Integrated Analysis of Biopsies from Inflammatory Bowel Disease Patients Identifies SAA1 as a Link Between Mucosal Microbes with TH17 and TH22 Cells
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel diseases (IBD) are believed to be driven by dysregulated interactions between the host and the gut microbiota. Our goal is to characterize and infer relationships between mucosal T cells, the host tissue environment, and microbial communities in patients with IBD who will serve as basis for mechanistic studies on human IBD. METHODS: We characterized mucosal CD4 T cells using flow cytometry, along with matching mucosal global gene expression and microbial communities data from 35 pinch biopsy samples from patients with IBD. We analyzed these data sets using an integrated framework to identify predictors of inflammatory states and then reproduced some of the putative relationships formed among these predictors by analyzing data from the pediatric RISK cohort. RESULTS: We identified 26 predictors from our combined data set that were effective in distinguishing between regions of the intestine undergoing active inflammation and regions that were normal. Network analysis on these 26 predictors revealed SAA1 as the most connected node linking the abundance of the genus Bacteroides with the production of IL17 and IL22 by CD4 T cells. These SAA1-linked microbial and transcriptome interactions were further reproduced with data from the pediatric IBD RISK cohort. CONCLUSIONS: This study identifies expression of SAA1 as an important link between mucosal T cells, microbial communities, and their tissue environment in patients with IBD. A combination of T cell effector function data, gene expression and microbial profiling can distinguish between intestinal inflammatory states in IBD regardless of disease types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".