB and no additional soluble cofactors (i.e., NAD(P)+, FAD+) are needed for the reaction [32]. Successful electron transfer from PQQ-GdhB has been demonstrated to both solid-phase [33,34,35] and soluble electron acceptors [36]. The latter is also true when PQQ-GdhB is immobilized, which allows independent measurements of certain properties of electrochemical biosensors. In our work, PQQ-GdhB was immobilized on a gold electrode using a mixed self-assembling monolayer (SAM) of 6-mercaptohexanol and 11-mercaptoundecanoic acid (6-MCH+11-MUA), crosslinked by 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and N-hydroxysulfosuccinimide (EDC+S-NHS), similar to the approach presented in [37]. The main challenge in developing a microfluidic system for a biosensor was to integrate the biological components with the fabricated transducers and microfluidic components. To immobilize the enzyme only on the working thin-film electrode, several integration approaches can be found in the literature, such as laminar co-flow with a carefully selected electrode position in the microchannel [38], applying a potential to the electrode that electrophoretically attracts the biological component to the electrode [39], and the electro-click chemistry method in a completely packaged freestanding channel [40]. Herein, we developed a method for selective microcontact printing [41] of mixed SAM onto a thin-film gold working electrode (WE). The key element of microcontact printing is a polymeric stamp with a relief pattern. This stamp is “inked” and put in contact with the WE surface. The ink is transferred from the stamp to the substrate only in the area of contact. After that, a microchannel was formed on the electrode chip, so both the activation of 11-MUA and the immobilization of the enzyme were performed with a controlled flow of substances through the microchannel. In this way the fabricated biosensor chip can be prepared in advance, without the enzyme, which can be immobilized just in time, i.e., just before using the sensor, which keeps the sensing area dry and inactive until the test begins. This approach reduces the burden during a microfluidic biosensor fabrication, keeping the required reagents stored outside of the detection structure in suitable wet conditions. In addition, our approach using the microcontact printing method reduced the time for mixed SAM deposition from several hours [37] or even a day to a few minutes compared to the conventional deposition approach by immersing the electrodes in the solution. Finally, miniaturization of the biosensor was implemented and proved to be advantageous in terms of sensor response magnitude and analyte consumption.
## 2.1. Materials and Instruments
For the biological part of biosensor construction, the following chemicals were used: $97\%$ purity 7 mM 6-MCH (Merck, Darmstadt, Germany), $95\%$ purity 5 mM 11-MUA (Merck, Darmstadt, Germany), 200 mM EDC (Thermo Fisher Scientific, Waltham, MA, USA) and 50 mM S-NHS (Merck, Darmstadt, Germany). The enzyme solution was a mixture of 8 μL 4.2 μg/mL PQQ-GdhB from *Acinetobacter calcoaceticus* (Novartis Technical Operations, Mengeš, Slovenia) [36], 20 μL $95\%$ 5 μM pyrroloquinoline quinone (PQQ) (Merck, Darmstadt, Germany), 40 μL $97\%$ 10 mM magnesium chloride (MgCl2) (Merck, Darmstadt, Germany) and 732 μL phosphate-buffered saline (PBS) with pH 7.2 (Gibco, Thermo Fisher Scientific, Waltham, MA, USA). The optimal concentration of the enzyme was chosen based on preliminary immobilization tests. For this purpose, various dilutions of purified GdhB were used for immobilization onto gold plated inserts, which were placed into standard 96 microtiter plates. Mixed SAM was used for immobilization (see Section 2.2); however, immersion rather than printing was used. To compare initial velocities, the spectrofluorometric method according to reference [36] was modified so that the final electron acceptor was resazurin instead of 2,6-dichlorophenolindophenol. The fluorescence of the reduced resazurin was measured at the wavelengths 530 (excitation) and 590 (emission) nm.
For polydimethylsiloxane (PDMS) microchannel fabrication, a two-part kit Sylgard 184 (Dow Corning Corporation, Midland, MI, USA) was applied in the standard mixing mass ratio of 10 parts a pre-polymer (base) and 1 part a crosslinker (curing agent). Biosensor measurements were performed using the following chemicals: 2 mM ferri/ferro-cyanide ([Fe(CN)6]3−/4−) (Merck, Darmstadt, Germany) in 0.1 M potassium chloride (KCl) (Fluka, Bornem, Belgium) and 0.1 mM ferrocenemethanol redox couple (FcMeOH/FcMeOH+) (Merck, Darmstadt, Germany) in 0.1 M PBS with pH 7.2 (Gibco, USA), D-(+)-glucose (Merck, Darmstadt, Germany) dissolved in [Fe(CN)6]3−/4− in KCl solution or FcMeOH in PBS solution. For basic cleaning and rinsing, isopropyl alcohol (Microchemicals, Ulm, Germany) and high-purity deionized water (in situ prepared) were used.
Electrochemical measurements were carried out using a potentiostat, EmStat3+ Blue, equipped with the PSTrace 5.9 software (PalmSens BV, GA Houten, The Netherlands). In some experiments, miniature Ag/AgCl with a 3 M KCl reference electrode, model ET073 (eDAQ Pty Ltd., Denistone East, NSW, Australia), was applied. For fabrication of PDMS microchannels, a Si mold was made by deep reactive ion etching using a Bosch technique (System 100 ICP 180, Oxford Instruments Plasma Technology, Bristol, UK). The plasma surface treatment system Atto (Diener electronic GmBh, Plasma Surface Technology, Nagold, Germany) was used for surface modification.
## 2.2. Design and Fabrication of Microfluidic Electrochemical PQQ-GdhB Biosensor
Figure 1 shows the sequence of the main process steps in the development of a microfluidic electrochemical enzyme biosensor for the detection of glucose. These steps are further described below.
Step 1: Thin-film electrode chip. The electrode chip was designed to provide two triplets of thin-film electrodes to enable parallel run tests. To fabricate them, Cr/Au layers ($\frac{30}{120}$ nm) were sputtered onto a precleaned Pyrex substrate. WE, counter (CE) and reference (RE) Au electrodes, Au conductive lines and Au connection pads were defined by photolithography and subsequent selective wet etchings of Cr/Au layers. Similarly, RE made from Ti/Ag thin films ($\frac{40}{1200}$ nm) was patterned next to WE and CE on the Cr/Au layer. The Ag layer was then partially transformed to Ag/AgCl electrode by the chemical oxidation reaction with FeCl3, according to Equation [1] [42]:[1]Ag+FeCl3 ⇄ AgCl+FeCl2 To avoid the contamination of neighboring WE and CE Au electrodes, a selective drop-casting method was utilized to apply FeCl3 for 1 min on the Ag electrode to confine the reaction exclusively to the RE area. The resulting Ag/AgCl thin-film electrode is considered a quasi-Ag/AgCl reference electrode [18,43].
Step 2: Selectively mixed SAM deposition on WE. The electrode chip was thoroughly cleaned, starting with wet cleaning in isopropyl alcohol, followed by ultrasonic cleaning in a 3:2 volume ratio isopropyl alcohol–deionized water mixture, and finally cleaning with oxygen plasma for 30 s at a working pressure of 1.0 mbar and an RF power of 50 W. After cleaning, the WE was modified by a mixed solution of 6-MCH and 11-MUA in ethanol to form, via a strong Au-thiolate bond, an oriented monolayer with its carboxylic group located at the monolayer–air or monolayer–liquid interface. 6-MCH was included to ensure that there is enough space for the enzyme to bind to 11-MUA. This mixed SAM selective deposition was performed by microcontact printing. Separately fabricated stamp chips from PDMS with the same shape and size as the WEs were firstly cleaned with oxygen plasma to form a hydrophilic surface, on which mixed SAM was then deposited by a pipette drop. After a few minutes, excess mixed SAM was removed by gently drying with N2. The stamp with only a thin layer of mixed SAM was precisely aligned on the WEs of the electrode chip for 20 s. Afterward, excess mixed SAM was removed by thoroughly rinsing the entire electrode chip with ethanol, followed by ultrapure water and drying with N2.
Step 3: Microfluidic chip formation. The PDMS microchannel was fabricated by soft lithography using a replica molding technique. The mold was fabricated by deep reactive ion etching of a patterned Si wafer. The PDMS replica formed was then exposed to an oxygen plasma with 0.8 mbar pressure and 30 W power for 20 s. The activated PDMS surface was pressed to the glass electrode chip and was irreversibly chemically bonded. The microfluidic connection to the chip was made with the polymer Tygon tubes, which were connected to the PDMS microchannel via stainless steel tube connectors.
Step 4: Selective enzyme immobilization on WE. First, 11-MUA was activated by a crosslinker, which involved stepwise formation and replacement of terminal EDC and S-NHS in sequence to form an S-NHS ester. Secondly, the active S-NHS ester was replaced by the primary amines of PQQ-GdhB, thus immobilizing the enzyme via the amide bond. This was performed in situ, i.e., by continuous flow of the crosslinker and the enzyme within the microchannel at a flow rate of 2 µL/min with exposure time of 15 and 120 min, respectively. Finally, this resulted in selective enzyme immobilization on mixed SAM-coated WE.
Step 5: Placement of the electrode chip with microchannel into the housing. The biosensor chip was installed in a custom-designed polyethylene housing that contained spring gold-coated pin connectors to ensure a reliable electrical connection between the electrodes and potentiostat. This configuration enables two sets of measurements to be performed simultaneously or in a sequence. To avoid mechanical damage of the glass, a custom-made gasket from PDMS was provided. Precise alignment of the electrode chip in the housing was achieved with a groove in the base and three insertion metal pins on the cover part of the housing (not shown).
## 2.3. Experimental Setup
Two measurement modes were used to characterize the developed biosensors, namely the immersion mode (IM), which allows static measurements, and the microfluidic mode (MM), which allows either static measurements or measurements during continuous flow of the solution. Each mode was tested with two different electrode and microchannel designs, i.e., a “large” and a “small” biosensor design (see Figure 2).
Immersion mode (IM). An IM biosensor was used explicitly to optimize crucial steps in the development of the sensor, such as evaluating the cleaning quality of thin-film Au electrodes, establishing an appropriate method of mixed SAM deposition with the degree of enzyme immobilization on Au WE and for the characterization of the biosensor storage stability. IM refers to the biosensor system in which WE was the constructed thin-film electrode (Figure 1), placed at the bottom of the reservoir, while CE and RE were externally immersed into the electrochemical cell. CE was a Pt wire placed next to a miniature commercial Ag/AgCl RE with a liquid junction. For stability, both were attached to the housing via a proper mechanical support. The volume of the reservoir was 5 mL. It should be noted that a different enzyme immobilization strategy from that presented in Figure 1 was employed in conjunction with the IM biosensor. Here, a mixed SAM was activated by immersing the electrode chip with the thin-film electrodes separately into the solution of EDC and S-NHS for two hours, followed by immobilization of PQQ-GdhB. The enzyme was introduced by immersing the entire electrode chip into the enzyme solution for one day.
Microfluidic mode (MM). In addition to static (off-line) measurements, MM enables measurements during continuous flow chemistry, appropriate for, e.g., monitoring pharmaceutical processes using in-line, on-line or at-line systems. MM refers to all three thin-film electrodes being integrated into the electrode chip and encapsulated within the PDMS microchannel. Microfluidic biosensor construction is presented in detail in Figure 1. The flow of the solution through the channel was provided by a syringe pump with a flow rate between 0 and 100 µL/min.
Large and small biosensor design. The biosensor electrodes were of two different designs, large and small electrode design, as shown in Figure 2. The diameters of the circular WE were 3.0 and 0.8 mm for the large and small designs, respectively. Due to the different electrode designs, the microchannel width was adjusted to each of them. The microchannels had the same length and height, 20 mm and 0.1 mm, respectively, and two different widths, 6.7 mm and 1.2 mm, for the large and small sizes of electrodes, respectively.
## 2.4. Biosensor Glucose Detection Principle
The PQQ-GdhB sensor is based on the indirect transfer of electrons from the substrate–enzyme biochemical reaction to the electrode via the mediator redox couple FcMeOH+/FcMeOH, which is considered to diffuse freely in the PBS electrolyte solution. The reaction scheme applied to this type of biosensor is presented in Figure 3.
The substrate molecule glucose is oxidized in the presence of GdhB-PQQ producing gluconolactone and reduced GdhB-PQQH2, with the latter being further oxidized in the presence of reducing diffusional electron mediator. The mediator is reoxidized at a sufficiently positive potential, producing a current signal proportional to the glucose concentration—this represents chronoamperometric detection, marked in blue in Figure 3. In cyclic voltammetry, in addition to the oxidation process of the mediator, the reduction in the mediator is also detected, which is correspondingly marked in red.
## 3. Results and Discussion
In the first subsection, the results of the electrochemical measurements of the IM are presented, while in the second subsection the results of the MM are shown and discussed.
## 3.1. Immersion Mode Biosensor Characterization
The results presented for the IM biosensor refer to an experimental setup in which three electrodes (WE, CE, RE) were immersed in a reservoir containing the solution, as shown in Figure 2a. The results were obtained using a large and a small electrode.
Evaluation of Au working electrode cleaning efficacy. After electrode fabrication, the remaining contaminants must be removed from the surface of the electrode chip. The method of electrode chip cleaning by isopropyl alcohol, deionized water and oxygen plasma was described in Section 2.2. The cleanliness of the electrodes was investigated by cyclic voltammetry in the presence of [Fe(CN)6]3−/4− in KCl solution by evaluating electrochemical reversibility. Figure 4a (dotted and solid blue curves) shows cyclic voltammograms obtained prior to and after electrode cleaning in oxygen plasma. The cyclic voltammogram was measured at a scan rate of 50 mV/s with 10 mV steps. As shown in Figure 4a, a difference in the cathodic and anodic peak current of 320 mV was measured prior to cleaning and 90 mV after cleaning. Therefore, the latter value is closer to the theoretical value of 57 mV for a single electron transfer reaction, which is characteristic of a pristine gold electrode surface [44]. In practice, peak voltage separation also depends on the resistance of the system and therefore differs from the theoretical one. In addition to plasma cleaning, we also obtained similar peak voltage separation within experimental uncertainty (91 mV with a standard deviation of 4 mV) using Piranha cleaning. Since both methods are considered rigorous in cleaning organic residues, the obtained values were considered to be a sufficient level of WE cleanliness. In addition, the process reversibility was tested by varying the scan rates from 20 mV/s to 120 mV/s. The reduction and oxidation peaks of the cyclic voltammograms followed a square root dependence of the scan rate, and the ratio of the anodic to cathodic peak current was ~0.98, which is close to 1, which is expected for a reversible process. This additionally confirms the proper cleanliness of our Au electrode.
Evaluation of selectively mixed SAM deposition on the WE. One of the challenges during the fabrication of the microfluidic biosensor was to deposit the mixed SAM only on the thin-film WE and not on the thin-film CE and RE, i.e., selective deposition on the WE. Mixed SAM deposition was performed by microcontact printing as described in Step 2 of Section 2.2. To evaluate the selectivity, we used three thin gold film electrodes correspondingly denoted as WE1, WE2 and WE3. Here each of the WEs was individually connected to the potentiostat, while CE and RE were external (IM, Figure 2a). Cyclic voltammetry was conducted before and after selective deposition of mixed SAM on each thin-film WE in the presence of ferri/ferro-cyanide in a KCl solution. The results obtained are reported by the three solid and three dashed traces in Figure 4a. Before deposition, each (oxidation and reduction) peak current was proportional to the surface area of the individual electrode (shown with solid traces). After depositing mixed SAM on the WE, the corresponding signal greatly decreased by $99\%$ (shown with dashed blue trace) compared to that obtained using an unmodified WE. The formation of the mixed SAM on the Au electrode resulted in a highly insulating surface and thus blocked the Faradaic current. The anodic peak from the WE2 and WE3 after mixed SAM deposition on the WE remained almost the same (decreased by $2\%$ and $17\%$, respectively; shown with dashed red and green traces), confirming the absence of mixed SAM contamination. This is solid proof that the deposition on the WE was specifically located, and no cross-contamination occurred on the other two thin-film electrodes WE2 and WE3. Figure 4b shows the results from the corresponding experiment involving a small electrode. After mixed SAM deposition on the WE, the signal from the mixed SAM-coated WE decreased by $99\%$ (shown with dashed blue trace), compared to the signal from uncoated WE. The anodic peak from the WE2 and WE3 after mixed SAM deposition on the WE decreased by $19\%$ and $31\%$, respectively (shown with dashed red and green traces). Despite the miniaturized electrode design, our deposition method proved to be suitable and still allowed selective deposition of mixed SAM without interference on the neighboring electrodes. As shown in the literature [45], the well-defined redox peaks commonly displayed on bare Au are drastically reduced for MUA-modified electrodes since the measured currents are at least one order of magnitude lower after the electrode functionalization. On the other hand, the FcMeOH redox couple in PBS does not show this isolating character for electron transfer through the mixed SAM to WE (as we obtain in the case of ferri/ferro-cyanide in KCl solution) and was selected for biosensor configuration.
Glucose detection by IM biosensor. The method of enzyme immobilization for the IM biosensor is described in Section 2.3. The efficacy of selective mixed SAM deposition together with enzyme immobilization on WE was analyzed by the response of the biosensor to glucose in the presence of FcMeOH in PBS. Figure 5a shows the cyclic voltammogram of the sensor response for different concentrations of glucose. The cyclic voltammogram is symmetrical when no glucose is present. As a response to the presence of glucose, a $27\%$ (for 1 mM glucose) and $69\%$ (for 10 mM glucose) increase in the anodic peak and a $22\%$ decrease in the cathodic peak were observed, compared to the measurements in the absence of glucose. In the case without glucose, the ratio between FcMeOH+ and FcMeOH was equal, resulting in a symmetrical voltammogram, while in the case of glucose presence, the ratio was different due to additional electrons brought by the oxidation of glucose (see Figure 3), leading to an asymmetrical voltammogram. Chronoamperometric current response versus time was measured for glucose concentrations between 0.01 and 10 mM and was carried out at a constant DC potentiostatic setpoint potential EDC of 0.35 V. Measured current values at 10 s are shown in a calibration plot (Figure 5b,c circles), while a full-time chronoamperometric response is shown in insets of Figure 5b,c. The response is in good agreement with the Cottrell equation [46] for simple redox events and diffusion-limited current dependence. The chronoamperometric current at 10 s in the range of 0.38 µA and 1.10 µA was measured in the case of large electrodes—see Figure 5b—and in the range of 0.12 µA and 0.28 µA in the case with small electrodes—see Figure 5c. The relation between measured current I and glucose concentration [glucose] is in good agreement with Michaelis–Menten kinetics and is confirmed with a fitted curve—see Figure 5b,c. Its formula is I=Imax[glucose]KM+[glucose]+b, where *Imax is* the maximum current achieved by the system, KM is the Michaelis constant and b is the background current at zero glucose. The obtained parameters are Imax = 0.86 µA, KM = 2.97 mM and $b = 0.42$ µA for large electrodes and Imax = 0.15 µA, KM = 1.48 mM and $b = 0.13$ µA for small electrodes. The obtained Michaelis constants are in a good agreement with the data from the literature [36].
The stability of the developed biosensor was evaluated by measuring the chronoamperometric response signal at different storage time points: immediately, and then 3 and 11 days after biosensor construction. Between measurements, the biosensor was stored in PBS at 4 °C. Figure 5d shows the biosensor response normalized by an initial signal for 1 mM glucose. While the reference signal (measurement without glucose) remained constant, the signal for 1 mM glucose decreased with time. After 11 days, the decrease in current was about $45\%$. It is most likely that the decrease in signal is due to the loss of the PQQ-GdhB enzyme or its activity.
## 3.2. Microfluidic Mode Biosensor Characterization
All results concerning the MM biosensor refer to an experimental setup in which three electrodes (WE, CE, RE) are integrated into the microchannel enabling static or continuous flow (dynamic) measurements (see Figure 2b). The results refer to the large and small electrode design with appropriately adapted microchannels.
Potential and flow dependence. MM characterization of the PQQ-GdhB biosensor on glucose is shown in Figure 6. The appropriate DC potential (EDC) for chronoamperometric measurements was determined from the cyclic voltammogram measured under static and dynamic conditions without glucose (see Figure 6a). The signal measured without flow is a typical symmetric peak-shaped reversible cyclic voltammogram, while the signal measured with flow has a $138\%$ higher oxidation current due to the increased mass transport of reaction species to the electrode. Accurate potential EDC was further determined and confirmed using chronoamperometry signals measured from 50 mV to 500 mV with a 50 mV step (see Figure 6b). From this dependence, an EDC working potential of 0.25 V was chosen as the lowest possible potential that enables the highest response with minimal effects on other characteristics. This value is approximately 80 mV lower in comparison to the IM biosensor with external RE (see Figure 5a). This shift is a consequence of the potential difference between the miniature liquid junction Ag/AgCl in 3 M KCl and the thin-film quasi-Ag/AgCl RE, which is placed directly in the solution. The results of the chronoamperometric current density for the MM of large and small electrode designs as a function of flow rate are shown in Figure 6c. Considering the large electrodes, a linear dependence on flow rate is observed up to 40 μL/min, after which saturation occurs. On the other hand, the small electrodes exhibit the end of the linear regime below 5 μL/min. However, the chronoamperometric current density significantly increases even at higher flow rates. Due to sufficient sensitivity and repeatability, a flow rate of 20 μL/min was chosen for further measurements at the large and the small electrodes.
Glucose detection by MM biosensor. Glucose solutions with concentrations from 10 μM to 10 mM were injected by a syringe pump into the microchannel of the microfluidic biosensor one at a time and detected by a chronoamperometric technique. The results for three selected cases of glucose concentration (0 mM, 1 mM, 10 mM) are shown in Figure 6d. The proportional increase in the electric current with increasing glucose concentration and flow was observed. The biosensor signal decreases with time when measured without flow, which is consistent with Cottrell’s equation, while in the presence of flow, after the initial few seconds, the biosensor signal remains independent of time. The transport of glucose to the activation sites of the enzyme is, in the case of zero flow, limited by diffusion, while in the case of flow, the convection transport mechanism ensures continuous access of the glucose to the reaction sites of the enzyme.
The current density sampled after 10 s of measurement is presented in Figure 6e. In the case of the large electrode design, the oxidation current density increases from 30 nA/mm2 to 203 nA /mm2 without flow and from 91 nA/mm2 to 259 nA/mm2 with flow. In the case of the small electrode design, an increase in oxidation current density from 201 nA/mm2 to 586 nA/mm2 and from 577 nA/mm2 to 988 nA/mm2 was observed without flow and with a flow of 20 µL/min, respectively. The results in Figure 6e also confirm that the biosensor response follows standard Michaelis–Menten enzyme kinetics. Therefore, the main three advantages of biosensors performing in the microfluidic channel are as follows: (i) continuous detection of the analyte is enabled; (ii) the flow through biosensor structure gives a higher response; and (iii) measurement readings are time-independent in the case of flow. Based on the calibration plot in Figure 6e, the upper limit of the linear range for both biosensor designs was at a glucose concentration of 200 µM.
Comparing the large and small electrodes, a pronounced increase in current density is shown by using the small electrode design. Regardless of the glucose concentration, about 3× higher current density is observed (Figure 6e). Moreover, an improvement is also shown when comparing biosensor sensitivities k (see Figure 6f). Here, based on the slopes of the respective calibration plots, a sensitivity of 0.21 and 0.24 nA/μM/mm2 was estimated at large electrodes and 0.79 and 0.61 nA/μM/mm2 at small electrodes, with and without flow, respectively. To summarize, approximately, a factor of 3 in sensitivity improvement is achieved when moving from the large electrode design to the small biosensor design in MM with continuous analyte flow.
The chronoamperometric response of the developed microfluidic PQQ-GdhB glucose biosensor at different glucose concentrations was further analyzed to determine the limit of detection (LOD). The LOD was calculated according to the formula 3* std/k [30], where std is the standard deviation of the chronoamperometric current of the blank sample (without glucose) and sensitivity k is the slope in the linear range of the calibration plot (0–200 μM) (Figure 6f). According to the methodology described above, the biosensor LOD was determined to be 30 μM.
The materials used for fabrication of the biosensor, and the sensitivity, linear range and LOD have been compared with the existing glucose electrochemical biosensors (Table 1).
Comparing the analytical performances of the proposed biosensor with various glucose electrochemical biosensors shows that the thin-film Au PQQ-GdhB sensor had a relatively highly sensitive response to glucose but only in the quite limited linear range of 30–200 μM. However, our biosensor still enables measurements up to 10 mM with reduced and nonlinear sensitivity. Additionally, we use mature microelectromechanical system technology, which makes the production more accessible.
## 4. Conclusions
We presented the development and characterization of an electrochemical biosensor in a microfluidic system for glucose measurement. Each step of the biosensor construction was verified separately, using IM and MM approaches. An IM biosensor was used for static measurements to optimize crucial steps regarding enzyme immobilization on the gold WE, while the MM biosensor was used for measurements in both static and continuous flow systems. The enzyme PQQ-GdhB was immobilized within the microchannel on the WE, where a mixed SAM of 6-MCH and 11-MUA was precisely deposited via microcontact printing and activated by crosslinker EDC+S-NHS. The advantage of microcontact printing for efficient mixed SAM deposition is a much shorter process, which was typically a few minutes in our work, compared to several hours for conventional deposition. Another advantage of this approach is that the complete fabrication of the device can be performed in advance, while the enzyme with the highest activity is applied to glucose detection. Two different sizes of thin film electrodes were used—large (WE diameter is 3.0 mm) and small (WE diameter is 0.8 mm) electrodes—both with customized microchannel designs. It has been shown that a fabricated microfluidic biosensor can detect a wide range of glucose concentrations from 30 µM to 10 mM. The response of the biosensor showed that the biosensor with the small electrode and microchannel design outperformed the biosensor with the large electrode and microchannel design in terms of current density. The sensitivity of the biosensor without flow is 0.61 nA/µM/mm2 in the linear range of the calibration plot, which is between 30 and 200 µM in glucose concentration. In the presence of a continuous glucose flow of 20 µL/min, the sensitivity of the biosensor further increased to 0.79 nA/µM/mm2. Good sensor sensitivity in this study under flow shows that the constructed biosensor is suitable for monitoring pharmaceutical processes. The benefit of our biosensor development approach is in the fabrication process, which comprises mature microelectromechanical system technology and enables a large-scale biosensor fabrication with high repeatability. While the biosensor was designed primarily for monitoring glucose in pharmaceutical processes, it also has potential for point-of-care testing. However, for those applications, validation of the sensor selectivity in biological samples would have to be performed. The presented development of a biosensor on a chip can be extended with automatization, various microfluidic components and multiplexed continuous biosensing.
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|
---
title: A Single Arm Clinical Study on the Effects of Continuous Erythropoietin Receptor
Activator Treatment in Non-Dialysis Patients with Chronic Heart Failure and Renal
Anemia
authors:
- Akira Sezai
- Hisakuni Sekino
- Makoto Taoka
- Shunji Osaka
- Masashi Tanaka
journal: Biomedicines
year: 2023
pmcid: PMC10046271
doi: 10.3390/biomedicines11030946
license: CC BY 4.0
---
# A Single Arm Clinical Study on the Effects of Continuous Erythropoietin Receptor Activator Treatment in Non-Dialysis Patients with Chronic Heart Failure and Renal Anemia
## Abstract
Erythropoiesis-stimulating agents improve the NYHA functional class and decrease the hospital readmission rates for heart failure; however, little is known about the influence of continuous erythropoietin receptor activator (CERA) on the heart. Therefore, a prospective study was conducted to investigate the effects of CERA on cardiac and renal function and oxidative stress in chronic heart failure with renal anemia. Sixty patients with chronic heart failure and renal anemia were enrolled and received CERA for 12 months. The primary endpoints were hemoglobin (Hb) and hematocrit, and the secondary endpoints were: [1] atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP); [2] NYHA class; [3] echocardiography; [4] blood urea nitrogen, creatinine, cystatin C, and urinary albumin; [5] high-sensitivity C-reactive protein; [6] oxidized low-density lipoprotein (Ox-LDL); and [7] renin, angiotensin-II, and aldosterone. There was a significant difference in the Hb levels measured before and after CERA administration. The BNP, ANP, NYHA, left ventricular mass index, renal function, and Ox-LDL decreased significantly after CERA administration. This study shows that CERA improves anemia and reduces renal impairment, as well as cardiac and oxidative stress. The result of this study is useful for a study in which switching from CERA to a new renal anemia drug, hypoxia-inducible factor prolyl-hydroxylase inhibitor, is investigated.
## 1. Introduction
Chronic kidney disease (CKD) and anemia are independent prognostic factors for heart failure and are associated with poor prognosis [1,2,3]. Furthermore, patients with CKD may develop cardio-renal-anemia syndrome, a vicious cycle involving heart disease, renal disease, and anemia. This syndrome leads to the exacerbation of both heart and renal disease because the associated ischemia causes fluid retention and inflammation and anemia causes oxidative stress [1,4]. Furthermore, the presence of anemia is considered to negatively affect the efficacy of heart failure drugs [5]. The treatment of anemia is also important in heart failure management. In the CHART-2 (Chronic Heart Failure Analysis and Registry in the Tohoku District-2) study performed in Japan, $35\%$ of patients with chronic cardiac failure had anemia [6]. The underlying mechanism of anemia in patients with heart failure are as follows: [1] Dilution by retained body fluid; and [2] decreased hematopoiesis. In the mechanism of [1], the renal blood flow is decreased, secondary to decreased cardiac output, thereby renin-angiotensin-aldosterone is activated. This results in the retention of body fluid. [ 2] is the decreased production of erythropoietin or decreased response by bone marrow cells to erythropoietin secondary to CKD complication [7]. Other causes of anemia include iron deficiency, decreased utilization of retained iron due to inflammation [5], high prevalence of malignancy in elderly patients with heart failure, use of anticoagulant/anti-platelet in patients with heart failure, and digestive tract bleeding. As the cause of anemia in heart failure is unknown, there is currently no established treatment. However, studies are currently underway to evaluate the effects of erythropoiesis-stimulating agents (ESA) in patients with anemia and heart failure. ESA were shown to improve the New York Heart Association (NYHA) functional class, decrease the hospital readmission rates for heart failure, and increase exercise tolerance [8,9,10]. No studies to date have evaluated whether ESA improve all-cause mortality, exacerbate heart failure, or improve hospital readmission rates, but the large-scale RED-HF (Reduction of Events by Darbepoetin Alfa in Heart Failure) study found that they can cause thromboembolism [11].
Among the many studies on the effects of ESA in renal anemia, one reported that continuous erythropoietin receptor activator (CERA; epoetin beta pegol, Mircera®, Chugai Pharmaceutical Co., Ltd., Tokyo, Japan) has a long half-life and good efficacy [12], and its clinical benefits have been reported [13,14]. However, there is no clinical study report of CERA in patients with heart failure. Accordingly, we believed this study was warranted. In clinical practice, it is found that the improvement of anemia sometimes prevents the worsening of heart failure. Therefore, a prospective clinical study was conducted to investigate the influence of CERA on cardiac function, renal function, and oxidative stress in patients with chronic heart failure and renal anemia.
## 2.1. Study Design
This was a single arm design at a single clinical site.
Subjects: Participants were patients with chronic heart failure and renal anemia being treated at Sekino Hospital, a logistical support hospital of the Nihon University Itabashi Hospital. The definition of chronic heart failure in the study population in this study is as follows: Those classified as NYHA I to III, receiving follow-up at an out-patient clinic who experienced acute decompensated heart failure, not re-admitted due to worsening of heart failure for at least past one year and receiving standard heart failure drugs (diuretics, β-blockers, and renin-angiotensin system inhibitors).
Renal anemia was defined as hemoglobin (Hb) levels of 11 g/dL or less; CKD stage 3b or lower; negative fecal occult blood test results; anemia that is not iron deficiency anemia, pernicious anemia, or hemolytic anemia; no vitamin B12 or folic acid deficiency; erythropoietin levels of 50 U/mL or less; and those whose anemia was not improved by iron treatment after the diagnosis of iron deficiency anemia.
Methods: CERA was administered intravenously as an add-on treatment once a month or once every two weeks for 12 months. The initial dose of CERA was 50 µg/month, and the dose was then adjusted to less than 13 g/dL in order to achieve Hb levels of 11. If the patient’s Hb level was 13 g/dL or higher, the administration of CERA was either stopped or reduced.
Endpoints: The primary endpoints were changes in the levels of Hb and hematocrit (Ht), and the secondary endpoints were changes in the following parameters: [1] atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP); [2] NYHA class; [3] echocardiography measurements of left ventricular ejection (LVEF), fractional shortening (%FS), left ventricular end-diastolic dimension (LVEDD), left ventricular end-systolic dimension (LVESD), left ventricular mass index (LVMI), and E/e’ ratio; [4] blood urea nitrogen (BUN), serum creatinine (sCr), estimated glomerular filtration rate (eGFR), cystatin C, and urinary albumin excretion adjusted for urinary albumin (U-Alb); [5] high-sensitivity C-reactive protein (hs-CRP); [6] oxidized low-density lipoprotein (Ox-LDL); and [7] renin, angiotensin-II, and aldosterone.
The Hb, Ht, ANP, BNP, sCr, eGFR, cystatin C, U-Alb, hs-CRP, and Ox-LDL levels were measured and the NYHA class was assessed at baseline before CERA administration and after 1, 3, 6, and 12 months of treatment. At baseline and after 6 and 12 months of treatment, the levels of renin, angiotensin-II, and aldosterone were measured and echocardiography was performed with a LOGIQ S8 ultrasound device (GE Healthcare Japan Corp., Tokyo, Japan) by a specialized echocardiographer. ( see Table 1 for study design).
The exclusion criteria were as follows: [1] hemodialysis; [2] hepatic dysfunction; [3] pregnancy; and [4] patients who were deemed unsuitable by the attending physician for other reasons. Adverse reactions included hypertension; tachycardia; bradycardia; renal dysfunction, which was defined as an increase in sCr levels of $50\%$ or more; hepatic dysfunction, which was defined as an increase of $50\%$ or more in aspartate transaminase and alanine transaminase levels; skin reactions; and allergic reactions. Adverse reactions management, such as the discontinuation of the test drug, was determined by the attending physician.
Written informed consent was obtained from all participants. The study was approved by the institutional review board and registered with the Hospital Medical Information Network (Study ID: UMIN000025419).
## 2.2. Statistical Analysis
The data are expressed as the mean ± standard error of the mean (SEM). The variables were compared by one-way analysis of variance (ANOVA). A p value of less than 0.05 indicated statistical significance. The statistical analysis in this study was conducted using Macintosh (MacBook Pro) with mac OS Big Sur, along with the analysis software, SPSS software (Version 28.0.0.0, IBM Inc., Chicago, IL, USA). The data were aggregated by Sekino Laboratory staff who were not involved in the study and analyzed by SATISTA (Kyoto, Japan), a company that was not involved in performing the study.
## 3. Results
A total of 60 patients were enrolled in the study (see Table 2 for the baseline characteristics). In the 12 month study period, no deaths, cardiac or renal events, thrombosis, or adverse reactions occurred. At month 12, the mean maintenance dose of CERA was 82.0 ± 4.8 μg/month. The hemoglobin levels reached 13 g/dL in 12 patients, so the CERA dose was either stopped (four patients: two at month 6 and another two at month 12) or reduced (eight patients: two at month 3, three at month 6, and another three at month 12). None of the patients who stopped taking CERA resumed intake, and the dose was not Increased in any of the patients in whom it was decreased. Two months after the initiation of CERA, all of the patients could maintain the target Hb level of between 11 and 13 g/dL with a monthly regimen.
## 3.1. Primary Endpoints
The Hb levels increased significantly from the baseline to all time points, as follows: baseline, 9.8 ± 0.1 g/dL; month 1, 11.0 ± 0.1 g/dL; month 3, 11.6 ± 0.2 g/dL; month 6, 11.7 ± 0.2 g/dL; and month 12, 12.2 ± 0.2 g/dL (all $p \leq 0.001$; Figure 1). Three months after the start of CERA, all of the patients could maintain the target Hb level (between 11 and 13 g/dL).
The Ht levels also increased significantly from baseline to all time points, as follows: baseline, 30.4 ± $0.4\%$; month 1, 33.8 ± $0.4\%$; month 3, 35.8 ± $0.5\%$; month 6, 36.4 ± $0.5\%$; and month 12, 37.5 ± $0.5\%$ (all $p \leq 0.001$; Figure 1).
## 3.2.1. BNP and ANP
The BNP and ANP results are shown in Figure 2. The BNP levels increased significantly from baseline to all time points, as follows: baseline, 174.2 ± 21.2 pg/mL; month 1, 121.7 ± 14.6 pg/mL; month 3, 110.1 ± 11.7 pg/mL; and month 12, 111.7 ± 12.2 pg/mL at 0 (all $$p \leq 0.001$$). The ANP levels also increased significantly from baseline to months 3, 6 and 12: baseline, 104.9 ± 9.1 pg/mL; month 3, 71.4 ± 6.3 pg/mL; month 6, 83.3 ± 7.8 pg/mL; and month 12, 83.9 ± 6.8 pg/mL (all $p \leq 0.001$).
## 3.2.2. NYHA Class
The NYHA class increased significantly from baseline to months 6 and 12 (both $$p \leq 0.007$$; Table 3).
The maintenance dose of CERA was analyzed by the NYHA. The maintenance doses of CERA at 12.5 U, 25U, 50 U, 62.5 U, 75 U, 100 U, 150 U, and >150 U were NYHA at 1.5 ± 0.5, 2.0 ± 0, 1.8 ± 0.1, 2.0 ± 0, 1.9 ± 0.1, 1.7 ± 0.2, 2.0 ± 0.3, and 2.0 ± 0.2, respectively. Thus, there was no significant relationship between the maintenance dose of CERA and the NYHA, and hence no dependency effect.
## 3.2.3. Echocardiography Measurements
The echocardiography results are shown in Table 2. There were no significant changes in the LVEF, %FS, LVEDD, LVESD, or E/e’, but the LVMI decreased significantly from baseline to month 12 ($$p \leq 0.008$$).
## 3.2.4. BUN, sCr, eGFR, Cystatin C, and U-Alb
The results of the renal variables are shown in Table 2 and Figure 2. Compared with the baseline, the BUN levels were significantly lower at months 3 ($$p \leq 0.003$$) and 6 ($$p \leq 0.029$$). At months 3, 6, and 12, significant decreases were seen in the sCr levels (month 3, $p \leq 0.001$; month 6, $p \leq 0.001$; month 12, $$p \leq 0.001$$), eGFR (all $p \leq 0.001$) and the cystatin C levels (month 3, $$p \leq 0.03$$; month 6, $$p \leq 0.033$$; month 12, $$p \leq 0.038$$). There were no significant changes in the U-Alb levels over the 12 months study period.
## 3.2.5. hs-CRP
There were no significant changes in the hs-CRP levels over the 12 months of CERA administration (Table 2).
## 3.2.6. Ox-LDL
The Ox-LDL levels decreased significantly from baseline to months 6 ($$p \leq 0.036$$) and 12 ($$p \leq 0.017$$; Figure 2).
## 3.2.7. Renin, Angiotensin-II, and Aldosterone
There were no significant changes in the levels of renin and aldosterone over the 12 months study period, but the angiotensin-II levels decreased significantly from baseline to month 12 ($$p \leq 0.003$$; Table 2).
## 4. Discussion
In this study, the administration of CERA improved not only anemia but also renal function, cardiac function, and oxidative stress. In addition, it was found that when the Hb levels were maintained between 11 and less than 13 g/dL, CERA was safe and did not cause any adverse reactions, such as thrombosis. Among the various ESA products, CERA was selected in this study for the following reasons: The study subjects were out-patients and visit a clinic on a monthly basis. Accordingly, a product with long-life is preferred. There is no head-to-head comparison study in heart failure between CERA and other ESA products. The half-life of CERA (4 to 6 days) is longer than that of other ESA products. A decreased dosing frequency is reported in a study that investigated switching from recombinant human Erythropoietin to CERA in dialysis patients [15]. In this study, all of the patients could successfully maintain the target Hb level of between 11 and 13 g/dL with a monthly dosing regimen, without complications.
The echocardiography showed no differences in the left ventricular function after CERA administration in many of the patients with heart failure and preserved LVEF who were in a low NYHA class. However, significant decreases were seen in the ANP, BNP, LVMI, and NYHA class after the administration of CERA, which improved the patients’ anemia and reduced the effects of cardiac stress. Another study reported that the ESA erythropoietin increased exercise tolerance, lowered the NYHA class, improved renal function, and reduced the BNP levels in patients with chronic heart failure [16]. Silverberg et al. reported that erythropoietin and an iron preparation improved the NYHA class, hospitalization rate, and diuretic requirements in patients with chronic heart failure with an LVEF of $40\%$ or less and Hb levels of between 10 and 11.5 g/dL [17]. In a clinical study on ESA treatment in 319 patients with chronic heart failure, the ESA improved the Hb levels from 11.3 to 13.4 g/dL, but did not significantly improve the exercise tolerance, NYHA class, or quality of life; however, the exercise time was reported to be significantly longer in patients whose serum Hb levels increased by 12.0 g/dL or more [6].
ESA reduce the BNP levels, NYHA class, and hospitalization rates in patients with cardio-renal anemia syndrome, so the correction of anemia by ESA plays a role in the improvement of the clinical outcome in this subset of patients with heart failure [8]. Kuwahara et al. reported that pre-dialysis patients with Hb levels below 8.9 g/dL have lower LVEF and significantly increased LVMI and deceleration time (DcT) than patients with higher Hb levels [18]. Hayashi et al. reported that the anemia improved and LVMI decreased in nine predialysis patients with chronic renal failure who were treated with ESA; furthermore, this study also found a correlation between anemia and LVMI [19].
Similar to previous studies, the present study found that CERA had positive renal effects, such as the improvement in the sCr levels and eGFR. CERA had favorable effects on the glomerular function because it significantly reduced the levels of cystatin C, a biomarker that closely reflects glomerular function without being influenced by the patient’s diet or muscle mass. Only a few other studies have reported on the cystatin C levels of patients undergoing ESA treatment, and only one described the effects of ESA treatment on tubular injuries and showed a correlation between the cystatin C levels and decreased levels of neutrophil gelatinase-associated lipocalin, a biomarker of tubular injury, after ESA administration [20].
The effects of CERA on the renin-angiotensin-aldosterone system (RAAS) were also investigated. The study participants with chronic heart and renal failure had increased RAAS before the CERA administration. The angiotensin-II levels decreased after 12 months of CERA administration. However, these results could not be analyzed in detail due to the fact that many of the study patients were taking renin-angiotensin system inhibitors, such as mineralocorticoid receptor antagonist (MRA). It is hypothesized that CERA may not directly affect the RAAS, but that it may improve anemia by having favorable effects on the heart and kidneys, which may lead to secondary effects on the RAAS.
Oxidative stress is known to have a strong influence on the kidneys. In this study, the Ox-LDL levels decreased significantly after CERA administration. Fujiwara et al. reported that erythropoietin improved anemia and significantly decreased the oxidative stress marker 8-OHdG, carotid artery intima-media thickness, brachial-ankle pulse wave velocity, and serum asymmetrical dimethylarginine levels, and consequently slowed the progression of renal insufficiency, oxidative stress, and atherosclerosis in 15 patients with renal anemia [21]. Our results showed that CERA not only improved anemia, but also reduced renal impairment, cardiac stress, and oxidative stress, and they suggest that using CERA as a treatment for renal anemia breaks the vicious cycle of damage to the heart and kidneys by improving oxidative stress and organ derangement, thus preventing the exacerbation of both heart disease and renal disease.
In this study, an improvement of anemia by CERA was anticipated. However, it is of interest to observe the improvements in the renal functions, ANP, BNP, LVMI, and oxidative stress. The study results suggest that these are unlikely to be direct effects of CERA, but are instead due to an increase in erythropoietin by CERA. It is indicated that the production of erythropoietin is decreased in patients with heart failure [22]. Accordingly, it appears meaningful to administer ESA to patients with renal anemia. It is reported that the treatment of patients with chronic heart failure by ESA resulted in anti-oxidative effects and anti-apoptosis effects [23]. This clinical study demonstrated that Ox-LDL was decreased by CERA, which is a significant finding. During the study period, SGLT2 inhibitors were not yet approved for use in heart failure in Japan, and SGLT2 inhibitors were not popular in diabetic patients. In recent years, the sub-analysis of large-scale studies of SGLT2 inhibitors demonstrated that an increase in erythropoietin by SGLT2 inhibitor affects the LVMI, oxidative stress, and oxygen delivery, which positively impacts the cardiac and renal functions [24,25]. In this study, there was no side effect, such as cardiovascular events. An improvement of anemia by CERA and safety was demonstrated. However, the study is ongoing for 12 months of follow-up, and it is not yet known if CERA can improve the outcome of patients with heart failure through an improvement in anemia. This is one of limitations of this study. The beneficial effects of erythropoietin on the cardiovascular system have long been discussed. Erythropoietin potentially confers the cardio-protective effects via anti-apoptosis, anti-inflammation, anti-oxidation, and neovascularization [26]. However, there are still ongoing discussions about whether these effects could result in an improvement in the prognosis of heart failure. Furthermore, with regard to the potential improvement of the prognosis of heart failure by CERA, more patients should be studied for a longer follow-up period.
New drugs, including hypoxia-inducible factor prolyl-hydroxylase (HIF-PH) inhibitors, have been developed for renal anemia and are now in clinical use. These agents work by stabilizing the hypoxia-inducible factor complex and stimulating endogenous erythropoietin production, even in patients with end-stage kidney disease. Five different HIF-PH inhibitors are currently available; they are administered orally, which may be a more favorable route for patients not undergoing hemodialysis [27,28,29,30,31]. A study is currently being conducted to evaluate the effects of switching from CERA to HIF-PH inhibitors and to clarify the differences between the two types of drugs and their efficacy and potential adverse effects (Study ID: UMIN000041651). In the future, the best way to treat heart failure with renal anemia will need to be clarified.
## 5. Limitation
This was a non-controlled, single arm study at a single clinical site, and a limited number of patients were studied. In this study, priority was given to treat renal anemia in clinical practice. Accordingly, a randomized controlled clinical trial could not ethically be conducted. However, to further investigate the efficacy of CERA and its limitations, a randomized, controlled clinical trial is warranted as a future endeavor. Accordingly, the study design is not robust enough to demonstrate the efficacy of CERA. Moreover, this study is still ongoing for 12 months of follow-up. It is not known whether an improvement of anemia by CERA affects the outcome of patients with heart failure. Future studies should include more patients for a longer-term follow-up at multiple centers.
## 6. Conclusions
This study determined that CERA improves anemia and also reduces renal impairment, cardiac stress, and oxidative stress in patients with chronic heart failure and renal anemia. The result of this study is useful as a study in which CERA is switched to a new renal anemia drug, hypoxia-inducible factor prolyl-hydroxylase (HIF-PH) inhibitors.
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|
---
title: On the Best Way to Cluster NCI-60 Molecules
authors:
- Saiveth Hernández-Hernández
- Pedro J. Ballester
journal: Biomolecules
year: 2023
pmcid: PMC10046274
doi: 10.3390/biom13030498
license: CC BY 4.0
---
# On the Best Way to Cluster NCI-60 Molecules
## Abstract
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. However, the poor clustering of compounds will compromise such validation, especially on test molecules dissimilar to those in the training set. This study aims at finding the best way to cluster the molecules screened by the National Cancer Institute (NCI)-60 project by comparing hierarchical, Taylor–Butina, and uniform manifold approximation and projection (UMAP) clustering methods. The best-performing algorithm can then be used to generate clusters for model validation strategies. This study also aims at measuring the impact of removing outlier molecules prior to the clustering step. Clustering results are evaluated using three well-known clustering quality metrics. In addition, we compute an average similarity matrix to assess the quality of each cluster. The results show variation in clustering quality from method to method. The clusters obtained by the hierarchical and Taylor–Butina methods are more computationally expensive to use in cross-validation strategies, and both cluster the molecules poorly. In contrast, the UMAP method provides the best quality, and therefore we recommend it to analyze this highly valuable dataset.
## 1. Introduction
In the past decades, artificial intelligence (AI) has been used to develop predictive models with a wide range of applications in biomedicine and healthcare [1]. In particular, machine learning (ML)—a subarea of AI—has become an important component in early drug discovery, for instance, by developing quantitative structure–activity relationship (QSAR) models [2]. The use of ML-based models in drug discovery has been possible due to the availability of preclinical data that can be reused to build and validate predictive models. Such is the case of the National Cancer Institute (NCI-60) human tumor cell lines screen, which since 1990 has been used by the cancer research community to find compounds with potential anticancer activity [3].
Although ML-based models have been extensively used, several challenges remain to be overcome along the drug-design pipeline, one of them related to model performance on unseen compounds. Indeed, many articles fail to account for the nearly inevitable reduction in predictive ability that may occur when something that is a useful predictor in one data set is not as useful in another dataset [4]. Models that perform well on an independent data set can be achieved using model validation strategies such as bootstrap or k-fold cross-validation (CV) [4]. More demanding model validation strategies include asymmetric validation embedding (AVE) [5], leave-dissimilar-target-out (LDTO) CV [6], leave-one-cell-line-out (LOCO) CV, leave-one-tissue-out (LOTO) CV, leave-one-compound-cluster-out (LOCCO) CV [7], and those using other similarity metrics between training and test data instances [8]. These complex model-validation methods pose additional challenges [9], particularly the LOCCO CV approach, which inherits the challenges of any clustering method.
Clustering methods are widely explored unsupervised ML-based algorithms whose aim is to discover the underlying structure or patterns existing in a given dataset. The clustering of biological entities, such as small molecules, can be performed using different approaches, including hierarchical clustering (HC), distribution-based clustering, and density-based clustering [10]. For example, several clustering algorithms, including hierarchical, Taylor–Butina, and UMAP clustering, have been compared on 29 data sets with between 100 and 5000 small molecules [11]. In addition, hierarchical clustering has been used to cluster molecules from the PubChem database [12], and Taylor–Butina clustering has been used to cluster molecules from the MolPort database [13].
A clustering analysis in a virtual chemical database can be used to quantify the diversity of compounds, which is relevant in several areas of chemistry such as in high-throughput screening (HTS) [14] and in QSAR-based virtual screening predictions [13]. In addition, the resulting clustering of compounds could lead to a comprehensive understanding of the underlying mechanism of action (MOA) of the drugs [15]. However, clustering chemical compounds also poses a challenge in terms of their representation. This is because chemical compounds can be represented in different ways, which can result in different clustering outcomes. It is possible for two compounds with different molecular structures to have comparable molecular descriptors or fingerprints, leading to them being clustered together using those representations. Similarly, two compounds with similar molecular structures may have different molecular descriptors or fingerprints, resulting in them being clustered separately using those representations. Structural fingerprints, such as the 1024-bit Morgan fingerprint, offer several advantages over other molecular representations for clustering tasks. For instance, they are computationally efficient and can be easily applied to large datasets. Moreover, Morgan fingerprints are robust to small variations in the molecular structure, making them a useful tool in virtual screening, where slight modifications to the molecular structure may occur due to synthetic or computational alterations.
In this study, we aim to evaluate and compare three different methods for clustering the NCI-60 molecules to determine the best-performing algorithm that can be used to generate clusters for model validation, ensuring that the resulting clusters are optimal for use in LOCCO-CV strategies or other model validation strategies. For this, we compare the hierarchical, Taylor–Butina, and UMAP clustering algorithms while tuning their essential hyperparameters. Additionally, we provide a definition of outlier molecules that aligns with the clustering problem we are addressing, allowing us to optimize outlier computation. By applying this definition, we investigate the impact of removing outlier molecules from the NCI-60 panel before clustering. To evaluate the clustering performance, we compute the silhouette coefficient, the Calinski–Harabaz score, and the Davies–Bouldin score for each method. Finally, alongside the clustering metrics, we compute a similarity matrix to achieve a better understanding of the molecules per cluster. The results show that, overall, removing outlier molecules results in better clustering. Moreover, the results show that UMAP clustering outperforms hierarchical and Taylor–Butina clustering.
## 2. Materials and Methods
The methodology of this study comprises three stages: data representation, clustering, and clustering evaluation. These stages are summarized in Figure 1 and are explained in detail in the following subsections. An important part of this study is to evaluate the impact of removing outlier molecules from the data set. Therefore, this study analyzes two scenarios per clustering method. In the first scenario, all of the unique molecules were clustered. In the second scenario, outlier molecules were removed before clustering. The outlier detection method is explained in Section 3.1.
It is important to note that the outlier detection method and clustering algorithms were applied to the entire set of molecules in the NCI-60 panel and not on a per-cell line basis. Although the clustering output provides information on molecule NSC ID, SMILES representations, and their assigned clusters, it is possible to map the clustering results to each individual cell line.
## 2.1. NCI-60 Dataset
The NCI-60 panel utilizes 60 different human tumor cell lines to identify and characterize novel compounds with growth inhibition or killing of tumor cell lines [3]. These 60 different human tumor cell lines comprise 9 cancer types: leukemia; melanoma; and cancers of the lung, colon, brain, ovary, breast, prostate, and kidney. All of the compounds that are screened have been initially tested in a one-dose assay on the full NCI-60 panel. Compounds showing significant growth inhibition at this assay are then evaluated against the NCI-60 panel in a 5-dose assay [16]. Each compound submitted to the NCI-60 panel for testing and evaluation is identified with a unique registration number called the National Service Center (NSC) ID.
Data quality is crucial in the development of AI/ML models during drug discovery. Therefore, data cleansing is necessary to ensure high-quality data is used to generate these models. To achieve this, we follow the preprocessing stage described by [17]. At the data-representation stage, each molecule is represented by a 1024-bit Morgan fingerprint (MFP) [18] with a radius of 2, which indicates the presence or absence of a particular substructure in the molecule. We chose this fingerprint for several reasons. Firstly, previous research has shown that the choice of fingerprint and metric has little effect on downstream predictions, such as target prediction based on molecular similarity [19]. Furthermore, our choice led to practically the best performance with respect to other fingerprints and metrics, suggesting that it is a near-optimal choice. In [20], the authors showed that the (extended-connectivity) fingerprints of radius 2 and 3 are among the best-performing fingerprints when ranking diverse structures by similarity. Previous research has shown that structural (or rule-based) fingerprints, such as E3FP, Morgan, or topological, should be considered for similarity-based clustering [21]. Finally, the 1024-bit MFP size has been successfully used in retrospective studies [22], for potency prediction [23], similarity searching, and bioactivity classification [24]. Therefore, in this study, the 1024-bit MFPs will be used as features to build the clustering models.
After the preprocessing stage, there remain around 2.7 M data points (50,555 small molecules screened against 60 cancer cell lines), which represent a matrix completeness of $89.25\%$. Figure 2 shows the distribution of the small molecules per cell line. We also analyze the Tanimoto similarity distribution of these small molecules (Figure 3a).
## 2.2. Clustering Methods
Hierarchical clustering consists in building a binary merge tree, starting from the molecules stored at the leaves and merging them until reaching the root of the tree that contains all the molecules of the dataset [25]. The linkage criteria determine the metric used for the merge strategy, for example, single linkage, complete linkage, or Ward linkage. In the context of clustering chemical structure databases, the Ward linkage is commonly used [26], which minimizes the sum of squared differences within all clusters. The graphical representation of the binary merge tree representing the resulting hierarchical clustering is called a dendrogram. The python implementation of this algorithm uses the RDkit library [27], where the first step is to calculate the similarity for each pair of molecules. Then, a distance matrix containing 1−similarity for each pairwise similarity value is created. This distance matrix is the model input (Figure 1). Finally, the linkage criteria used in this study is the Ward linkage.
Taylor–Butina clustering is an algorithm based on exclusion spheres at a given Tanimoto level [28]. The way the clusters are built allows all of the molecules belonging to each cluster to have a Tanimoto value above or equal to the similarity cutoff used. At each iteration, the molecules are visited and labeled, either as a cluster centroid or as a cluster member. A disadvantage of this algorithm is that at the end of the clustering, molecules that have not been labeled are considered as singletons, even if they have neighbors. The reason is that their neighbors have been attracted by a better centroid. With this approach, we benefit from fast clustering since in each iteration only unlabeled molecules are compared, and we avoid the formation of highly heterogeneous clusters [28]. The python implementation of the Taylor–Butina algorithm employs the RDkit [27] library. The distance matrix is calculated in the same way as in hierarchical clustering (Figure 1); then, based on the similarity cutoff given, each molecule is assigned to a cluster id.
The uniform manifold approximation and projection (UMAP) is a non-linear dimensionality reduction algorithm that seeks to learn the manifold structure of the data and find a low-dimensional embedding while preserving the essential topological structure of that manifold [29]. While UMAP has been used for dimensionality reduction [30], it has also been used for clustering [11]. UMAP has four basic parameters to control the impact on the resulting embedding. These are n_neighbors, which controls how UMAP balances local versus global structure in the data; min_dist, which controls how tightly UMAP is allowed to pack points together; n_components, which allows the user to determine the dimensionality of the reduced dimension space we will be embedding the data into; and metric, which controls how distance is computed in the ambient space of the input data. After the dimensionality reduction is completed, this new representation of the molecules is clustered using the AgglomerativeClustering function from the Scikit-learn library [31].
## 2.3. Clustering Evaluation
The last step is the evaluation of the clustering algorithms. In this study, we consider three unsupervised metrics to evaluate the clustering quality results, the silhouette coefficient, the Calinski–Harabasz score, and the Davies–Bouldin score. These metrics are available in the Scikit-learn library [31].
To calculate the silhouette coefficient [32], we first calculate the mean intra-cluster distance ai for each molecule i in the cluster CI as follows: [1]ai=1|CI|−1∑j∈CI,i≠jd(i,j), where d(i,j) is the distance between molecules i and j in the cluster CI. Next, we calculate the mean inter-cluster distance bi for each molecule i to some cluster CJ as follows: [2]bi=minJ≠I1|CJ|∑j∈CJd(i,j), where d(i,j) is the distance between the molecule i to all molecules in CJ, with CJ≠CI.
Then, the silhouette coefficient is defined for each molecule using the mean intra-cluster distance ai and the mean inter-cluster distance bi as: [3]SC(i)=bi−aimax(ai,bi).
In summary, the silhouette coefficient is calculated using the mean distance between a given molecule and all other molecules in the same cluster (ai), and the mean distance between a given molecule and all other molecules in the next nearest-cluster (bi) [15]. The SC function implemented in the Scikit-learn library returns the mean silhouette coefficient over all molecules. The best value is 1, and the worst value is −1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a molecule has been assigned to the wrong cluster as a different cluster is more similar.
The Calinski–Harabasz score [33], also known as the variance ratio criterion, is defined as the ratio of the sum of inter-clusters dispersion and of within-cluster dispersion for all clusters, where dispersion is defined as the sum of distances squared. This score is calculated for k clusters as follows: [4]CH=tr(Bk)tr(Wk)×nE−kk−1, where the tr(Bk) is the trace of the between-cluster dispersion matrix, and tr(Wk) is the trace of the within-cluster dispersion matrix, defined by: [5]Wk=∑$q = 1$k∑x∈Cq(x−cq)(x−cq)T, [6]Bk=∑$q = 1$knq(cq−cE)(cq−cE)T, with Cq the set of points in cluster q, cq the center of cluster q, cE the center of cluster E, and nq the number of points in cluster q. A higher Calinski–Harabasz score relates to a model with better-defined clusters.
Finally, the Davies–Bouldin score [34] is defined as the average similarity measure of each cluster Ci with its most similar cluster Cj: [7]DB=1k∑$i = 1$kmaxi≠jRij, where *Rij is* defined as: [8]Rij=si+sjdij, and si is the average distance between each molecule of cluster and the centroid of that cluster, and dij is the distance between cluster centroids i and j. The minimum score is zero, with lower values indicating better clustering.
We complement this evaluation by calculating a matrix with the average Tanimoto similarity between the molecules of cluster i and those in cluster j. The average of these similarity scores represents the position (i,j) of the final matrix. In the case of average similarity between molecules from a cluster and itself (position (i,i)), the similarity was calculated between two different molecules. This means that values in position (i,i) do not include similarity scores of a molecule with itself. Note that the resulting matrix is a symmetric matrix (the similarity scores between cluster i and cluster j are equal to similarity scores between cluster j and cluster i); thus, for display purposes, the similarity matrix is shown as a lower triangular matrix.
## 3.1. Outlier Detection
One of the aspects to consider when using ML-based algorithms, in particular those used for clustering, is that many of them are sensitive to outliers and fluctuations in the density of data points [35]. In this study, we defined an outlier as the molecule whose Tanimoto similarity value to their most similar molecule is lower than or equal to a given cutoff. Thus, an outlier molecule is different from any other molecule in the set at the predefined outlier cutoff.
To remove outlier molecules, we first calculated the Tanimoto similarity for each pair of molecules (1,277,878,735 pairs). Then, we retained the similarity value of each molecule to its closest molecule (other than itself). In this way, we only have 50,555 similarity values. We retain those molecules because all the other molecules will be even less similar. The next step was to find the best outlier cutoff based on the three clustering quality metrics. We used the hierarchical clustering (with Ward linkage = 3) as the baseline algorithm for this experiment and evaluated the outlier cutoffs from 0.2 to 0.5, with a step size of 0.1.
Table 1 shows that as we increase the outlier cutoff, we obtain fewer clusters. This may be because at each outlier cutoff we have fewer molecules to cluster, and they are more similar to each other. Additionally, increasing the outlier cutoff generally leads to all three metrics improving their results with respect to the previous outlier cutoff. Moreover, the outlier cutoff of 0.5 seems to be the best tradeoff between the number of clusters and their quality since the silhouette coefficient and the Davies–Bouldin score achieve better values at this outlier cutoff. Therefore, the outlier cutoff of 0.5 was used for the following experiments. Figure 3 shows the distribution of similarity values before and after the outlier detection method.
## 3.2. Hierarchical Clustering
To perform hierarchical clustering of molecules we have to specify the Ward linkage cutoff to be used. We evaluated the impact of different cutoffs on the three selected clustering quality metrics, as well as on the number of clusters obtained. The Ward linkage cutoffs explored ranged from 0.5 to 3.0, with a step size of 0.5. These cutoffs were applied to either all molecules or non-outlier molecules and are reported in Table 2. By using the Ward linkage as the merging criterion, we are requesting that at each step the hierarchical clustering algorithm has to find the pair of clusters that leads to the minimum increase in total intra-cluster variance after merging. Table 2 shows that stricter (smaller) Ward linkage cutoffs result in a larger number of clusters.
Regarding the clustering quality metrics, the silhouette coefficient suggests that some molecules have been assigned to the wrong cluster as negative values are obtained at each cutoff. The Calinski–Harabasz score suggests that better-defined clusters are obtained at higher cutoff values, 2.5 and 3.0 for instance. In contrast, the Davies–Bouldin score suggests that better-defined clusters are obtained at smaller cutoff values but at the cost of a larger number of clusters. This behavior is repeated for all molecules as well as for non-outlier molecules.
Considering that the main purpose of this clustering problem is to use the clusters for ML model validation, the results in Table 2 suggest that a cutoff value between 2.0 and 3.0 generates a number of clusters that is less computationally expensive to use in a cross-validation strategy. In particular, the Ward linkage cutoff of 3.0 achieves the best clustering quality results, and it improves between all molecules and non-outlier molecules. Indeed, at this cutoff the silhouette coefficient improves by $50\%$, the Calinski–Harabasz score improves by $10\%$, and the Davies–Bouldin score improves by $13\%$ when using non-outlier molecules. Therefore, we chose the Ward linkage cutoff of 3.0 for the following experiments.
The next step was to analyze the number of molecules per cluster. Figure 4 shows the dendrogram for both all 50,555 molecules and 32,971 non-outlier molecules. When all of the molecules are clustered (Figure 4a), one cluster concentrates about $35\%$ of the molecules (17,691 molecules in cluster 2), whereas when non-outlier molecules are clustered (Figure 4b), one cluster concentrates about $46\%$ of the molecules (15,389 molecules in cluster 2). Even with this high concentration of molecules in a single cluster, the remaining clusters have enough information as the smallest clusters obtained in all molecules and non-outlier molecules concentrate $3.5\%$ and $10.5\%$ of the molecules, respectively.
To complement the analysis of the clustering quality, we now calculate the average similarity matrix for both all molecules and non-outlier molecules. This matrix was calculated as explained in Section 2.3. In a good clustering result, molecules from different clusters should have much lower similarity than molecules from the same cluster. In the average similarity matrix, this means that the similarity values on the diagonal must always be greater than the off-diagonal values. Figure 5 shows that molecules from the same cluster are, on average, very similar to molecules from different clusters since the off-diagonal values are mostly equal to the diagonal values, and in some cases even greater than the diagonal values. This suggests a poor clustering of molecules by using hierarchical clustering (with Ward linkage = 3), even when outlier molecules are removed. This assumption is consistent with the values of the clustering quality metrics reported in Table 2.
## 3.3. Taylor–Butina Clustering
One of the advantages of Taylor–Butina clustering is that molecules in the resulting clusters will have a Tanimoto value greater than or equal to the established similarity cutoff. In this study, we analyzed similarity cutoffs 0.35, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.97, and 0.99. These cutoffs were applied to either all molecules or non-outlier molecules and evaluated in terms of the three clustering quality metrics.
Table 3 shows that smaller similarity cutoffs result in a larger number of clusters. As for the clustering quality metrics, the silhouette coefficient and the Davies–Bouldin score suggest that better-defined clusters are obtained with smaller similarity cutoff values but at the cost of a higher number of clusters. This trend is observed for all molecules as well as for non-outlier molecules. The number of clusters should also be considered as it impacts the number of cross-validations and hence the computational cost. Therefore, if we consider the number of clusters then the Calinski–Harabasz score suggests that the similarity cutoff of 0.9 is the best option. Overall, the three clustering quality metrics improve between all molecules and non-outlier molecules. Figure 5Hierarchical clustering with Ward linkage = 3. ( a) Clustering of all 50,555 molecules results in 7 clusters. ( b) The clustering of 32,971 non-outlier molecules results in 4 clusters. Outlier molecules were removed using an outlier cutoff of 0.5 (see Section 3.1 for more details about outlier detection). The matrix is calculated as the average Tanimoto similarity between the molecules of cluster i and those in cluster j, and the average similarity between molecules of a cluster and itself (see Section 2.3 for more details on how this matrix is calculated). The matrices have been adjusted to the same scale to facilitate clustering comparison between all molecules and non-outlier molecules. For readability, similarity scores were rounded to three decimal places. The average similarity between a cluster and itself (diagonal) is close to the average similarity between two different clusters (off-diagonal), indicating poor clustering of both all molecules and non-outlier molecules.
Since one of the drawbacks of the Taylor–Butina algorithm is the possibility of obtaining clusters that are singletons, the next step in this analysis is to evaluate the number of molecules per cluster. Based on results from Table 3, we choose the similarity cutoff of 0.9 to perform this experiment. Indeed, Table 4 shows that when all molecules are clustered, $17\%$ of the clusters obtained are singletons. This value decreases to $10\%$ in the non-outlier molecules. Moreover, $90\%$ of all molecules and $93\%$ of the non-outlier molecules have been assigned to a single cluster.
The last step in the clustering quality analysis is to calculate the average similarity matrix for both all molecules and non-outlier molecules. To analyze these matrices, which are calculated as the average within-cluster and between-cluster similarity, the sizes of the clusters must be taken into account. Indeed, since both cases (all molecules and non-outlier molecules) have singletons, it is not possible to calculate the average within-cluster similarity, so these values are missing on the matrix diagonal. For the average between-cluster similarity, it can be the similarity between two molecules. Overall, Figure 6 shows that molecules from different clusters have lower similarity than molecules from the same cluster, notably for non-outlier molecules. However, these values may be biased by the number of molecules per cluster.
## 3.4. UMAP Clustering
UMAP has four basic hyperparameters (n_neighbors, min_dist, n_components, and metric) that control the dimensionality reduction result and one that controls the clustering (n_clusters). In this study, MFPs are embedded in two dimensions (n_components=2) and the Jaccard metric is used to calculate the distance between MFPs. For the remaining hyperparameters (n_neighbors, min_dist, and n_clusters), we implemented a two-step hyperparameter tuning process to find their optimal values. In the first step, we fixed the number of clusters and tune the values of the number of neighbors (n_neighbors) and the distance (min_dist). Based on the results obtained with hierarchical clustering, when all molecules are clustered, the number of clusters was set to 7. The evaluation of this experiment was done in terms of the three clustering quality metrics. Once these hyperparameters have been set, the second step of hyperparameter tuning uses these values and focuses on finding the optimal number of clusters by using the elbow method [36]. The elbow method fits the clustering model for a range of n_clusters values. If the data are very clustered, the optimal number of clusters is given by the point of inflection on the curve (i.e., the elbow); otherwise, the elbow will be unclear.
In the first step of the hyperparameter tuning process, we performed a grid search to look for the best n_neighbors and min_dist hyperparameters. The values of n_neighbors evaluated are 20, 30, 50, 100, 150, and 200, while for min_dist the values evaluated are in the range of 0.0 to 0.9, with a step size of 0.1. Figure 7 shows the results of each of the clustering quality metrics obtained at each hyperparameter combination. Overall, the results suggest that using 100 or 200 neighbors UMAP is able to balance local versus global structure in the molecules in a better way than with fewer neighbors. Additionally, at these numbers of neighbors, the best min_dist is 0.0, which means molecules represented in the embedded space are close to each other. Since the combination (100, 0.0) is the best in two of the three clustering quality metrics, these values were used in the second step of the hyperparameter tuning.
Given the values n_neighbors=100 and min_dist=0.0, we now look for the optimal number of clusters to be used. Since our evaluation of clustering quality goes beyond the three clustering metrics, we use the elbow method to narrow the search for the number of clusters rather than to find the optimal number. Here, we explored the range of clusters from 2 to 25. We also compared two scoring metrics to evaluate the clusters. These scoring metrics are the distortion, defined by the sum of squared distances between each observation and its closest centroid, and the silhouette metric, defined by the mean ratio of intra-cluster and nearest-cluster distance.
Figure 8a suggests that the optimal number of clusters is 7 when using the distortion score. However, Figure 8b suggests that our data are not highly clustered as the elbow is not clearly defined when the Silhouette score is used. Instead, the method returns as the optimal number of clusters the value at which the highest Silhouette score was obtained; in this case, the suggested optimal number is 2 clusters. Since we are using the elbow method to narrow the search for the optimal number of clusters, from Figure 8 we analyze clusters 6, 7, and 8. In addition, to find out whether a larger number of clusters improves the clustering results, we also explored the UMAP clustering when 20 groups are requested. These values were applied to either all molecules or non-outlier molecules and evaluated in terms of the three clustering quality metrics.
With the best combination of hyperparameters for UMAP found, we evaluated the clustering results in terms of the clustering quality metrics obtained at each number of clusters. Table 5 shows these results, where, in summary, all clustering quality metrics suggest well-defined clusters. Even when all metrics reach their best value with different numbers of clusters, the difference in the results with respect to the best value is small. In fact, the difference of the best silhouette coefficient with respect to the other values is between 3–$6\%$; for the Calinski–Harabasz score, this difference ranges from $0.5\%$ to $12\%$; and for the Davies–Bouldin score the difference ranges from $2\%$ to $4\%$. For non-outlier molecules, the difference between the best and worst value for each of the clustering metrics is between 4.5 and $11\%$ for the silhouette coefficient, between 6 and $12\%$ for the Calinski–Harabasz score, and between 1 and $5.5\%$ for the Davies–Bouldin score.
The next step was to analyze the number of molecules per cluster when 7 and 20 clusters are requested. These two values were selected considering the optimal number of clusters obtained with the elbow method (Figure 8a) and the results of the clustering metrics (Table 5). Table 6 shows the descriptive statistics of the cluster sizes, for both all 50,555 molecules and 32,971 non-outlier molecules. *In* general, the size of the clusters is well distributed (mean cluster size is close to median cluster size) for all molecules as well as for non-outlier molecules. Moreover, each of the 7 clusters concentrates between 5 and $22\%$ of all molecules, and between 4 and $24\%$ of non-outlier molecules. However, when 20 clusters are required, the smallest clusters in each case concentrate $1.19\%$ and $0.96\%$ of the molecules, respectively. This should be taken into account when performing cross-validation since having few molecules could affect the model performance.
The last step in the clustering quality analysis is to calculate the average similarity matrix for both all molecules and non-outlier molecules. Overall, Figure 9 shows that molecules from different clusters have lower similarity than molecules from the same cluster, which indicates good clustering results. This is consistent with the clustering quality metrics shown in Table 5. Moreover, the similarity values between a cluster and itself (diagonal) improve when only non-outlier molecules are clustered. As for the number of clusters, increasing this number (from 7 to 20) increases the similarity between a cluster and itself from 0.16 to 0.22, for the upper bound.
## 3.5. Comparison of Clustering Algorithms
Finally, we compare the best clustering results obtained with each of the three methods explored in this study. In the case of UMAP clustering, we compare the results when 7 and 20 clusters are required. This comparison is performed for all molecules as well as for non-outlier molecules. Additionally, we include the run time (in minutes) taken by each method. Table 7 summarizes the results of this comparison.
When all molecules are clustered, the UMAP model obtains the best results on all metrics, whether using 7 or 20 clusters. Indeed, the silhouette coefficient improves between 0.339 and 0.521 units with respect to hierarchical and Taylor–Butina clustering. With respect to the Calinski–Harabaz score, although there is no upper limit for this score, the improvement is evident, with more than 34K units. As for the Davies–Bouldin score, the improvement is between 3–76 units. Regarding the run time, the Taylor–Butina clustering consumes 10x more time than the UMAP clustering, which is the fastest method. Similar behavior occurs when non-outlier molecules are clustered. That is, the UMAP clustering achieves the best results in terms of the three quality metrics as well as the run time. Figure 9UMAP clustering with n_neighbors = 100 and min_dist = 0.0. Clustering of (a) all 50,555 molecules into 7 clusters, (b) 32,971 non-outlier molecules into 7 clusters, (c) all 50,555 molecules into 20 clusters, and (d) 32,971 non-outlier molecules into 20 clusters. Outlier molecules were removed using an outlier cutoff of 0.5 (see Section 3.1 for more details about outlier detection). The matrix is calculated as the average Tanimoto similarity between the molecules of cluster i and those in cluster j, and the average similarity between molecules of a cluster and itself (see Section 2.3 for more details on how this matrix is calculated). The matrices have been adjusted to the same scale to facilitate clustering comparison between all molecules and non-outlier molecules. For readability, similarity scores were rounded to three decimal places when seven clusters were requested. When 20 clusters were requested, the similarity scores are not displayed. The average similarity between one cluster and itself (diagonal) is substantially higher than the average similarity in different clusters (off-diagonal), indicating well-defined clusters in both all molecules and non-outlier molecules.
*In* general, removing outlier molecules prior to clustering leads to an improvement in hierarchical and UMAP clustering (with 20 clusters). In the case of the Taylor–Butina clustering, there is no improvement by performing this step; in fact, the silhouette coefficient decreases by $2.5\%$, the Calinski–Harabasz score decreases by $17\%$, and the Davies–Bouldin score decreases by $1.3\%$. The absence of improvement in UMAP with seven clusters when outliers are removed may be due to the fact that according to the elbow method (Figure 8a), this is the optimal number of clusters, and therefore an improvement could be difficult to achieve.
## 4. Discussion
The main aim of this study is to provide an optimal clustering of molecules from the NCI-60 panel, which can be used to generate clusters for model validation. As model validation is a technique to assess model generalization, high-quality clustering results could improve the generalization of ML-based models. However, in studies using clustering methods that derive clusters to be used in cross-validation (LOCCO-CV, for example), the analysis of clustering quality is usually omitted and at best restricted to a single metric. To provide a comprehensive comparison of the clustering results obtained by hierarchical, Taylor–Butina, and UMAP clustering, in this study we show three well-known clustering metrics, along with the similarity matrix. The latter provides information on the structure of each cluster obtained.
The results show that the clustering quality metrics vary from method to method (Table 2, Table 3 and Table 5). Using different cutoffs in hierarchical and Taylor–Butina clustering leads to results that are computationally expensive to use under a cross-validation strategy. Even at cutoff values where there is a trade-off between the number of clusters obtained and the quality of the clustering, according to the similarity matrix (Figure 5 and Figure 6), the molecules in different clusters are similar to each other. This inter-cluster similarity may affect the model performance since similar molecules are present in the training set and the test set. This highlights the importance of complementing the usual clustering metrics by calculating the average similarity matrix.
In methods such as hierarchical or Taylor–Butina clustering, the number of clusters is not an input but a consequence of the selected cutoff level. In the case of UMAP clustering, the number of clusters must be provided by the user. Since this parameter can affect the clustering quality, we address this problem by using the elbow method. However, instead of using it to find the optimal number of clusters, we use it to narrow the search for this number. The results suggest that we can generally get higher-quality clustering if we request a higher number of clusters. However, we require a compromise between the number of clusters and clustering quality as more clusters imply more cross-validations and thus more expensive computation.
In addition to the optimal number of clusters, the elbow method also provides insights into the clusterability of the data set (Figure 8). In this case, this analysis suggests that the molecules in the NCI-60 panel are not very clustered as the inflection point of the curve is not clearly observed (Figure 8b). This has a greater impact on hierarchical and Taylor–Butina clustering, in addition to the similarity distribution of molecules (Figure 3), since these methods also require a cutoff that determines the behavior of the clusters (intra-cluster distance or similarity between molecules).
Hierarchical, Taylor–Butina, and UMAP clustering were also tested to evaluate if the removal of outlier molecules improves clustering quality. We define outlier molecules and evaluate the results according to the desired clustering. In this case, the results demonstrate a benefit when non-outlier molecules are clustered using hierarchical and UMAP clustering. This is not the case for the Taylor–Butina clustering, where a decrease in the metrics is observed (Table 7). This suggests that Taylor–*Butina is* not suitable for clustering molecules having a distribution as shown in Figure 3 since in both scenarios (all molecules or non-outlier molecules) we obtain clusters that concentrate most of the molecules or singletons. While the adopted outlier detection method improves clustering, we believe that a comprehensive search for an optimal method using high-quality packages such as PyOD [37] is likely to result in further improvement.
In summary, there are many factors influencing clustering problems, particularly the clustering of molecules. Models that are not properly validated are susceptible to reduced performance in unseen compounds; therefore, poor clustering of molecules can lead to the poor estimation of model generalizability. Since results showed different clustering quality metrics with respect to the method used, special care must be taken in this task.
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|
---
title: Improving the Assessment and Diagnosis of Breast Lymphedema after Treatment
for Breast Cancer
authors:
- Katie Riches
- Kwok-Leung Cheung
- Vaughan Keeley
journal: Cancers
year: 2023
pmcid: PMC10046280
doi: 10.3390/cancers15061758
license: CC BY 4.0
---
# Improving the Assessment and Diagnosis of Breast Lymphedema after Treatment for Breast Cancer
## Abstract
### Simple Summary
Lymphedema affecting the breast can develop after breast cancer treatment. Currently breast lymphedema isn’t well recognised nor techniques to measure lymphedema affecting the breast as well studied. This paper explores the validity and reliability of measures which can be used to determine the presence of breast lymphedema. Women with and without breast lymphedema were included in this study to enable comparisons to be made. Improving the assessment of breast lymphedema will advance clinical practice and enable the outcome of treatment to be reported. Ultrasound and tissue dielectric constant were found to be able to reliably distinguish between edema and non edematous breast tissue. In addition certain patient characteristics and breast caner treatments were found to associated with the development of breast lymphedema.
### Abstract
Lymphedema can develop after treatment for breast cancer (BCRL). Lymphedema of the breast is not well studied. Currently, the main techniques used to diagnose and monitor the effectiveness of treatment are subjective clinician assessment and patient reports. Eighty-nine women who had undergone breast cancer treatment were recruited with and without breast lymphedema. Blinded clinical assessment determined the presence or absence of breast lymphedema. Measurement of skin thickness by ultrasound scanning, local tissue water by tissue dielectric constant (TDC) and tissue indentation by tonometry was recorded. Breast cancer treatment and demographic details were documented. Descriptive statistics were undertaken to compare sample characteristics, including the Chi-squared test, Odds Ratio (OR) and Relative Risks (RR) calculated. Increased body mass index (BMI), larger bra size, increased number of positive lymph nodes, axillary surgery, chemotherapy and increased Nottingham Prognostic Index (NPI) were all associated with breast lymphedema ($p \leq 0.05$). Ultrasound and TDC measurements were significantly higher in the lymphedema group ($p \leq 0.05$). Receiver Operator Characteristic (ROC) curves demonstrated that ultrasound and TDC measurements could distinguish between edematous and non-edematous breasts. Threshold levels were produced, which demonstrated good levels of sensitivity and specificity. These findings have the potential to improve the diagnosis of breast lymphedema.
## 1. Introduction
Lymphedema or chronic oedema are terms used interchangeably to describe the lymphatic system’s failure or inadequacy [1]. Oedema is often the most recognised consequence of lymphedema, but other effects include skin and tissue changes and a predisposition to infection. Lymphedema arises when there is an imbalance between capillary filtration and lymphatic drainage from the interstitial spaces, which can be due to a variety of causes [1].
Lymphedema can develop after treatment for breast cancer due to damage caused to the lymphatic drainage affecting the arm, breast, and/or chest wall. Most of the research on this topic is focused on the incidence and outcomes of breast cancer-related lymphedema (BCRL) of the arm. Lymphedema affecting the breast is not as well studied nor recognized as often in clinical practice. In some cases, the presenting signs and symptoms of breast lymphedema are misdiagnosed as inflammation occurring post-radiotherapy. Lymphedema affecting the breast can occur alongside or without arm lymphedema.
Patients often report breast heaviness, pain/tenderness, skin changes and swelling. These symptoms can be significant and impact the clothes that the patient feels comfortable wearing and the activities they are able to complete.
The assessment and diagnosis of breast lymphedema are further complicated as, currently, there are no recognised objective assessment techniques for quantifying the degree or presence of breast lymphedema. At present, it is assessed following clinical examination, patient report and, on occasion, the use of pre-and post-treatment photographs. In addition, the risk factors pertaining to breast lymphedema are not as well studied, and confounding results have been presented. This presents challenges not only in the recognition and diagnosis of the condition but in evaluating treatment outcomes.
There appears to be a paucity of studies on breast lymphedema, and this area often is omitted when lymphedema following breast cancer treatment is considered. The reasons for the lack of research in this area are not known. Breast-conserving surgery (BCS) is well-established as a surgical technique with proven effectiveness in overall breast cancer survival [2]. Breast cancer screening programmes enable earlier diagnosis of breast cancer, influencing the treatment required and enabling more women to be treated with BCS [3,4]. Therefore, the proportion of women who are at risk of mid-line lymphedema, particularly breast lymphedema, is considerable.
Breast lymphedema has been reported in several studies since as early as 1982 but does not appear to be as well recognised clinically or researched as frequently as arm lymphedema [5]. In addition, the reported prevalence varies depending on the methodology and diagnostic criteria of the study but has been reported to be as high as $75.5\%$ [6]. Therefore, further research is required to improve the recognition and impact of breast lymphedema. In addition to the physical changes and symptoms associated with lymphedema, the literature reports that lymphedema can negatively impact a patient’s quality of life. Furthermore, this relationship is not purely linear in that quality of life does not decline as the severity of measured lymphedema increases [6,7,8,9]. Therefore, it cannot be inferred that assessing lymphedema using physical measures only can holistically assess the impact of this condition on the patient. Having breast lymphedema has been associated with poorer body image and reduced quality of life compared to those who have had breast cancer but without breast lymphedema [6].
There are only a few published studies that focus on breast lymphedema. The majority of previous work used subjective reporting to identify the presence of breast lymphedema; however, there was inconsistency in the signs and symptoms used to define it [6,10,11]. Additionally, the methodologies applied vary, including breast lymphedema diagnosed after retrospective reviewing of medical notes and prospective multiple assessment studies [5,10,11,12]. The reported risk factors associated with the development of breast lymphedema are similar to those associated with arm lymphedema. These include axillary node surgery, radiotherapy, raised body mass index (BMI) and bra size [5,10,11,12,13,14,15,16]. However, from the literature, not all risk factors were found to demonstrate statistically significant relationships, and in several studies, conflicting results were found.
Quantitative measurement techniques that are used to assess lymphedema affecting other areas have been used in studies focusing on breast lymphedema. These include ultrasound, tonometry and local tissue water measured by tissue dielectric constant (TDC) [11,12,13,14,17,18,19,20,21,22].
These have all demonstrated some potential to distinguish between edematous and non-edematous breast tissue but have limitations in their application. Additionally, diagnostic thresholds have not been proposed for each of these measurements, or the ranges suggested have not yet been validated in breast lymphedema. A diagnostic threshold for breast TDC has been produced from a cohort of women without breast lymphedema. This was calculated using the mean plus 2 standard deviations of breast TDC in a group of healthy volunteers ($$n = 15$$). A ratio of >1.4 was deemed to represent breast oedema [19]. Table 1 provides an overview of the strengths and limitations identified in using these measurements in the assessment of breast lymphedema.
The limitations identified from the available techniques to assess breast lymphedema demonstrate why no gold standard quantitative measurement technique exists.
This study’s principal aim and focus was to improve the assessment and recognition of breast lymphedema and to increase the understanding of this type of lymphedema. The overarching objective was to validate objective measures to accurately assess for the presence of breast lymphedema, which will improve clinical practice. The different objectives included:I.To consider the risk factors previously associated with the development of arm and breast lymphedema and to determine whether they apply to this study sample;II.To test and validate the objective measurement techniques identified from the literature and determine whether they can be used in the assessment of breast lymphedema;III.It was hypothesised that the measurements would differ significantly between those with and without breast lymphedema. It would be expected that they would have higher values in the group with breast lymphedema.
## 2. Materials and Methods
The design of this study enabled several objectives to be proposed with different hypotheses tested.
## 2.1. Study Design and Ethical Approval
Prior to any data collection, Research Ethics approval was obtained from the NRES Committee North West—Haydock, reference 15/NW/0608. Local approval and study sponsorship were obtained from the University of Nottingham.
The study recruited a convenience sample of women who attended the Breast Care Unit or the Lymphedema clinic. This included women with and without breast lymphedema.
## 2.2. Participants
A sample size calculation was undertaken. Applying a confidence level of 0.95, using a 2-sided interval with an expected proportion of 0.80 for sensitivity and specificity and a precision (width of confidence interval) of 0.12, a sample of 86 participants was required. This was based on 43 participants being enrolled who had clinically assessed breast oedema and 43 without.
For the measurement techniques observed to be able to distinguish between edematous and non-edematous breast tissue receiver operating characteristic (ROC) analysis was performed alongside sensitivity and specificity calculations.
The inclusion criterion for the study was that all patients were; female; >18 years of age; had undergone previous treatment for breast cancer, which included wide local excision (WLE); able to provide informed consent and had no clinical evidence of breast cancer recurrence to ensure that the presence of active disease or treatment for active disease did not influence the measurements.
## 2.3. Data Collection
All participants attended at least one study appointment at which written informed consent for study participation was obtained. At the appointment, each participant underwent blinded clinical assessment and measurement of the four breast quadrants of both breasts using tonometry, ultrasound and TDC. Breast cancer treatment data and demographic information were collected.
Although the literature review identified limitations for each measurement technique, they have all been used to determine changes in the size, tissue composition and tone of limbs to indicate the presence or absence of lymphedema. This study explored whether they can be considered valid and reliable techniques in assessing breast lymphedema. Measurement difficulties or concerns with the accuracy of each measurement were recorded to enable the application of each method to be evaluated. Measurements were undertaken in each of the four quadrants of the breast to enable comparisons to be made with the corresponding quadrant of the contralateral breast. With the exception of tonometry, repeated measurements were obtained, 2 ultrasound measurements for each quadrant and 3 TDC measurements for each quadrant. The mean of each of the measurements was calculated.
## 2.3.1. Blinded Clinical Expert Assessment/Confirmation of Breast Oedema
The blinded clinical assessment was completed by a consultant physician with over 25 years of experience in lymphology. All participants were examined whilst lying supine. Assessment of the breast was undertaken by examining each of the four quadrants for pitting oedema individually. The “pitting” test required the clinician to press firmly for several seconds with a thumb or finger onto the examined area; once removed, if the finger/thumbprint remained, then pitting oedema was deemed present. Pitting oedema was required to be present in at least one of the four breast quadrants for that participant to be identified as having breast lymphedema. Other signs and symptoms that were assessed as part of the clinical examination included skin changes; recognised as a thickening or a peau d’orange appearance, redness or inflammation, tenderness on palpation, an increase in temperature compared to the contralateral breast and the presence of seam marks or indentations from clothes or bra. These were all assessed as present or absent.
## 2.3.2. Measurement Using Tonometer
Prior to use, the tissue tonometer (Flinders, Australia) was calibrated using the supplied calibration plate. If it was not possible to measure all four of the breast quadrants as the tonometer could not be applied to the lower quadrants of some breasts, particularly for participants with large, ptotic breasts, this was noted. Tonometry was not repeated because the weighted probe will indent edematous tissue and, therefore, cannot be repeated in the same area until fluid in the tissues has re-accumulated.
## 2.3.3. Measurement Using Ultrasound
Ultrasound measurements were obtained using the Sonosite Edge Ultrasound (FujiFilm Sonosite, Amsterdam, The Netherlands). A high-frequency 6–15 MHz probe was used. The participant was laid supine with the corresponding arm raised above the head. Participants were positioned this way to improve sound penetration and enable good visualisation of the breast quadrants [14]. A thick layer of ultrasound gel was applied, and the transducer was positioned perpendicular to the skin with gentle pressure applied. This was undertaken to ensure complete contact with the breast and eliminate any air pockets which could block sound waves passing through. Live images were produced by the ultrasound device from which individual frames were saved and measurements obtained. The area chosen to measure was always perpendicular, and the measurement start/end points were from the anterior echogenic border of the epidermis to the posterior echogenic border of the dermis.
## 2.3.4. Measurement Using Tissue Dielectric Constant (TDC)
The moisture meter (Delfin Technologies, Kuopio, Finland) was used to obtain TDC measurements. An electromagnetic wave is directed into the tissue by an open-ended coaxial probe, creating an electromagnetic field in the tissue. Depending on the relative permittivity of the tissue, or dielectric constant of the tissue, the alterations in magnitude and phase of the electromagnetic wave that travels through the tissue vary. The dielectric properties of a tissue responsible for this wave shift are directly influenced by the total amount of water in a tissue. The TDC value is directly proportional to the water content in the tissue being assessed; therefore, higher values were expected in an edematous breast. Theoretically, the value obtained can range from 1, indicating no water, to 78, indicating $100\%$ water in the measured area.
## 2.3.5. Patient Characteristics and Breast Cancer Treatment
Patient and treatment characteristics were recorded, including; breast cancer surgery specifying the type and grade of cancer, number of lymph nodes removed and number of lymph nodes positive, adjuvant breast cancer treatment (chemotherapy, radiotherapy and hormone treatment), postoperative complications such as infections, wounds and seromas, BMI and bra size (cup and chest circumference).
## 2.4. Data Analysis
Data analysis included descriptive statistics to explore the characteristics of the whole sample group and whether there were differences between those who did and did not have breast lymphedema. This included the Chi-squared test, Odds Ratio (OR) and Relative Risks (RR).
The data from the objective measurement tools were compared and analysed using several methods. Two sample and paired t-tests or their non-parametric equivalents were undertaken to compare the different groups. Measurements from the affected breast were compared with those from the same quadrant on the contralateral breast. It was hypothesised that the measurements of both the individual quadrants and mean total breast measurements would differ in the group with clinically diagnosed breast lymphedema. It was postulated that the TDC values for the affected quadrants and the overall ratios would be higher in the lymphedema group. For the ultrasound measurements, it was expected that total cutaneous skin thickness would be thicker in the lymphedematous breast quadrants. Finally, the tonometer readings were expected to be higher in the breast quadrants with lymphedema.
Receiver Operator Characteristic (ROC) curves and the Area Under the Curve (AUC) statistics were undertaken to enable diagnostic threshold level to be produced. From these, the sensitivity and specificity calculations plus positive (PPV) and negative predictive values (NPV) and positive and negative likelihood ratios were calculated, and comparisons made of the threshold levels against the “gold standard” clinical assessment as the determinant of the presence/absence of breast lymphedema for each of the objective measurement techniques.
## 3. Results
Patients were approached over a 20-month period, and 89 women consented to participate in the study.
## 3.1. Sample Characteristics
Of the 89 participants, 40 ($44.9\%$) were found to have breast lymphedema present. The majority of participants ($\frac{29}{40}$) had at least two quadrants of the breast that were found to have pitting oedema, with six participants having all four quadrants affected. The lower half of the breast was most commonly affected when the individual breast quadrants were compared.
The mean age of the sample was 61.1 years (standard deviation, sd = 9.6 years, range 29–80 years). The length of time from surgery to study participation ranged from 6 months to 12 years. The mean BMI was 29.25 (sd = 5.81, range 19.2–45.14). The majority of the sample was right-hand dominant ($92.1\%$). Bra size by cup and band circumference was recorded for 81 participants. Fifty-two different bra sizes were worn by the sample, and the most common bra size was 36C. Cup size ranged from an A cup to a HH cup, and band circumference was from 34 inches to 50 inches. The entire sample had undergone a WLE, with 67 participants undergoing a sentinel node biopsy (SNB) and 23 having had an axillary node clearance (ANC). Six women had undergone both procedures, and five had not undergone any axillary procedure. The most common location of breast cancer was in the upper outer (UO) quadrant, being affected in $59.8\%$ of the sample, followed by the lower outer quadrant ($20.7\%$). Approximately half of the group had a grade II tumour, and one-third had a grade III tumour. Twenty-two ($26.2\%$) were found to have lymphatic or vascular invasion (LVI) present on histopathology. The Nottingham Prognostic Index (NPI) ranged from 2.06–6.8, with a mean NPI of 3.98 (sd 1.102). The number of lymph nodes (LN) removed ranged from 0–38. The mean number of LN removed was 6.17 (sd 8.442), and the median number of nodes removed was two (interquartile range, IQR, 1–10). The number of LN removed, and the presence of metastatic deposits varied depending on the extent of the procedure(s) to the axilla. Adjuvant treatments included chemotherapy ($\frac{40}{84}$, $44.9\%$), radiotherapy ($\frac{88}{89}$, $98.9\%$) and hormone therapy such as Tamoxifen, Anastrazole and Letrozole ($\frac{65}{84}$, $73\%$). The chemotherapy regimens varied depending on the length of time between cancer treatment and study participation. Details of radiotherapy treatment were recorded for $\frac{87}{88}$ participants who received it. The entire group received radiotherapy to the breast, the most common dose being 40 Gray (Gy) ($70\%$).
## Breast Cancer Treatment and Breast Lymphedema
Comparison of the type of axillary surgery or at least one positive lymph node in the presence of breast lymphedema was undertaken using the X2 test. More participants were observed than expected with breast lymphedema in the ANC or positive lymph node group; this difference was statistically significant in both cases (see Table 2).
Further calculation of the relative risk demonstrates that patients who underwent an ANC or had at least one positive LN were almost twice as likely to develop breast lymphedema as those with an SNB or negative lymph nodes. These groups were not mutually exclusive. Although most participants with lymph node-positive disease had undergone an ANC, some participants with lymph node-positive breast cancer had only undergone an SNB.
The entire sample except one patient received radiotherapy; therefore, no comparisons could be made between those who had or did not have radiotherapy. Table 3 displays the number of participants who received chemotherapy and hormone therapy. The X2 test was not significant when hormone therapy was considered. However, more participants in the breast lymphedema group had received chemotherapy ($$p \leq 0.031$$, RR = 1.657).
There was no significant difference between the age of the participant and the presence or absence of breast lymphedema (t-test, $$p \leq 0.375$$).
Other characteristics did demonstrate statistically significant differences between those with and without breast lymphedema. The mean values for weight, BMI and NPI were higher in the lymphedema group ($p \leq 0.001$, 0.001 and 0.04, respectively).
Median chest circumference was higher in the lymphedema group at 40 inches, compared to 36 in the non-lymphedema group ($p \leq 0.001$), see Figure 1.
Comparing bra cup size, the distribution varied between the two groups; the lymphedema group wore larger cup-sized bras (Figure 2). The majority of the group without breast lymphedema wore a C cup or smaller ($63.8\%$); however, this cup size was less frequently worn in the lymphedema group ($23.5\%$). The most commonly worn bra cup in the lymphedema group was a DD ($20.6\%$). The Mann-Whitney U test confirmed a statistically significant difference between these groups ($p \leq 0.001$).
## 3.2. Ability to Distinguish Edematous and Non-Edematous Breast Quadrants Using Tonometry
Comparing the affected and unaffected measurements obtained by the tonometer, p values were >0.05 for each of the four breast quadrants. This result supports that the null hypothesis is accepted and that there is no significant difference in the tonometer measurements for any of the four quadrants A comparison of the median values demonstrated that these are similar in both groups.
## 3.3. Ability to Distinguish Edematous and Non-Edematous Breast Tissue by Ultrasound
Using the paired sample t-test, mean skin thickness ultrasound measurements were significantly higher in the affected breast quadrant compared to the contralateral (unaffected) breast quadrant (all $p \leq 0.05$) for each of the four quadrants (Table 4). In each of the four quadrants, the mean skin thickness in the affected group was approximately double the measurements of the corresponding unaffected quadrant. The inner quadrants were thicker in both the affected and unaffected groups compared to the outer quadrants. As there were fewer participants with lymphedema in the upper quadrants, the numbers in these groups were smaller.
## 3.4. Ability to Distinguish Edematous and Non-Edematous Breast Quadrants Using TDC
Applying the paired samples t-test, mean TDC readings were significantly higher in each of the affected breast quadrants compared to the unaffected breast quadrants. These were comparable with the ultrasound measurements. The inner quadrants had higher TDC readings, but the difference between these and the outer quadrants was by a few units only (Table 5).
When TDC is measured commonly, the raw data is not used, but a comparison is made between ratios comparing affected and unaffected area(s). Using the Independent samples t-test, the mean ratio was significantly higher in the group with breast lymphedema than in the non-lymphedema group ($p \leq 0.001$) (Table 6). The measurements from the four breast quadrants were added together, and a ratio of the affected to the unaffected breast was produced (Table 7).
## 3.5.1. ROC Analysis for Tissue Dielectric Constant for the Whole Breast
The AUC statistic for TDC is 0.901, standard error = 0.032 and produced a $95\%$ confidence interval of 0.839–0.964 (Figure 3). Analysis of the ROC curve identified a TDC threshold using a ratio of 1.34, producing a sensitivity of $87.5\%$ and specificity of $79.6\%$ (Table 8).
The high PPV and NPV identified that approximately $78\%$ and $87\%$ of participants with or without breast lymphedema were correctly identified using the TDC threshold of 1.34. The TDC threshold ratio of >1.34, the positive likelihood ratio (+LR) of 4.29 and the negative likelihood ratio (-LR) of 0.157 indicate that these tests may improve the assessment of breast lymphedema.
## 3.5.2. ROC Analysis for Ultrasound Measurement
The AUC, associated p values, sensitivity and specificity results support the use of skin thickness measurement of the breast quadrants by ultrasound scanning in the identification of breast lymphedema (Table 9). The PPV identified that approximately 44–$58\%$ of the sample with a positive test did have breast lymphedema (Table 10). However, the NPV values are much higher, 87–$99\%$ indicating that participants with a “normal” ultrasound measurement did not have breast lymphedema. Comparable to TDC data, the positive and negative likelihood ratios for the USS thresholds again indicate that these tests add value to the assessment of breast lymphedema.
## 4. Discussion
This study had several aims, all associated with improving the diagnosis and recognition of breast lymphedema after breast cancer. It appears that some of the risk factors associated with the development of lymphedema affecting the arm are also risk factors for the development of breast lymphedema. In addition, the relationship between increased breast size and the development of breast lymphedema was established in this study. The measurement of skin thickness by ultrasound scanning and skin water measurement by tissue dielectric constant were both valid methods for assessing breast lymphedema. ROC analysis produced threshold values that could be applied in practice to distinguish between edematous and non-edematous breast tissue. In this study, the tissue tonometer was not found to be reliable in distinguishing between the edematous and non-edematous breast tissue and, therefore, not recommended as an assessment tool for breast lymphedema.
The analysis identified that the treated breast skin was thicker by ultrasound measurement and had a higher TDC reading than the non-treated breast, even in participants who did not have breast lymphedema.
Repeated assessment confirmed the reliability of the ultrasound and TDC measurements. Repeated measurements did not differ significantly when comparing the initial and second assessment measurements.
The characteristic of the study sample in relation to age, breast cancer diagnosis and treatment are comparable to the UK breast cancer population, which supports the findings being applied to clinical practice [24,25,26]. Breast lymphedema is not well studied but is a growing topic. Participants recruited in this study had undergone breast cancer surgery up to 12 years earlier, recognising breast lymphedema as an ongoing problem for some women.
## 4.1. Risk Factors
Several risk factors pertaining to the development of breast lymphedema were identified, including lymph node-positive breast cancer, ANC, NPI, BMI and bra size. This is valuable information which will support the education of patients undergoing breast cancer treatment. It has been suggested that breast lymphedema should be considered and the risks of developing this explained to patients to ensure informed treatment decisions are made [27]. In addition, identifying those at higher risk could be used to determine who might benefit from monitoring post-operatively.
Having lymph node-positive disease and ANC demonstrated an increased risk of breast lymphedema than those with lymph node-negative disease or requiring an SNB. This is unsurprising as the participants who underwent an ANC would have done so due to having had a malignant axillary lymph node identified as part of their breast cancer diagnosis. Previously, in a large prospective study of breast oedema, ANC and adjuvant chemotherapy were identified as associated risk factors at time points up to 18 months post-radiotherapy [27]. This finding may also be related to the risk factors; receipt of chemotherapy and high NPI, reflecting that breast lymphedema may develop because of more advanced breast cancer and the intensive treatment it requires. Future research focusing on confounding variables and multicollinearity would provide information on whether these are individual risk factors or interrelated.
In this study, larger chest circumference and larger bra cup size were associated with the presence of breast lymphedema. In the general population, bra or bust size has been recognised to be increasing as the body size of the population increases [28]. The average UK bra size was reported to have increased from 34B in 2008 to a 36DD in 2019 [28].
Over two-thirds of participants received surgery to remove a tumour from the upper breast quadrants; however, only one participant had oedema present in the upper quadrant only. For those who had oedema in one or two of the four breast quadrants, it was in the lower quadrants. This raises the question of whether it is breast cancer treatment, including surgery and radiotherapy, which impairs the lymphatic drainage from the lower half of the breast, which causes oedema. A similar presentation of lymphedema is observed when hand and forearm swelling develops after breast cancer treatment, which is focused away from the operated area of the breast and axilla. Questions regarding the lymphatic drainage pathways of the arm and breast following axillary surgery have recently been raised, specifically whether, in some cases, they are able to regenerate themselves, the surgical breaks filled and the drainage pathways repaired [29]. In a recent study, imaging of the arm and breast lymphatics using indocyanine green (ICG) fluorescence lymphography after ANC treatment for breast cancer demonstrated several different variations in lymphatic flow. In addition to regenerated lymphatic pathways seen, lymphatic drainage appeared to cross the midline and drain into the contralateral axilla [29]. An alternative consideration is whether increased breast size creates a venous hypertension effect on the breast resulting in more fluid for the lymph system to drain. This may be contributed to by the gravitational effect of larger, ptotic breasts. Such a phenomenon is recognised in lymphedema associated with significant obesity that affects the abdominal apron or other areas and the development of massive localised lymphedema [30]. In such cases, increased capillary filtration with overloaded regional lymphatics results in oedema development [30]. These symptoms are also associated with the presence of chronic oedema of the legs due to venous hypertension.
## 4.2. Assessment of Breast Edema
In this study, the tissue tonometer could not distinguish edematous and non-edematous tissue, the median readings obtained were similar, and the analysis did not reach significant levels. The defining characteristic for determining oedema in any breast quadrant was the presence of pitting oedema during the clinical assessment. This mimics the technique of the tonometer. It was, therefore, surprising that the tonometer did not record higher readings in the edematous breast quadrants.
In addition, readings could not be obtained for all the study participants as the tonometer could not be positioned correctly to enable the measurement to be undertaken; this was more common when measuring the lower breast quadrants.
Measurement of dermal thickness by high-frequency ultrasound scanning demonstrated this technique to be valid and reliable in assessing breast lymphedema. This study has shown that ultrasound measurements can be used to distinguish edematous and non-edematous breast tissue, and reproducible measurements were obtained at repeated assessments. The procedure was well tolerated, and measurements were able to be performed on all of the participants in this study.
Similar to other studies, the measurements obtained in this study differed depending on the quadrant or part of the breast being measured. The ultrasound measurement points, and the number of measurements obtained in this study vary from other studies, including a recently published study [31]. The medial (inner) aspect(s) of the breast in all of the studies, including this study, were thicker than the lateral (outer) aspect(s). This relationship was consistent for the affected and unaffected breasts. In this current study, the highest measurement for the edematous breast was in the upper inner quadrant (UIQ), and this varied from the literature, which identified the lower breast quadrants as the thickest. The upper outer quadrant (UOQ) was the thinnest in this and all of the studies reported in the literature for both the affected and unaffected breasts. In addition, the TDC readings were lowest in this quadrant. This was an unexpected result as if the lymphatic drainage of the breast is through the axillary lymph nodes, it would be postulated that there would be more fluid draining through this quadrant resulting in higher USS and TDC measurements. Additional research using live imaging techniques to assess lymphatic flow, such as lymphoscintigraphy or ICG lymphography, would be useful to study this finding further.
In this study, there were only 11 participants who had oedema in the upper inner breast quadrant; therefore, any outliers in this group would potentially have increased the mean of the measurements obtained. However, the confidence interval and standard deviation produced in this subgroup are similar to those produced for the other subgroups for both USS and TDC measurements.
However, the USS measurements proposed in this current study differ from the thresholds suggested in a recently published study [31]. In that study, the USS cut-offs were 1.6 mm (outer quadrants) and 2.0 mm (inner quadrants). The thresholds proposed in this study are higher for each of the four quadrants. This difference may result from the measurement points not being identical in the two studies. In this study, the measurements were obtained in the middle of the breast quadrants and on the boundary between quadrants in the other study. The high negative predictive values from this current study demonstrated that using these measurements as a threshold for diagnosis would result in few false negative classifications. Therefore, the clinicians would be able to provide reassurance or confidence in diagnosing breast lymphedema. Normal breast skin thickness varies between 1 and 2 mm, with a mean of 1.7 mm [32]. The thresholds suggested in this study all exceed 2 mm. Therefore, a higher diagnostic threshold, such as that proposed in this current study, might be preferred to reduce overdiagnosis and false positives.
The TDC ratio threshold proposed in this study (1.34) is at the midpoint between the two previously proposed ratios, 1.4 for breast oedema and the initial TDC threshold of 1.26 for determining the presence of forearm lymphedema [20,23]. Using either the 1.4 or 1.34 ratio threshold increases the specificity and PPV but reduces the sensitivity and NPV. None of the TDC thresholds correctly diagnosed the entire sample, which was to be expected. If an assessment technique is used to screen patients and identify those who may have the condition in question for further assessment or to reassure those at risk of the condition that they do not have it, then a threshold may be selected due to the desired sensitivity, specificity, PPV or NPV.
The price, portability and simple training required to use the moisture meter, compared to obtaining ultrasound images, would identify this as a technique that could be used in the screening and preliminary assessment of breast lymphedema. Therefore, a lower threshold, such as the 1.34 proposed in this study, could be used as the threshold for breast lymphedema screening when referral to a lymphedema service is recommended for a more detailed assessment to be undertaken.
## 4.3. Limitations of the Study
Limitations of this study include that a single clinician undertook the clinical assessment. It had been hoped that an additional physician would also be present to complete a second assessment, enabling inter-rater reliability to be examined. On reflection, we could have obtained photographs of the participants’ breasts which could have been reviewed by other clinicians in the team. In addition, the temperature of the breasts could have been recorded, which would have provided a physical measurement and strengthened the assessment. The identification of raised breast temperature was a subjective assessment following palpation of each breast.
The analysis identified that some patients appear more likely to develop breast lymphedema due to individual characteristics, breast cancer treatment and cancer histology. This study was not powered to undertake multivariate analysis; further research with a larger sample is needed. This information would enable clinicians to discuss individual risks and identify which patients might benefit from additional monitoring.
## 5. Conclusions
The findings from this study add to the existing knowledge base and have a strong clinical application that can be applied to improve the assessment and diagnosis of breast lymphedema. Identifying potential risk factors provides information that can be shared with patients before breast cancer treatment to educate them on the potential risk of lymphedema development. Breast lymphedema appears more common in patients with larger breasts and/or more advanced breast cancer.
In addition, the measurement thresholds proposed for USS and TDC could be applied in clinical practice to aid the diagnosis of breast lymphedema. Further research using these techniques would enable the treatments provided for breast lymphedema to be evaluated.
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|
---
title: 'Body Composition of Patients Undergoing Radical Cystectomy for Bladder Cancer:
Sarcopenia, Low Psoas Muscle Index, and Myosteatosis Are Independent Risk Factors
for Mortality'
authors:
- Simon U. Engelmann
- Christoph Pickl
- Maximilian Haas
- Sebastian Kaelble
- Valerie Hartmann
- Maximilian Firsching
- Laura Lehmann
- Miodrag Gužvić
- Bas W. G. van Rhijn
- Johannes Breyer
- Maximilian Burger
- Roman Mayr
journal: Cancers
year: 2023
pmcid: PMC10046300
doi: 10.3390/cancers15061778
license: CC BY 4.0
---
# Body Composition of Patients Undergoing Radical Cystectomy for Bladder Cancer: Sarcopenia, Low Psoas Muscle Index, and Myosteatosis Are Independent Risk Factors for Mortality
## Abstract
### Simple Summary
Assessment of body composition in bladder cancer patients has not been sufficiently performed in larger patient cohorts. In other tumor entities, implications of the prognostic value of certain body composition traits have been made. The aim of our retrospective single-center study on 657 patients was to assess different muscle and adipose tissue indices in order to identify those relevant as prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in bladder cancer patients. We also aimed to assess different thresholds described in the literature for other tumor entities. Unification and consensus of definitions are urgently needed in this field of research. We identified sarcopenia, low psoas muscle index (PMI), and myosteatosis as independent risk factors for OS and CSS.
### Abstract
Background: We assessed a wide array of body composition parameters to identify those most relevant as prognostic tools for patients undergoing radical cystectomy (RC) due to bladder cancer (BC). Methods: *In this* retrospective, single-center study, preoperative computed tomography (CT) scans of 657 patients were measured at the level of the 3rd lumbar vertebra (L3) to determine common body composition indices including sarcopenia, myosteatosis, psoas muscle index (PMI), subcutaneous and visceral fat index (SFI and VFI), visceral-to-subcutaneous fat ratio (VSR), and visceral obesity. Predictors of overall survival (OS) and cancer-specific survival (CSS) were identified in univariate and multivariate survival analysis. Results: Sarcopenia and a low PMI were independently associated with shorter OS (Sarcopenia: HR 1.30; $95\%$ CI 1.02–1.66; $$p \leq 0.04$$ and a low PMI: HR 1.32; $95\%$ CI 1.02–1.70; $$p \leq 0.03$$) and CSS (Sarcopenia: HR 1.64; $95\%$ CI 1.19–2.25; $p \leq 0.01$ and a low PMI: HR 1.41; $95\%$ CI 1.02–1.96; $$p \leq 0.04$$). Myosteatosis, measured as decreasing average Hounsfield units of skeletal muscle, was an independent risk factor for OS (HR 0.98; $95\%$ CI 0.97–1.00; $$p \leq 0.01$$) and CSS (HR 0.98; $95\%$ CI 0.96–1.00; $p \leq 0.05$). The assessed adipose tissue indices were not significant predictors for OS and CSS. Conclusions: Sarcopenia, a low PMI, and myosteatosis are independent predictors for OS and CSS in patients undergoing radical cystectomy for bladder cancer.
## 1. Introduction
Bladder cancer (BC) is the ninth most common cancer worldwide with a yearly incidence of approximately 430,000 cases [1]. BC is male predominant and is the fourth most common in men in industrial nations such as the USA and Germany [2,3]. For muscle invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) refractory to instillation therapy, radical cystectomy (RC) represents the gold standard therapy [4]. Although radical cystectomy is performed with curative intent, patients have a 5-year overall survival (OS) rate of approximately 50–$60\%$ [2,5]. Known variables affecting survival after RC include age, histopathologic characteristics, and comorbidity [6,7]. In recent years, sarcopenia was found to be an independent predictor of survival in several tumor entities, including BC [8,9]. Further investigations have shown that aside from sarcopenia, other constitutions of body composition also seem to influence the outcome of various cancer entities [10,11,12].
A wide array of relevant body composition parameters has been identified. Although different nomenclature and thresholds are used throughout the literature, the main parameters include the skeletal muscle index (SMI) [13,14,15,16], the psoas muscle index (PMI) [17], skeletal muscle Hounsfield units (SMHU, also known as skeletal muscle radiation attenuation [SMRA] or skeletal muscle density [SMD]) [14,15,18,19], the subcutaneous fat index (SFI, also known as the subcutaneous adipose tissue index [SATI]) [13,20], the visceral fat index (VFI, also known as the visceral adipose tissue index [VATI]) [16,20], the visceral adipose tissue area (visceral obesity) [13,15,20], and the visceral-to-subcutaneous fat ratio (VSR) [13,16].
Sarcopenia, being the most commonly assessed body composition parameter, was found to be a predictor for survival in several malignancies including ovarian cancer, colorectal cancer, cholangiocarcinoma, gastric cancer, pancreatic cancer, prostate cancer, and BC [13,16,21,22,23,24]. Similarly, the psoas muscle index and myosteatosis are prognostic factors in different tumor entities including urologic tumors [10,17,25,26,27,28,29]. Adipose tissue distribution measurements are more complex to grasp due to their heterogeneity. Increased adipose tissue was associated with both positive and negative effects in previous studies. By observing the adipose tissue distribution and differentiating between short- and long term effects, an “obesity paradox” has been postulated for the protective effect of adipose tissue for long term survival in different tumor entities [30].
In BC research, currently there are only few existing studies on sarcopenia and body composition. Sarcopenia was shown to be a significant predictor for shorter overall and cancer-specific survival [9,31]. The PMI in BC patients has shown diverging significance in past studies [17,32]. Myosteatosis has rarely been investigated in BC patients. One study identified myosteatosis as a risk factor for survival after radical cystectomy, showing a similar effect as described in other tumors [33]. Adipose tissue distribution has not yet found its place in risk stratification for survival in BC patients. Previous studies have indicated a protective effect of adipose tissue on survival outcome. However, no clear significant implications have been made so far [34,35].
The aim of this study was to assess the wide array of commonly established body composition parameters and different thresholds described in the literature as prognostic factors for survival after radical cystectomy due to BC and to place these parameters into context for BC research. Furthermore, we aimed to identify certain body composition prognostic factors for this tumor entity that can be easily assessed and used to evaluate prognosis after RC.
## 2. Patients and Methods
Ethical approval was granted by the institutional ethics committee of the university hospital Regensburg (approval number: 16-101-0095).
## 2.1. Patients
In this single-center retrospective study, 807 patients who underwent RC due to BC between 1 August 2004 and 31 December 2020 were selected. Further inclusion criteria were a preoperative computed tomography (CT), no longer than 3 months prior to RC, and availability of follow-up data. A total of 657 met the eligibility criteria and remained in the study cohort (Figure 1). Patient data, tumor staging information, and comorbidities were collected using in-hospital patient records. Comorbidities were quantified using the Adult Comorbidity Evaluation 27 (ACE-27) scoring model [36]. Tumor staging, age, comorbidities (ACE-27), and perioperative chemotherapy were identified as confounding variables that might influence survival. Follow-up data were collected from hospital patient records, patient telephone interviews, local urologists, and general practitioners. The data collected included last follow-up date, date of death, and cause of death. Overall survival is defined as the time from radical cystectomy to death, regardless of cause of death, and cancer-specific survival was defined as the time from radical cystectomy to death due to bladder cancer progression or metastasis causing death.
## 2.2. Body Composition Measurements
Body composition parameters were measured using preoperative CT scans of the abdomen, at the height of the third lumbar vertebra (L3) using Osirix DICOM viewer software (OsiriX MD version 13.0.0, Pixmeo, Geneva, Switzerland) as previously described [16,17]. All measurements were performed on two consecutive transversal CT images at height of the third lumbar vertebra, on which both transverse processes were visible. The mean measurements were used for further calculations and analyses. Skeletal muscle was identified as Hounsfield units (HUs) of −29 to +150 [37]. Adipose tissue was identified as −150 to −50 HU [38]. Muscles measured at L3 included the psoas, paraspinal, transverse abdominal, external oblique, internal oblique, and rectus abdominis muscles. Intramuscular adipose tissue measurements to determine myosteatosis were performed by determining the mean HU of the skeletal muscle at L3. Different common body composition indices were calculated using the obtained measurements. For the PMI, the average area of one psoas muscle was used as previously described [17]. The SMI and PMI were calculated by normalizing for height in meters squared. The same was performed for the SFI and VFI [16]. The VSR was calculated by dividing visceral fat area by subcutaneous fat area [16]. An overview of measurements and indices used is shown in Figure 2.
The most common values and thresholds that could be found in the literature were used for sarcopenia [14,18,39,40], PMI [17], myosteatosis [14,18,19,24], SFI [11] and visceral obesity [41]. For the VFI and VSR, no suitable validated thresholds were found. For these indices, we used a validated online biomarker optimization software to find optimal cutoffs [42]. The thresholds used for each parameter are shown in Table 1.
## 2.3. Statistical Analysis
Frequencies are presented as absolute numbers and percentages. Continuous data are presented as median with interquartile range (IQR). Differences between groups were analyzed using the Pearson χ2 test for dichotomous parameters and the Wilcoxon-Mann–Whitney U test for continuous data. Survival data were analyzed using univariate and multivariate Cox regression. Kaplan–Meier curves were used to illustrate overall (OS) and cancer-specific survival (CSS). Statistical analysis was performed using SPSS software (version 29.0; SPSS Inc., Chicago, IL, USA). Graphs were created using Prism software (Prism 9 for macOS, version 9.4.1).
## 3.1. Descriptive Data
The distribution of body composition parameters is depicted in Table 1. Patient characteristics for the entire study cohort are depicted in Table 2. The median age was 70 (IQR 63–77) with 167 ($25.4\%$) females and 490 ($74.6\%$) males. In total, 351 ($53.4\%$) patients were alive at the censor date with a median follow-up of 40 months (IQR 15–76 months). As for histopathologic characteristics, 343 ($52\%$) had a pT3 staged tumor or worse, 93 ($14.2\%$) had positive surgical margins, 198 ($30.1\%$) had a positive lymph node status, and 32 ($4.9\%$) had metastases preoperatively. The locations of positive surgical margins were ureteric ($$n = 6$$, $0.9\%$), at soft tissue ($$n = 69$$, $10.5\%$), at the urethra ($$n = 19$$, $2.9\%$) and 2 ($0.3\%$) were undefined in the pathological report.
## 3.2. Relationship of Sarcopenia with Other Parameters
The relationships between sarcopenia, clinical parameters, histopathologic results, and other body composition parameters were assessed and presented in Table 2. Sarcopenia was more common in older patients ($p \leq 0.01$) and sarcopenic patients had more severe comorbidities according to the ACE-27 scoring model ($$p \leq 0.01$$). Higher tumor stages and positive surgical margins were more common in sarcopenic patients ($$p \leq 0.04$$ and $$p \leq 0.02$$, respectively). There was no significant association between sarcopenia and over- or underweight in the entire cohort ($$p \leq 0.06$$). More smokers were not sarcopenic ($$p \leq 0.05$$). Sarcopenia (Martin) was significantly associated with other definitions of sarcopenia, myosteatosis (all definitions), and low psoas muscle index (all $p \leq 0.01$).
## 3.3. Body Composition and Overall Survival
As shown in Table 3, sarcopenia (all definitions), myosteatosis (all definitions), and low PMI were all significant risk factors for overall survival (all $p \leq 0.01$) analyzed by univariate Cox regression. Sarcopenia defined by Martin et al., low PMI, and myosteatosis defined by Martin et al. were the strongest body composition risk factors for OS identified in this study (Table 3) [14]. The association of these risk factors with OS is illustrated in Figure 3. Sarcopenia (Martin), myosteatosis (Xiao) and SMHU, representing myosteatosis as a continuous variable, and low PMI were significant independent risk factors for OS in a multivariate Cox-regression model adjusted for age, ACE-27, pT-stage, surgical margins, pN-stage, cM-stage, and BMI category (Table 4). None of the analyzed adipose tissue components (SFI, VFI, VSR, and visceral obesity) could be identified as risk factors for OS in our study cohort (Table 3).
## 3.4. Body Composition and Cancer-Specific Survival
All defined thresholds of sarcopenia are significantly associated with CSS in univariate analysis (all $p \leq 0.01$; Table 3). Myosteatosis as a continuous variable and a low PMI were shown as significant risk factors for CSS in univariate Cox-regression analysis. Visceral obesity (HR 0.70; $95\%$ CI 0.52–0.93; $$p \leq 0.01$$) was found to be protective for CSS (Table 3 and Figure 3). In a multivariate Cox-regression analysis adjusted for age, pT-stage, surgical margins, pN-stage, cM-stage, perioperative chemotherapy, and BMI, sarcopenia (Martin) and low PMI and myosteatosis (continuous variable) were found to be independent risk factors for CSS (Table 5). Apart from visceral obesity, which was found to be protective for CSS, none of the other adipose tissue components could be found to be significantly associated with CSS for the entire study cohort.
## 4. Discussion
The aim of our study was to assess different body composition parameters in order to identify those relevant for survival after RC for BC. Our study supports the findings that sarcopenia (Martin) is a strong independent risk predictor for OS (HR 1.30; $95\%$ CI 1.02–1.66; $$p \leq 0.04$$) and CSS (HR 1.64; $95\%$ CI 1.19–2.25; $p \leq 0.01$) as previously described by Mayr et al. and other research groups [9,33]. Low PMI also represents an independent predictor of OS (HR 1.32; $95\%$ CI 1.02–1.70; $$p \leq 0.03$$) and CSS (HR 1.41; $95\%$ CI 1.02–1.96; $$p \leq 0.04$$) in multivariate analyses. PMI measurements are easier to obtain than the SMI and may even be measured by sonography [44]. Hence, we propose low PMI as a relevant and applicable prognostic factor for patients undergoing RC for BC. Myosteatosis defined by different thresholds was a significant predictor for OS in univariate analysis. Myosteatosis was an independent risk factor for OS as defined by Xiao (HR 1.33; $95\%$ CI 1.00–1.76; $p \leq 0.05$). Myosteatosis as a continuous variable was shown to be a risk factor for OS (HR 0.98; $95\%$ CI 0.97–1.00, $$p \leq 0.01$$) and CSS (HR 0.98; $95\%$ CI 0.96–1.00; $p \leq 0.05$). None of the indices involving adipose tissue measurements that were examined in this study could be identified as independent prognostic factors.
## 4.1. Choice of Body Composition Thresholds
We aimed to assess commonly used body composition parameters in cancer research. We identified those often referred to in the literature and where possible, those assessed in sizable cohorts (Table 1) [14,18,19,39,43]. Where possible, thresholds that were assessed in cohorts with urothelial cancer were utilized [17]. By using different thresholds for analyses of body composition, statistical analysis yielded different results (Table 3, Table 4 and Table 5). This underlines the heterogeneity of thresholds used in research due to differing study populations, cancer entities, and baseline characteristics. We found the *Martin criteria* for sarcopenia, the Kasaharas threshold for low PMI, and the Xiaos threshold for myosteatosis to be most significant for our study cohort (Table 3, Table 4 and Table 5).
## 4.2. Sarcopenia
Different definitions of sarcopenia were used in data analysis (Table 1). While statistical findings between them remain similar, the *Martin criteria* are most commonly used in the existing literature and are adjusted for gender and BMI [14]. In relevant earlier studies on patients undergoing RC for BC, sarcopenia was also defined by the *Martin criteria* [9,45]. Nonetheless, the *Martin criteria* recently have been discussed controversially because the thresholds for men are discontinuous, dependent on BMI [46]. Nevertheless, no consensus was found to this date. Using the Martin criteria, $52\%$ of patients of the entire cohort were defined as sarcopenic. This is in line with a Japanese single-center study published in 2016, where $48\%$ of patients were sarcopenic [45]. In another Japanese single-center study by, $39\%$ of patients were classified as sarcopenic [33]. A limitation to comparability is the use of another threshold for sarcopenia not considering BMI. A key finding of our current study is the independent association of sarcopenia and OS and CSS (Table 4 and Table 5 and Figure 3). These results align with the findings of Mayr et al. in 2018 where sarcopenia was also an independent predictor for CSS (HR 1.42; $95\%$ CI 1.00–2.02; $p \leq 0.05$) and OS (HR 1.43; $95\%$ CI 1.09–1.87; $$p \leq 0.01$$) [9]. Our current results also align with findings made in earlier studies by Psutka et al., Hirasawa et al., and Yamashita et al. [ 31,33,45]. Of note, the study cohorts of Psutka and Hirasawa were both limited by sample size. The study cohort by Mayr et al. had a sufficient sample size with 500 patients and 234 events in total [9]. However, it was a multicenter, multinational study. The current study underlines the suggested findings in a large single-center cohort of 657 patients.
## 4.3. Psoas Muscle Index
We have shown that a low PMI is an independent risk factor for OS and CSS (Table 4 and Table 5). Only few studies on the PMI exist in patients undergoing RC due to BC. The PMI is often used as a shortcut-index to determine sarcopenia [32]. Although we have shown that sarcopenia is significantly associated with a low PMI (Table 2), these terms should not be used in a synonymic manner. Not all patients with a low PMI are identified as sarcopenic (Martin) and vice versa (Table 2). Nevertheless, we do believe that the PMI is a very useful prognostic measurement that can also be easily obtained via sonography during patient consultation [44].
In a single-center retrospective study with a sizable study cohort of 441 patients, Stangl-Kremser et al. did not find a low PMI to be a statistically significant independent risk factor for OS, although a statistical trend was shown (HR 1.59; $95\%$ CI 0.98–2.59; $$p \leq 0.06$$) [35]. A major difference between the current study is the thresholds used for the PMI. Stangl-Kremser et al. used thresholds determined in a healthy population with a mean age of 31 years [47]. In the current study, we used threshold values described by Kasahara et al. in a population with advanced bladder cancer with a mean age of 61.9 years [17]. The latter is better comparable to patients undergoing RC due to bladder cancer. Kasahara et al. identified a low PMI as a risk factor for shorter survival (log-rank $$p \leq 0.015$$). While a low PMI is a strong independent predictor of OS and CSS after radical cystectomy (Figure 3), it is also one of the simplest measurements to take. Thus, we propose the PMI as a diagnostic and prognostic tool that should be used on a day-to-day basis during patient consultation.
## 4.4. Myosteatosis
To date, Myosteatosis has barely been mentioned in the literature as a prognostic factor in patients with BC. In other tumor entities, myosteatosis has found its place as a risk factor. For instance, Martin et al. have identified myosteatosis as an independent risk for OS in a large cohort of 1473 patients with lung and gastrointestinal cancer [14]. For BC, only one comparable study was found. Yamashita et al. assessed 123 patients who underwent RC for BC [33]. They found that myosteatosis was a strong independent predictor for CSS (HR 3.53; $95\%$ CI 1.30–12.50; $$p \leq 0.04$$) and not for OS ($$p \leq 0.10$$). In our study, myosteatosis had a statistically significant association in univariate analyses with OS in all definitions used, whereby the definition posed by Martin et al. was the strongest risk factor (HR 1.63; $95\%$ CI 1.27–2.10; $p \leq 0.01$; Figure 2) [14]. An independent association in multivariate analysis could be shown for OS if using myosteatosis (SMHU) as a continuous variable and the definition by Xiao et al. ( Table 4) [18]. Regarding the continuous variable, one must note that lower SMHU values represent a higher adipose tissue content of muscle, thus myosteatosis. An independent risk by myosteatosis for CSS could be shown using the continuous variable (HR 0.98; $95\%$ CI 0.96–1.0; $p \leq 0.05$, Table 5). The thresholds for myosteatosis in our current study were different to those used by Yamashita et al. ( SMD < 38.5 HU for men and SMD < 28.6 HU for women) [33]. The fact that the continuous variable is an independent statistically significant risk factor for OS and CSS may suggest that we did not find an appropriate threshold for our dataset by using previously mentioned thresholds.
## 4.5. Adipose Tissue Indices
To the best of our knowledge, no comparable study assessing adipose tissue distribution indices in patients undergoing RC exists to this date. We examined adipose tissue parameters that have been used in body composition research for other diseases [11,15,16,41]. In our study, we did not find statistically significant associations between adipose tissue indices (SFI, VFI, VSR) and OS or CSS in the entire cohort (Table 3). Visceral obesity seemed to have a protective effect for CSS (HR 0.70; $95\%$ CI 0.52–0.93; $$p \leq 0.01$$) in univariate analysis. Psutka et al. made implications of adipose tissue having protective effects in a study published in 2015 of 262 patients undergoing RC. Here, the total fat area at the height of L3 was measured and the whole-body fat mass was calculated. Psutka et al. conclude “…among patients with normal muscularity there is a trend toward improved survival in those with increasing weight and adiposity…” [34]. The protective value of adipose tissue was also identified by Martini et al. in a study of 70 patients with advanced urothelial cancer treated with immune checkpoint inhibitors [10]. High VFI was significantly associated with improved progression-free survival (HR 1.76, $$p \leq 0.04$$) and showed a trend toward longer OS. High SFI was significantly associated with prolonged OS (HR 1.99, $$p \leq 0.043$$). In another study conducted by Stangl-Kremser et al. on 68 patients treated with radiation for BC, no association with survival could be found for visceral fat area, subcutaneous fat area, and visceral-to-subcutaneous fat ratio, i.e., similar to our findings [35].
## 4.6. Limitations
Our study has several limitations. Due to the retrospective nature of our study, we collected data from 657 patients in a non-consecutive manner. The follow-up information we were able to retrieve was heterogenic depending on reachability of either patients, general practitioners, or local urologists. Therefore, we were not able to reliably collect information such as progression-free survival. Furthermore, due to the retrospective data, our study lacks other frailty measurements such as hand-grip strength, performance status on questionnaires, or other potentially interesting examinations such as blood analysis for inflammation or nutritional status (albumin levels). Although having selected patients undergoing RC due to BC, there is still a certain heterogeneity in our cohort. Tumor stages ranging from pTa up to pT4 were all included; also, patients who received neoadjuvant chemotherapy were included. However, neoadjuvant chemotherapy was not associated with sarcopenia in our cohort ($$p \leq 0.084$$, Table 2). Due to the long retrospective period of our study, we have a low rate of neoadjuvant therapies ($7.2\%$) compared to recent cystectomy studies. In the early years of our study cohort, barely any neoadjuvant chemotherapy was given, whereas in the later years rates exceeded $20\%$. Our study cohort has a high rate of positive surgical margins ($14.2\%$) compared to the literature, where the rates are reported at $5\%$ [48]. This may be due to the high portion of pT3 ($35\%$) and pT4 ($17\%$) in our study. In the literature, much lower rates of pT3 ($22\%$) and pT4 ($7\%$) are described [49]. There is a need for a prospective study with follow-up appointments, including CT scans for further body composition analysis during the follow-up period.
## 5. Conclusions
To the best of our knowledge, this study represents the largest cohort assessed for body composition parameters in patients undergoing RC due to BC.
Sarcopenia and the psoas muscle index proved to be strong independent predictors for OS and CSS. The psoas muscle index can be used as a simple prognostic tool during patient consultation. Myosteatosis is an independent predictor for OS in this patient cohort. Due to the risks posed by sarcopenia, we are planning a preoperative exercise and nutritional support program in collaboration with the department of oncology in our center, similar to the program proposed by Yamamoto et al. [ 50]. Body composition parameters such as myosteatosis, subcutaneous and visceral fat indices, visceral-to-subcutaneous fat ratio, and visceral obesity have only been investigated in a low number of BC patients up to this date. Hence, there is a lack of comparable studies, making it difficult to place these findings into context. There is a need for further large studies assessing the effect of adipose tissue distribution in BC. Outside of BC research, sarcopenia and body composition research are a rapidly growing field. As demonstrated in our study, there are several definitions and thresholds for different measurements in the literature. There is an urgent need for unification and consensus on thresholds to increase comparability between studies and tumor entities.
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|
---
title: Interleukin-18 and Gelsolin Are Associated with Acute Kidney Disease after
Cardiac Catheterization
authors:
- Po-Yen Kuo
- Kai-Fan Tsai
- Po-Jung Wu
- Pai-Chin Hsu
- Chien-Hsing Wu
- Wen-Chin Lee
- Hsiu-Yu Fang
- Chih-Yuan Fang
- Sheng-Ying Chung
- Yung-Lung Chen
- Terry Ting-Yu Chiou
journal: Biomolecules
year: 2023
pmcid: PMC10046301
doi: 10.3390/biom13030487
license: CC BY 4.0
---
# Interleukin-18 and Gelsolin Are Associated with Acute Kidney Disease after Cardiac Catheterization
## Abstract
Patients undergoing cardiac catheterization are at high risk of post-procedure acute kidney injury (AKI) and may experience persistent renal damage after an initial insult, a state known as acute kidney disease (AKD). However, the association between AKD and urinary renal biomarkers has not yet been evaluated in this population. We enrolled 94 patients who underwent elective cardiac catheterization to investigate patterns of urinary renal biomarkers and their associations with post-procedure AKD. Serial urinary renal biomarker levels were measured during pre-procedure, early post-procedure (12–24 h), and late post-procedure (7–10 days) periods. In our investigation, $42.55\%$ of the enrolled patients developed AKD during the late post-procedure period. While the liver-type free-fatty-acid-binding protein level increased sharply during the early post-procedure period, it returned to baseline during the late post-procedure period. In contrast, interleukin-18 (IL-18) levels increased steadily during the post-procedure period. Early post-procedure ratios of IL-18 and gelsolin (GSN) were independently associated with subsequent AKD (odds ratio ($95\%$ confidence interval), 4.742 (1.523–14.759) for IL-18 ratio, $$p \leq 0.007$$; 1.812 (1.027–3.198) for GSN ratio, $$p \leq 0.040$$). In conclusion, post-procedure AKD is common and associated with early changes in urinary IL-18 and GSN in patients undergoing cardiac catheterization.
## 1. Introduction
Acute kidney injury (AKI) after exposure to an iodinated contrast medium is a common and problematic condition owing to the extensive utilization of contrast-requiring procedures such as computed tomography and angiography [1]. Contrast exposure is the third most common cause of AKI during hospitalization. In high-risk populations, the incidence of contrast-associated AKI is approximately 11–$12\%$ [2,3]. It is also associated with adverse clinical outcomes including chronic kidney disease (CKD), end-stage renal disease (ESRD), cardiovascular events, prolonged hospital stays, and mortality [4,5,6,7]. Among contrast-requiring procedures, cardiac catheterization is particularly associated with a high risk of post-procedure AKI, ranging between 1.9–$49\%$ in the literature [2,8]. Apart from contrast-related factors (such as direct agent toxicity or post-exposure renal vasoconstriction), procedure-specific (such as atheromatous embolism) and patient-specific (such as hypotension or pre-existing renal disease) factors also play important roles in renal damage after cardiac catheterization [9,10,11,12]. Thus, renal injury after cardiac catheterization warrants particular attention and subsequent clinical monitoring [7,13,14].
Recently, the concept of acute kidney disease (AKD) was established to define persistent kidney damage 7–90 days after an initial nephrotoxic insult [13,15,16]. As a critical phase between AKI and CKD, AKD may represent a window of opportunity for modifying the disease course and preventing further kidney injury [4,17,18]. Therefore, AKD evaluation and prediction are essential in post-AKI management. Biomarkers such as urinary albumin-creatinine ratio (UACr), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), insulin-like growth factor-binding protein-7 (IGFBP-7), gelsolin (GSN), liver-type free-fatty-acid-binding protein (L-FABP), and interleukin-18 (IL-18) have been regarded as early predictors of kidney injury [19,20,21,22]. With the availability of enzyme-linked immunosorbent assay (ELISA)-based methods for their detection in urine samples, these renal biomarkers can serve as feasible and noninvasive tools for serial assessment in the post-AKI period [23]. While most renal biomarker studies have focused on a single time point after nephrotoxic events, the longitudinal changes in urinary renal biomarkers after cardiac catheterization and their associations with AKD are yet to be elucidated.
In this prospective observational study involving patients undergoing cardiac catheterization, we evaluated the incidence of AKD, time-course patterns of urinary renal biomarkers, and associations between urinary renal biomarkers and the occurrence of AKD after the procedure.
## 2.1. Patient Enrollment
Patients undergoing cardiac catheterization were recruited from the cardiology ward of the Kaohsiung Chang Gung Memorial Hospital between August 2020 and May 2021. Inclusion criteria were as follows: [1] adult patients (≥20 years of age) undergoing elective cardiac catheterization; [2] patients who had at least one risk factor for renal injury after cardiac catheterization (i.e., ≥60 years of age, diabetes, pre-existing renal impairment, coronary artery disease, cerebrovascular disease, heart failure (HF), or malignancy); and [3] patients with non-dialysis-dependent status. Patients were excluded if they had an AKI event within one month before enrollment, advanced CKD (Kidney Disease Improving Global Outcomes (KDIGO) stage 4–5 or ESRD) [24], documented anaphylactic allergy to iodinated contrast medium, hemodynamic instability at enrollment, or previous intravenous contrast exposure within one month before enrollment. All the participants received an intravenous fluid infusion with physiological saline at 60 mL/h for 8 h before cardiac catheterization as per the hospital’s practice for AKI prevention. The contrast medium utilized in the procedure was Omnipaque® 350 (GE HealthCare, Chicago, IL, USA), which is a non-ionic, low-osmolality contrast agent containing 755 mg of iohexol (equivalent to 350 mg of organic iodine) per mL of solution. The study protocol was approved by the Institutional Review Board and Ethics Committee of the Chang Gung Medical Foundation, Taipei, Taiwan (IRB Nos. 201802329B0 and 201902059B0). The study adhered to the principles of the Declaration of Helsinki. Written informed consent was obtained from all the participants.
## 2.2. Measurement of Urinary Biomarkers and Identification of Post-Procedure Renal Injury
To assess the patterns of urinary renal biomarkers and occurrence of renal injury after cardiac catheterization, pre-procedure (on the procedure day), early post-procedure (12–24 h after the procedure), and late post-procedure (7–10 days after the procedure) levels of urinary renal biomarkers and serum creatinine (Cr) were measured in the study population. Urinary renal biomarkers including UACr, urinary IL-18, urinary GSN, and urinary L-FABP were measured in first-void spot urine samples in the morning using ELISA-based methods. The measurement of UACr was performed in the certificated laboratory of the hospital, and the measurement of other urinary biomarkers was performed by an experienced technician using commercial ELISA kits (IL-18: RayBio® Human IL-18 ELISA Kit (ELH-IL18), RayBiotech Life, Inc., Peachtree Corners, GA, USA; GSN: ELISA Kit for Gelsolin (SEA372Hu), Wuhan USCN Business Co., Ltd., Wuhan, Hubei, China; L-FABP: NORUDIA L-FABP Kit (no. 84051000), Sekisui Medical Co., Ltd., Tokyo, Japan). The ELISA-based renal biomarkers were measured in accordance with the standard protocols of the manufacturers, and each result was corrected using the urinary Cr level of the same urine sample. A post-procedure AKI event was defined as an increase in serum Cr ≥26.53 μmol/L or ≥$50\%$ of the pre-procedure level during the early post-procedure period, according to the 2012 KDIGO criteria [25]. The post-procedure AKD was defined as an increase in serum Cr ≥26.53 μmol/L or ≥$50\%$ of the pre-procedure level or an increase in UACr ≥$30\%$ of the pre-procedure level during the late post-procedure period, which was based on the consensus of the Acute Dialysis Quality Initiative 16 Workgroup and previous studies regarding albuminuria as a surrogate marker of renal damage [15,26,27].
## 2.3. Collection of Baseline Demographic and Clinical Characteristics
Baseline demographic profiles of the enrolled patients were collected via the electronic medical record system of the hospital. Before the cardiac catheterization, we obtained information including age, sex, body mass index (BMI), smoking habit, pre-procedure blood pressure measurement, and comorbidities such as hypertension, diabetes, pre-existing renal impairment, coronary artery disease, cerebrovascular disease, HF, and malignancy history. Data on pre-existing renal impairment, including estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and microalbuminuria (UACr ≥30 mg/g Cr), were recorded according to the data within three months before the enrollment. The contrast volume used during the cardiac catheterization was also collected from the hospital procedure records. Additionally, baseline hematological and biochemical profiles of the study population, including hemoglobin, hematocrit, glycated hemoglobin (HbA1c), lipid profiles, and eGFR levels, were measured before the cardiac catheterization. The Modification of Diet in Renal Disease equation, which is eGFR (mL/min/1.73 m2) = 175 × serum Cr (µmol/L)−1.154 × 0.0113 × age (year)−0.203 × 0.742 (if female), was used to retrieve the eGFR [28].
## 2.4. Statistical Analysis
Categorical variables are presented as numbers (n) with percentages, and continuous variables are presented as medians with interquartile ranges (IQRs) owing to the non-normal distribution revealed by the Kolmogorov–Smirnov method. The levels of serial urinary renal biomarkers (pre-procedure, early post-procedure, and late post-procedure) in the entire cohort were compared using the Wilcoxon signed-rank test. To evaluate the associations between urinary biomarkers and the occurrence of post-procedure AKD, we stratified the study population into two groups according to AKD occurrence during the late post-procedure period (AKD and non-AKD groups) and compared patient characteristics and serial urinary biomarker levels between the groups. Categorical variables were analyzed using the chi-square test, and continuous variables were analyzed using the Mann–Whitney U test for univariate analysis. Furthermore, the post-procedure ratios of all urinary biomarkers (defined as the ratio of the post-procedure level to the corresponding pre-procedure level) were also calculated during the early and late post-procedure periods and compared between the groups using the Mann–Whitney U test. To identify the factors independently associated with the occurrence of post-procedure AKD, urinary biomarker levels during the pre-procedure and early post-procedure periods and their post-procedure ratios during the early post-procedure period were assessed using multivariate logistic regression analysis via the forward stepwise selection method. Multivariate analysis was also adjusted for age, sex, BMI, pre-existing renal impairment, hypertension, diabetes, and baseline covariates with a p-value of <0.1 in the univariate analyses using the enter method. Statistical significance was set at a p-value of <0.05. Statistical Product and Service Solutions software (version 22.0; IBM, Armonk, NY, USA) was used for all the analyses.
## 3.1. Characteristics of the Study Population
During the study period, 94 patients undergoing elective cardiac catheterization were enrolled for the analysis. The characteristics of the enrolled patients are summarized in Table 1. The median age of the study population was 66 years (IQR, 60–73), and $77.66\%$ of the enrolled patients were males. The median BMI of the cohort was 25.55 kg/m2 (IQR, 23.20–28.31), and 15 ($15.96\%$) of the participants were smokers. The median baseline eGFR level of the cohort was 76.33 mL/min/1.73 m2 (IQR, 60.33–96.21), and $19.15\%$ and $44.68\%$ of the participants had a baseline eGFR level of <60 mL/min/1.73 m2 and microalbuminuria, respectively. The most common comorbidity was hypertension ($70.21\%$), followed by diabetes ($38.30\%$), HF ($19.15\%$), coronary artery disease ($13.83\%$), cerebrovascular disease ($5.32\%$), and malignancy ($5.32\%$). The median contrast volume used in the procedure was 150.00 mL (IQR, 80.00–170.00). During the post-procedure period, none of the participants had repeated contrast exposure, nephrotoxic agent use, or shock episodes. The median serum Cr levels were 87.54 µmol/L (IQR, 69.85–111.63), 86.65 µmol/L (IQR, 64.10–109.42), and 92.84 µmol/L (IQR, 76.93–122.02) during the pre-procedure, early post-procedure (12–24 h), and late post-procedure (7–10 days) periods, respectively. During the early post-procedure period, $1.06\%$ of the enrolled patients experienced an AKI event, and 40 ($42.55\%$) participants had AKD during the late post-procedure period. Among the AKD group, 11 participants had an increase in serum Cr ≥26.53 μmol/L or ≥$50\%$ of the baseline level, 28 had an elevation in UACr ≥$30\%$ of the baseline level, and 1 met both the serum Cr and UACr criteria for AKD diagnosis.
## 3.2. Patterns of Urinary Renal Biomarkers in the Total Cohort after Cardiac Catheterization
The serial levels of urinary renal biomarkers are shown in Figure 1. The urinary IL-18 levels increased markedly during the late post-procedure period compared with those during the pre-procedure ($$p \leq 0.001$$) and early post-procedure ($$p \leq 0.045$$) periods; however, the early post-procedure levels were similar with the baseline levels ($$p \leq 0.178$$) (median (IQR), pre-procedure vs. early post-procedure vs. late post-procedure, 26.10 (17.40–40.73) vs. 30.90 (18.25–49.10) vs. 32.65 (19.95–51.90) ng/g Cr). In $65.96\%$ of patients, urinary IL-18 levels were elevated above the baseline levels during the late post-procedure period. Furthermore, compared to pre-procedure levels, urinary L-FABP levels increased remarkably ($p \leq 0.001$) during the early post-procedure period. However, the late post-procedure L-FABP levels declined significantly compared to those during the early post-procedure period ($p \leq 0.001$) and were similar with the baseline levels ($$p \leq 0.067$$) (median (IQR), pre-procedure vs. early post-procedure vs. late post-procedure, 3.19 (1.60–9.36) vs. 13.52 (7.04–30.89) vs. 2.81 (1.13–5.75) μg/g Cr). Most of the enrolled patients ($90.43\%$) presented with a higher L-FABP level during the early post-procedure period than during the pre-procedure period, and the late post-procedure L-FABP levels were lower in $93.62\%$ of the patients than in the early post-procedure period. In contrast, UACr and urinary GSN levels were similar between serial measurements in the total population.
## 3.3. Patient Characteristics and Urinary Biomarkers Stratified by the Occurrence of AKD
To assess the association between urinary renal biomarkers and the occurrence of post-procedure AKD, the study population was stratified into two groups: AKD ($$n = 40$$) and non-AKD ($$n = 54$$). Compared to the non-AKD group, the proportion of patients with HF was higher in the AKD group (AKD vs. non-AKD, $30.00\%$ vs. $11.11\%$, $$p \leq 0.021$$), and patients in the AKD group received a lower contrast volume during the procedure (median (IQR), AKD vs. non-AKD, 100.00 (75.00–150.00) vs. 150.00 (100.00–200.00) mL, $$p \leq 0.008$$). Additionally, diabetes was slightly less common in the AKD group than in the non-AKD group (AKD vs. non-AKD, $27.50\%$ vs. $46.30\%$, $$p \leq 0.064$$). Although the late post-procedure serum Cr level was higher in the AKD group (median (IQR), AKD vs. non-AKD, 111.41 (83.11–133.51) vs. 88.42 (73.17–108.31) µmol/L, $$p \leq 0.020$$), the pre-procedure and early post-procedure serum Cr levels were similar between the groups. Other baseline demographic and clinical profiles, including the proportions of pre-existing renal impairment, such as microalbuminuria and baseline eGFR < 60 mL/min/1.73 m2, were similar between the groups (Table 1).
Comparisons of serial urinary biomarkers between the groups are presented in Figure 2 and Figure 3. The AKD group had significantly higher urinary L-FABP levels during the early and late post-procedure periods (median (IQR), AKD vs. non-AKD, early post-procedure: 18.24 (8.61–37.79) vs. 10.83 (5.57–18.48) μg/g Cr, $$p \leq 0.017$$; late post-procedure: 3.94 (1.86–8.06) vs. 2.58 (0.82–4.63) μg/g Cr, $$p \leq 0.035$$). The post-procedure ratios of UACr, urinary IL-18, and urinary GSN were markedly higher in the AKD group during the early post-procedure period (median (IQR), AKD vs. non-AKD, UACr: 1.29 (1.00–1.77) vs. 0.87 (0.62–1.23), $$p \leq 0.001$$; IL-18:1.30 (0.89–1.79) vs. 0.91 (0.69–1.34), $$p \leq 0.013$$; GSN: 1.20 (0.76–1.92) vs. 0.71 (0.43–1.42), $$p \leq 0.008$$). During the late post-procedure period, the post-procedure ratios of UACr, urinary IL-18, and urinary GSN were persistently higher in the AKD group (median (IQR), AKD vs. non-AKD, UACr: 1.68 (1.10–2.62) vs. 0.74 (0.37–0.97), $p \leq 0.001$; IL-18:1.55 (1.07–2.29) vs. 1.09 (0.70–1.68), $$p \leq 0.018$$; GSN: 1.28 (0.69–2.41) vs. 0.70 (0.24–1.67), $$p \leq 0.007$$). In contrast, the post-procedure ratio of urinary L-FABP was only slightly higher in the AKD group during the late post-procedure period (median (IQR), AKD vs. non-AKD, 1.14 (0.48–1.61) vs. 0.71 (0.33–1.27), $$p \leq 0.092$$).
## 3.4. Urinary Biomarkers Independently Associated with the Occurrence of Post-Procedure AKD
All the urinary biomarker levels during the pre-procedure and early post-procedure periods and their ratios during the early post-procedure period were evaluated by multivariate logistic regression analysis using a forward stepwise selection method to identify factors independently associated with post-procedure AKD. With adjustment for age, sex, BMI, pre-existing renal impairment, hypertension, diabetes, and baseline covariates with a p-value of <0.1 in the univariate analyses via the enter method, our analysis demonstrated that the post-procedure ratios of urinary IL-18 and GSN during the early post-procedure period were independently associated with the occurrence of AKD after cardiac catheterization (odds ratio (OR) ($95\%$ confidence interval (CI)), 4.742 (1.523–14.759) for IL-18 ratio, $$p \leq 0.007$$; 1.812 (1.027–3.198) for GSN ratio, $$p \leq 0.040$$). Moreover, the BMI was inversely associated with post-procedure AKD (OR ($95\%$ CI), 0.837 (0.709–0.989) per kg/m2, $$p \leq 0.036$$), whereas HF was positively correlated with AKD after the cardiac catheterization (OR ($95\%$ CI), 6.521 (1.356–31.351), $$p \leq 0.019$$) (Table 2).
## 4. Discussion
In this study involving patients undergoing cardiac catheterization and at risk of post-procedure renal injury, we demonstrated that the incidence of AKD was $42.55\%$ and that the early post-procedure ratios of urinary IL-18 and GSN were independently associated with subsequent AKD. Additionally, distinct time-course patterns of urinary IL-18 and L-FABP after cardiac catheterization were also revealed in our investigation. While L-FABP increased sharply during the early post-procedure period (12–24 h), its level dropped toward baseline during the late post-procedure period (7–10 days). In contrast, IL-18 levels increased steadily throughout the post-procedure periods. Compared with the low AKI incidence during the early post-procedure period, the AKD incidence was remarkably high during the late post-procedure period in our study. Of note, the high AKD incidence might be partly ascribed to the inclusion of both serum Cr and UACr criteria for AKD diagnosis in our study. Additionally, previous studies have revealed that the incidence of contrast-associated AKI might be relatively lower during the immediate post-exposure period (24–36 h), and contrast-associated renal damage could become more prominent 2–5 days after exposure [3,4,29]. Considering the surrogate role of albuminuria for kidney damage [30,31] and the time-course variations of renal injury after contrast exposure, our study emphasized the requirement for serial monitoring of serum Cr and albuminuria (or other reliable renal biomarkers) in patients undergoing cardiac catheterization, especially in the high-risk population.
In our analysis, early post-procedure ratios of urinary IL-18 and GSN were independently associated with AKD occurrence. As a proinflammatory cytokine expressed in renal tubular cells, IL-18 can activate macrophages during renal inflammation and may play a crucial role in the pathogenesis of CKD [32,33,34]. Furthermore, in a large cohort of patients undergoing coronary angiography, urinary IL-18 levels were prominently elevated in those with major kidney events, thereby supporting its utilization in detecting acute renal injury [35]. Similarly, the potential role of GSN as a novel renal biomarker has been proposed [36,37]. GSN is a multifunctional actin-regulatory protein that influences cellular motility [38,39]. Altered renal GSN expression may disrupt the actin cytoskeleton in podocytes and result in podocytopathy [40]. In addition, plasma GSN can modulate immune response during inflammatory processes and is associated with adverse outcomes in patients with CKD and various illnesses [22,38,41,42,43,44]. In an animal study of AKI, gentamicin-induced nephropathy was linked to elevated urinary GSN excretion [21]. Moreover, an association between plasma GSN and AKI occurrence has been demonstrated in patients with sepsis or those receiving cardiopulmonary bypass, indicating its role in facilitating AKI diagnosis and monitoring [36,37]. Despite current evidence suggesting the predictive roles of IL-18 and GSN in AKI occurrence, their utilization in the AKD period has not been investigated. The association between AKD and urinary levels of these renal biomarkers is also a topic of interest because urine measurements are more feasible for serial renal monitoring [23]. However, since the reference ranges for urinary IL-18 and GSN have not been established, their optimal applications in clinical and investigatory settings remain unknown. In our study, by calculating the ratios during the early post-procedure period, we found that early elevations in IL-18 and GSN after cardiac catheterization were independently associated with the occurrence of subsequent AKD. Additionally, the steady increase in urinary IL-18 levels during the post-procedure period might also indicate escalating renal inflammation after cardiac catheterization in our study population [32]. While there are several other renal biomarkers with the potential to predict acute renal damage in various situations, such as NGAL, KIM-1, and IGFBP-7, the clinical application of IL-18 and GSN is still under investigation [45]. Our analysis highlights the potential utility of urinary IL-18 and GSN in identifying persistent kidney injury among patients undergoing cardiac catheterization.
Although the levels and post-procedure ratios of urinary L-FABP were not independently associated with subsequent AKD, a distinct pattern of urinary L-FABP levels was observed after cardiac catheterization in our study population. As an endogenous antioxidant abundant in the proximal renal tubules, urinary L-FABP levels reflect tissue oxidative stress during renal ischemia [46,47]. Previous studies also revealed that increased urinary excretion of L-FABP correlated with renal deterioration in patients undergoing cardiac catheterization and those with CKD [48,49]. In our study, a sharp increase in urinary L-FABP levels during the early post-procedure period occurred in $90.43\%$ of the enrolled patients, and these levels returned to baseline during the late post-procedure period. This phenomenon may implicate transient renal ischemia after cardiac catheterization, resulting in an almost universal elevation of intrarenal oxidative stress in the study population, even in those without subsequent AKD. Further investigations combining different renal biomarkers are warranted to clarify the clinical significance of changes in L-FABP patterns and elucidate the pathogenesis of AKD after cardiac catheterization.
Our analysis also revealed that HF was positively associated with post-procedure AKD, and baseline BMI was inversely correlated with AKD after cardiac catheterization. Pre-existing HF is a well-documented risk factor for contrast-associated AKI, and the severity of HF may also serve as a positive predictor of AKI after contrast exposure [12,50,51]. Although obesity is a risk factor for AKI in critically ill patients, previous studies addressing the relationship between BMI and AKI have revealed mixed results [52]. Additionally, higher BMI has been reported to be paradoxically associated with fewer adverse outcomes and mortality events in patients with AKI after cardiac catheterization [53]. These observations and our findings underscore the importance and complexity of patient-specific factors in AKD pathogenesis after cardiac catheterization. Notably, while the AKD group received a lower contrast volume and had slightly fewer patients with diabetes, contrast volume and diabetes were not independently associated with post-procedure AKD in our study. In an investigation by Nikolsky et al., each 100 mL increment in contrast volume resulted in a $30\%$ increase in the AKI risk in patients receiving percutaneous coronary intervention [54]. However, the effect of contrast volume on the AKI risk after percutaneous coronary intervention appears to have been smaller than those of other factors in the Mehran risk score, such as HF and other comorbidities [12,50]. In our study, the difference in median contrast volume between the AKD and non-AKD groups was only 50 mL, which might attenuate its impact on the risk of post-procedure AKD. Furthermore, although diabetes has been considered a risk factor for AKI in several scenarios, its effect might be substantially altered by the status of glycemic control [55]. Evidence has indicated that an elevated HbA1c is associated with a higher risk of AKI after cardiac catheterization, and aggressive glycemic control could reduce the AKI risk in patients with CKD and diabetes [55,56,57]. In our study, both AKD and non-AKD groups demonstrated fair glycemic control (HbA1c, median (IQR), AKD vs. non-AKD, 6.05 (5.70–6.85) vs. 6.10 (5.60–6.70) %, $$p \leq 0.888$$), which might minimize the influence of diabetes in the AKD risk after cardiac catheterization. Our findings reflect the multifactorial nature of AKD after cardiac catheterization, which requires further comprehensive studies to clarify its underlying mechanisms and preventive strategies.
This study has certain limitations. Because the occurrence of AKI was defined according to serum Cr levels during the early post-procedure period (12–24 h), the incidence of post-procedure AKI might be underestimated in our investigation. Additionally, as we identified post-procedure AKD events based on late post-procedure levels (7–10 days) of serum Cr and UACr, the possible occurrence and progression of AKD between 11–90 days after cardiac catheterization were not assessed in this study. Although we recognized the association between urinary renal biomarkers and AKD after cardiac catheterization, the predictive potential of these biomarkers for AKD was not suitable to be evaluated in this study because of the sample size. Similarly, the roles of other patient-specific and procedure-specific factors (such as diabetes and contrast volume) on the risk of post-procedure AKD require additional research with a larger sample size. Finally, the correlations between post-procedure AKD and other promising novel renal biomarkers (such as KIM-1 and NGAL) in the enrolled patients were not evaluated due to the study design. Further comprehensive investigations are warranted to address these issues and to clarify the clinical roles of urinary IL-18 and GSN in predicting AKD after contrast exposure. Despite these limitations, our analysis highlights the high incidence of post-procedure AKD, distinct time-course patterns of urinary IL-18 and L-FABP, and associations between AKD occurrence and early post-procedure ratios of IL-18 and GSN in patients undergoing cardiac catheterization, which have not been investigated in the literature. Therefore, our study provides a foundation for future research to elucidate the utility of these renal biomarkers in patients undergoing cardiac catheterization, who have a particularly high risk of post-procedure renal damage.
## 5. Conclusions
In patients undergoing cardiac catheterization and at risk of post-procedure renal injury, post-procedure AKD is common and associated with early changes in urinary IL-18 and GSN. The steady elevation of urinary IL-18 and transient increase in urinary L-FABP levels during the post-procedure period might imply intrarenal inflammation and ischemia after cardiac catheterization. Recognizing the different patterns of urinary renal biomarkers in various kidney injury etiologies may offer new insights into their mechanisms and pathogenesis, leading to innovative diagnostic and therapeutic paradigms.
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|
---
title: Unique Characteristics of Patients with Von Hippel–Lindau Disease Defined by
Various Diagnostic Criteria
authors:
- Reut Halperin
- Liat Arnon
- Yehudit Eden-Friedman
- Amit Tirosh
journal: Cancers
year: 2023
pmcid: PMC10046302
doi: 10.3390/cancers15061657
license: CC BY 4.0
---
# Unique Characteristics of Patients with Von Hippel–Lindau Disease Defined by Various Diagnostic Criteria
## Abstract
### Simple Summary
Von Hippel–*Lindau is* a rare endocrine (and other organ) multi-neoplasia syndrome. A clinical diagnosis may be defined using different diagnostic criteria. However, the validity of these criteria has not been evaluated thus far. Here, we assess the patient population defined by the main sets of diagnostic criteria and demonstrate the different diagnostic accuracy of each diagnostic criterion based on patients’ characteristics and genetic analysis.
### Abstract
Von Hippel–Lindau (VHL) disease diagnosis is based on two criteria sets: *International criteria* (IC, two hemangioblastomas, one hemangioblastoma plus one visceral lesion, or VHL family history/pathogenic variant plus hemangioblastoma/visceral lesion); or *Danish criteria* (DC, two clinical manifestations, or VHL family history/pathogenic variant plus hemangioblastoma/visceral lesion). We aimed to compare the characteristics of patients with VHL-related pancreatic neuroendocrine tumor (vPNET) meeting either the clinical *Danish criteria* only (DOC) or IC to those with sporadic PNET (sPNET). The cohort included 33 patients with VHL (20 vPNETs) and 65 with sPNET. In terms of genetic testing and family history of VHL, $90.0\%$ of the patients with vPNET in the IC group had a germline VHL pathogenic variant, and $70.0\%$ had a family history of VHL vs. $20\%$ and $10\%$ in the DOC group, respectively ($p \leq 0.05$ for both). Patients with vPNET were younger at diagnosis compared with sPNET (51.6 ± 4.1 vs. 62.8 ± 1.5 years, $p \leq 0.05$). Patients in the IC group were younger at diagnosis with VHL, vPNET, pheochromocytoma, or paraganglioma (PPGL) and renal-cell carcinoma (RCC) than those in the DOC group ($p \leq 0.05$ for all comparisons). The most prevalent presenting manifestations were hemangioblastoma ($42.8\%$) and PPGL ($33.3\%$) vs. RCC ($58.3\%$) and PNET ($41.7\%$) in the IC vs. DOC groups. In conclusion, patients with vPNET meeting DOC criteria show greater similarity to sPNET. We suggest performing genetic testing, rather than solely using clinical criteria, for establishing the diagnosis of VHL.
## 1. Introduction
Von Hippel–Lindau disease (VHL) is an autosomal dominant inherited disorder caused by a germline pathogenic variant (PV) in the VHL gene, a regulator of hypoxia-inducible factors (HIF) [1]. Reduced activity of the VHL protein prevents degradation of HIF, thus promoting cell proliferation and survival, increased glucose utilization, and angiogenesis [2,3]. Clinical manifestations of patients with VHL include hemangioblastomas (HB) of the retina and central nervous system (CNS), renal cell carcinomas (RCC), pancreatic neuroendocrine tumors (PNET), and adrenal pheochromocytomas or paragangliomas (PPGL), as well as cysts of the pancreas, kidney, epididymis, and broad ligament, and endolymphatic sac tumors [4].
A clinical diagnosis of VHL can be established based on one of two sets of criteria: the “International” [4,5]; or “Danish” [6] criteria, as detailed in Table 1. The International Criteria are based on Melmon and Rosen’s original criteria published in 1964 [7] requiring any clinical manifestation of VHL, and a CNS HB harbored either by the patient or a family member. The Danish Criteria, developed based on the Danish VHL registry, were revised in 2013 and are in use mainly in Denmark [8].
Discovering the genetic background of VHL allowed an extension of these criteria over time [4]. Thus, today both criteria allow diagnosis of VHL if a patient has a genetic or familial background of VHL with at least one clinical manifestation while also allowing a clinically based diagnosis. Yet, the clinical diagnosis differs between the two criteria: while the International clinical criteria require the presence of at least one HB, the *Danish criteria* allow establishing a clinical diagnosis of VHL based solely on visceral manifestations.
The leading cause of death in VHL is CNS HB, followed by RCC [9,10,11,12,13], while PNET is an infrequent cause of death. Nevertheless, the presence of metastatic PNET is associated with increased mortality, with tumor size above 3 cm and a pathogenic variant in exon 3 of the VHL gene associated with an increased chance of metastatic disease [14,15,16,17,18]. The prevalence of VHL-related PNET (vPNET) is, on average, $5\%$, reaching up to $17\%$ in specific cohorts [4,5,19,20,21,22]. Compared with sporadic PNET (sPNET), vPNET is diagnosed at a relatively young age, is often non-functional, multifocal, is usually grade 1 or 2, and typically carries a more benign course compared with sPNET [17,23,24,25,26]. Based on the distinct clinical course of vPNET compared to sPNET, supported by retrospective data on differences in mortality risk [13], specific management recommendations for vPNET were developed by Laks et al. in 2022 [27], which differ from the guidelines for sPNET management in various parameters [28,29].
In the current study, we aimed to compare the characteristics of vPNET, as defined by the clinical definitions of International and Danish Criteria for the diagnosis of VHL. We compared both subgroups to sPNET to assess whether the more liberal definition of VHL, based on the Danish criteria, defines a clinically distinct patient population.
## 2. Materials and Methods
This research was approved by the Sheba MC Institutional Review Board (SMC-18-5735 and SMC-18-5674). Patients, who required genetic testing as part of this study, signed a written informed consent. This was a retrospective study, based on data retrieved using the MD Clone system (Beer-Sheva, Israel), of patients diagnosed with PNET between 1995 to 2021 at a tertiary medical center. Data collected included demographic characteristics (age at diagnosis, sex, and diagnoses of VHL manifestations); PNET-specific characteristics: location of the tumor within the pancreas; grade (differentiation, percentage of positive staining for KI-67, and mitotic index); and stage (based on the 8th Edition of the American Joint Committee on Cancer [30]); imaging results: computed tomography (CT) and/or magnetic resonant imaging (MRI) scans; ultrasonography (US); and/or positron emitting tomography (PET). The parameters collected for each modality included timing and report of progression, regression, or stability based on expert radiologist interpretation; intervention types: none; pharmacological; radionuclide; invasive radiology or surgical; and mortality of any cause. In addition, data on VHL family history and results of VHL germline genetic testing (indels or point pathogenic variants) were gathered.
The patients were sub-grouped based on whether they harbored vPNET or sPNET and whether the VHL diagnosis was determined by the *Danish criteria* (DC) or the *International criteria* (IC, see Table 1 for definitions of each criterion). Due to an overlap between the IC and DC, all patients diagnosed according to the IC also fulfill the DC. We, therefore, formed the following comparison groups: International (IC, patients fulfilling the international clinical criteria); Danish (DC, all patients fulfilling the Danish clinical criteria); Danish-only (DOC, patients fulfilling the Danish clinical criteria but not the International clinical criteria); and sPNET (fulfilling neither criteria for VHL diagnosis). Progression-free survival was calculated as the interval (in months) between the date of diagnosis and the timing of the earliest report of progression based on any imaging modality.
There were nineteen patients in the sPNET group with partial or missing pathology data. Six patients did not undergo biopsy: four patients had poor health status, and two had a longstanding stable small PNET, in which the diagnosis was based on the pathognomonic radiological appearance. Three patients had indeterminate grades: two had a well-differentiated PNET per pathology report, not defining KI67 or mitotic index, and one patient underwent endoscopic ultrasound-guided biopsy, but the material was too scarce to allow grading. For ten patients, grading was not defined, as they were sampled before 2010 when the World Health Organization defined the NET grading system.
Eight patients with vPNET did not undergo biopsy/surgical resection of the lesion, including seven from the IC group. The vPNET in these patients was not sampled following institutional multidisciplinary team discussion, considering the diagnosis of VHL, the high likelihood for vPNET, and the tendency for diagnosing vPNETs using non-interventional manners. Two patients with no grades available were from the DOC group: one patient, 81 years-old male, was at high risk for intervention, and for the other, tumor tissue quantity was not sufficient for determining tumor grade. In two additional patients from the DC group, pathological data were not available.
## Statistical Analysis
The statistical analysis was performed via SPSS Statistics (version 20.0.0, IBM, 2011, Armonk, NY, USA). Continuous variables with normal distribution were compared via the Student’s t-test, and categorical parameters were compared using the chi-square test or Fisher’s exact test. Continuous variables are presented as mean ± standard error of the mean (SEM) unless stated otherwise, and categorical parameters are presented as n (%). Non-parametric tests were used as appropriate. Two-tailed p-value < 0.05 was set as a threshold for statistical significance.
## 3.1. sPNET vs. vPNET
Patients and tumor characteristics of both sPNET and vPNET are presented in Table 2. Thirty-three patients with a diagnosis of VHL according to either the International ($$n = 21$$) or Danish-only ($$n = 12$$) criteria were identified, of which 20 patients were diagnosed with PNET (mean age at diagnosis with vPNET was 51.6 ± 4.1 years, $60.0\%$ males). In 65 patients with sPNET ($56.9\%$ males) mean age at diagnosis with PNET was 62.8 ± 1.5 years. The vPNETs were detected at a younger age compared with sPNET ($p \leq 0.001$). There was a significant difference in the stage but not in the grade between sPNET and vPNET (Table 2). A higher fraction of patients with sporadic PNET had stage III/IV diseases as compared with patients with vPNET ($$p \leq 0.02$$). There was a smaller proportion of vPNET located at the body/tail of the pancreas vs. the sPNET group ($36.8\%$ vs. $64.6\%$, $$p \leq 0.012$$).
## 3.2. DOC vs. IC Criteria–PNET Characteristics
To assess the differences between patients diagnosed with VHL based on the DOC vs. IC, we compared their demographic characteristics (Table 3) and the various manifestations of VHL (Table 4) between the two groups. Patients with IC-based diagnosis were younger at diagnosis with PNET compared with the DOC group (40.5 ± 5.5 vs. 62.6 ± 4.1 years, $$p \leq 0.003$$, respectively). Stage, grade, and PNET location did not differ between groups (Table 3).
## 3.3. VHL Characteristics in Patients with PNET-DOC vs. IC Criteria
Nine ($90\%$) of the ten patients that underwent germline DNA genotyping of the VHL gene in the IC group had a pathogenic variant, as compared to one patient out of five tested ($20\%$) in the DOC group ($$p \leq 0.007$$). Among those genetically diagnosed with VHL, 15 had a missense PV, and three had a deletion (additional two patients had an unknown PV type). The most frequent pathogenic variant identified was p. Gly93Arg (identified in five patients of two kindreds). Seventy percent ($\frac{7}{10}$) of the patients diagnosed with VHL based on the IC had a family history of VHL manifestations in a total of 15 different kindreds, compared with ten percent (one patient) in the DOC group ($$p \leq 0.006$$, Table 4). Age at diagnosis of VHL was younger among patients diagnosed based on the IC vs. DOC (26.6 ± 4.1 vs. 58.8 ± 4.3 years, $p \leq 0.001$, respectively). Furthermore, diagnoses with RCC and PPGL were also made at a significantly younger age in the IC compared with the DOC group ($$p \leq 0.05$$ for RCC and $$p \leq 0.01$$ for PPGL, Table 4). By definition, all patients ($\frac{10}{10}$) in the IC group had CNS HB, and $30.0\%$ ($\frac{3}{10}$) had retinal HB compared with none in the DOC group.
## 3.4. Clinical Characteristics of Patients Diagnosed with VHL Based on the DOC and IC
Out of the 33 patients diagnosed with VHL, 21 were diagnosed according to the IC and 12 according to the DOC (Table 5). Relevant family history was reported in $61.9\%$ of the patients in the IC group compared with one patient ($8.3\%$) in the DOC group ($$p \leq 0.003$$). Of those tested, a confirmed pathogenic VHL variant was found in $95.3\%$ percent of patients in the IC group vs. $20.0\%$ in the DOC group ($p \leq 0.001$). Age at diagnosis of VHL was significantly younger in the IC (22.6 ± 2.3 years) vs. DOC group (57.1 ± 4.0 years, $p \leq 0.001$). This was also true for age at diagnosis with PNET ($$p \leq 0.003$$), RCC ($$p \leq 0.009$$), and PPGL ($p \leq 0.001$, Table 5). The most frequent first VHL manifestation in the IC group was CNS HB ($42.8\%$), followed by PPGL ($33.3\%$) vs. RCC ($58.3\%$) and PNET ($41.7\%$) in DOC.
## 4. Discussion
In this study, we aimed to assess the difference in the population selected by using different sets of VHL clinical diagnostic criteria. We assessed the difference in terms of patient characteristics and PNET characteristics, both between the two criteria sets (Danish and International) and vs. sporadic PNET.
We show that the patients diagnosed solely based on the DOC are mostly VHL-mutation negative (albeit the small number of patients undergoing germline genetic evaluation), compared with ~$95\%$ positivity among those diagnosed based on the IC. *In* general, VHL-associated PNETs were diagnosed at a younger age than sporadic PNETs. However, we found that this is criteria-dependent, as PNETs were detected at a younger age in patients with IC-VHL vs. DOC-VHL, while age at DOC-VHL PNET diagnosis was comparable to sPNET diagnosis. Similarly, the age at diagnosis of VHL, PNET, RCC, and PPGL was significantly younger for patients in the IC-VHL vs. DOC-VHL groups.
The finding that PNET arises at a younger age in VHL patients compared with patients with the sporadic disease is well documented [24,25], with a mean age of vPNET at the presentation of 31–49 years [10,17,20] and of sPNET of 60.2 ± 13 years [15]. Hence, the absence of such differences between DOC-vPNET and sPNET raises a question on the validity of these clinical criteria. In contrast to the distinctly younger age of vPNET diagnosis vs. sPNET, the difference in pancreatic tumor location was not previously recognized. In a retrospective study based on Surveillance, Epidemiology, and End Results (SEER) database, approximately two-thirds of PNETs were located at the body or tail of the pancreas, with worse prognosis in those located at the head of the pancreas, possibly explained by the greater mass of Langerhans islets in the pancreatic body and tail compared with the head of pancreas [31]. Since the composition of the endocrine cells within the pancreatic islets differs between pancreatic anatomic locations [32], the higher rate of pancreatic head PNET in the context of VHL may suggest a specific cell of origin that is more abundant in the pancreatic head islets.
In the current study, we found striking differences in VHL characteristics between patients diagnosed according to the IC and DOC criteria. The IC group had a significantly younger age of diagnosis of VHL, PNET, PPGL, and RCC. An earlier VHL diagnosis may be attributed to the young age of occurrence of CNS HB (averaging around 30 years of age [9,20]) and retinal HB (peaking before 20 years of age [8]), that were not included in the DOC group by definition.
PNET is a rare diagnosis, 0.8 per 100,000 patients [33], with increasing incidence in the past decades, mostly of low-grade PNET with a decrease in high-grade PNET seen over time [34]. This rise is at least partly related to trends in imaging and improved recognition in histology [35]. RCC is a common diagnosis accounting for $2\%$ of all cancer diagnoses in the general population, with a rising incidence estimated to be 9.6–10.9 per 100,000 patients, with most cases discovered incidentally on imaging [36]. Overall, from all reported cases, only <$5\%$ of all RCC cases and <$1\%$ of all PNET cases are VHL-related [8]. In the DOC group, $80\%$ of patients had RCC and PNET, both at much higher rates than that reported in patients with genetically proven VHL [4]. The high prevalence and the difference in age of diagnosis of RCC and PNET between these VHL groups may stem from the high frequency of RCC and rising frequency of PNET in the general population, leading to an erroneous clinically-based diagnosis of VHL of patients that, in fact, co-carry sporadic RCC and PNET. Indeed, when looking at the genetic basis of VHL diagnosis, we found that while $95\%$ of the patients in the IC group that underwent genetic testing carried a pathogenic variant, only $20\%$ (one patient) of those tested in the DOC group carried a variant, this was also true for family history of VHL.
It should be noted, though, that patients in the DOC group were less likely to undergo genetic testing. Clinically, patients diagnosed with VHL according to the *Danish criteria* were older with incidental identification of RCC and PNET; thus, they were rarely suspected of having the genetic syndrome. While this is reflected in the low likelihood of having a VHL gene pathogenic variant, it also explains the low rate of genetic testing performed in this patient population. We, thus, suggest refraining from diagnosing patients with VHL based on the combination of RCC and PNET without confirming the diagnosis with the identification of a pathogenic variant. VHL testing genotyping is a highly accurate test for ruling out a diagnosis of VHL. DNA sequencing (Sanger) identifies $89\%$ of the variants, with only $11\%$ potential false negatives with this technique due to whole exon or whole gene deletions [37]. Analyses for copy number alterations might be needed in cases with high clinical suspicion, with negative results per Sanger. The rare possibility of mosaicism should also be considered, necessitating the use of next-generation sequencing [38].
Further supporting this suggestion is the revision of VHL diagnosis in the current Danish VHL guidelines (published in 2022), now allowing a clinical diagnosis based only on a combination of HB and another clinical manifestation [39]. In our view, in the rare cases in which genetic testing is not feasible and VHL diagnosis is based solely on clinical criteria, VHL diagnosis should be supported by implementing a maximal age cutoff. For example, VHL penetrance is over $95\%$ by age 60 years and $99\%$ by age 70 years [9]; therefore, a sole clinical-based VHL diagnosis in a patient over 70 years old should be avoided.
This study has several limitations. First, this is a retrospective study, and as such, not all patients had a complete medical record, specifically regarding the grade of vPNET. The lack of data on PNET grade for the majority of patients with vPNETs stems from the unique diagnostic approach for PNET in patients with VHL as delineated in the vPNET management guidelines [27] explains the high rate of non-invasive diagnosis of vPNET in the IC group, in which $\frac{7}{10}$ patients did not undergo biopsy of their pancreatic lesion, and the diagnosis of PNET was based on the pathognomonic appearance on imaging as a biopsy in not regularly recommended for patients with VHL. Second, while this is the practice these days, we are unable to carry genetic testing to all patients diagnosed with VHL in retrospect. Third, the VHL patient population in this study may be characteristic of our clinic and differ from other geographic sites since the genotype distribution is population- and geography dependent, albeit the large number of kindreds reported in this study may suggest differently. Fourth, patients with VHL undergo regular screening for vPNET and, thus, detection is expected at an earlier stage (and perhaps also age) compared to patients with sPNET. Finally, the sporadic group had a relatively low frequency of high-grade PNET, most probably stemming from documentation bias due to the referral of high-grade patients for follow-up at the oncology institute in previous years.
## 5. Conclusions
Diagnosis of VHL according to different criteria results in different patient characteristics and may include patients with sporadic disease. Our findings question the role of clinical diagnosis of monogenic hereditary syndromes with a known causing gene. Such diagnosis is straightforward and highly affordable and, thus, should be performed considering its huge impact on patient management. The follow-up required for patients with VHL is lifelong and comprises numerous tests and clinic visits, associated with the heavy emotional and administrative burden on the patients and a profound impact on their lives and their families’ daily living. In addition to this burden, the diagnosis of VHL affects the decision-making of its various related manifestation. This includes, for example, less stringent sampling of new lesions, a different threshold for surgical interventions, and VHL-targeted medical therapy. Hence, the diagnosis of VHL should be determined using the most specific clinical and genetic assessment tools with the highest positive predictive value. Based on our data, the clinical *Danish criteria* specificity is not comparable to genetic testing and is inferior to the *International criteria* that are based on unique clinical manifestations of VHL (mainly multiple hemangioblastomas). Thus, we suggest using genetic testing for the diagnosis of VHL, and in cases in which such resources are not available—using the international clinical criteria.
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|
---
title: Uncoupled nitric oxide synthase activity promotes colorectal cancer progression
authors:
- Asim Alam
- Steven C. Smith
- Sundaresan Gobalakrishnan
- Mina McGinn
- Vasily A. Yakovlev
- Christopher S. Rabender
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10046306
doi: 10.3389/fonc.2023.1165326
license: CC BY 4.0
---
# Uncoupled nitric oxide synthase activity promotes colorectal cancer progression
## Abstract
Increased levels of reactive oxygen/nitrogen species are one hallmark of chronic inflammation contributing to the activation of pro-inflammatory/proliferative pathways. In the cancers analyzed, the tetrahydrobiopterin:dihydrobiopterin ratio is lower than that of the corresponding normal tissue, leading to an uncoupled nitric oxide synthase activity and increased generation of reactive oxygen/nitrogen species. Previously, we demonstrated that prophylactic treatment with sepiapterin, a salvage pathway precursor of tetrahydrobiopterin, prevents dextran sodium sulfate–induced colitis in mice and associated azoxymethane-induced colorectal cancer. Herein, we report that increasing the tetrahydrobiopterin:dihydrobiopterin ratio and recoupling nitric oxide synthase with sepiapterin in the colon cancer cell lines, HCT116 and HT29, inhibit their proliferation and enhance cell death, in part, by Akt/GSK-3β–mediated downregulation of β-catenin. Therapeutic oral gavage with sepiapterin of mice bearing azoxymethane/dextran sodium sulfate–induced colorectal cancer decreased metabolic uptake of [18F]-fluorodeoxyglucose and enhanced apoptosis nine-fold in these tumors. Immunohistochemical analysis of both mouse and human tissues indicated downregulated expression of key enzymes in tetrahydrobiopterin biosynthesis in the colorectal cancer tumors. Human stage 1 colon tumors exhibited a significant decrease in the expression of quinoid dihydropteridine reductase, a key enzyme involved in recycling tetrahydrobiopterin suggesting a potential mechanism for the reduced tetrahydrobiopterin:dihydrobiopterin ratio in these tumors. In summary, sepiapterin treatment of colorectal cancer cells increases the tetrahydrobiopterin:dihydrobiopterin ratio, recouples nitric oxide synthase, and reduces tumor growth. We conclude that nitric oxide synthase coupling may provide a useful therapeutic target for treating patients with colorectal cancer.
## Introduction
Wnt signaling is a critical regulator of normal cell turnover in healthy colonic epithelial cells. Thus, it is not surprising that more than $90\%$ of spontaneous colorectal cancer (CRC) cases have activating mutations in Wnt signaling in the early stages of tumor progression [1]. The major effector of Wnt signaling in both normal and malignant colonic epithelial cells is β-catenin, and the most common activating mutation of Wnt signaling in CRC is in the adenomatous polyposis coli (APC) gene. In the absence of Wnt ligand, APC binds to phosphorylated β-catenin, forming a destruction complex in which β-catenin is degraded by the proteasome [2]. There are other activating mutations of the Wnt pathway including constitutive activation of Wnt receptor without ligand, mutated forms of β-catenin unable to bind the APC protein, and loss of function of glycogen synthase kinase-3β (GSK-3β), the kinase responsible for β-catenin phosphorylation [3]. Regardless of the underlying mechanism, an important consequence of aberrant Wnt signaling is nuclear accumulation of β-catenin. Cooperating with members of the TCF/LEF transcription factor family, β-catenin activates a number of target genes including Cyclin D1 (CCND1), cell division cycle 25a (CDC25A), Claudin-7 (CLDN7), vascular endothelial growth factor (VEGF), and matrix metalloproteinase 7 (MMP7), that modulate cell proliferation, metastasis, and tumor progression [1].
Patients with inflammatory bowel diseases have a 5- to 15-fold increased risk of developing CRC in their lifetimes [4]. The tumors arising in these patients are difficult to treat and have a >$50\%$ mortality rate [5, 6]. Patients with inflammatory bowel disease and a family history of CRC are also twice as likely to develop cancer compared with patients with inflammatory bowel disease without a positive family history [7]. The histopathology of colitis-associated CRC is similar to hereditary or sporadic CRC, and similar genetic mutations in the Wnt signaling pathway leading to increased nuclear accumulation of β-catenin are also present in colitis-associated CRC. Furthermore, there are robust pro-inflammatory infiltrates and increased pro-inflammatory cytokine levels in CRC without any clinically detectable signs of gastrointestinal distress [3]. These findings suggest a mechanistic overlap and a dependence on an inflammatory microenvironment in all etiologies of CRC.
A hallmark of an inflammatory microenvironment is increased levels of reactive oxygen/nitrogen species (ROS/RNS). The major sources of ROS/RNS are the three isoforms of nitric oxide synthase (NOS), neuronal (nNOS), inducible (iNOS), and endothelial (eNOS). These isoforms are differentially expressed depending on tumor type, but all three have common cofactor requirements including tetrahydrobiopterin (BH4), nicotinamide adenine dinucleotide phosphate (NADPH), Flavin adenine dinucleotide (FAD), and flavin mononucleotide (FMN) [8, 9]. Under normal physiological conditions with a complete complement of cofactors and substrate, NOS produces nitric oxide (•NO). At the •NO concentrations produced by nNOS and eNOS, •NO binds to the heme of soluble guanylate cyclase initiating Guanosine 3’,5’-cyclic monophosphate (cGMP) synthesis from Guanosine 5’-triphosphate (GTP) and activation of protein kinase G. By this mechanism, •NO acts as principle vasoregulator stimulating phosphorylation of the vasodilator-stimulated phosphoprotein and inositol 1,4,5-triphosphate receptor [10]. •NO/protein kinase G signaling is also involved in cell proliferation inhibiting both non-canonical transforming growth factor–β signaling and the Wnt/β-catenin pathways. Thus, sildenafil and other inhibitors of phosphodiesterases that hydrolyze cGMP suppress inflammation-driven CRC progression (11–14).
Although well described in the vasculature literature, studies of NOS in cancer cells generally ignore the fact that NOS can have two activities: “coupled” that generates •NO or “uncoupled” that generates O2−/ONOO−. A key factor determining the state of coupling is the ratio of [BH4] to its oxidation product [BH2] because both bind to NOS with equal affinity. When the BH4:BH2 ratio is low as found in inflammatory conditions, uncoupling is observed and more O2−/ONOO− and less •NO are generated [15, 16]. Because ONOO− oxidizes BH4 to BH2, a futile feed forward destruction mechanism of BH4 can be established. This switch in activities can have important consequences for downstream pro- and anti-growth signaling pathways.
We [17] recently showed that diverse tumor cell types in vitro and in vivo have low BH4:BH2 ratios (≤2) compared to normal tissues, including human colorectal tumor biopsies compared with paired adjacent “normal tissue” biopsies (>4). Furthermore, when sepiapterin (SP), a BH4 salvage pathway precursor, was included in the medium of cultured cells or provided orally to animals bearing tumors cells, the BH4:BH2 ratio increased in tissue culture, in breast cancer xenografts, and in a spontaneous MMTVneu breast tumor model. SP enhanced tumor cell killing both in vitro and in vivo in these breast cancer models. Previously, we also demonstrated that administering SP prophylactically in the azoxymethane dextran sodium sulfate (AOM/DSS) mouse model for colitis-associated CRC inhibited not only protein tyrosine nitration as a marker of NOS uncoupling but also reduced both colitis and tumor development [18]. Herein, the therapeutic administration of SP is shown to inhibit CRC growth through a mechanism targeting Akt-β-catenin signaling. Furthermore, the observed changes in the expression levels of key biopterin metabolic enzymes provide a mechanism for sustaining a low BH4:BH2 ratio during CRC progression in mice and in humans.
## Reagents and tissue culture
L-Sepiapterin was purchased from Schirks, Laboratories (Jona, Switzerland), Nω-nitro-L arginine, Euk134 and S-nitrosoglutathione from Sigma-Aldrich (St Louis, MO), and GP91ds-tat from Anaspec (Fremont, CA). The following primary antibodies (and sources) were used: goat anti-actin (sc-1615, Santa Cruz Biotechnology, Dallas, TX) and mouse monoclonal anti-GAPDH (MAB374, Millipore, Burlington, MA); Cell Signaling Technology (Danvers, MA) provided rabbit polyclonal anti-cdc25A (cst-3652), rabbit polyclonal anti-Akt (cst-9272), rabbit polyclonal anti pS$\frac{33}{37}$-β-catenin (cst-2009), rabbit monoclonal anti-non-phospho (active) β-catenin (cst-8814), mouse monoclonal anti-pS9-GSK3β (cst-9832), and rabbit monoclonal anti-GSK3β (cst-9323) and anti-pS473-Akt (cst-4060). Other antibodies included mouse monoclonal anti-β-catenin (#610153, BD Transduction Laboratories, San Jose, CA); anti-CBR1, anti-CBR3, and anti-AKR1C3 rabbit polyclonal antibodies (A5446, A7545, and A13568, ABclonal, Woburn, MA); rabbit polyclonal anti-SPR, and anti-quinoid dihydropteridine reductase (QDPR) (#22293 and 28041, ThermoFisher Scientific, Waltham, MA) and mouse monoclonal anti-dihydrofolate reductase (DHFR) (#MAB7934, R&D Systems, Minneapolis, MN). The Apotag kit was from Millipore. CRC cell lines (HCT116 and HT29) were purchased from American type culture collection (ATCC) and grown as monolayers in McCoy’s 5A medium (Thermo Fisher) supplemented with $10\%$ fetal bovine serum (FBS) and penicillin and streptomycin (50 U/ml). Experiments were performed only on cells grown up to 10 passages. Clonogenic assays were performed as described [17].
## Induction of colitis and CRC
Colitis/carcinogenesis was induced by the intraperitoneal injection of AOM (10 mg/kg) followed by three cycles of $2\%$ dextran sodium sulfate (DSS) treatment as previously described [18]. Two weeks after the last DSS treatment, animals were treated with three daily doses of SP (every 8 h) for a total daily dose of 1 mg/kg/day via oral gavage for 8 days. A subset of animals were treated with SP in their drinking water (170 μM SP) to provide an approximate dose of 0.64 mg/kg/day for 3 weeks as previously described [18] All procedures were approved by the Institutional Animal Care and Use Committee of Virginia Commonwealth University (protocol number AM10185) and conformed to the guidelines established by the National Institutes of Health.
## Biochemical analyses
For all analyses involving SP [clonogenic assays, Western blot, and high performance liquid chromatography (HPLC) experiments] a stock solution of fresh 1 mM SP with $0.25\%$ dimethyl sulfoxide (DMSO) in a serum-free culture medium was made and subsequently diluted appropriately. For control/untreated experimental conditions, a serum-free culture medium with $0.25\%$ DMSO was diluted appropriately depending on amount used from stock 1 mM SP solution. Fresh SP and media were replaced daily for long course treatment with SP.
Biopterin measurements of cultured cells were as described using HPLC analysis [17]. To analyze tumor biopterin levels in vivo, mice were anesthetized and euthanized by cervical dislocation. The distal colon was excised, and colonic epithelial cells were separated from the mucosal layer and tumor polyps as described [19]. Tumors were surgically excised after removal of epithelial cells. The tumors or normal epithelial cells were either placed in the −80°C freezer or immediately homogenized in 10 volumes of 0.1N HCl with a pestle and mortar on ice. The homogenates were centrifuged for 20 min at 13,200 rpm, and the supernatants were stored at −80°C prior to HPLC analysis.
GSK3β shRNA Lentiviral Particles-A and control shRNA Lentiviral Particles-A were purchased from Santa Cruz Biotechnology (Dallas, TX). In a 12-well plate, HCT-116 cells were plated in complete McCoy’s medium with $10\%$ FBS to achieve ~$50\%$ confluence the following day. Complete medium (1 ml per well) with Polybrene (5 µg/ml; sc-134220) and appropriate lentiviral particles (20 µl) were prepared as infection medium. Complete growth medium was replaced with infection medium and incubated overnight for 12 h. The medium was changed to a fresh complete growth medium and incubated overnight. To select stable clones, cells were split 1:3 from original 12-well plates and grown for 48 h in 10-cm dishes. shRNA expressing clones were selected by treating dishes with puromycin dihydrochloride (10 µg/ml) in a complete growth medium. Fresh puromycin was replaced every 3–4 days. After 2 weeks of growth under puromycin selection, cells were split 1:50 into multiple dishes. Single colonies were picked under sterile conditions and grown to confluence under puromycin selection. These clones were then analyzed via Western blot analysis for GSK3β expression.
TCF/LEF promoter activity was measured with a luciferase reporter assay using HCT-116 cells as described by the manufacturer (Addgene, Cambridge, MA). Briefly, for T cell factor/lef-1 family of transcription factors (TCF/LEF) promoter activity, cells were treated with SP. Twenty-four hours before analysis cells were transfected by lipofectamine/plus with a luciferase tagged TCF/LEF reporter construct (Addgene) in SP-free medium. Three hours later, the medium was changed with fresh SP and luciferase activity measured 24 h later.
S-nitrosylated proteins were purified and analyzed as previously described using biotin switch methodology [20].
## Immunochemical analysis
For immunohistochemical analysis, mouse colons were prepared as “Swiss rolls” [18]. In some experiments, tumors were excised from the colons, rinsed in phosphate buffered saline (PBS), and frozen in liquid nitrogen. Swiss rolls or excised tumors were embedded in optimal cutting temperature (OCT) medium and cryosections prepared. De-identified human patient colon samples frozen in OCT were obtained from the Massey Cancer Center Tissue and Data Acquisition and Analysis Core through a local Institutional review board (IRB)-approved protocol that abides by the Declaration of Helsinki principles. Multiple frozen sections were fixed in ice-cold $4\%$ paraformaldehyde in PBS for 10 min. The middle section was used for Hematoxylin & Eosin (H&E) staining and delineation of normal and cancer areas. Sections on either side were stained for the different proteins as follows. Mouse sections were blocked with the M.O.M. kit from Vector Laboratories (Burlington, CA). Human samples were blocked with goat serum for 60 min. Sections were stained with primary antibody at 1:200 dilution overnight at 4°C, followed by incubation with Alexa-conjugated appropriate secondary antibody at 1:500 dilution at room temperature for 1 h. After washing, sections were stained with 4′,6-diamidino-2-phenylindole (DAPI). Images were captured using the Ariol Digital Pathology Platform. Quantitation was achieved with ImageJ software using nuclear DAPI staining for normalization.
## PET/CT imaging
For positron emission tomography/computed tomography (PET/CT) imaging animals were fasted, anesthetized ($2\%$ isoflurane in oxygen), and injected intravenous (iv) with 300 µCi [18F]-FDG (fluorodeoxyglucose) from IBA Molecular (Sterling, VA). After 60 min of FDG uptake, animals were positioned in the Inveon Preclinical System (Siemens Healthcare, Malvern, PA), and PET/CT images were acquired for 10 min with no attenuation correction. The PET images were processed using manufacturer recommended calibration procedures and a phantom of known volume and activity acquired prior to the study. OSEM3D-MAP reconstructions were done using Inveon Acquisition Workplace 1.5 and were used for region-of-interest analysis in the Inveon Research Workplace 4.1. The percent injected dose/gram of tissue (%ID/g) values were calculated after appropriate decay corrections using the formula, %ID/g = Ct/ID × 100, where *Ct is* the concentration of radiotracer in the tissue (MBq/cc), obtained from the PET mages after region-of-interest analysis.
## SP increases the BH4:BH2 ratio in CRC cells and decreases proliferation in vitro and in vivo
Our previous studies demonstrated in HT29 human colon cancer cells in vitro and in paired tumor and adjacent normal human colon biopsies that the BH4:BH2 ratio is lower in the tumor cells relative to normal tissue [17]. To extend these findings, the BH4:BH2 ratio was measured in another colon cancer cell line, HCT116, and in isolated tumors from the colons of AOM/DSS-treated mice. Figure 1A shows that incubating HCT-116 cells with 20 µM SP for 24 h significantly increased the BH4:BH2 ratio, 2.3 ± 0.2 to 11.6 ± 2.3. SP treatment elevated the BH4:BH2 ratio by more than 2.5-fold in HT29 cells and about five-fold in HCT-116 cells compared to baseline.
**Figure 1:** *SP raises the BH4:BH2 ratio in CRC cells and decreases proliferation both in vitro and in vivo.
(A) BH4:BH2 ratios were measured in vitro cell lines and from tumors obtained from AOM/DSS mice treated with and without SP at 1 mg/kg. Values are given ± SEM. (B) Cells were treated with SP for 24 h, plated and grown to viable colonies of 50 or more cells. Statistical significance was evaluated with the Student’s t-test. *p<0.05. (C) At 2 weeks following completion of three cycles of DSS treatment, animals were imaged to obtain baseline tumor FDG activity. Two weeks later, animals were re-imaged to assess tumor progression by [18F]-FDG tumor activity. Animals were then treated with SP by oral gavage (1 mg/kg/day) and imaged at 3 and 8 days. (Red arrows: tumor; B = Bladder) (D) [18F]-FDG uptake values were calculated and normalized to the fold change from week 12 and averaged for N = 10 animals. Fold change values are shown as ± SD. *p ≤ 0.05 using Student’s t-test. (E) At 2 weeks after final DSS treatment, animals were administered with SP (4 mg/L) in their drinking water for 3 weeks after which the animals were sacrificed and colons collected.*
A colony formation assay was used to determine whether SP has effects on the proliferation capacity of tumor cells in vitro. Increasing concentrations of SP resulted in a progressive reduction in colony formation in both HCT-116 and HT29 cell lines (Figure 1B). At the SP concentration used in subsequent experiments, 20 μM, there is a $30\%$–$40\%$ decrease in colonies compared to untreated controls.
In vivo, after the final round of DSS treatment, animals were returned to normal drinking water for 4 weeks to allow for colitis to subside and tumors to continue growing. Animals were then treated with SP for 8 days after which the tumors were excised and the BH4:BH2 ratio determined by HPLC. In all cases the BH4:BH2 ratio of tumor cells was significantly lower than that of normal mouse colon epithelial cells (7.1+/−0.6 [19], Supplementary Information Table 1). In vivo, there was an approximate three-fold increase in the BH4:BH2 ratio in the tumors from SP-treated animals (Figure 1A).
[18F]-FDG/PET/CT imaging was used to determine the effects SP on CRC progression in vivo. Two weeks after the last DSS treatment and prior to SP treatment, animals were imaged two times separated by 14 days ($$n = 10$$). Animals were then imaged on days 3 and 8 of SP treatment (1 mg/kg/day by oral gavage). Figures 1C, D show representative PET images at different time points and the quantification of PET images from 10 animals normalized to their initial [18F]-FDG uptake at week 12, respectively. There is an approximate two-fold increase in [18F]-FDG uptake during the 2 weeks without any treatment followed by a $40\%$ reduction in [18F]-FDG uptake from baseline after 3 and 8 days of SP treatment.
The effect of SP on tumor burden was also assessed by gross examination and by assay for apoptosis in tumor-bearing animals treated with SP for a longer duration. Two weeks after the last DSS administration, the drinking water of half the mice was supplemented with 170 μM SP for 3 weeks at which time the mice were sacrificed. Figure 1E shows with two representative colons that SP for 3 weeks reduced the number of tumors per colon. In agreement, Supplementary Information Figure 1 shows an increase in tumor apoptosis measured by immunofluorescence. Collectively, these data suggest that SP has anti-proliferative and cytotoxic effects on CRC cell lines in vitro and spontaneous colitis-associated cancer in vivo.
## SP treatment decreases expression of β-catenin protein both in vitro and in vivo
The Wnt signaling pathway involving β-catenin transcriptional activity is a key driver of epithelial tumor progression. Our previous studies demonstrated that SP downregulated β-catenin protein expression in breast cancer cells [17]. We tested whether SP also reduced β-catenin protein levels in HT29 and HCT-116 cell lines. As shown in Figure 2A, the level of active (non-phosphorylated) form of β-catenin protein in both HT29 and HCT116 cells decreased within 1 day of SP treatment. To confirm that SP was able to decrease the transcriptional activity of β-catenin, a LEF/TCF luciferase promoter activity assay was employed. In HCT-116 cells, LEF/TCF-driven luciferase expression was significantly inhibited after 3 days of SP treatment (Figure 2B), with similar results in HT29 cells (data not shown).
**Figure 2:** *SP decreases β-catenin expression in vivo and ex vivo.
(A) Western blot analysis for active (non-phosphorylated) β-catenin expression in HTC-116 and HT-29 cells treated with 20 µM SP. (B) LET/TCF luciferase promotor activity assay in HCT-116 cells incubated with 20 µM SP for 3 and 5 days. Results are normalized to untreated cells and reported as ± SD. Student’s t-test was used to assess statistical significance (*p< 0.05). (C)
Ex vivo immunofluorescence analysis of β-catenin in CRC tumors. At 2 weeks after the final DSS treatment, animals were administered SP in their drinking water for 3 weeks as described in Materials and Methods. Tumors were excised from colon, and histological slides were made and stained for β-catenin expression. Representative sections are shown with β-catenin staining in green and DAPI nuclear staining in blue. (D) Quantification of β-catenin fluorescence was normalized to DAPI for control (N = 3) and SP treated (N = 3). Values are shown as ± SD; *p< 0.05 by t-test.*
We obtained similar results in the AOM/DSS-treated animals with respect to β-catenin expression. Immunofluorescence staining (Figures 2C, D) shows that tumor β-catenin protein expression is significantly downregulated after 3 weeks of consuming drinking water supplemented with 170 μM SP. In summary, both in vitro and in vivo experiments demonstrate that SP treatment results in reduced β-catenin protein levels.
## SP treatment leads to S-nitrosylation of Akt and decreased Akt activity
β-catenin is targeted for proteomic degradation after phosphorylation by GSK3β. Akt is a potent activator of Wnt signaling by phosphorylating and inhibiting GSK3β, thereby preventing proteasomal β-catenin degradation [21]. Because *Akt is* activated during times of oxidative stress, and SP decreases oxidative stress by increasing the BH4:BH2 ratio and *NO synthesis while reducing generation of oxidants O2− and ONOO−, we tested whether Akt activity was inhibited by SP. Phosphorylation of Akt at Ser473 (a marker for its activation) was assayed by Western blot analysis in HCT-116 and HT-29 cells. Phosphorylation at Ser473 was decreased with SP treatment in both cell lines (Figure 3A; Supplemental Information Figure 2A). Given the published reports that show •NO donors inactivate Akt by S-nitrosylation of Cys224, we tested whether incubating HCT116 cells with either the *NO donor, S-nitrosoglutathione, or with SP stimulated S-nitrosylation of Akt and thereby inhibited its activity [22]. As shown in Figure 3B, treating cells with either a *NO donor or SP results in S-nitrosylation of Akt. The loss of Akt activity was confirmed by assaying for GSK3β Ser9 phosphorylation, a known Akt phosphorylation site [23]. For both HCT-116 and HT-29 cells, incubation with SP blocked phosphorylation of Ser9-GSK3β (Figure 3A; Supplementary Information Figure 2A). To confirm increased activity of GSK3β, Ser$\frac{33}{37}$ phosphorylation of β-catenin and expression of cdc25A, which is also degraded as a consequence of GSK3β activity [24], were analyzed by Western blot (Supplementary Information Figure 2B). Phosphorylation of β-catenin was enhanced in SP- treated HCT-116 cells by a mechanism sensitive to the GSK3β inhibitor, SB216763. Expression levels of cdc25A were also decreased in cells incubated with SP. Thus, in these CRC cells, SP leads to decreased activation of AKT and a corresponding increased activation of GSK3β contributing to β-catenin degradation.
**Figure 3:** *SP treatment leads to S-nitrosylation of AKT and decreased pSer473 AKT. (A) Western blot analysis of pAkt and pGSK3β after 20 µM SP treatment in HCT-116 cells. (B) The biotin switch assay, previously described (20), was used to determine s-nitrosylation of Akt in HCT-116 cells with 20 µM SP treatment after 1 or 3 days. As a positive control s-nitrosoglutathione (GSNO), a nitric oxide (NO) donor, was added to the cell lysates for 5 min. Representative blots from two separate experiments are shown.*
## Inhibiting GSK3β expression blocks SP effects on β-catenin, cdc25A expression, and HCT116 growth
To confirm GSK3β’s role in SP’s anti-proliferative effects, HCT-116 cells were stably transfected with shRNA to knockdown GSK3β expression, with several clones isolated (Figure 4A). Subsequent experiments compared the effects of SP on cells expressing scrambled shRNA (clones shConC or shConD) and GSK3β (shGSK3.I or shGSK3.G) knockdown cells. *With* genetic knockdown of GSK3β, the reduced expression of β-catenin and cdc25A observed with SP treatment is lost [compare Figure 4B (shconC) and Figure 4C (shGSK3.I)]. Similarly, GSK3β knockdown prevented SP inhibition of LEF/TCF luciferase activity without effecting overall LEF/TCF activity (Figure 4D). We also tested the effects of GSK3β knockdown on the cytotoxic activity of SP. A colony-forming assay shows that the cytotoxic activity of SP is partially suppressed by inhibiting GSK3β expression (Figure 4E). This suggests that the cell growth inhibiting activity of SP involves more than the Akt/GSK3β pathway.
**Figure 4:** *GSK-3β knockdown mitigates the effects of SP in HCT-116 cells: (A) Confirmation by Western blot of GSK-3β knockdown in shGSK3.I and shGSK3.G clones. shConC (B) cells or shGSK3.I (C) cells were treated with 20 µM of SP for 1, 3, or 5 days, and protein expression of β-catenin, cdc25A, and pSer473-Akt were measured by Western blot. Representative blots are shown from three separate experiments. (D) LEF-TCF promotor activity was measured in shConC, shGSK3.G, and shGSK3.I clones. Results are normalized to untreated cells and reported as ± SD. Student’s t-test was used to assess for statistical significance; *p<0.05. (E) For in vitro clonogenic assays, scrambled and GSK3β knockdown HCT-116 cells were treated with 20 µM SP for 24 h before plating and growing colonies to 50 cells or more. Colony counts were normalized to untreated cells and assessed for statistical significance using the Student’s t-test. *p< 0.05.*
## An oxidative environment and biopterin metabolism contribute to a decreased BH4:BH2
After a myocardial infarction, eNOS becomes uncoupled via the oxidation of BH4 to BH2 in cardiac tissue. Several groups have postulated that this oxidation is due to an increase in O2− and ONOO− resulting from ischemia and inflammatory cell infiltration [25]. Given the inflammatory microenvironment and hypoxic nature of tumors, we tested whether ROS/RNS have a role in decreasing the BH4:BH2 ratio. For this, we used inhibitors and scavengers of ROS/RNS. Nω-nitro-l-arginine (L-NNA) is a specific inhibitor of all NOS isoforms, GP91ds-tat is a small peptide inhibitor of NADPH oxidases, which generate O2−, and EUK134 is a scavenger of O2− and ONOO−. Supplementary Table 1 shows that these inhibitors individually or combined and at concentrations known to be effective in vitro (26–28) have only minimal effects on BH4:BH2 in HCT116 cells. This suggests that there must be a fundamental change in biopterin metabolism to account for the low BH4:BH2 in these cells.
To determine a possible mechanism for the low BH4:BH2, protein expression levels of key enzymes in the de novo synthesis and salvage pathways of BH4 of normal colonic epithelial cells from non-tumor bearing animals and CRC cells from animals treated with AOM/DSS were compared. The four enzymes studied were GTP cyclohydrolase-1 (GCH-1), SP reductase (SPR), quinoid dihydropteridine reductase (QDPR), and dihydrofolate reductase (DHFR). GCH-1 is the enzyme responsible for catalyzing the rate limiting step in de novo synthesis. We previously demonstrated that overexpressing GCH-1 increased the BH4:BH2 ratio in MCF-7 breast cancer cells [17]. SPR is an enzyme responsible for reducing 6-pyruvoyltetrahydrobiopterin to BH4 in the de novo pathway and also converting BH2 (and SP) back to BH4 in the salvage pathway. QDPR is important in both the salvage and de novo pathways, and, much like SPR, it is responsible for reducing upstream pteridine derivatives into BH4 [25, 29, 30]. DHFR reduces BH2 to BH4 [25].
Representative immunofluorescence staining of SPR, QDPR, GCH-1, and DHFR is shown in Figure 5A with quantification of staining after normalization to DAPI nuclear staining provided in Figure 5B. With the exception of DHFR, the normal colon expressed significantly higher amounts of these enzymes compared to the colons of tumor bearing mice. DHFR levels of normal and tumor tissues were approximately identical. These data collectively indicate that changes in expression of the BH4 synthesis and salvage pathway enzymes may be the major mechanism of how AOM/DSS induced CRC cells maintain a decreased BH4:BH2.
**Figure 5:** *Ex vivo analysis of biopterin metabolic enzymes. (A) Colons from control or tumor bearing animals after treatment were harvested and probed for SPR, QDPR, GCH-1, and DHFR. Representative images are shown. (B) Quantification was done by fluorescence of specific protein normalized to DAPI fluorescence. Relative normalized expression is shown as ± SD. Student’s t-test was used to assess for statistical significance (*p<0.05).*
The decreased expression of SPR found in the mouse CRC raises questions of how exogenous SP is converted to BH2 and ultimately to BH4 through further reduction. Previous studies have demonstrated that aldose and carbonyl reductases can reduce SP to BH2 [31]. To confirm that these reductases are present in AOM/DSS induced CRC, Swiss rolls and tumors were stained for CBR1, CBR3, and AKR1C3. As shown in Supplementary Figure 4, these enzymes are expressed throughout the colon and in isolated tumors.
The immunohistochemical analysis was repeated with human CRC biopsies. H&E-stained sections were delineated into regions of normal and abnormal histology by a pathologist. These histological classifications were used to assess the relative expression levels in normal and diseased areas as a function of tumor stage (Supplementary Information Figure 3). The patient samples were stained for GCH-1, SPR, QDPR, and DHFR. Results from representative sections of each stage of tumor progression and quantified relative expression levels for all samples as a function of tumor stage are shown in Figure 6. The most notable finding is the significantly decreased expression of QDPR found in stage 1 that persists throughout tumor progression. QDPR is associated with regeneration of BH4 from an intermediate, quinoid dihydrobiopterin, formed during the catalytic cycle of aromatic amino acid hydroxylases and not NOS. As a cofactor for NOS, BH4 is oxidized to a protonated trihydrobiopterin cation radical which is subsequently reduced in the next catalytic cycle by the NOS flavins. However, QDPR is ubiquitously expressed in most if not all cells and as discussed below may likely play a key role in maintaining cellular BH4 levels under conditions of oxidative stress (30, 32–35).
**Figure 6:** *Ex vivo analysis of GCH-1, SPR, QDPR, and DHFR in normal mucosa and colon adenocarcinoma human tissue samples. (A–D) Representative sections of whole tissue sections analyzed for normal and tumor tissue by a pathologist as shown in Supporting Information
Figure 5
. In the bar graphs, GCH-1, SPR, and QDPR expression levels from five patient samples and for DHFR from three patient samples were measured for each tumor stage and normalized to DAPI. These values were subsequently compared to normal tissue expression set to 1. Relative expression is shown for each enzyme at different stages of tumor progression. Values are shown as ± SD, and unpaired t-test was used to determine statistical significance (*p<0.05).*
## Discussion
Herein, it is demonstrated that treating colorectal tumor cells with SP in vitro and in vivo increases BH4:BH2 and recouples NOS activity. One consequence is the S-nitrosylation and inhibition of Akt activity and decreased phosphorylation at the inhibitory site of GSK3β. The activation of GSK3β in colorectal tumors in mice treated with SP contributes to decreased β-catenin expression and to reduced tumor growth as assessed by enhanced apoptosis and reduced [18F]-FDG uptake measured by PET/CT. Given the multiple mechanisms by which ROS/RNS can modulate growth promoting pathways, other mechanisms for the pro-tumor growth activity of SP are also likely involved, e.g., inhibition of NFκB signaling [17]. This is evident from the experimental results in Figure 4, where SP induced cell death in HCT116 cells was only partially inhibited by GSK3β knockdown. Figure 7 shows a graphical representation of our findings in this study.
**Figure 7:** *NOS coupling and NO/RNS-dependent downstream signal transduction. We have demonstrated that NOS is uncoupled in tumor cells and can be recoupled with exogenous BH4 or SP resulting in a switch from pro-survival/pro-inflammatory to anti-survival/anti-inflammatory.*
Several studies including our own show that inhibiting NOS leads to a decrease in tumor growth and proliferation (26, 36–38). Others, however, have demonstrated that activating NOS or treating cells with •NO donors enhances tumor cell proliferation (39–42). One explanation for these apparently conflicting reports is the state of NOS coupling. In all solid tumors examined to date, NOS has been shown to be uncoupled producing ROS/RNS at levels that are potentially growth promoting, for example, by activating NFκB transcriptional activity [17, 43]. Thus, inhibition of uncoupled NOS by decreasing ROS/RNS production inhibits activation of proliferative pathways, as seen with L-NNA, a selective NOS inhibitor [26]. When *NO donors are added to cells with an uncoupled NOS generating O2−, the released *NO reacts with O2− producing ONOO− and activating pro-proliferative and anti-apoptotic pathways, e.g., NFkB activation [43, 44].
Activation of the protein kinase G pathway of CRC and breast cancer cells with cGMP phosphodiesterase inhibitors (e.g., sulfinadic) or by overexpressing protein kinase G results in decreased β-catenin expression and inhibition of tumor cell growth [45, 46]. We have previously shown with breast cancer cells that SP by recoupling NOS also enhanced cGMP/protein kinase G signaling resulting in downregulated β-catenin expression [17]. In contrast, SP treatment of HCT-116 cells did not lead to an increase in cGMP levels or protein kinase G signaling in our hands (unpublished data). One explanation comes from the growing evidence that CRC cells overexpress various cGMP phosphodiesterase isoforms [13, 47]. Thus, future investigations will involve combining cGMP phosphodiesterase inhibitors and SP to determine whether this will further inhibit tumor growth.
In mammary carcinoma cells, the NOS inhibitor, L-NNA, increased the BH4:BH2 ratio indicating that NOS generated ROS/RNS was, in part, responsible for the low BH4:BH2 ratio [17]. The present investigation reveals an alternative mechanism in CRC cells. The expression of key biopterin metabolizing enzymes was shown to be perturbed as the tumors progressed in both mice and humans. In mice, expression of GCH1, SPR, and QDPR was shown to be significantly downregulated in AOM/DSS tumors. In humans, QDPR was also significantly downregulated. Thus, in CRC tumors, a low BH4:BH2 ratio is, in part, due to a reduced recycling of BH2 back to BH4. However, the expression levels of DHFR, which are known to reduce BH2 to BH4, remained unchanged. Recent studies with a QDPR knockout mouse provide a potential explanation [32]. In the QDPR knockout mouse, a decline in the BH4:BH2+biopterin ratio was observed in all tissues except the kidney when compared to the ratio in the wild-type mouse. Furthermore, a methotrexate-induced decline in the ratio was observed in all tissues of the QDPR-deficient mouse but only in the kidney of the wild-type mouse. Given the relatively low affinity of mouse and human DHFR for BH2 and these findings with the QDPR knockout mouse, QDPR deficiency may partially account for the reduced BH4:BH2 found in tumor tissues [33]. DHFR may be critical in reducing BH2 to BH4 under conditions of elevated intracellular BH2 as when cells are incubated with SP.
Future studies will need to explore alternative mechanisms of NOS uncoupling including conditions of low [Arg] as found with elevated expression of arginase or S-glutathionylation of eNOS [48, 49]. Regardless of the mechanism, NOS uncoupling represents a critical switching mechanism for tumor cell growth, initiating and sustaining different downstream signaling pathways that are pro-proliferative and anti-apoptotic, e.g., NF-κB and β-catenin. When coupled, the primary product of all NOS isoforms is *NO, and downstream signaling is dominated by *NO-dependent anti-proliferative pathways (e.g., soluble guanylate cyclase/protein kinase G). In addition, we have recently shown that SP normalizes MMTVneu breast tumor vasculature increasing tumor blood flow and oxygenation and in the process enhancing tumor uptake and cytotoxicity of daunorubicin and tumor radiosensitivity [50]. This shift in NOS activity thus represents an anti-tumor target that is potentially exploitable by repurposing a therapeutic already in clinical use. Synthetic BH4 (Kuvan) is used to treat a certain type of phenylketonuria and is in clinical trials for some cardiovascular diseases including patients with endothelial dysfunction. SP has also been used in phase I and II clinical trials at five times the concentrations that we are using here and was well tolerated with minimal toxicity and no reports of serious side effects.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by IACUC.
## Author contributions
Conception and design: AA and CR. Experiments: AA, MM, VY, GS, SS, and CR. Data analysis and interpretation: AA, SS, GS, VY, and CR. Writing and review: AA, VY, and CR. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Researchers were provided Sepiapterin by PTC Therapeutics free of charge.
## 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.
## Author disclaimer
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2023.1165326/full#supplementary-material
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|
---
title: Retinal Alterations as Potential Biomarkers of Structural Brain Changes in
Alzheimer’s Disease Spectrum Patients
authors:
- Zheqi Hu
- Lianlian Wang
- Dandan Zhu
- Ruomeng Qin
- Xiaoning Sheng
- Zhihong Ke
- Pengfei Shao
- Hui Zhao
- Yun Xu
- Feng Bai
journal: Brain Sciences
year: 2023
pmcid: PMC10046312
doi: 10.3390/brainsci13030460
license: CC BY 4.0
---
# Retinal Alterations as Potential Biomarkers of Structural Brain Changes in Alzheimer’s Disease Spectrum Patients
## Abstract
Retinal imaging being a potential biomarker for Alzheimer’s disease is gradually attracting the attention of researchers. However, the association between retinal parameters and AD neuroimaging biomarkers, particularly structural changes, is still unclear. In this cross-sectional study, we recruited 25 cognitively impaired (CI) and 21 cognitively normal (CN) individuals. All subjects underwent retinal layer thickness and microvascular measurements with optical coherence tomography angiography (OCTA). Gray matter and white matter (WM) data such as T1-weighted magnetic resonance imaging and diffusion tensor imaging, respectively, were also collected. In addition, hippocampal subfield volumes and WM tract microstructural alterations were investigated as classical AD neuroimaging biomarkers. The microvascular and retinal features and their correlation with brain structural imaging markers were further analyzed. We observed a reduction in vessel density (VD) at the inferior outer (IO) sector ($$p \leq 0.049$$), atrophy in hippocampal subfield volumes, such as the subiculum ($$p \leq 0.012$$), presubiculum ($$p \leq 0.015$$), molecular_layer_HP ($$p \leq 0.033$$), GC-ML-DG ($$p \leq 0.043$$) and whole hippocampus ($$p \leq 0.033$$) in CI patients. Altered microstructural integrity of WM tracts in CI patients was also discovered in the cingulum hippocampal part (CgH). Importantly, we detected significant associations between retinal VD and gray matter volumes of the hippocampal subfield in CI patients. These findings suggested that the retinal microvascular measures acquired by OCTA may be markers for the early prediction of AD-related structural brain changes.
## 1. Introduction
Alzheimer’s disease (AD) is the most prevalent form of dementia among elderly individuals worldwide, affecting over 50 million people [1]. The pathophysiological alterations in AD involve the loss of neurons, brain atrophy, the deposition of extracellular β-amyloid (Aβ) plaques and the accumulation of intracellular neurofibrillary tangles. Early diagnosis of AD is critical for effective prevention and management. Mild cognitive impairment (MCI) is defined as a transitional phase between normal aging and AD, and patients with MCI have a 10–$15\%$ higher risk of developing AD per year [2]. Established markers for the diagnosis of AD include cerebrospinal fluid markers as well as imaging biomarkers such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Brain structural decline offers a versatile means of understanding and targeting pathophysiological mechanisms [3].
The retina is considered an extension of the central nervous system and neurodegenerative changes resulting from diseases are mirrored in the eye [4]. At the molecular level, several studies have demonstrated the presence of classical AD biomarkers, such as amyloid β and phosphorylated tau, within the retinal structure and vasculature. These deposits cause retinal and capillary degeneration, such as apoptosis of retinal ganglion cells (RGCs), thinning of the retinal nerve fiber layer (RNFL) and other structural and functional impairments in AD patients [5,6,7,8,9,10,11,12,13,14,15,16,17,18]. Optical coherence tomography (OCT) is an optical imaging technique that enables both structural and functional imaging of the retina in humans, as well as a wide range of veterinary animals [19,20,21]. OCT angiography (OCTA) allows for the extraction of blood vessels in the retina from OCT data. Various reviews have highlighted characteristic pathological retinal changes in AD patients, including a significant reduction in RNFL thickness and macular and choroidal thickness, a decline in vessel density and an increase in the foveal avascular zone (FAZ) in patients with AD, even in MCI subjects, as measured by OCTA [22,23,24,25,26]. Furthermore, some studies have reported an association between a thinner retinal structure and worse cognitive function, as well as a greater likelihood of future cognitive decline [27,28,29].
The relationship between changes in retinal structure or microvasculature and the neuroimaging of Alzheimer’s disease (AD) has received increasing attention in recent studies. Previous research has shown that a thinner macular thickness is negatively correlated with cerebral cortical atrophy, particularly parietal cortical atrophy in AD patients [30,31]. Moreover, a thicker retinal nerve fiber layer (RNFL) has been linked to better MRI variables, such as greater hippocampal volumes and improved diffusion tensor imaging variables in elderly individuals without dementia [32,33,34]. In an OCTA study, the authors found that reductions in retinal vessel density (VD) were significantly associated with inferolateral ventricle (ILV) enlargement in MCI and AD. However, most of these studies focused on the hippocampus in large scale measures despite its heterogeneous composition consisting of distinct subfields with varying anatomical, functional and electrophysiological properties, including the dentate gyrus, subiculum, parasubiculum and presubiculum [32,35,36,37,38]. Multiple studies had reported that atrophy in the CA1, subiculum and presubiculum represent the earliest sites for AD pathologic changes (i.e., amyloid deposits, tau aggregation) [39,40,41]. Additionally, diffusion tensor imaging (DTI) is the most common technique to assess pathophysiology in neurodegenerative diseases, especially the detection of microstructural changes in the cerebral white matter (WM) in AD. A multicenter study with a large cohort found a significantly decreased fractional anisotropy (FA) and an increased mean diffusivity (MD) in areas with AD pathology, including the corpus callosum, cingulate gyrus, fornix, precuneus, medial and lateral temporal lobes and prefrontal lobe WM [42]. Previous studies also showed the potential correlation between retinal layer thickness changes and WM variables [32,43,44].
Here, we aimed to investigate the changes in retinal measures between groups and determine the correlation between retinal layer thickness/microvascular measures and classical AD structural biomarkers in cognitively normal (CN) subjects and cognitively impaired (CI) patients. Our hypothesis was that retinal characteristics can reflect altered AD-related structural brain changes, providing a simple and non-invasive biomarker with potential clinical applications.
## 2.1. Participants
The present study recruited 52 right-handed Chinese Han elderly individuals at the Department of Neurology of the Nanjing Drum Tower Hospital of Nanjing University Medical School from September 2020 to January 2021, including 10 AD subjects (4 males and 6 females), 21 MCI subjects (6 males and 15 females) and 21 CN individuals (7 males and 14 females). This study was approved by the ethics committees of the Nanjing Drum Tower Hospital and Nanjing University Medical School, and written informed consent was obtained from each subject prior to participation. All subjects underwent a comprehensive neuropsychological test, 3.0 T whole brain MRI scanning and a general medical examination by an experienced neurologist. The cognitive functions of all the subjects were evaluated by an experienced neuropsychologist using the Chinese version of the Mini-Mental State Examination (MMSE) and the Beijing version of the Montreal Cognitive Assessment (MoCA) as general cognitive function screening. The Clinical Dementia Rating (CDR) scale, activities of daily living (ADL) assessment, Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA) were also tested.
The inclusion criteria for CN subjects were as follows: [1] the absence of reported cognitive complaints; [2] normal MMSE and MoCA scores that were adjusted for age and education. The MoCA was used to identify CN and CI. Optimal cutoff points were determined based on education level. For individuals with 7 or more years of education, the MoCA cutoff was $\frac{24}{25.}$ For those with 1–6 years of education, the MoCA cutoff was $\frac{19}{20.}$ Finally, for those with no formal education, the cutoff was $\frac{13}{14}$; and [3] CDR = 0 [45,46]. These CI patients included MCI and AD patients. In contrast, the inclusion criteria for CI patients were determined using the National Institute of Neurological and Communicative Disorders and Stroke and the AD and Related Disorders Association (NINCDS-ADRDA) criteria for AD patients and the *Petersen criteria* for MCI patients [47,48]: [1] subjective memory complaint confirmed by an informant; [2] objective memory impairment as detected by the MoCA or Auditory Verbal Learning Test (AVLT) scores that were at least 1.5 standard deviations below normative values for age and/or education; [3] preserved general cognitive function (MMSE ≥ 24); [4] CDR score = 0.5; [5] no or minimal impairment in activities of daily living (ADL); and [6] not sufficient to diagnose dementia. Exclusion criteria for all the subjects were as follows: [1] age less than 50 years old; [2] a history of stroke, diabetes or hypertension; [3] central nervous system diseases that could potentially result in cognitive decline; [4] severe mental health conditions such as schizophrenia, anxiety (Hamilton Rating Scale for Anxiety (HAMA) score ≥ 21) or depression (Hamilton Rating Scale for Depression (HAMD) score ≥ 17); [5] severe systemic diseases such as heart failure and kidney dysfunction; [6] a history of drug or alcohol abuse; [7] intolerance of MRI examination or the inability to complete neuropsychological testing; [8] other medical conditions that may impact cognition; and [9] the presence of concurrent retinal diseases such as diabetic neuropathy, epiretinal membrane or macular degeneration, as well as any history of glaucoma, optic neuropathies or ocular surgery, except for cataract surgery. Additionally, all participants underwent thorough ophthalmic evaluations, with eyes having a history of retinal surgery, evidence of epiretinal membrane, glaucoma or branch retinal vein occlusion being excluded. A flowchart (Figure 1) visually represented the three main processing sectors of the study, which will be discussed in further detail. Finally, a total of 46 subjects (90 eyes) were enrolled in this study, including 25 CI patients (17 MCI patients, 8 AD patients) and 21 CN subjects (Supplementary Figure S1).
## 2.2. MRI Data Acquisition
In this study, imaging data were acquired using a 3.0T scanner, specifically the 3.0T Ingenia (32-channel head coil), manufactured by Philips in Eindhoven, Netherlands, which was located at Nanjing Drum Tower Hospital. To obtain sagittal T1-weighted MR images covering the entire brain, participants were positioned in the supine position and underwent a three-dimensional turbo fast echo acquisition. The T1-weighted imaging acquisition parameters were: a repetition time (TR) of 8.2 ms, an echo time (TE) of 3.7 ms, a field of view (FOV) of 256 × 256 mm, an acquisition matrix of 256 × 256, a flip angle (FA) of 8°, a slice thickness of 1.0 mm, no gap, voxel resolution = 1 × 1 × 1 mm3 and a number of slices of 192. Diffusion-weighted imaging was performed using an echo planar imaging (EPI) sequence, with diffusion-sensitizing gradients applied along 32 non-collinear directions ($b = 1000$ s/mm2), and an acquisition without diffusion weighting ($b = 0$). The acquisition parameters for diffusion-weighted imaging were: a TR of 8500 ms, a TE of 71 ms, a flip angle of 90°, a matrix size of 112 × 112 and an FOV of 224 × 224 mm. In addition, an axial T2-weighted, diffusion-weighted imaging (DWI) sequence and fluid-attenuated inversion recovery (FLAIR) sequence were acquired to detect acute or subacute infarctions and visible white matter damage.
## 2.3.1. Hippocampal Subfield Acquisition
The present study employed the FreeSurfer software (version 6.0) and the integrated hippocampal subfield segmentation software package to perform hippocampal subfield segmentation and grey/white matter volumetric segmentation [49]. The standard FreeSurfer processing pipeline using the “recon-all” script was utilized to preprocess all T1-weighted images [50]. The volumetric segmentation process for hippocampal subfields, which has been described previously, involved dividing a total of 12 subfields for each side of the hippocampus: the parasubiculum, presubiculum, subiculum, CA1, CA2-CA3, CA4, granule cell layer of the dentate gyrus, hippocampus–amygdala transition area, fimbria, molecular layer, hippocampal fissure and hippocampal tail (Figure 2). However, because the fimbria (white matter) and hippocampal fissure (cerebrospinal fluid) are not part of the grey matter and have shown relatively lower segmentation accuracies than other subfields in previous research, they were excluded from the subsequent analysis [51,52]. To ensure accuracy, a quality control process and manual editing were implemented and poor quality of segmentation were excluded [53].
## 2.3.2. DTI Processing
In this study, the Atlas-based segmentation approach was utilized to investigate diffusion abnormalities. DTI data processing was conducted using PANDA software (http://www.nitrc.org/projects/panda/, accessed on 12 February 2021), following the default pipeline setting [54]. PANDA is a pipeline tool that integrates FSL, the Diffusion Toolkit and MRIcron for diffusion MRI analysis. The data processing steps included converting the original DICOM data to a NIFIT format, removing non-brain tissue, correcting for eddy current and head motion, adjusting the diffusion gradient direction and calculating the diffusion tensor metrics, such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD). The FA images in native space were registered to the FA standard template in Montreal Neurological Institute (MNI) space using FSL’s FNIRT command. To evaluate changes in the major white matter tracts, all DTI metrics were registered to the JHU White Matter Tractography Atlas [55]. In the present research, we obtained FA/MD/AxD/RD diffusion tensor metrics.
Specific calculation indexes are as follows: [1]FA=3(λ1−λ¯)2+(λ2−λ¯)2+(λ3−λ¯)$\frac{2}{2}$(λ12+λ22+λ32) MD = (λ1 +λ2 + λ3)/3; AxD = λ1; RD = (λ1 + λ3)/2. In this study, the diffusion of water molecules was characterized by three dispersion directions, namely λ1, λ2 and λ3 [56]. Specifically, λ1 indicates the axial direction of the fiber bundle within the voxel, while λ2 and λ3 represent the radial directions perpendicular to the axis. The WM fiber pathways of interest included anterior thalamic radiation (ATR); cingulum in the cingulated cortex area (CgC); cingulum in the hippocampal area (CgH); corticospinal tract (CST); forceps major (FMa); inferior fronto-occipital fasciculus (IFO); inferior longitudinal fasciculus (ILF); superior longitudinal fasciculus (SLF); temporal projection of the SLF (tSLF); and uncinate fasciculus (UF). Bilateral evaluations were conducted for all tracts, with the exception of the corpus callosum (forceps major) and corpus callosum (forceps minor).
## 2.4. Retinal Image Acquisition
Retinal imaging was carried out with a spectral-domain OCTA machine (Cirrus HD-5000 AngioPlex; Carl Zeiss Meditec, Dublin, CA, USA) capable of scanning at 68,000 A-scans/s. Images of 6 × 6 mm centered on the fovea, 512 × 128 for the macular cube and 200 × 200 for the optic disc cube scans were captured. Images that were poor quality (less than $\frac{7}{10}$ signal strength) or had low resolution, uncorrectable segmentation errors, projection artifacts or motion artifacts were excluded. Segmentation of full-thickness retinal scans in the superficial capillary plexus (SCP) was automated using OCTA software (Version 10.0; Carl Zeiss Meditec, Dublin, CA, USA). The software automatically quantified the average VD for the 6 × 6 mm SCP images over the central macula using an Early Treatment Diabetic Retinopathy Study (ETDRS) grid overlay, and it was automatically calculated for the 6 mm circle, 6 mm ring and 3 mm ring regions of the 6 × 6 mm OCTA images (Supplementary Figure S2). The software also automatically segmented and quantified the FAZ area. RNFL thickness (using a 3.46-mm diameter circle centered on the optic disc) was recorded [57,58].
## 2.5. Statistical Analysis
The measurement data were presented as mean ± standard deviation (SD). Independent sample t-test and Mann–Whitney U test were used to compare normally and non-normally distributed measurement data between groups, respectively. The χ2 test was employed to examine between-group sex differences, which is categorical data. All p values were two-sided, and no correction was made for multiple analyses. To minimize Type 1 errors caused by conducting multiple tests, we averaged all retinal measures, DTI metrics and hippocampal subfield volumes for both hemispheres. We performed an analysis of covariance (ANCOVA) to investigate whether retinal measures, hippocampal subfield volumes or alterations in WM integrity were changed in CI participants, while adjusting for sex, age, years of education and estimated Total Intracranial Volume (eTIV) [59]. To evaluate the linear correlation between the averaged retinal parameters in the optic disc and macula with AD neuroimaging biomarkers, partial correlation analysis was used, with age, gender, education and eTIV acting as calibration control variables. All statistical analyses were performed using SPSS for Windows (version 26.0, IBM, Chicago, IL, USA). Statistical significance was considered at $p \leq 0.05$ (two-tailed).
## 3.1. Demographic, Neuropsychological Characteristics and Retinal Measures
The demographic and clinical data of the CN and CI groups are presented in Table 1. No significant differences were observed between the groups with regard to gender ($$p \leq 0.924$$), eTIV ($$p \leq 0.526$$), HAMD ($$p \leq 0.58$$) or HAMA scores ($$p \leq 0.74$$). However, the individuals in the CI group were found to be significantly older than those in the CN group ($$p \leq 0.016$$) and had a lower level of education ($p \leq 0.001$). The CI group exhibited lower scores for MMSE and MoCA-BJ assessments, in comparison to the CN group. The difference in retinal measures between the CI and CN groups is shown (Supplementary Table S1, Figure 3). The results show a decrease in vessel density (VD) at the inferior outer (IO) sector in the CI group when compared with the CN group ($$p \leq 0.049$$). RNFL and macular thickness in any sectors or rings did not discriminate CI patients from CN subjects. Trends of lower retinal VD and thinner RNFL and macular thickness in the CI group were found (Supplementary Table S1).
## 3.2. Hippocampal Volume and Its Association with Retinal Measures
In the comparison of hippocampal subfield volumes between these two groups (Table 2, Figure 4), volumes were significantly decreased in the CI group compared with the CN group in the subiculum ($$p \leq 0.012$$), presubiculum ($$p \leq 0.015$$), molecular_layer_HP ($$p \leq 0.033$$), GC-ML-DG ($$p \leq 0.043$$) and the whole hippocampus ($$p \leq 0.033$$). In addition, the CI group showed a trend of atrophy in other hippocampal subfield volumes when compared with the CN group, but the differences were not statistically significant.
To test our major hypothesis, we employed hippocampal subfields that have demonstrated significant differences previously as proxies for AD disease severity. We determined the association between retinal parameters and hippocampal subfield volumes, controlling for age, gender, education and eTIV (Supplementary Table S3). As hypothesized, we found structural and vascular retinal degeneration mirror atrophy of the hippocampal subfield volumes. As shown in Figure 5, the volumes of the subiculum were positively associated with VD_TO ($r = 0.523$, $$p \leq 0.018$$, Figure 5A). When comparing the presubiculum with retinal measures, we found consistent moderate to strong positive associations between the presubiculum and VD_TO ($r = 0.593$, $$p \leq 0.006$$, Figure 5B) and VD_IO ($r = 0.559$, $$p \leq 0.01$$, Figure 5F). Similarly, when investigating the association between retinal vascular changes and hippocampal subfield volumes, we found that a higher VD in the TO sector was associated with greater molecular_layer_HP ($r = 0.463$, $$p \leq 0.04$$) and GC-ML-DG ($r = 0.452$, $$p \leq 0.046$$) and whole hippocampus ($r = 0.49$, $$p \leq 0.029$$) volumes (Figure 5C–E). It is worth noting that there were no significant correlations between hippocampal subfield volumes and macular or RNFL OCT parameters in the CI group (all $p \leq 0.05$, Supplementary Table S3).
## 3.3. WM Integrity and Its Association with Retinal Measures
We performed an analysis to estimate the WM integrity for all fiber bundles in both groups. The findings revealed a significant difference in the MD and AxD of the cingulum in the hippocampal area (CgH) ($$p \leq 0.046$$ and $$p \leq 0.03$$, respectively) between the CI and CN groups (Table 3, Figure 6, Supplementary Table S2).
*In* general, increased MD values and decreased FA values are indicative of compromised fiber integrity, which may result from increased diffusion in the major fiber directions and a loss of fiber myelination. Thus, we proceeded to investigate the potential association between retinal parameters and WM integrity in fibers that showed significant damage in the CI group, while controlling for age, gender and years of education. However, in the present study, we did not observe any significant correlation in the CI group (see Supplementary Table S5). Conversely, in the CN group, we found a positive correlation between the axial diffusivity (AxD) value of the cingulum in the hippocampal area (CgH) and the VD_TI retina, with a correlation coefficient of 0.495 and a p-value of 0.037 (Supplementary Table S6).
## 4. Discussion
Our cross-sectional study investigated the relationship between retinal layer thickness/microvascular measures and classical AD-related structural biomarkers. Results found that (i) in the CI group, a statistically significant decrease in vessel density (VD) was observed in the inferior outer (IO) sector when compared to the CN group. While the RNFL and macular thickness did not differentiate between the CI and CN groups, a decreasing trend was noted in our study. Retinal changes, as an extension of the brain, may reflect pathophysiological processes in the central nervous system, as previously demonstrated in other studies [4,22,23,24,60,61]. ( ii) The present CI patients showed atrophy in hippocampal subfield volumes and an altered integrity of the microstructure of WM tracts. Importantly, we found significant associations between retinal parameters and hippocampal subfield volumes in the CI group.
## 4.1. Volumetric Comparisons of the Hippocampal Subfield Volumes and Correlations with Retinal OCTA Parameters
In contrast to our expectations, widespread hippocampal subfield losses in CI were found, which contained subiculum, presubiculum, molecular_layer_HP and GC-ML-DG. This is consistent with a previous study that indicated CA1, subiculum, presubiculum, molecular layer and fimbria showed a trend toward significant volume reduction in the progression of AD [62]. Early research described the relation between retinal layers and MRI features in AD spectrum populations. One study found an inverse correlation between macular thickness and cerebral cortical atrophy, especially parietal cortical atrophy in AD patients [30]. Others revealed a significant positive correlation between RNFL thickness and hippocampal volume, with this association being more pronounced in the m-RNFL in people without dementia [35,36,37]. However, these studies were focused on the relationship between retinal structure and a relatively large area of the brain (i.e., temporal lobe, whole hippocampus) [35,36,37,63]. Instead, we focused on the association between retinal measures and hippocampal subfields. We found that thicker RNFL in the special sector showed significantly increased subiculum, presubiculum, GC-ML-DG and molecular_layer_HP volumes, which is consistent with previous studies [35,36,37].
Growing evidence from epidemiological, clinical and experimental studies suggests that cerebromicrovascular and microcirculatory damage related to aging play crucial roles in the development of various forms of dementia, including Alzheimer’s disease (AD), in the elderly population. The significance of microvascular contributions to AD in the elderly cannot be overstated [64]. Due to the resemblance of retinal arterioles and venules to cerebral small blood vessels, changes in retinal microvasculature may indicate similar pathological processes occurring in the cerebral microvasculature. Therefore, we also explored the association between hippocampal subfield atrophy and retinal microvasculature and discovered that the volumes of the whole hippocampus and subfields (i.e., subiculum, presubiculum, GC-ML-DG and molecular_layer_HP) were positively correlated with retinal VD. One recent study indicated a positive correlation between retinal VD and some cognitive function domains [28]. Another study found that larger presubiculum, subiculum and CA4/dentate gyrus volumes were associated with higher delayed recall scores and fewer informant reports of memory difficulties [38]. Combined with our results, special hippocampal subfields may play a mediating role in the association between retinal VD and cognitive function domains. However, no association between macular thickness or RNFL and hippocampal subfield volumes was observed. This may be because previous studies measured macular ganglion cell-inner plexiform layer (GCL-IPL) thickness; in contrast, our software automatically assessed the thickness of the ILM-RPE. From the retinal structure, we can conclude that GCL-IPL is included in the ILM-RPE and that ILM thickness is less sensitive to changes during AD progression than GCs, which may lead to such results.
In addition, we further explored whether this relationship also existed in healthy subjects and results showed a positive correlation between retinal VD and the volumes of the presubiculum (Supplementary Table S4), which is consistent with previous findings [33,63]. The findings have shown that this association exists at different stages of AD and becomes more pronounced as the disease progresses; therefore, the retina is becoming a potential marker for predicting the degree of AD progression.
## 4.2. Microstructural Comparisons of WM Integrity and Correlations with Retinal OCTA Parameters
Diffusion tensor imaging (DTI) is a widely used technique in diffusion MRI for examining the pathophysiology of neurodegenerative disorders, including AD, Parkinson’s disease (PD) and multiple sclerosis [65]. This technique utilizes four metrics, including FA, MD, AxD and RD, to describe the brain’s structure based on water diffusion [66]. In this study, we investigated the relationship between DTI metrics and retinal parameters. Compared to normal elderly adults, patients with CI displayed elevated MD/AxD values in the CgH tracts, which are part of the limbic system and are known to be affected early in AD. Previous DTI research has shown that WM changes in AD patients primarily affect the medial temporal limbic-associated tracts, which is consistent with the disease’s pathological involvement of the temporal lobe [67,68]. Consistent with previous studies, our findings indicated decreased WM integrity in the bilateral limbic tracts (CgH) of CI patients [69,70].
Few studies have investigated the associations between WM microstructure and retinal measures. One previous study found that thicker RNFL was associated with better MRI variables in both the cingulum and posterior thalamic radiations in elderly individuals without dementia [32]. Another population-based study revealed that a thinner RNFL and ganglion cell layer (GCL) was associated with lower FA and higher MD in WM tracts, which are part of optic radiation [44]. A study exploring whether ganglion cell-inner plexiform layer (GC-IPL) thickness is associated with brain WM microstructure and how this association differs between normal and cognitively impaired subjects found that thinner GCL-IPL was linked with lower WM microstructure integrity in subjects without cognitive impairment, suggesting a possible physiological relationship between the retina and the brain in healthy aging [43]. Our study also investigated the association between retinal measures, including microvascular, macular, and RNFL and WM alterations. Interestingly, no significant correlation was found in the CI group, but a correlation existed in the CN group. We speculate that this contradictory result may be due to the limited sample size and the disruption of retina–brain associations in the presence of cognitive impairment [43].
## 5. Limitations
Our study has several limitations that should be taken into account. Firstly, the sample size was relatively small, and the data was collected cross-sectionally, preventing us from determining the temporal sequence and directionality of the observed associations. Initiating longitudinal studies with larger cohorts, including individuals at various stages of cognitive decline, and beginning retinal measurements at an early age are necessary steps to investigate the potential direct relationship between retinal and brain degeneration. Secondly, our study lacked amyloid imaging data and CSF biomarkers, which are proposed by the AT(N) biological framework [71]. Early brain Aβ pathology may be a more sensitive biomarker of AD processes, and investigating the relationship between retinal measures and Aβ levels could provide insight into the potential utility of retinal imaging as a preclinical AD biomarker [72]. Thirdly, although the retina is composed of many different structures, our measurement of macular thickness only considered the full layer thickness. In future studies, we plan to analyze the retina in greater detail, including the structure of the ONL.
## 6. Conclusions
In this study, we found a significant decrease in vessel density and associations between retinal parameters and atrophy of the hippocampal subfield volumes in CI patients. These findings suggested that retinal layer thickness and microvascular measures detected by OCTA may be markers for the early prediction of AD-related structural brain changes.
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|
---
title: FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis
(GSA) in Application to Constraint-Based Models
authors:
- Anatoly Sorokin
- Igor Goryanin
journal: Biomolecules
year: 2023
pmcid: PMC10046323
doi: 10.3390/biom13030500
license: CC BY 4.0
---
# FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models
## Abstract
Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. Methods: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. Results: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. Conclusions: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.
## 1. Introduction
Genome-scale metabolic models (GEMs) that combine functional annotation of the genome with available metabolic knowledge are an valuable tool for modern computational and systems biology [1,2]. GEMs were used in biotechnology for strain engineering [3,4,5] for a better understanding of the metabolic consequences of various pathological processes [6,7,8], such as cancer [9,10], metabolic syndrome, and obesity [11], to name a few. Over the past decade, GEMs have been created for several hundreds of unicellular organisms (BiGG [12], MEMOTE [13], AGORA [14]) and dozens of human body tissue types [15]. However, most of these models were created by either semi- or fully-automated processes, which could suffer from incorrect gene annotation, arbitrary reactions added by the gap-filling process [13,14,16], etc. All of these inflate the size of the model, tangle it with unnecessary reactions, and make its analysis more complicated. There is a quote attributed to Einstein: “You should make things as simple as possible, but not simpler”. The model reduction methods applicable to the GEMs are an active area of research [17,18]. Here we propose another approach to model simplification, which is based on flux balance analysis (FBA) and global sensitivity analysis (GSA).
FBA is a computational approach used to analyze and predict the metabolic behavior of an organism using GEM [19]. FBA is commonly used in systems biology and metabolic engineering to study cellular metabolism and to predict the growth and behavior of an organism in different conditions. FBA is based on the principle of mass balance, where the input and output of each metabolite in a metabolic network are balanced. The metabolic network is represented as a set of reactions, which are connected by metabolites. Each reaction has an associated flux, which represents the rate at which the reaction occurs. FBA uses linear programming to optimize the flux through the metabolic network of GEM, subject to constraints such as the availability of nutrients and the capacity of enzymes. The goal of FBA is to find the set of fluxes that maximizes a specific objective function, such as the growth rate of the organism. The FBA approach can be used to predict the effect of genetic and environmental perturbations on cellular metabolism, and to identify metabolic engineering targets for improving the performance of industrial bioprocesses. FBA has been successfully applied to a wide range of organisms, including bacteria, yeast, plants, and humans.
Kinetic modeling involves creating mathematical models that describe the behavior of complex systems, such as chemical reactions, biological processes, or ecological systems. These models often contain a large number of parameters, each representing a specific aspect of the system, such as reaction rates, enzyme concentrations, or external inputs. However, not all of these parameters are equally important for the behavior of the system. Some parameters may have little influence on the system trajectory, and their values may be difficult or impossible to estimate from experimental data. These unimportant parameters are called unobservable parameters. Identifying unobservable parameters is important because it can help simplify the model and reduce the number of unknowns that need to be estimated. One approach to identifying unobservable parameters is global sensitivity analysis (GSA), which estimates how variations in each parameter value affect the behavior of the system as a whole [20]. GSA can help identify parameters that have little influence on the system behavior, and therefore can be considered unobservable.
One approach to identifying unobservable parameters in FBA is Flux Variability Analysis (FVA). FVA estimates the range of possible flux values for each reaction in the network, while keeping the objective near its optimal value. This allows researchers to identify reactions that are essential for the network’s behavior, which have narrow flux distribution, as well as reactions that can be varied without significantly affecting the network’s output. However, FVA does not provide information about flux interactions and how fluxes influence the objective value when it is far from optimum, which can be important for understanding the behavior of complex networks. To analyze flux interactions, Kelk and colleagues have developed a method called CoPE-FBA [21], which utilizes a decomposition approach to break down alternative flux distributions into three topological features: vertices, rays, and linealities. These features correspond to paths, irreversible cycles, and reversible cycles in a metabolic network, respectively. The authors demonstrated that the optimal solution space is often determined by a few subnetworks or modules consisting of numerous reactions, each with multiple internal routes. By analyzing the solution space using this method, it is possible to characterize the entire space based on these subnetworks or modules. As a result, two reactions would be present in the same module if their flux values across all vertices are correlated, regardless of whether they are in the same flux route or in exclusive ones. To analyze how flux perturbation influences other fluxes and objectives, a local version of sensitivity analysis in FBA that is combining FVA with Monte-Carlo sampling was developed [22]. In this approach, all reactions are divided into three groups: ‘stable’ reactions have a low FVA range, ‘robust’ reactions vary a little with perturbations of the other reaction fluxes, and ‘sensitive’ reactions significantly change their fluxes in response to the perturbation. Then, the fraction of each group was compared between different constraint types and mutations. However, neither the CoPE-FBA nor Monte-Carlo approach show how variation in the flux influences the objective value.
Recently, a global sensitivity analysis (GSA) of constraint-based models was published in the literature [23]. This type of analysis is useful for identifying which model parameters have the greatest impact on the model output, and for understanding the behavior of the model in response to changes in those parameters. However, the authors of the study chose to use a relatively complicated and computationally expensive method of GSA called Sobol variance-based sensitivity analysis.
Sobol variance-based sensitivity analysis is a powerful tool for quantifying the contribution of individual parameters and interactions between parameters to the variability of the model output. It is based on the decomposition of the variance of the model output into contributions from individual parameters, as well as combinations of parameters. This allows the authors to identify which parameters have the greatest impact on the output, and to quantify the degree to which the interactions between parameters affect the model behavior. This method of GSA is computationally expensive and requires the development of a complex computational infrastructure. This may limit its applicability in some contexts, particularly for models that are computationally intensive or have a large number of parameters. Moreover, it may require specialized expertise in order to implement and analyze the results of the method.
Despite its limitations, Sobol variance-based sensitivity analysis remains a powerful tool for GSA and can provide valuable insights into the behavior of complex models. It is important for researchers to carefully consider the trade-offs between computational cost and analytical power when selecting a method for GSA, and to choose a method that is well-suited to the specific needs of their study.
In response to the limitations of Sobol variance-based sensitivity analysis, a new approach has been proposed for estimating objective function sensitivity to flux boundary values using Partial Rank Correlation Coefficient (PRCC) calculations [20]. The PRCC approach is based on calculating the partial correlation coefficient between the ranks of each parameter and the rank of the objective function value:[1]rxj,y=Cov(xj^,y^)Var(xj^)Var(y^); y^=y−y˜; xj^=xj−xj˜ where y˜ and xj˜ are obtained from linear regression models:[2]xj˜=c0+∑$$p \leq 1$$,p≠jNcpxp; y˜=b0+∑$$p \leq 1$$,p≠jNbpxp This approach provides a measure of the sensitivity of the objective function to each parameter, while taking into account the interactions between parameters. Rank-transformed data are used to take into account possible nonlinearity in the data.
One advantage of the PRCC approach is that it does not require extensive coding and can be implemented using standard flux balance analysis (FBA) tools, such as the Cobrapy toolbox [24]. The calculation time for the PRCC approach depends on the number of available CPUs in the high-performance computing (HPC) cluster, as parallelization is applied at the level of flux boundaries. This approach is therefore computationally efficient and can be applied to large-scale models with many parameters.
Another benefit of the PRCC approach is that the sensitivity coefficient is signed, allowing researchers to distinguish between parameters that positively or negatively influence the objective function. This provides additional insight into the behavior of the model and can help guide the selection of interventions or modifications to the system.
To calculate the PRCC sensitivity coefficient, a set of random points in the parameter set is sampled using the Sobol low discrepancy sequence, as described in previous work [25,26,27,28]. The PRCC sensitivity coefficient is then calculated as a partial correlation coefficient between each parameter and the objective function value, with the influence of other parameters controlled for.
Marino and co-authors [20] provide methods to estimate both the significance and saturation of the PRCC sensitivity coefficient. The significance of the coefficient is determined by comparing its magnitude to the distribution of coefficients obtained from randomized permutations of the data. The saturation of the coefficient is a measure of how much of the variation in the objective function can be explained by the variation in the parameter value, with higher saturation indicating a stronger relationship between the parameter and the objective function.
In summary, the PRCC approach provides a computationally efficient and flexible alternative to Sobol variance-based sensitivity analysis for conducting GSA in constraint-based models. Its ease of implementation and ability to provide signed sensitivity coefficients make it a valuable tool for studying the behavior of complex systems.
## 2. Materials and Methods
The metabolic network with m metabolites and r reactions is described by an m·r stoichiometry matrix, N. The (i, j)-th entry of N, nij, is the stoichiometric coefficient of the i-th metabolite in the j-th reaction. Any reaction flux vector v that satisfies Nv=0 contains reaction fluxes such that the system is in a steady state. In Flux Balance Analysis (FBA) [18], some optimization problem is solved to identify a unique solution vector vo, such that wvo=maxvwv for vl≤v≤vu, where w is the objective coefficient vector and vl and vu are reaction bounds. We are interested in the estimation of the sensitivity of the objective function to the values of reaction boundaries.
There is a special type of reaction in the constraint-based modelling called ‘boundary reactions’, which usually describe the exchange of metabolites between the internal ‘cell’ and the external ‘environment’.
Our approach consists of three steps: Define parameter space: for non-boundary irreversible reactions only one parameter vu is created, for reversible and boundary reactions two parameters are created for each reaction—vl and vu. Generate a set of quasi-random low-discrepancy points in the parameter space. Update parameters (reaction bounds) and find the optimal objective value for each point in the parameter space. Calculate Partial Rank Correlation Coefficient (PRCC) for each parameter and objective value. The statistical significance of the PRCC value is estimated as described by Marino et al. [ 20]. The sufficiency of the sample size for reliable PRCC estimation is controlled by the top-down coefficient of concordance (TDCC): when TDCC between PRCC vectors calculated at different sample sizes exceeds the threshold of 0.9, the sample size is considered sufficient for analysis.
The toy model (Figure 1) was created with Cobrapy v.0.25.0 [24] and saved as JSON for the model diagram drawing with Escher web interface [29] and SBML [30] format for further simulations. E. coli str. K-12 substr. W3110 WGMM was taken from BiGG database [12] in SBML format.
For network distance calculations, all metabolites participating in more than 20 reactions in any compartment, except amino acids, succinate, PEP, and fructose-6-phospate, were removed. Network distance was calculated as the number of reaction steps between the node of interest and objective reaction. Calculations were performed with R package ‘igraph’ version 1.3.5 [31].
The Sobol quasi-random low-discrepancy sequence was generated with the python Quasi-Monte Carlo submodule of the SciPy v.1.7.3 [32]. Model simulation was performed with Cobrapy v.0.25.0 [24]. All calculations were performed using Python version 3.10.2.
PRCC calculations were performed with R package ‘sensitivity’ version 1.28.0 [33]. TDCC values were calculated by ‘ODEsensitivity’ R package version 1.1.2 [34]. All calculations were performed with R version 4.2.1 [35].
All simulations were performed on the OIST HPC cluster with 8CPU and 64GB per job. Sobol points generation, application to the reaction boundaries and optimization of objectives were performed in chunks of 8192 per job. Calculations of the PRCC sensitivity coefficients were performed on 262,144 Sobol points in chunks of 10 features per job. Convergence of the calculation was controlled by TDCC between consecutive datasets different in 8192 Sobol points. The TDCC value between 262,144 and 253,952 was 0.909. The average execution time was 30 min per job for the Sobol point calculations and 7 h per job for the PRCC calculation.
## 3. Results
Techniques such as Flux Balance Analysis (FBA) utilize the stoichiometry matrix of the reaction system to estimate steady-state fluxes, which are compatible with the viable state of the system. In FBA, an objective is optimized over the steady-state flux vectors, usually by maximizing the flux through certain reactions. The behavior of constraint-based models is controlled by parameters, such as reaction flux boundaries. Our approach estimates the sensitivity of the model’s objective function to these boundary values.
## 3.1. FBA-PRCC Can Identify the Backbone of the Flux-Related Network
To construct the parameter space, we consider that reversible reactions have two boundaries, whereas irreversible reactions have only one, with the lower bound usually set to zero. Boundary reactions, which describe the transport of matter through the model boundary, require special treatment. To evaluate the sensitivity of the objective function to the presence of various nutrients, all boundary reactions are considered reversible, contributing two parameters to the parameter space.
As an example, we consider the toy model described in the Kelk paper [21] (Figure 1), consisting of 27 reactions, of which, two are boundary, nine are reversible, and EX_Y is the objective reaction; there are 37 parameters in our GSA. The parameter space is sampled with a Sobol low-discrepancy sequence, which is designed for uniform coverage of multidimensional spaces with quasi-random points. With 20 K points, we obtain a stable estimation of the PRCC coefficients (Table 1). As expected, the upper boundaries for reactions R5, R12, and R22 are among the most sensitive parameters, and the upper bounds for reactions R8 and R11 control the reaction module between R5 and R12. The presence of the reversible loop R19-R20-R21-R14 renders reactions R15 and R18 less important for the EX_Y flux. As expected from the model structure, EX_Y flux appears to be sensitive to no one of the lower bound parameters.
## 3.2. FBA-PRCC Can Identify Controlling Steps in the Flux-Based Network
For a more biologically relevant example, we calculated the sensitivity of the Lysine production pathway in E. coli str. K-12 substr. W3110 (BiGG iEC1372_W3110). Using lower bounds for reversible and boundary reactions and upper bounds for all reactions in the model, we obtained a total of 3730 parameters. The objective coefficient was set to one for the lysine exchange reaction EX_lys__L_e_u. We simulated over 262 K points to obtain reliable values for the PRCC coefficients. Out of 3730 parameters, 55 were significant at the $1\%$ threshold, with 19 lower and 36 upper bounds (Supplementary Table S1). The PRCC plot against the network distance from the objective reactions in Figure 2 shows that the highest sensitivity coefficients correspond to the last three steps of lysine production, including the exchange reaction, transport to extracellular compartment and transport to periplasmic compartment.
Figure 3 shows that the majority of reactions with positive PRCC values form the backbone of the lysine biosynthesis pathway. Experimental analysis of lysine biosynthesis in E. coli has shown that overexpression of diaminopimelate decarboxylase (lysA) and aspartate kinase (lysC) increased lysine titers by $78.1\%$ and $123.6\%$, respectively [36]. In our model, this corresponds to reactions DAPDC and ASPK, respectively. The PRCC values for the upper boundary of the irreversible DAPDC reaction are 0.12 (p-value < 1 × 10−16), and for the reversible ASPK reaction, the PRCC coefficients for its upper and lower bounds are 0.0037 (p-value $5.9\%$) and −0.004 (p-value $3.9\%$), respectively. Although FBA-PRCC identifies the reactions important for lysine production and their contribution, the order is different from the experimental data. For instance, DAPDC has a higher PRCC value but a lower increase in lysine production. However, it is important to note that lysine biosynthesis is highly regulated in the cell, as mentioned in [37] and in [36], and accurately describing the regulatory relationships in FBA models is challenging.
The majority of the 15 parameters that are negatively correlated with lysine production correspond to redox balance by decreasing H+ production or by shifting NAD/NADH balance.
## 3.3. FBA-PRCC Is Computationally Efficient
Unlike the recently published Sobol variance-based sensitivity analysis for conducting GSA in constraint-based models [23], the FBA-PRCC approach does not require development of special low-level software. All its steps were implemented in Python and R using standard Cobrapy software [24] for FBA calculations and R ‘sensitivity’ package [33]. Calculations of the PRCC values for each flux are independent, so we were using OIST HPC computation clusters to run all these calculations in parallel. Sobol points generation, application to the reaction boundaries, and optimization of objectives were performed in chunks of 8192 per job. Calculations of the PRCC sensitivity coefficients were performed on 262,144 Sobol points in chunks of 10 features per job. The average execution time is 30 min per job for the Sobol point calculations and 7 h per job for the PRCC calculation.
## 4. Discussion
In this work, we have presented a new, fast, and parallelizable framework for estimating the sensitivity coefficients of reaction boundaries in constraint-based models. We demonstrated the performance of our framework using a 27-reaction toy model and the whole-genome metabolic reconstruction of E. coli metabolism. This is the first time that sensitivity coefficients have been calculated for all boundary values in the whole-genome model. Previous analyses, such as those published in [23], have been focused on only a small subset of exchange reactions.
One area where our approach could be particularly useful is in identifying new antibacterial drug targets. We can do this by identifying reaction boundaries that negatively correlate with biomass production and then finding inhibitors for these reactions. Additionally, we can model combinational therapy by analyzing the FBA-PRCC of perturbed models where certain reactions are inhibited or blocked, similar to our analysis in our previous work, Lebedeva et al. [ 25].
In the future, we plan to expand the application of our approach by exploring its potential in engineering chimeric bacterial cells or microbial communities. To achieve this, we plan to combine our FBA-PRCC method with our metagenomic analysis platform, ASAR [38]. By doing this, we hope to uncover new insights into microbial metabolism and how it can be manipulated for various applications.
Overall, we believe that our approach has the potential to be a powerful tool for both fundamental research and practical applications in biotechnology and medicine.
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|
---
title: Evaluation of Specific Cellular and Humoral Immune Response to Toxoplasma gondii
in Patients with Autoimmune Rheumatic Diseases Immunomodulated Due to the Use of
TNF Blockers
authors:
- Cristhianne Molinero Ratkevicius Andrade
- Aline Caroline de Lima Marques
- Rodolfo Pessato Timóteo
- Ana Carolina de Morais Oliveira-Scussel
- Fernanda Bernadelli De Vito
- Marcos Vinícius da Silva
- José Roberto Mineo
- Reginaldo Botelho Teodoro
- Denise Bertulucci Rocha Rodrigues
- Virmondes Rodrigues Júnior
journal: Biomedicines
year: 2023
pmcid: PMC10046324
doi: 10.3390/biomedicines11030930
license: CC BY 4.0
---
# Evaluation of Specific Cellular and Humoral Immune Response to Toxoplasma gondii in Patients with Autoimmune Rheumatic Diseases Immunomodulated Due to the Use of TNF Blockers
## Abstract
[1] Background: TNF antagonists have been used to treat autoimmune diseases (AD). However, during the chronic phase of toxoplasmosis, TNF-α and TNFR play a significant role in maintaining disease resistance and latency. Several studies have demonstrated the risk of latent infections’ reactivation in patients infected with toxoplasmosis. Our objective was to verify whether patients with autoimmune rheumatic diseases, who use TNF antagonists and/or synthetic drugs and had previous contact with *Toxoplasma gondii* (IgG+), present any indication of an increased risk of toxoplasmosis reactivation. [ 2] Methods: Blood samples were collected, and peripheral blood mononuclear cells (PBMCs) were evaluated after stimulation with antigens of Toxoplasma gondii, with anti-CD3/anti-CD28 or without stimulus, at 48 and 96 h. CD69+, CD28+, and PD-1 stains were evaluated, in addition to intracellular expression of IFN-γ, IL-17, and IL-10 by CD4+ and the presence of regulatory CD4+ T cells by labeling CD25+, FOXP3, and LAP. The cytokines IL-2, IL-4, IL-6, IL-10, IFN-γ, TNF-α, and IL-17 were measured in the culture supernatant after 96 h. Serology for IgG and IgG1 was evaluated. [ 3] Results: There were no differences in the levels of IgG and IgG1 between the groups, but the IgG1 avidity was reduced in the immunobiological group compared to the control group. All groups exhibited a significant correlation between IgG and IgG1 positivity. CD4+ T lymphocytes expressing PD-1 were increased in individuals suffering from autoimmune rheumatic diseases and using disease-modifying antirheumatic drugs. In addition, treatment with TNF blockers did not seem to influence the populations of regulatory T cells and did not interfere with the expression of the cytokines IFN-γ, IL-17, and IL-10 by CD4+ cells or the production of cytokines by PBMCs from patients with AD. [ 4] Conclusions: This study presents evidence that the use of TNF-α blockers did not promote an immunological imbalance to the extent of impairing the anti-*Toxoplasma gondii* immune response and predisposing to toxoplasmosis reactivation.
## 1. Introduction
Toxoplasmosis is one of the most prevalent zoonoses in Brazil and worldwide, with a varied distribution according to the population studied. Although it is a self-limited disease in immunocompetent subjects, it can have serious consequences in immunosuppressed patients [1,2]. The immune response is crucial for maintaining toxoplasmosis latency. For this reason, there is great concern about the prevention and monitoring of acute toxoplasmosis in immunocompromised patients, such as transplanted or HIV-positive patients, pregnant women, and newborns, who are at increased risk of developing severe diseases [3,4,5].
Interferon-gamma (IFN-γ) and its receptor, IFN-γR, play a major role in containing the *Toxoplasma gondii* infection during the acute phase. However, during the chronic phase, the action of this cytokine alone is not sufficient. The tumor necrosis factor alpha (TNF-α) and TNFR are therefore required to maintain disease resistance and latency in hematopoietic cells and other host cells [6].
In recent years, TNF antagonists have been used to treat autoimmune diseases and have provided, in many cases, better control and quality of life for related patients [7]. However, several studies have shown some risks associated with the use of these medications [8,9,10,11], such as the appearance of opportunistic infections [12,13]. Some diseases, such as tuberculosis, are closely monitored due to their high risk of reactivation in these patients [12,14]. With regard to this disease, the use of anti-TNF-α in vitro promotes a negative modulation of cytokines that are essential for the development of the Th1 [15].
In toxoplasmosis, the treatment with the TNF-α antagonist, etanercept, in mice infected with T. gondii promotes a significant decrease in TNF-α levels which are associated with a higher number of brain cysts [16]. In addition, there are other experiments that demonstrated the risk of reactivation in mice [16,17] and some case reports of patients who developed manifestations of toxoplasmic infection [18,19].
These data suggest that individuals using TNF blockers may be at risk of toxoplasmosis reactivation [16,17,18,19]. However, in Brazil, there is still no screening for toxoplasmosis before the administration of TNF-α blockers or during monitoring throughout the treatment [20,21]. Thus, the fact that there are reports on the reactivation of this disease, associated with the scarcity of data on the maintenance of latent toxoplasmosis before and during therapy with immunobiologicals, reinforces the need for further studies that evaluate the immune response against T. gondii in these patients.
Since the inhibition of TNF-α may predispose to failure to maintain the chronicity of the disease, with the consequent conversion of bradyzoites into tachyzoites due to the impairment of anti-T. gondii immune response [16,22], our study evaluated cellular markers and the production of important cytokines for controlling the parasite in patients with rheumatic diseases using TNF antagonists.
## 2.1. Study Design
Patients treated at the rheumatology service of the UFTM General Hospital (Uberaba, State of Minas Gerais, Brazil) were invited to participate in the study. Individuals with positive serology for T. gondii and without autoimmune diseases, who were treated in the other sectors of the hospital, were invited to join the control group (CG). Patients aged between 18 and 60 years old and who had IgG anti-T. gondii were selected, and their blood was collected for cell culture. They had to be taking their medication properly and should not have presented any diagnosis of opportunistic disease or any other acute condition, such as flu, recent heart attack, and bone fracture, among others that could interfere in the analysis of the in vitro response against Toxoplasma gondii.
Additionally, using the Management Application for University Hospitals (AGHU), an online medical record survey was conducted to verify the following data: diagnosed autoimmune disease, date of diagnosis, treatment time, and type of medication used.
The research participants were grouped according to the type of medication they were using, namely synthetic drugs or biological immunomodulators. The synthetic drugs include medications that control symptoms, such as analgesics, non-steroidal anti-inflammatory drugs, and corticosteroids (they have immunosuppressive action when used for a long time). Disease-modifying antirheumatic drugs (DMARDs) were also included [23], such as sulfasalazine, hydroxychloroquine sulfate, leflunomide, and methotrexate (MTX). Biological immunomodulators are considered biological DMARDs and include infliximab, etanercept, certolizumab pegol, golimumab, and adalimumab, all blockers of the TNF-α cytokine [24], which was the focus of the present study. Figure 1 presents the groups of patients and the criteria for their inclusion in each group.
All patients were informed about the purpose of the study, and only those who accepted and signed the free and informed consent form (ICF) were included. This project was previously approved by the Research Ethics Committee (CEP) of the Federal University of Triângulo Mineiro (UFTM), under the protocol number 1,870,741, and complies with the Declaration of Helsinki.
## 2.2. Maintenance of T. gondii Cultures and Obtaining Parasites
The tachyzoite forms of the T. gondii RH strain were maintained in cell culture using HeLa cell lines [25]. These cells were infected with T. gondii tachyzoites that were maintained through serial passages in Roswell Park Memorial Institute (RPMI, Gibco Thermo Fisher Scientific, Waltham, MA, USA) medium with $5\%$ fetal bovine serum every 48–72 h. Free parasites were collected with a cell scraper and partially purified by forced passage through a 13 × 4 mm needle and rapid centrifugation (70× g for two minutes at 4 °C), which was performed to remove cell debris. The supernatant was collected and washed twice (900× g for 10 min at 4 °C) with 0.01 M phosphate buffered saline (PBS, pH 7.2). The final pellet of the parasite suspension was resuspended in PBS and stored at −80 °C until the soluble T. gondii antigen was prepared.
## 2.3. Preparation of Soluble Toxoplasma gondii Antigens (STAg) for Immunoenzymatic Assays and for Cell Culture
Parasitic suspensions containing approximately 108 tachyzoites/mL were resuspended in an ultrapure water containing the cocktail of COMPLETETM protease inhibitors (COMPLETETM ULTRA Tablets, Mini, EASYpack, Roche Applied Science, Switzerland) and subjected to 10 rapid cycles of freezing in liquid nitrogen and heating in a water bath at 37 °C. Then, osmolarity was adjusted with 10× sterile PBS, and eight cycles of ultrasound at 60 Hz in an ice bath were conducted for five minutes. After centrifugation at 10,000× g for 30 min at 4 °C, the supernatant was collected and aliquoted. For use in cell culture, the same protocol was followed but with the exclusion of the COMPLETETM protease inhibitor cocktail and the ultrasound cycles and with the addition of a 0.22 μm pore membrane antigen filtration step (Millipore, USA). Crude antigens were aliquoted into sterile tubes. Protein concentration was determined [26] in both crude soluble antigens, and aliquots were stored in a freezer at −80 °C until use.
## 2.4. ELISA Test for the Detection of Anti-Toxoplasma gondii Total IgG, Its Subclass IgG1, and ELISA-Avidity for Total IgG and IgG1 Anti-T. gondii
The tests for the detection of anti-*Toxoplasma gondii* antibodies, total IgG, and its subclass, IgG1, in the patients’ serum samples were performed using the enzyme-linked immunosorbent assay (ELISA). The high-affinity plaques (Thermo ScientificTM NuncTM, Waltham, Massachusetts, USA) were sensitized with STAg (10 μg/mL) diluted in 0.06 M carbonate-bicarbonate buffer (pH 9.6) and incubated for 18 h at 4 °C. Subsequently, the plaque for IgG test was washed three times with PBS containing $0.05\%$ Tween 20 (PBS-T) and blocked with PBS-T containing $5\%$ skimmed milk powder (Molico, Nestle, São Paulo, SP, Brazil, PBS -T-M$5\%$) for one hour at room temperature. After being washed again, the serum samples were diluted 1:64 in PBS-T-M$5\%$ and incubated for one hour at 37 °C. After six washes, anti-human IgG antibody (1:2000) conjugated to peroxidase (IgG/HRP, DAKO) was added and incubated for one hour at 37 °C. Following another washing, the reaction was revealed by the addition of the enzymatic substrate 1,2 orthophenylenediamine (OPD, Dako) with $3\%$ of H2O2 ($30\%$) diluted in ultrapure water.
For the evaluation of the subclasses, the same processes were followed for the sensitizing plaques and washes. Blocking was performed with PBS-T containing $1\%$ bovine serum albumin ($1\%$ PBS-T-BSA) for one hour at room temperature. After the washes, the serum samples were also diluted to the ratio 1:64 in $1\%$ PBS-T-BSA and incubated for two hours at 37 °C. After six washes, the anti-human IgG1 (1:1000, BD PharmigenTM), conjugated with biotin, was incubated for one hour at 37 °C. The plates were then washed six times, and streptavidin conjugated to peroxidase was added (1:1000) and incubated for 30 min in the dark at room temperature. After another washing, the reaction was revealed.
The absorbance values were determined on a microtiter plate reader at 490 nm. Both the positive and negative controls were included on the plate. The levels of antibodies were expressed in the ELISA index (EI) according to the formula EI = optical density (OD) sample/cut off, where the cut off was calculated as the mean OD of the negative control sera plus three standards deviations. EI values > 1.2 were considered positive for total IgG and IgG1, while borderline reactivity values close to EI = 1.0 were not considered positive.
A test was also performed to evaluate the avidity of total IgG and subclass IgG1. The same ELISA procedure was performed for the positive serum samples, but there was an additional step after incubation with samples from the patients where 8 M urea solution diluted in PBS 1× was placed in one of the plates, while only PBS 1× was placed in the other plate—so it could fit as a control plate—for 15 min at room temperature in order to evaluate the binding strength between the antibodies of the patients and the *Toxoplasma gondii* antigens.
The results of this test were expressed with the avidity index (AI) according to the formula AI = EI of the urea cavity/EI of the cavity without urea × 100. The interpretation for the results obtained for the percent avidity of the IgG1 and IgG antibody were as follows: 0–$30\%$, low avidity—suggests that recent infection was acquired within the past three months; 31–$59\%$, moderate avidity—suggests that the period of infection cannot be defined and is characterized as an indeterminate period; and 60–$100\%$, high avidity—suggests that the infection was acquired more than three months ago [27].
## 2.5. Blood Collection and Isolation and Culture of Peripheral Blood Mononuclear Cells (PBMCs)
Venous blood (20 mL) was collected by venipuncture into heparinized tubes. PBMCs were isolated by Ficoll-Hypaque density gradient centrifugation (GE Healthcare, Uppsala, Sweden) at 430× g and 25 °C for 30 min. The cells were washed three times and resuspended in RPMI 1640 medium (GE Healthcare) containing 50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer (Gibco), $10\%$ inactivated fetal calf serum (Gibco), 2 mM L-glutamine (Gibco), 50 mM b-mercaptoethanol (Gibco), 24 mM sodium bicarbonate, and 40 ug/mL gentamicin (Neoquímica, Anápolis, GO, Brazil) to reach a final concentration of 2 × 106 cells/mL. PBMCs were cultured in 24-well microplates (Falcon, San Jose, CA, USA) in the presence of 5 ug/mL T. gondii antigens (strain RH), 1 µg/mL of αCD3 (BD PharmigenTM, BD Biosciences, San Jose, California, USA), and 0.5 µg/mL of αCD28 (BD PharmigenTM, USA) or maintained in a culture medium at 37 °C in a humidified atmosphere with $5\%$ of CO2. All procedures were performed under sterile conditions using a laminar flow hood. Supernatants were collected and stored at −80 °C for the quantification of cytokines.
## 2.6. Flow Cytometry
After the 48 h supernatant was collected, the PBMCs contained in each culture condition, αCD3αCD28, STAg, and absence of stimulation, from each patient were removed from the culture plate and divided into two microplate wells with U-bottom (SARSTEDT, Germany) diluted in PBS containing $10\%$ AB serum (PBS-AB$10\%$) to a final volume of 100 µL per well—the first containing unstained or blank cells (B) and the second containing cells to be stained. The PBMCs of each patient cultured for 96 h were previously incubated for five hours with GolgistopTM solution (BD Biosciences, San Jose, CA, USA). Subsequently, the supernatant was collected, and the cells were removed from the culture plate and divided into five microplate wells with U-bottom, 100 µL per well diluted in PBS-AB$10\%$ and one well containing the blank, and the others containing the cells to be stained, for each of the three culture conditions. The extracellular probes made on cytometry after 48 h and the extra- and intra-cellular probes made after 96 h are presented in Table 1 below.
The protocol for cytometry analysis was the same as described in [28]. Briefly, the cells were resuspended in 150 µL of $4\%$ paraformaldehyde fixative. Subsequently, the cells were passed into tubes to enable the reading on the BD FACSCantoTM II flow cytometer (BD Biosciences, USA) and visualization of the acquisition of 50,000 events per tube using the CellQuest software (BD Biosciences, USA). Antibodies that were appropriate for the control isotypes were used. The acquisition analysis (Figures S1–S4) was performed using the FlowJo program, version 10.6.1 (BD Biosciences, USA).
## 2.7. Determination of Cytokines in the Culture Supernatant by Cytometric Bead Array (CBA)
The cytokines, IL-2, IFN-γ, IL-6, TNF-α, IL-4, IL-10, and IL-17, present in culture supernatants were simultaneously quantified by the CBA technique (BD ™ CBA Human Th1/Th2/Th17 Cytokine Kit, San Jose, CA, USA) according to the manufacturer’s protocol. Then, the beads bound with the cytokines were resuspended in 200 µL of the wash buffer and transferred to cytometry tubes for acquisition that was made on the same day on the FACSCalibur BD flow cytometer (BD Biosciences, USA). The acquisition analysis was performed using the FCAP ArrayTM version 2.0 program (BD Biosciences, USA), and the concentration of cytokines was estimated by linear regression analysis with the fluorescence obtained on the standard curve of each cytokine and expressed in pg/mL.
For this analysis, 10 individuals from the CG, 10 patients from the SD group, and 9 from the IB group were selected, and the levels of cytokines of the three stimulus conditions, namely αCD3αCD28, STAg, and absence of stimulus, were measured.
## 2.8. Statistical Analysis
A statistical analysis of all data was performed using the GraphPad Prism software version 7.04 (GraphPad Software Inc., San Diego, CA, USA). The normal distribution of the quantitative variables was verified with the Shapiro–Wilk normality test [29,30]. Continuous variables that presented a normal distribution were expressed as mean ± standard deviation, and those with non-normal distribution were expressed in medians and interquartiles. The Kruskal–Wallis non-parametric test with Dunn’s post-test was used for data that did not follow normal distribution, and one-way ANOVA with Tukey’s post-test was employed when the distribution was normal. For the analysis of paired samples, the Wilcoxon test was used when the distribution was not normal and the paired t-test was employed for samples with normal distribution. Correlation analyses were performed using the Spearman correlation test. Values of $p \leq 0.05$ were considered statistically significant.
## 3.1. General Characteristics of the Studied Individuals
The mean age among the tested individuals was 50.14 (±8.25) for the SD group, 48.38 (±11.39) for the IB group, and 38 (±10.00) for the CG. The other characteristics are described in Supplementary Table S1. The patients’ treatment schedules and medication use time are shown in Supplementary Table S2.
## 3.2. Detection of Total IgG Antibodies and Subclass IgG1 Anti-Toxoplasma gondii and Avidity of Total IgG and IgG1
Patients who had previous contact with *Toxoplasma gondii* were selected from the serology assessment for total IgG anti-T. gondii, as presented in Figure 2A. When the levels of total IgG were compared among the groups, there was no significant difference; however, a tendency to decrease in group IB compared to the CG was observed ($$p \leq 0.05$$) (Figure 2A). There were no significant variations in the evaluation of avidity, and all subjects presented antibodies with high avidity of total IgG, except for one patient in the IB group with moderate avidity (Figure 2B).
The presence of the IgG1 subclass, which is described in the literature as the most frequent in the anti-T. gondii response [31], was also assessed. The levels of serum IgG1 (Figure 2C) did not reveal statistically significant differences in the comparison among the groups; however, the IgG1 avidity test showed a reduction in avidity with significant difference in the IB group compared to the CG ($$p \leq 0.04$$) (Figure 2D). There was also a significant positive correlation between the total IgG and IgG1 levels in all groups, with $$p \leq 0.0003$$ and $r = 0.86$ in the CG, $p \leq 0.0001$ and $r = 0.915$ in the SD group, and $p \leq 0.0001$ and $r = 0.97$ in the IB group (Figure 2E).
## 3.3. CD4+ Expressing the Exhaustion Marker PD-1 and Activation Markers CD69+ and CD28+
PBMCs were cultured for 48 h and 96 h in the presence of medium alone or with STAg or αCD3αCD28 stimulation, respectively. Then, CD4+ and CD8+ cells were analyzed for the presence of activation or exhaustion markers. These results were analyzed for cell gain or loss in the stimuli used compared to cells that were not stimulated within the same group. A further analysis was conducted with the same stimulus between groups.
The expression of activation marker CD69+ in CD4+ lymphocytes demonstrated a significant increase in αCD3αCD28 after stimulation compared to the medium alone in the SD and IB groups ($$p \leq 0.0017$$ and $$p \leq 0.007$$, respectively). On the other hand, in the CG, there was only a tendency to increase in that same stimulus ($$p \leq 0.06$$). There was no difference after the STAg stimulation (Figure 3A). In CD8+ lymphocytes, the expression of CD69+, after αCD3αCD28 stimulation, was significantly increased in all groups compared to medium alone ($$p \leq 0.0007$$ in the CG, $$p \leq 0.0009$$ in the SD group, and $$p \leq 0.0003$$ in the IB group). STAg stimulation also promoted a significant increase in CD69 expression in all groups ($$p \leq 0.03$$ in the CG, $$p \leq 0.024$$ in the SD group, and $$p \leq 0.004$$ in the IB group). There was a significant low percentage of activated CD8+ cells in the absence of stimulus only in the IB group compared to the CG ($$p \leq 0.04$$) (Figure 3B).
CD4+ and CD8+ T cells were also analyzed for the expression of CD28+, a co-stimulatory molecule that interacts with B7 present in APC [32]. On CD4+ cells, the expression of CD28 was down-modulated in cultures treated with αCD3αCD28 when compared to the absence of stimuli in all groups ($$p \leq 0.0002$$ in CG, SD, and IB). The stimulation with STAg did not show significant changes (Figure 4A). Regarding CD8+CD28+ T lymphocytes, αCD3αCD28 presented a lower percentage than the medium in the CG ($$p \leq 0.014$$), SD ($$p \leq 0.0209$$), and IB ($$p \leq 0.015$$), and there were no significant changes in any groups in STAg. The cellular percentage in the stimulus with αCD3αCD28 in the IB group was reduced compared to the same stimulus in the CG ($$p \leq 0.03$$), and also in the stimulus with STAg in comparison between IB × CG ($$p \leq 0.03$$) (Figure 4B).
Cell exhaustion was also assessed by the expression of Programmed cell death protein 1 (PD-1). In CD4+, the increase in cells expressing this protein after stimulation with αCD3αCD28 was evident when compared to cells without any stimulus in the three groups ($$p \leq 0.0005$$ in the CG, $$p \leq 0.0012$$ in the SD group, and $p \leq 0.0001$ in the IB group). This increase in PD-1 expression was also observed in cells stimulated with STAg when compared to the absence of stimulus in the three groups ($$p \leq 0.0005$$ in the CG, $$p \leq 0.04$$ in the SD group, and $$p \leq 0.007$$ in the IB group). However, when the expression was compared among the groups, there was a statistical difference only between the non-stimulated cells of SD × CG ($$p \leq 0.008$$) and between the IB group and the CG ($$p \leq 0.0121$$) (Figure 4C). In CD8+ T lymphocytes, there was the same increase in expression of PD-1 in the cells stimulated with αCD3αCD28 when compared to the absence of stimulus in the three groups ($$p \leq 0.0002$$ in the CG, $$p \leq 0.0001$$ in the SD group, and $$p \leq 0.0003$$ in the IB group), as well as in the cells stimulated with STAg compared to the medium, although this increase occurred only in the CG ($$p \leq 0.003$$) and in the SD group ($$p \leq 0.0012$$). There were no statistically significant differences in CD8+ cells expressing PD-1 in the analysis between groups (Figure 4D).
Cells expressing both the CD28+ and PD-1 molecules were also analyzed. An increase in double expression was observed in CD4+ T lymphocytes stimulated with αCD3αCD28 when compared to unstimulated cells in all groups, with $$p \leq 0.03$$ for the CG, $$p \leq 0.04$$ for the SD group, and $$p \leq 0.0110$$ for the IB group. This same increase was also observed in the analysis of STAg × medium in the three groups: CG ($$p \leq 0.0005$$), SD ($$p \leq 0.04$$), and IB ($$p \leq 0.006$$). In the analysis among the groups, only the non-stimulated cells had a statistically significant difference between the SD group and the CG, demonstrating an increase in the percentage of cells with double expression in the SD group ($$p \leq 0.03$$) (Figure 4E).
The evaluation of CD8+ double positives demonstrated an increase in the percentage of these cells in the αCD3αCD28 stimulus compared to the medium in the CG ($$p \leq 0.0002$$) and SD ($$p \leq 0.0001$$) and IB ($$p \leq 0.0021$$) groups and in the STAg when compared to the medium in the CG ($$p \leq 0.003$$) and the SD group ($$p \leq 0.0105$$). A decrease was observed in the percentage of cells stimulated with STAg that presented this double staining in the IB group when compared to the CG ($$p \leq 0.04$$) (Figure 4F).
## 3.4. Intracellular Expression of IFN-γ, IL-17, and IL-10 Cytokines by CD4+ Cells
CD4+ T cells were also evaluated for their expression of IFN-γ, IL-17, and IL-10 and double expression of these cytokines. IFN-γ producing cells had an increase in their percentage after stimulation with αCD3αCD28 compared to those not stimulated in the CG ($$p \leq 0.0002$$) and SD ($$p \leq 0.03$$) and IB ($$p \leq 0.0013$$) groups. This same increase in comparison to the medium was observed after stimulation with STAg in the CG ($$p \leq 0.0002$$) and in the IB group ($$p \leq 0.0013$$) but not in the SD group. The analysis among the groups did not reveal significant differences (Figure 5A).
The percentage of CD4+IL-17+ in the cells stimulated with αCD3αCD28 was higher than those that were not stimulated in the CG ($$p \leq 0.006$$) and in the IB group ($$p \leq 0.0016$$). The same occurred in the cells after stimulation with STAg ($$p \leq 0.0215$$ in the CG and $$p \leq 0.03$$ in the IB group). However, there were no differences in the stimuli in the analysis among the groups surveyed (Figure 5B).
The intracellular expression of IL-10 was increased in CD4+ cells after stimulation with αCD3αCD28 compared to the medium in groups CG ($$p \leq 0.013$$) and IB ($$p \leq 0.007$$) and after stimulation with STAg compared to the medium in the CG ($$p \leq 0.03$$) and SD ($$p \leq 0.024$$) and IB ($$p \leq 0.025$$) groups (Figure 5C); there was no difference among the groups.
With regard to the double intracellular expression of cytokines, the evaluation of CD4+IFN-γ+IL-17+ demonstrated an increase in the percentage of cells stimulated with αCD3αCD28 and STAg compared to unstimulated lymphocytes in the CG ($$p \leq 0.006$$ and $$p \leq 0.03$$, respectively) and IB group ($$p \leq 0.006$$ and $$p \leq 0.029$$, respectively). However, there were no differences in the assessment among the groups (Figure 5D). In turn, double cells producing IFN-γ+ and IL-10 presented an increase in their percentage only in the CG when αCD3αCD28 and STAg were compared with unstimulated cells ($$p \leq 0.008$$ and $$p \leq 0.013$$, respectively), while in the IB group, there was only a tendency to increase the percentage of cells stimulated with STAg in comparison to the medium ($$p \leq 0.073$$). However, the analysis among the groups did not show any significant difference (Figure 5E). Finally, the CD4+IL-17+IL-10+ lymphocytes demonstrated a significant increase in the CG only when the cells stimulated with αCD3αCD28 were compared to non-stimulated cells ($$p \leq 0.0105$$). They also showed a tendency to increase in STAg × medium in the CG ($$p \leq 0.05$$) and αCD3αCD28 × medium in the IB group ($$p \leq 0.07$$). Similarly, there were no significant changes when the differences among the groups in each stimulus were analyzed (Figure 5F).
## 3.5. Assessment of Regulatory CD4+ T Populations
The profile of regulatory T lymphocytes was also analyzed. CD4+ T cells were evaluated for the presence of the alpha subunit of the receptor of the interleukin-2 or CD25, the Foxp3 transcription factor, and the latency-associated peptide (LAP). During the cytometry analysis, cells were separated into CD25High, CD25Low, and CD25 negative, being considered as classic Tregs, stained as CD4+CD25HighFoxP3+.
An increase in CD4+CD25High T lymphocytes was observed in the stimuli: αCD3αCD28 compared to cells without any stimulus in the three groups ($$p \leq 0.0002$$ in the CG, $$p \leq 0.0001$$ in SD group, and $p \leq 0.0001$ in the IB group), as well as in STAg when compared to the medium in groups CG, SD, and IB ($$p \leq 0.0002$$, $$p \leq 0.04$$, and $$p \leq 0.0004$$, respectively). When the difference among the groups was analyzed, the percentage of these cells was reduced after being stimulated with STAg in the SD group compared to the CG ($$p \leq 0.017$$); however, there were no differences in the IB (Figure 6A).
CD4+CD25HighFoxp3+ cells had an increase in their percentage in the stimulus with αCD3αCD28 compared to the medium only in the IB group ($$p \leq 0.03$$) and did not present significant changes in the analysis among the groups (Figure 6B). In turn, CD4+CD25HighLAP+ cells had an increase in their percentage when stimulated with STAg compared to those without any stimulus in all groups ($$p \leq 0.0105$$ in the CG, $$p \leq 0.04$$ in the SD group, and $$p \leq 0.006$$ in the IB group) and they had no gain or loss in any stimulus when compared among the groups (Figure 6C). However, regulatory T cells expressing Foxp3+ and LAP+ did not present significant differences between the stimuli of the groups but only a tendency to increase in STAg compared to the medium in group IB ($$p \leq 0.07$$); there was no difference in the analysis among the groups (Figure 6D).
## 3.6. Production of Cytokines by PBMCs in Patients with Autoimmune Rheumatic Diseases
The supernatant was analyzed for the presence of the cytokines IL-2, IFN-γ, IL-6, TNF-α, IL-4, IL-10, and IL-17 after 96 h of cell culture. In the analysis of IL-2 production, an increase in this cytokine was observed in STAg when compared to the absence of stimulation in the three groups, with $$p \leq 0.003$$ in the CG and in the SD group and $$p \leq 0.019$$ in the IB group (Figure 7A). The next cytokine that was analyzed was IFN-γ, which demonstrated a significant increase in its production in αCD3αCD28 and STAg when compared to the medium in all groups ($$p \leq 0.002$$ in the CG and SD group and $$p \leq 0.003$$ in the IB group for both stimuli), as shown in Figure 7B.
With regard to IFN-γ, IL-6 demonstrated an increase in its production in both stimuli when compared to its production in unstimulated cells in all groups. The significant differences for αCD3αCD28 were $p \leq 0.0001$ for the CG and SD group and $$p \leq 0.0001$$ for the IB group, while for STAg, the significant differences were $$p \leq 0.0004$$, $$p \leq 0.004$$, and $$p \leq 0.026$$ for the CG, SD group, and IB group, respectively (Figure 7C). TNF-α, in turn, increased its production after stimulation of cells with αCD3αCD28 compared to the medium in the SD and IB groups ($$p \leq 0.007$$ for both) and showed a tendency to increase its production in the CG ($$p \leq 0.07$$). The STAg stimulation also resulted in an increase in this cytokine in the three groups, with $$p \leq 0.002$$ in the CG, $$p \leq 00.78$$ in the SD group, and $$p \leq 0.003$$ in the IB group (Figure 7D).
The analysis of IL-4 had no statistically significant differences between stimuli when compared with cells that were not stimulated in any of the groups (Figure 7E). On the other hand, IL-10 presented a significant increase in its production after stimulation with αCD3αCD28 when compared to the absence of stimuli in the three groups: CG and SD group ($$p \leq 0.002$$) and IB group ($$p \leq 0.003$$). However, only the IB group demonstrated an increase in the production of this cytokine in STAg compared to the medium ($$p \leq 0.007$$) (Figure 7F).
Finally, IL-17 was increased only in αCD3αCD28 when compared to the medium, significantly statistically in the three groups ($$p \leq 0.007$$ in the CG and SD group and $$p \leq 0.015$$ in the IB group), as demonstrated in Figure 7G.
The analysis of cytokine production in the same stimulus among the study groups was also carried out, but no increase or decrease in any of the cytokines surveyed was observed (Figure 7A–G).
## 4. Discussion
An appropriate response against *Toxoplasma gondii* depends on the action of cytokines, such as IL-12, IFN-γ, and TNF-α, which are predominantly of the Th1 profile, in addition to other pro-inflammatory cytokines [33]. In turn, the control of autoimmune diseases requires the reduction in the excessive action of pro-inflammatory cytokines in individuals affected by these pathologies. In this context, TNF blockers are considered a great advance in the fight against autoimmune diseases, contributing to their control and providing a good quality of life for patients in most cases [34]. However, there are some cases in which the medication may not work as expected (therapeutic failure) [35] or may work but cause an immunosuppression that predisposes the individual to opportunistic diseases [18,19]. Previously in our laboratory, it was demonstrated that the use of infliximab, a TNF-α blocker, in an in vitro granuloma model using PBMCs from patients with active or treated tuberculosis or positive PPD, was able to promote a negative modulation of Treg, Th1, and Th17 profiles, which was evaluated through the observation of decreased production of cytokines IFN-γ, IL-12p40, IL-10, and IL-17 [15]. Thus, given the importance of a balance in the immune responses of individuals to avoid tissue damage or involvement by opportunistic diseases, this study sought to verify whether, as has already been shown in tuberculosis, the use of these immunosuppressive drugs in carrier patients of autoimmune diseases impairs their ability to respond against Toxoplasma gondii.
There are reports of individuals who can develop neurological or ocular symptoms [18,19,36]; however, this is the first study, as far as we know, that tried to establish and discover which immunological changes are related to a possible reactivation. Moreover, recently, our study group showed that pregnant women with gestational diabetes had lower anti-T. gondii than those without diabetes. They also had a higher number of T lymphocytes expressing activation and exhaustion markers (CD28+ and PD-1), a lower number of CD4+ T cells producing IFN-γ, IL-10, and IL-17, and lower secretion of IL-17, IL-4, TNF, and IL-2 after their PBMCs are challenged in vitro with T. gondii antigens, which indicates the importance of monitoring patients with immunosuppression, even if it is transient [37]. For this reason, we try to evaluate some parameters of cell expression and cytokine production in vitro after nonspecific stimulation and a stimulation with total soluble *Toxoplasma gondii* antigens.
In our study, we found that CD4+ cells expressing the PD-1 exhaustion marker were increased in patients using synthetic and biological drugs compared to healthy individuals; however, this exhaustion was only observed in the absence of stimuli. It has been demonstrated that the increase in PD-1 occurs in response to the exhaustion in T lymphocytes [38,39]. For this reason, PD-1 is also involved in autoimmunity and peripheral tolerance mechanisms, and there are studies associating it with the development of therapeutic approaches to alleviate the effects of some autoimmune diseases [39]. We found that this change confirms its relationship of increased expression in autoimmune diseases, as described in the literature [39], since the cells of these patients may present signs of exhaustion due to being constantly activated. The increase in its expression may also be due to the attempt to decrease the activation of T cells in order to make them less reactive, thus reducing the inflammatory effects observed in autoimmune diseases [40].
There is also evidence that chronic toxoplasmosis infection can promote cell exhaustion and increase PD-1 expression mainly in the presence of bradyzoites, as demonstrated in mice [41,42,43]. In fact, when we analyzed the cells stimulated with STAg compared to the non-stimulated ones, we were able to observe an increase in CD4+ exhaustion in the three groups and in CD8+ in CG and SD, although this change also occurred after exposure to the mitogen in the three groups for both cell populations.
One study revealed that lymphocyte choriomeningitis virus-specific CD8+ cells presented increased expression of this receptor accompanied by impaired proliferation and decreased production of cytokines in effector cells. One of its ligands, PD-L1, was also increased in splenocytes from infected mice. Moreover, blocking PD-L1 with antibodies led to an increase in responses in these cells again, including an increase in the production of cytokines TNF-α and IFN-γ and a decrease in the viral load in organs of these animals even in the absence of CD4+ lymphocytes. Anti-PD-L1 also allowed an increase in the proliferation of this population since it does not allow a communication between PD-1 and its ligand, as it still remains expressed on the cell surface. Blocking PD-1 itself is able to improve CD8+ exhaustion again, although to a lesser extent [38]. In toxoplasmosis, PD-1 expression in CD8+ T cells has been associated with increased apoptosis and decreased proliferation [42]. Furthermore, mice infected with T. gondii and in the chronic phase that presented antibodies against MAG1—which is an antigen present in the cyst containing bradyzoites—expressed higher levels of PD-1 and its two ligands, namely PD-L1 and PD-L2. It was seen that the greater the number of cysts, the higher the antibody levels and the greater the expression of these molecules. However, once treated with anti-PD-L1, the mice showed a decrease in the number of brain cysts containing bradyzoites and decreased expression of the BAG1, a protein which is also present in bradyzoites. Another positive point was that this blockade promoted an increase in cytokines, such as IL-12p70 and IL-10, which were correlated with a decrease in cysts [43].
Here, we observe that PD-1 expression was equally modulated in the CG and SD and IB groups in CD4+ or CD8+ after αCD3αCD28 and STAg stimuli. This finding suggests that these cells may present the capacity of being responsive when stimulated, although they were obtained from patients who were suffering from an autoimmune disease and who were under treatment. Given the significance of the PD-1 PD-1L signaling pathway in homeostasis preservation and in the anti-infectious response, it is important to note that according to our results, treatment with TNF blockers does not represent a potential risk to modulate PD-1 expression.
Furthermore, CD4+ cells with simultaneous expression of CD28 and PD-1 were increased in the absence of SD stimuli, confirming once again that the presence of PD-1 did not completely inhibit the presence of CD28 and a possible activation via CD28-B7. This may be because although PD-1 can interfere with CD28-mediated PCK-ϴ activation and decrease T cell activation, unlike CTLA-4, it does not exclude the presence of this molecule. It is not positioned in a similar location and therefore does not compete with CD28 [40]. Thus, it is possible for a cell to express both molecules even though PD-1 indirectly impairs the proper function of CD28.
When it comes to Tregs, we know that the TNF-α cytokine is capable of compromising the proper function of this population in rheumatoid arthritis, and one of the mechanisms is to reduce the phosphorylation of the FoxP3 transcription factor so that the treatment with anti- TNF contributes to the reestablishment of the function of these cells, reversing this effect and further reducing the action of IFN-γ and IL-17 and promoting a balance between cell profiles [44].
Due to the use of TNF blockers, which, despite being one of the main cytokines causing inflammation in autoimmune diseases, are also one of those responsible for maintaining the anti-T. gondii, we thought that there could be a reduction in the percentage of cells expressing IFN-γ and an increase in regulatory T profile cells; however, this did not occur. Not only the cells expressing IFN-γ were able to present similar percentages in the study groups and in the CG but there was also a decrease in the CD4CD25High population in patients using synthetic drugs after stimulation with STAg when compared to cells from individuals of the CG. This suggests that it might be possible to develop an appropriate Th1 response in these patients in face of a possible infection by the parasite. However, when the transcription factors FoxP3 and LAP were analyzed in this population, there were no significant differences. Hence, it is not possible to affirm that all these cells in fact belonged to the regulatory profile.
Contrary to our results, Yang et al. [ 2020] found that the percentage of regulatory T cells was lower in patients with ankylosing spondylitis than that in the CG; however, after patients were treated with anbainuo, which is a recombinant TNF-α receptor (biosimilar etanercept), for 12 weeks, they found that the medication promoted an increase in the number of these cells [45]. This was also seen in a murine model of arthritis, in which TNF blockade was accompanied by an increase in this population [46]. The fact that we found a decrease rather than an increase in Tregs may suggest that the effects of the anti-TNF assessed did not stand out from an adequate response to the stimuli. During T. gondii infection, there is a large production of IFN-γ, expression of T-bet, and an increase in Th1 cells; therefore, there is a reduction in Foxp3+ Tregs, which should be balanced to avoid the inflammatory effects caused by defense against the parasite to increase the pathogenesis of the disease [47]. Another explanation for this reduction in Tregs would be that, for some reasons, these patients did not present the maximum effects of immunobiologicals since even in the absence of stimulus, the Tregs were reduced. However, it is noteworthy that this change in regulatory T cells may or may not occur depending on how the disease progresses in these individuals and even on the type of TNF blocker used for treatment. Patients using adalimumab, for example, may have increased Tregs, but if the disease activity persists despite immunobiologicals, the increase in those cells will not be detected [48]. In addition, there is variation in the immune response according to the type of TNF blocker used, since this increase in Tregs due to the use of adalimumab does not occur in the treatment with etanercept [48].
Finally, when we evaluated the expression of cytokines IFN-γ, IL-17, and IL-10 through flow cytometry, we noted that stimulation with STAg and αCD3αCD28 did not increase or decrease the capacity to express cytokines in any of the groups, which indicates that the use of immunomodulating medication, whether synthetic or biological, still allows the response to be similar to that observed in individuals without autoimmune disease, such as those who composed the CG. Accordingly, an analysis of peripheral blood CD4+ and CD8+ T cells from patients with rheumatoid arthritis, placed in culture and stimulated with PMA and ionomycin or with αCD3αCD28 in the presence of brefeldin A, verified the expression of Th1 and Th2 profile cytokines IL-2, IFN-γ, IL-4, IL-5, IL-13, and IL-10 in these cells and concluded that the percentages of expression of these cytokines were very similar in patients with rheumatoid arthritis compared to controls without any type of autoimmune disease [49]. Just as we evaluated possible differences between cytokines in patients using immunobiologicals (IB), synthetic drugs that are also immunosuppressive (SD), and the CG, this study also evaluated these possible differences between patients receiving immunosuppressants and those receiving only nonsteroidal anti-inflammatory drugs and also did not find significant differences in cytokine expression [49].
Regarding cytokine analyses in the culture supernatant, Sauzullo et al. [ 2018] examined the production of the cytokine IFN-γ in response to stimulation with the mitogen phytohemagglutinin (PHA) in patients with rheumatologic immune-mediated inflammatory diseases (IMID) for one to eight years of prolonged use of TNF-α blockers. The study discovered that over these years, there were fluctuations in the production of IFN-γ, with the lowest levels in the first dose and after four years of use, while the doses after one, two, and eight years were even larger than the initial one, and there were no differences between the production of this cytokine in the two TNF blockers evaluated, namely etanercept and adalimumab, or between the two diseases, rheumatoid and psoriatic arthritis. It is also interesting to note that after these individuals used these immunobiologicals for eight years, no statistical differences were found in the production of IFN-γ in them when compared to healthy donors [50]; this is similar to our study, which also did not find differences in the production of any of the analyzed cytokines, IL-2, IFN-γ, IL-6, TNF-α, IL-4, IL-10, and IL-17, in the supernatant in IB and SD groups when compared to the CG, neither in the stimulation with the mitogen nor in the stimulus with T. gondii antigen.
Another study [51] evaluated the effects of an anti-TNF after seven days of drug application and, contrary to our results, the researchers found that TNF production after stimulation with αCD3αCD28 was lower in patients without treatment, patients treated with methotrexate, and patients using anti-TNF compared to the CG. Additionally, the production of IL-10 and IL-6 was lower in patients treated with methotrexate or biological drugs. They also found that CD4+CD69+ cells were decreased in individuals with MTX and IB after stimulation with αCD3αCD28 [51], while our study did not indicate alterations in the CD4+ population but only in CD8+. However, as provided in our study, Furiati et al. [ 2019] did not observe differences in the production of IFN-γ and IL-17 when comparing the CGs, patients with untreated psoriasis, and patients with psoriasis being treated with synthetic or biological DMARDs [51].
These findings of cell expression of cytokines and cytokine production demonstrating similar levels between individuals using immunosuppressive drugs and the CG, although preliminary, are surprisingly encouraging, since we can suggest, at least initially, that the risk of toxoplasmosis reactivation is not as increased as that observed for the reactivation of tuberculosis, for example. In fact, the changes caused by the autoimmune disease alone are enough to increase the risk of infections in these patients by at least twice, including tuberculosis [9]. In addition, when the treatment is conducted with immunobiologicals, this risk increases even more, having already been exposed that, depending on the type of blocker used, it can reach about 30 times, as presented by Seong et al. [ 2007] regarding the use of infliximab [52]. However, there are few studies that relate the use of biological therapies to the risk of toxoplasmosis reactivation, and, as far as we know, there are no other studies presenting details of the immune responses of these individuals to stimuli with T. gondii antigens; therefore, we would not be able to establish what degree of immunosuppression would be necessary for a possible reactivation based on the results observed here.
However, it is known that in individuals with AIDS, who have an immunosuppression that is much more pronounced than the immunosuppression caused by DMARDs used by patients with autoimmune diseases, the risk of neurological disorders triggered by the reactivation of tuberculosis is much greater than the risk of attacks caused by neurotoxoplasmosis; therefore, while tuberculous meningitis is associated with CD4+ levels below 400/mm3, toxoplasmosis is associated with CD4+ levels below 200/mm3 [53]. However, even HIV-positive patients can use immunobiologicals to treat autoimmune diseases. Some studies demonstrate that immunosuppressants could be administered to these individuals as long as they are well-monitored. The number of viral copies, as well as the CD4+ cell count, can be useful for proper follow-up [54,55]. A review published in 2016 by Gallitano et al. described that of 27 cases of patients treated with TNF blockers, only 4 had complications due to infections [55]. Another study followed HIV-positive patients with rheumatic disease using TNF inhibitors, between 2003 and 2021, and observed that in general, these drugs did not cause serious infectious episodes, being considered relatively safe even in the long term [56]. This reaffirms the need for a balance between monitoring and treating individuals using this medication.
Therefore, it is possible to understand why, although there are reports of neurological and ocular manifestations in patients using anti-TNF and other immunobiologicals, there is still no monitoring or screening for toxoplasmosis during treatment, since at first, it seemed that other factors might be involved in this imbalance in immunity that caused reactivation in some of these patients.
Another fact to consider is that there are different responses to different types of TNF blockers. In cultures of cells from healthy individuals stimulated with M. tuberculosis antigens or PHA and incubated with TNF blockers, it was shown that both adalimumab and infliximab were able to inhibit the early activation of T cells, evaluated by the expression of CD69+, while etanercept was not able to do so. Furthermore, it was observed that TNF blockade with infliximab and adalimumab could suppress IFN-γ production, while again, etanercept did not differ from non-blocked cells [57]. It is also necessary to consider that the immune response, even in individuals without autoimmune diseases, may vary from one another. In this context, autoimmune disease is another confounding factor, since even when evaluating the use of only one type of immunobiological intervention, one must keep in mind that this medication is used for several autoimmune diseases with different pathophysiologies. There is also the issue of other associated comorbidities that can interfere with the assessment (although we have tried our best to reduce this bias), and finally, it must be considered that an evaluation with a broader cell staining panel and a larger n is necessary.
## 5. Conclusions
To our knowledge, this is the first study that analyzed cellular immune response in patients with rheumatic autoimmune diseases and serum positive to toxoplasmosis under the treatment of TNF blockers. The absence of significant changes in the analyzed immune response suggests that these patients have a low risk of reactivation of toxoplasmosis.
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|
---
title: 'Immunoexpression Pattern of Autophagy Markers in Developing and Postnatal
Kidneys of Dab1−/−
(yotari) Mice'
authors:
- Mirko Maglica
- Nela Kelam
- Ejazul Haque
- Ilija Perutina
- Anita Racetin
- Natalija Filipović
- Yu Katsuyama
- Katarina Vukojević
journal: Biomolecules
year: 2023
pmcid: PMC10046325
doi: 10.3390/biom13030402
license: CC BY 4.0
---
# Immunoexpression Pattern of Autophagy Markers in Developing and Postnatal Kidneys of Dab1−/−
(yotari) Mice
## Abstract
The purpose of this study was to compare the immunofluorescence patterns of autophagic markers: Light chain 3 beta (LC3B), Glucose regulating protein 78 (GRP78), Heat shock cognate 71 (HSC70) and Lysosomal-associated membrane protein 2A (LAMP2A) in the developing and postnatal kidneys of Dab1−/− (yotari) mice to those of wild-type samples. Embryos were obtained on gestation days 13.5 and 15.5 (E13.5 and E15.5), and adult animals were sacrificed at postnatal days 4, 11 and 14 (P4, P11, and P14). After fixation and dehydration, paraffin-embedded kidney tissues were sectioned and incubated with specific antibodies. Using an immunofluorescence microscope, sections were analyzed. For statistical analysis, a two-way ANOVA test and a Tukey’s multiple comparison test were performed with a probability level of $p \leq 0.05.$ A significant increase in GRP78 and LAMP2A expression was observed in the renal vesicles and convoluted tubules of yotari in embryonic stages. In postnatal kidneys, all observed proteins showed higher signal intensities in proximal and distal convoluted tubules of yotari, while a higher percentage of LC3B-positive cells was also observed in glomeruli. Our findings suggest that all of the examined autophagic markers play an important role in normal kidney development, as well as the potential importance of these proteins in renal pathology, where they primarily serve a protective function and thus may be used as diagnostic and therapeutic targets.
## 1. Introduction
Congenital anomalies of the kidney and urinary tract (CAKUT) are disorders that emerge during renal development and result in a spectrum of structural defects in kidneys and outflow tracts (ureters, bladder and urethra). Depending on the registry, these malformations occur in 4–60 per 10,000 children [1,2]. Kidney development is a multi-stage process that begins with the induction of the ureteric bud from the nephric duct, followed by mesenchymal-to-epithelial transition and branching morphogenesis and terminates with the completion of nephron patterning and elongation [3]. Interferences to normal nephrogenesis, due to exposure to environmental risk factors or the dysfunction of genes that control this process, can lead to CAKUT [4,5]. Using mouse models, we obtained most of the current knowledge regarding CAKUT and genes involved in renal development and nephrogenesis [6]. From minor defects to syndromic presentations affecting not just the genitourinary tract but also other fetal structures and amniotic fluid, CAKUT presents in a variety of ways and can be identified as early as 11 weeks of gestation in humans. Consequently, identifying the genes responsible for CAKUT can help physicians in early diagnostics, as well as in the therapy of these frequent anomalies [7]. The Disabed-1 (Dab1) and Reelin regulate neuronal migration during brain development. The binding of Reelin to its receptors induces Dab1 tyrosine phosphorylation. Tyrosine-phosphorylated Dab1 recruits a wide range of SH2 domain-containing proteins and activates multiple signaling cascades. Their signaling pathway has a well-established role in regulating the “inside-out” lamination of the cerebral cortex and the activation of intracellular signaling cascades and rearrangement of the cytoskeleton to guide neuronal migration [8,9]. Recently, our group demonstrated the appearance of DAB1 and REELIN during human fetal kidney development and confirmed their potentially significant role in early kidney nephrogenesis [10]. Additionally, we found that homozygous Dab1−/− mutation causes autosomal recessive CAKUT displayed with kidney hypoplasia [11]. Therefore, this study aims to look into the potential causes of kidney hypoplasia in yotari and the role of autophagy in kidney cell degradation via lysosome-dependent cell removal.
In contrast with apoptosis, autophagy maintains cellular homeostasis by recycling selective organelles and useful molecules. During autophagy, unique autophagosomes are formed and fused with lysosomes to form autolysosomes [12,13]. Light chain 3 (LC3) family member, LC3B, is recognized as an autophagy-related protein [14]. During the fusion of autophagosomes with lysosomes, intra-autophagosomal LC3B-II (a post-translation modification form of LC3B) is degraded by lysosomal proteases, which makes these proteins markers of autophagic activity [15,16]. It was shown to be down-regulated in kidneys after injury, suggesting that autophagy might be involved in repairing of kidney injuries [17].
A specific type of autophagy called chaperone-mediated autophagy (CMA) is important for eliminating oxidized proteins. The main characteristic of this mode is the presence of so-called lysosomal-associated membrane protein 2A (LAMP2A) [18,19]. A study from 2017 by Zhang et al. analyzed the distribution of LAMP2A in disease-relevant kidneys. When compared to wild-type mouse kidneys, their findings revealed an increase in the percentage of LAMP2A-positive cells apically but a lack of lysosomal proteins in the basal areas of proximal tubule cells, implying defective LAMP2A trafficking and thus inhibited CMA activity, potentially leading to progressive renal injury in cystinosis [20].
Chaperones, also called heat shock proteins, and their cofactors are important for regulating the apoptotic pathway. They facilitate the restoration of normal function by refolding denatured and degrading irreparably damaged proteins via autophagy [21]. Glucose-regulated protein 78 (GRP78) belongs to the Heat shock protein 70 (HSP70) family. Its expression is induced during endoplasmic reticulum stress and its main role is in preventing ER stress-induced cell death [22,23,24]. ER stress was observed in different kidney diseases, such as diabetic nephropathy, renal fibrosis, genetic mutations of renal proteins, proteinuria, idiopathic nephrotic syndrome, and minimal change renal disease [25].
Heat shock cognate 71 (HSC70) protein displays significant homology with HSP70 (around $75\%$). It is mostly found in the cytoplasm, where it has been demonstrated to support a variety of oncogenic processes by controlling client proteins [26]. To perform CMA, HSC70 binds unfolded/misfolded proteins with an exposed KFERQ (consensus peptide sequence found in various native proteins) amino acid motif and brings them to the lysosome. The HSC70/KFERQ-containing client protein complex then interacts with and binds to the cytosolic tail of the monomer form of the receptor for CMA, lysosome-associated membrane protein 2A (LAMP-2A) [27]. In a recent study, Zhang et al. demonstrated that kidney cancer cells, particularly those from patients with metastases, expressed HSC70 staining in the nucleus and/or cytoplasm in a significantly higher percentage than the kidneys of the control group of patients [28].
This study aimed to analyze the effects of *Dab1* gene functional silencing on the expression and localization of LC3B, LAMP2A, GRP78 and HSC70 in the developing and postnatal kidneys of Dab1−/− (yotari) mice. Understanding the normal expression of these autophagy markers can lead to better diagnostic and treatment modalities for kidney diseases.
## 2.1. Ethics
Animal use was approved by the Guidelines for the Care and Use of Laboratory Animals at the Shiga University of Medical Science. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of the University of Split School of Medicine (UP/1-322-$\frac{01}{17}$-$\frac{01}{13}$; 525-$\frac{10}{0255}$-17-7; 13 October 2017).
## 2.2. Generation of Dab1 Null Conventional Mutants and Sample Collection
The yotari (Dab1−/−) mouse is a neurological mutant mouse with a phenotype similar to a reeler (Reelin−/−) mouse. These mice arose unexpectedly in the descendants of a male chimeric mouse carrying a gene mutation encoding the receptor for inositol-1,4,5-trisphosphate (IP3R1) K.O. mice [29,30]. Yotari mouse, which spontaneously arose from a mutation in Dab1, and the reeler mouse, whose mutation is caused by deletion of the 3′ coding region of reelin cDNA, both exhibit the same phenotype pattern, including unstable gait, tremors, and early death around the time of weaning [31,32,33,34]. These similarities in the phenotypes of yotari and reeler mice suggest that the gene mutated in yotari encodes a molecule on the same signaling pathway as Reelin, the product of the reelin gene [29].
In a temperature-controlled (23 ± 2 °C) setting, yotari (yot) and C57BL/6N (wt) mice, colonies genetically identical within each strain, making them free of genetic differences that could impact research results [35], were grown and housed separately in groups of three to four in typical polycarbonate cages with free access to food and tap water. Three mice were used for each genotype (yotari and wt) at every observed time point. The photoperiod consisted of 12 h of artificial light and 12 h of darkness. The following PCR primers were used for genotyping: yotari—GCCCTTCAG-CATCACCATGCT and CAGTGAGTACATATTGTGTGAGTTCC, wild-type of Dab1 locus—GCCCTTCAGCATCACCATGCT and CCTTGTTTCTTTGCTTTAAGGCTGT [36].
On gestation days 13.5 (E13.5) and 15.5 (E15.5), the gravid mice were sacrificed, and their embryos were obtained. Postnatal groups of mice were sacrificed on their 4th, 11th and 14th postnatal days (P4, P11, P14). With pentobarbital, mice were deeply anesthetized before being transcardially perfused with phosphate buffer saline (PBS, pH 7.2) followed by $4\%$ paraformaldehyde (PFA) in 0.1 M PBS. Kidneys were removed and $4\%$ PFA in 0.1 M PBS was used to fix them overnight for conventional histological analyses (hematoxylin-eosin immunofluorescence staining). All chemicals used were cerified by the Biological Stain Commision and were used as received without any further purification, and were obtained from Sigma-Aldrich (St. Louis, MO, USA)
## 2.3. Immunofluorescence
After fixation, tissue was dehydrated with graded ethanol solutions (Sigma-Aldrich, St. Louis, MO, USA), embedded in paraffin blocks and serially cut as 5 µm-thick sections, which were then mounted on glass slides. Proper tissue preservation was confirmed by hematoxylin–eosin staining of every 10th section.. The mounted tissue sections were deparaffinized in xylol, followed by rehydration in graded ethanol and distilled water, subsequently heated in a sodium citrate buffer (Sigma Aldrich, St. Louis, MO, USA) for 20 min at 95 °C in a water steamer, and gradually blocking buffer (ab64226, Abcam, Cambridge, UK) was administered for 30 min. The samples were incubated with primary antibodies (Table 1) overnight in a humidity chamber. The following day, they were rinsed with PBS before incubating with suitable secondary antibodies (Table 1) for one hour. The samples were then cover-slipped after being given a final PBS wash, after which the nuclei were stained with 40,6-diamidino-2-phenylindole (DAPI) (Immuno-Mount, Thermo Shandon, Pittsburgh, PA, USA). No staining was observed when primary antibodies were omitted from the immunofluorescence protocol.
Each primary antibody was diluted in blocking solution to the exact concentration before the preadsorption test was conducted. After introducing an appropriate peptide antigen, the sections were treated with the mixture. The outcomes revealed no antibody staining. When primary antibodies were left out of the immunofluorescence technique, neither non-specific secondary antibody binding nor false-positive results were seen.
## 2.4. Data Acquisition and Analysis
Sections were examined by an immunofluorescence microscope (BX51, Olympus, Tokyo, Japan) equipped with a Nikon DS-Ri2 camera (Nikon Corporation, Tokyo, Japan). In order to quantify the immunoexpression of proteins of interest, non-overlapping visual fields were captured at ×40 magnification and constant exposure times for analysis. At least ten images of the embryonic kidney structures—metanephric mesenchyme (mm), renal vesicles (rv), immature glomeruli (G), convoluted tubules (Ct), ampullae (A), and collecting ducts (Cd)—were taken at embryonic days E13.5 and E15.5 and at least twenty images of the postnatal kidney structures—glomeruli (G), proximal convoluted tubules (PCT) and distal convoluted tubules (DCT) at postnatal days P4, P11, and P14. The captured photos were then processed in ImageJ software (National Institutes of Health, Bethesda, MD, USA) and Adobe Photoshop (Adobe, San Jose, CA, USA). The cells that gave an immunoreactive signal were counted and expressed as a percentage of total cells per animal group in the previously created Excel table. Any degree of staining with the used markers in the cytoplasm, nucleus, or membrane was considered positive. The staining intensity of distinct kidney structures was semi-quantitatively evaluated at four degrees: the absence of any reactivity (−), mild reactivity (+), moderate reactivity (++), and strong reactivity (+++). Three researchers, blinded to the strain of the mice and the time points, independently evaluated the microphotographs. Interclass correlation analysis was used to test interrater agreement, and the result showed high agreement with a coefficient of >0.75 [37].
## 2.5. Statistical Analyses
GraphPad Prism 9.0.0 was used to conduct the statistical analyses (GraphPad Software, San Diego, CA, USA) with a probability level of $p \leq 0.05$ regarded as statistically significant. A two-way ANOVA test followed by Tukey’s multiple comparison test was used to identify significant differences in the percentage of positive cells between mm, rv/G, Ct, and A/Cd on E13.5 and E15.5 and G, PCT, and DCT at P4, P11, and P14. The mean ± standard deviation was used to express the percentage of positive cells (SD).
## 3. Results
At embryonic days E13.5 and E15.5, immunoexpression of LC3B, GRP78, HSC70, and LAMP2A were examined on metanephric mesenchyme (mm), renal vesicles (rv), convoluted tubules (Ct), ampullae (A), and collecting ducts (Cd), as well as at postnatal days P4, P11, and P14 on glomeruli (G), proximal convoluted tubules (PCT), and distal convoluted tubules (DCT) on kidneys of three animals of wild-type (wt) and yotari mice genotype, per for each observed time point.
## 3.1. LC3B Expression
At E13.5 wt, a mild diffuse signal was observed in developing nephrons (renal vesicles) but weakly in collecting ducts, including ampullae, ureteric buds, and convoluted tubules. In the surrounding undifferentiated cells of the metanephric mesenchyme (interstitium), there was a mild punctate expression of LC3B, weakening towards more differentiated kidney structures (Figure 1a). In E13.5 yotari mice, there was no reactivity in renal vesicles, ampullae/convoluted tubules, or collecting ducts, but LC3B signal was observed in cells of the metanephric mesenchyme, both in undifferentiated and differentiated cells (Figure 1b). Compared to the wt, the LC3B signal in mm was stronger both on the periphery and between other structures but without statistical significance ($$p \leq 0.661$$).
The signal pattern for kidneys at E15.5 wt was entirely different from that of E13.5. Visceral cells of developing glomeruli in the control group displayed weak punctate LC3B signal. In terms of other structures, convoluted tubules and ampullae/collecting ducts showed a weak punctate signal of the apical membrane. Cells in the metanephric mesenchyme surrounding the collecting ducts exhibited a predominance of LC3B expression, whereas the pattern was reversed in the periphery (Figure 1c). A much stronger LC3B signal with a somewhat different pattern was observed in E15.5 yotari. A strong diffuse signal of both visceral cells and the 0,0. layer of the Bowman’s capsule in glomeruli were present. The punctate signal of convoluted tubules and ampullae/collecting ducts in the wt was replaced with a strong diffuse signal of the apical membrane of the structures mentioned above. The signal in the metanephric mesenchyme did not increase (Figure 1d). The percentage of LC3B-positive cells within all observed structures, except metanephric mesenchyme, was higher in yotari mice ($p \leq 0.05$).
Semi-quantitative analysis of both animal genotypes at E13.5 revealed staining in metanephric mesenchyme, with mild intensity in wt and moderate in yotari (Table 2). In contrast to mild reactivity in the cells of collecting ducts and convoluted tubules/ampullae of the wt mice, yotari showed moderate reactivity according to semi-quantitative analysis. Furthermore, renal vesicles in mutant animals showed mild reactivity at E15.5 (Table 2).
When postnatal kidneys from wt genotype were analyzed, it was observed that DCT cells had a weak diffuse signal in their cytoplasm, while PCT cells had no staining in P4. The apical membrane contained the majority of this signal. Glomeruli had weak diffuse cytoplasmatic staining in the juxtaglomerular apparatus (JGA) accompanied with a weak punctate staining in the endothelial cells of capillary loops (Figure 2a). The yotari genotype P4 had a considerably greater percentage of LC3B-positive cells. All observed structures had substantially stronger, more diffuse signals (Figure 2b). DCT staining was observed in both the apical and basolateral membranes, whereas PCT had an apically dispersed diffuse signal ($p \leq 0.0001$). Glomeruli displayed a similar pattern to wt, with significantly stronger staining near JGA and blood vessel endothelial cells ($$p \leq 0.0004$$).
There was a significant increment in LC3B staining in the postnatal kidneys at later developmental stages P11 and P14. In both the aforementioned time points, yotari mice had a percentage of positive cells significantly higher than the control group in glomeruli ($p \leq 0.05$), as well as in DCT and PCT ($p \leq 0.001$). At P11 and P14, diffuse visceral cell glomeruli staining was seen, along with a strong punctate signal in Bowman’s capsule and JGA. In the convoluted tubules of yotari mice, the LC3B staining was punctate and dispersed throughout the cytoplasm. The immunoexpression pattern was similar in wt genotypes, but most cells were LC3B negative (Figure 2c–f).
Semi-quantitative analysis revealed mild reactivity in glomeruli for both animal genotypes and moderate reactivity in PCT and DCT of yotari mice at P4. Staining intensity overall increased in later developmental days, with mild staining in glomeruli and DCT for wt animals at P11 and moderate staining in the same structures for yotari. Additionally, PCT showed mild signal reactivity in mutated animals. P14 yotari mice displayed moderate staining intensity in all structures, in contrast to P14 wt mice, which displayed the same staining pattern as P11 wt mice (Table 3).
## 3.2. GRP78 Expression
Both genotypes displayed increased GRP78 expression in the collecting ducts and ampullae at E13.5. In control animals, staining was strong and diffuse throughout the cytoplasm of the aforementioned structures, while in mutated ones, the signal was mainly located in the apical membrane (Figure 3a). In yotari mice, a strong punctate signal was visible in metanephric mesenchyme, both in the undifferentiated and differentiating regions. Renal vesicles and collecting ducts had mild punctate staining that was mostly concentrated in the cytoplasm of cells, both apically and basolaterally (Figure 3b). There were a few statistically significant results for this time point; the control group had a higher signal intensity in a/cd ($$p \leq 0.0002$$), whereas yotari had a much higher intensity in ct and mm ($p \leq 0.0001$). At E13.5, a semi-quantitative analysis revealed strong reactivity in the wt ampullae/collecting ducts. The yotari exhibits mild reactivity in glomeruli and moderate reactivity for other observed structures (Table 2).
The staining pattern remained the same at E15.5, but yotari mice displayed substantially greater signal intensities throughout all structures (Figure 3c,d). These animals’ glomeruli stage had a much higher percentage of GRP78-positive cells than the renal vesicle stage at E13.5 ($p \leq 0.0001$). As for the semi-quantitative analysis, wt mice at E15.5 showed weak reactivity for all observed structures, while yotari structures were moderate to strongly positive (Table 2).
GRP78 positive cells displayed a strong diffuse signal in the postnatal stages of development, with mainly positive cells in convoluted tubules. In wt animals, immunoexpression was observed in visceral cells of the glomeruli, with the punctate signal being localized mainly within the nucleus (Figure 4a,c). In the more advanced developmental phases, the glomeruli staining became faint and almost completely absent at P14 (Figure 4e). However, expression of the positive GRP78 signal showed a completely reversed pattern in convoluted tubules, with the signal rapidly increasing throughout time (Figure 4b,d,f). Yotari demonstrated a significant increase in expression rate in both PCT and DCT at all observed time points, with the signal located primarily in the apical cytoplasm of these structures ($p \leq 0.0001$).
In the semi-quantitative evaluation, wt mice revealed mild staining in the G and PCT at P4. Same-age yotari specimens showed moderate intensity in cortical tubules. At P11 the control group of animals displayed mild signals for all observed structures, while yotari had mild intensity in G but a moderate to the strong percentage of positive cells in PCT and DCT. Lastly, G and DCT were mildly stained in P14 wt mice, while yotari convoluted tubules showed moderate signal intensity (Table 3).
## 3.3. HSC70 Expression
In the analysis of HSC70 at the embryonic phase of development, fluorescence was completely absent in wt animals (Figure 5a–d). Both E13.5 and E15.5 yotari animals had weak staining of HSC70-positive cells, with mild punctate immunoexpression in the cytoplasm of convoluted tubule cells and metanephric mesenchyme. The signal in Ct for yotari mice showed the same pattern for both embryonic time points, but the statistical difference was significant only for older specimens ($p \leq 0.0001$). Even though there was some punctate signal in the interstitium between differentiating structures, there was no significant difference between wt and yotari in the embryonic developmental phases ($$p \leq 0.064$$).
Semi-quantitative analysis of yotari on E15.5 showed mild to moderate reactivity in mm and Ct (Table 2).
Concerning postnatal stages, the same pattern of staining continued for wt mice. The signal was completely absent; there were a few sporadic positive HSC70 cells, but the overall percentage was negligible (Figure 6a,c,e). However, the pattern was somewhat different for yotari. The percentage of positive HSC70 cells gradually increased over time, with the strongest signal present in cortical tubules. At P4, the signal was diffuse and mostly concentrated in the apical membrane of DCT (Figure 6b). As mice grew, so did the HSC70 signal; at P11, the percentage of positive cells increased to around $30\%$ in the tubules of all examined animals (Figure 6d). In PCT, the signal was dispersed throughout the cytoplasm, and in DCT, it was still predominantly found in the apical membrane. At P11, the glomeruli collected some fluorescence, mostly perinuclear in visceral cells. On the 14th day of postnatal development, HSC70 staining reached its peak. Around $60\%$ of PCT and DCT cells were positive, with the same pattern of signal distribution as P11 yotari (Figure 6f). As for the statistical analysis, there was no significant difference in glomeruli staining between the control and yotari groups of animals in any postnatal phase. However, there were significant results regarding other structures, such as PCT at P11 ($$p \leq 0.0023$$) and P14 ($p \leq 0.0001$), as well as DCT at P4 ($$p \leq 0.035$$), P11 ($p \leq 0.0001$) and P14 ($p \leq 0.0001$).
Based on the semi-quantitative analysis, wt animals displayed minimal PCT reactivity on the 14th postnatal day. Yotari samples exhibited a more complicated pattern of reactivity; semi-quantitative analysis for P4 yotari revealed a moderate intensity for both PCT and DCT; at P11, the signal sample remained unchanged for PCT and DCT, but at this time point, there was also minor reactivity in glomeruli cells. Yotari P14 exhibited strong reactivity in PCT and mild in G and DCT (Table 3).
## 3.4. LAMP2A Expression
LAMP2A-positive cells were identified as green staining in both wt and yotari mice embryonic stages. At E13.5, collecting ducts and ampullae of both genotypes showed a weak fluorescence intensity, with staining concentrating primarily on the apical membrane of the aforementioned structures (Figure 7a,b). Renal vesicles of yotari specimens displayed stronger reactivity at E13.5, with staining concentrated perinuclearly ($p \leq 0.0001$).
For wt at E15.5, the pattern of reactivity remained the same (Figure 7c), while in the yotari genotype, the proportion of LAMP2A-positive cells varied considerably between E13.5 and E15.5 (Figure 7d). Perinuclearly in immature glomeruli, a large diffuse signal was still present, but convoluted tubules showed the biggest variation. The cytoplasm of about half of the examined ct had strong staining that was at times punctate and at other times diffuse. Statistical analysis revealed significant differences between the two genotypes: the LAMP2A expression in rv/G, A/Cd and Ct was greater at E15.5 yotari than in wt of the same age ($p \leq 0.0001$).
Semi-quantitative analysis of wt on E13.5 revealed mild staining intensity in immature glomeruli and convoluted tubules. Same-age yotari specimens displayed moderate staining intensity in renal vesicles. At E15.5, the wild-type displayed mild staining reactivity in rv/G and a/cd, while yotari specimens had the same reactivity in rv/G and a/cd but also displayed moderate intensity in ct (Table 2).
The same expression pattern continued in the early postnatal stage. At P4, both genotypes expressed staining in convoluted tubules (Figure 8a,b), but the intensity in yotari specimens was more robust both in DCT and PCT ($p \leq 0.0001$). Almost $60\%$ of all observed convoluted tubules displayed a strong, diffuse signal, predominantly concentrated in the apical membranes of yotari animals. The same pattern was noticed in wt animals but in a much lesser percentage.
A similar pattern was present at later postnatal stages (P11 and P14), with a smaller percentage of LAMP2A-positive cells in distal convoluted tubules. Throughout postnatal development, the signal redistribution in apical membranes of convoluted tubules remained unaltered (Figure 8c–f). A significant difference was present for PCT ($p \leq 0.0001$).
Semi-quantitative analysis revealed mild PCT staining for wt at P4; for the same-age yotari, there was mild DCT staining and strong PCT staining. Yotari displayed a moderate PCT signal intensity at P11, which remained constant at P14 for the same genotype, whereas wt displayed mild PCT reactivity at P14 (Table 3).
## 4. Discussion
Kidney development, combined with nephron maturing, is one of the most precisely organized and integrated processes, coordinated through the interaction of different genes and cellular mechanisms. Interference with this highly complicated multi-stage process may lead to errors in nephrogenesis, resulting in different structural defects of the kidneys and outflow tracts referred to as CAKUT. These malformations are the most common congenital disabilities and the leading cause of end-stage renal disease in children [38,39]. In our earlier research with yotari mice, we discovered a series of different markers and how disruption in one step of their signaling pathways can result in CAKUT [11,40,41].
We discovered the most about the genetic elements that affect CAKUT development using mouse models and are getting closer to understanding their etiology with each new gene uncovered [6]. As our previous research showed strong expression of DAB1 during fetal kidney development and diminishing that same expression in postnatal healthy kidneys [10], in this study, we assumed that Dab1 knockout mice would have a different pattern in autophagy and apoptosis, as these two mechanisms are extremely important for normal kidney function and development [42,43]. Using a Dab1−/− mouse model, we explored various LC3B, GRP78, HSC70, and LAMP2A expression patterns during both embryonic and postnatal developmental periods to confirm our hypothesis.
This study showed no significant LC3B immunoexpression in the early stages of embryonic development at E13.5. The staining signal was mild for both genotypes, suggesting that autolysosomes were not yet formed and, consequently, autophagy was not yet initiated. However, embryos obtained at E15.5 showed a significant increase of positive LC3B cells in all observed structures except the metanephric mesenchyme. The same immunoexpression pattern carried on into the postnatal period, where yotari had a significantly higher percentage of LC3B-positive cells than control animals in all observed structures. In our recent study, which examined postnatal yotari specimens, higher LC3B expression was observed in the glomeruli of P11 and P14 mice. These findings probably reflect higher autophagy activity in podocytes, which could be associated with foot process effacement and could lead to nephrotic syndrome development [11]. However, it has been reported that inhibition of autophagy may lead to the activation of pro-apoptotic pathways of endoplasmatic reticulum stress, which induces podocyte apoptosis and could be a possible explanation for autophagy’s protective role [44,45]. Several studies have shown that in most renal diseases, autophagy largely serves a preventive function. Its activity is essential in preventing the onset of podocytopathies, especially minimal change disease (MCD), a glomerular condition linked to the nephrotic syndrome that can result in chronic renal failure [46]. In addition, downregulation of autophagy, such as in blood hyperglycemia, can cause diabetic kidney disease [47], leading to fibrosis in renal epithelial cells, including podocytes, proximal tubular, as well as in mesangial, and endothelial cells. The nephrotoxin and chemotherapeutic agent cisplatin elevates LC3B expression in proximal tubular cells. The acute kidney injury and renal dysfunction caused by cisplatin were significantly exacerbated by autophagy inhibitors (such as the antimalarial drug chloroquine) [48]. Most studies mentioned above suggest an entirely cytoprotective role for autophagy in acute kidney injuries and chronic kidney diseases through recycling damaged cellular components, making its pathway a potential therapeutic target. Still, this process can also be detrimental in certain conditions, and further rigorous investigations are required.
Both genotypes expressed a high percentage of GRP78-positive cells at embryonic stages, with yotari having a stronger signal in convoluted tubules and metanephric mesenchyme. At E15.5, yotari exhibits elevated expression proportions in the cytoplasm of maturing glomeruli cells. In the postnatal period, yotari has a significantly higher percentage of positive cells in both PCT and DCT for every observed period. This immunoexpression pattern suggests an essential role for GRP78 in the early embryonic maturation of ampullae and collecting ducts but also signalizes significant levels of ER stress in all of the examined postnatal yotari structures. GRP78, a well-known chaperone engaged in numerous intracellular processes and a sensor for ER stress, prevents programmed cell death by promoting protein folding and eliminating misfolded proteins. However, Runbing et al. suggest that it may also serve an opposite function by promoting renal tubular fibrosis via the IRE1/XBP1 signaling pathway [24,49,50]. Earlier studies using GRP78 knockout mice provided direct evidence that GRP78 is essential for early embryo development and that its absence could lead to a series of pro-apoptotic pathways and early embryonic lethality [51,52], thus explaining the high percentage of GRP78-positive cells during embryonic stages in wt mice. Although there are currently no studies comparing the immunoexpression of Dab1 and GRP78 proteins in kidneys, a study by Mimura et al. showed that in knock-in GRP78 mice, higher expression of dephosphorylated Dab1 in the brain was observed, with reduced expression of Reelin, an extracellular matrix glycoprotein upstream of Dab1. This study suggested that GRP78 is essential for proper brain development and adequate Reelin/Dab1 pathway regulation [53]. During ER stress, multiple so-called stress proteins dissociate from GRP78 and then activate the downstream signaling pathway to promote the degradation of unfolded or misfolded proteins. GRP78 staining in yotari postnatal convoluted tubules could thus indicate induction of ER stress, as has already been described in most acute and chronic renal pathologies [23,54,55]. These findings suggest that functional *Dab1* gene silencing may cause an accumulation of unfolded or misfolded proteins in the ER lumen, thereby upregulating the GRP78 pathway and, as a result of autophagy capacity oversaturation, making apoptosis hypothetically the central mechanism of renal dysfunction in yotari mice.
The significant difference at embryonic stages in the percentage of HSC70-positive cells was found at E15.5, where yotari exhibited an increase in convoluted tubules. The same pattern was observed in the postnatal developmental stages analysis. Yotari mice expressed higher signal intensity at every timepoint except for P4, where there was not a significant difference in the reactivity of PCT cells. Chaperone-mediated autophagy (CMA) is a selective protein degradation process precisely mediated by HSC70. In the study of Cuervo et al., it is demonstrated that levels of α2-microglobulin (CMA substrate) in kidneys and liver after exposure to 2,2,4-trimethylpentane are increased, thus inducing the lysosome overload and resulting in severe cellular damage [56]. During acute diabetes mellitus, CMA was significantly inhibited in the rat renal cortex, and a mass of proteins with a KFERQ-like motif increased [57]. Unfortunately, no research has demonstrated a connection between the Reelin/Dab1 pathway and CMA mediators such as HSC70. However, based on the previous results, we can infer that in yotari mice, a higher percentage of HSC70-positive cells correlates with increased CMA activity and plays a protective role in cortical tubules.
During gestation, LAMP2A immunoexpression was observed in immature glomeruli, where yotari mice exhibit a significantly higher percentage of LAMP2A-positive cells than wt mice. The convoluted tubule LAMP2A staining pattern resembled HSC70 immunoexpression, which was significantly higher in the later stages of yotari mouse embryonic development. While PCT showed a significant difference in all examined postnatal periods compared to control animals, DCT of the yotari mice had a higher percentage of positive cells only in P4. It was predicted that LAMP2A’s staining pattern would be similar to that of HSC70 since LAMP2A serves as a receptor for chaperones carrying unfolded or misfolded proteins. The only study explaining the localization of LAMP2A in mutated kidneys is by Zhang et al. They discovered the apical distribution of LAMP2A in the cystinosis-relevant kidney PCT but its absence in the basal areas of the proximal tubule cells using Ctns−/− mice. These findings could implicate that absence of Dab1 results in the accumulation of CMA substrates in proximal tubule cells, which consequently leads to higher expression of the lysosomal receptor LAMP2A [20,58]. Recent research using LAMP2A knockout mice showed that cells are more vulnerable to stresses when this receptor is absent. Earlier work has demonstrated the significance of LAMP2A as a CMA regulator, demonstrating how CMA activity to eliminate proteins oxidized during mild oxidative stress appears to occur through increased LAMP2A transcription [59,60].
The dynamic and expression patterns of LC3B, GRP78, HSC70, and LAMP2A in control and yotari animals demonstrate the significance of these markers both during kidney development and later on in maintaining proper renal function. We can assume that silencing the *Dab1* gene causes an accumulation of unfolded or misfolded proteins, leading to increased expression of chaperones and other autophagic biomarkers. Different stimuli control CMA activity, including hunger, growth hormones, oxidative stress, lipids, aging, and retinoic acid signaling. The exact mechanism that initiates the autophagic sequence in Dab1−/− mice remains unknown but based on the increased percentage of GRP78-positive cells, we can assume that oxidative stress plays a significant role, particularly in convoluted tubules in both embryonic and adult kidneys.
In conclusion, our research emphasizes the importance of autophagy in kidney dysfunction and suggests that it plays a protective role in preventing programmed cell death. All observed proteins play significant roles in the autophagy process, making them excellent biomarkers for quantifying it. For various renal diseases, modifying autophagic activity may be a promising therapeutic approach; however, more studies are required to fully understand the role of autophagy in Dab1−/− kidney hypoplasia.
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|
---
title: Effects of Monoamino-Oxidase-A (MAO-A) Inhibition on Skeletal Muscle Inflammation
and Wasting through Pancreatic Ductal Adenocarcinoma in Triple Transgenic Mice
authors:
- Simon K. P. Schmich
- Jan Keck
- Gabriel A. Bonaterra
- Mirjam Bertoune
- Anna Adam
- Beate Wilhelm
- Emily P. Slater
- Hans Schwarzbach
- Volker Fendrich
- Ralf Kinscherf
- Wulf Hildebrandt
journal: Biomedicines
year: 2023
pmcid: PMC10046345
doi: 10.3390/biomedicines11030912
license: CC BY 4.0
---
# Effects of Monoamino-Oxidase-A (MAO-A) Inhibition on Skeletal Muscle Inflammation and Wasting through Pancreatic Ductal Adenocarcinoma in Triple Transgenic Mice
## Abstract
Cancer cachexia describes a syndrome of muscle wasting and lipolysis that is still largely untreatable and negatively impacts prognosis, mobility, and healthcare costs. Since upregulation of skeletal muscle monoamine-oxidase-A (MAO-A), a source of reactive oxygen species, may contribute to cachexia, we investigated the effects of the MAO-inhibitor harmine-hydrochloride (HH, intraperitoneal, 8 weeks) on muscle wasting in a triple-transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC) and wild type (WT) mice. Gastrocnemius and soleus muscle cryo-cross-sections were analyzed for fiber type-specific cross-sectional area (CSA), fraction and capillarization using ATPase- and lectin-stainings. Transcripts of pro-apoptotic, -atrophic, and -inflammatory signals were determined by RT-qPCR. Furthermore, we evaluated the integrity of neuromuscular junction (NMJ, pre-/post-synaptic co-staining) and mitochondrial ultrastructure (transmission electron microscopy). MAO-A expression in gastrocnemius muscle was increased with PDAC vs. WT (immunohistochemistry: $p \leq 0.05$; Western blot: by trend). PDAC expectedly reduced fiber CSA and upregulated IL-1β in both calf muscles, while MuRF1 expression increased in soleus muscle only. Although IL-1β decreased, HH caused an additional $38.65\%$ ($p \leq 0.001$) decrease in gastrocnemius muscle (IIBX) fiber CSA. Moreover, soleus muscle CSA remained unchanged despite the downregulation of E3-ligases FBXO32 ($p \leq 0.05$) and MuRF1 ($p \leq 0.01$) through HH. Notably, HH significantly decreased the post-synaptic NMJ area (quadriceps muscle) and glutathione levels (gastrocnemius muscle), thereby increasing mitochondrial damage and centronucleation in soleus and gastrocnemius type IIBX fibers. Moreover, although pro-atrophic/-inflammatory signals are reversed, HH unfortunately fails to stop and rather promotes PDAC-related muscle wasting, possibly via denervation or mitochondrial damage. These differential adverse vs. therapeutic effects warrant studies regarding dose-dependent benefits and risks with consideration of other targets of HH, such as the dual-specificity tyrosine phosphorylation regulated kinases 1A and B (DYRK1A/B).
## 1. Introduction
Cachexia describes a complex inflammatory, catabolic syndrome that is highly prevalent in chronic diseases, such as cancer and manifests itself by wasting of skeletal muscle with or without loss of fat mass [1,2], both of which cannot be compensated for by food intake [3]. Cachexia has a massively negative impact on the patients’ quality of life [1,4,5] and imposes high costs on healthcare systems [6,7]. Despite multiple therapeutic approaches, including nutrition, exercise, anti-inflammatory drugs, such as NSAID, megestrol, or cannabis [8,9,10,11,12,13], a causal treatment does not exist until now.
Although cachexia is a multi-organ syndrome [14], the role of skeletal muscle tissue during its development is crucial. Cachectic patients show a dramatic decrease in life expectancy [15], which is aggravated by the occurrence of obesity and occult muscle loss [16,17]. On a histological level, a wide range of human and animal studies, including pancreatic, lung, and colon cancer, show that a loss of fiber types I and II cross-sectional area (CSA) is the main feature of muscle wasting, in addition to altered myofibrillar structure, local apoptosis, or impaired regeneration, and, in some cases, fiber-type transition [4,18,19,20,21,22,23]. Thereby, fiber atrophy is found in comparison to both healthy controls and non-cachectic tumor-bearing animals or humans. This phenotype varies widely between muscle and fiber type, the entity, mass, or inoculation site of tumor, and the definition and stage of cachexia, among other factors [18,24,25,26,27,28,29].
Oxidative stress is considered to be a major factor of muscle wasting [30,31]. In glucocorticoid-induced proteolysis, gene expression of monoamine oxidase A (MAO-A) is upregulated in skeletal muscle, and thus leads to an increased production of H2O2 and related reactive oxygen species (ROS) [32]. In several studies on cardiac and skeletal muscle, ROS have been shown to induce mitochondrial damages, apoptosis, fibrosis, etc. [ 33,34,35,36,37].
Since to date, the impact of MAO-derived ROS on cancer cachexia has remained unclear, we presently aimed to evaluate for the first time the expression of MAO-A protein and the effect of MAO-A inhibition in cancer-related muscle wasting. Since PDAC is clinically associated with the highest incidence and most rapid progression of cachexia as a major contributor to this 4th and 5th leading cause of cancer-associated death [14,38,39,40,41,42,43], we chose a mouse model of orthotopic PDAC established by Hingurani et al. [ 44]. It is based on the 3x-transgenig KrasG12D/+; LSL-Trp53R172H/+; Pdx-1-Cre (KPC) mutants (Kras: Kirsten Rat Sarcoma, Trp53: Transformation-related protein 53, Pdx-1-Cre: Pancreatic and duodenal homeobox 1-cyclization recombination) and commonly utilized for PDAC research and preclinical therapeutic testing, since it mimics well clinical features of human PDAC, such as metastatic pattern (liver $80\%$, lung 50–$60\%$, adrenal gland $20\%$, and peritoneum 20–$30\%$), ascites and, importantly, of cachexia development [44,45,46]. Moreover, it recapitulates the mutant-based cancerogenesis from pancreatic intraepithelial neoplasia (PanIN) stages 1–3 to PDAC [44,45,47,48], with a PDAC incidence of $96.3\%$ [44]. According to a previous study of our group, this KPC model develops a likely inflammation-driven fiber atrophy in both, ‘red’ (soleus) and ‘white’ (gastrocnemius) muscle [49]. This muscle wasting coincides with the completion of cancerogenesis and occurs in the absence of altered integrity of mitochondrial ultrastructure or neuromuscular junction (NMJ).
Using this KPC mouse model, we presently evaluated the effect of MAO inhibition via the β-carboline alkaloid harmine-hydrochloride (HH), a competitive and selective MAO-A inhibitor [50,51,52] on cancer-related muscle wasting, with regard to histomorphometry of fiber types, pro-inflammatory, -atrophic, and -apoptotic gene expression, capillarization, NMJ, intramyocellular glutathione (GSH) content and redox state, and relevant transcripts. In addition, the effect of this therapeutic approach was assessed in WT mice without cachexia. The study focused on the hindlimb muscles as commonly performed in the field of cancer- or other disease-related cachexia [4,18,31,49] and evaluated the mixed slow-twitch (‘red’) soleus muscle (containing fiber types I, IIA, and IIBX) as well as the fast-twitch (‘white’) gastrocnemius or quadriceps muscle, which predominantly consists of type IIBX fibers.
## 2.1. Tumor Mouse Model
The present study used a triple transgenic mouse model of PDAC first described by Hingorani et al. [ 45]. It is based on three mutations—Trp53, Kras, and Pdx-1-Cre (Figure 1)—and largely recapitulates the development of PDAC in humans [45]. Mice were cross-bred by the Biomedical Research Centre of the University of Marburg and received food and water ad libitum with a periodic day-night cycle of 12 h. At the age of 3 months, each group of triple transgenic and wild type (WT) mice was treated intraperitoneally with HH (30 mg/kg/day) for 2 months.
After the treatment, the mice were sacrificed by cervical dislocation and the right triceps surae (i.e., gastrocnemius, soleus, and plantaris muscle) as well as the quadriceps muscle were removed and snap-frozen in liquid nitrogen-cooled isopentane. Small samples of gastrocnemius and soleus muscles were fixed for transmission electron microscopy (TEM) as previously described [49]. The post-mortem histopathological diagnosis of PDAC was made by two independent experienced investigators, and triple transgenic mice without PDAC were excluded from the study. In addition, WT mice were assessed for normal pancreatic histology. The study was approved by the Regional Commission Giessen (MR $\frac{20}{11}$-Nr$\frac{.70}{2009}$) and performed in compliance with the regulations for animal experiments at the Philipps-University Marburg.
## 2.2. Histological Examination
For lectin-staining [53], cross-sections (8 μm) of snap-frozen gastrocnemius and soleus muscles were cut in a cryostat, fixed in $4\%$ paraformaldehyde/phosphate-buffered saline (PFA/PBS) for 10 min at room temperature. Hydrogen peroxide ($0.05\%$ in PBS; pH 7.4) was used to block endogenous peroxidases. Then, cross-sections were incubated (30 min; 37 °C) with 40 μg/mL horseradish-peroxidase conjugated Isolectin B4 of *Bandeiraea simplicifolia* (BSI–B4) (Merck-Sigma-Aldrich Co. LLC, St. Louis, MO, USA) in PBS. A negative control with D-Galactose was performed. Thereafter, cross-sections were washed with PBS, incubated in a solution of 25 mg 3-3′-diaminobenzidine (Merck-Sigma), 50 µL hydrogen peroxide $30\%$, solved in 100 mL PBS, rinsed with PBS, and immediately counterstained with Mayer’s hematoxylin (Carl Roth GmbH, Karlsruhe, Germany).
Fiber types I, IIA, and IIBX were identified according to the acid-sensitive activity of their adenosine triphosphatase (ATPase) in unfixed serial cross-sections (8 μm), as previously described [54,55] (Figure 2). Microscopic images of the ATPase- and lectin-stained cross-sections were recorded using an Axio Imager M2 microscope (Carl Zeiss GmbH, Oberkochen, Germany) including the high-resolution imaging system Axio-Cam HRc/AxioVision Rel. 4.8 (Carl Zeiss GmbH). After ATPase-staining or lectin-staining, serial cross-sections with at least 100 fibers were selected and edged by a rectangle (region of interest—ROI) (Figure 2A,B). Only fibers with a cross-sectional area (CSA) >$50\%$ within the ROI were examined. Histomorphological parameters assessed were fiber-type-specific CSA, minimal Feret diameter (minFeret), fraction of total fiber population, capillary contacts, and centronucleation. For capillary density, only capillaries inside the rectangle were considered. For fiber-capillary contacts, all capillaries around the fibers were considered. Image analyses were performed using the ImageJ/Fiji (National Institute of Health, Bethesda, MD, USA a.o.). Analyses in the gastrocnemius muscle were limited to its large homogenous ‘white’ (i.e., superficial) area consisting of type IIBX only, since its soleus muscle-adjacent portion may variably contain type IIA, and sometimes type I fibers (Figure 2C).
For immunohistochemistry of MAO-A expression, gastrocnemius muscle cryo-cross-sections were fixed by $5\%$ paraformaldehyde (10 min), blocked with bovine serum albumin (BSA), and incubated with MAO-A monoclonal rabbit IgG antibody (Abcam, Cambridge, UK; 1:100) detected by an anti-rabbit secondary antibody that was conjugated with horseradish peroxidase (HRP) for metabolization of 3,3′-diaminobenzidine (DAB) with H2O2 as chromogene substrate. Nuclei were counterstained with Mayer’s hematoxylin (Carl Roth GmbH, Germany). In digital images (200×) obtained by the Zeiss Axio Imager. M2 microscope (Carl Zeiss AG; Oberkochen, Germany) combined with Axio-Cam HRc/AxioVision (Carl Zeiss GmbH), the MAO-A positive area in terms of percentage of intact muscle tissue was quantified in two representative areas with >150 type IIBX fibers using ImageJ/Fiji.
To evaluate NMJ integrity (innervation status) in quadriceps muscle, every 6th out of 60 serial cryo-cross-sections (8 µm), i.e., 10 cross-sections per mouse were used for pre- and post-synaptic NMJ co-staining in subgroups ($$n = 5$$ WT, $$n = 4$$ PDAC, $$n = 5$$ WT with HH and $$n = 5$$ PDAC with HH). After fixation with $4\%$ PFA/PBS and block by $1\%$ BSA/PBS for 30 min, PBS-washed cross-sections were incubated in a humidified chamber overnight with biotin-XX-conjugated α-bungarotoxin (BTX, 1:500; Invitrogen, Eugene, OR 97402, USA) for post-synaptic staining, and an antibody against the vesicular acetylcholine transporter (vAChT antibody (1:1000, Lee Eiden, 80259, Lot No.: bl. $\frac{6}{97}$) for pre-synaptic staining. After washing, cross-sections were incubated for 2 h with Cy3-conjugated streptavidin (1:200, Dianova GmbH, Hamburg, Germany) or Alexa Fluor®488 labeled donkey anti-rabbit IgG (1:200, MoBiTec GmbH, Göttingen, Germany) for the detection of BTX and vAChT antibodies, respectively. Slides were mounted using Immu-Mount™ (Fisher Scientific GmbH, Schwerte, Germany) and glass coverslips. Cross-sections were treated identically except for incubation with BTX or the primary vAChT antibodies served as controls. Con-focal images were collected with a C2 system on an Eclipse Ti2 inverted microscope (Ni-kon GmbH, Düsseldorf, Germany) using the software NIS-Elements AR 4.30.01 (Nikon GmbH). Each NMJ was scanned in a 630-fold magnification at 250 Hz with an image size of 1024 × 1024 pixels. Subsequently, Fiji software was used for morphometrical analysis, determining the BTX and vAChT immunolabeled areas, as well as their overlap area.
To assess the NMJ density of BTX+ post-synapses, every 7th cross-section was treated for 10 min with $3\%$ H2O2 to block endogenous peroxidases, and thereafter, post-synaptic NMJ were detected by incubation with biotin-XX-conjugated α-BTX and horseradish peroxidase (HRP)-conjugated streptavidin (Jackson ImmunoResearch Laboratories. Inc., West Grove, PA, USA) using DAB as a chromogen substrate. Nuclei were counterstained with Mayer’s hematoxylin (Carl Roth GmbH, Germany). NMJ count per area was determined in digital images (200-fold magnification) obtained by the Zeiss Axio Imager. M2 microscope combined with Axio-Cam HRc/AxioVision.
## 2.3. Mitochondrial Ultrastructure
Using TEM, the ultrastructural integrity of gastrocnemius and soleus muscle mitochondria was studied at 25,000-fold magnification as previously described [56], in 5–8 mice per WT, Ca, WTHH, or CaHH group and >10 images per animal. Mitochondrial ultrastructure was categorized as ‘damaged’ in the case of >$50\%$ loss of the cristae, and/or >$50\%$ disruption of the outer membrane, or as ‘normal’ otherwise. The ‘damaged’ fractions (%) of each muscle were compared between the groups.
## 2.4. Western Blotting of MAO-A Expression
Gastrocnemius samples were lysed using RIPA (radioimmunoprecipitation assay) buffer pH 7.5 (Cell Signaling Technology Europa, Leiden, The Netherlands), containing the protease/phosphatase inhibitor cocktail (Cell Signaling Technology, Boston, MA, USA). The total protein concentrations were determined using the Pierce BCA assay (bicinchoninic acid) (Thermo Scientific, Rockford, IL, USA) according to the manufacturer’s instructions. Proteins were loaded on pre-cast polyacrylamide NuPAGE® 4–$12\%$ Bis-Tris gels (Life Technologies GmbH, Darmstadt, Germany). After SDS-PAGE, proteins were transferred onto 0.45 µm nitrocellulose membranes [Millipore (Billerica, MA, USA)]. Before performing the immunoreactions, the membranes were stained for 1 min with Ponceau S solution ($0.1\%$ in $3\%$ trichloroacetic acid) at RT, and then rinsed with distilled water to remove the background and documented by the Fusion-SL Advance™ imaging system (Peqlab) according to the instruction manual. Membranes were destained in blocking buffer ($5\%$ milk in tris-buffered saline [TBS]). Thereafter, membranes were incubated with the primary antibodies (rabbit monoclonal anti-Monoamine Oxidase A [MAO-A] antibody, ab126751, Abcam plc., Cambridge, UK) overnight at 4 °C in blocking buffer. After washing with TBS $0.1\%$ Tween 20, membranes were incubated with ECL-anti-rabbit IgG-horseradish peroxidase (HRP) antibody (MA9340, GE Healthcare, Amersham, UK). Moreover, after washing, the peroxidase reaction was visualized with ImmobilonTM Western (HRP) Substrat (Merck Chemicals GmbH. Darmstadt, Germany). The intensity of WB bands and Ponceau S total protein was quantified using ImageJ/Fiji software from the National Institutes of Health (Bethesda, MD, USA). Total proteins according to Ponceau staining < 140 kDa were used to normalize the intensity of the bands [57].
## 2.5. Intramyocellular Glutathione (GSH) Content and Glutathione Redox State
Gastrocnemius muscle GSH content and glutathione redox state were determined according to Tietze [1969] [58] as previously described [59]. Briefly, 20–30 mg of muscle tissue were homogenized, deproteinized with 500 µL $2.5\%$ sulfosalicylic acid (SSA), sonicated, and centrifugated for 10 min at 13,000× g and 5 °C. The supernatant was used to determine its content of total GSH (tGSH) and its oxidized disulfide GSSG form. Reduced GSH (rGSH) was calculated by subtraction of GSSG from tGSH. The total protein content of the pellet, corresponding to the supernatant volume was quantified by the colorimetric Bio-Rad protein assay (BioradLaboratories, Munich, Germany) to normalize the intracellular tGSH and GSSG.
## 2.6. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR)
RNA was extracted from tissue using peqGOLDTriFast™ (VWR International GmbH, Darmstadt, Germany) according to the manufacturer’s instructions. RNA concentration was assessed using a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) by optical density (OD) 260 nm, and purity by OD260 nm/OD280 nm. A ratio of 1.8 to 2.0 was accepted as pure. RNA integrity (RIN) was confirmed using a RNA 6000 NanoChip kit on an Agilent 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA). RNA samples with a RIN between 8 and 10 were considered as suitable for reverse transcription (RT). An aliquot of total RNA was treated with 1 unit DNAse (Thermo Fisher Scientific Inc.) (30 min; 37 °C). The treated RNA was employed to perform the RT for 1 h at 42 °C, using oligo primer (dT)12–18 (Agilent Technologies Inc.), 20 units of the reverse transcriptase, included in the Affinity Script multiple temperature cDNA synthesis kit (Agilent Technologies Inc.), 24 units of Ribo Lock™ RNAse inhibitor (Thermo Fisher Scientific Inc.), and 4 mM dNTP mix (Agilent Technologies Inc.). The cDNA was used for RT-qPCR using the QuantiTect-primer assays (Table 1) (Qiagen N.V., Venlo, The Netherlands) along with Takyon™ Low Rox Probe Master-Mix dTTP Blue (Eurogentec, Seraing, Belgium) or Agilent™ Brilliant III Ultra-Fast SYBR® Green QPCR Master-Mix (Agilent Technologies Inc.). The thermal profile consisted of 3 min at 95 °C followed by 45 cycles at 95 °C for 10 s and 60 °C for 20 s. The qPCR and data analyses were performed using the Stratagene Mx3005P™ qPCR System (Agilent Technologies Inc.). For each unknown sample, the relative amount was calculated by linear regression analysis from their respective standard curves, which were generated from a pool of cDNA. Specificity of the amplified product was confirmed by the melting curve analysis (55–95 °C). The expression of housekeeping (Table 1) genes (RER1 for soleus muscle and β-actin for gastrocnemius muscle) were selected with the use of the software Normfinder [60].
## 2.7. Statistical Analyses
Statistical analyses were performed using R Deducer [https://www.R-project.org/]. Shapiro-Wilk normality test, Levene’s test for homogeneity of variance, two-factorial ANOVA, and Tukey’s test were performed. Two-factorial ANOVA was applied to the total study population to assess the effect of PDAC (Ca and CaHH group) vs. WT (WT and WTHH groups) as well as the effect of HH (WTHH and CaHH group) vs. untreated mice (WT and Ca group) with significant differences being presented in separate boxes within the graphs. Significant differences between two groups (post hoc) are indicated within the graph by x (for PDAC effects) or # (for HH effects). A significance level of ≤$5\%$ was chosen for this study.
## 3. Results
The average weight of the mice was between 24.03 and 27.88 g, with no significant difference between groups. Concerning age, a significant difference by PDAC and an interaction existed by ANOVA. In the post-hoc test, all groups were significantly different in age, except for the comparison between CaHH and WTHH (Ca—WT $p \leq 0.001$; WTHH—WT $p \leq 0.01$; CaHH—WT $p \leq 0.05$; WTHH—Ca $p \leq 0.01$; CaHH—Ca $p \leq 0.01$) (Table 2).
## 3.1. Gastrocnemius Muscle
As a target for HH treatment, MAO-A expression was confirmed by Western blot with normalization for total protein in gastrocnemius muscle of all four groups, i.e., WT, Ca WTHH, and CaHH (Figure 3A). There was a trend toward a PDAC-related increase in the highly variable MAO-A expression, which tended to decrease with HH treatment in both WT and PDAC-bearing mice. In addition, immunohistochemistry revealed a significantly higher MAO-A expression in Ca compared to WT mice (Figure 3B). Notably, no data were obtained regarding MAO-A enzyme activity and its change with HH treatment.
PDAC ($p \leq 0.05$) and HH ($p \leq 0.001$) led to a significant decrease in type IIBX CSA (Figure 4). A significant HH-mediated, as well as a PDAC-induced, reduction in CSA was found (Figure 4A). Additionally, HH treatment led to a decrease (Ca—CaHH: −9.85 µm; WT—WTHH: −4.64 µm) in the minFeret (Figure 4B), which is a CSA-corresponding length indicating the shortest distance between two parallel tangents on the fiber diameter. Thereby, in line with fiber atrophy, HH treatment significantly increased capillary density by $38.10\%$ in WT and by $44.21\%$ in PDAC mice (Figure 4C), while capillary fiber contacts remained unchanged (not shown).
Moreover, RT-qPCR analyses revealed that neither the expression of apoptosis-relevant genes (BAX, Caspase 3, BCL-2) nor E3 ligases MuRF1 and FBXO32, or of MAO-A and MAO-B were significantly altered with PDAC or HH. Similarly, the expression of proinflammatory genes, such as TNFα, IL-6, and COX2 showed no related differences (Table 3).
However, IL-1β expression was found to be significantly increased by PDAC ($p \leq 0.01$) and, importantly, significantly decreased by HH ($p \leq 0.05$) (Figure 5A).
Furthermore, ANOVA revealed a non-synergistic interaction at the mRNA expression of Socs3, increasing in Ca compared to WT mice by $160.43\%$ and decreasing in CaHH compared to Ca mice by $195.7\%$ (Figure 5B). As another significant effect in gastrocnemius muscle, HH led to a decreased mRNA expression of Ppargc1a by $81.52\%$ in WTHH compared to WT mice (Figure 5C).
Analyses of GSH content and redox state in gastrocnemius muscle homogenate by ANOVA revealed (Table 4) that, overall, PDAC had no significant impact. However, HH treatment led to a significant decrease in total (WT—WTHH $p \leq 0.001$; WT—CaHH $p \leq 0.05$) as well as in reduced GSH (WT—WTHH $p \leq 0.05$). Since HH and PDAC tended to decrease GSSG as well (WT—Ca $p \leq 0.01$; WT—WTHH $p \leq 0.001$), the ratio of reduced GSH to GSSG (WT—Ca $p \leq 0.05$; Ca—WTHH $p \leq 0.05$) was not significantly changed by HH. Overall, HH appeared to impair intra(myo)cellular GSH availability, however, without a major shift in oxidation status.
## 3.2. Soleus Muscle—Histomorphometry and Gene Expression
In soleus muscle, PDAC led to a decrease in CSA in fiber types I ($p \leq 0.01$) and IIA ($p \leq 0.05$) (Figure 6A,B) and to a corresponding reduction in minFeret in fiber types I ($p \leq 0.05$) and IIA ($p \leq 0.05$) (Figure 6D,E). Furthermore, interaction of PDAC and HH resulted in a significantly ($p \leq 0.05$) increased number of type I fibers by $14.59\%$ in soleus muscle of CaHH mice in comparison to the WTHH or Ca group (Figure 6G). Driven by PDAC, the number of type IIA fibers in soleus muscle of CaHH mice significantly ($p \leq 0.05$) decreased by $16.27\%$ compared to the WTHH group (Figure 6H). In soleus muscle of PDAC mice, the number of type IIBX fibers significantly ($p \leq 0.01$) increased by $9.57\%$ compared to WT mice (Figure 6I). Capillary density and fiber-capillary contacts were similar in all groups under test (Table 5).
Regarding relevant proteolytic signals and markers in soleus muscle, a PDAC-driven increase in MuRF1 mRNA, but not in FBXO32 mRNA expression was found (Figure 7A). As a potentially anti-cachectic effect, HH treatment led to a significant decrease in MuRF1 and FBXO32 expression by $64.78\%$ ($p \leq 0.01$) and $49.55\%$ ($p \leq 0.05$), respectively (Figure 7B). Due to an interaction between PDAC and HH ($p \leq 0.05$), BAX expression in CaHH significantly increased by $68.81\%$ ($p \leq 0.05$) compared to WTHH mice and by $84.07\%$ ($p \leq 0.01$) compared to Ca mice (Figure 7C). ANOVA and Tukey’s test showed a significant ($p \leq 0.001$) increase in IL-1β expression driven by PDAC (Figure 7D). As with gastrocnemius muscle, Caspase 3, BCL-2, MAO-A, and MAO-B gene expressions were unchanged in soleus muscle (Table 6). The expressions of proinflammatory CD68, TNFα, IL-6, and COX2 genes were not altered, as well (Table 6). However, HH led to a significant downregulation of MMP9 overall. Furthermore, MyoG was found to be significantly upregulated through HH by interaction with PDAC.
## 3.3. Gastrocnemius and Soleus Muscles—Mitochondrial Integrity and Centronucleation
In gastrocnemius muscle, no significant global effect of PDAC ($$p \leq 0.595$$) or HH ($$p \leq 0.160$$) was detected regarding the mitochondrial integrity as assessed by TEM. Among the total mitochondrial number analyzed per animal in WT ($$n = 220$.0$ ± 50.4), Ca ($$n = 230$.8$ ± 44.9), WTHH ($$n = 137$.5$ ± 13.0), and CaHH ($$n = 153$.1$ ± 14.8), the ‘damaged’ fractions amounted to $39.8\%$ ± $3.7\%$, $37.8\%$ ± $4.3\%$, $42.5\%$ ± $5.8\%$, and $50.1\%$ ± $5.0\%$, respectively.
Similarly, no significant global effect of PDAC ($$p \leq 0.513$$) or HH ($$p \leq 0.075$$) was found regarding the mitochondrial integrity in soleus muscle. Among the total mitochondrial number analyzed per animal in WT ($$n = 364$.6$ ± 25.7), Ca ($$n = 311$.3$ ± 42.0), WTHH ($$n = 307$.5$ ± 49.3), and CaHH ($$n = 370$.3$ ± 57.0), the ‘damaged’ fractions were $41.1\%$ ± $3.1\%$, $39.4\%$ ± $2.4\%$, $49.2\%$ ± $4.4\%$, and $45.8\%$ ± $4.40\%$, respectively.
However, a combined analysis of both muscles revealed a significant effect of HH ($$p \leq 0.022$$) but not PDAC ($$p \leq 0.930$$) in terms of increased mitochondrial damage.
No significant global effect of PDAC or HH was detected on the fraction of centronucleated cells in gastrocnemius muscle or within the total fiber population, type I or type IIA fibers in soleus muscle (Table 7). However, within type IIBX fibers of soleus muscle alone or in combination with gastrocnemius and soleus muscles, a significant increase in centronucleated fraction with HH but not PDAC was detected.
## 3.4. Quadriceps Muscle: Morphological NMJ Integrity
Evaluation of NMJ integrity by pre-/post-synaptic co-staining in subgroups (Table 8, Figure 8) indicated that the post-synaptic BTX+ area was significantly reduced with HH treatment, while PDAC had no impact (Ca—CaHH $p \leq 0.05$). Moreover, there was a significant interaction of HH treatment with PDAC in a way that the HH treatment decreased vAChT+ area (Ca—CaHH $p \leq 0.05$) and the absolute percentage overlap of vAChT+ with BTX+ areas (Tukey’s test: vAchT/BTX overlap [µm2] Ca—CaHH $p \leq 0.05$; vACht/BTX overlap [%] WT—Ca $p \leq 0.05$). Furthermore, significant correlations were found between rGSH/GSSG ratios in gastrocnemius muscle and NMJ vAChT/BTX overlap [%] in quadriceps muscle ($r = 0.580$, $$p \leq 0.015$$), as well as between rGSH content and NMJ BTX+ area ($r = 0.512$ $$p \leq 0.035$$) of these muscles, which both consist of type IIBX fibers.
## 4. Discussion
In the presently studied triple transgenic KPC mouse model, a recently reported cachexia-inducing effect of PDAC was confirmed in terms of reduced CSA of muscle fiber type IIBX in gastrocnemius and, as a global effect across all groups, of fiber types I and IIA in soleus muscle [49]. No significant pro-atrophic impact of PDAC was, however, detected in soleus muscle IIBX fibers, which represented a rather small fraction only. Our data on PDAC-related muscle wasting in these most frequently studied muscle types [4,18,31,49] are in line with the pro-cachectic effect of various other carcinoma in [19,26,27,28,29,61], which may vary widely between different types of cancer, fiber composition, and other factors [18]. Notably, changes in body weight were detected neither with PDAC nor with HH treatment. The fact that it did not reflect significant muscle wasting induced by PDAC and, surprisingly, also with HH in gastrocnemius muscle, may be due to concomitant variable changes in fat mass and increases in ascites fluid volume and/or tumor mass, all of which could not be measured.
Regarding fiber type distribution, a shift toward types I and IIBX in soleus muscle was observed solely due to a combined PDAC and HH effect. Alterations in fiber type composition with cancer cachexia may involve apoptosis, fiber transition, or impaired fiber type regeneration [18], all of which may contribute to differences and contradiction between findings on fiber composition with cancer [19,25,26,27,28,29]. It is very likely that the significant increase in capillary density, as presently observed, results from the reduction in fiber CSA in the gastrocnemius muscle, as changes in capillary-fiber contacts were absent.
Muscle IL-1β, a predictor of cachexia and higher mortality rates in patients with PDAC [62], was the only consistent parameter that was increased in both muscles under study and might have triggered acute phase reaction leading to cachexia [63]. In gastrocnemius muscle, a concomitant upregulation of Socs3 was found, that might be linked to the higher IL-1β [64,65], whereas in soleus muscle, MuRF1 was found to be increased as a protein-catabolic signal. No significant PDAC-related changes were detected in the expression of Ppargc1a, the role of which in metabolism and prognosis of cancer cachexia is still controversial [66,67,68,69].
As a presently addressed target of HH, skeletal muscle MAO-A was shown by Western blot (gastrocnemius muscle) and RT-qPCR (soleus and gastrocnemius muscles) to be expressed in both WT and PDAC mice. Thereby, a PDAC-related increase in MAO-A expression was neither detected on the RNA level nor on the protein level. However, there was a trend toward higher MAO-A protein expression with PDAC in Western blot and immunochemistry revealed significantly higher MAO-A expression in Ca compared to WT. These data reveal high variability and are only partly in line with a previous study showing a massive MAO-A upregulation along with increased H2O2 production upon glucocorticoid treatment in myocytes in vitro [32]. Even in the absence of significant changes in MAO-A expression, the present HH treatment can, however, be expected to substantially inhibit the enzyme activity of MAO-A, thereby decreasing H2O2 production as a pro-inflammatory and -cachectic signal within skeletal muscle.
Surprisingly, however, rather than reversing muscle atrophy, HH treatment led to a (further) reduced CSA of type IIBX fibers in the gastrocnemius muscle. As a serious adverse HH effect in the context of cachexia that is not described before, it occurred despite the observed significant reduction in the likely pro-cachectic IL-1β mRNA expression in gastrocnemius muscle. This protective anti-inflammatory effect may be in line with previous findings in human neurons after death following traumatic brain injury [70], or in mice serum [71,72]. Importantly, HH treatment also decreased the expression of MuRF1 and FBXO32 in soleus muscle, but was not able to diminish the PDAC-triggered muscle atrophy. Therefore, this, in theory, anti-cachectic, i.e., beneficial HH effect presently appeared to be overridden by another consequence of HH treatment. It cannot be excluded that apoptosis as suggested by BAX upregulation with HH (by interaction with PDAC) may have contributed to losses in muscle mass, as histomorphometry assesses fiber atrophy only.
Though obtained in subgroups only, our present data on NMJ integrity in the quadriceps muscle as another IIBX fiber-containing muscle suggest that NMJ may be compromised through HH, especially in PDAC mice. Of interest, IL-1β and possibly increased MAO-A activity is required for regeneration after peripheral nerve injury [73,74], while inhibition of MAO-B, not achieved by HH, may be beneficial for regeneration [75]. Therefore, anti-inflammatory effects through the presently applied HH dose may not be generally desirable in the context of cachexia treatment, though inhibition of MAO-A/B may reduce ROS production following denervation [76]. It remains to be studied as to what extent the adverse effect of HH is dose-dependent and overrides a beneficial anti-cachectic effect through downregulation of E3-ligases and IL-1β.
The assumption of compromised innervation through HH is presently supported by the finding that tGSH and rGSH availability in gastrocnemius muscle decreased significantly with HH treatment [73,76,77] and, furthermore, by the finding of increased centronucleation [78] in soleus type IIBX, or combined gastrocnemius and soleus IIBX fibers. In addition, as a source of ROS, dysfunctional mitochondria have indeed been implicated as a cause or a consequence of muscle denervation [76,79]. Increased ROS production of dysfunctional mitochondria may not necessarily be associated with detectable ultrastructural damage [18,80]. However, we presently detected an overall increase in the fraction of damaged mitochondria through HH when considering the combined data of both muscles under study, while PDAC was without any significant effect. Whether the adverse effect of HH primarily targets the NMJ or mitochondria remains open at present, however, both may lead to oxidative shifts in intracellular GSH status and proteasomal activation as a major cause of fiber atrophy. Notably, in the context of its possible adverse effects, cytotoxic effects of HH have also been described in pulmonal fibroblasts [81] and hepatocytes [82]. Possible triggers may be the ability of many β-carbolines to intercalate with DNA [83,84] and to provoke a G2 phase arrest [85,86] by the inhibition of topoisomerase I [87] or cyclin-dependent kinase [88]. *More* generally, it is noteworthy that HH may induce psychomotor alterations, such as reduced physical activity and anhedonia in rats, which presently cannot be excluded as a possible explanation for the HH-related muscle atrophy [89].
Importantly, the presently reported effects through HH may also, at least in part, be conveyed via HH-related inhibition of the x-chromosomal multifunctional dual-specificity tyrosine phosphorylation regulated kinases 1A (DYRK1A) and the 1B (DYRK1B/MIRK). DYRK1A is required for normal brain development and axonal transport and is expressed in adult NMJ [90], where DYRK1A inhibitors, such as HH might possibly have adverse effects, though data in this respect are lacking. However, since DYRK1A overexpression in Down’s Syndrome (DS) is strongly implicated in DS-associated oxidative stress, Alzheimer’s disease [91], and motor dysfunction [90], DYRK1A inhibitors may be beneficial in this condition, e.g., by upregulating the nuclear factor erythroid 2-like 2 (NRF2) [92]. However, DYRK1B is mainly expressed in skeletal muscle, where it plays a role in antioxidative defense [93,94] somewhat in contrast to the role of MAO-A. Therefore, HH may differentially alter the oxidative milieu through MAO-A and DYRK1B inhibition. However, since myocellular DYRK1B expression is critical for myogenesis, limited apoptosis, myocellular fusion, fiber differentiation, antioxidative defense, and autophagy (e.g., of damaged mitochondria) [94,95], its inhibition by HH may contribute to the presently observed muscle alterations, such as centronucleation, mitochondrial damage, fiber shift, and atrophy. Given that MAO-A was presently found to be upregulated in quadriceps muscle, the HH-related reversal of pro-cachectic PDAC effects (i.e., lowering IL-1β in gastrocnemius and MURF1 in soleus muscle) may be attributable to MOA-O inhibition, i.e., to reducing oxidative stress rather than to its augmentation via DYRK1B (DYRK1A) inhibition. Certainly, further studies on the relative contribution of these players to cachexia development and to therapeutic benefit or adverse events of HH are warranted.
Owing to the limited sample size, some gender imbalance between the groups, and the lack of a sham control, the present study may overall be considered as hypothesis-generating, especially with regard to soleus muscle histomorphometry in both HH-treated groups and the small subgroups explored for mitochondrial and NMJ integrity. Due to limitations in tissue/blood samples for further Western/ELISA analyses, the present results were mainly based on muscle qRT-PCR and histomorphometry, and thus unable to assess a potential contribution of systemic inflammation to the effect of PDAC or HH.
## 5. Conclusions
This hypothesis-generating study provides first insight into an understudied topic, i.e., MAO-A expression with cancer-related muscle wasting and the therapeutic potential of the MAO-A inhibition with HH. Using a transgenic mouse model of PDAC with confirmed fiber atrophy, we show a desirable inhibiting effect of HH on inflammation (IL-1β and Socs3) in gastrocnemius muscle and on signals of fiber atrophy via proteolysis (MuRF1 and FBX032), however, with a possible pro-apoptotic effect (BAX). Overall, this, however, did not translate into inhibition of PDAC-related muscle fiber atrophy. Rather, HH promotes fiber atrophy, at least in ‘white’ glycolytic muscle. Our preliminary data indicate that this adverse effect of HH may arise from compromised innervations, as also supported by GSH depletion, mitochondrial damage, and centronucleation. Notably, these adverse prooxidative effects impairing neuromuscular maintenance may also arise from HH-related inhibition of neuronal DYRK1A and/or DYRK1B. Whether different HH dosages may more effectively reduce inflammatory and fiber atrophy signals while avoiding neuromuscular adverse effects warrants further studies. In this context, it would be interesting to investigate the impact of other commonly used MAO inhibitors, such as Moclobemide, Rasagiline, or Selegiline in the present experimental animal model.
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---
title: Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare
Professionals Attending COVID-19
authors:
- Juan Luis Delgado-Gallegos
- Gener Avilés-Rodriguez
- Gerardo R. Padilla-Rivas
- María De los Ángeles Cosío-León
- Héctor Franco-Villareal
- Juan Iván Nieto-Hipólito
- Juan de Dios Sánchez López
- Erika Zuñiga-Violante
- Jose Francisco Islas
- Gerardo Salvador Romo-Cardenas
journal: Brain Sciences
year: 2023
pmcid: PMC10046351
doi: 10.3390/brainsci13030513
license: CC BY 4.0
---
# Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
## Abstract
Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation ($p \leq 0.05$) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of $94.1\%$). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.
## 1. Introduction
With the global spread of the COVID-19, both medical and allied healthcare professionals have become the most highly affected sectors [1,2,3]. In developing democracies, the public health system became engulfed by the overwhelming levels of stress [4,5]. In addition, the situation becomes even more taxing for attending personnel as they not only deal with the burdened system [6] but also with the enemy (COVID-19) upfront. It is here, where they can also become prey to the disease [7]. Recently in Mexico, reports for the period of late February to 23 August showed that over 97,600 healthcare professionals had become infected with COVID-19 [8]. Hence, Mexico showed atop of all Latin America countries in infection-to-death rate (>$10\%$) [9]. The number of “total confirmed”, possible, active cases, and mortality of COVID-19 amongst physicians, almost doubled during the period of 16 August up to 3 November, potentially generating high levels of stress on them. This is of particular interest when we consider stress as a potential trigger to lose focus during procedures or while attending to patients; therefore, enabling conditions for COVID-19 infection, or making costly mistakes [10].
According to the Pan American Health Organization (PAHO), Mexico has the highest number of healthcare workers infected with COVID-19 in Latin America [11]. In 28 December 2020, the number of health care professionals affected by COVID-19, as reported by the National health ministry, was just over 182,200 [12]. Reports show that both physicians and nurses have similar levels of burnout and emotional fatigue [3,13,14,15]. Physicians typically work in a more independent manner. This, along with their long shift hours, high-sense of duty, work ethics, and the fact that they partake in multiple jobs normally of low wages, becomes a source of additional stress [8]. With the data being generated while facing the disease, it is important the apply rapid methods that allow study of this scenario and allow development of policies or strategies. For this purpose, machine learning algorithms have proved efficient in the analysis of stress in working employees [16,17,18]. Still, for medical applications, it is important for the algorithm to provide explainability for computer-aided diagnosis [19]. Therefore, in this study, we propose the use of the C5.0 algorithm to assess perceived stress in healthcare workers exposed to COVID-19, generating an explainable classification diagram that contributes to the understanding of mental health in pandemic scenarios.
Recent developments in computational modeling have led to the ever-evolving field of artificial intelligence, which, when combined with neuro- and behavioral science, has created the new field of computational psychiatry [20,21]. Computational psychiatry helps to model and understand underlying mental illness, allowing the prediction of potential behavioral patterns, improving classification, and assisting the physician to provide a faster and personalized medical attention [22]. Nowadays, machine learning algorithms are promising technologies used by various healthcare providers, as they result in better scale-up, speed-up, processing power, and reliability, which translates into a more efficient performance of the clinical team [23,24,25]. Therefore, a trend is to use these techniques to better understand, and fight against the current pandemic and other chronic diseases, especially when the resulting model could have a graphical-based explanation [26]. Using well-known machine learning algorithms, such as decision trees, for establishing classification systems are but one of the many features of their application [27]. Typically, it is possible to classify a population into branch-like segments that generate an inverted tree [27,28]. These algorithms can efficiently deal with large, complicated datasets without imposing a complicated parametric structure [28]. Researchers have reported the use of these types of algorithms for applications in the study of behavioral and mental health [29] and on the use of computational based methods to classify stress from data generated by sensor devices [30]. Thus, it is possible to use these tools to better understand disease and propose different clinical paths, and to classify subgroups of patients for different diagnostic tests, treatment strategies, and assessment of mental health-related conditions [31,32].
Several approaches on machine learning-based stress assessments have been reported. A common method considers the use of bio-marker data to stratify stress on several levels [33,34], since these algorithms are able to asses not only stress, but depression and anxiety as well [18,35]. For COVID-19-related stress evaluation, the use of these type of algorithms has been previously explored for general population studies, based on distributed questionnaires data [36,37], which allows for exploration of data acquired from these kinds of questionnaires for clinical applications [38].
Specifically, decision tree algorithms have been able to obtain $92\%$ accuracy, providing not only a reliable stress categorization [39], but they also generate a visual model that allows to analysis of the actual scenario of the problem, which is not common with machine learning algorithms.
In machine learning, a common strategy used for data analytics is the cross-industry standard process for data mining method (CRISP-DM). This method defines six steps for data-based knowledge projects. This strategy begins with defining problems and objectives (business understanding), followed by data insights (data understanding). Next, defining a dataset and its analysis (data preparation), and results from this analysis generates a model (modeling). *Once* generated, it is evaluated (evaluation), and if the goal is achieved, it can be implemented [40].
Given that it is possible to use decision tree algorithms to identify prominent features that influence stress [16], it is feasible to apply this type of algorithms to obtain an explicative model of the studied scenario. Additionally, the proven efficiency of the C5.0 algorithm as a biomedical decision support tool for assisted diagnosis makes it a likely tool for the case [41,42,43]. In the current work, we studied the application of a C5.0 decision tree algorithm, as proposed in the literature, to locate the combination of factors needed to classify, correctly, healthcare professionals attending to COVID-19 patients, by the category of perceived stress. This provides a graphical tool that allows a better understanding of the mental health of healthcare professionals at the beginning of the COVID-19 pandemic in northeast Mexico.
## 2. Materials and Methods
Some other work on stress perception during the COVID-19 pandemic has been reported regarding healthcare workers [44,45]. Our work is based on previously reported adapted COVID-19 stress scale (ACSS) data [1], at the beginning of the pandemic, in healthcare workers in northeast Mexico. The dataset was previously classified into different categories of perceived stress for healthcare professionals attending to COVID-19 patients: five variables were defined (danger and contamination, xenophobia, traumatic stress, compulsive checking, and social economical) and four different results were defined, with scores per area: 0–6 absent, 7–23 mild, 13–18, moderate, 19–24 severe. A total tallied score of all the areas was obtained, and further analyzed and correlated in accordance to job-specific characteristics [1]. We adapted the analysis method using the CRISP-DM model, commonly used in data analytics [40], having the same number of stages and sequences, as shown in Figure 1. Initially, we performed a data structure study from the data analytics scope to consider the type of variables from the ACSS. This was to establish the type of variables and how they contributed to the context of the ACSS, along with the four categories of stress defined from the scores as outcomes: absent, mild, moderate and severe.
Next, we performed a data validation analysis considering statistical tests to confirm relations between variables from the scales and classification outcomes from the raw data, and to confirm internal consistency [46]. This was completed by obtaining both Cronbach’s alpha and McDonald’s omega from the raw ACSS responses and a Pearson chi-square statistic applied to the ACSS and the resulting stress scale. We followed the validation process with a data distribution analysis to study stress components for model selection and interpretation. This measured the central tendency of the professional profile, which included the profession and work area from the healthcare workers who participated in the study, as well as for the ACSS and the resulting stress class.
Given that the approach of this work is to provide an AI-based method that could become a tool for clinical decision making, we selected a decision tree (DT) model to study the relations and classification routes for stress level according to data from its respective scales.
We carried out an accuracy analysis based on the results from algorithm training, as well as a sensitivity and specificity analysis by splitting the categories defined for stress into different subgroups for healthy and disease states.
## 2.1. Descriptive Statistical Analysis
We performed both the statistical and algorithm performance analysis in R language to obtain behavior patterns and understanding of data distribution. For data preparation and preprocessing, we also carried out a descriptive statistical study to understand data structure and distribution. To obtain valuable information for model interpretation, measures of central tendency were obtained from the professional profile data of the healthcare workers who participated in the study, as well as from the ACSS.
For the instrument validation purposes, we estimated the value of Cronbach’s alpha considering the numerical values from all participant responses [47]. Finally, we applied Pearson chi-square statistics using SPSS (ver. 21) to the ACSS areas to show results robustness [48].
## 2.2. Application of C5.0 Algorithm
Following the statistical analysis on the instrument results, we developed a DT to behave as a computational supportive scaffold for the study of mental illness. We opted to use a C5.0 algorithm to analyze and classify the stress level from the dataset and for construction of a classification tree, as used in previous health-related scenarios [49]. This algorithm uses information gain as its splitting criteria and the binomial confidence limit method for the pruning technique, improving the feature selection and reducing error pruning. These methods have been reported useful to build efficient classifying models having small datasets, given the mathematical background of the model [50]. Additonally, DT outperforms other algorithms with smaller datasets, as in this case.
Following both the statistical and computational analysis of the instrument and dataset, we analyzed the performance based on sensitivity and accuracy on the generated model [51]. Given the size of the dataset, the confusion matrix obtained from the algorithm training was used to define the accuracy of the obtained model. Then, sensitivity and specificity calculations were completed using the results of the confusion matrix. Given that there are four different levels of stress defined as outcome, three different combination subgroups were used to define healthy and disease states. Conclusions were drawn from the results of the analysis, as well as routes defined by the tree model branches considering initial statistical analysis. The application of this algorithm is not intended as a classification tool but as a computer-aided tool that provides a wider scope of stress in healthcare workers. For this, the whole dataset was used to train the algorithm and to obtain the DT with the use of R and RStudio.
## 2.3. Dataset
As mentioned before, the study considers a dataset obtained from 106 participants from which information related to medical or healthcare education, work field and experience. Then, the data is built into a stress concept conformed by five components, which are: danger + fear of contamination, socioeconomical, xenophobia, traumatic stress and compulsive checking. Danger + fear of contamination refers to perceived stress related to the probability of being exposed and contracting the disease. The socioeconomical factor refers to financial-related stress that is associated with the chance of losing their job and the financial burden of becoming unemployed. Xenophobia is a scale that refers to the fact that the disease comes from abroad and it might not be possible to stop it. Traumatic stress refers to the emotional burden related to work with COVID-19 patients, and compulsive checking it related to compulsive behavior around the need to look for information about the disease.
## 3. Results
We applied an initial preprocessing statistical analysis to the 106 entries dataset. After eliminating missing data entries for statistical and algorithm-based analysis preparation, we used a group of 101 entries for the study. Besides explainability from the graphical output, decision trees have proved useful for small datasets [52]. Still, the dataset is greater than the minimal size of 62 required for decision tree models [50].
From the total entries, we counted the frequency of the profession and work area variables, as shown in Table 1.
We then built upon the five areas of the ACSS, calculating the central tendency metrics for each of these components based on the cumulative result of each participant, as shown in Table 2.
Given that we based each feature on the addition of the responses from the survey, we considered all the values from each question and participant for the calculation of Cronbach’s alpha, which shows a good internal consistency (0.94) for the whole survey instrument and data, and a similar result for McDonald’s omega (0.95). In addition, Supplementary Table S1 shows chi-square tests to each question in order to define significance in the relationship of the variables. Table 3 shows the result of the test for each scale area and each question, and for the cumulative ACSS.
Both results from Cronbach’s alpha and the chi-square test show internal consistency of the data and validate the dependence for stress level calculation, ensuring the dataset quality for algorithm-based analysis. Distribution for the stress level classification in healthcare personnel calculated from the ACSS is shown in Figure 2.
Figure 3 shows a scatter plot from the intersection from the xenophobia and danger + fear of contamination scales from the ACSS, allowing to observe the distribution of the stress levels based on these two variables in some areas of the graph.
Stress scale distribution showed in Figure 2 shows the general incidence of the stress level in healthcare professionals at the beginning of the pandemic. Although imbalanced, commonly in medical data, correlation distribution showed in Figure 3 confirms the feasibility to use the dataset, despite the size and imbalance, for the purpose to decipher medical context [53].
Following the descriptive statistical analysis, we trained a decision tree model with the preprocessed dataset ($$n = 101$$) using the C5.0 algorithm [28,49], considering the stress level to be the target variable. We used all areas of the ACSS including participant profession and work area as the predictive variables to find any relationship between them to predict stress level. Figure 4 shows the decision tree obtained from the dataset.
At the class level, a set of boxes with all four levels of stress are observed. In each box, the extreme right bar corresponds to the severe level indicator, followed, to the left, by moderate, mild and absent levels, respectively. Despite declaring the features related to the participant profession and work area, these variables did not provide valuable information gain to be considered in the model. Table 4 shows the confusion matrix from the obtained decision tree model, where only 6 out of 101 entries were incorrectly classified, missing two cases for mild level, three for moderate and one for severe. All these errors were classified only in neighboring levels, giving the model an accuracy of $94.1\%$.
To analyze model performance, a sensitivity and specificity calculation were carried out. For these, three different scenarios were considered based on the stress classification outcome from the dataset, dividing entries into healthy and disease groups. Calculation was completed with the figures from the confusion matrix. Results are shown in Table 5.
## 4. Discussion
Our purpose was to define a statistical and computational framework algorithm to analyze and understand stress levels in healthcare professionals for the impact of the COVID-19 pandemic and to potentially define a graphical self-explainable clinical tool, which can be further used as a severity predictor of stress.
A dataset related to the ACSS, as defined by Delgado-Gallegos et al., was studied with a calculated Cronbach’s alpha of 0.94, which shows a good internal consistency; stress levels were calculated as a geometrical result from the addition of five scales from the survey defined as danger + fear of contamination, socioeconomic stress, xenophobia, traumatic stress and compulsive checking. Chi-square tests were carried out for all questions individually, looking to validate stress level calculation. Statistical significance ($p \leq 0.05$) was found in most of the questions, considering the answers of all participants, except one question for the traumatic scale, and four for the compulsive checking scale (all shown in Supplementary Table S1). However, all scales showed statistical significance when the test was applied to the accumulated value for each of these scales, as seen in Table 3; thus, validating, the use of the ACSS in a population [1,54]. Therefore, the use of this model can be re-adapted to help in correctly assessing and providing a faster diagnosis and opportune treatment.
From the central tendency metrics statistical analysis, no relation was observed between participant profession and work area, similar analysis was done for the stress scales which showed an exception for danger + fear of contamination joint scale, all other areas had a similar maximum value but with different means. Therefore, considering the results from the preprocessing stage, the dataset shows good quality, independence, and internal consistency for algorithm analysis. All 101 entries from the dataset were used to train a decision tree model by the C5.0 algorithm, where stress level was defined as the target variable, with participant profession, work area, and cumulative stress scales as predictors. The resulting model showed an accuracy of $94\%$, adding a more precise assessment to the initial stress classification. Nonetheless, the algorithm did not find enough information gain from the participant profession, work area, and the socioeconomic scale. Neglecting these variables from the resulting model allows to understand that experience and day-to-day work routine are not a factor on how healthcare professionals perceive stress. Resilience could help explain this pattern, as it is an adaptation mechanism in which a person, overtime, can handle stress in overwhelming situations [15,55].
Computational psychiatry states the similarity between the brain and a computer and proposes the use of computational terminology for the study of mental illness [56]. Our results show interesting data denoting hypothetical tendencies based on the purity of the resulting branches of the decision tree, where severe stress cases can be related mostly to high levels of xenophobia and compulsive stress, as shown by the relation of the threshold values from the extreme right route of the decision tree, which are above the 3rd quartile for xenophobia and compulsive stress scales, and from the measures of central tendency shown in Table 2. In a similar manner, absent stress level comes from the scenario of combined thresholds below the 1st quartile from xenophobic, compulsive and traumatic stress scales. It is interesting to note that the danger + fear of contamination scale can be used to find both mild and moderate cases, despite being a larger joint scale.
Even though there are various classification algorithms, such as K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Random Forest, Radial Basis Function or Adaptive Boosting (AdaBoost) that are used for classification process with prominent accuracy and performance, it has been previously reported that with the use of decision tree algorithms, it is possible to rely on a few variables from a health-related problem to stratify patients with a visual tool that empowers clinical decision [41,42,43]. Given the size of our dataset ($$n = 101$$), this constitutes an efficient input for the C5.0 algorithm, which was further confirmed with the sensitivity and specificity analysis. In addition, the sensitivity and specificity analysis showed acceptable results despite the few severe stress cases. Supplementary content shows the analysis of the studied dataset with the algorithms mentioned above.
Currently, machine learning and decision tree algorithms are still in their initial stages of application in the medical field. Recently, Yu et al. published a retrospective study on the conditions to predict metabolic syndrome [57] and Peng et al. published a recent study on the prediction of exacerbation of chronic obstructive pulmonary disease using key indicators, the result had an overall accuracy of $80.3\%$ with a confidence level of $95\%$ [58]. Machine learning has also been used to identify complex patterns in emergency hospital services, which implies intelligence data-driven decisions even under overwhelming circumstances [59].
## 5. Conclusions
This is only a fist approximation based on recent data from healthcare professionals in the northeast part of Mexico [1] and the first study of its kind using the C5.0 decision tree algorithm model on the assessment of stress on self-explainable model basis. Because of the mathematical foundation of these algorithms, it allows not only to obtain a better understanding of a problem, but also to generate accurate predictions. The need of larger datasets and machine learning methodological approaches is well established. Therefore, the impact of applying machine learning algorithms represents a window of opportunity in actual global health and in the decision-making process of developing health policies, based on large-scale studies. For clinical decision-making scenarios, decision trees are specifically useful to simplify assisted diagnosis given the ease of understanding, expanding the scope of computer assisted diagnosis.
This work contributes to mathematical-informed understanding of mental illness and computational psychiatry; thus, forming a diagnostic tool to help in the assessment of patients. In this study, we analyzed healthcare professionals’ answers, as they are one of the most affected sectors in the pandemic [60]. In addition, an expansion of this method with the use of algorithm combinations could provide efficient clinical-assisted tools that could apply to scenarios of the internet of medical things; real-time measurement of compounds or metabolites could be analyzed to decipher medical context, as in this work, or even to reach customizable medicine. In addition to uses from the COVID-19 pandemic, it can be used to understand different stress factors and how they can interfere with performance and the social dynamics in different populations.
## 6. Limitation
The main goal of this work is to show that the mathematical-/computer-based analysis applied to a very specific population allowed to identify patterns in behavior and mental health, despite the fact that the sample size could not be big enough for a formal data analytics study. Applying synthetic methods to increase sample size or to balance the target variable could affect the actual scenario of the data from the population analyzed during a small and very specific period of time, making the founded patterns meaningless. The use of a decision tree to the diagnosed population during the COVID-19 pandemic contributes to the understanding of mental health and behavioral patterns within an emblematic event in human history.
A formal analytics study was added as supplementary material. The application for computer-aided diagnosis is suggested for future work.
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|
---
title: Surface Micro-Patterned Biofunctionalized Hydrogel for Direct Nucleic Acid
Hybridization Detection
authors:
- Paola Zezza
- María Isabel Lucío
- Estrella Fernández
- Ángel Maquieira
- María-José Bañuls
journal: Biosensors
year: 2023
pmcid: PMC10046352
doi: 10.3390/bios13030312
license: CC BY 4.0
---
# Surface Micro-Patterned Biofunctionalized Hydrogel for Direct Nucleic Acid Hybridization Detection
## Abstract
The present research is focused on the development of a biofunctionalized hydrogel with a surface diffractive micropattern as a label-free biosensing platform. The biosensors described in this paper were fabricated with a holographic recording of polyethylene terephthalate (PET) surface micro-structures, which were then transferred into a hydrogel material. Acrylamide-based hydrogels were obtained with free radical polymerization, and propargyl acrylate was added as a comonomer, which allowed for covalent immobilization of thiolated oligonucleotide probes into the hydrogel network, via thiol-yne photoclick chemistry. The comonomer was shown to significantly contribute to the immobilization of the probes based on fluorescence imaging. Two different immobilization approaches were demonstrated: during or after hydrogel synthesis. The second approach showed better loading capacity of the bioreceptor groups. Diffraction efficiency measurements of hydrogel gratings at 532 nm showed a selective response reaching a limit of detection in the complementary DNA strand of 2.47 µM. The label-free biosensor as designed could significantly contribute to direct and accurate analysis in medical diagnosis as it is cheap, easy to fabricate, and works without the need for further reagents.
## 1. Introduction
Nowadays, the interest in developing affordable and mass-producible clinical diagnostics devices is increasing to improve accessibility to healthcare worldwide. Having fast and self-monitoring tests that allow detection onsite is a global interest to avoid hospital crowding and the spreading of contagious diseases. Definitely, the development of portable devices for point-of-care testing (POCT), which allows fast analyte detection with an easily interpretable readout, is crucial for the future [1]. POCT is presently available for a variety of analyses, for example, pregnancy tests, infectious disease tests (such as respiratory infections and sexually transmitted diseases), glucose tests, and several other applications [2,3,4,5,6]. Among various types of sensors, optical biosensors present great advantages over conventional analytical techniques because they enable direct, real-time, and label-free detection of many biological and chemical substances [7,8,9]. Their advantages include high sensitivity, small size, light weight, cost-effectiveness, and the ability to provide multiplexed or distributed sensing. In this context, holographic biosensors offer an appealing approach for label-free optical biosensing. Holographic sensors are gratings, recorded with holographic techniques, of functionalized polymers capable of quantifying the concentration of the target analyte [10]. As a transducer, a holographic pattern is recorded in the sensitive polymer structure, which consists of a 3D periodic structure with alternating strips of differing refractive index (RI), and thus it diffracts the light. After the holographic recording, the polymer matrix, permeable to the target analyte, changes its physical and chemical characteristics, such as lattice spacing and/or refractive index based on its interaction with the target analyte, and produces a change in the diffraction pattern. So far, various hydrophilic and hydrophobic polymers have been used for the fabrication of holographic sensors including gelatin, poly(2-hydroxyethyl methacrylate) (pHEMA), poly(acrylamide) (pAAM), and polyvinyl alcohol (PVA). Their application includes humidity, temperature, and pressure sensors, as well as glucose, lactate, electrolytes, and pH chemical sensors [11]. However, there are very few examples using bioreceptors, mainly antibodies, to achieve holographic biosensing, with their use for nucleic acid hybridizations not being reported. In this work, an Acrylamide/Propargyl acrylate (AM/PA) hydrogel is used, simultaneously, as a matrix for the holographic pattern fabrication and for the functionalization with single-strand thiolated oligonucleotides as a biorecognition element. Hydrogels are attractive platforms for bioanalysis thanks to their ability to retain large amounts of water, acting like biological tissues, optimal for biological interactions [12,13,14,15,16]. Hydrogel-based sensors found numerous applications in clinical diagnostics, biomedical research, environmental monitoring, and food testing [17,18,19,20,21]. Thus, because of their properties, hydrogels have been employed in POC systems for different purposes, which include cell and tissue immunostaining [22], localized photothermal heating [23], microneedle fabrication for drug delivery [24] or for interstitial fluid sampling [25], ion sensing [20], and cocaine, ochratoxin A [26], and glucose detection [27] as well as mRNA detection with hydrogel microparticles [28].
Here, a rapid, specific, and label-free detection system for nucleic acid hybridization based on surface relief holographic gratings was demonstrated. To this aim, the surface of an oligonucleotide probe-functionalized hydrogel was micro-patterned [29]. Briefly, Acrylamide/Propargyl acrylate (AM/PA) hydrogels were obtained using the free radical polymerization (FRP) reaction, both thermally and photochemically activated. Using replica molding of holographic molds [30], a diffractive micropattern on the hydrogel surface was fabricated. It acts as a transducer that diffracts light, producing a measurable signal proportional to the probe–target interaction. The surface micropatterning technique that was used has some advantages: it is easy to manufacture, does not require expensive instrumentation, and allows the creation of patterns of micrometer size. To apply this surface micropatterned hydrogel in biosensing, DNA probes were incorporated into the network as bioreceptors for the target. In particular, covalent functionalization of thiol-modified ssDNA probes in acrylamide-based hydrogels was obtained using a photoclick thiol-ene reaction [31]. Hence, when hybridizing with the complementary strand, the hydrogel underwent changes that were monitored with optical diffraction measurements. The change in the diffraction efficiency of hydrogel gratings was specific for the complementary strand, given that this is the first time that holographic hydrogel gratings are used to detect the direct hybridization of oligonucleotides.
## 2.1. Chemicals
Acrylamide (AM), propargyl acrylate (PA), N, N′-methylenebis (acrylamide) (MBA), Potassium persulfate (KPS), 2,2-Dimethoxy-2-phenylacetophenone (DMPA) and Tetrahydrofuran (THF), sodium phosphate dibasic, potassium phosphate monobasic, sodium chloride, potassium chloride, sodium acetate, sodium citrate, ethylenediaminetetraacetic acid, and Tween-20 were purchased from Sigma–Aldrich (Madrid, Spain). The acetate-Tris (2-carboxyethyl) phosphine buffer (Ac-TCEP, pH 4.5) consists of 25 mM of TCEP, 0.15 M sodium acetate, 0.1 M Ethylenediaminetetraacetic acid, and 0.1 M NaCl in DI water; the phosphate-buffered saline solution with $0.1\%$ (v/v) of Tween 20 detergent (PBS-T, pH 7.4) consists of 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4; and the saline-sodium citrate buffer (SSC1x, pH 7.4) consists of 0.15 M NaCl and 0.015 M sodium citrate. Polydimethylsiloxane (PDMS) Sylgard 184 was purchased from Dow Corning (Wiesbaden, Germany). The oligonucleotides were supplied by Sumilab (Valencia, Spain), and the sequences used are listed in Table S1.
## 2.2. Equipment
Hydrogel photopolymerization and bioreceptor immobilization with UV irradiation was carried out using a UV photoreactor LightOx PhotoReact 365 nm (13 mW/cm2 light power) (Sigma–Aldrich, Madrid, Spain). Hydrogel fluorescence measurements were registered with a fluorescence microarray analyzer SensoSpot (Miltenyi Imaging GmbH, Radolfzell, Germany) (λex = 633 nm, λem = 670 nm). Fluorescence image data processing was performed with the GenePix Pro 4.0 software from Molecular Devices, Inc. (Sunnyvale, CA, USA).
The morphological characterization of hydrogels was carried out using scanning electron microscopy (SEM, Gemini SEM 500 system, Zeiss, Oxford Instruments, Oxford, UK). Hydrogels were completely swollen in distilled water and frozen at −20 °C. Then, they were lyophilized overnight (Telstar Lyoquest freeze-drier, Azbil Telstar Technologies, S. L. U., Terrasa, Spain) to yield completely dry aerogel samples. Finally, dry samples were prepared using sputter coating with a Au layer of about 15 nm (BAL-TEC SCD 005 sputter coater, Leica microsystems, Wetzlar, Germany).
Fourier transform infrared (FT-IR) spectroscopy of lyophilized hydrogels was performed using a Tensor 27 FT-IR-spectrophotometer (Bruker, MA, USA). UV-*Visible spectra* of hydrogels immersed in H2O were collected in an Agilent 8453 spectrophotometer (Santa Clara, CA, USA). For the analysis, hydrogels were polymerized inside an Eppendorf and, after washing, they were placed inside a 1 × 1 cm cuvette filled with H2O.
Swelling behavior studies were carried out with lyophilized hydrogel samples. Samples with a size of approximately 1 cm3 were immersed in PBS-T (10 mL) at room temperature. The weight of the swollen hydrogels was recorded at different times until they were totally swollen (reaching a constant weight). Buffer excess on the surface of the hydrogel was removed with filter paper before weighing. The swelling degree was calculated using Equation [1], where *Wt is* the weight of the hydrogel after being immersed in the buffer during time “t” and W0 is the weight of the lyophilized hydrogel before the immersion. [ 1]Swelling (%)=Wt−W0Wo ×100
## 2.3. Hydrogel Synthesis
Acrylamide/Propargyl acrylate (AM/PA) and acrylamide (AM) hydrogels were prepared using free radical polymerization (FRP) either with photochemical or thermal activation (Scheme S1). Different hydrogel compositions were optimized: AM[25]/PA, AM[8]/PA, AM[25], and AM[8]. The AM[25]/PA hydrogel was prepared by mixing $25\%$ (w/v) of AM monomer, $0.05\%$ (w/v) of MBA crosslinker, and 15 μL of PA co-monomer in 1 mL of distilled water. The AM[8]/PA hydrogel was prepared by mixing $8\%$ (w/v) of AM monomer, $0.25\%$ (w/v) of MBA crosslinker, and 15 μL of PA co-monomer in 1 mL of distilled water. The control hydrogel AM[25] was prepared by mixing $25\%$ (w/v) of AM monomer and $0.05\%$ (w/v) of MBA crosslinker, while the control hydrogel AM[8] was prepared by mixing $8\%$ (w/v) of AM monomer and $0.25\%$ (w/v) of MBA crosslinker. For the synthesis of the hydrogel using thermal activation, potassium persulfate (KPS) at $1\%$ (v/v) was added to the solution as a thermal initiator, and the reaction mixtures were placed in an oven at 60 °C for 90 min. For the synthesis of hydrogels with photochemical activation, 2,2-Dimethoxy-2-phenylacetophenone (DMPA) photoinitiator at $1\%$ (w/v) was added to the reaction mixture and hydrogels were polymerized irradiating at 365 nm in a UV photoreactor (13 mW/cm2) for 10 min. Once polymerized, the hydrogels were washed with immersion in distilled water for at least 2 h using three times fresh water to ensure that non-polymerized monomers were eliminated. The obtained hydrogels were stored completely swollen in distilled water at 4 °C.
## 2.4. Probe Immobilization and Hybridization Assay
For potential biosensing applications, AM/PA hydrogels and their control systems, AM hydrogels, were covalently functionalized with a thiol-modified oligonucleotide probe, and hybridization capacity was tested with a fluorescence-labeled target. All probes used are listed in Table S1. The bioreceptor immobilization was studied either during or after the hydrogel synthesis. In the first approach, after monomers and crosslinker homogenization in water, 1 μM of Probe 1 and $1\%$ (w/v) of DMPA photoinitiator in water were added to the mixture, and the solution was irradiated at 365 nm (13 mW/cm2) for 10 min. In this strategy, polymerization and bioreceptor immobilization were carried out simultaneously in one step. In the second approach, the already thermally synthesized hydrogels were cut into squares (0.5 × 0.5 cm) and immersed in 100 µL of 1 μM of Probe 1 and $1\%$ (w/v) of DMPA photoinitiator in THF:Ac-TCEP 1:1. Then, the hydrogels were irradiated at 365 nm (13 mW/cm2) for 30 min. In both approaches, after the immobilization step, the hydrogels were placed on an oscillator plate and washed overnight with PBS-T.
For the hybridization assays, Probe 1-functionalized hydrogels of 0.5 × 0.5 cm were placed in a transparent ELISA (enzyme-linked immunosorbent assay) plate and equilibrated in 250 µL of SSC1x for 24 h. Then, SSC1x was discarded, and the hydrogels were incubated with 50 µL of Cy5-labeled, complementary strand Target 2, in SSC1x, at growing concentrations (0; 0.2; 0.4; 0.8; 1; 1.5; and 2 µM) for one hour at 37 °C. Fluorescence signals were collected immediately after the hybridization and after overnight washing with SSC1x. Control hydrogels having immobilized a non-complementary sequence (Probe 2) were also hybridized as described.
## 2.5. Surface Micropattern Fabrication
Surface microstructures made of Polyethylene terephthalate (PET) were fabricated using the direct laser interference patterning (DLIP) technique [32]. The DLIP system was equipped with a frequency quadrupled Q-switched laser head (TECH-263 Advanced Laser-export Co., Ltd., Moscow, Russia) with a maximum pulse energy of 50 μJ, operating at a wavelength of 263 nm and with a pulse duration shorter than 3 ns. A fluence of 0.09 J/cm2 was used to obtain PET masters with a period of approximately 4 μm. The structural features of the original PET master were characterized with a 3D optical profilometer (Sensofar, PLu neon, Terrasa, Spain). Hydrogel surface micropatterns were fabricated using the replica molding technique (REM) from the original PET master. The micro-pattern obtained on the hydrogel surface was observed with optical microscopy (OM, Leica microsystems, MZ APO, Wetzlar, Germany).
Micropatterns were obtained in the hydrogel surface using replica molding (Scheme 1). Firstly, the original PET micropattern was copied onto PDMS. The PDMS solution was poured onto the PET surface, a vacuum was applied for 10 min to aid the solution-pattern adhesion, and then it was placed in the oven at 60 °C for 2 h. Secondly, the PDMS negative pattern was transferred onto the hydrogel surface. Initially, pre-polymeric solutions with monomers and crosslinkers of hydrogels AM[25]/PA, AM[25], AM[8]/PA, and AM[8] were stirred for 20 min until homogenization. Then, KPS was added, and the solution was sonicated for 2 min. The solutions were poured onto different PDMS micropatterned surfaces, a vacuum was applied for 10 min, and then they were placed in an oven at 60 °C for 1.5 h. Once polymerized, they were peeled off and washed with immersion in distilled water for at least 2 h using three times fresh water to ensure that non-polymerized monomers were eliminated. The micropatterned hydrogels were stored completely swollen in distilled water at 4 °C.
The micropatterns obtained on the PDMS and the swollen hydrogel surface were observed with optical microscopy (OM, Leica microsystems, MZ APO, Wetzlar, Germany). Surface pattern characterization was also carried out with an optical set-up as shown in Figure 1. From the bottom, a continuous green laser beam (532 nm, 100 mW) is attenuated and orthogonally directed to the sample holder using a mirror. The sample holder is a 3D-printed platform provided with a pinhole and patterned lanes that allow the x–y movement of a 96-well ELISA plate so the laser beam can be unequivocally directed toward every well. Then, movable silicon photodiodes are placed after the sample holder to record the intensity of the different laser beams (incident or diffracted). A concave spherical lens ($f = 30$ mm) was placed on the top of the 96-well plate to focus the diffracted beams produced by the hydrogel micropatterns.
Diffraction efficiency (DE%) of the micropatterns was calculated with Equation [2]:[2]DE (%)=I1I0 × 100 where I0 was the intensity of the zero-diffraction order and I1 was the intensity of the first diffracted order.
## 2.6. Label-Free Hybridization Assay
Bioreceptor immobilization in micropatterned hydrogels was carried out in two steps. Firstly, thermally polymerized micropatterned hydrogel (AM[25]/PA) was functionalized with 5 µM of Probe 1. For that, micropatterned hydrogels were cut in squares (0.5 × 0.5cm) and treated with 100 µL of a 5 μM solution of Probe 1 and $1\%$ (w/v) of DMPA photoinitiator in THF:Ac-TCEP 1:1. Then, the hydrogels were irradiated at 365 nm (13 mW/cm2) for 30 min. The functionalized micropatterned hydrogels were washed overnight with PBS-T to eliminate the non-covalently attached probes. For the label-free hybridization assays, the probe-functionalized micropatterned hydrogels were placed in separated wells of a transparent ELISA plate and equilibrated in 250 µL of SSC1x. The day after, SSC1x buffer solution was replaced with a fresh one and the initial diffraction efficiencies (DEi) of the hydrogels were obtained using the optical set-up (Figure 1) and Equation [2]. Hybridization assay was performed using incubation of the hydrogels with growing concentrations of Target 1 (0; 2; 5; 10; and 25 µM) in 50 µL SSC1x for one hour at 37 °C. The hybridization experiment was also carried out with the AM[25]/PA hydrogel functionalized with a non-complementary, thiol-bearing oligonucleotide sequence (Probe 2), and hybridized at 10 and 25 µM of Target 1, as a negative control. Then, the hydrogels were washed overnight with SSC1x to be sure that all the non-specifically bound targets were removed. The final diffraction efficiencies of the hydrogels (DEf) were obtained using the optical set-up (Figure 1) and Equation [2]. The relative diffraction efficiency was used to characterize the response of the hydrogel to the target concentration, as described in Equation [3]:[3]RDE%=DEf−DEiDEi×100 where RDE is the relative diffraction efficiency, DEi is the initial diffraction efficiency (after the equilibration step with SSC1x), and DEf is the final diffraction efficiency (after incubation and washing steps) for the first diffraction order. All experiments were repeated three times.
## 3.1. Optimized Hydrogel Compositions
First, hydrogel composition was optimized from both a physical and a chemical point of view. Polyacrylamide hydrogels are one of the most utilized materials in the synthesis of holographic and photonic hydrogel due, among other things, to their excellent optical properties [11]. AM was chosen as the main monomer for the synthesis of the hydrogel networks and MBA as one of the most common crosslinkers for polyacrylamide. The PA co-monomer was incorporated to introduce the alkyne moiety, which was necessary for the further thiolated-probe covalent attachment through thiol-yne photo-click coupling chemistry [33]. Apart from reaching adequate physical and optical properties such as good porosity, transparency, and low optical background, the chemical formulation was adapted to increase the immobilization density of the biorecognition Probe 1. For that, different ratios of monomer (AM), co-monomer (PA), and crosslinker (MBA) were assayed. All the assay compositions are shown in Table S2. As expected, all the hydrogels were transparent with almost zero absorbance at the working wavelength of our system (532 nm). Figure S1 shows the UV-*Visible spectra* of all hydrogels. However, not all the synthesized hydrogels showed the consistency required for part of our purposes: the fabrication of surface relief diffraction grating using replica molding. The requirements of hydrogels for potentially yielding suitable gratings include the following: they must adapt the form of the container used for the polymerization and they need to be manipulable, easy to cut, not brittle, and to keep the macroscopical form after washing and swelling. The consistency of the different synthesized hydrogels polymerized with thermal activation is indicated in Table S2. In addition, Figure S2 shows photographs of hydrogels with different consistencies.
AM[25]/PA and AM[8]/PA showed the best consistency and potential to be used as surface relief gratings for DNA hybridization, so they, and their counterpart controls without PA, were selected for further optimization. The selected compositions are shown in Table 1 and photographs of the hydrogels are shown in Figure S3. As the activation process for polymerization can affect the final properties of the hydrogel, i.e., porosity, swelling, etc., the polymerization was carried out following two different activation processes: thermally and photochemically.
The morphology of the optimized hydrogel compositions that contain PA was comparatively observed for thermal and photochemical activation, as poor homogeneity has been previously reported in hydrogels polymerized with UV-light [34,35,36]. For that, lyophilized hydrogels were analyzed with SEM (Figure 2 and Figure S4). As can be observed in the SEM micrographs, the thermal activation provided higher homogeneity and porosity to the hydrogel network for both the AM[25]/PA and AM[8]/PA compositions, although both activation procedures resulted in adequate porosity levels.
As the hydrogels obtained with thermal activation showed the best homogeneity based on the SEM, swelling behavior studies of these hydrogels were carried out to test the hydrogel buffer absorption capacity. In Figure S5 of the Supplementary Materials, the swelling studies show how the chemical composition affects the hydrogel water uptake. Hydrogels AM[8] demonstrated a higher swelling degree than hydrogels AM[25]. This is probably because the larger quantity of monomer used in AM[25] hydrogels counteracts the higher crosslinker degree present in AM[8] hydrogels. Equally, the propargyl acrylate co-monomer contributed to the polymer swelling capacity. PA reduces the buffer absorption in AM[25]/PA and AM[8]/PA hydrogels in comparison to AM[25] and AM[8] reference systems, probably due to the higher hydrophobicity of the alkyne moiety. However, in both compositions, the swelling capacity was over $400\%$. Thus, the optimized compositions were tested for subsequent bioreceptor immobilization and surface micropatterning.
## 3.2. Probe Immobilization and Hybridization Assay
AM/PA hydrogels and their corresponding controls (without PA) were covalently functionalized with a thiol-bearing oligonucleotide probe for potential biosensing applications. The oligonucleotide probe acts as the specific biorecognition element for its complementary sequence (target). In the hydrogel formulation, the propargyl acrylate (PA) co-monomer had a C–C triple bond that was expected to enhance the binding with thiol-probes, in comparison to the control system [37]. Thiolated probes incorporation was carried out using the thiol-yne photoclick coupling reaction with UV irradiation at 365 nm (Scheme S2). Previous work by our group performed in microarray format had demonstrated that these irradiation conditions did not affect the probes stability and bioavailability to hybridize with the complementary strands [38]. Firstly, the thermally polymerized AM[25]/PA and AM[8]/PA hydrogels were biofunctionalized as the thermal activation yielded hydrogels with higher homogeneity and porosity, and, in addition, they showed a high swelling degree. Hydrogels were functionalized with Probe 1, complementary to the target, and, additionally, with Probe 2, which was a thiolated, non-complementary sequence. In addition, hydrogels without PA, AM[25] and AM[8], were also submitted to functionalization with Probe 1 to assess the role of PA in the probe immobilization process. The immobilization was carried out in 1:1 THF:Ac-TCEP, and TCEP was added to facilitate the reduction of disulfide bonds established between the thiolated probes. After probe immobilization, a fluorescence-labeled target sequence was used for hybridization assays to verify the successful incorporation of the thiol probe and its bioavailability for the specific hybridization. Therefore, thermally activated, probe-biofunctionalized hydrogels AM[25]/PA, AM[25], AM[8]/PA, and AM [8] were hybridized with increasing concentrations of the Cy5-labeled target sequence (Target 2) for 1h at 37 °C, and the fluorescence was registered after washing overnight (Figure 3a,b). As a control, a fluorescence signal was also registered after hybridization in several cross-section pieces of the hydrogels AM[8]/PA and AM[25]/PA to demonstrate that target 2 could reach the probe within 1h (Figure S7). Figure 3a,b show that significantly higher fluorescence signals (4-fold to 5-fold) were observed for AM[25]/PA and AM[8]/PA hydrogels compared to their control systems AM[25] and AM[8] when they were functionalized with Probe 1, complementary to the target. As expected, the introduction of the PA co-monomer allowed a much more effective probe immobilization, thanks to the thiol-yne coupling chemistry, increasing the probe loading in the hydrogels. Therefore, the immobilization strategy was successful for both AM[25]/PA and AM[8]/PA hydrogels. Moreover, a higher fluorescence signal was measured for the AM[25]/PA hydrogel in comparison to the AM[8]/PA hydrogel. In addition, almost no fluorescence was observed when AM[25]/PA and AM[8]/PA hydrogels were functionalized with Probe 2, having the non-complementary sequence, which demonstrated that specific hybridization was taking place, and non-specific binding was negligible inside the hydrogel supports. As polymerization could be also activated photochemically using the same wavelength needed for the thiol-yne coupling reaction, a second strategy was assessed for the hydrogels biofunctionalization: a one-step process that consisted of the immobilization of the thiolated probe during hydrogel polymerization. In this strategy, the thiol-yne photoclick coupling reaction and acrylamide polymerization, using DMPA as a photoinitiator, were triggered with UV irradiation at the same time. Therefore, pre-polymeric solutions of AM[25]/PA and AM[25] hydrogels were mixed with 1 μM of complementary Probe 1 and DMAP, and then irradiated at 365 nm for 30 min. Additionally, a control experiment was carried out with AM[25]/PA hydrogel and the non-complementary Probe 2. Once hydrogels were washed and equilibrated with SSC1x, hybridization assays with the Cy5-labeled target sequence (Target 2) at increasing concentrations, as above, were carried out, and fluorescence was registered after washing (Figure 3c). In this case, the highest fluorescence signal was also observed for hydrogels AM[25]/PA functionalized with Probe 1. However, the high fluorescence observed in the hybridization curve of hydrogel AM[25] showed that the thiolated probe resulted in being immobilized without the presence of (PA) co-monomer. This is due to the thiol-acrylate coupling reaction which follows the same principle as thiol-yne photocoupling reaction [39]. Figure S6 shows the IR spectrum of a lyophilized AM[25] hydrogel, which showed a spectral profile compatible with the presence of residual unreacted acrylamide groups. However, even in this case, the presence of PA increased the hydrogel probe immobilization capability. As before, AM[25]/PA hydrogels biofunctionalized with Probe 2 did not show a significant fluorescence signal after hybridization, which reveals that non-specific binding is also avoided with the one-pot functionalization strategy. Comparing the two strategies for AM[25]/PA hydrogels functionalized with Probe 1, complementary to Target 2, the ones biofunctionalized after polymerization (Figure 3a) showed two-fold the fluorescence signal of the ones biofunctionalized during the polymerization (Figure 3c). Probably, in the case of the biofunctionalization after the polymer synthesis, a larger number of bioreceptors are introduced and, in addition, these probes are more accessible to the target. Thus, thermally polymerized AM[25]/PA hydrogels biofunctionalized after their synthesis showed the best performance for the detection of the complementary target using fluorescence.
## 3.3. Surface Micropattern Fabrication and Characterization
For the surface micropatterning of hydrogels, PET masters were used to obtain a negative in PDMS which was in turn replicated with the above optimized hydrogel compositions. The fabricated PET master was characterized using confocal microscopy (Figure 4a). The profile obtained from the confocal images shows that the gratings have a period of 4 µm and a depth of 2.1 µm. The PDMS negative copy was characterized with optical microscopy where, as expected, a period of 4 µm was observed, which confirmed the correct replica of the PET master (Figure 4b). In addition, the original PET master and its PDMS copies were irradiated with a continuous green laser at 532 nm using the optical set-up described in the Materials and Methods section (Figure 1), and the diffraction efficiency (DE%) was calculated using Equation [2]. Both fabricated microstructures showed good diffraction efficiency.
Hydrogel surface micropatterning was realized, during the polymerization, for the optimized compositions using replica molding. The thermally activated curing process, for Acrylamide/Propargyl acrylate hydrogels, took place in 1.30 h, supposedly a sufficient time for obtaining a good copy of the original PET microstructure. For the AM[25]/PA and AM[25] compositions, a good copy of the microstructure was obtained during the thermal curing. Figure 4c shows the optical microscopy image of the AM[25]/PA hydrogel grating which correctly replicated the pattern. It should be noticed that a higher period is observed in the hydrogel compared to the PDMS master, as the first one is swollen in water. The diffraction of the AM[25]/PA hydrogel, thermally polymerized, was also evaluated after its irradiation with a continuous green laser at 532 nm using the optical set-up of Figure 1. Figure 4d shows the diffraction pattern of the AM[25]/PA hydrogel. Zero, first, and second diffractive orders are present and distinguishable, so it could be very useful for label-free biosensing based on diffractive measurements. The diffraction efficiency (DE%) was calculated for the first diffraction order using Equation [2], resulting in 4.6 ± 0.5, 9.8 ± 0.5, and 1.1 ± 0.2, for PET, PDMS, and AM[25]/PA gratings, respectively. Lower values were observed in comparison with the PET and PDMS master, which was expected as the hydrogel has a watery nature and the PET and PDMS are plastics. The replica of the microstructure using thermal activation was not possible for the AM[8]/PA and AM[8] compositions. This was attributed to the amount of monomer used, which was too low to achieve the right viscosity for the replication process. On the other hand, trials of the grating replica molding using photochemical polymerization resulted unsuccessful, since the polymerization proceeded too fast to permit the correct molding.
On the other hand, by varying UV photoreactor parameters individually for each hydrogel composition, such as UV light power and irradiation time, hydrogel surface micropatterns were successfully obtained for all the optimized hydrogel compositions. However, the peeling-off of the hydrogel surface pattern copied from the PDMS, using photochemical activation, was cumbersome, and thus 20 μL of glycerol was added to promote the detachment. For the (AM[25]/PA), AM[25], and (AM[8]/PA) hydrogels, micro-patterned replicas were obtained using 15 min of UV irradiation and 10 mW/cm2 of light power, whereas for the AM[8] hydrogel, 10 min of UV irradiation and 0.6 mW/cm2 of light power were used.
Although it was possible to replicate the grating using both thermal and photochemical activation, it was concluded that better reproducibility in surface micropatterns copies was obtained for the AM[25]/PA hydrogel composition during thermal curing. Thus, the AM[25]/PA hydrogel composition showed the best results in terms of micropattern fabrication and biorecognition properties. Consequently, it was chosen for further label-free biosensing studies.
## 3.4. Label-Free Biorecognition
To evaluate the potential label-free sensing of surface relief gratings of the probe-functionalized hydrogels, a hybridization assay was performed using unlabeled probes. Firstly, surface microstructures were obtained for the AM[25]/PA hydrogels during the thermal curing as, according to previous results, this hydrogel composition and reaction conditions produced the hydrogel with the best properties for the selective detection of targets with fluorescent sensing, and, in addition, they yielded micropatterned hydrogels that were able to correctly diffract the light. Therefore, the same conditions were expected to produce hydrogels with the best properties for the label-free detection of targets. After the hydrogel synthesis, the AM[25]/PA hydrogel was functionalized with 5 μM of Probe 1. The functionalized hydrogel patterns were placed in a Petri dish and washed overnight with SSC1x buffer. The day after, they were cut into squares (0.5 × 0.5cm) and positioned in separated wells of a transparent ELISA plate with 250 µL of SSC1x. The size of the hydrogel was chosen to perfectly fit within the ELISA wells and, thus, avoid the crushing of their walls and their free flotation. Diffraction efficiencies (DE%) of the functionalized hydrogel patterns were registered using the optical set-up (Figure 1) at controlled conditions (RH 45 ± $5\%$ and 24 ± 1°C). Ambient conditions were reached with domestic air conditioning and humidifier systems. Figure S12 shows that signals were stable for at least 30 min. Therefore, the signal was not affected by the incidence of the focused laser beam and slight delays in the reading time would not affect the obtained results. After that, the hybridization assay was performed in triplicate. Hydrogels were incubated with a growing concentration of Target 1 (0; 5; 10; and 25 µM) in 50 µL of SSC1x for 1h at 37 °C. After overnight washing with SSC1x, DE was registered at 532 nm and RDE was calculated according to Equation [3] to assess the direct detection of complementary DNA-sequence (Target 1) (Figure 5). As a control experiment, the AM[25]/PA hydrogel was also functionalized with a non-complementary DNA sequence (Probe 2), and hybridization assays were performed with Target 1 at 25 µM following exactly the same procedure. A gradual decrease in the DE% with increasing concentration of the unlabelled target was observed for the (AM[25]/PA) hydrogel functionalized with Probe 1, while for the control system, having immobilized the non-complementary sequence Probe 2, no tendency was observed. The DE (%) data obtained with probe 1 can be best fitted using a Hill 1 correlation curve, obtaining a correlation coefficient of R2 = 0.991. The RDE (%) data obtained with Probe 1 can also be best fitted using a Hill 1 correlation curve, obtaining a correlation coefficient of R2 = 0.997. The limit of detection (LOD) of 2.47 µM was calculated from the RDE (%) curve as the concentration associated with the mean signal of ten blank measurements plus three times their standard deviation. Thus, it was possible to detect the analyte in the range from 2.47 to 10 μM using the micropatterned hydrogels as an optical transducer.
Therefore, the label-free biosensing assay using unlabeled probes, performed for (AM[25]/PA) hydrogels with the surface micropattern, showed excellent preliminary results. The LOD of DNA in our system is higher lower than most of the hydrogel-based systems described in the literature [40]. However, most of the approaches are based on labels or/and elaborate DNA architectures. DNA hybridization with hydrogel has also been explored for actuators and other purposes [41], but poor consideration of the analytical performance is contemplated in these studies. Baba and co-workers have reported the use of diffraction gratings for the label-free detection of DNA with very low LOD, but the DNA was amplified during the analysis [42]. Our results are very promising, but the diffraction efficiency calculated for the obtained hydrogel surface-micropattern is not high. Hence, further improvements in the micropattern fabrication can be realized to increase the initial DE% and, accordingly, the sensitivity for the analyte detection. These improvements involve the fabrication of thinner surface relief gratings as well as the replication with lower-period PET masters. Although fabrication of these gratings can be challenging, technologies such as two-photon polymerization can be used for fabricating 2D/3D microstructures with high accuracy [43,44]. In addition, quicker data acquisition and automatization of hydrogel SRGs will allow for increasing the number of replicates and lowering the experimental error. Despite those facts, it was possible to directly detect the analyte with good selectivity and sensitivity, given that this is the first time that surface micro-patterned hydrogels were used to directly detect hybridization events.
## 4. Conclusions and Future Outlook
Optical biosensors are emerging for point-of-care testing (POCT) as they present some advantages such as increased sensitivity and suitability for being integrated into a compact device with the purpose of being utilized out-of-the-lab. Overall, line-like periodic microstructures were successfully fabricated on a bioresponsive hydrogel surface and used as transducers for converting the analyte–bioreceptor binding into a measurable optical signal. The planned approach for the covalent immobilization of the bioreceptor probes had notable outcomes. Furthermore, different bioreceptors with thiol terminal groups could be used, depending on the analyte to be detected. Accordingly, the developed biosensor can sense multiple analytes. Results obtained from the label-free biorecognition assay have shown a direct correlation between the diffraction efficiency measured and the target concentration. The label-free biosensor as designed could significantly contribute to direct and accurate analysis in medical diagnosis, being cheap, easy to fabricate and working without the need for further reagents. To fully achieve this, further aspects should be considered, such as the minimization of biofouling of hydrogels when they are immersed in real fluids. This can be achieved by tuning the composition of hydrogels, for instance, using polyacrylamide copolymers or zwitterionic moieties [45].
## Figures, Scheme and Table
**Scheme 1:** *Micropatterning process steps for hydrogel surface structure manufacturing.* **Figure 1:** *(a) Optical set-up used for diffraction efficiency measurement. (b) Analyte sensing principle: after analyte biorecognition, the intensity of zero and first diffraction order changes, and thus the diffraction efficiency.* **Figure 2:** *Porosity observed with SEM (scanning electron microscopy) for selected hydrogel compositions (AM(25)/PA) and (AM(8)/PA) prepared using thermal and photochemical activation.* **Figure 3:** *Fluorescence intensity measured after hybridization with increasing concentrations of labeled Target 2 (a) in AM(25)/PA and AM(25) hydrogels and (b) in AM(8)/PA and AM(8), biofunctionalized with Probe 1 or 2 after their polymerization; and (c) in AM(25)/PA and AM(25) hydrogels with Probe 1 or 2 covalently attached during the polymerization step. The Probe 1 sequence was complementary to Target 2, while the Probe 2 sequence was non-complementary; both probes bear the thiol moiety needed for thiol-yne or thiol-ene coupling. Details of the obtained fluorescence signals are shown in Section S-VI of the Supplementary Materials (Figures S8, S9, S10 and S11).* **Figure 4:** *(a) Images and cross section profile of the microstructure PET (polyethylene terephthalate) master fabricated with Direct Laser Interference Patterning obtained using a 3D Optical Profilometer (Sensofar, Spain). Optical microscopy image of (b) the negative micropattern copied in PDMS using thermal curing and (c) the Surface Relief Grating (SRG) replicated in (AM(25)/PA) hydrogel from the PDMS micropattern. (d) Optical diffraction observed for the SRG, obtained in (c), measured with green laser irradiation (λ = 532 nm) after complete swelling in distilled water.* **Figure 5:** *Change in the diffraction efficiency of SRG made of probe-functionalized hydrogels after hybridization with Target 1. (a) Diffraction efficiency (DE) measured at λ = 532 nm and, (b) relative diffraction efficiency (RDE) of SRG functionalized with Probe 1 (blue) and Probe 2 (orange) hybridized with increasing concentrations of Target 1 (complementary to Probe 1). The DE changes with the amount of Target hybridized only for the SRG hydrogels functionalized with the complementary strand.* TABLE_PLACEHOLDER:Table 1
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