Additional file 1 of Dynamics of the gut microbiome, IgA response, and plasma metabolome in the development of pediatric celiac disease

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.