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In SU.VI.MAX 2, subjects reported their comorbidities by a self-administered questionnaire. Several chronic conditions were identified in 12 categories: cardiovascular diseases or other cardiovascular impairments; cancers; respiratory diseases or impairments; diseases or impairments related to the ears, nose, or throat; digestive diseases or impairments of digestive function; diseases or impairments related to bones and joints; urinary/genital diseases or impairments; endocrine or metabolic diseases; eye-related diseases or impairments; neurological/psychiatric diseases or impairments; infectious diseases; and other health problems. The year of diagnosis was collected, as was the presence of a medication or actual treatment for the condition. On the basis of the potential relevance of conditions for a participant’s overall health status, one of the authors (CPG), a geriatrician, selected 19 chronic conditions: hypertension, heart failure, arrhythmias and palpitations, ischemic cardiovascular/vascular impairments, respiratory impairments, hearing impairments, ear, nose and throat impairments, digestive impairments, vertebral diseases, osteoporosis, arthritis and rheumatism, adenoma or prostatic hyperplasia, thyroid disease, diabetes, vision impairments, anxiety/depression, sleeping troubles, memory impairments and cancer. Multimorbidity was defined as having at least 2 of these 19 chronic conditions.
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HRQoL was assessed (2007–2009) at enrollment by using the French version of the Medical Outcome Study Short Form 36 (SF-36) and the French version of the Duke Health Profile (Duke) . For each questionnaire, two main dimensions were retained: the physical component (SF-36: physical component summary [PCS]; Duke: Phys) and the mental component (SF-36: mental component summary; Duke: Ment). The scores were linearized from 0 to 100 to compare scores between the questionnaires: (0: worst HRQoL, 100: best HRQoL).
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We performed an exploratory factor analysis of the 19 morbidities to determine multimorbidity patterns. We identified the tendencies of diseases to co-occur by selecting sets of variables with potentially common underlying causal factors. Factor analysis was used with a tetrachoric correlation matrix because conditions were expressed as binary variables . The extraction of the multimorbidity patterns involved the principal factor method, and the number of factors to extract was determined by the scree-test , with minimal eigenvalue of 1.0 (Kaiser criterion). A condition with loading factor > 0.25 had greater importance in a pattern, which indicates a stronger association . The Kaiser-Meyer-Olkin (KMO) method was used to estimate the adequacy of the data for our model on factor analysis. This parameter takes values between 0 and 1, which, with a greater goodness of fit, are close to 1. Cumulative variance of the sample was determined to describe the variance of the diagnostic data explained by the patterns. An oblique rotation (Oblimin) was applied to correlate factors with one another to obtain a better interpretation of the analysis factor. The results of this analysis could be interpreted as multimorbidity patterns (i.e., diseases that are non-randomly associated with each other).
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For each participant, a multimorbidity score was calculated for every identified pattern. These individual scores corresponded to the sum of each loading factor from the factor analysis multiplied by the presence (= 1) or absence (= 0) of each condition. The mean of each multimorbidity score was calculated for every pattern. The higher the multimorbidity score, the greater the number and association of multimorbidities.
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We hypothesized that multimorbidity scores’ impact on HRQoL was different according age and gender. Significant interactions between age groups and multimorbidity scores and between gender and multimorbidity scores confirmed this hypothesis. So multiple linear regression used to examine the association between multimorbidity and HRQoL scores (PCS and Phys; MCS and Ment) were realized for each age group and by gender. Models were adjusted for others sociodemographic variables which p<0.2 in bivariate analyses. Analyses involved use of SAS 9.4. Two-sided p < 0.001 was considered statistically significant (after Bonferroni correction).
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Among the 5647 adults aged 55 years or older (51.5% women), the mean (SD) age was 63.2 years (4.9); 71.2% were retired and 66% declared a good general health status (data not shown). For participants 60–64 years old, 12.8% of women were working as compared with 18.7% of men (Table 1). For participants 55–59 years old, 36.0% of men were retired as compared with 23.6% of women. Among those ≥ 70 years old, 46.3% of men had a university education and 10.9% were single as compared with 36.9% and 35.3% of women, respectively. Among those 65–69 years, 29.6% of men never smoked as compared with 64.1% of women.
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More than 87% of the participants reported having at least one chronic condition, and more than 63% had at least two chronic conditions. The most frequent chronic diseases were arthritis/rheumatism, vision impairments and hypertension, with an overall prevalence of 41.0%, 40.5% and 23.9%, respectively (Table 2). For women, the most represented chronic conditions were arthritis and rheumatism (47.7%), vision impairments (43.3%), anxiety/depression (24.6%) and sleeping troubles (25.3%) (Table 2). For men, the most represented chronic conditions were vision impairments (37.5%), arthritis and rheumatism (33.8%) and hypertension (27.4%). The proportion of multimorbidity was greater for women than men (67.3% and 60.0%, respectively). For men and women, presence of an increasing number of chronic conditions increased with increasing age (Table 2). The proportion of each morbidity increased with age for men (hypertension– 55–59 years: 20.3%, ≥ 70 years: 32.6%; arthritis and rheumatism– 55–59 years: 22.7%, ≥70 years: 43.5%) and women (hypertension– 55–59 years: 16.9%, ≥70 years: 26.6%; arthritis and rheumatism– 55–59 years: 38.7%, ≥70 years: 60.9%), except for 4 conditions for which no increase was observed (hearing impairment, nose and throat impairments, digestive impairments, osteoporosis, sleeping troubles). The prevalence of only anxiety/depression tended to decreased with age for men (55–59 years: 15.3%, ≥70 years: 9.9%) and women (55–59 years: 26.9%, ≥70 years: 22.2%), but this decrease was greater for women. Other chronic conditions, such as sleeping troubles, had a higher frequency among women than men regardless of age group [men (55–59 years: 13.1%, ≥70 years: 14.6%) and women (55–59 years: 26.6%, ≥70 years: 27.2%)].
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We identified two multimorbidity patterns according to the eigenvalues of the factor analysis and the results of the scree-test, which explained 62.2% of the total variance (Table 4). The KMO value was 0.61, which was considered acceptable adequacy. Three conditions were less associated with the two patterns, A and B, because of factor loading < 0.25 (hearing impairment, vision impairments and cancer). With the threshold of 0.25, the variable respiratory impairments were correlated with both patterns (factor loading 0.26 and 0.30).
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We established a pattern A and B score for each participant corresponding to the overall impact of these chronic conditions (mean [SD] score 0.73 [0.69] and 0.27 [0.40], respectively) (data not shown). The mean pattern A score for men was 0.55 [range: -0.29; 3.63] and for women 0.89 [range: -0.15; 4.31]. The mean pattern B score for men was 0.38 [range: -0.46; 2.70] and for women 0.17 [range: -0.54; 3.24].
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Mean pattern A scores for men were 0.46, 0.54, 0.57 and 0.63 for ages 55–59, 60–64, 65–69 and ≥70, respectively, and for women 0.83, 0.87, 0.94 and 1.09, respectively. Mean pattern B scores for men were 0.25, 0.32, 0.46 and 0.52, respectively, and for women 0.13, 0.15, 0.22 and 0.27, respectively.
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Tables 5 and 6 show the linear regression analysis of the association of multimorbidity and HRQoL scores by age groups for men and women, adjusted for covariates (BMI, professional status, education level, family status and smoking status). For men, after adjusting for covariates, increased pattern A score was associated with reduced HRQoL score for all four dimensions studied whatever the age group (Table 5). Increased adjusted pattern B score was associated with only Duke Phys dimension (60–64 years: -5.3; p<0.0001) and the association tended to decrease with age (55–59 years: -5.7; 60–64 years: -5.3; 65–69 years: -3.4; ≥70 years: -3.5).
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The strongest association was between an increase in both morbidity scores and reduced HRQoL measured by the DUKE questionnaire. This association remained strong after adjusting for covariates for the pattern A but not pattern B score (e.g., DUKE Ment, 55–59 years: adjusted pattern A score: -11.5, p<0.0001; adjusted pattern B score: not significant).
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For women, the same results were found (Table 6). Increased adjusted pattern A score was associated with reduced HRQoL score for all four dimensions studied whatever the age group. Increased adjusted pattern B score was associated with some age groups for the SF-36 PCS (55–59 years: -3.9, p<0.0001; 60–64 years: -2.8, p<0.0001) and the DUKE Phys (55–59 years: -7.8, p<0.0001; ≥ 70 years: -9.0, p = 0.0006). This association tended to decrease for the SF-36 PCS (55–59 years: -3.9; 60–64 years, -2.8; ≥70 years: -2.3), whereas this decrease was maximal for the extreme age groups for the DUKE Phys (55–59 years: -7.8; ≤70 years: -9.0).
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In a sample of 5647 subjects aged 55 years or older, an exploratory factor analysis allowed for identifying two multimorbidity patterns: A and B. On exploring the cross-sectional association of individual multimorbidity and HRQoL scores (assessed by the SF-36 and DUKE questionnaires), pattern A explained more of the HRQoL score decrease than pattern B, with mean multimorbidity scores of 0.73 and 0.27 for the two patterns, respectively. HRQoL was better for men in general, and mental dimension scores tended to increase with age and physical dimension decreased.
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The characteristics of the patterns we extracted were similar to those observed previously . The most important conditions in pattern A concerned mental illness and bone impairments. Anxiety is associated with depression problems . These conditions could induce sleep disorders, thereby causing some potential memory impairments that may explain the mental part of this pattern. Nevertheless, depression problems may increase with increasing pain and significant limitations in movement. Regarding the relationship between mental disorders and bone disorders, the World Mental Health Surveys, conducted across 17 countries, found greater risk of developing mood disorders and anxiety with presence of osteoarthritis . Being in constant pain, with limited movement, may lead to doubts about the ability to be autonomous and thus a negative self-image inducing anxiety and depressive disorders.
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Pattern B was found in previous studies . Its composition is largely associated with cardiovascular and metabolic disorders. Blood vessels are involved in diabetes, so its presence in this pattern is justified. However, prostate impairments constitute a non-cardiovascular chronic condition, so their inclusion in this pattern is more questionable. Older men mainly have this pattern, so gender may explain the mix of prostate and cardiovascular impairments in this pattern without any relationship between them other than shared risk factors.
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We have observed a reduction in the proportion of anxiety and depression with increasing of age. The onset of physical illness common in older persons has been shown to increase proportion of depression and anxiety . However, the literature showed that studies that have examined the incidence of anxiety or depression across the life span have inconclusive results. Often, anxiety or depression measures depend on cohort characteristics such as age, cultural background…. Our sample is constituted of healthy voluntary participants who may represent a healthy cohort bias.
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The impact on HRQoL was greater with pattern A than B multimorbidity score. The greatest effects were found with the Duke Health Profile. A high pattern A score was associated with a lower score in both mental and physical dimensions of HRQoL and a high pattern B score was essentially associated with a lower score in the physical dimension. Indeed, no significant association was found between mental HRQoL score and pattern B score. Pattern B is related to cardiovascular and metabolic disorders. The literature showed that cardiovascular diseases were associated with a reduced in physical and mental dimensions of HRQoL . In addition, a strong association was found between depression and cardiovascular diseases . So we could have expected in our study a significant impact of pattern B multimorbidity score on mental HRQoL. In the absence of this result, we can make the hypothesis that our method allows us to better identify participants with cardiovascular pathology in absence of psychological disorders. In fact, anxiety/depression condition is represented in pattern A.
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According to our analysis by age groups and gender, decrease in HRQoL was associated more with the DUKE than the SF-36, especially for pattern A score. This result was expected, considering that the Duke Health Profile has more items oriented toward mental health than the SF-36, which is a more general questionnaire. Several studies have shown that among all morbidities, mental disorders have the highest impact on HRQoL . Nevertheless, other studies did not account for these conditions in HRQoL assessment . Our results highlight the importance of considering mental disorders in HRQoL studies.
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Many studies have shown decreased HRQoL with increasing number of chronic conditions . With the methodology we used, we accounted for not only the number of morbidities but also their association in the population, which allowed for measuring the effect of morbidities on HRQoL as accurately as possible and comparing participants with each other by age group and gender.
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Our approach allowed us to account for the interaction between chronic conditions and determine the multimorbidity status of each participant in contrast to studies that conducted a latent class analysis, seeking to consolidate clustered participants, which were unable to account for the complexity of the possibilities. This type of study incurs misclassification error and the model can be applied to only a limited number of participants . When studies analyze morbidities individually, they can consider only a limited number of morbidity interactions . Also it is not necessary to apply a method dedicated to multimorbidity for a specific population because our method allows for self-determination of a multimorbidity pattern.
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Another strength of our study is the sample size. In addition, more than 5000 participants received a geriatric consultation. Nevertheless, our study could feature an underreporting of some diseases/conditions because of the self-administered questionnaires. Moreover, to assess the multimorbidity measure, we did not use a validated instrument such as the Cumulative Illness Rating Scale or the Duke Severity Illness Checklist , both of which cannot be used for an existing cohort. Finally, we did not assess the severity of conditions and did not use an exhaustive list of conditions.
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The results of our study of a relatively healthy sample, including a low prevalence of morbidities and high HRQoL, suggest that multimorbidity affects HRQoL differently depending on gender or age. Nonetheless, our study is a novel use of multimorbidity patterns to test the impact of multimorbidity on HRQoL. We found two patterns, which were clinically recognizable and theoretically plausible. Further investigations and research in older populations should consider multimorbidity patterns to confirm these findings.
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Our analysis of more than 5,000 participants of 55 years and older, revealed two multimorbidity patterns which were clinically recognizable and theoretically plausible. These two identified patterns affected both HRQoL, notably a strong association was found between a multimorbidity pattern related to mental illness and deteriorated bone health (pattern A)–and a decrease in physical and mental HRQoL. The multimorbidity pattern related to cardiovascular and metabolic disorders (pattern B) seems to have no impact on mental HRQoL. The strength of theses associations differed according to age groups.
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Our study is a new integrating approach of accounting for multimorbidity patterns in studying HRQoL in healthy population. Indeed, available multimorbidity indices were based on specific outcomes, such as mortality, costs, or function, and therefore may not address a patient’s overall condition . In addition, they were validated on very specific populations and then it is difficult to apply them to other populations. The multimorbidity scores we identified (counted and weighted) can be used in clinical research to control for the effect of multimorbidity on patients’ HRQoL and may be useful for clinical practice.
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To conclude, the results of this study could lead to a deeper understanding of the association of multimorbidity and HRQoL. Nevertheless, this method should be deepened through further studies to integrate the severity of conditions and to enrich the methodology.
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One of the major changes in human history was the emergence of agricultural societies . About 13,000 years ago, farmers began to domesticated plants and animals for agriculture. Domestication was done by selecting plants and animals with suitable traits for farming like increased yield. As a result, the morphology of our cultivated plants was reshaped by human selection for a period certainly spanning thousands of years [2–4]. The domestication process offers an interesting glimpse of the broad adaptation process and of the genetic basis of morphological and physiological traits [5, 6]. It helps understand how a relatively lowly productive wild relative can be transformed into a high yielding cultivated variety. Insights into crop domestication have primarily come from cereals . Root and tuber crops are also a major contributor of starch to the human diet. These crops have the particularity of very often being vegetatively propagated . The domestication process increased their ability to store starch in their roots or tubers and other specialized storage organs as well as the size of these organs . Today it is not clear if the knowledge we have of the process of domestication of cereal crops can be extrapolated to root and tuber crops. For example, selection on several genes responsible for starch biosynthesis has been documented in maize [8, 9]. So, one would expect that domestication also allows more efficient production and/or storage of starch in root and tuber crops. One would also expect that domestication reshaped the formation and development of roots as a support for efficient starch storage.
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The most widely grown root and tuber crops in Africa are cassava and yam. The two main species of yam, Dioscorea spp., were domesticated independently, D. rotundata in Africa and D. alata in Asia. D. rotundata, the most widely cultivated yam species in Africa is a staple food for over 100 million people . This species has two close wild relatives D. abyssinica and D. praehensilis [11–14]. The three species are diploid and have 20 chromosomes [2n = 40] [14–16]. The African cultivated yam and its closest wild relatives are compulsory out-crossers because they are dioecious. However, D. rotundata is preferentially propagated through vegetative multiplication . Interestingly, the two wild species have distinct ecological distribution: D. abyssinica is found in the wooded savanna areas while D. praehensilis is found in tropical forested areas . The diploid African yam is cultivated in both ecological areas, thereby allowing gene flow between cultivated and the two wild species . Several key phenotypes differentiate cultivated varieties from their wild relatives. Cultivated yams are characterized by larger and less ramified roots than their wild relatives, and some cultivated varieties do not develop inflorescences . Finally, the wild relatives of yam are vines which grow partly in the shade of their tutor tree, while cultivated yams grow in full sunlight. This change of habitat might be associated with major adaptation.
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Our objective was to uncover the molecular basis of yam domestication. To find what genes and specific functions were selected during yam domestication, we sequenced the genome of wild and cultivated African yams. Using this dataset, we then scanned for selection signature to pinpoint genes associated with domestication.
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Thirty plants were collected in 15 villages in Benin (Additional file 2: Table S1). Sampling included 10 individuals belonging to the cultivated species D. rotundata, and 10 individuals belonging to each of its two closest wild relatives, D. abyssinica and D. praehensilis. Plants were identified by Serge Tostain (yam specialist, IRD), Nora Scarcelli (yam specialist, IRD) and local yam farmers. DNA was extracted as previously described using a standard protocol . Genomic libraries were constructed using a recent protocol . The genomic libraries were 2 × 100 bp paired-end sequenced by sample multiplexing using the Illumina HiSeq 2000 technology (GeT_Genotoul, Toulouse, France).
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Raw data were first filtered using a previously described pipeline . Briefly, we performed a demultiplexing python script demuladapt (https://github.com/Maillol/demultadapt). Adaptors and low-quality bases were eliminated using cutadapt 1.2.1 . Reads with a mean quality score < 30 were removed using a free perl script https://github.com/SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_Quality.pl . Mapping was performed using default options of BWA aln-sampe V0.7.5a–r405 , and using the D. rotundata transcriptome reference . We validated by modelling that the mapping of genomic DNA reads on a transcriptome reference did not lead to major bias of SNP identification (Additional file 1: Table S1).
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We estimated the genotype likelihood (GL) for each site using the option “-GL 3” (SOAPsnp model) implemented in angsd 0.700 . We also performed SNP calling using the HaplotypeCaller in the Genome Analysis Toolkit (GATK) V-3.4-46 . Default options of GATK and the “-rf BadCigar” options were used. SNPs were filtered for low missing rate < 5% and a mean depth ≥ 4. The complete script from the raw data to the GL or SNP data analysis is available as a Additional file 1: Table S1.
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Genetic structure was assessed using a least-squares optimization approach implemented in the sNMF program . This approach is based on SNP calling and consists in estimating admixture coefficients based on sparse non-negative matrix factorization . We assessed a number of K populations varying from 1 to 6 clusters. Ten replications were performed for each K value. To select the best K value, we used the minimum value of the cross entropy criterion . We also used the maximum likelihood structure approach implemented in the NgsAdmix program . This approach directly uses the genotype likelihood given by angsd, without calling genotypes. The most relevant K number of population was selected by comparing the results obtained with NgsAdmix and sNMF. Genetic diversity was estimated using nucleotide diversity π and nucleotide polymorphism θ computed using the option “-doThetas” implemented in angsd 0.700 . We calculated the ratio of diversity between the cultivated species D. rotundata and each of the wild species D. praehensilis and D. abyssinica using the R package. Pairwise linkage disequilibrium (LD) was calculated with the squared allele frequency correlation r 2 using the R packages SNPRelate and LDcorSV . A set of contigs corresponding to 1% of all contigs was randomly selected and used as reference. Intra-contig LDs within these contigs were performed for pairs of SNPs with minor allele frequencies (MAF) higher than 0.01.
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We used four different approaches to identify regions under selection: two methods allowing identifying a reduction of diversity for the selected genes, two methods allowing identifying an excess of differentiation. The diversity reduction was assessed using Tajima’s D and by the ratio of cultivated to wild diversity. The excess of differentiation was assessed using the FST between cultivated and wild populations and a principal component based analysis. Tajima’s D value of each contig was calculated for the species using vcftools v0.1.13 . (1) We plotted the distribution of Tajima’s D values and then used a 1% threshold to identify extremely low values. (2) The ratio of the cultivated genetic diversity divided by the mean diversity of the two wild relative species using π and θ . We used a 1% threshold to identify outlier contigs with extremely low ratios. (3) We estimated the differentiation index FST between the cultivated group and each of the two wild groups for each contig using vcftools v0.1.13 . Using the cutoff of the 1% top values, contigs with extreme FST between the cultivated and both two wild relatives were selected as candidates. (4) Based on principal component analysis at the SNP level we used the program Pcadapt V2.2 to identify SNPs with extreme differentiation between the three species. The Mahalanobis distance was calculated and we used the 5% threshold of the false discovery rate (FDR) to detect candidate SNPs. The four selection tests were compared using a Venn diagram to reveal the most likely candidate regions for selection. The annotation of the candidate selected genes was retrieved from a previous study .
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First, all the candidate contigs annotated in the reference transcriptome were tested for enrichment of gene ontology (GO) molecular function terms. Standard Fisher’s exact tests implemented in the R package TopGO were performed. A minimum of five annotated genes were required per term in order to limit statistical artifacts of GO terms with less annotated genes. Then, to control for false positive effects, only candidate contigs identified by at least two different selection tests were chosen, and the enrichment of GO terms analysis was rerun.
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We generated 162 million 100-bp paired-end reads. The yam transcriptome size has been estimated to be approximately 64 Mb and the genome size to be 550 Mb. We obtained an average mapping rate of ~ 12.6% of our genomic reads i.e. close to the expected 12.4% based on the relative transcriptome size compared to the whole genome (Additional file 2: Table S2). We identified a total of 308,840 SNPs. These SNPs were found in 23,136 contigs with a mean contig length of 1316 bp (ranging from 250 to 15,691). A low correlation was observed between the length of the contigs and the number of SNPs detected (r = 0.34, p < 0.001).
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Analysis of the population structure using sNMF led to three major genetic groups (Additional file 2: Figure S1), corresponding to the three species (Fig. 1-a). We identified four individuals (A420, P599, A433 and P624) as interspecific hybrids. One individual (A3085) was certainly misclassified in the field: it was recorded as D. abyssinica in the field but was genetically close to the D. praehensilis group. The exact structuration was similarly found using the NgsAdmix approach, with only minor differences in the estimated proportion of admixture (Fig. 1-b). As hybrids could bias the calculation of diversity; the differentiation tests; and Tajima’s D statistics, we removed the four hybrids for further analysis. Departures for neutrality or extreme differentiation were consequently assessed on 26 individuals.Fig. 1Structure analysis using sNMF(a) and NgsAdmix (b). Each color represents one population. The length of each segment in each vertical bar represents the proportion of ancestry in each population
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We compared nucleotide diversity π and the nucleotide polymorphism θ between the cultivated species and each of the wild species. First, the cultivated diversity π was 26% and 36% respectively lower than D. abyssinica and D. praehensilis (Additional file 2: Table S3 a and b). Secondly, the cultivated diversity θ was 28% and 44% lower than D. abyssinica and D. praehensilis respectively. Linkage disequilibrium (LD) computed between 400,760 pairs of SNP decreased rapidly at r 2 = 0.1 after 100 bp (Additional file 2: Figure S2).
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Contigs were searched for selection signatures using four different methods: Tajima’s D, marked reduction in the diversity in the cultivated samples, differentiation between wild and cultivated species, and principal component analysis. Using the four methods, a total of 998 candidate contigs were identified (Additional file 2: Table S4), among which 81 were detected by at least two methods (Additional file 2: Figure S3).
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(i) Tajima’s D in the cultivated yam showed a skewed distribution to positive values (Fig. 2-a), with a mean of 0.77. The distribution reflected an excess of contigs with low diversity (Fig. 2-a). The distribution of Tajima’s values in the two wild species is centered on zero and consequently reflects a more global equilibrium between SNP occurrence and their frequencies (Additional file 2: Figure S4). Using a 1% threshold (Tajima D < −1.84), a total of 187 contigs were identified as potential candidates under selection in the cultivated sample.Fig. 2Summary of the different tests used to identify outlier contigs. In the distribution of Tajima’s D value of the cultivated species (a), the red line indicates the 1% threshold used to consider contigs as candidates. In the of reduction of nucleotide diversity π (b), the -log10 (πc/πw) for each contig is represented by one dot. The gray line corresponds to the 1% threshold used to consider contigs as candidates. In the comparison of FST between the cultivated and the two-wild species (c), each dot represents a contigs. The grey lines indicate the 1% threshold used to consider contigs as candidates. Finally, in the histogram of p-value (d), the peak of SNP close to zero indicates the presence of outliers. Here, the SNPs were considered as candidates using an FDR of 0.05
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Summary of the different tests used to identify outlier contigs. In the distribution of Tajima’s D value of the cultivated species (a), the red line indicates the 1% threshold used to consider contigs as candidates. In the of reduction of nucleotide diversity π (b), the -log10 (πc/πw) for each contig is represented by one dot. The gray line corresponds to the 1% threshold used to consider contigs as candidates. In the comparison of FST between the cultivated and the two-wild species (c), each dot represents a contigs. The grey lines indicate the 1% threshold used to consider contigs as candidates. Finally, in the histogram of p-value (d), the peak of SNP close to zero indicates the presence of outliers. Here, the SNPs were considered as candidates using an FDR of 0.05
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(ii) The reduction of nucleotide diversity and the nucleotide polymorphism were highly correlated (r = 0.997, p < 0.001, (Additional file 2: Figure S5). Consequently, we only used the reduction of nucleotide diversity (πc/πw) for further analysis. Using a threshold of 1% (−log10 (πc/πw) > 1.34), a total of 232 contigs were identified as having an extremely low diversity in the cultivated sample compared to their wild relatives, and were therefore considered as candidates. (Fig. 2-b).
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(iii) The average differentiation between D. rotundata and D. praehensilis was higher than between D. rotundata and D. abyssinica, (FST = 0.21 and 0.16, respectively, p-value <0.001). Using a 1% threshold (FST > 0.73 and 0.84 for D. rotundata with D. praehensilis and D. abyssinica respectively), 422 contigs were identified with extremely high FST values with one or the other wild species. Among them, 12 showed extreme values with the two wild species simultaneously (Fig. 2-c).
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(iv) Last, we used a SNP-based approach. The two first principal components were used to perform the genome scan for selection using Pcadapt V.2.2 (Additional file 2: Figure S6a). The Mahalanobis statistic distance fitted a normal distribution (Additional file 2: Figure S6b). The histogram of p-values showed an excess of small p-values, indicating the presence of outliers (Fig. 2d). Using a 5% threshold, we identified 2502 SNPs in 1602 candidate contigs with extremely low p-values. A total of 238 contigs that showed at least two SNPs putatively under selection were retained as candidates.
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We compared the candidate contigs with the available annotation of the yam transcriptome reference . Thus, we retrieved some genes corresponding to putative targets for selection during yam domestication. In particular, among the genes annotated for the candidate genes, we identified five candidate contigs that were relevant in the light of yam domestication (Fig. 3 and Additional file 2: Table S5). These five candidate contigs showed strong diversity loss in the cultivated group compared to the wild species (Additional file 2: Figure S7). A candidate contig was a putative SCARECROW-LIKE gene involved in root development [42, 43]. Two other genes were associated with the earliest stages of starch biosynthesis and storage i.e., genes coding for the sucrose synthase 4 and the sucrose-phosphate synthase 1 . We also identified two genes associated with growth and phototropism, respectively: Ethylene Insensitive 4 genes (EIN4) and Phototropin 2 gene (Phot2, . The 998 candidate contigs were significantly enriched for a total of 21 significant GO terms (Additional file 2: Table S6). When we restricted our analysis to the 81 candidate contigs detected by at least two methods, we obtained nine significant GO terms (Additional file 2: Table S7). The most significant GO terms were identical whether we considered all the candidate contigs or only the 81 candidate contigs. The set of GO terms found across these two enrichment tests was associated with dehydrogenase and oxidoreductase (NADH DH) activities (Fig. 4).Fig. 3Key genes associated with yam domestication. SCARECROW-LIKE, Phot2, EIN4, SUS4 and SPS1 are some interesting genes probably selected during domestication Fig. 4TreeMap view of the 10 most significant “Go Terms” identified. The 10 most significant GO terms were reported with their respective p-values. We group them in 4 major clusters: “oxidase activity” in green, “transferase activity” in blue, “catalytic activity” in pink. “cofactor binding” in yellow
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TreeMap view of the 10 most significant “Go Terms” identified. The 10 most significant GO terms were reported with their respective p-values. We group them in 4 major clusters: “oxidase activity” in green, “transferase activity” in blue, “catalytic activity” in pink. “cofactor binding” in yellow
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study
| 99.9 |
Today, the D. rotundata yam species is vegetatively propagated. However, the nucleotide diversity loss associated with domestication is relatively modest: the cultivated sample had 26% and 36% diversity loss respectively relative to D. abyssinica and D. praehensilis. In out-crossing species like pearl millet and maize, diversity losses of 32% and 35% were reported. In self-pollinating species, the diversity loss can be much higher, for example, 62% in barley , and 70% in wheat . The loss of diversity observed in our study is more similar to outcrossing crops. We do not know when the transition from an outcrossing crop to a preferentially vegetative crop occurred. It is likely that during the first step of domestication, the crop reproduced mainly through seed. Even today, the reproduction system of D. rotundata is not purely vegetative [13, 52], and some cultivated varieties were found to have been recently obtained by cross-pollination. So, this modest loss of diversity is not surprising.
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study
| 100.0 |
Linkage disequilibrium (LD) also decreased rapidly, like in other outcrossing crops. This LD decay is more similar to that observed in maize [53–55] than to that reported in self-pollinating crops such as rice . However, our estimation of LD is based on a small sample and we might overestimate the rapidity of its decrease.
|
study
| 99.94 |
We found 2% of yam genome classified as candidates for selected genes during domestication. A very similar rate of genome under selection was previously observed in maize, ranging from 2 to 5% [49, 57, 58]. Among the contigs we identified, roughly 10% of the candidate contigs were commonly identified by a least two different methods used for detecting signatures of selection.
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study
| 100.0 |
Depending of the strength and the timing of selection, its resulting impact on diversity could differ. Consequently, each test has different strength and power to detect these specific signatures of selection. For example, when strongly selected, alleles could be fixed. These specific genes showing strong selection could be detected by differentiation FST based test, but not by Tajima’s D test because of their fixed polymorphism . So, the specificity of each test could lead to the discovery of only a small set of the same contigs by all different methods. However, each method could also identify false positives . These false positives could be specific of a test. In conclusion, both false positives and different impacts of selection on diversity resulted in roughly 10% of genes being simultaneously identified by all the methods performed. Furthermore, signature of selection on two contigs could be associated with a single selection events one of them. Even if we found that linkage disequilibrium decreased fast, our list of selected genes might represent fewer selection events than their actual numbers.
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study
| 100.0 |
Cultivated yams are known to have less ramified and larger roots than wild yams. Remarkably, we found a contig homologous to a gene coding for a SCARECROW-LIKE protein. As demonstrated in Arabidopsis, this gene is a key player in root development [42, 43] and consequently may have been mobilized during yam domestication. We also pinpointed a contig homologous to an EIN4 gene. EIN4 is a receptor of ethylene involved in growth regulation and many developmental processes including seed germination, leaf and flower senescence . At this stage, we do not know if this gene may affect root development itself or its above ground development.
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study
| 100.0 |
Domestication of root and cereal crops is notably associated with the increase of starch production. Several studies on cereals suggest that starch biosynthesis and storage were important targets for selection . In our study, we observed the selection of two genes involved in the production of sugar: SUS4 and SPS1. SUS catalysis is the first step leading to starch formation by converting sucrose to fructose and UDP-glucose. In wheat, selection for increased starch content was associated with selection of SUS genes , and enhancing SUS activities also resulted in increasing starch content in maize . The SPS gene has also been reported to play a major role in sucrose biosynthesis under osmotic stress conditions . In conclusion, similar set of genes were selected during cereal, root and tuber crops.
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study
| 100.0 |
Beyond starch production, cultivated yam underwent a major change in its living environment during domestication. Yams are now grown in open fields, whereas its wild relatives grow as vines in the shade of tutor trees. This environmental change during domestication certainly required adaptation due to such changes in light and heat. We observed strong signatures of selection in genes associated with physiological processes of regulation of photosynthesis for light tracking and for plant growth. Indeed, one of our candidate contigs is homologous to the Phototropin 2 gene (Phot2). In higher plants, Phot2 enables perception of blue light and consequently optimization of photosynthetic performance and growth .
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study
| 100.0 |
Beyond specific genes associated with the change from shade to light environment, we also found a significant enrichment of interesting gene ontology terms. The most significant GO terms observed were and oxidoreductase activities associated with NADPH DH complex genes [64, 65]. Whatever the strategy of enrichment test used, the results were robust for these functions. The NADH DH complex is an important set of enzymes for chlororespiration . The NADH DH complex is involved in photosynthesis , more specifically in the photosystems I (PSI) and II (PSII). It plays a role in protection against photo-oxidative stresses associated with the formation of reactive oxygen species (ROS) . High light and heat could favour the production of ROS [69, 70]. In oats, NADH DH is over-expressed with increasing light . Consequently, it has been postulated that this type of complex plays a role in mitigating ROS stress associated with increasing intensity of light or heat. In Brassica plants, the same NADH DH complex has also been reported to be associated with the domestication process . The wild species of Brassica showed higher tolerance to high light and heat intensity than the cultivated species . In this specific case, domestication was associated with a decrease in photosynthetic parameters under stress conditions in the cultivated species . The two wild species of yam are vines that grow in partial shade. The cultivated species D. rotundata grows under full sunlight in the field. We hypothesize that adaptation of the cultivated yam led to the selection of genes that enable efficient photosynthesis with increasing light and heat intensity. Optimizing photosynthesis is also an important way to enhance production of carbohydrate, later stored as starch in the tuber.
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study
| 100.0 |
Selection in the early step of sugar biosynthesis is detected in yam, and previously detected in cereal. This result suggests that key step in starch biosynthesis were necessary both in cereal as well as in root and tuber crops. More interestingly, drastic changes in habitat associated with domestication is certainly retraced in selection in phototropism genes. Selection on dehydrogenase and oxidoreductase activities associated with NADPH DH complex genes, was certainly the consequence of adaptation to optimize photosynthesis in full light. If some convergence is observed at the molecular level, very specific adaptations were necessary for the domestication of African yam. Beyond domestication, this study highlight the molecular mechanism associated with changes from shade-tolerant plant to a full light environment.
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study
| 100.0 |
Additional file 1:We assess if the mapping of genomic DNA reads on a transcriptome reference could impact SNP calling in our special case. Table S1. Summary of mapping and SNP calling using simulated data. (DOCX 15 kb) Additional file 2:Molecular basis of African yam domestication: analyses of selection point to starch biosynthesis, root development and photosynthesis related genes. Table S1. Passport data of plant material collected from Benin. Table S2. Metric information of data filtering and mapping. Table S3. Mean Nucleotide diversity (π) and polymorphism (ɵ). Table S4. List of the contigs detected as selected by at least one method. Table S5. Remarkable candidate genes showing selection signature. Table S6. Gene Ontology (GO) terms significantly enriched (p-value ≤ 0.05) among the 998 candidate contigs. Table S7. Gene Ontology (GO) terms significantly enriched (p-value ≤ 0.05) among the 81 candidates contigs detected by a least two methods. Figure S1. Cross-entropy calculated using sNMF (Frichot et al., 2014) for K = 1 to 6. Ten repetitions of the run were done. Figure S2. Intra-contigs linkage disequilibrium (LD) as a function of physical distance between SNPs pairs from 1% of all contigs. Figure S3. Venn Diagram comparing the candidate contigs obtained using the 4 methods. Figure S4. Distribution of Tajima’s D value calculated for D. abyssinica (a) and D. praehensilis (b). Figure S5. Comparison of diversity lost. Figure S6. Variance explained by PCA axis (a) and distribution of Mahalanobis distance (b) from PCAdapt. Figure S7. Nucleotide diversity within five candidate contigs for cultivated and the wild species (XLSX 45 kb)
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study
| 100.0 |
Molecular basis of African yam domestication: analyses of selection point to starch biosynthesis, root development and photosynthesis related genes. Table S1. Passport data of plant material collected from Benin. Table S2. Metric information of data filtering and mapping. Table S3. Mean Nucleotide diversity (π) and polymorphism (ɵ). Table S4. List of the contigs detected as selected by at least one method. Table S5. Remarkable candidate genes showing selection signature. Table S6. Gene Ontology (GO) terms significantly enriched (p-value ≤ 0.05) among the 998 candidate contigs. Table S7. Gene Ontology (GO) terms significantly enriched (p-value ≤ 0.05) among the 81 candidates contigs detected by a least two methods. Figure S1. Cross-entropy calculated using sNMF (Frichot et al., 2014) for K = 1 to 6. Ten repetitions of the run were done. Figure S2. Intra-contigs linkage disequilibrium (LD) as a function of physical distance between SNPs pairs from 1% of all contigs. Figure S3. Venn Diagram comparing the candidate contigs obtained using the 4 methods. Figure S4. Distribution of Tajima’s D value calculated for D. abyssinica (a) and D. praehensilis (b). Figure S5. Comparison of diversity lost. Figure S6. Variance explained by PCA axis (a) and distribution of Mahalanobis distance (b) from PCAdapt. Figure S7. Nucleotide diversity within five candidate contigs for cultivated and the wild species (XLSX 45 kb)
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study
| 100.0 |
Specifications TableTableSubject areaComputational and Insilico ChemistryMore specific subject areaGroup Quantitative Structure-Activity Relationship(QSAR)Type of dataEquation, Tables,GraphsHow data was acquiredGroup based QSAR modellingData formatAnalysisExperimental factorsMultiple linear regression GQSAR models for predicting the inhibitory potential of benzothiazole dataset were created. 17 molecules were utilized as training dataset and 8 molecules utilized as test dataset.Experimental featuresFragment descriptors and pMIC values were utilized in GQSAR analysis via stepwise variable selection method using dataset of 25 molecules.Data source locationPharmaceutical chemistry of Laboratory of Progressive Education Society's, Modern College of Pharmacy, Sector 21, Yamunanagar, Nigdi, Pune 411044, Maharashtra, IndiaData accessibilityThe data is with this article
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study
| 99.44 |
Value of the data•Tuberculosis is one of most lethal disease in the current decade; development of potent antitubercular compounds is need of time.•GQSAR modelling data was developed for predicting structural properties of benzothiazole dataset which are infusing antitubercular activity.•The GQSAR models generated will be utilized to screen various heterocyclic datasets for antitubercular potency, which will lead to development of novel antitubercular compounds.
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other
| 95.6 |
Molecular data set for current study were taken from literature reported by Telvekar et al. . All the 24 structures of benzothiazole derivatives were drawn using 2D builder module of Vlife MDS 4.3. These 2D structures were converted into 3D via using V life engine platform. Geometry and structures of 3D molecules were optimized via energy minimization process using Merck molecular force field (MMFF) and Gasteiger charges. A common template which is a representative of the entire molecules under study was prepared with the presence of a dummy atom (X) at the substitution site.
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study
| 100.0 |
The common chemical structure as shown in Fig. 1 was utilized for development of GQSAR model. The molecules in the data set were fragmented in six different fragments (R-R6). The fragmented molecules were incorporated into the QSAR module of V life MDS for calculation of molecular descriptors. Molecular descriptors are nothing but the numerical values which represents physical and chemical information of the molecules. In GQSAR studies descriptors are representation of the physical and chemical behavior of substituents present.Fig. 1Molecular Template Utilized for Fragmentation pattern.Fig. 1
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study
| 100.0 |
Generated dataset of 25 benzothiazole derivatives were randomly divided into training set and test set 17 and 8 molecules respectively. Random distribution of training and test set will results into uniform distribution of biological activity across the molecules under study. Multiple linear regression analysis was utilized for development GQSAR models, with number of dependent variable limited to not more than 3 per model (Table 1).Table 1Table Showing Molecules under Study.Table 1Mole. NoRR1R2R3R4R51.HHHHHH2.HClHHHH3.HHHClHH4.HClHClHH5.HHCH3ClHH6.ClHHHHH7.ClClHHHH8.ClHHClHH9.ClClHClHH10.ClHCH3ClHH11.CH3HHHHH12.CH3ClHHHH13.CH3HHClHH14.CH3ClHClHH15.CH3HCH3ClHH16.OCH3HHHHH17.OCH3ClHHHH18.OCH3HHClHH19.OCH3ClHClHH20.OCH3HCH3ClHH21.NO2HHHHH22.NO2ClHHHH23.NO2HHClHH24.NO2ClHClHH25.NO2HCH3ClHH
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study
| 100.0 |
Validation is a critical step in the QSAR model development. Validation methods are required for establishing predictability of QSAR model on unseen data and for determination of complexity of QSAR model which is justified by the data under study. Number of methods like the methods of least squares fit (R2), cross validation (Q2), adjusted R2 (R2adj), chi-squared test (χ2), root mean squared error (RMSE), bootstrapping and scrambling (Y-Randomization) are reported for internal validation of QSAR models. Observed activity of molecules in dataset was expressed in MIC(μg/ml) and converted into pMIC for QSAR analysis. All the molecules in the dataset are having activity (MIC) in the range 1.5–29.00 μg/ml.
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study
| 100.0 |
Congeneric nature of the dataset is basis prerequisite for any QSAR analysis. Fragment based QSAR is recent methodology were complex structures can be analyzed. 30 different G-QSAR models were generated and best one of them are selected on basis of the statistical values like r2, q2, pred_r2, F-test and standard error. The predicted activity data via QSAR models was in accordance with the observed biological activity with small variations which were clearly identified in the correlation plot of different model (Table 2 and Fig. 2). Selected model is given by.Fig. 2Figure Showing Correlation Plot for Selected GQSAR model having r2 0.88.Fig. 2Table 2Table showing observed and predicted activity of selected GQSAR model.Table 2Molecule. NoObserved Activity pMIC(μg/ml)Predicted Activity pMIC(μg/ml)Molecule NoObserved Activity pMIC(μg/ml)Predicted Activity pMIC(μg/ml)12.12.7140.91.220.90.7150.80.831.21.2160.80.74 #2.31.217#0.71.35#1.20.918#1.10.761.31.6190.61.373.34.1200.60.882.83.621#0.60.795.64.022#0.61.1101.53.7230.70.711#1.00.7240.71.1121.41.225#0.70.8130.80.7# Test Set Molecules
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study
| 100.0 |
Splenic aneurysm with more than 5cm diameter is rarely seen. The giant splenic artery aneurysm, greater than 10cm, is even rare. In 2005 there were only 12 cases of true splenic artery aneurysms described in the literature with such dimensions.( 1 ) Such case requires a careful plan and execution because of its intimal relation with surrounding abdominal organs. In Brazil, no cases of this disease have been reported so far.
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other
| 52.16 |
This was a 61-year-old woman without complains who was referred by her vascular surgeon after a routine abdominal ultrasonography that demonstrated a large proportion tumor in the mesogastric area. The patient had blood hypertension, which was controlled with 40mg furosemide taken once a day, hypercholesterolemia treated with 10mg rosuvastatin daily. There was a history of three previous gestations and she had smoked until the age of 19-year-old.
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clinical case
| 100.0 |
Upon physical examination, she reported pain on palpation in the upper left quadrant of abdomen. The patient underwent abdominal computed tomography without contrast agent that identified a giant splenic artery aneurysm measuring 11x10cm (Figures 1A and B). An intraoperative angiography confirmed the initial suspicion (Figure 2).
|
clinical case
| 100.0 |
Patient underwent surgical treatment by median xyphopubic laparotomy. The opened small omentum did not enable to approach celiac artery by epiplon retrocavity because aneurysm do not enabled its visualization. A dissection, therefore, was carried out resealing partly the right lateral wall of aneurysmatic sac with the aim to identify celiac artery that also enable its visualization. We opted to perform a medial visceral rotation with descending colon mobilization by the incision of lateral reflection of peritoneum, cranially prolonged with section of phrenicocolic and splenorenal ligament. The plan was achieved between pancreas and Gerota’s fascia, promoting anteromedial rotation of the spleen, pancreas and stomach, therefore identifying the tumors (Figure 3).
|
clinical case
| 100.0 |
Left lobe of the liver was mobilized and retracted, sectioning the triangle liking, whereas gonadal vein, ureter, left kidney, left renal vein and adrenal vein remained in situ. During mobilization of spleen a laceration of its capsule occurred, which determined the splenectomy. The Omni-Tract retractor was placed to maintain the viscera to right, left pile of diaphragm was partial sectioned and suprarenal aorta dissected with identification of higher mesenteric artery and initial short segment of celiac artery that was initially clamped, verifying the persistence of pulsability in liver artery; a ligature of celiac artery was performed.
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clinical case
| 99.94 |
The aneurysm had a sac aspect and it affected splenic artery in its proximal and medium segments. After the opening of aneurysmatic sac, we observed retrograde bleeding of two orifices of pancreatic branches that were sutured and its anterior wall was resected, leaving intact the remain aneurysm that was adhered to pancreas.
|
clinical case
| 99.94 |
The anatomopathological exam of the aneurysm wall showed atheroma plaque in the intimal and hyalinization of medium tunic, in addition to aggregated of compatible fibrin with mural trunk without malignant signs. The patient evolved without intercurrences and remained asymptomatic 6 months after surgical intervention.
|
clinical case
| 99.94 |
Splenic artery aneurysm can be true or pseudoaneurysms.( 2 ) The majority of aneurysms is true, and seen among women and presents as important risk factors the astherosclerosis, arterial hypertension, and multiparity – all these factors are described in our case. About 10% of patients with giant arterial aneurysm described in the literature had hepatic cirrhosis and 2.5% portal hypertension( 3 ) such conditions possible constitutes predisposing factors and they were not seen in our patient. In splenic artery pseudoaneurysm the main risk factor is pancreatitis in which pancreatic enzimes can promove desistegration of the arterial wall and, in case of pseudocist, it can establish a fistulae between pancreatitis and the artery( 2 ) – initially suggested in the our case, based on size of the tumor with blood flow in its interior. This hypothesis was ignored because there was no previous history of pancreatic disease, and more accurate computed tomography exam of abdomen revealed a normal pancreas.
|
clinical case
| 99.9 |
Most of splenic aneurysm is located in distal third of the artery and they are saccular, a fact not observed in our case, in which aneurysmatic dilatation, only saccular,( 3 ) evolved to proximal segments of vessels, which seems to characterize the giant aneurysms.( 1 ) Despite of enlarged dimensions, the patient was asymptomatic and aneurysm was found in routine ultrasonography exam of the abdomen, and these finding are compatible with other series.( 4 )
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clinical case
| 99.9 |
There are several treatment options for these aneurysms, depending on age, patient’s general conditions, artery site and dimensions of aneurysmatic sac. The progress of endovascular techniques and they good results have stimulate their employment in such cases, i.e., presenting little aggression and low morbidity.( 5 , 6 ) Embolization with moles or cyanoacrylate glue have been used successfully in less voluminous and short neck saccular aneurysm.( 6 ) However, in giant aneurysms, such as in our case, the use of large number of moles to exclude aneurysm become unviable, not only for the cost of the procedure, but also for high probability of not achieving total exclusion. In addition, infectious complications, splenic infarct, and intense inflammatory process have been described, and they constitute an inconvenience for employment of this technique in such circumstances.( 1 , 7 )
|
review
| 91.5 |
Other endovascular treatment option, in cases that aneurysm affect proximal and medial segments of splenic artery, comprises the implantation of endoprosthesis to preserve irrigation of spleen and promote exclusion of aneurysm. However, accentuated tortuosity of artery can avoid the implantation, although there are successful reports using this technique.( 8 ) In our case, implantation of endoprosthesis was not considered because there was no proximal neck of splenic artery for endoprosthesis fixation.
|
clinical case
| 99.25 |
Although endovascular techniques are highly developed, conventional open surgical treatment remains the gold-standard to approach splenic artery aneurysms( 10 ) mainly giant aneurysms such as the one reported in this case. Patients with intact aneurysm have low morbidity and mortality, and complete resolution with no need of later control. The open approach is complex and constitutes a true challenge to the surgical team, because of the impossibility to approach celiac artery by an anterior route due to the presence of large putative mass. Our case resemble the one reported by Yadav et al.,( 10 ) in which the patient had good general condition, lack of pancreatic disease, large size aneurysm, and who underwent open surgical treatment. However, distal aneurysm site in artery and decision of totally resection led Yaday and colleagues to perform an aneurysmectomy associated with splenectomy and caudal pancreatectomy, which constitutes a more aggressive surgical intervention than the one used in our case. We used only a partial aneurysmectomy with presentation of aneurysmatic sac wall adhered to pancreas – the approach adopted by Pescarus et al.,( 1 ) in a similar case.
|
clinical case
| 99.9 |
Large visceral aneurysms and, particularly, splenic aneurysms promote distortions in their arteries of origin, in addition to adherences and compressions of surrounding organs, therefore causing difficult and/or turning impossible endovascular access, avoiding open surgical correction, which is the first choice for treatment of giant aneurysms.
|
other
| 99.9 |
Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitation caused by destruction of the lung parenchyma and/or airway obstruction [1–3]. COPD is currently the third leading cause of death worldwide and poses a major public health burden globally . Moreover, COPD is associated with the development of lung cancer . There is no cure available for COPD and current drugs are mainly effective in improving symptoms and exacerbations but generally do not slow down the progression of the disease . Therefore, it is important to understand the cellular and molecular mechanisms of COPD for developing effective treatments of the disease.
|
review
| 99.9 |
COPD is a chronic inflammatory disease caused by inhalation of toxic particles and gases, mostly cigarette smoke (CS) [1–3,7]. Despite the fact that CS is the major risk factor for COPD, many chronic smokers maintain normal lung function (so-called resistant smokers) , so do some smokers even after more than 40 pack years of smoking , while only ~20–30% of chronic smokers develop the disease [1, 2,7,9]. This suggests that the susceptibility of smokers to COPD can vary significantly [1, 2, 8, 9]. However, the cellular and molecular basis for the disease susceptibility remains to be elucidated albeit genetic or environmental factors may play a role [1, 2]. As chronic cigarette smokers with normal lung function also have increased pulmonary inflammation, this inflammation seems to be magnified in COPD. Understanding of the amplification of inflammation is not yet complete . Cigarette smoking cessation is considered currently as the most important intervention to reduce COPD progression . While quitting smoking can prevent the COPD progression in some patients, who are referred as (reversibly) susceptible smokers, cigarette smoking cessation fails to slow or preclude the COPD progression in others (referred as severely susceptible smokers) [2, 11]. The precise understanding of different effects of smoking cessation has not yet been fully achieved [1–2].
|
review
| 99.9 |
The CS-induced inflammatory response in COPD progression involving both innate and adaptive immunity [1, 2] is mediated via a complex network that encompasses multiple immune cell types, molecular mediators and lung tissues. Several different types of immune cells and molecular mediators are found to accumulate in the lungs of patients with COPD [1–3, 5–7, 12]. Important immune cells include macrophages, neutrophils, dentritic cells, and T lymphocytes and molecular mediators include cytokines, chemokines, and protein proteases such as metalloproteases (MMPs). There exists an enormous amount of literature regarding these individual network elements. However, little is known about combined interactions between these elements or the associated pathways in the network. In particular, while COPD progression is a multistage and dynamic process, studies on the temporal sequence of inflammation in the disease are lacking . It is not clear how immune cells and molecular mediators are dynamically linked and which of these elements are determinants in the disease progression. This is particularly important for identification of biomarkers in the disease [6, 13–17]. For example, the levels of proinflammatory cytokines, TNF-α and IL-1β are increased in the lungs of COPD patients and were suggested as potential targets . However, inhibition of TNF-α or IL-1β has been proved to be unsuccessful in clinical trials of patients with COPD . A pilot study on patients with COPD revealed no change in levels of inflammatory markers following inhibition of TNF-α , but the underlying reason remains to be elucidated [6, 19]. Recent studies have shown that the levels of IL-6 as well as the associated proteins, C-reactive protein (CRP) and fibrinogen, are significantly increased in COPD patients compared to those in smokers with normal lung function and healthy non-smokers . IL-6 is considered to be a potential biomarker for COPD, but the detailed mechanism of IL-6 action in the disease progression is not yet fully understood.
|
review
| 99.9 |
In this study, we develop a network model for CS-induced immune response in the lung to address the issues described above. Our model analysis identifies several positive feedback loops that play a determinant role in the CS-induced immune response and COPD progression, providing novel insight into the cellular and molecular mechanisms of the disease.
|
study
| 100.0 |
The associated immune system is highly complicated as it involves many mediator molecules, multiple immune cell types, and lung tissues, presenting a challenge for quantifying the dynamics of CS-induced COPD. For simplicity in the following discussion, an immune response network model is constructed by treating important immune cells, cytokines, and lung tissues as network nodes. There are two types of inputs initiated from a node: a positive or an up-regulation input (denoted by→) indicates that an increasing of the concentration of the tail node will lead to an increasing of that of the head node or an up-regulation of the process when the input arrow ends at an edge between two nodes, and vice versa for a negative or a down-regulation (inhibition) input (denoted by ┫).
|
study
| 99.94 |
A network model for CS-induced immune response in COPD progression in this work is developed based on established literature knowledge. Upon exposure to CS, immature alveolar macrophages (M0) are classically activated directly by the CS particulate phase and polarized to the inflammatory phenotype (M1) in the lung [20, 21]. The M1 cells produce inflammatory cytokines such as TNF-α, which activates M1 conversely, IL-6 and IL-12 [22, 23]. M1 can cause tissue damage (TD) in the lung by releasing reactive oxygen species (ROS) leading to oxidative stress, proteases such as macrophage elastase and metalloproteases (MMPs) to ingest pathogens and apoptotic cells, and chemokines to recruit neutrophils into the lung [24–26]. Neutrophils, which are short-lived and subsequently cleared by macrophages , can also cause TD in a manner similar to macrophage . Furthermore, the tissues damaged by M1 can produce elastin fragments (EFs) as strong attractors to recruit monocytes (precursors of macrophages, M0) into the lung from circulation .Subsequently, these M0 cells are differentiated into M1, thus forming a positive feedback loop, M1→TD→M1. In addition, TD can also be caused directly by CS .
|
study
| 100.0 |
TD is an early source of IL-4 production (by basophils and mast cells in TD) that leads to alternatively activated macrophages (M2) [26, 28]. M2 can release IL-10, which can conversely activate M2, and transforming growth factor, TGF-β . While IL-10 is a potent anti-inflammatory cytokine that down-regulates almost all important proinflammatory and TD-related processes, TGF-β is a multi-functional growth factor. In small airways, TGF-β is a potent inducer for extracellular matrix target genes such as collagens, and fibroblast proliferation and activation which both are key events in the fibrogenic process . However, in the lung parenchyma, TGF-β down-regulates tissue damage through inhibition of MMP-12 and MMP-9 .
|
study
| 99.94 |
Dendritic cells (DCs) are antigen-presenting and play a critical role in linking the innate to the adaptive immune response [1, 30]. Immature dendritic cells (DC0) near the epithelial surface are activated directly by CS or dangerous signals generated from TD . Dendritic cells undergo a maturation process and migrate towards the local lymph nodes. Naïve, quiescent T cells cannot enter the lung parenchyma. But once activated by matured DC, they can move into the lung and differentiate into Th1, Th2, Th17, T-regulatory (Treg) and CD8+T cells, which are proliferated by themselves, in their corresponding cytokine environments [31, 32], e.g., in the presence of IL-12 secreted by M1 (as well as DC), naïve CD4+T cells (Th0) differentiate into T helper 1 (Th1) cells [33–35]. The Th1 cells secrete IFN-γ to up-regulate the polarization process from M0 to M1 [22, 36]. A multi-node positive feedback loop, M1→IL12→Th1→IFN-γ→M1, is thus formed. In contrast to Th1, Th2 is polarized from Th0 in the presence of IL-4. Th2 produces IL-13 and IL-4, release of which further enhances the production of IL-10 and TGF-β by M2. In the presence of TGF-β, Th0 cells differentiate into Treg, which secrete IL-10 [22, 36]. TGF-β and IL-6 (rather than IL-23) together induce Th17 differentiation, leading to the production of IL-17 [37–41]. While IL-17 acts on epithelial cells to recruit neutrophils to cause tissue damage further, the activated epithelial cells in TD secrete IL-6, forming a positive feedback loop, IL-6→Th17→IL-17→TD→IL-6 . Th17 cells also produce IL-21 for the differentiation of CD8+T cells from naïve CD8 cytotoxic T lymphocytes (T0) [37, 42]. While CD8+T cells produce IFN-γ to enhance the M1 inflammatory activities, they also release granzyme B and perforins, causing apoptosis/necrosis of targeted cells and leading to TD further . In addition, IL-6 can down-regulate the activation of Treg that secretes IL-10 to inhibit Th17 . Consequently, a positive feedback loop, IL-6┫Treg→IL-10 ┫Th17→IL-17→TD → IL-6, is formed.
|
study
| 99.9 |
As proposed earlier, the immune cells, cytokines and TD discussed above can be treated as network nodes. The aforementioned interactions between these nodes are then integrated into the network model shown in Fig 1 in the next section. The constructed network bears a multiple timescale feature. Cytokine regulation of cell function through signal transduction usually occurs on a sub-second timescale, for example, whereas cell production of cytokines takes minutes to hours . The cytokine regulation activity can thus be considered to be at steady state in the equations that describes the slow timescale activities of the cells. In this way a positive or a negative input can be modeled using an increasing or decreasing Hill function, respectively . The dynamics of the cytokines, the immune cells and TD can thus be described using a set of ordinary differential equations (ODEs). In this study, the system of ODEs is solved numerically using MATLAB (version R2013a Mathworks) with a variable order and multistep solver, ode15s. MATLAB is also used to plot the simulation data to generate the figures presented below.
|
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| 100.0 |
In the present network model shown in Fig 1, M1, DC, Th1, CD8+T and Th17 cells along with their corresponding cytokines, TNF-α, IL-6, IL-12, IFN-γ, and IL-17 form multiple proinflammatory pathways, whereas M2, Th2 and Treg cells with the associated cytokines, IL-4, TGF-β, and IL-10, form anti-inflammatory/regulatory pathways. The inflammatory and anti-inflammatory/regulatory pathways are interlinked with each other through several nodes representing molecular mediators such as IL-6, TGF-β and IL-10 (Fig 1). These pathways eventually converge at the TD node that represents the tissue damage. Here, we focus on the immunologic aspects of COPD and the TD node is highly coarse-grained, involving neutrophil-induced tissue damage, epithelial and endothelial cell injury and extracellular matrix degradation etc. As discussed above, for example, M1 produces chemokines to recruit neutrophils (not shown in Fig 1) for the release of neutrophil elastase and ROS, contributing to TD. The representation of this process is included indirectly in the M1→TD motif shown in Fig 1. In addition, while M1 secretes MMPs to cause TD, TD also produces EFs to recruit monocytes for generating M1. This process is represented in the M1→TD→M1 positive feedback loop where MMPs and EFs are included indirectly (Fig 1). As TD is the major feature of CODP in the lung parenchyma, the dynamics of TD is used to characterize the progression of COPD in this work.
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As mentioned above, the dynamics of the network elements can be described by a set of ODEs. Here, the ODEs involve the following 18 variables: M1, M2, DC, T1, T2, T8, T17, and Tg represent the densities of M1, M2, DC, Th1, Th2, CD8+T, Th17, and Treg cells (in units of cell numbers in a cubic millimeter of tissue), respectively, whereas I4, I6, I10, I12, I17, I21, Iα, Iγ, and Iβ denote concentrations of the cytokines, IL-4, IL-6, IL-10, IL-12, IL-17, IL-21,TNF-α, IFN-γ, and TGF-β. The variable, TD, is defined as a ratio (in terms of a percentage) of damaged tissue to the whole lung parenchymal tissue (normal tissue plus damaged tissue). This TD definition is similar to that of destructive index (DI) [46–47] and can be measured experimentally. While a TD value of 0–30% is considered normal, a TD value larger than 30% is associated with COPD .
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The M1 population originates from a constant source of M0 whose differentiation is stimulated by the external stimulus, CS . The M0 differentiation process, which is inhibited by I10, is up-regulated by Iγ and Iα at maximal rate k2 . As shown in Fig 1, TD also increases the M1 population and this process is also down-regulated by I10. Equation for describing the population dynamics of M1 with a decay rate, dM1, is then given by dM1dt=k1S11+(I10K1)2+k2(IγK2)2+(IαK3)21+(IγK2)2+(IαK3)2+(I10K4)2+k3TD11+(I10K5)2−dM1M1,(1) where k1=k1'M0, k2=k2'M0 (k1' and k2' are rate constants), and the regulation of the M0→M1 differentiation process is characterized by the Hill function whose coefficients are often chosen to be 2 to allow sufficient nonlinearity . In Eq 1, S is cigarette smoking intensity, whose values are given below.
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The population of dendritic cells, DC, originates from a constant source of immature D0 activated and up-regulated by the external stimulus, S and the damage signal from TD . This process is inhibited by I10. With a decay rate coefficient, dDC, equation for the DC dynamics is given by dDCdt=k5S11+(I10K8)2+k6TD11+(I10K9)2−dDCDC,(3) where k5=k5'D0, k6=k6'D0.
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| 99.94 |
T cells derive from a constant source of immature T-cells (Th0 or T0), which are recruited and activated by DC in combination with specific cytokines as mentioned previously. For T1, T2, T8, and T17, the associated T-cell differentiation processes are down-regulated by I10. With inclusion of a decay rate, equations describing the dynamics of these T-cell populations can be expressed by dTidt=ki(IiKTi)21+(IiKTi)2+(I10KTi,10)2+kipTi11+(I10KTi,I10p)2−dTiTi(4) where Ti represents T1, T2, or T8, ki = k7, k8, or k9; Ii = I12, I4, or I21, and kpi = kp1, kp2, or kp8, which are the maximum rates for the self-proliferation of T1, T2, and T8, respectively. The T17 dynamics, which is up-regulated by both I6 and Iβ and is down- regulated by I10, can be described by dT17dt=k10(IβI6KT17,I6)21+(IβI6KT17,I6)2+(I10KT17,I10)2+k17pT1711+(I10KT17,I10p)2−dT17T17(5)
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As shown in Fig 1, the cytokines are secreted from their associated immune cells. In particular, TD also contributes to the I4 and I6 productions, respectively, as discussed previously. The cytokine secretion processes are often down-regulated by I10. Accordingly, the population dynamics of the cytokines can be described by dIidt=∑j=1Nki,jCjfj(I10)+kIi,TDTDfjD(I10)−diIi(7) where Cj is the density of the j-th cell, which produces Ii with a maxima rate ki,j, kIi,TD is the rate constant of the Ii production by TD, fj(I10) is the regulation function, i.e., the Hill function fj(I10)=11+(I10KCj,I10)2(8)
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The Hill function, fDj (I10), in Eq 7 describes the I10 down-regulation of the process for I4 or I6 production by TD (Fig 1). Thus, for I4 and I6, fjD(I10)=11+(I10KIi,I10)2(9) whereas fD (I10) = 0 for other cytokines, whose population dynamics are governed by Equations A-G given in S1 File. For example, Eqs 10–11 describing the dynamics of I6 and I10 are given by dI6dt=kI6,M1M111+(I10KM1,I10)2+kI6,TDTD11+(I10KI6,I10)2−dI6I6(10) dI10dt=(kI10,M2M2+kI10,TgTg)11+(I10/KI10,I10)2−dI10I10(11)
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As discussed above, TD can be generated by S , M1 [24–26] and T8 , respectively. Iγ and I17 also cause TD . These TD generation processes are all down-regulated by I10 . As mentioned earlier, in the lung parenchyma the process for MMP-induced TD generation is also inhibited by Iβ . Equation describing the TD dynamics thus is dTDdt=k12S(1−TD)11+(I10K10)2+k13M1(1−TD)11+(I10K11)2+k14T8(1−TD)11+(I10K12)2+k15(IγK14)2+(I17K15)21+(IγK13)2+(I17K14)2+(I10K15)2+(IβK16)2−dTDTD(12) where TD is defined by damaged tissue/(normal tissue+damaged tissue)×100% and tissue repair is involved indirectly in the last term in Eq 12.
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The parameter values in the above equations were taken or estimated from literature in the following discussion. For those whose experimental data are not available, we performed a global sensitivity analysis to obtain order-of-magnitude estimates. Sensitivity analysis is useful to determine parameters that play a critical role in affecting the model outcome . In this study, a global sensitivity analysis outlined by Marino et al. is applied to assess the sensitivity of the model outcome, TD, at t = 4000 day in the stable stage of COPD to variations of all parameters in the model. Baseline values of these parameters are listed in Table A in S1 File. A range of 10% to 200% of the baseline values was specified for each parameter in a way similar to that in ref. 49. All parameters are assumed to be uniformly distributed in their corresponding interval and 2000 samples are generated for each parameter using the Latin Hypercube Sampling method . The partial rank correlation coefficient (PRCC) as well as p-value for each parameter is then calculated. The calculated PPRCs with the p-values smaller than 0.01 are presented in Figure A in S1 File. The values of PRCC range between -1 and +1 with the sign determining whether an increase in the parameter will increase (+) or decrease (-) the TD output.
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Our sensitivity analysis shows that a set of parameters including k13, k14, k15, and dTD have relatively large PRCCs (>0.1, Figure A in S1 File), suggesting that TD outcomes are sensitive to these parameters. This set of parameters, whose values can be changed from individual to individual, are associated with different network elements, positive feedback loops and pathways, which are critical in the CS-induced immune response and COPD progression. Therefore, investigation of model outcome changes with variations in these parameters can be of great use to disclose the cellular and molecular mechanisms of COPD as discussed below.
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Eqs 1–12 are applied in the following discussion with the parameters given in Table A in S1 File. The time course of the simulations is marked in days and units of cell populations and cytokine concentrations are in terms of cells per milliliter (cells/ml) and pmol per liter (pmol/L). The initial conditions of the variables for all activated cells and secreted cytokines are set to zero. CS-induced immune response and COPD progression are dependent upon the dose of CS inhalation . Figs 2–4 respectively show the time courses of the changes in the immune cells, cytokines and tissue damage (TD) in response to CS at a relatively high level of the cigarette smoking intensity, S (S = 1.67). Here, S is defined as the ratio of a CS dose to the minimal CS dose required to cause COPD with the parameters given in Table A in S1 File.
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As shown in Fig 2(B), M1 along with Iα and I6 [Fig 3(B)] rises to a peak at 12 days of CS exposure and then decays until day 60 because of the suppression of IL-10 secreted mainly by M2, showing an acute inflammatory response to the CS exposure. These results are qualitatively consistent with experiments in mice ) (see Figure B in S1 File for comparison with mice experiments). This period of time can be referred as step 1 in the progression of COPD as proposed by Agusti et al. [2–3]. After step 1, however, the inhibitory effect of IL-10 on M1 (as well as proinflammatory cytokines) is counteracted by the production of M1 up-regulated by TNF-α, TD, and INF-γ (Fig 1). It turns out that M1 is increased (but slowly) up to day 180 [Fig 2(B)]. This time period is referred as step 2 in the COPD progression [2, 3]. During this period, DC along with I12 (data not shown), I6 and Iβ [Fig 3(B)], and I21 (data not shown) increases slowly and gradually, resulting in the slow productions of T1, T17, and T8, respectively. Consequently, TD rises gradually during this period. After step 2, innate proinflammatory network elements start to go up quickly as M1 predominates over M2 again [Fig 2(A)]. In particular, I6 is increased more rapidly than Iα [Fig 3(A)], enhancing the production of T17 and T8 and allowing the adaptive immunity to play an increasingly important role in response to the CS exposure and in tissue damage (Fig 4). Notably, T8 goes up more quickly than T1 and T17 [Fig 4(A)], dominating in the late phase in the COPD progression. Eventually, TD together with the immune cells and cytokines goes to a steady state (stable COPD) as shown in Fig 4(B). The results for the above immune cells and cytokines at the steady state are listed in Table B in S1 File. Our results are in agreement with experiments [55–63].
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When S decreases to be less than unity, e.g., S = 0.7, the network exhibits a different dynamical feature. Upon CS exposure (S = 0.7), the network dynamical behaviors [Figs 5(B) and 6(B)] are similar (with smaller amplitudes) to those at S = 1.67 in the acute phase [Figs 2(B) and 3(B)]. After step 1, however, M1 together with other proinflammatory elements including Iα and I6 decreases to a low-level steady state (Figs 5 and 6) owing to the suppression of IL-10. Consequently, TD maintains at a rather low level (<5%) when the system reaches a steady state (Fig 7), indicating that COPD does not occur. Compared to the case of S = 1.67 (Figs 2–4), M2 (along with Tg, I10, and Iβ) (Figs 5–7) starts to predominate over M1 (with other proinflammatory components) during step 2 and remains predominant thereafter to a steady state where COPD does not occur.
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The results for the cases of S = 1.67 and S = 0.7 indicate that during the transition from the innate to the adaptive immunity, when M1 predominates over M2, the system would proceed to high-grade chronic inflammation and eventually toward COPD (Figs 2–4); while M2 (Fig 5) [Treg (Fig 7A)] is predominant, the acute inflammation turns into the low-grade chronic inflammation (Fig 6), and COPD does not occur [Fig 7(B)].
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