The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

Heather T Essigmann; Kristi L Hoffman; Joseph F Petrosino; Goo Jun; David Aguilar; Craig L Hanis; Herbert L DuPont; Eric L Brown

doi:10.1371/journal.pone.0258812

. 2021 Oct 20;16(10):e0258812. doi: 10.1371/journal.pone.0258812

The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

Heather T Essigmann ¹, Kristi L Hoffman ², Joseph F Petrosino ², Goo Jun ³, David Aguilar ³, Craig L Hanis ³, Herbert L DuPont ^1,⁴, Eric L Brown ^1,^*

Editor: Jane Foster⁵

PMCID: PMC8528330 PMID: 34669745

Abstract

Secretory IgA (SIgA) is released into mucosal surfaces where its function extends beyond that of host defense to include the shaping of resident microbial communities by mediating exclusion/inclusion of respective microbes and regulating bacterial gene expression. In this capacity, SIgA acts as the fulcrum on which host immunity and the health of the microbiota are balanced. We recently completed an analysis of the gut and salivary IgA-Biomes (16S rDNA sequencing of SIgA-coated/uncoated bacteria) in Mexican-American adults that identified IgA-Biome differences across the glycemic spectrum. As Th17:Treg ratio imbalances are associated with gut microbiome dysbiosis and chronic inflammatory conditions such as type 2 diabetes, the present study extends our prior work by examining the impact of Th17:Treg ratios (pro-inflammatory:anti-inflammatory T-cell ratios) and the SIgA response (Th17:Treg-SIgA axis) in shaping microbial communities. Examining the impact of Th17:Treg ratios (determined by epigenetic qPCR lymphocyte subset quantification) on the IgA-Biome across diabetes phenotypes identified a proportional relationship between Th17:Treg ratios and alpha diversity in the stool IgA-Biome of those with dysglycemia, significant changes in community composition of the stool and salivary microbiomes across glycemic profiles, and genera preferentially abundant by T-cell inflammatory phenotype. This is the first study to associate epigenetically quantified Th17:Treg ratios with both the larger and SIgA-fractionated microbiome, assess these associations in the context of a chronic inflammatory disease, and offers a novel frame through which to evaluate mucosal microbiomes in the context of host responses and inflammation.

Introduction

Secretory immunoglobulin A (SIgA), the most abundant immunoglobulin subtype produced by humans, maintains intestinal homeostasis by actively selecting the residents of the gut microbiota [1–3] and providing protection against pathogenic microorganisms and toxins [4, 5]. Additionally, SIgA regulates bacterial gene expression and motility, thereby impacting host metabolism, innate immune responses, and the integrity of the gut epithelium [6–11].

The fitness of the mucosal associated lymphatic tissue (MALT) (e.g., the gut, bronchus, conjunctiva, lacrimal ducts, salivary ducts, nasopharynx) can be measured by the diversity of the SIgA repertoire [11, 12]. The primary purpose of the MALT is to generate and transport SIgA to these host/microbiota interfaces where they target select taxa, thereby generating distinct SIgA coated (SIgA⁺) or SIgA uncoated (SIgA^-) microbial communities, i.e.: the IgA-Biome [12–14]. Because the quality of the SIgA response can be affected by T-cell phenotypes and mucosally-imprinted/activated plasma cells circulate across the MALT (including in the oral cavity lymphatics and Peyer’s patches and lymphoid follicles of the small and large intestines) [11], we examined the impact of Th17:T-regulatory (Treg) T-cell ratios on both the salivary and gut IgA-Biomes (i.e., the SIgA-coated and SIgA-uncoated microbes) in the context of diabetes phenotypes because: i) the oral cavity is the point of entry where microbes first encounter SIgA and some SIgA-coated taxa are present both in the oral cavity and the gut e.g., Lactobacillus, Haemophilus, and Prevotella [15–17], and ii) the impact of the Th17:Treg ratios in the context of the IgA-Biome and dysglycemia has not yet been explored and will better define the impact of the Th17:Treg/SIgA axis across mucosal microbiomes (Fig 1).

Fig 1 — IgA is produced at an approximate rate of 1 gram per day and is the primary acquired immune component secreted into mucosal surfaces [14]. Although B-cells populating these tissues can produce IgA without T-cell help (primarily in isolated lymphoid follicles; not depicted), somatic hypermutation resulting in the generation of affinity-matured (and highly specific) IgA is a product of germinal center reactions which involve T-cell help and take place almost exclusively within Peyer’s patches [14]. Because T-cells play a central role in the generation of affinity matured IgAs, the type of T-cell (Th17; green cells vs. Treg; red cells) providing B-cell help can impact the quantity and quality of the mucosal IgA response (red vs. green SIgAs and their respective targets). Therefore, the Th17:Treg-SIgA axis is likely to impact the IgA repertoire secreted into mucosal surfaces that in turn shapes the architecture of mucosal microbiomes. Inflammation and associated cytokine profiles triggered by chronic disease states like diabetes (i.e., dysglycemia) affect immune responses and can therefore alter Th17:Treg ratios and the resulting IgA response both in the gut and systemically. Activated T- and B-cells can migrate out of the gut via the efferent lymphatics and enter mesenteric lymph nodes or reach the circulation where they can migrate to other mucosal sites or return to the gut. T-cells in the circulation can then be isolated and Th17/Treg profiles defined. Created with Biorender.com.

Production of SIgA of high affinity and specificity is T-cell-dependent [18–22], and the nature of the T-cells populating the lamina propria and the lymphatic tissues of the gut and other mucosal sites can significantly impact the SIgA repertoire [23–26]. Without T-cells, high affinity, antigen-specific antibody responses are virtually nonexistent [27, 28], and poly-specific, cross-reactive SIgAs of low affinity and specificity dominate [19, 20, 22, 29–33]. The importance of a highly diversified (specific) SIgA repertoire in the maintenance of a complex gut microbiome is exemplified in mice with a specific mutation in activation induced cytidine deaminase (AID) that allows for antibody class switching in the absence of affinity maturation resulting in the production of normal amounts of low-affinity SIgA [34, 35]. The absence of high-affinity SIgA in such AID mutants skews the composition of the gut microbiome [36].

Immune dysregulation plays a key role in the pathogenesis and progression of type 2 diabetes [37]. Type 2 diabetes presents with impaired natural killer cell and B-cell function, abnormal proliferation of macrophages, and imbalances in the Th17:Treg T-cell axis [37–39]. Despite developing from a common precursor, Th17 and Treg T-cells have opposing inflammatory roles evident in autoimmune disease, infection, diabetes, and gut microbiome health [23, 24, 26, 40]. Tregs maintain homeostasis in part by regulating SIgA abundance and specificity, while SIgA reciprocally controls T-cell responses in the gut [41]. Skewed Th17:Treg profiles, observed in a number of chronic inflammatory conditions [40, 42], are due to both an increase in Th17 cells [23, 43–46] and a reduction in Tregs—the latter of which are vital in reversing the insulin resistance that typifies diabetes progression [46–49]. IgA production is also altered in those with hyperglycemia [21, 50, 51], with elevated serum IgA correlated with diabetes sequelae, including nephropathy, neuropathy, and vascular complications [3, 37, 50–57].

We recently conducted a preliminary study of the IgA-Biome, 16Sv4 rDNA profiling of the microbiota based on their SIgA coating, by diabetes status in a group of Mexican American adults [13]. We observed relative shifts in the SIgA⁺ and SIgA^- bacterial communities distinct from that of the overall microbiome [13], highlighting the unique perspective of IgA-Biome sequencing to identify novel relationships between the gut and salivary microbiota and the immune system in dysglycemia (those with prediabetes and diabetes). To better understand the complex dynamic between T-helper cell profiles and SIgA in the gut and saliva in the context of diabetes, we used qPCR to epigenetically quantify T-cell ratios to examine associations between the Th17:Treg axis and the gut/salivary IgA-Biomes across the glycemic spectrum [40, 58–61]. Each qPCR assay is specific for either demethylated FOXP3 (for Tregs) or demethylated IL17A (for Th17 cells) [40]. This technique provides relative and absolute immune cell counts applicable to fresh-frozen blood samples. Signals are digital; they indicate either one positive or negative value per cell rather than arbitrarily defined thresholds for “positivity” as in flow cytometric methods limited to liquid blood. These preliminary analyses identified significant associations by taxa and between alpha and beta diversity metrics of the gut and salivary microbiomes with changes in Th17:Treg ratios in the context of both the IgA-Biome and glycemic status.

Materials and methods

Study population and sample collection

The Diabetes Prevention Microbiome Study is an ongoing longitudinal cohort study designed to explore the complex etiologies of type 2 diabetes and relationships with the gut, nasal, and salivary microbiomes in Mexican American adults residing in Starr County, Texas, where obesity and type 2 diabetes prevalence far exceeds U.S. averages [62]. This study was approved by the Institutional Review Board Committee for the Protection of Human Subjects of the University of Texas Health Science Center (HSC-SPH-06-0225), and informed written consent was obtained from each participant. Stool, saliva, and buffy coat samples were collected upon enrollment from an age matched subset of 24 participants, 8 with normal glycemia, 8 with prediabetes, and 8 with diabetes [13]. Characterization of the gut and salivary IgA-Biomes and demographics of the study participants and were previously published by our group [13]. Briefly, 16.67% (4/24) of subjects were male and the mean age of participants was 47.75 years (standard deviation (SD) = 5.96). The body mass index (BMI) varied by diabetes phenotype (ANOVA = 0.05) with mean BMIs of 30.6 kg/m² (SD = 4.8), 33.0 kg/m² (SD = 3.0), and 37.7 kg/m² (SD = 7.6) for those classified as normoglycemic, prediabetic, and diabetic, respectively. In the present study, the buffy coats were used to define the Th17:Treg ratio (see below) to establish correlations between the IgA-Biome and diabetes profiles.

Diabetes classifications

Participants were defined as having normoglycemia (n = 8), prediabetes (n = 8), or diabetes (n = 8) based on the clinical cut-points for fasting glucose, 2-hour post load glucose levels, or percent glycated hemoglobin (%HbA1c) [13, 63]. Those with an fasting glucose level of 100 mg/dL– 125 mg/dL, a glucose tolerance with a 2-hour post glucose load of 140–199 mg/dL, or with %HbA1c 5.7% < HbA1c < 6.4% were classified as having prediabetes. Participants with a fasting glucose level ≥ 126 mg/dL, a 2-hour post load glucose level ≥ 200 mg/dL, or %HbA1c ≥ 6.5% were classified as having diabetes. Participants with blood values sufficient for classification with prediabetes or diabetes were newly diagnosed and treatment naïve to any diabetes-related medications. A definition of normoglycemic required all 3 measures be within normal range, while an abnormal value on any one test was sufficient to classify a subject as prediabetic or diabetic [13].

IgA-Biome analyses

Bacteria from stool and saliva samples were coated with fluorescently labeled antibodies to separate IgA-coated and uncoated organisms as previously described [13]. Sorting was conducted using a BD SORP FACS Aria™ II (Special Order Research Product from Beckton Dickinson, San Jose, CA), equipped with forward scatter photomultiplier tube (FSC-PMT) for improved small particle detection, at the Baylor College of Medicine Cytometry and Cell Sorting Core [13]. Sorted samples were collected into chilled, pre-labeled epi tubes, and stored at -80°C until subjected to 16S rRNA gene sequencing. Most stool and saliva IgA-Biome analyses were conducted on half a million sorted bacteria although some sorts contained fewer bacteria [13].

16Sv4 rDNA sequencing

Genomic DNA was extracted from sorted bacterial cells using the MagAttract PowerSoil DNA kit (Qiagen) following methods adapted from the NIH Human Microbiome Project [64]. In addition to sequencing IgA⁺ and IgA^- bacteria from each sample, unsorted (Presort) samples were collected for each sample prior to sorting and sequenced in parallel. The V4 region of the 16S rDNA gene was amplified by PCR using barcoded primers (515F, 806R) and sequenced on the MiSeq platform (Illumina) via the 2x250 bp paired-end protocol, targeting at least 10,000 reads per sample. The IgA-Biome profiles defined for the 24 participants described in the present study were previously published by our group [13].

Demultiplexed read pairs were merged using USEARCH v7.0.1090 [65] with the following parameters: merged length ≥252 bp, minimum 50 bp overlap with zero mismatches, and truncation quality >5. Merged reads were further filtered against a maximum expected error rate of 0.05 and PhiX sequences were removed. Reads were clustered into Operational Taxonomic Units (OTUs) at a similarity cutoff value of 97% using UPARSE [66] and underwent chimera removal via USEARCH [65]. OTUs were mapped against an optimized version of SILVA Database v132 [67] containing only sequences from the v4 region of the 16S rRNA gene.

Epigenetic characterization of T-helper cell profiles

Relative counts of CD3 T-cells, Treg, Th17 subpopulations were measured by quantitative epigenetic real-time PCR at the Berlin laboratory of Precision for Medicine [58]. DNA was extracted from buffy coats at the German Cancer Research Center from frozen pellets of nonintact, nucleated blood cells (EDTA buffy coats). Buffy coats were collected at the same time stool and saliva samples for IgA-Biome analyses were collected. DNA quality was assessed using Quant-iT PicoGreen dsDNA Assay (Life Technologies) and an OD 260/280 ratio between 1.7 and 2.0 was considered acceptable. Precision for Medicine personnel were blinded to the status of the samples received. Genomic DNA was treated with ammonium bisulphite, converting unmethylated cytosine to uracil while leaving methylated cytosine unchanged, and relative quantities of different lymphocytes were determined through methylation-specific qPCR assays for unmethylated CpG sites that were shown to be stably associated with specific immune cell type lineages as described [59–61]. Absence of CpG methylation in those gene loci (amplicon regions) was used for the epigenetic cell counting. These loci were CD3G/CD3D, FOXP3, and IL-17A for quantification of total CD3⁺ T-cells, regulatory T-cells (Tregs), and Th17 [59–61]. Total cell count was determined by methylation-specific qPCR for the GAPDH gene locus as described in Sehouli et al. [60]. The relative content of CD3 T-cells, Treg, and Th17 cells in the sample was determined by calculating the ratio of the respective three loci and the number of unmethylated GAPDH genes in the sample. In addition, the relative content of Treg and Th17 cells within the overall CD3 T-cell compartment was determined by calculating the ratios of unmethylated copies for FOXP3/CD3 and IL-17A/CD3 [58, 60, 61]. In addition, the relative composition of pro-inflammatory Th17 cells and immune suppressive Treg cells was calculated based on the copy numbers of unmethylated IL-17A/FOXP3 [58, 60, 61]. All measurements were run in duplicate and subjected to rigorous quality controls according to the laboratory’s quality management system that is accredited under ISO 17025. All plate runs conformed to performance limits set for standards, calibrators, and references that monitor standard linearity, bisulfite conversion, and qPCR efficiency. No samples failed quality control and the buffy coat samples used for the present study fell within established standards set by a healthy control cohort.

Statistical analysis

A two tailed t-test and ANOVA were used to assess Th17:Treg ratios by diabetes phenotypes using Stata 16 statistical software (Statacorp, College Station, TX). As described previously [13], the Agile Toolkit for Incisive Microbial Analyses 2 (ATIMA2) was used as an integrated solution for analyzing and visualizing microbiome data (https://atima.research.bcm.edu). Analyses of alpha diversity metrics were performed in ATIMA2 (supplementary figures) and in Stata 16 using ordinary least squares regression adjusted for body mass index and graphed using GraphPad Prism version 8.0 (San Diego, CA). Comparisons of community dispersion were assessed using unweighted UniFrac distance and visualized using ATIMA2 and statistical significance determined via Mann-Whitney U test or PERMANOVA adjusted for body mass index using the adonis and betadisper functions in the R package vegan [68, 69]. Differentially abundant taxa by Th17:Treg ratios were determined via Linear discriminant analysis effect size (LEfSe) [70] using the Galaxy web platform with parameters of alpha of 0.05 and threshold of logarithmic linear discriminative analysis (LDA) score of 2.0 [71]. Analyses using LEfSe were limited to taxa present in at least 10% of samples and visualized using GraphPad Prism version 8.0.

Results

Epigenetic quantification of the Th17:Treg ratio

Relative Th17 and Treg cell counts were defined for individuals with previously established stool and salivary IgA-Biomes using quantitative epigenetic real-time PCR and the Th17:Treg ratios calculated (Fig 2) [13]. Comparisons of Th17:Treg ratio between individuals with normoglycemia (n = 8), prediabetes (n = 8), or diabetes (n = 8) identified similarly elevated Th17:Treg ratios in dysglycemic individuals, those classified as having prediabetes (mean ± standard deviation (SD): 2.22±0.20; range: 1.514–3.239) and diabetes (mean ± SD: 1.96±0.19; range: 1.428–3.222), compared to individuals with normal blood sugar values (i.e., normoglycemia; mean ± SD: 1.58±0.16; range: 0.8177–2.006). For this reason, those with prediabetes and diabetes were combined into a dysglycemic category for some of the analyses. The Th17:Treg ratios of those with dysglycemia were significantly higher than those without dysglycemia (Fig 2A; p<0.042). Analyses of differences across the three diabetic phenotypes approached the margin of statistical significance (Fig 1B; ANOVA, p<0.068).

Fig 2 — Box plots depict the median (center line in each box) and box boundaries represent the range. (A) Th17:Treg ratio comparisons between those with normoglycemia and dysglycemia; *p<0.042 two-tailed t-test. (B) Th17:Treg ratio comparisons between those with normoglycemia, prediabetes, and diabetes; ANOVA p<0.0685.

Th17:Treg T-cell ratios were normally distributed (Shapiro-Wilk test of normality p-value = 0.08) with a median of 1.79 and mean of 1.90 (SD: 0.57, range: 0.82–3.24). Consequently, and because there is no established definitions for what constitutes high or low Th17:Treg ratios, the 24 participants were dichotomized into 2 categories above and below the median Th17:Treg ratio, indicative of a (pro-inflammatory profile or anti-inflammatory profile, respectively, for analyses requiring categorical characterization (beta diversity, LEfSe) [40, 72].

Th17:Treg ratios and glycemic status affect alpha and beta diversity

The impact of Th17:Treg ratios on the microbiota was first assessed independently of the IgA-Biome by analyzing the Presort samples (i.e. stool and saliva samples not subjected to SIgA⁺ or SIgA^- sorting) across the diabetes spectrum. Analyses of alpha diversity, an evaluation of within sample diversity, did not identify differences in bacterial evenness (Shannon diversity index) between those with normoglycemia or dysglycemia for either the stool or salivary microbiomes by T-cell ratio. Bacterial richness (observed OTUs [operational taxonomic units]) of stool, however, significantly increased with increasing Th17:Treg ratios in those with dysglycemia (Fig 3A; p<0.005). Although not statistically significant, the opposite trend, i.e., decreasing richness with increasing Th17:Treg ratio, was observed in those with normoglycemia (Fig 3A; p<0.066). When the same analyses were conducted across the three diabetes phenotypes those with prediabetes and diabetes followed a similar trend i.e., increasing OTUs with increasing Th17:Treg ratios (p<0.063 and p<0.035, respectively) (Fig 3B). Analyses of salivary samples did not identify statistically significant differences across the glycemic spectrum; however, a similar trend to that observed with stool samples was present, i.e., directly and inversely proportional relationships between OTU counts and Th17:Treg ratios for those with dysglycemia and normoglycemia, respectively (S1A and S1B Fig).

Fig 3 — Linear regression of OTU counts from Presort stool samples on Th17:Treg ratios by (A) normoglycemia and dysglycemia profiles, p-value of <0.066 (normoglycemic) and <0.005 (dysglycemic) or (B) by normoglycemia, prediabetes, and diabetes profiles, p-value of p<0.063 (prediabetes) p-value of p<0.035 (diabetes). P-values obtained using ordinary least squares regression adjusted for body mass index and result from a two-sided hypothesis.

Comparisons of individuals grouped as having pro-inflammatory or anti-inflammatory profiles based on Th17:Treg ratios were used to investigate beta, or between sample, diversity of stool and salivary Presort samples using unweighted UniFrac distances, a distance metric that incorporates phylogenetic relatedness and multivariate statistical techniques to determine whether microbial communities are significantly different. Principle coordinate analysis of unweighted Unifrac distances identified significant differences in community composition between individuals with different inflammatory phenotypes in both stool and saliva samples of those with dysglycemia (Fig 4B and 4D; p<0.021 and p<0.036, respectively). Significant differences in the stool or saliva Presort samples of those with normoglycemia were not observed (Fig 4A and 4C). Analyses by prediabetes and diabetes phenotypes identified significant differences in the stool microbiome of those with prediabetes (S2 Fig; p<0.03). No significant differences in the salivary microbiome in those with prediabetes or diabetes were observed (S3 Fig).

Fig 4 — Principal coordinate analyses of unweighted UniFrac distances of stool (A, B) and (C, D) saliva Presort samples in those with (A, C) normoglycemia and (B, D) dysglycemia. P-values determined using PERMANOVA and include adjustments for body mass index.

We used the biomarker discovery tool Linear Discriminant Analysis Effect Size (LEfSe) [73] to identify preferentially abundant genera in the stool and saliva Presort samples of individuals classified as having pro- or anti-inflammatory profiles (Fig 5). Six organisms were found to be differentially abundant of the stool of those with pro-inflammatory Th17:Treg ratios, including members of the genus Faecalibacterium (p = 0.008), while only one genus, Fusicatenbacter (p = 0.039), was found to be differentially abundant of the stool of those with an anti-inflammatory profile. In saliva, Granulicatella (p = 0.031) and members of genera Selenomonas (p = 0.036) and Eubacterium (p = 0.040) were preferentially abundant in the Presort salivary microbiome of those with anti- and pro-inflammatory Th17:Treg profiles, respectively.

Fig 5 — LEfSe identified genera (or lowest taxonomic designation available) significantly associated with pro- or anti-inflammatory Th17:Treg profiles in (A) stool and (B) saliva Presort samples. Analyses were conducted using parameters α<0.05 and linear discriminant analysis (LDA) threshold ≥2.0.

Associations between the IgA-Biome, Th17:Treg ratios, and glycemic status

The observation that almost all human SIgA protein sequences show evidence of somatic hypermutation and affinity maturation indicates that T-cell-dependent responses likely play a dominant role in shaping the IgA-Biome. More specifically as it relates to IgA production, B-cells of the mucosal lymphoid tissues are different form B-cells populating non-mucosal lymphoid tissues in that they can undergo class switching from IgM/IgD to IgA without T-cell help; however, like B-cells at all other anatomic sites, mucosal B-cells cannot produce high affinity antibodies, the products of germinal center reactions and somatic hypermutation, without the aid of T-cells [11, 74]. We therefore evaluated the interaction between Th17:Treg ratios and the IgA-Biome in the context of glycemic status. Linear regression of alpha diversity metrics on Th17:Treg ratios by stool IgA-Biome compartments (SIgA⁺ and SIgA^-) identified significant increases in richness (OTU count) with increasing Th17:Treg ratios in those with dysglycemia in both compartments (Fig 6A; p<0.001). When the analysis of Th17:Treg ratios and the IgA-Biome was conducted across the three diabetes phenotypes similar trends were observed (Fig 6B; p<0.132 and p<0.018 in the IgA⁺ and IgA^- compartments, respectively of those with prediabetes and p<0.012 and p<0.022 in the IgA⁺ and IgA^- compartments, respectively of those with diabetes). The opposite trend, although not significant, was observed in normoglycemic individuals, whereby richness decreased in both SIgA compartments as the Th17:Treg ratio increased (Fig 6). Analysis of alpha diversity metrics in the salivary IgA-Biome did not identify significant associations (S4A and S4B Fig).

Fig 6 — (A) Linear regression of OTU counts on Th17:Treg ratios in individuals with normoglycemia and dysglycemia. p<0.314 and p<0.118 in the IgA⁺ and IgA^- compartments, respectively, for those in the normoglycemic group and p<0.001 for both the IgA⁺ and IgA^- compartments for those in the dysglycemic group. (B) Linear regression of OTU counts on Th17:Treg ratios in those individuals with normoglycemia, prediabetes, and diabetes. p<0.132 and p<0.018 in the IgA⁺ and IgA^- compartments, respectively, for individuals with prediabetes and p<0.012 and p<0.022 in the IgA⁺ and IgA^- compartments, respectively, for individuals with diabetes. P-values obtained using ordinary least squares regression adjusted for body mass index and result from a two-sided hypothesis.

Principle coordinate analyses of unweighted UniFrac distances (Fig 7) identified significant differences in microbial community composition by Th17:Treg ratio-derived inflammatory phenotypes in the SIgA⁺ stool samples of those with dysglycemia (Fig 7B; p<0.003). No differences by inflammatory phenotype were observed in those without diabetes in the SIgA^- fraction of stool samples (Fig 7A) nor in either SIgA compartment of the salivary microbiome. Within the dysglycemic group, similar analyses comparing inflammatory phenotype of those with prediabetes and diabetes identified significant differences only in the stool SIgA⁺ and SIgA^- compartments of those with prediabetes (S5 Fig). No significant differences in the salivary IgA-Biome by inflammatory phenotype were observed in subjects with prediabetes or diabetes (S6 Fig).

Comparisons across diabetes phenotypes of unweighted UniFrac distances between each subject within an inflammatory profile, however, revealed significant differences in community composition within the SIgA compartments of stool (Fig 8) and saliva (S7 Fig). The gut microbial communities from individuals with a pro-inflammatory phenotype within the SIgA⁺ fraction of those with normoglycemia (p<0.0152) and the SIgA^- fraction in those with dysglycemia (Fig 8; p<0.023) were significantly less diverse than those with a lower inflammatory profile. In saliva, independent of SIgA coating (i.e., Presort sample) the microbial communities of normoglycemic individuals with a pro-inflammatory profile were more diverse than those with an anti-inflammatory profile (S7 Fig).

Fig 8 — Box plots compare unweighted UniFrac distances between all individuals within each inflammatory phenotype, respectively, by SIgA compartment and glycemic profile. Normoglycemic subjects with a pro-inflammatory phenotype have SIgA⁺ bacterial community compositions more similar to each other than do those with an anti-inflammatory phenotype (*p<0.0152). Similarly, dysglycemic subjects with a pro-inflammatory phenotype have SIgA^- bacterial community compositions more similar to each other than do those with an anti-inflammatory phenotype (**p<0.023). P-values determined using the Mann-Whitney U-test.

LEfSe also identified preferentially abundant genera between individuals with Th17:Treg ratios above and below the median value in both the stool and salivary IgA-Biomes (Fig 9). Similar to what was observed for Presort samples, the genus Faecalibacterium had the highest LDA score in subjects with a pro-inflammatory phenotype in the SIgA⁺ compartment (p = 0.014). The similarity between taxa identified in both the Presort (Fig 5) and SIgA⁺ fractions (Fig 9), compared to the taxa present in the SIgA^- fraction, suggested SIgA⁺ taxa drive the significant associations observed in the Presort analyses. Only Allinsonella was present in both the Presort (p = 0.018) and SIgA^- (0.014) fractions and Faecalibacterium was present across all three compartments (Fig 9). Dominant taxa in the salivary IgA-Biome included Mogibacterium in the SIgA⁺ (p = 0.038) compartment of those with a pro-inflammatory phenotype. No significant taxa in the SIgA^- compartment in those with a pro-inflammatory phenotype were identified. Analysis of individuals classified as having an anti-inflammatory phenotype identified Cardiobacterium (p = 0.032) and Selenomonas (p = 0.041) as preferentially abundant in the SIgA⁺ compartment and Gemella (p = 0.007), Oribacterium (p = 0.034), and Stomatobaculum (p = 0.024) in the SIgA^- compartment.

Fig 9 — LEfSe identified genera (or next taxonomic designation available) associated with the Th17:Treg ratios in (A) stool SIgA⁺, (B) stool SIgA^-, (C) saliva SIgA⁺, or (D) saliva SIgA^- compartments. Analyses were conducted using parameters α<0.05 and linear discriminant analysis (LDA) threshold ≥2.0.

Discussion

The relationship between the gut microbiome and human health has become increasingly evident [2, 11, 25, 37, 75–82]. We now recognize that disturbances of the gut microbiota trigger ripple effects that impact immunoglobulin production, T-helper cell responses, vaccine efficacy, infection [2, 8, 21, 83–88], metabolism [89–91], nutrition [92], and even the composition of microbiota in the lungs and oral mucosa [11, 21, 93–96]. That the gut microbiome can influence inflammation and host metabolism in a manner consistent with what is observed at the onset of type 2 diabetes, intimates a possible causal role of the gut microbiome in type 2 diabetes initiation or progression [97, 98]. Therefore, identifying the microbial constituents that have the host’s immunological attention or, equally important, organisms that should have the host’s attention but for unknown reasons do not, is critical to fully understanding the complex etiology of type 2 diabetes.

In the present study we aimed to identify associations across the Th17:Treg-SIgA axis and glycemic profiles by studying a population whose stool and salivary IgA-Biomes had been previously characterized [13]. Even with the small sample size examined, these preliminary analyses identified significant differences in alpha and beta diversity metrics and taxonomy across T-cell ratios. Differences were most notable in the stool IgA-Biome using both continuously measured and dichotomized Th17:Treg ratios, with categorical classification based on the premise that higher ratios corresponded to higher inflammatory profiles [40, 72].

Analysis of within-subject variation revealed significant increases in bacterial richness (OTU counts) with increasing Th17:Treg ratios in those with dysglycemia (e.g., prediabetes and diabetes phenotypes combined). When the dysglycemic group was separated into those with prediabetes and diabetes, the association between Th17:Treg ratios and OTU counts persisted. These significant effects held across the unsorted Presort sample and both SIgA compartments (SIgA⁺ and SIgA^-) demonstrating that bacterial richness positively correlated with measures of inflammation only in those with abnormal blood sugar values. Although analyses of saliva did not identify significant associations between relative abundance and Th17:Treg ratios, it should be noted that same trend for those with dysglycemia (increasing Th17:Treg rations correlated with increasing OTUs) and normoglycemia (increasing Th17:Treg rations correlated with decreasing OTUs) was observed. This pattern implicates a roll for Th17 and Treg cells in shaping the microbiota across the MALT.

The premise that increasing diversity is associated with health may therefore need re-evaluation, particularly in the context of the IgA-Biome [99, 100]. That is, increased diversity may not be beneficial, particularly in SIgA⁺ or SIgA^- fractions, if SIgA responses are focused on microbiota critical to gut homeostasis or if the increase in diversity is the result of an outgrowth of pathobionts associated with disease as observed in some irritable bowel disease patients [11]. A recent study that observed healthy ageing and longevity was associated with reduced microbial diversity and loss of specific taxa further disrupts the paradigm that increased diversity is always healthful [101].

We also observed significant differences in community composition (beta diversity) between individuals categorized by their inflammatory profiles in the stool and salivary microbiomes of those with dysglycemia (Fig 4). In stool, these changes were most strongly associated with the SIgA⁺ compartment (Fig 7) and may implicate SIgA-coated organisms in the pathological processes associated with higher Th17:Treg ratios and inflammation [102]. A similar but not significant trend in the SIgA⁺ fraction of those with normal blood sugar values may indicate that changes to SIgA-targeted communities is heightened in the context of an inflammatory disease like type 2 diabetes. It is also remarkable that even in this small data set, a general trend was observed, particularly in stool IgA-Biome compartments and the Presort samples towards decreasing community diversity (i.e., lower UniFrac distance) in individuals with a more inflammatory T-cell profile independent of glycemic status (Fig 8). This suggested that individuals with a higher inflammatory profile have communities more similar to each other compared to those with a lower inflammatory profile (Fig 8). This general trend was not observed for the salivary IgA-Biome. In fact, the Presort of those with normoglycemia had the opposite association i.e., less diversity between salivary communities of those with reduced inflammatory profiles compared to those with higher inflammatory profiles (S7 Fig).

Preferentially abundant genera across the IgA-Biome in the context of inflammatory phenotypes based Th17:Treg ratios were identified using LEfSe. In the stool Presort samples, genera significantly associated with pro-inflammatory profiles appeared to be driven by SIgA-targeted organisms since many taxa (Colinsella, Lachnospiraceeae, Phascolarcitobacterium, and Faecalibacterium) were found enriched in both the Presort and SIgA⁺ fractions. Parabacteroides and Erysipelotrichaceae were unique to the SIgA⁺ fraction and Allinsonella and Negativibacillus unique to the stool Presort. The only genus present in both SIgA⁺ and SIgA^- compartments was Faecalibacterium, a genus that only contains 22 species, 12 of which inhabit the human gut [103]. Although F. prausnitzii has been associated with chronic inflammation, other Faecalibacterium species are leading candidates for the development of probiotics and biotherapeutics [103, 104]. It is possible that different Faecalibacterium species/strains with varying levels of antigenicity may be responsible for the presence of this species in both the SIgA⁺ and SIgA^- compartments; similar to the two strains of Bacteroides fragilis described by Palm et al., one associated with irritable bowel disease and the other with a healthy gut [102]. Identification of Collinsella was also interesting since increasing relative abundance of this organism has previously been positively correlated to diabetes and negatively correlated to weight loss, suggesting that manipulation/reduction of diabetes-associated taxa such as Collinsella may be an avenue through which to improve glucose homeostasis [105–107].

Although the predominance of our findings involved the gut, developing an understanding of the salivary IgA-Biome in the context of health and disease may be as important since i) the oral mucosa is the first point of contact between SIgA and the microbiota, ii) SIgA responses stimulated at distinct mucosal sites recirculate between compartments thereby affecting each site’s SIgA responses, iii) many SIgA-targeted oral microbiota can be detected in the gut, and iv) oral dysbiosis impacts gut microbiome homeostasis [11, 108]. Although the differences observed in the salivary IgA-Biome in the present analyses were not as striking as those described for the gut IgA-Biome, our small sample size and the fewer number of taxa that colonize the oral mucosa (relative to the number colonizing the gut) may have muted our sensitivity to alterations in salivary microbiota with changing Th17:Treg ratios [108]. Nevertheless, we observed significant changes in salivary IgA-Biome beta diversity between individuals dichotomized by Th17:Treg ratio in those with dysglycemia. This supported the hypothesis that inflammatory changes as a consequence of changing Th17:Treg ratios and disease status have fundamental impacts across mucosal-associated lymphoid tissues and that interventions targeting multiple mucosal sites as a means of re-establishing systemic symbiosis across mucosal compartments may be possible.

The role of the gut microbiota in shaping and maintaining immune health cannot be overstated. Gut microbial communities significantly impact the Th17:Treg balance, an association easily appreciated in germ free mice that present with impaired Treg function [24, 100]. Conversely, in both mice and humans, Treg deficiency resulted in chronic inflammation and the elicitation of autoimmune responses in various organs [100] and specific strains of Bacteroides fragilis and Clostridia species have been observed as key drivers of Treg development and suppressors of Th17 responses [24]. We propose a scenario where host factors (e.g., genetics, SIgA) and the environment (e.g., diet, antibiotics, infections) shape a core microbiota. The microbiota, via interactions with immune components such as innate immune receptors in the gut and dendritic cells (in addition to other antigen presenting cells of the lamina propria), establish a cytokine milieu that shapes the T-helper cell profiles of the mucosa. B-cells responding to the same microbial stimuli will in turn receive help signals from activated T-cells required for the establishment diverse and specific SIgA. The cycle is completed when SIgA interacts with and re-shapes the microbiota that originally stimulated its generation [11].

Our preliminary results demonstrated that an association exists between dysglycemic status and epigenetically quantified Th17:Treg ratios across the SIgA Biomes. This pilot project is the first of its kind to associate epigenetically quantified Th17:Treg ratios with both the larger and SIgA-fractionated microbiome, assess these associations in the context of a chronic inflammatory disease with increasing global prevalence and offers a novel frame through which to evaluate mucosal microbiomes in the context of host responses and inflammation. Larger sample sizes and deeper analyses into the specific species/strains associated with these phenotypic changes are needed to more fully illuminate the effects of inflammation on the microbiome and will be crucial to developing interventions designed to alter microbial populations as a means of establishing gut/immune homeostasis that in turn drive systemic health.

Supporting information

S1 Fig. Impact of Th17:Treg ratios on alpha diversity of the salivary microbiome by diabetes phenotype.

Linear regression of OTU counts from Presort stool samples on Th17:Treg ratios by (A) normoglycemia and dysglycemia profiles, p-value of <0.066 (normoglycemic) and <0.198 (dysglycemic) or (B) by normoglycemia, prediabetes, and diabetes profiles, p-value of p<0.136 (prediabetes) p-value of p<0.381 (diabetes). P-values obtained using ordinary least squares regression and result from a two-sided hypothesis.

(TIFF)

Click here for additional data file.^{(5.6MB, tiff)}

S2 Fig. Principle coordinate analyses of UniFrac distances of stool Presort samples by Th17:Treg ratios in those with normoglycemia, prediabetes, and diabetes.

Principal coordinate analyses of unweighted UniFrac distances of the stool microbiomes of those with (A) normoglycemia, (B) prediabetes, or (C) diabetes. P-values determined using PERMANOVA.

(TIFF)

Click here for additional data file.^{(4.6MB, tiff)}

S3 Fig. Principle coordinate analyses of UniFrac distances of saliva Presort samples by Th17:Treg ratios in those with normoglycemia, prediabetes, and diabetes.

Principal coordinate analyses of unweighted UniFrac distances of the stool microbiomes of those with (A) normoglycemia, (B) prediabetes, or (C) diabetes. P-values determined using PERMANOVA.

(TIFF)

Click here for additional data file.^{(4.6MB, tiff)}

S4 Fig. Impact of Th17:Treg ratios on alpha diversity of the saliva IgA-Biome by diabetes phenotype.

(A) Linear regression of OTU counts on Th17:Treg ratios in those individuals with normoglycemia and dysglycemia. Normoglycemic p<0.958 and p<0.359 in the IgA⁺ and IgA^- compartments, respectively, and dysglycemic p<0.715 and p<0.533 in the IgA⁺ and IgA^- compartments, respectively. (B) Linear regression of OTU counts on Th17:Treg ratios in those individuals with normoglycemia, prediabetes, and diabetes by SIgA coating. For those individuals with normoglycemia, p<0.958 and p<0.359 in the IgA⁺ and IgA^- compartments, respectively, for those individuals with prediabetes, p<0.819 and p<0.376 in the IgA⁺ and IgA^- compartments, respectively, and in those with diabetes, p<0.097 and p<0.567 in the IgA⁺ and IgA^- compartments, respectively.

(TIFF)

Click here for additional data file.^{(6.9MB, tiff)}

S5 Fig. Principal coordinate analyses of UniFrac distances of the stool IgA-Biome by dichotomized Th17:Treg ratios across diabetes phenotype.

Principal coordinate analyses of unweighted UniFrac distances of the (A, B, C) SIgA⁺ and (C, D, E) SIgA^- compartments of individuals dichotomized by their inflammatory profiles. (A, D) Normoglycemic and (B, E) prediabetic, and (C, F) diabetes. Statistical significance was determined using PERMANOVA.

(TIFF)

Click here for additional data file.^{(4.9MB, tiff)}

S6 Fig. Principal coordinate analyses of UniFrac distances of the salivary IgA-Biome by dichotomized Th17:Treg ratios across diabetes phenotype.

(TIFF)

Click here for additional data file.^{(4.8MB, tiff)}

S7 Fig. Comparisons of UniFrac distances between of the salivary IgA-Biome of normal and dysglycemic individuals categorized by Th17:Treg ratio.

Box plots compare unweighted UniFrac distances between all individuals within each inflammatory phenotype, respectively, by SIgA compartment and glycemic profile. *p<0.023 determined using the Mann-Whitney U-test.

(TIFF)

Click here for additional data file.^{(2.3MB, tiff)}

Acknowledgments

We would like to thank the participants for their willingness to be a part of our study and the amazing, tireless Starr County Health Studies’ staff that are responsible for enrolling, consenting, and collecting the samples that allow us to conduct our various studies.

Data Availability

The 16S RNA sequencing data are available in NCBI under BioProject accession number PRJNA662100. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA662100.

Funding Statement

National Institutes of Health grant R01DK116378 to ELB and CLH.

References

1.Swiatczak B, Cohen IR. Gut feelings of safety: tolerance to the microbiota mediated by innate immune receptors. Microbiol Immunol. 2015;59(10):573–85. Epub 2015/08/27. doi: 10.1111/1348-0421.12318 . [DOI] [PubMed] [Google Scholar]
2.Shulzhenko N, Morgun A, Hsiao W, Battle M, Yao M, Gavrilova O, et al. Crosstalk between B lymphocytes, microbiota and the intestinal epithelium governs immunity versus metabolism in the gut. Nat Med. 2011;17(12):1585–93. Epub 2011/11/22. doi: 10.1038/nm.2505 ; PubMed Central PMCID: PMC3902046. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Vehkala L, Ukkola O, Kesaniemi YA, Kahonen M, Nieminen MS, Salomaa V, et al. Plasma IgA antibody levels to malondialdehyde acetaldehyde-adducts are associated with inflammatory mediators, obesity and type 2 diabetes. Ann Med. 2013;45(8):501–10. Epub 2013/10/18. doi: 10.3109/07853890.2013.841322 . [DOI] [PubMed] [Google Scholar]
4.Bunker JJ, Erickson SA, Flynn TM, Henry C, Koval JC, Meisel M, et al. Natural polyreactive IgA antibodies coat the intestinal microbiota. Science. 2017;358(6361). Epub 2017/10/04. doi: 10.1126/science.aan6619 ; PubMed Central PMCID: PMC5790183. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Pabst O. New concepts in the generation and functions of IgA. Nat Rev Immunol. 2012;12(12):821–32. Epub 2012/10/30. doi: 10.1038/nri3322 . [DOI] [PubMed] [Google Scholar]
6.Cullender TC, Chassaing B, Janzon A, Kumar K, Muller CE, Werner JJ, et al. Innate and adaptive immunity interact to quench microbiome flagellar motility in the gut. Cell Host Microbe. 2013;14(5):571–81. Epub 2013/11/19. doi: 10.1016/j.chom.2013.10.009 ; PubMed Central PMCID: PMC3920589. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Fagarasan S, Muramatsu M, Suzuki K, Nagaoka H, Hiai H, Honjo T. Critical roles of activation-induced cytidine deaminase in the homeostasis of gut flora. Science. 2002;298(5597):1424–7. Epub 2002/11/16. doi: 10.1126/science.1077336 . [DOI] [PubMed] [Google Scholar]
8.Kawamoto S, Maruya M, Kato LM, Suda W, Atarashi K, Doi Y, et al. Foxp3(+) T cells regulate immunoglobulin a selection and facilitate diversification of bacterial species responsible for immune homeostasis. Immunity. 2014;41(1):152–65. Epub 2014/07/16. doi: 10.1016/j.immuni.2014.05.016 . [DOI] [PubMed] [Google Scholar]
9.Kubinak JL, Round JL. Do antibodies select a healthy microbiota? Nat Rev Immunol. 2016;16(12):767–74. Epub 2016/11/08. doi: 10.1038/nri.2016.114 . [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Chen K, Magri G, Grasset EK, Cerutti A. Rethinking mucosal antibody responses: IgM, IgG and IgD join IgA. Nat Rev Immunol. 2020. Epub 2020/02/06. doi: 10.1038/s41577-019-0261-1 . [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Huus KE, Petersen C, Finlay BB. Diversity and dynamism of IgA-microbiota interactions. Nat Rev Immunol. 2021. Epub 2021/02/12. doi: 10.1038/s41577-021-00506-1 . [DOI] [PubMed] [Google Scholar]
12.Cesta MF. Normal structure, function, and histology of mucosa-associated lymphoid tissue. Toxicol Pathol. 2006;34(5):599–608. Epub 2006/10/28. doi: 10.1080/01926230600865531 . [DOI] [PubMed] [Google Scholar]
13.Brown EL, Essigmann HT, Hoffman KL, Palm NW, Gunter SM, Sederstrom JM, et al. Impact of Diabetes on the Gut and Salivary IgA Microbiomes. Infect Immun. 2020;88(12). Epub 2020/09/10. doi: 10.1128/IAI.00301-20 ; PubMed Central PMCID: PMC7671898. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Hand TW, Reboldi A. Production and Function of Immunoglobulin A. Annu Rev Immunol. 2021;39:695–718. Epub 2021/03/02. doi: 10.1146/annurev-immunol-102119-074236 . [DOI] [PubMed] [Google Scholar]
15.Fadlallah J, El Kafsi H, Sterlin D, Juste C, Parizot C, Dorgham K, et al. Microbial ecology perturbation in human IgA deficiency. Sci Transl Med. 2018;10(439). Epub 2018/05/04. doi: 10.1126/scitranslmed.aan1217 . [DOI] [PubMed] [Google Scholar]
16.Goncalves P, Charbit B, Lenoir C, Milieu Interieur C, Duffy D, Fischer A, et al. Antibody-coated microbiota in nasopharynx of healthy individuals and IVIg-treated patients with hypogammaglobulinemia. J Allergy Clin Immunol. 2020;145(6):1686–90 e4. Epub 2020/01/20. doi: 10.1016/j.jaci.2020.01.002 . [DOI] [PubMed] [Google Scholar]
17.Madhwani T, McBain AJ. The Application of Magnetic Bead Selection to Investigate Interactions between the Oral Microbiota and Salivary Immunoglobulins. PLoS One. 2016;11(8):e0158288. Epub 2016/08/03. doi: 10.1371/journal.pone.0158288 ; PubMed Central PMCID: PMC4970730. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Lycke N, Eriksen L, Holmgren J. Protection against cholera toxin after oral immunization is thymus-dependent and associated with intestinal production of neutralizing IgA antitoxin. Scand J Immunol. 1987;25(4):413–9. Epub 1987/04/01. doi: 10.1111/j.1365-3083.1987.tb02208.x [DOI] [PubMed] [Google Scholar]
19.Harris NL, Spoerri I, Schopfer JF, Nembrini C, Merky P, Massacand J, et al. Mechanisms of neonatal mucosal antibody protection. J Immunol. 2006;177(9):6256–62. Epub 2006/10/24. doi: 10.4049/jimmunol.177.9.6256 . [DOI] [PubMed] [Google Scholar]
20.Peterson DA, Planer JD, Guruge JL, Xue L, Downey-Virgin W, Goodman AL, et al. Characterizing the interactions between a naturally primed immunoglobulin A and its conserved Bacteroides thetaiotaomicron species-specific epitope in gnotobiotic mice. J Biol Chem. 2015;290(20):12630–49. Epub 2015/03/22. doi: 10.1074/jbc.M114.633800 ; PubMed Central PMCID: PMC4432283. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Macpherson AJ, Yilmaz B, Limenitakis JP, Ganal-Vonarburg SC. IgA Function in Relation to the Intestinal Microbiota. Annu Rev Immunol. 2018;36:359–81. Epub 2018/02/06. doi: 10.1146/annurev-immunol-042617-053238 . [DOI] [PubMed] [Google Scholar]
22.Macpherson AJ, Gatto D, Sainsbury E, Harriman GR, Hengartner H, Zinkernagel RM. A primitive T cell-independent mechanism of intestinal mucosal IgA responses to commensal bacteria. Science. 2000;288(5474):2222–6. Epub 2000/06/24. doi: 10.1126/science.288.5474.2222 . [DOI] [PubMed] [Google Scholar]
23.Ip B, Cilfone NA, Belkina AC, DeFuria J, Jagannathan-Bogdan M, Zhu M, et al. Th17 cytokines differentiate obesity from obesity-associated type 2 diabetes and promote TNFalpha production. Obesity (Silver Spring). 2016;24(1):102–12. Epub 2015/11/19. doi: 10.1002/oby.21243 ; PubMed Central PMCID: PMC4688084. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Omenetti S, Pizarro TT. The Treg/Th17 Axis: A Dynamic Balance Regulated by the Gut Microbiome. Front Immunol. 2015;6:639. Epub 2016/01/07. doi: 10.3389/fimmu.2015.00639 ; PubMed Central PMCID: PMC4681807. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Viladomiu M, Kivolowitz C, Abdulhamid A, Dogan B, Victorio D, Castellanos JG, et al. IgA-coated E. coli enriched in Crohn’s disease spondyloarthritis promote TH17-dependent inflammation. Sci Transl Med. 2017;9(376). Epub 2017/02/10. doi: 10.1126/scitranslmed.aaf9655 ; PubMed Central PMCID: PMC6159892. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Wang M, Chen F, Wang J, Zeng Z, Yang Q, Shao S. Th17 and Treg lymphocytes in obesity and Type 2 diabetic patients. Clin Immunol. 2018;197:77–85. Epub 2018/09/16. doi: 10.1016/j.clim.2018.09.005 . [DOI] [PubMed] [Google Scholar]
27.Turner JS, Benet ZL, Grigorova IL. Signals 1, 2 and B cell fate or: Where, when and for how long? Immunol Rev. 2020;296(1):9–23. Epub 2020/05/30. doi: 10.1111/imr.12865 . [DOI] [PubMed] [Google Scholar]
28.Yoshitomi H, Ueno H. Shared and distinct roles of T peripheral helper and T follicular helper cells in human diseases. Cell Mol Immunol. 2020. Epub 2020/09/02. doi: 10.1038/s41423-020-00529-z . [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Quan CP, Berneman A, Pires R, Avrameas S, Bouvet JP. Natural polyreactive secretory immunoglobulin A autoantibodies as a possible barrier to infection in humans. Infect Immun. 1997;65(10):3997–4004. Epub 1997/10/08. PubMed Central PMCID: PMC175574. doi: 10.1128/iai.65.10.3997-4004.1997 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.He B, Xu W, Santini PA, Polydorides AD, Chiu A, Estrella J, et al. Intestinal bacteria trigger T cell-independent immunoglobulin A(2) class switching by inducing epithelial-cell secretion of the cytokine APRIL. Immunity. 2007;26(6):812–26. Epub 2007/06/16. doi: 10.1016/j.immuni.2007.04.014 . [DOI] [PubMed] [Google Scholar]
31.Shimoda M, Inoue Y, Azuma N, Kanno C. Natural polyreactive immunoglobulin A antibodies produced in mouse Peyer’s patches. Immunology. 1999;97(1):9–17. Epub 1999/08/14. doi: 10.1046/j.1365-2567.1999.00755.x ; PubMed Central PMCID: PMC2326812. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Bunker JJ, Flynn TM, Koval JC, Shaw DG, Meisel M, McDonald BD, et al. Innate and Adaptive Humoral Responses Coat Distinct Commensal Bacteria with Immunoglobulin A. Immunity. 2015;43(3):541–53. Epub 2015/09/01. doi: 10.1016/j.immuni.2015.08.007 ; PubMed Central PMCID: PMC4575282. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Okai S, Usui F, Yokota S, Hori IY, Hasegawa M, Nakamura T, et al. High-affinity monoclonal IgA regulates gut microbiota and prevents colitis in mice. Nat Microbiol. 2016;1(9):16103. Epub 2016/08/27. doi: 10.1038/nmicrobiol.2016.103 . [DOI] [PubMed] [Google Scholar]
34.Shinkura R, Ito S, Begum NA, Nagaoka H, Muramatsu M, Kinoshita K, et al. Separate domains of AID are required for somatic hypermutation and class-switch recombination. Nat Immunol. 2004;5(7):707–12. Epub 2004/06/15. doi: 10.1038/ni1086 . [DOI] [PubMed] [Google Scholar]
35.Wei M, Shinkura R, Doi Y, Maruya M, Fagarasan S, Honjo T. Mice carrying a knock-in mutation of Aicda resulting in a defect in somatic hypermutation have impaired gut homeostasis and compromised mucosal defense. Nat Immunol. 2011;12(3):264–70. Epub 2011/01/25. doi: 10.1038/ni.1991 . [DOI] [PubMed] [Google Scholar]
36.Kato LM, Kawamoto S, Maruya M, Fagarasan S. The role of the adaptive immune system in regulation of gut microbiota. Immunol Rev. 2014;260(1):67–75. Epub 2014/06/20. doi: 10.1111/imr.12185 . [DOI] [PubMed] [Google Scholar]
37.Zhou T, Hu Z, Yang S, Sun L, Yu Z, Wang G. Role of Adaptive and Innate Immunity in Type 2 Diabetes Mellitus. J Diabetes Res. 2018;2018:7457269. Epub 2018/12/12. doi: 10.1155/2018/7457269 ; PubMed Central PMCID: PMC6250017. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Ferraro A, Socci C, Stabilini A, Valle A, Monti P, Piemonti L, et al. Expansion of Th17 cells and functional defects in T regulatory cells are key features of the pancreatic lymph nodes in patients with type 1 diabetes. Diabetes. 2011;60(11):2903–13. Epub 2011/09/08. doi: 10.2337/db11-0090 ; PubMed Central PMCID: PMC3198077. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Zhang C, Xiao C, Wang P, Xu W, Zhang A, Li Q, et al. The alteration of Th1/Th2/Th17/Treg paradigm in patients with type 2 diabetes mellitus: Relationship with diabetic nephropathy. Hum Immunol. 2014;75(4):289–96. Epub 2014/02/18. doi: 10.1016/j.humimm.2014.02.007 . [DOI] [PubMed] [Google Scholar]
40.Singh A, Yamamoto M, Ruan J, Choi JY, Gauvreau GM, Olek S, et al. Th17/Treg ratio derived using DNA methylation analysis is associated with the late phase asthmatic response. Allergy Asthma Clin Immunol. 2014;10(1):32. Epub 2014/07/06. doi: 10.1186/1710-1492-10-32 ; PubMed Central PMCID: PMC4078401. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Feng T, Elson CO, Cong Y. Treg cell-IgA axis in maintenance of host immune homeostasis with microbiota. Int Immunopharmacol. 2011;11(5):589–92. Epub 2010/11/30. doi: 10.1016/j.intimp.2010.11.016 ; PubMed Central PMCID: PMC3078992. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Cheng X, Yu X, Ding YJ, Fu QQ, Xie JJ, Tang TT, et al. The Th17/Treg imbalance in patients with acute coronary syndrome. Clin Immunol. 2008;127(1):89–97. Epub 2008/02/26. doi: 10.1016/j.clim.2008.01.009 . [DOI] [PubMed] [Google Scholar]
43.Dalmas E, Venteclef N, Caer C, Poitou C, Cremer I, Aron-Wisnewsky J, et al. T cell-derived IL-22 amplifies IL-1beta-driven inflammation in human adipose tissue: relevance to obesity and type 2 diabetes. Diabetes. 2014;63(6):1966–77. Epub 2014/02/13. doi: 10.2337/db13-1511 . [DOI] [PubMed] [Google Scholar]
44.Fabbrini E, Cella M, McCartney SA, Fuchs A, Abumrad NA, Pietka TA, et al. Association between specific adipose tissue CD4+ T-cell populations and insulin resistance in obese individuals. Gastroenterology. 2013;145(2):366–74 e1-3. Epub 2013/04/20. doi: 10.1053/j.gastro.2013.04.010 ; PubMed Central PMCID: PMC3756481. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Chen H, Ren X, Liao N, Wen F. Th17 cell frequency and IL-17A concentrations in peripheral blood mononuclear cells and vitreous fluid from patients with diabetic retinopathy. J Int Med Res. 2016;44(6):1403–13. Epub 2016/11/26. doi: 10.1177/0300060516672369 ; PubMed Central PMCID: PMC5536736. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Jagannathan-Bogdan M, McDonnell ME, Shin H, Rehman Q, Hasturk H, Apovian CM, et al. Elevated proinflammatory cytokine production by a skewed T cell compartment requires monocytes and promotes inflammation in type 2 diabetes. J Immunol. 2011;186(2):1162–72. Epub 2010/12/21. doi: 10.4049/jimmunol.1002615 ; PubMed Central PMCID: PMC3089774. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Yuan N, Zhang HF, Wei Q, Wang P, Guo WY. Expression of CD4+CD25+Foxp3+ Regulatory T Cells, Interleukin 10 and Transforming Growth Factor beta in Newly Diagnosed Type 2 Diabetic Patients. Exp Clin Endocrinol Diabetes. 2018;126(2):96–101. Epub 2017/09/28. doi: 10.1055/s-0043-113454 . [DOI] [PubMed] [Google Scholar]
48.Han JM, Patterson SJ, Speck M, Ehses JA, Levings MK. Insulin inhibits IL-10-mediated regulatory T cell function: implications for obesity. J Immunol. 2014;192(2):623–9. Epub 2013/12/11. doi: 10.4049/jimmunol.1302181 . [DOI] [PubMed] [Google Scholar]
49.Qiao YC, Shen J, He L, Hong XZ, Tian F, Pan YH, et al. Changes of Regulatory T Cells and of Proinflammatory and Immunosuppressive Cytokines in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis. J Diabetes Res. 2016;2016:3694957. Epub 2016/10/26. doi: 10.1155/2016/3694957 ; PubMed Central PMCID: PMC5061980. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Awartani F. Serum immunoglobulin levels in type 2 diabetes patients with chronic periodontitis. J Contemp Dent Pract. 2010;11(3):001–8. Epub 2010/05/13. . [PubMed] [Google Scholar]
51.Triolo G, Giardina E, Zarcone MR, Giordano C, Rinaldi A, Bompiani GD. Contribution of secretory IgA, polymeric IgA and IgA/secretory component-containing circulating immune complexes to the serum hyper-IgA in diabetes mellitus. Diabetologia. 1984;27 Suppl:157–9. Epub 1984/07/01. doi: 10.1007/BF00275677 [DOI] [PubMed] [Google Scholar]
52.Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537–47. Epub 2016/05/09. doi: 10.1016/S2213-8587(16)30010-9 . [DOI] [PubMed] [Google Scholar]
53.Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract. 2014;105(2):141–50. Epub 2014/05/07. doi: 10.1016/j.diabres.2014.04.006 . [DOI] [PubMed] [Google Scholar]
54.Zhou W, Sailani MR, Contrepois K, Zhou Y, Ahadi S, Leopold SR, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569(7758):663–71. Epub 2019/05/31. doi: 10.1038/s41586-019-1236-x ; PubMed Central PMCID: PMC6666404. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Kumar Nathella P, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis. Immunology. 2017;152(1):13–24. Epub 2017/05/26. doi: 10.1111/imm.12762 ; PubMed Central PMCID: PMC5543489. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Agashe S, Petak S. Cardiac Autonomic Neuropathy in Diabetes Mellitus. Methodist Debakey Cardiovasc J. 2018;14(4):251–6. Epub 2019/02/23. doi: 10.14797/mdcj-14-4-251 ; PubMed Central PMCID: PMC6369622. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Guo M, Ding J, Li J, Wang J, Zhang T, Liu C, et al. SGLT2 inhibitors and risk of stroke in patients with type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab. 2018;20(8):1977–82. Epub 2018/03/25. doi: 10.1111/dom.13295 . [DOI] [PubMed] [Google Scholar]
58.Baron U, Werner J, Schildknecht K, Schulze JJ, Mulu A, Liebert UG, et al. Epigenetic immune cell counting in human blood samples for immunodiagnostics. Sci Transl Med. 2018;10(452). Epub 2018/08/03. doi: 10.1126/scitranslmed.aan3508 . [DOI] [PubMed] [Google Scholar]
59.Burska AN, Thu A, Parmar R, Bzoma I, Samans B, Raschke E, et al. Quantifying circulating Th17 cells by qPCR: potential as diagnostic biomarker for rheumatoid arthritis. Rheumatology (Oxford). 2019;58(11):2015–24. Epub 2019/05/14. doi: 10.1093/rheumatology/kez162 . [DOI] [PubMed] [Google Scholar]
60.Sehouli J, Loddenkemper C, Cornu T, Schwachula T, Hoffmuller U, Grutzkau A, et al. Epigenetic quantification of tumor-infiltrating T-lymphocytes. Epigenetics. 2011;6(2):236–46. Epub 2010/10/22. doi: 10.4161/epi.6.2.13755 ; PubMed Central PMCID: PMC3278789. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Wieczorek G, Asemissen A, Model F, Turbachova I, Floess S, Liebenberg V, et al. Quantitative DNA methylation analysis of FOXP3 as a new method for counting regulatory T cells in peripheral blood and solid tissue. Cancer Res. 2009;69(2):599–608. Epub 2009/01/17. doi: 10.1158/0008-5472.CAN-08-2361 . [DOI] [PubMed] [Google Scholar]
62.Hanis CL, Redline S, Cade BE, Bell GI, Cox NJ, Below JE, et al. Beyond type 2 diabetes, obesity and hypertension: an axis including sleep apnea, left ventricular hypertrophy, endothelial dysfunction, and aortic stiffness among Mexican Americans in Starr County, Texas. Cardiovasc Diabetol. 2016;15:86. doi: 10.1186/s12933-016-0405-6 ; PubMed Central PMCID: PMC4897940. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Standards of medical care in diabetes—2015: summary of revisions. Diabetes Care. 2015;38 Suppl:S4. Epub 2014/12/30. doi: 10.2337/dc15-S003 . [DOI] [PubMed] [Google Scholar]
64.Human Microbiome Project C. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14. doi: 10.1038/nature11234 ; PubMed Central PMCID: PMC3564958. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. doi: 10.1093/bioinformatics/btq461 . [DOI] [PubMed] [Google Scholar]
66.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8. Epub 2013/08/21. doi: 10.1038/nmeth.2604 . [DOI] [PubMed] [Google Scholar]
67.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6. Epub 2012/11/30. doi: 10.1093/nar/gks1219 ; PubMed Central PMCID: PMC3531112. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Jari Oksanen FGB, Michael Friendly, Roeland Kindt, Pierre Legendre, Dan McGlinn, Peter R. Minchin, et al. Vegan: Community Ecology Package. R package version 2.5–7. 2020.
69.Team R. RStudio: Integrated development for R. Boston, MA: RStudio, PBC; 2020. [Google Scholar]
70.Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. Epub 2011/06/28. doi: 10.1186/gb-2011-12-6-r60 ; PubMed Central PMCID: PMC3218848. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Cech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537–W44. Epub 2018/05/24. doi: 10.1093/nar/gky379 ; PubMed Central PMCID: PMC6030816. [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Yang J, Chu Y, Yang X, Gao D, Zhu L, Yang X, et al. Th17 and natural Treg cell population dynamics in systemic lupus erythematosus. Arthritis Rheum. 2009;60(5):1472–83. Epub 2009/05/01. doi: 10.1002/art.24499 . [DOI] [PubMed] [Google Scholar]
73.Madan A, Thompson D, Fowler JC, Ajami NJ, Salas R, Frueh BC, et al. The gut microbiota is associated with psychiatric symptom severity and treatment outcome among individuals with serious mental illness. J Affect Disord. 2020;264:98–106. Epub 2020/02/15. doi: 10.1016/j.jad.2019.12.020 . [DOI] [PubMed] [Google Scholar]
74.Brown EL, Essigmann HT. Original Antigenic Sin: the Downside of Immunological Memory and Implications for COVID-19. mSphere. 2021;6(2). Epub 2021/03/12. doi: 10.1128/mSphere.00056-21 . [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Vuong HE, Hsiao EY. Emerging Roles for the Gut Microbiome in Autism Spectrum Disorder. Biol Psychiatry. 2017;81(5):411–23. Epub 2016/10/25. doi: 10.1016/j.biopsych.2016.08.024 ; PubMed Central PMCID: PMC5285286. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13(4):260–70. Epub 2012/03/14. doi: 10.1038/nrg3182 ; PubMed Central PMCID: PMC3418802. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Rhee SH, Pothoulakis C, Mayer EA. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol. 2009;6(5):306–14. Epub 2009/05/01. doi: 10.1038/nrgastro.2009.35 ; PubMed Central PMCID: PMC3817714. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Shreiner AB, Kao JY, Young VB. The gut microbiome in health and in disease. Curr Opin Gastroenterol. 2015;31(1):69–75. Epub 2014/11/14. doi: 10.1097/MOG.0000000000000139 ; PubMed Central PMCID: PMC4290017. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Tang WH, Kitai T, Hazen SL. Gut Microbiota in Cardiovascular Health and Disease. Circ Res. 2017;120(7):1183–96. Epub 2017/04/01. doi: 10.1161/CIRCRESAHA.117.309715 ; PubMed Central PMCID: PMC5390330. [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Tilg H, Moschen AR. Microbiota and diabetes: an evolving relationship. Gut. 2014;63(9):1513–21. Epub 2014/05/17. doi: 10.1136/gutjnl-2014-306928 . [DOI] [PubMed] [Google Scholar]
81.van Olden C, Groen AK, Nieuwdorp M. Role of Intestinal Microbiome in Lipid and Glucose Metabolism in Diabetes Mellitus. Clin Ther. 2015;37(6):1172–7. Epub 2015/04/30. doi: 10.1016/j.clinthera.2015.03.008 . [DOI] [PubMed] [Google Scholar]
82.Webb DR, Khunti K, Silverman R, Gray LJ, Srinivasan B, Lacy PS, et al. Impact of metabolic indices on central artery stiffness: independent association of insulin resistance and glucose with aortic pulse wave velocity. Diabetologia. 2010;53(6):1190–8. Epub 2010/03/10. doi: 10.1007/s00125-010-1689-9 . [DOI] [PubMed] [Google Scholar]
83.Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A. 2009;106(10):3698–703. Epub 2009/02/24. doi: 10.1073/pnas.0812874106 ; PubMed Central PMCID: PMC2656143. [DOI] [PMC free article] [PubMed] [Google Scholar]
84.De Angelis M, Montemurno E, Piccolo M, Vannini L, Lauriero G, Maranzano V, et al. Microbiota and metabolome associated with immunoglobulin A nephropathy (IgAN). PLoS One. 2014;9(6):e99006. Epub 2014/06/13. doi: 10.1371/journal.pone.0099006 ; PubMed Central PMCID: PMC4055632. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Kayama H, Okumura R, Takeda K. Interaction Between the Microbiota, Epithelia, and Immune Cells in the Intestine. Annu Rev Immunol. 2020;38:23–48. Epub 2020/04/29. doi: 10.1146/annurev-immunol-070119-115104 . [DOI] [PubMed] [Google Scholar]
86.McCarville JL, Chen GY, Cuevas VD, Troha K, Ayres JS. Microbiota Metabolites in Health and Disease. Annu Rev Immunol. 2020;38:147–70. Epub 2020/04/29. doi: 10.1146/annurev-immunol-071219-125715 . [DOI] [PubMed] [Google Scholar]
87.Hagan T, Cortese M, Rouphael N, Boudreau C, Linde C, Maddur MS, et al. Antibiotics-Driven Gut Microbiome Perturbation Alters Immunity to Vaccines in Humans. Cell. 2019;178(6):1313–28 e13. Epub 2019/09/07. doi: 10.1016/j.cell.2019.08.010 ; PubMed Central PMCID: PMC6750738. [DOI] [PMC free article] [PubMed] [Google Scholar]
88.Pamer EG. Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science. 2016;352(6285):535–8. Epub 2016/04/30. doi: 10.1126/science.aad9382 ; PubMed Central PMCID: PMC4984266. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Cresci GAM. Metabolome of the Microbiome: A Foundation for Better Health? Altern Ther Health Med. 2018;24(S1):25–7. Epub 2019/04/15. . [PubMed] [Google Scholar]
90.Yang Y, Misra BB, Liang L, Bi D, Weng W, Wu W, et al. Integrated microbiome and metabolome analysis reveals a novel interplay between commensal bacteria and metabolites in colorectal cancer. Theranostics. 2019;9(14):4101–14. Epub 2019/07/10. doi: 10.7150/thno.35186 ; PubMed Central PMCID: PMC6592169. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Tang ZZ, Chen G, Hong Q, Huang S, Smith HM, Shah RD, et al. Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites. Front Genet. 2019;10:454. Epub 2019/06/06. doi: 10.3389/fgene.2019.00454 ; PubMed Central PMCID: PMC6534069. [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Kolodziejczyk AA, Zheng D, Elinav E. Diet-microbiota interactions and personalized nutrition. Nat Rev Microbiol. 2019;17(12):742–53. Epub 2019/09/22. doi: 10.1038/s41579-019-0256-8 . [DOI] [PubMed] [Google Scholar]
93.Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science. 2012;336(6086):1268–73. Epub 2012/06/08. doi: 10.1126/science.1223490 ; PubMed Central PMCID: PMC4420145. [DOI] [PMC free article] [PubMed] [Google Scholar]
94.Barko PC, McMichael MA, Swanson KS, Williams DA. The Gastrointestinal Microbiome: A Review. J Vet Intern Med. 2018;32(1):9–25. Epub 2017/11/25. doi: 10.1111/jvim.14875 ; PubMed Central PMCID: PMC5787212. [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Noverr MC, Huffnagle GB. Does the microbiota regulate immune responses outside the gut? Trends Microbiol. 2004;12(12):562–8. Epub 2004/11/13. doi: 10.1016/j.tim.2004.10.008 . [DOI] [PubMed] [Google Scholar]
96.Brandtzaeg P. Do salivary antibodies reliably reflect both mucosal and systemic immunity? Ann N Y Acad Sci. 2007;1098:288–311. Epub 2007/04/17. doi: 10.1196/annals.1384.012 . [DOI] [PubMed] [Google Scholar]
97.Bleau C, Karelis AD, St-Pierre DH, Lamontagne L. Crosstalk between intestinal microbiota, adipose tissue and skeletal muscle as an early event in systemic low-grade inflammation and the development of obesity and diabetes. Diabetes Metab Res Rev. 2015;31(6):545–61. doi: 10.1002/dmrr.2617 . [DOI] [PubMed] [Google Scholar]
98.Moreno-Indias I, Cardona F, Tinahones FJ, Queipo-Ortuno MI. Impact of the gut microbiota on the development of obesity and type 2 diabetes mellitus. Front Microbiol. 2014;5:190. doi: 10.3389/fmicb.2014.00190 ; PubMed Central PMCID: PMC4010744. [DOI] [PMC free article] [PubMed] [Google Scholar]
99.Van den Abbeele P, Verstraete W, El Aidy S, Geirnaert A, Van de Wiele T. Prebiotics, faecal transplants and microbial network units to stimulate biodiversity of the human gut microbiome. Microb Biotechnol. 2013;6(4):335–40. Epub 2013/04/19. doi: 10.1111/1751-7915.12049 ; PubMed Central PMCID: PMC3917468. [DOI] [PMC free article] [PubMed] [Google Scholar]
100.Petersen C, Round JL. Defining dysbiosis and its influence on host immunity and disease. Cell Microbiol. 2014;16(7):1024–33. Epub 2014/05/07. doi: 10.1111/cmi.12308 ; PubMed Central PMCID: PMC4143175. [DOI] [PMC free article] [PubMed] [Google Scholar]
101.Wilmanski T, Diener C, Rappaport N, Patwardhan S, Wiedrick J, Lapidus J, et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat Metab. 2021;3(2):274–86. Epub 2021/02/24. doi: 10.1038/s42255-021-00348-0 . [DOI] [PMC free article] [PubMed] [Google Scholar]
102.Palm NW, de Zoete MR, Cullen TW, Barry NA, Stefanowski J, Hao L, et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell. 2014;158(5):1000–10. Epub 2014/08/30. doi: 10.1016/j.cell.2014.08.006 ; PubMed Central PMCID: PMC4174347. [DOI] [PMC free article] [PubMed] [Google Scholar]
103.De Filippis F, Pasolli E, Ercolini D. Newly Explored Faecalibacterium Diversity Is Connected to Age, Lifestyle, Geography, and Disease. Curr Biol. 2020;30(24):4932–43 e4. Epub 2020/10/17. doi: 10.1016/j.cub.2020.09.063 . [DOI] [PubMed] [Google Scholar]
104.Laursen MF, Laursen RP, Larnkjaer A, Molgaard C, Michaelsen KF, Frokiaer H, et al. Faecalibacterium Gut Colonization Is Accelerated by Presence of Older Siblings. mSphere. 2017;2(6). Epub 2017/12/05. doi: 10.1128/mSphere.00448-17 ; PubMed Central PMCID: PMC5705805. [DOI] [PMC free article] [PubMed] [Google Scholar]
105.Lambeth SM, Carson T, Lowe J, Ramaraj T, Leff JW, Luo L, et al. Composition, Diversity and Abundance of Gut Microbiome in Prediabetes and Type 2 Diabetes. J Diabetes Obes. 2015;2(3):1–7. Epub 2016/01/13. doi: 10.15436/2376-0949.15.031 ; PubMed Central PMCID: PMC4705851. [DOI] [PMC free article] [PubMed] [Google Scholar]
106.Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One. 2013;8(8):e71108. Epub 2013/09/10. doi: 10.1371/journal.pone.0071108 ; PubMed Central PMCID: PMC3754967. [DOI] [PMC free article] [PubMed] [Google Scholar]
107.Walker AW, Ince J, Duncan SH, Webster LM, Holtrop G, Ze X, et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 2011;5(2):220–30. Epub 2010/08/06. doi: 10.1038/ismej.2010.118 ; PubMed Central PMCID: PMC3105703. [DOI] [PMC free article] [PubMed] [Google Scholar]
108.Silva-Boghossian CM, Cesario PC, Leao ATT, Colombo APV. Subgingival microbial profile of obese women with periodontal disease. J Periodontol. 2018;89(2):186–94. Epub 2018/03/10. doi: 10.1002/JPER.17-0236 . [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0258812.r001

Decision Letter 0

Jane Foster

26 Aug 2021

PONE-D-21-17583

The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

PLOS ONE

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Reviewer #1: Overall this manuscript presents very exciting data that helps to clarify the intricate relationships between secretory signaling molecules, immune cells and the microbiomes in the context of glycemic control. However, I feel that perhaps the authors know their study and data so well, they are leaving out key details that help the reader to follow this complex system and multiple comparisons. I strongly encourage the authors to follow the comments below and also seek internal review from their own colleagues who similarly do not know the study as well to provide important feedback for additional clarity.

Abstract: I feel like the abstract is very heavy with background (though it does provide very helpful outlines of the field and give needed context to the manuscript). Can the authors work to try to remove small details in the Abstract to provide room for a little more explanation for the study at hand and the results? The bit about B cells could maybe be moved to the Introduction and given a little more space to explain the biological relationships, as well as saving some space in the Abstract.

Introduction: I fully appreciate that this is complex and difficult system to clearly explain. However, I think the Introduction could warrant a little more structure around explaining the different mucosal compartments/microbial environments under examination in the study: saliva vs. stool vs. blood (buffy coat). It may be difficult for some readers to follow how changes in one compartment vs. another or broad/systemic changes across all confers biological consequences. Give some time in the Intro to explain this and you save yourself headache in the later aspects of the paper (you also mention the consequences of differential change like this in the Discussion, so set it up better earlier).

Methods: What were the BMI differences between groups? Ideally, the participants could be matched on BMI as well but that is challenging. Please report the BMI for each sample group and determine any differences.

I would state about the plate runs and performance limits in only one location, either Method or Results (probably Method), no need to repeat the sentence in both places.

Results: Consider adding a Demographics reporting table that states the ranges for age, BMI, blood glucose measurements and %HbA1c values for each group. Are these exactly the same participants reported in Brown et al., 2020? I suggest rewording the Study Population description in that case to be more transparent that these are “pre-matched” from another study and not specifically matched for this current study. It seems like the current manuscript is a direct extension of this earlier manuscript and thus should perhaps be more clearly tied to the previous findings also. The mention of the “Presort” samples on Pg. 11, Line 213 definitely suggests that there is some overlap between samples/findings. This needs to be much more clear as to what samples/ranges/analyses are being directly compared in this current manuscript.

Figure and Figure Legend 7: I’m totally lost as to what is being presented when it’s telling me that I’m seeing “above median vs. above median” and “below median vs. below median” across these 3 sample sets…one of which contains the other two??? I’m very lost. Please add clarifying language, ideally to both the Figure itself and the legend.

Discussion: As much as I appreciate the reference to Hippocrates and the importance of the gut, the language might be a bit too strongly flowery for application in this manuscript. Strongly suggest toning that down.

Reviewer #2: The authors aimed to identify associations across the Th17:Treg-SIgA axis and glycemic profiles. The authors detected a relationship between Th17:Treg ratios and alpha diversity in the stool IgA-Biome of those with dysglycemia as well as changes in the composition of the stool and salivary microbiomes across glycemic profiles. This research is novel. The manuscript is well-written but requires some adjustments:

1) The abstract contains a lot of information on the background. However, the aim and the used methods are not mentioned clearly. The results are listed within a long sentence. Conclusion and outlook are missing. Please revise your abstract to increase readability and understanding.

2) The term IgA-Biome is interesting as it reflects specific bacteria as you have defined. Would it be perhaps an idea to adjust it to IgA-Bacteriome since I assume you are not including / assessing other microorganisms? Secondly, It would be useful to understand what bacteria are SIgA-coated/uncoated bacteria. The term has been not introduced when it appears first time in the introduction in line 72.

3) V4 represents a hypervariable region of 16S which could lead to taxonomic inaccuracy compared to sequencing the full 16S gene. Have you validated your results?

4) The figures were very pixeled and of low resolution. It was hard to evaluate them properly. Please update.

5) “Although not statistically significant, the opposite trend, i.e., decreasing richness with increasing Th17:Treg ratio, was observed in those with normoglycemia (Fig 2) indicating that bacteria richness is positively correlated with measures of inflammation only in those with abnormal blood sugar values.” It is unclear how the authors concluded here on a positive correlation.

6) It would be useful to quantify secretory IgA in this population and relate it to your findings, particularly the bacterial results, to understand the interrelationship better.

7) “8 with normal glycemia, 8 with prediabetes, and 8 with diabetes, were used in the current study”. It would be helpful for the reader to list the relevant metabolic parameters in this paper again. This can be added as a supplementary table.

8) Line 202 - relatively gaussian?

9) Fig. 1 - Th17:Treg ratios by diabetes profile: It would be still more accurate to differentiate between prediabetic and diabetic.

10) Fig. 2ff – it would be interesting to investigate whether there is a trajectory between normoglycemic and prediabetic towards diabetic. As such it would be helpful to re-graph all figures to include all 3 groups. From Fig. 2 onwards, it remains unclear why both groups prediabetes and diabetes are still combined.

11) “The observation that almost all human SIgA show evidence of somatic hypermutation and affinity maturation indicates that T-cell-dependent responses play a dominant role in shaping the IgA-Biome” The conclusion is unclear here. Please provide more context.

12) “Similar analysis of alpha diversity metrics in the salivary IgA-Biome did not identify significant associations” Please clarify what is meant with “similar analysis of alpha diversity”.

13) Please provide more clarification on what the Th17:Treg-SIgA axis entails. This could be done for instance by a graphic.

14) Line 374 - “categorized by T-cell phenotype”. Since not a range of T-cell phenotypes has been characterized, this should be revised.

15) “Faecalibacterium, a genus that only contains one species, F. prausnitzii, previously associated with chronic inflammation” That F. prausnitzii is not the only one species in this genera has been recently revised (de Filippis et al. 2020, Current Biology).

Typos

- Line 379: trended towards significance?

**********

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PLoS One. 2021 Oct 20;16(10):e0258812. doi: 10.1371/journal.pone.0258812.r002

Author response to Decision Letter 0

17 Sep 2021

ONE-D-21-17583

The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

PLOS ONE

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Response:

Corrected.

3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

“National Institutes of Health grant R01DK116378 to ELB and CLH.”

Response:

Funding-related text has been removed from the manuscript. The funding source number of R01DK116378 is correct. It will be corrected in the on-line portion of the submission.

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Response:

All ‘data not shown’ phrases have been removed from the modified manuscript. Seven supplementary Figs and 2 new additional Figs have been added to the revised manuscript.

Response:

The section in the Methods pertaining to the consent has been revised.

Comments to the Author

Reviewer #1:

Overall this manuscript presents very exciting data that helps to clarify the intricate relationships between secretory signaling molecules, immune cells and the microbiomes in the context of glycemic control. However, I feel that perhaps the authors know their study and data so well, they are leaving out key details that help the reader to follow this complex system and multiple comparisons. I strongly encourage the authors to follow the comments below and also seek internal review from their own colleagues who similarly do not know the study as well to provide important feedback for additional clarity.

1. Abstract: I feel like the abstract is very heavy with background (though it does provide very helpful outlines of the field and give needed context to the manuscript). Can the authors work to try to remove small details in the Abstract to provide room for a little more explanation for the study at hand and the results? The bit about B cells could maybe be moved to the Introduction and given a little more space to explain the biological relationships, as well as saving some space in the Abstract.

Response:

The Abstract has been modified as recommended.

2. Introduction: I fully appreciate that this is complex and difficult system to clearly explain. However, I think the Introduction could warrant a little more structure around explaining the different mucosal compartments/microbial environments under examination in the study: saliva vs. stool vs. blood (buffy coat). It may be difficult for some readers to follow how changes in one compartment vs. another or broad/systemic changes across all confers biological consequences. Give some time in the Intro to explain this and you save yourself headache in the later aspects of the paper (you also mention the consequences of differential change like this in the Discussion, so set it up better earlier).

Response:

Thank you for the suggestion. A new paragraph in the introduction has been added to address this comment (Lines: 42-82). In addition, a new Fig 1 has been added to aid with the explanation of the Th17:Treg/SIgA Axis and the microbiome.

3. Methods: What were the BMI differences between groups? Ideally, the participants could be matched on BMI as well but that is challenging. Please report the BMI for each sample group and determine any differences.

Response:

The following section was added to the Materials (Lines 144-151):

‘Characterization of the gut and salivary IgA-Biomes and demographics of the study participants and were previously published by our group [13]. Briefly, 16.67% (4/24) of subjects were male and the mean age of participants was 47.75 years (standard deviation (SD) = 5.96). The body mass index (BMI) varied by diabetes phenotype (ANOVA = 0.05) with mean BMIs of 30.6 kg/m2 (SD = 4.8), 33.0 kg/m2 (SD = 3.0), and 37.7 kg/m2 (SD = 7.6) for those classified as normoglycemic, prediabetic, and diabetic, respectively. In the present study, the buffy coats were used to define the Th17:Treg ratios (see below) to establish correlations between the IgA-Biome and diabetes profiles.’

In addition, differences in BMI across groups were not statistically significant by Kruskal Wallis H-test (p=0.08); however, figures 2 A-B, figures 4 A-D, figures 6 A-B, and figures 7 A-D have been reanalyzed adjusting for BMI. Please see revised ‘Statistical analyses’ section in the Methods (Lines: 225-240).

4. I would state about the plate runs and performance limits in only one location, either Methods or Results (probably Methods), no need to repeat the sentence in both places.

Response:

This has been corrected and only appears in the Methods section as suggested.

5. Results: Consider adding a Demographics reporting table that states the ranges for age, BMI, blood glucose measurements and %HbA1c values for each group. Are these exactly the same participants reported in Brown et al., 2020? I suggest rewording the Study Population description in that case to be more transparent that these are “pre-matched” from another study and not specifically matched for this current study. It seems like the current manuscript is a direct extension of this earlier manuscript and thus should perhaps be more clearly tied to the previous findings also. The mention of the “Presort” samples on Pg. 11, Line 213 definitely suggests that there is some overlap between samples/findings. This needs to be much more clear as to what samples/ranges/analyses are being directly compared in this current manuscript.

Response:

We agree with the Reviewer that it is not clear that the IgA-Biome was established in our prior publication (Brown et al., 2020) [reference 13].

In the ‘Study population and sample collection’ in the Methods section the following sentence was added (Lines: 141-151)

‘Participants were age matched and their respective gut and salivary IgA-Biomes and demographics were previously published by our group [13]. Briefly, 16.67% (4/24) of subjects were male and the mean age of participants was 47.75 years (standard deviation (SD) = 5.96). The body mass index (BMI) varied by diabetes phenotype (ANOVA = 0.05) with mean BMIs of 30.6 kg/m2 (SD = 4.8), 33.0 kg/m2 (SD = 3.0), and 37.7 kg/m2 (SD = 7.6) for those classified as normoglycemic, prediabetic, and diabetic, respectively. In the present study, the buffy coats were used to define the Th17:Treg ratios (see below) to establish correlations between the IgA-Biome and diabetes profiles.’

In the ‘Epigenetic characterization of T-helper cell profiles’ section of the Methods the following sentence was added (Lines: 197-198):

‘Buffy coats were collected at the same time stool and saliva samples for IgA-Biome analyses were collected.’

In the ‘16S rRNA gene sequencing’ section of the Methods the following sentence was added (Lines: 182-184):

‘The IgA-Biome profiles defined for the 24 participants described in the present study were previously published by our group [13].’

We also agree that the description of the Presort is unclear. Prior to sorting samples into IgA+ and IgA- groups a portion of each sample was collected and sequenced with the sorted samples in parallel. This unsorted sample is referred to as the Presort. In the ‘16S RNA gene sequencing’ section of the Methods the following sentence was added (Lines: 178-180):

‘In addition to sequencing IgA+ and IgA- bacteria from each sample, unsorted (Presort) samples were collected for each sample prior to sorting and sequenced in parallel.”

6. Figure and Figure Legend 7: I’m totally lost as to what is being presented when it’s telling me that I’m seeing “above median vs. above median” and “below median vs. below median” across these 3 sample sets…one of which contains the other two??? I’m very lost. Please add clarifying language, ideally to both the Figure itself and the legend.

Response:

We agree with the Reviewer that this is a difficult analysis to explain. First, instead of using below the median or above the median we have now explained that above the median ratio is a ‘pro-inflammatory’ profile and below the median ratio is ‘anti-inflammatory. Second, the box plots to the Fig in question (now Fig 8) have been changed to ‘Pro-Inflammatory Phenotype’ or ‘Anti-Inflammatory Phenotype.’ The reason the data were presented as they were i.e., comparing ‘below vs. below’ to ‘above vs. above’ is because the UniFrac test measures the evolutionary distance (distances between communities as branch length percentage) between taxa in each ‘above’ or ‘below’ group. Therefore, it was written as, for example, ‘below vs. below’ because the test was determining distances between communities in the ‘below group’ and the same to those in the ‘above group’ by comparing distances within in each group, that is, above vs. above and below vs. below. That is all gone! In addition, a sentence was added to better explain UniFrac and the legend to the new Fig. 8 has been modified as well.

Lines: 396-401:

‘Comparisons of individuals grouped as having pro-inflammatory or anti-inflammatory profiles based on Th17:Treg ratios were used to investigate beta, or between sample, diversity of stool and salivary Presort samples using unweighted UniFrac distances, a distance metric that incorporates phylogenetic relatedness and multivariate statistical techniques to determine whether microbial communities are significantly different.’

New Fig 8 legend:

Fig 8. Comparisons of UniFrac distances between of the stool IgA-Biome of normal and dysglycemic individuals categorized by inflammatory phenotype. Box plots compare unweighted UniFrac distances between all individuals within each inflammatory phenotype, respectively, by SIgA compartment and glycemic profile. Normoglycemic subjects with a pro-inflammatory phenotype have SIgA+ bacterial community compositions more similar to each other than do those with an anti-inflammatory phenotype (*p<0.0152). Similarly, dysglycemic subjects with a pro-inflammatory phenotype have SIgA- bacterial community compositions more similar to each other than do those with an anti-inflammatory phenotype (**p<0.023). P-values determined using the Mann-Whitney U-test.

7. Discussion: As much as I appreciate the reference to Hippocrates and the importance of the gut, the language might be a bit too strongly flowery for application in this manuscript. Strongly suggest toning that down.

Response:

This sentence has been removed.

Reviewer #2:

The authors aimed to identify associations across the Th17:Treg-SIgA axis and glycemic profiles. The authors detected a relationship between Th17:Treg ratios and alpha diversity in the stool IgA-Biome of those with dysglycemia as well as changes in the composition of the stool and salivary microbiomes across glycemic profiles. This research is novel. The manuscript is well-written but requires some adjustments:

1. The abstract contains a lot of information on the background. However, the aim and the used methods are not mentioned clearly. The results are listed within a long sentence. Conclusion and outlook are missing. Please revise your abstract to increase readability and understanding.

Response:

Reviewer 1 also had recommendations regarding the Abstract. The modified form attempts to encompass both Reviewers’ comments.

2. The term IgA-Biome is interesting as it reflects specific bacteria as you have defined. Would it be perhaps an idea to adjust it to IgA-Bacteriome since I assume you are not including / assessing other microorganisms? Secondly, it would be useful to understand what bacteria are SIgA-coated/uncoated bacteria. The term has been not introduced when it appears first time in the introduction in line 72.

Response:

We agree with the Reviewer that we are at this time only looking at bacteria. However, as new taxonomic sequences are added to the pipelines, additional taxa including fungi and viruses can be added to the analyses. We have also already published one manuscript using ‘IgA-Biome’ for consistency would like to maintain it as it is. The IgA-Biome is now part of the Abstract and of the revised Introduction.

3. V4 represents a hypervariable region of 16S which could lead to taxonomic inaccuracy compared to sequencing the full 16S gene. Have you validated your results?

Response:

While long read full length (FL) 16S sequencing was not available through our collaborators at the CMMR Sequencing Core at the time sequencing was performed, we fully agree that FL 16S information would provide improved taxonomic accuracy, including species/strain level assignments. Hence, future research with these and related samples from the larger cohort will use FL 16S sequencing or, pending greater availability of material, whole genome shotgun (WGS) sequencing, enabling validation of the results presented here. For the current work, we selected the V4 region to be consistent with previously published data on the IgA-Biome and the stool microbiome more generally, allowing us to make more broadly appropriate comparisons with the literature, and limited analysis to genus-level assessments as appropriate for short read amplicon data.

4) The figures were very pixeled and of low resolution. It was hard to evaluate them properly.

Response:

All Figs have been uploaded with a minimum 1200 dpi.

Response:

We think the confusion arises because the previous sentence is focused on those with dysglycemia and the sentence in question is supposed to be focusing on the normoglycemic group but ends with a mention of the association between Th17:Treg ratios and those with dysglycemia. The sentence now reads (Lines 280-282):

“Although not statistically significant, the opposite trend, i.e., decreasing richness with increasing Th17:Treg ratio, was observed in those with normoglycemia (Fig 3A; p<0.066).

6. It would be useful to quantify secretory IgA in this population and relate it to your findings, particularly the bacterial results, to understand the interrelationship better.

Response:

We agree with the Reviewer; however, we feel this is beyond the scope of the present study. In addition, we feel that the focus of the IgA work is what is to examine taxa that are SIgA coated or uncoated. We are not sure that knowing the amount of SIgA would provide additional information. The percent coating of bacteria for each participant is reported in our earlier publication [ref 13].

7. “8 with normal glycemia, 8 with prediabetes, and 8 with diabetes, were used in the current study”. It would be helpful for the reader to list the relevant metabolic parameters in this paper again. This can be added as a supplementary table.

Response:

The requested information has now been added (Lines: 141-151):

‘Stool, saliva, and buffy coat samples collected upon enrollment from an age matched subset of 24 participants, 8 with normal glycemia, 8 with prediabetes, and 8 with diabetes [13]. Characterization of the gut and salivary IgA-Biomes and demographics of the study participants and were previously published by our group [13]. Briefly, 16.67% (4/24) of subjects were male and the mean age of participants was 47.75 years (standard deviation (SD) = 5.96). The body mass index (BMI) varied by diabetes phenotype (ANOVA = 0.05) with mean BMIs of 30.6 kg/m2 (SD = 4.8), 33.0 kg/m2 (SD = 3.0), and 37.7 kg/m2 (SD = 7.6) for those classified as normoglycemic, prediabetic, and diabetic, respectively. In the present study, the buffy coats were used to define the Th17:Treg ratios (see below) to establish correlations between the IgA-Biome and diabetes profiles.’

8. Line 202 - relatively gaussian?

Response:

‘Relatively’ has been removed. The sentence now reads (Lines: 263-264):

‘Th17:Treg T-cell ratios were normally distributed (Shapiro-Wil test of normality p-value = 0.08) with a median of 1.79 and mean of 1.90 (SD: 057, range: 0.82 – 3.24).’

9. Fig. 1 - Th17:Treg ratios by diabetes profile: It would be still more accurate to differentiate between prediabetic and diabetic.

Response:

We now show these data in Figure 2. Fig 2A is the same as the original Fig 1 and Fig 2B shows the ratios for those with prediabetes and diabetes.

10. Fig. 2ff – it would be interesting to investigate whether there is a trajectory between normoglycemic and prediabetic towards diabetic. As such it would be helpful to re-graph all figures to include all 3 groups. From Fig. 2 onwards, it remains unclear why both groups prediabetes and diabetes are still combined.

Response:

The prediabetic and diabetics had similar associations with the Th17:Treg ratios and were therefore combined into the dysglycemia group to increase sample sizes and our ability to detect differences between these phenotypes; however, we agree with the Reviewer and now include either in the manuscript or in the supplementary Figs data for the prediabetes and diabetes groups.

11. “The observation that almost all human SIgA show evidence of somatic hypermutation and affinity maturation indicates that T-cell-dependent responses play a dominant role in shaping the IgA-Biome” The conclusion is unclear here. Please provide more context.

Response:

We have added an additional sentence below to hopefully clarify this statement (Lines: 340-346):

‘More specifically as it relates to IgA production, B-cells of the mucosal lymphoid tissues are different form B-cells populating non-mucosal lymphoid tissues in that they can undergo class switching from IgM/IgD to IgA without T-cell help; however, like B-cells at all other anatomic sites, mucosal B-cells cannot produce high affinity antibodies, the products of germinal center reactions and somatic hypermutation, without the aid of T-cells [11, 74].’

12. “Similar analysis of alpha diversity metrics in the salivary IgA-Biome did not identify significant associations” Please clarify what is meant with “similar analysis of alpha diversity”.

Response:

‘Similar’ was removed from the sentence and now reads as follows (Lines 356-358):

‘Analysis of alpha diversity metrics in the salivary IgA-Biome did not identify significant associations (Fig S4A and S4B).’

13. Please provide more clarification on what the Th17:Treg-SIgA axis entails. This could be done for instance by a graphic.

Response:

We have generated a new Fig 1 graphic describing the Th17:Treg-SIgA axis.

14. Line 374 - “categorized by T-cell phenotype”. Since not a range of T-cell phenotypes has been characterized, this should be revised.

Response:

This terminology has been modified throughout the manuscript. The sentence now reads as follows (Lines: 475-476):

‘We also observed significant differences in community composition (beta diversity) between individuals categorized by their inflammatory profiles in the stool…’

15. “Faecalibacterium, a genus that only contains one species, F. prausnitzii, previously associated with chronic inflammation” That F. prausnitzii is not the only one species in this genera has been recently revised (de Filippis et al. 2020, Current Biology).

Response:

Many thanks for catching that! The Filippis reference has been added and this portion of the Discussion has been modified as follows (Lines: 498-502):

‘The only genus present in both SIgA+ and SIgA- compartments was Faecalibacterium, a genus that only contains 22 species, 12 of which inhabit the human gut [103]. Although F. prausnitzii has been associated with chronic inflammation, other Faecalibacterium species are leading candidates for the development of probiotics and biotherapeutics [103, 104].

Typos

- Line 379: trended towards significance?

Response:

This sentence now reads as follows (Lines 480-482):

A similar but not significant trend in the SIgA+ fraction of those with normal blood sugar values may indicate that changes to SIgA-targeted communities is heightened in the context of an inflammatory disease like type 2 diabetes.

PLoS One. doi: 10.1371/journal.pone.0258812.r003

Decision Letter 1

Jane Foster

6 Oct 2021

The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

PONE-D-21-17583R1

Dear Dr. Brown,

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Reviewer #1: All comments have been addressed

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Reviewer #1: Yes

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**********

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**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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6. Review Comments to the Author

Reviewer #1: My compliments to the authors, this is an excellent revision. The manuscript reads much more clearly. Great work here.

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Thank you for your comprehensive response to the raised concerns. I have no further questions.

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PLoS One. doi: 10.1371/journal.pone.0258812.r004

Acceptance letter

Jane Foster

12 Oct 2021

PONE-D-21-17583R1

The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

Dear Dr. Brown:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Impact of Th17:Treg ratios on alpha diversity of the salivary microbiome by diabetes phenotype.

(TIFF)

Click here for additional data file.^{(5.6MB, tiff)}

S2 Fig. Principle coordinate analyses of UniFrac distances of stool Presort samples by Th17:Treg ratios in those with normoglycemia, prediabetes, and diabetes.

Principal coordinate analyses of unweighted UniFrac distances of the stool microbiomes of those with (A) normoglycemia, (B) prediabetes, or (C) diabetes. P-values determined using PERMANOVA.

(TIFF)

Click here for additional data file.^{(4.6MB, tiff)}

S3 Fig. Principle coordinate analyses of UniFrac distances of saliva Presort samples by Th17:Treg ratios in those with normoglycemia, prediabetes, and diabetes.

Principal coordinate analyses of unweighted UniFrac distances of the stool microbiomes of those with (A) normoglycemia, (B) prediabetes, or (C) diabetes. P-values determined using PERMANOVA.

(TIFF)

Click here for additional data file.^{(4.6MB, tiff)}

S4 Fig. Impact of Th17:Treg ratios on alpha diversity of the saliva IgA-Biome by diabetes phenotype.

(TIFF)

Click here for additional data file.^{(6.9MB, tiff)}

S5 Fig. Principal coordinate analyses of UniFrac distances of the stool IgA-Biome by dichotomized Th17:Treg ratios across diabetes phenotype.

(TIFF)

Click here for additional data file.^{(4.9MB, tiff)}

S6 Fig. Principal coordinate analyses of UniFrac distances of the salivary IgA-Biome by dichotomized Th17:Treg ratios across diabetes phenotype.

(TIFF)

Click here for additional data file.^{(4.8MB, tiff)}

S7 Fig. Comparisons of UniFrac distances between of the salivary IgA-Biome of normal and dysglycemic individuals categorized by Th17:Treg ratio.

(TIFF)

Click here for additional data file.^{(2.3MB, tiff)}

Data Availability Statement

The 16S RNA sequencing data are available in NCBI under BioProject accession number PRJNA662100. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA662100.

[pone.0258812.ref001] 1.Swiatczak B, Cohen IR. Gut feelings of safety: tolerance to the microbiota mediated by innate immune receptors. Microbiol Immunol. 2015;59(10):573–85. Epub 2015/08/27. doi: 10.1111/1348-0421.12318 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref002] 2.Shulzhenko N, Morgun A, Hsiao W, Battle M, Yao M, Gavrilova O, et al. Crosstalk between B lymphocytes, microbiota and the intestinal epithelium governs immunity versus metabolism in the gut. Nat Med. 2011;17(12):1585–93. Epub 2011/11/22. doi: 10.1038/nm.2505 ; PubMed Central PMCID: PMC3902046. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref003] 3.Vehkala L, Ukkola O, Kesaniemi YA, Kahonen M, Nieminen MS, Salomaa V, et al. Plasma IgA antibody levels to malondialdehyde acetaldehyde-adducts are associated with inflammatory mediators, obesity and type 2 diabetes. Ann Med. 2013;45(8):501–10. Epub 2013/10/18. doi: 10.3109/07853890.2013.841322 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref004] 4.Bunker JJ, Erickson SA, Flynn TM, Henry C, Koval JC, Meisel M, et al. Natural polyreactive IgA antibodies coat the intestinal microbiota. Science. 2017;358(6361). Epub 2017/10/04. doi: 10.1126/science.aan6619 ; PubMed Central PMCID: PMC5790183. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref005] 5.Pabst O. New concepts in the generation and functions of IgA. Nat Rev Immunol. 2012;12(12):821–32. Epub 2012/10/30. doi: 10.1038/nri3322 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref006] 6.Cullender TC, Chassaing B, Janzon A, Kumar K, Muller CE, Werner JJ, et al. Innate and adaptive immunity interact to quench microbiome flagellar motility in the gut. Cell Host Microbe. 2013;14(5):571–81. Epub 2013/11/19. doi: 10.1016/j.chom.2013.10.009 ; PubMed Central PMCID: PMC3920589. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref007] 7.Fagarasan S, Muramatsu M, Suzuki K, Nagaoka H, Hiai H, Honjo T. Critical roles of activation-induced cytidine deaminase in the homeostasis of gut flora. Science. 2002;298(5597):1424–7. Epub 2002/11/16. doi: 10.1126/science.1077336 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref008] 8.Kawamoto S, Maruya M, Kato LM, Suda W, Atarashi K, Doi Y, et al. Foxp3(+) T cells regulate immunoglobulin a selection and facilitate diversification of bacterial species responsible for immune homeostasis. Immunity. 2014;41(1):152–65. Epub 2014/07/16. doi: 10.1016/j.immuni.2014.05.016 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref009] 9.Kubinak JL, Round JL. Do antibodies select a healthy microbiota? Nat Rev Immunol. 2016;16(12):767–74. Epub 2016/11/08. doi: 10.1038/nri.2016.114 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref010] 10.Chen K, Magri G, Grasset EK, Cerutti A. Rethinking mucosal antibody responses: IgM, IgG and IgD join IgA. Nat Rev Immunol. 2020. Epub 2020/02/06. doi: 10.1038/s41577-019-0261-1 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref011] 11.Huus KE, Petersen C, Finlay BB. Diversity and dynamism of IgA-microbiota interactions. Nat Rev Immunol. 2021. Epub 2021/02/12. doi: 10.1038/s41577-021-00506-1 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref012] 12.Cesta MF. Normal structure, function, and histology of mucosa-associated lymphoid tissue. Toxicol Pathol. 2006;34(5):599–608. Epub 2006/10/28. doi: 10.1080/01926230600865531 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref013] 13.Brown EL, Essigmann HT, Hoffman KL, Palm NW, Gunter SM, Sederstrom JM, et al. Impact of Diabetes on the Gut and Salivary IgA Microbiomes. Infect Immun. 2020;88(12). Epub 2020/09/10. doi: 10.1128/IAI.00301-20 ; PubMed Central PMCID: PMC7671898. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref014] 14.Hand TW, Reboldi A. Production and Function of Immunoglobulin A. Annu Rev Immunol. 2021;39:695–718. Epub 2021/03/02. doi: 10.1146/annurev-immunol-102119-074236 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref015] 15.Fadlallah J, El Kafsi H, Sterlin D, Juste C, Parizot C, Dorgham K, et al. Microbial ecology perturbation in human IgA deficiency. Sci Transl Med. 2018;10(439). Epub 2018/05/04. doi: 10.1126/scitranslmed.aan1217 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref016] 16.Goncalves P, Charbit B, Lenoir C, Milieu Interieur C, Duffy D, Fischer A, et al. Antibody-coated microbiota in nasopharynx of healthy individuals and IVIg-treated patients with hypogammaglobulinemia. J Allergy Clin Immunol. 2020;145(6):1686–90 e4. Epub 2020/01/20. doi: 10.1016/j.jaci.2020.01.002 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref017] 17.Madhwani T, McBain AJ. The Application of Magnetic Bead Selection to Investigate Interactions between the Oral Microbiota and Salivary Immunoglobulins. PLoS One. 2016;11(8):e0158288. Epub 2016/08/03. doi: 10.1371/journal.pone.0158288 ; PubMed Central PMCID: PMC4970730. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref018] 18.Lycke N, Eriksen L, Holmgren J. Protection against cholera toxin after oral immunization is thymus-dependent and associated with intestinal production of neutralizing IgA antitoxin. Scand J Immunol. 1987;25(4):413–9. Epub 1987/04/01. doi: 10.1111/j.1365-3083.1987.tb02208.x [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref019] 19.Harris NL, Spoerri I, Schopfer JF, Nembrini C, Merky P, Massacand J, et al. Mechanisms of neonatal mucosal antibody protection. J Immunol. 2006;177(9):6256–62. Epub 2006/10/24. doi: 10.4049/jimmunol.177.9.6256 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref020] 20.Peterson DA, Planer JD, Guruge JL, Xue L, Downey-Virgin W, Goodman AL, et al. Characterizing the interactions between a naturally primed immunoglobulin A and its conserved Bacteroides thetaiotaomicron species-specific epitope in gnotobiotic mice. J Biol Chem. 2015;290(20):12630–49. Epub 2015/03/22. doi: 10.1074/jbc.M114.633800 ; PubMed Central PMCID: PMC4432283. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref021] 21.Macpherson AJ, Yilmaz B, Limenitakis JP, Ganal-Vonarburg SC. IgA Function in Relation to the Intestinal Microbiota. Annu Rev Immunol. 2018;36:359–81. Epub 2018/02/06. doi: 10.1146/annurev-immunol-042617-053238 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref022] 22.Macpherson AJ, Gatto D, Sainsbury E, Harriman GR, Hengartner H, Zinkernagel RM. A primitive T cell-independent mechanism of intestinal mucosal IgA responses to commensal bacteria. Science. 2000;288(5474):2222–6. Epub 2000/06/24. doi: 10.1126/science.288.5474.2222 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref023] 23.Ip B, Cilfone NA, Belkina AC, DeFuria J, Jagannathan-Bogdan M, Zhu M, et al. Th17 cytokines differentiate obesity from obesity-associated type 2 diabetes and promote TNFalpha production. Obesity (Silver Spring). 2016;24(1):102–12. Epub 2015/11/19. doi: 10.1002/oby.21243 ; PubMed Central PMCID: PMC4688084. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref024] 24.Omenetti S, Pizarro TT. The Treg/Th17 Axis: A Dynamic Balance Regulated by the Gut Microbiome. Front Immunol. 2015;6:639. Epub 2016/01/07. doi: 10.3389/fimmu.2015.00639 ; PubMed Central PMCID: PMC4681807. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref025] 25.Viladomiu M, Kivolowitz C, Abdulhamid A, Dogan B, Victorio D, Castellanos JG, et al. IgA-coated E. coli enriched in Crohn’s disease spondyloarthritis promote TH17-dependent inflammation. Sci Transl Med. 2017;9(376). Epub 2017/02/10. doi: 10.1126/scitranslmed.aaf9655 ; PubMed Central PMCID: PMC6159892. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref026] 26.Wang M, Chen F, Wang J, Zeng Z, Yang Q, Shao S. Th17 and Treg lymphocytes in obesity and Type 2 diabetic patients. Clin Immunol. 2018;197:77–85. Epub 2018/09/16. doi: 10.1016/j.clim.2018.09.005 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref027] 27.Turner JS, Benet ZL, Grigorova IL. Signals 1, 2 and B cell fate or: Where, when and for how long? Immunol Rev. 2020;296(1):9–23. Epub 2020/05/30. doi: 10.1111/imr.12865 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref028] 28.Yoshitomi H, Ueno H. Shared and distinct roles of T peripheral helper and T follicular helper cells in human diseases. Cell Mol Immunol. 2020. Epub 2020/09/02. doi: 10.1038/s41423-020-00529-z . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref029] 29.Quan CP, Berneman A, Pires R, Avrameas S, Bouvet JP. Natural polyreactive secretory immunoglobulin A autoantibodies as a possible barrier to infection in humans. Infect Immun. 1997;65(10):3997–4004. Epub 1997/10/08. PubMed Central PMCID: PMC175574. doi: 10.1128/iai.65.10.3997-4004.1997 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref030] 30.He B, Xu W, Santini PA, Polydorides AD, Chiu A, Estrella J, et al. Intestinal bacteria trigger T cell-independent immunoglobulin A(2) class switching by inducing epithelial-cell secretion of the cytokine APRIL. Immunity. 2007;26(6):812–26. Epub 2007/06/16. doi: 10.1016/j.immuni.2007.04.014 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref031] 31.Shimoda M, Inoue Y, Azuma N, Kanno C. Natural polyreactive immunoglobulin A antibodies produced in mouse Peyer’s patches. Immunology. 1999;97(1):9–17. Epub 1999/08/14. doi: 10.1046/j.1365-2567.1999.00755.x ; PubMed Central PMCID: PMC2326812. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref032] 32.Bunker JJ, Flynn TM, Koval JC, Shaw DG, Meisel M, McDonald BD, et al. Innate and Adaptive Humoral Responses Coat Distinct Commensal Bacteria with Immunoglobulin A. Immunity. 2015;43(3):541–53. Epub 2015/09/01. doi: 10.1016/j.immuni.2015.08.007 ; PubMed Central PMCID: PMC4575282. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref033] 33.Okai S, Usui F, Yokota S, Hori IY, Hasegawa M, Nakamura T, et al. High-affinity monoclonal IgA regulates gut microbiota and prevents colitis in mice. Nat Microbiol. 2016;1(9):16103. Epub 2016/08/27. doi: 10.1038/nmicrobiol.2016.103 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref034] 34.Shinkura R, Ito S, Begum NA, Nagaoka H, Muramatsu M, Kinoshita K, et al. Separate domains of AID are required for somatic hypermutation and class-switch recombination. Nat Immunol. 2004;5(7):707–12. Epub 2004/06/15. doi: 10.1038/ni1086 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref035] 35.Wei M, Shinkura R, Doi Y, Maruya M, Fagarasan S, Honjo T. Mice carrying a knock-in mutation of Aicda resulting in a defect in somatic hypermutation have impaired gut homeostasis and compromised mucosal defense. Nat Immunol. 2011;12(3):264–70. Epub 2011/01/25. doi: 10.1038/ni.1991 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref036] 36.Kato LM, Kawamoto S, Maruya M, Fagarasan S. The role of the adaptive immune system in regulation of gut microbiota. Immunol Rev. 2014;260(1):67–75. Epub 2014/06/20. doi: 10.1111/imr.12185 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref037] 37.Zhou T, Hu Z, Yang S, Sun L, Yu Z, Wang G. Role of Adaptive and Innate Immunity in Type 2 Diabetes Mellitus. J Diabetes Res. 2018;2018:7457269. Epub 2018/12/12. doi: 10.1155/2018/7457269 ; PubMed Central PMCID: PMC6250017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref038] 38.Ferraro A, Socci C, Stabilini A, Valle A, Monti P, Piemonti L, et al. Expansion of Th17 cells and functional defects in T regulatory cells are key features of the pancreatic lymph nodes in patients with type 1 diabetes. Diabetes. 2011;60(11):2903–13. Epub 2011/09/08. doi: 10.2337/db11-0090 ; PubMed Central PMCID: PMC3198077. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref039] 39.Zhang C, Xiao C, Wang P, Xu W, Zhang A, Li Q, et al. The alteration of Th1/Th2/Th17/Treg paradigm in patients with type 2 diabetes mellitus: Relationship with diabetic nephropathy. Hum Immunol. 2014;75(4):289–96. Epub 2014/02/18. doi: 10.1016/j.humimm.2014.02.007 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref040] 40.Singh A, Yamamoto M, Ruan J, Choi JY, Gauvreau GM, Olek S, et al. Th17/Treg ratio derived using DNA methylation analysis is associated with the late phase asthmatic response. Allergy Asthma Clin Immunol. 2014;10(1):32. Epub 2014/07/06. doi: 10.1186/1710-1492-10-32 ; PubMed Central PMCID: PMC4078401. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref041] 41.Feng T, Elson CO, Cong Y. Treg cell-IgA axis in maintenance of host immune homeostasis with microbiota. Int Immunopharmacol. 2011;11(5):589–92. Epub 2010/11/30. doi: 10.1016/j.intimp.2010.11.016 ; PubMed Central PMCID: PMC3078992. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref042] 42.Cheng X, Yu X, Ding YJ, Fu QQ, Xie JJ, Tang TT, et al. The Th17/Treg imbalance in patients with acute coronary syndrome. Clin Immunol. 2008;127(1):89–97. Epub 2008/02/26. doi: 10.1016/j.clim.2008.01.009 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref043] 43.Dalmas E, Venteclef N, Caer C, Poitou C, Cremer I, Aron-Wisnewsky J, et al. T cell-derived IL-22 amplifies IL-1beta-driven inflammation in human adipose tissue: relevance to obesity and type 2 diabetes. Diabetes. 2014;63(6):1966–77. Epub 2014/02/13. doi: 10.2337/db13-1511 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref044] 44.Fabbrini E, Cella M, McCartney SA, Fuchs A, Abumrad NA, Pietka TA, et al. Association between specific adipose tissue CD4+ T-cell populations and insulin resistance in obese individuals. Gastroenterology. 2013;145(2):366–74 e1-3. Epub 2013/04/20. doi: 10.1053/j.gastro.2013.04.010 ; PubMed Central PMCID: PMC3756481. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref045] 45.Chen H, Ren X, Liao N, Wen F. Th17 cell frequency and IL-17A concentrations in peripheral blood mononuclear cells and vitreous fluid from patients with diabetic retinopathy. J Int Med Res. 2016;44(6):1403–13. Epub 2016/11/26. doi: 10.1177/0300060516672369 ; PubMed Central PMCID: PMC5536736. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref046] 46.Jagannathan-Bogdan M, McDonnell ME, Shin H, Rehman Q, Hasturk H, Apovian CM, et al. Elevated proinflammatory cytokine production by a skewed T cell compartment requires monocytes and promotes inflammation in type 2 diabetes. J Immunol. 2011;186(2):1162–72. Epub 2010/12/21. doi: 10.4049/jimmunol.1002615 ; PubMed Central PMCID: PMC3089774. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref047] 47.Yuan N, Zhang HF, Wei Q, Wang P, Guo WY. Expression of CD4+CD25+Foxp3+ Regulatory T Cells, Interleukin 10 and Transforming Growth Factor beta in Newly Diagnosed Type 2 Diabetic Patients. Exp Clin Endocrinol Diabetes. 2018;126(2):96–101. Epub 2017/09/28. doi: 10.1055/s-0043-113454 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref048] 48.Han JM, Patterson SJ, Speck M, Ehses JA, Levings MK. Insulin inhibits IL-10-mediated regulatory T cell function: implications for obesity. J Immunol. 2014;192(2):623–9. Epub 2013/12/11. doi: 10.4049/jimmunol.1302181 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref049] 49.Qiao YC, Shen J, He L, Hong XZ, Tian F, Pan YH, et al. Changes of Regulatory T Cells and of Proinflammatory and Immunosuppressive Cytokines in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis. J Diabetes Res. 2016;2016:3694957. Epub 2016/10/26. doi: 10.1155/2016/3694957 ; PubMed Central PMCID: PMC5061980. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref050] 50.Awartani F. Serum immunoglobulin levels in type 2 diabetes patients with chronic periodontitis. J Contemp Dent Pract. 2010;11(3):001–8. Epub 2010/05/13. . [PubMed] [Google Scholar]

[pone.0258812.ref051] 51.Triolo G, Giardina E, Zarcone MR, Giordano C, Rinaldi A, Bompiani GD. Contribution of secretory IgA, polymeric IgA and IgA/secretory component-containing circulating immune complexes to the serum hyper-IgA in diabetes mellitus. Diabetologia. 1984;27 Suppl:157–9. Epub 1984/07/01. doi: 10.1007/BF00275677 [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref052] 52.Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537–47. Epub 2016/05/09. doi: 10.1016/S2213-8587(16)30010-9 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref053] 53.Esser N, Legrand-Poels S, Piette J, Scheen AJ, Paquot N. Inflammation as a link between obesity, metabolic syndrome and type 2 diabetes. Diabetes Res Clin Pract. 2014;105(2):141–50. Epub 2014/05/07. doi: 10.1016/j.diabres.2014.04.006 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref054] 54.Zhou W, Sailani MR, Contrepois K, Zhou Y, Ahadi S, Leopold SR, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569(7758):663–71. Epub 2019/05/31. doi: 10.1038/s41586-019-1236-x ; PubMed Central PMCID: PMC6666404. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref055] 55.Kumar Nathella P, Babu S. Influence of diabetes mellitus on immunity to human tuberculosis. Immunology. 2017;152(1):13–24. Epub 2017/05/26. doi: 10.1111/imm.12762 ; PubMed Central PMCID: PMC5543489. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref056] 56.Agashe S, Petak S. Cardiac Autonomic Neuropathy in Diabetes Mellitus. Methodist Debakey Cardiovasc J. 2018;14(4):251–6. Epub 2019/02/23. doi: 10.14797/mdcj-14-4-251 ; PubMed Central PMCID: PMC6369622. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref057] 57.Guo M, Ding J, Li J, Wang J, Zhang T, Liu C, et al. SGLT2 inhibitors and risk of stroke in patients with type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metab. 2018;20(8):1977–82. Epub 2018/03/25. doi: 10.1111/dom.13295 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref058] 58.Baron U, Werner J, Schildknecht K, Schulze JJ, Mulu A, Liebert UG, et al. Epigenetic immune cell counting in human blood samples for immunodiagnostics. Sci Transl Med. 2018;10(452). Epub 2018/08/03. doi: 10.1126/scitranslmed.aan3508 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref059] 59.Burska AN, Thu A, Parmar R, Bzoma I, Samans B, Raschke E, et al. Quantifying circulating Th17 cells by qPCR: potential as diagnostic biomarker for rheumatoid arthritis. Rheumatology (Oxford). 2019;58(11):2015–24. Epub 2019/05/14. doi: 10.1093/rheumatology/kez162 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref060] 60.Sehouli J, Loddenkemper C, Cornu T, Schwachula T, Hoffmuller U, Grutzkau A, et al. Epigenetic quantification of tumor-infiltrating T-lymphocytes. Epigenetics. 2011;6(2):236–46. Epub 2010/10/22. doi: 10.4161/epi.6.2.13755 ; PubMed Central PMCID: PMC3278789. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref061] 61.Wieczorek G, Asemissen A, Model F, Turbachova I, Floess S, Liebenberg V, et al. Quantitative DNA methylation analysis of FOXP3 as a new method for counting regulatory T cells in peripheral blood and solid tissue. Cancer Res. 2009;69(2):599–608. Epub 2009/01/17. doi: 10.1158/0008-5472.CAN-08-2361 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref062] 62.Hanis CL, Redline S, Cade BE, Bell GI, Cox NJ, Below JE, et al. Beyond type 2 diabetes, obesity and hypertension: an axis including sleep apnea, left ventricular hypertrophy, endothelial dysfunction, and aortic stiffness among Mexican Americans in Starr County, Texas. Cardiovasc Diabetol. 2016;15:86. doi: 10.1186/s12933-016-0405-6 ; PubMed Central PMCID: PMC4897940. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref063] 63.Standards of medical care in diabetes—2015: summary of revisions. Diabetes Care. 2015;38 Suppl:S4. Epub 2014/12/30. doi: 10.2337/dc15-S003 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref064] 64.Human Microbiome Project C. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14. doi: 10.1038/nature11234 ; PubMed Central PMCID: PMC3564958. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref065] 65.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. doi: 10.1093/bioinformatics/btq461 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref066] 66.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8. Epub 2013/08/21. doi: 10.1038/nmeth.2604 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref067] 67.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590–6. Epub 2012/11/30. doi: 10.1093/nar/gks1219 ; PubMed Central PMCID: PMC3531112. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref068] 68.Jari Oksanen FGB, Michael Friendly, Roeland Kindt, Pierre Legendre, Dan McGlinn, Peter R. Minchin, et al. Vegan: Community Ecology Package. R package version 2.5–7. 2020.

[pone.0258812.ref069] 69.Team R. RStudio: Integrated development for R. Boston, MA: RStudio, PBC; 2020. [Google Scholar]

[pone.0258812.ref070] 70.Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. Epub 2011/06/28. doi: 10.1186/gb-2011-12-6-r60 ; PubMed Central PMCID: PMC3218848. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref071] 71.Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Cech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537–W44. Epub 2018/05/24. doi: 10.1093/nar/gky379 ; PubMed Central PMCID: PMC6030816. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref072] 72.Yang J, Chu Y, Yang X, Gao D, Zhu L, Yang X, et al. Th17 and natural Treg cell population dynamics in systemic lupus erythematosus. Arthritis Rheum. 2009;60(5):1472–83. Epub 2009/05/01. doi: 10.1002/art.24499 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref073] 73.Madan A, Thompson D, Fowler JC, Ajami NJ, Salas R, Frueh BC, et al. The gut microbiota is associated with psychiatric symptom severity and treatment outcome among individuals with serious mental illness. J Affect Disord. 2020;264:98–106. Epub 2020/02/15. doi: 10.1016/j.jad.2019.12.020 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref074] 74.Brown EL, Essigmann HT. Original Antigenic Sin: the Downside of Immunological Memory and Implications for COVID-19. mSphere. 2021;6(2). Epub 2021/03/12. doi: 10.1128/mSphere.00056-21 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref075] 75.Vuong HE, Hsiao EY. Emerging Roles for the Gut Microbiome in Autism Spectrum Disorder. Biol Psychiatry. 2017;81(5):411–23. Epub 2016/10/25. doi: 10.1016/j.biopsych.2016.08.024 ; PubMed Central PMCID: PMC5285286. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref076] 76.Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13(4):260–70. Epub 2012/03/14. doi: 10.1038/nrg3182 ; PubMed Central PMCID: PMC3418802. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref077] 77.Rhee SH, Pothoulakis C, Mayer EA. Principles and clinical implications of the brain-gut-enteric microbiota axis. Nat Rev Gastroenterol Hepatol. 2009;6(5):306–14. Epub 2009/05/01. doi: 10.1038/nrgastro.2009.35 ; PubMed Central PMCID: PMC3817714. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref078] 78.Shreiner AB, Kao JY, Young VB. The gut microbiome in health and in disease. Curr Opin Gastroenterol. 2015;31(1):69–75. Epub 2014/11/14. doi: 10.1097/MOG.0000000000000139 ; PubMed Central PMCID: PMC4290017. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref079] 79.Tang WH, Kitai T, Hazen SL. Gut Microbiota in Cardiovascular Health and Disease. Circ Res. 2017;120(7):1183–96. Epub 2017/04/01. doi: 10.1161/CIRCRESAHA.117.309715 ; PubMed Central PMCID: PMC5390330. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref080] 80.Tilg H, Moschen AR. Microbiota and diabetes: an evolving relationship. Gut. 2014;63(9):1513–21. Epub 2014/05/17. doi: 10.1136/gutjnl-2014-306928 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref081] 81.van Olden C, Groen AK, Nieuwdorp M. Role of Intestinal Microbiome in Lipid and Glucose Metabolism in Diabetes Mellitus. Clin Ther. 2015;37(6):1172–7. Epub 2015/04/30. doi: 10.1016/j.clinthera.2015.03.008 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref082] 82.Webb DR, Khunti K, Silverman R, Gray LJ, Srinivasan B, Lacy PS, et al. Impact of metabolic indices on central artery stiffness: independent association of insulin resistance and glucose with aortic pulse wave velocity. Diabetologia. 2010;53(6):1190–8. Epub 2010/03/10. doi: 10.1007/s00125-010-1689-9 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref083] 83.Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A. 2009;106(10):3698–703. Epub 2009/02/24. doi: 10.1073/pnas.0812874106 ; PubMed Central PMCID: PMC2656143. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref084] 84.De Angelis M, Montemurno E, Piccolo M, Vannini L, Lauriero G, Maranzano V, et al. Microbiota and metabolome associated with immunoglobulin A nephropathy (IgAN). PLoS One. 2014;9(6):e99006. Epub 2014/06/13. doi: 10.1371/journal.pone.0099006 ; PubMed Central PMCID: PMC4055632. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref085] 85.Kayama H, Okumura R, Takeda K. Interaction Between the Microbiota, Epithelia, and Immune Cells in the Intestine. Annu Rev Immunol. 2020;38:23–48. Epub 2020/04/29. doi: 10.1146/annurev-immunol-070119-115104 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref086] 86.McCarville JL, Chen GY, Cuevas VD, Troha K, Ayres JS. Microbiota Metabolites in Health and Disease. Annu Rev Immunol. 2020;38:147–70. Epub 2020/04/29. doi: 10.1146/annurev-immunol-071219-125715 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref087] 87.Hagan T, Cortese M, Rouphael N, Boudreau C, Linde C, Maddur MS, et al. Antibiotics-Driven Gut Microbiome Perturbation Alters Immunity to Vaccines in Humans. Cell. 2019;178(6):1313–28 e13. Epub 2019/09/07. doi: 10.1016/j.cell.2019.08.010 ; PubMed Central PMCID: PMC6750738. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref088] 88.Pamer EG. Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science. 2016;352(6285):535–8. Epub 2016/04/30. doi: 10.1126/science.aad9382 ; PubMed Central PMCID: PMC4984266. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref089] 89.Cresci GAM. Metabolome of the Microbiome: A Foundation for Better Health? Altern Ther Health Med. 2018;24(S1):25–7. Epub 2019/04/15. . [PubMed] [Google Scholar]

[pone.0258812.ref090] 90.Yang Y, Misra BB, Liang L, Bi D, Weng W, Wu W, et al. Integrated microbiome and metabolome analysis reveals a novel interplay between commensal bacteria and metabolites in colorectal cancer. Theranostics. 2019;9(14):4101–14. Epub 2019/07/10. doi: 10.7150/thno.35186 ; PubMed Central PMCID: PMC6592169. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref091] 91.Tang ZZ, Chen G, Hong Q, Huang S, Smith HM, Shah RD, et al. Multi-Omic Analysis of the Microbiome and Metabolome in Healthy Subjects Reveals Microbiome-Dependent Relationships Between Diet and Metabolites. Front Genet. 2019;10:454. Epub 2019/06/06. doi: 10.3389/fgene.2019.00454 ; PubMed Central PMCID: PMC6534069. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref092] 92.Kolodziejczyk AA, Zheng D, Elinav E. Diet-microbiota interactions and personalized nutrition. Nat Rev Microbiol. 2019;17(12):742–53. Epub 2019/09/22. doi: 10.1038/s41579-019-0256-8 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref093] 93.Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science. 2012;336(6086):1268–73. Epub 2012/06/08. doi: 10.1126/science.1223490 ; PubMed Central PMCID: PMC4420145. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref094] 94.Barko PC, McMichael MA, Swanson KS, Williams DA. The Gastrointestinal Microbiome: A Review. J Vet Intern Med. 2018;32(1):9–25. Epub 2017/11/25. doi: 10.1111/jvim.14875 ; PubMed Central PMCID: PMC5787212. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref095] 95.Noverr MC, Huffnagle GB. Does the microbiota regulate immune responses outside the gut? Trends Microbiol. 2004;12(12):562–8. Epub 2004/11/13. doi: 10.1016/j.tim.2004.10.008 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref096] 96.Brandtzaeg P. Do salivary antibodies reliably reflect both mucosal and systemic immunity? Ann N Y Acad Sci. 2007;1098:288–311. Epub 2007/04/17. doi: 10.1196/annals.1384.012 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref097] 97.Bleau C, Karelis AD, St-Pierre DH, Lamontagne L. Crosstalk between intestinal microbiota, adipose tissue and skeletal muscle as an early event in systemic low-grade inflammation and the development of obesity and diabetes. Diabetes Metab Res Rev. 2015;31(6):545–61. doi: 10.1002/dmrr.2617 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref098] 98.Moreno-Indias I, Cardona F, Tinahones FJ, Queipo-Ortuno MI. Impact of the gut microbiota on the development of obesity and type 2 diabetes mellitus. Front Microbiol. 2014;5:190. doi: 10.3389/fmicb.2014.00190 ; PubMed Central PMCID: PMC4010744. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref099] 99.Van den Abbeele P, Verstraete W, El Aidy S, Geirnaert A, Van de Wiele T. Prebiotics, faecal transplants and microbial network units to stimulate biodiversity of the human gut microbiome. Microb Biotechnol. 2013;6(4):335–40. Epub 2013/04/19. doi: 10.1111/1751-7915.12049 ; PubMed Central PMCID: PMC3917468. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref100] 100.Petersen C, Round JL. Defining dysbiosis and its influence on host immunity and disease. Cell Microbiol. 2014;16(7):1024–33. Epub 2014/05/07. doi: 10.1111/cmi.12308 ; PubMed Central PMCID: PMC4143175. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref101] 101.Wilmanski T, Diener C, Rappaport N, Patwardhan S, Wiedrick J, Lapidus J, et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat Metab. 2021;3(2):274–86. Epub 2021/02/24. doi: 10.1038/s42255-021-00348-0 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref102] 102.Palm NW, de Zoete MR, Cullen TW, Barry NA, Stefanowski J, Hao L, et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell. 2014;158(5):1000–10. Epub 2014/08/30. doi: 10.1016/j.cell.2014.08.006 ; PubMed Central PMCID: PMC4174347. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref103] 103.De Filippis F, Pasolli E, Ercolini D. Newly Explored Faecalibacterium Diversity Is Connected to Age, Lifestyle, Geography, and Disease. Curr Biol. 2020;30(24):4932–43 e4. Epub 2020/10/17. doi: 10.1016/j.cub.2020.09.063 . [DOI] [PubMed] [Google Scholar]

[pone.0258812.ref104] 104.Laursen MF, Laursen RP, Larnkjaer A, Molgaard C, Michaelsen KF, Frokiaer H, et al. Faecalibacterium Gut Colonization Is Accelerated by Presence of Older Siblings. mSphere. 2017;2(6). Epub 2017/12/05. doi: 10.1128/mSphere.00448-17 ; PubMed Central PMCID: PMC5705805. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref105] 105.Lambeth SM, Carson T, Lowe J, Ramaraj T, Leff JW, Luo L, et al. Composition, Diversity and Abundance of Gut Microbiome in Prediabetes and Type 2 Diabetes. J Diabetes Obes. 2015;2(3):1–7. Epub 2016/01/13. doi: 10.15436/2376-0949.15.031 ; PubMed Central PMCID: PMC4705851. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref106] 106.Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One. 2013;8(8):e71108. Epub 2013/09/10. doi: 10.1371/journal.pone.0071108 ; PubMed Central PMCID: PMC3754967. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref107] 107.Walker AW, Ince J, Duncan SH, Webster LM, Holtrop G, Ze X, et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J. 2011;5(2):220–30. Epub 2010/08/06. doi: 10.1038/ismej.2010.118 ; PubMed Central PMCID: PMC3105703. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0258812.ref108] 108.Silva-Boghossian CM, Cesario PC, Leao ATT, Colombo APV. Subgingival microbial profile of obese women with periodontal disease. J Periodontol. 2018;89(2):186–94. Epub 2018/03/10. doi: 10.1002/JPER.17-0236 . [DOI] [PubMed] [Google Scholar]

PERMALINK

The impact of the Th17:Treg axis on the IgA-Biome across the glycemic spectrum

Heather T Essigmann

Kristi L Hoffman

Joseph F Petrosino

Goo Jun

David Aguilar

Craig L Hanis

Herbert L DuPont

Eric L Brown

Roles

Abstract

Introduction

Fig 1. The Th17:Treg SIgA axis.

Materials and methods

Study population and sample collection

Diabetes classifications

IgA-Biome analyses

16Sv4 rDNA sequencing

Epigenetic characterization of T-helper cell profiles

Statistical analysis

Results

Epigenetic quantification of the Th17:Treg ratio

Fig 2. Th17:Treg ratios by diabetes profile.

Th17:Treg ratios and glycemic status affect alpha and beta diversity

Fig 3. Impact of Th17:Treg ratios on alpha diversity of the stool microbiome by diabetes phenotype.

Fig 4. Principle coordinate analyses of UniFrac distances of stool and saliva Presort samples by Th17:Treg ratios in those with normoglycemia and dysglycemia.

Fig 5. LEfSe of stool and salivary Presort samples by inflammatory profiles.

Associations between the IgA-Biome, Th17:Treg ratios, and glycemic status

Fig 6. Impact of Th17:Treg ratios on alpha diversity of the stool IgA-Biome by diabetes phenotype.

Fig 7. Principal coordinate analyses of UniFrac distances of the stool IgA-Biome by Th17:Treg ratio-derived inflammatory phenotypes across diabetes status.

Fig 8. Comparisons of UniFrac distances between of the stool IgA-Biome of normal and dysglycemic individuals categorized by inflammatory phenotype.

Fig 9. LEfSe of stool and salivary IgA-Biomes by Th17:Treg ratio-derived inflammatory phenotypes.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jane Foster

Roles

Author response to Decision Letter 0

Decision Letter 1

Jane Foster

Roles

Acceptance letter

Jane Foster

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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