DOP08 Transcriptional signatures of blood derived immune cells associated with disease location-based heterogeneity in IBD
Bibliographic record
Abstract
Abstract Background Disease location is a prominent axis of heterogeneity in Inflammatory Bowel Disease (IBD) with many implications. Using genome-wide profiling of the transcriptome of monocytes and CD4+ T cells isolated and purified from whole blood, we aimed to identify molecular signatures and mechanisms associated with different locations among IBD patients. Methods Blood was collected from 125 IBD patients (87 CD, 38 UC) with endoscopy-proven active disease (presence of ulcerations). Cell separation and fluorescence activated cell sorting were performed to separate the monocyte and CD4+ T cell fractions, from which RNA was subsequently isolated and sequenced (Illumina HiSeq 4000NGS). We used different supervised and unsupervised approaches (differential expression, pathway based data integration, latent factor based models, regularized generalized canonical correlation analysis and co-expression networks) to interpret the differences in the gene expression datasets of monocytes and CD4+ T cells from patients with different disease locations (Montreal classification). Functional enrichment analysis was performed using the ReactomePA package. Regulatory relationships and therapeutic relevance information were retrieved from the ChEA3 and the OpenTargets resources respectively. Comparison with single-cell and bulk-derived gene expression signatures from other auto-immune diseases were performed using the ADEX resource. Results Highly variant disease-location (DL)-associated genes (FDR <= 0.1) in monocytes and CD4+ T cells were identified using latent factor based unsupervised models. These genes were known to be involved in IBD pathogenesis and/or intestinal inflammation. Additional supervised analysis revealed significant differences in CD4+ T cells between ileal CD patients and UC patients. RAF-independent MAPK-activation pathway and FOXO-mediated transcriptional pathway (downregulated in UC patients) were over-represented (FDR <= 0.05) among the features distinguishing ileal CD and UC patients based on signature sets derived from the above-mentioned multiple approaches. Of note was the finding that 12.5% of the DL associated co-expression modules were also annotated as IBD drug targets. Based on gene expression signature from bulk and single-cell sources, the DL associated genes were found to be active in many other auto-immune diseases such as rheumatoid arthritis, systemic sclerosis, Sjögren’s syndrome, type 1 diabetes and Systemic lupus erythematosus, suggesting their role in mediating immune malfunctions. Conclusion We identified signaling pathways and transcription factors which could drive the expression differences observed in the circulating immune cells between ileal CD and UC patients.
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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 | 0.000 |
| 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 | 0.001 | 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".