Analysis of Flagellin-Specific Adaptive Immunity Reveals Links to Dysbiosis in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
Background & Aims Bacterial flagellin is an important antigen in inflammatory bowel disease, but the role of flagellin-specific CD4 + T cells in disease pathogenesis remains unclear. Also unknown is how changes in intestinal microbiome intersect with those in microbiota-specific CD4 + T cells. We aimed to quantify and characterize flagellin-specific CD4 + T cells in Crohn's disease (CD) and ulcerative colitis (UC) patients and study their relationship with intestinal microbiome diversity. Methods Blood was collected from 3 cohorts that included CD patients, UC patients, and healthy controls. Flow cytometry analyzed CD4 + T cells specific for Lachnospiraceae -derived A4-Fla2 and Escherichia coli H18 FliC flagellins, or control vaccine antigens. Serum antiflagellin IgG and IgA antibodies were detected by enzyme-linked immunosorbent assay and stool samples were collected and subjected to 16S ribosomal DNA sequencing. Results Compared with healthy controls, CD and UC patients had lower frequencies of vaccine-antigen–specific CD4 + T cells and, as a proportion of vaccine-specific cells, higher frequencies of flagellin-specific CD4 + T cells. The proportion of flagellin-specific CD4 + T cells that were CXCR3 neg CCR4 + CCR6 + Th17 cells was reduced in CD and UC patients, with increased proportions of CD39 + , PD-1 + , and integrin β7 + cells. Microbiome analysis showed differentially abundant bacterial species in patient groups that correlated with immune responses to flagellin. Conclusions Both CD and UC patients have relative increases in the proportion of circulating Fla2-specific CD4 + T cells, which may be associated with changes in the intestinal microbiome. Evidence that the phenotype of these cells strongly correlate with disease severity provides insight into the potential roles of flagellin-specific CD4 + T cells in inflammatory bowel disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".