Additional file 1: Figure S1. Flow chart describing CD progressors demographics and sample size. Figure S2. Violin plot representation of most enriched genus/species in CD progressors compared to healthy controls. A. Fold change in ASVs at age 2.5 CD progressors (left panel, n=15) and healthy controls (right panel, n=16). B. Fold change in ASVs at age 5 in CD progressors (left panel, n=10) and healthy controls (right panel, n=13). Figure S3. Gating strategy for IgA sequencing and analysis. A. Schematic overview of IgA-based fecal bacteria separation combined with 16S rRNA gene sequencing (IgA-seq) for stool samples from CD progressors and healthy controls. MACS: Magnetic-activated cell separation. B. Gating strategy for the isolation of IgA-/+ bacteria from the CD progressors and healthy controls’ fecal samples. C. Empirical Bayes quasi-likelihood F-tests analysis for comparing IgA-coated and non-coated gut microbiota ASVs in healthy controls (upper row) and CD progressors (lower row) at ages 2.5, and 5. Frequency: number of ASVs. FDR: False Discovery Rate. D. Empirical Bayes quasi-likelihood F-tests analysis for the comparisons of IgA-coated or non-coated gut microbiota ASVs between CD progressors and healthy controls (upper row: age 2.5 years old; lower row: age 5 years old). F. Box plots showing representative ASVs in which abundances were similar in the gut microbiota (presorting samples) but differently targeted by IgA at age 2.5. E. Violin plots showing representative ASVs in which abundances were similar in the gut microbiota (presorting samples) but differently targeted by IgA at age 5. Figure S4. Heat Map showing IgA target in CD progressors and healthy subjects. A. Heat map showing the relative abundance of the top ASVs significantly different between IgA+ and IgA- samples of CD progressors and healthy controls (ASVs=51, selected based on p-value) at age 2.5 B. at age 5. Each column represents an individual participant and each row represents an ASV. Figure S5. The cytokine and plasma metabolome profiles of CD progressors and CD patients. A. Comparison of all 48 cytokines analyzed in plasma samples obtained from CD progressors (n=10) and healthy controls (n=10) at age 5. Data were expressed as means ±SEM. *p<0.05, **p<0.01, ***p<0.001. Statistical analysis was performed by a two-tailed, unpaired student’s t-test. B. Violin plots showing the representative Clostridium XIVa bacteria abundance between CD progressors and healthy controls (Left: before separation by IgA coating, Right: in IgA+ bacteria). Figure S6. Gating Strategy for the Flow cytometry analysis. Strategy 1- Gating strategy for NK1.1 and Qa-1 expression in TCRβ+ cells. Strategy 2- Gating strategy for CD8, CD4, NKG2D, CD103, and NKp46. Figure S7. TDCA diet induces changes in T-cell composition in different cell subsets. A. H&E images of ileum tissue sections of control and TDCA treated female mice. Full image (upper panel) and image at high magnification (lower panel). Scale =20μm. B. Villi/ Crypt ratio in ileum tissue sections of control and the TDCA treated female mice. C. Number of plasma cells in the lamina propria of the ileum section of female mice. D. TCRβ+ cells as % of total CD45+ cells. E. NKG2D+ cells as % of total TCRβ+ CD45+ cells. F. CD103+ cells as % of total CD4+ cells. G. Qa-1+ cells as % of total CD4+ cells in the IELs, PP, LP, and spleen of female (left panel) and male (right panel) mice. H Relative gene expression of Qa-1 and IL-10 in the ileum tissue analyzed using qPCR. Female (left panel) and male (right panel) mice after 10 weeks of TDCA treatment compared to controls. Data were expressed as mean ± SEM. *p<0.05, **p <0.01, ***p<0.001. Statistical analysis was performed by a two-tailed unpaired student’s t-test.
ABSTRACT Type 1 Diabetes (T1D) is an autoimmune disease characterized by destruction of pancreatic β-cells. Focusing on the main insulin epitope, insulin B-chain 9-23 (insB:9-23), we explored whether a microbial insB:9-23 mimic could modulate T1D. We now demonstrate that a microbial insB:9-23 mimic of Parabacteroides distasonis, a human gut commensal, exclusively stimulates non-obese diabetic (NOD) mouse T cells specific to insB:9-23. Indeed, immunization of NOD mice with either the bacterial mimic peptide or insB:9-23 further verified the cross-reactivity in vivo. Modeling P. distasonis peptide revealed a potential pathogenic register 3 binding. P. distasonis colonization of the female NOD mice gut accelerated T1D onset. In addition, adoptive transfer of splenocytes from NOD mice colonized with P. distasonis to NOD.SCID recipients conferred the enhanced disease phenotype. Integration analysis of published infant T1D gut microbiome data revealed that P. distasonis peptide is not present in the gut microbiota in the first year of life of infants that eventually develop T1D. Furthermore, P. distasonis peptide can stimulate human T cell clones specific to insB:9-23 and T1D patients demonstrated a strong humoral immune response to P. distasonis than controls. Taken together, our studies define a potential molecular mimicry link between T1D pathogenesis and the gut microbiota. One Sentence Summary The human gut commensal bacterium, Parabacteroides distasonis, accelerates type 1 diabetes in the NOD mouse model of the disease and involves expression of an insulin B:9-23 epitope mimic, supporting a potential disease mechanism involving molecular mimicry.
Growing evidence indicates an important link between gut microbiota, obesity, and metabolic syndrome. Alterations in exocrine pancreatic function are also widely present in patients with diabetes and obesity. To examine this interaction, C57BL/6J mice were fed either a chow diet, high-fat diet (HFD) or HFD plus oral vancomycin or metronidazole to modify the gut microbiome. HFD alone leads to a 40% increase in pancreas weight, decreased glucagon-like peptide-1 and peptide YY levels, and increased glucose-dependent insulinotropic peptide in the plasma. Quantitative proteomics identified 138 host proteins in fecal samples of these mice, of which 32 were significantly changed by HFD. The most significant of these were the pancreatic enzymes. These changes in amylase and elastase were reversed by antibiotic treatment. These alterations could be reproduced by transferring gut microbiota from donor C57BL/6J mice to germ-free. By contrast, antibiotics had no effect on pancreatic size or exocrine function in C57BL/6J mice fed a chow diet<a>.</a> Further, one week vancomycin administration significantly increased amylase and elastase levels in obese prediabetic men. Thus, the alterations in gut microbiota in obesity can alter pancreatic growth, exocrine function and gut endocrine function, and may contribute to the alterations observed in patients with obesity and diabetes.
AbstractPurpose: To evaluate the utility of apparent diffusion coefficient (ADC) values of extraocular muscles (EOMs) in differentiating activity of thyroid eye disease (TED). Method: Forty-two TED patients who underwent diffusion tensor imaging(DTI) were retrospectively enrolled in this study, including 29 patients in analysis group and 13 patients in validation group.The mean,maximum and minimum ADC value of each EOM were regarded as ADCmean, ADCmax and ADCmin.The difference between ADCmax and ADCmin was regarded as △ADC.The correlations between ADCmean or △ADC of each EOM and clinical activity score (CAS) were assessed. Results: In analysis group, ADCmean differed between active and inactive eyes and positively correlated with CAS in IR (P<0.05), not in SR,LR and MR(all p>0.05). While △ADC differed between two groups and negatively correlated with CAS in all EOMs (all P<0.05). ADCmean predicted active disease at cut-off value of 1259.3×10−6mm2s-1 with sensitivity of 66.7% and specificity of 71.4% in IR[area under curve =0.667, P<0.05].△ADC predicted disease activity in all EOMs [area under curve 0.658–0.746,all P<0.05].The cut-off values of △ADC were 382, 823,520 and 572 ×10−6mm2s-1 with sensitivity of 80.0%, 50.0%,43.3%,83.3% and specificity of 67.9%,85.7%, 89.3%, 60.7% in SR,IR,MR, and LR respectively.There were no significant differences in the predictive efficacy among all cut-off values. Conclusions: Our results showed that DTI is an valuable tool in the assessment of disease activity of TED.Both ADCmean of IR and △ADC of all four EOMs can be used in discriminating disease activity with the same predictive power.
To evaluate the utility of apparent diffusion coefficient (ADC) values of extraocular muscles (EOMs) in differentiating activity of thyroid eye disease (TED). Forty-two TED patients who underwent diffusion tensor imaging(DTI) were retrospectively enrolled in this study, including 29 patients in analysis group and 13 patients in validation group. The mean, maximum and minimum ADC value of each EOM were regarded as ADCmean, ADCmax and ADCmin. The difference between ADCmax and ADCmin was regarded as △ADC. The correlations between ADCmean or △ADC of each EOM and clinical activity score (CAS) were assessed. In analysis group, ADCmean differed between active and inactive eyes and positively correlated with CAS in IR (P < 0.05), not in SR,LR and MR(all p > 0.05). While △ADC differed between two groups and negatively correlated with CAS in all EOMs (all P < 0.05). ADCmean predicted active disease at cut-off value of 1259.3 × 10−6mm2s−1 with sensitivity of 66.7% and specificity of 71.4% in IR[area under curve = 0.667, P < 0.05]. △ADC predicted disease activity in all EOMs [area under curve 0.658–0.746,all P < 0.05]. The cut-off values of △ADC were 382, 823,520 and 572 × 10−6mm2s−1 with sensitivity of 80.0%, 50.0%, 43.3%, 83.3% and specificity of 67.9%, 85.7%, 89.3%, 60.7% in SR, IR, MR, and LR respectively. There were no significant differences in the predictive efficacy among all cut-off values. Our results showed that DTI is a valuable tool in the assessment of disease activity of TED. Both ADCmean of IR and △ADC of all four EOMs can be used in discriminating disease activity with the same predictive power.
Abstract Objectives Mesenchymal stem cells (MSCs) play important roles in multiple myeloma (MM) pathogenesis. Previous studies have discovered a group of MM-associated potential biomarkers in MSCs derived from bone marrow (BM-MSCs). However, no study of the bioinformatics analysis was conducted to explore the key genes and pathways of MSCs derived from adipose (AD-MSCs) in MM. The aim of this study was to screen potential biomarkers or therapeutic targets of AD-MSCs and BM-MSCs in MM. Methods The gene expression profiles of AD-MSCs (GSE133346) and BM-MSCs (GSE36474) were downloaded from Gene Expression Omnibus (GEO) database. Gene Oncology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network of differentially expressed genes (DEGs) were performed. Results A total of 456 common downregulated DEGs in two datasets were identified and the remaining DEGs in GSE133346 were further identified as specific DEGs of AD-MSCs. Furthermore, a PPI network of common downregulated DEGs was constructed and seven hub genes were identified. Importantly, cell cycle was the most significantly enrichment pathway both in AD-MSCs and BM-MSCs from MM patients. Conclusion We identified key genes and pathways closely related with MM progression, which may act as potential biomarkers or therapeutic targets of MM.
Type 1 diabetes (T1D) is an autoimmune disease characterized by the selective destruction of pancreatic β-cells by autoreactive T-cells. Genome-wide association studies (GWAS) have identified over 150 regions that influence the risk of developing T1D, but genetics alone cannot account for the increasing incidence of T1D. Various environmental factors have been examined to identify their potential contributions, however, no report has demonstrated a direct role of these environmental factors in T1D onset. In the nonobese diabetic (NOD) mouse model of T1D, over 90% of the anti-insulin CD4+ T-cell clones target amino acids 9-23 of the insulin B chain (insB:9-23). More importantly, insB:9-23 specific T-cells have been identified in islets obtained from human organ donors with T1D as well as peripheral blood lymphocytes of living T1D patients. In this study, focusing on the main insulin epitope, insB:9-23, we explored whether a microbial insB:9-23 mimic could modulate T1D. We demonstrate that a microbial insB:9-23 mimic of Parabacteroides distasonis, a human gut commensal, exclusively stimulates NOD mouse T-cells specific to insB:9-23. Indeed, immunization of NOD mice with either the bacterial mimic peptide or insB:9-23 further verified the cross-reactivity in vivo. P. distasonis colonization of the female NOD mice gut accelerated T1D onset. In addition, adoptive transfer of splenocytes from NOD mice colonized with P. distasonis to NOD.SCID recipients conferred the enhanced disease phenotype. Integration analysis of published DIABIMMUNE T1D gut microbiome data revealed that P. distasonis is lacking in the gut microbiota of the children below age 1 who will eventually develop T1D in Russia and Estonia. Notably, P. distasonis insB:9-23 mimic can stimulate human T-cell clones specific to insB:9-23. Taken together, our studies define a potential molecular mimicry link between T1D pathogenesis and the gut microbiota. Disclosure K. Girdhar: None. E. Altindis: None. Q. Huang: None. I. Chow: None. C. Brady: None. A. Raisingani: None. T. Tran: None. W. Kwok: None. M. A. Atkinson: None. C. Kahn: Advisory Panel; Self; ERX Pharmaceuticals, Kaleido Biosciences, Inc., Consultant; Self; Flagship Pioneering, Sana/Cobalt. Funding National Institutes of Health (1K01DK117967-01, R01DK031026, R01DK033201, P01AI042288); G. Harold & Leila Y. Mathers Foundation; Iacocca Family Foundation
Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of pancreatic β-cells. One of the earliest aspects of this process is the development of autoantibodies and T cells directed at an epitope in the B-chain of insulin (insB:9–23). Analysis of microbial protein sequences with homology to the insB:9–23 sequence revealed 17 peptides showing >50% identity to insB:9–23. Of these 17 peptides, the hprt4–18 peptide, found in the normal human gut commensal Parabacteroides distasonis , activated both human T cell clones from T1D patients and T cell hybridomas from nonobese diabetic (NOD) mice specific to insB:9–23. Immunization of NOD mice with P. distasonis insB:9–23 peptide mimic or insB:9–23 peptide verified immune cross-reactivity. Colonization of female NOD mice with P. distasonis accelerated the development of T1D, increasing macrophages, dendritic cells, and destructive CD8 + T cells, while decreasing FoxP3 + regulatory T cells. Western blot analysis identified P. distasonis –reacting antibodies in sera of NOD mice colonized with P. distasonis and human T1D patients. Furthermore, adoptive transfer of splenocytes from P. distasonis –treated mice to NOD/SCID mice enhanced disease phenotype in the recipients. Finally, analysis of human children gut microbiome data from a longitudinal DIABIMMUNE study revealed that seroconversion rates (i.e., the proportion of individuals developing two or more autoantibodies) were consistently higher in children whose microbiome harbored sequences capable of producing the hprt4–18 peptide compared to individuals who did not harbor it. Taken together, these data demonstrate the potential role of a gut microbiota-derived insB:9–23-mimic peptide as a molecular trigger of T1D pathogenesis.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
