O10 TNF signalling associated with high risk compared to low risk disease in primary biliary cholangitis: a transcriptomic analysis of peripheral immune cells
Bibliographic record
Abstract
Primary biliary cholangitis (PBC) is an immune-mediated inflammatory disorder of the interlobular bile ducts which leads in many cases to cirrhosis. In patients with PBC, inadequate biochemical response to first-line treatment with ursodeoxycholic acid (UDCA) identifies those at high risk of progressive liver disease. In this study, we used transcriptional profiling of peripheral immune cells to gain insight into the immunobiology of high- vs. low-risk disease. We performed bulk RNA-sequencing of monocytes, NK cells, CD4+ T cells, CD8+ T cells, and B cells isolated from the peripheral blood of 40 treatment-naïve PBC patients; 36 high-risk patients (ALP ≥1.67 times the upper limit of normal [ULN] despite treatment with UDCA); 32 low-risk patients (ALP <1×ULN on UDCA); and 32 matched controls. We used Weighted Gene Co-expression Network Analysis (WGCNA) to identify networks of co-expressed genes (“modules”) associated with high-risk, low-risk or any PBC, and the most highly connected genes (“hub genes”) within them. Finally, we performed Multi-Omics Factor Analysis (MOFA) of WGCNA modules to identify the principal axes of biological variation (“latent factors”) across all immune cell subsets. We identified modules associated with high-risk, low-risk, or any PBC patient (q < 0.05) in each PBMC subset. Hub genes and functional annotations suggested that: (1) CD4+ T cells, CD8+ T cells and monocytes are active in PBC patients irrespective of disease activity, with enrichment of genes involved in TNF, IL-2, IL-6, and INFγ signalling, amongst other pathways; and (2) TNF signalling, implicated in all five cell types studied, is associated with high risk compared to low risk disease. Using MOFA, we identified one latent factor which bridged all cell types; was heavily weighted for modules enriched for TNF signalling; and showed significant difference in high compared to low risk disease (q < 0.05). Using this approach we found evidence of pro-inflammatory signalling in all stages of PBC disease irrespective of high-risk or low-risk disease and we identified TNF signalling as key in high risk compared to low risk 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".