A160 MOLECULAR ANALYSIS OF THE INJURY-REPAIR RESPONSE IN ULCERATIVE COLITIS REVEALS HETEROGENEITY IN DISEASE ACTIVIT
Bibliographic record
Abstract
Abstract Background Ulcerative colitis (UC) is a chronic inflammatory condition affecting the colonic epithelium. We used an established microarray-based system to analyze a set of 128 UC biopsies (113 patients), assessing gene expression associated with the colon’s response to injury in UC. Aims Our aim was to describe the burden of injury in UC biopsies and to explore molecular heterogeneity across disease activity, as assessed by the endoscopic Mayo score. Methods 128 UC colon biopsies were collected at the University of Alberta Hospital (Edmonton, AB) and Cedars-Sinai Hospital (Los Angeles, CA) during standard of care colonoscopy and processed using Affymetrix microarrays. Principal component analysis (PCA) and archetypal analysis (AA) visualized relationships between biopsies and previously annotated injury-associated transcript sets. AA assigned each biopsy to one of three groups, and scores to each biopsy relating it to all three groups. Results Spearman correlations (Table 1A) were highest between the endoscopic Mayo score and the injury-repair-associated transcripts (IRRAT, 0.64, P=4.7x10-16), immunoglobulin transcripts highly associated with chronic injury and fibrosis (IGT, 0.63, P=3.0x10-15), endothelial transcripts (ENDAT, 0.61, P=1.8x10-14), and parenchymal dedifferentiation i.e. epithelial solute carrier loss (CT2, -0.60, P=6.5x10-14). PCA separated injury from no injury in PC1 (Figure 1A). T cell transcripts (QCATs), interferon-gamma inducible transcripts (GRITs) and targets of biologics (IL12, TNFA, ITGA4/B7) separated from injury transcripts in PC2. We assigned three AA groups and visualized biopsies in PCA (Figure 1B, colored by AA membership). Group 1 (grey, N=44) biopsies had little parenchymal dedifferentiation and low expression of injury-associated transcripts. Groups 2 (red, N=44) and 3 (blue, N=40) had increased expression of injury-associated transcript sets and dedifferentiation compared to Group 1 (Table 1). Although Group 3 was endoscopically similar to Group 1 (P>0.05), Group 3 showed elevated injury-associated transcript set expression (e.g. IRRAT) and increased parenchymal dedifferentiation (CT2). Conclusions Assessment of UC biopsies using AA and previously annotated injury-associated gene sets reveals two groups of biopsies that are endoscopically similar though one group has increased molecular abnormalities, thus revealing heterogeneity unrelated to the Mayo score. A molecular system based around PCA and AA could enhance and refine UC disease assessment by allowing for quantitation and qualification of injury in biopsies obtained at endoscopy i.e. a level of resolution beyond conventional endoscopic scoring. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".