Multiomic Sequencing Reveals Distinctive Gene Expression and Epigenetic Alterations Associated With Primary Sclerosing Cholangitis Development in Treatment-Naïve Pediatric Ulcerative Colitis
Bibliographic record
Abstract
Background and Aims: Primary sclerosing cholangitis (PSC) is a progressive cholestatic disease with up to 80% of patients also suffering from ulcerative colitis (PSC-UC). The difficulty in the diagnosis along with the increased risk for developing cancer represents a clinical challenge. Furthermore, the precise molecular factors regulating the phenotype of this disease subtype remain unknown. Methods: We applied methyl-capture sequencing and mRNA sequencing to colonic mucosal biopsies from 3 groups of treatment-naïve children at diagnosis from the Determinants and Outcomes in CHildren and AdolescentS study: UC (n = 10), PSC-UC (n = 10), and healthy controls (n = 10). Results: Differential gene expression between UC and PSC-UC showed significantly higher gene expression changes in PSC-UC patients when compared to UC. Specifically, expression of these genes was regulated by master transcriptional regulators (NLRP3, DLL1) and transcription factors (RELA, Myogenin, and FOXO1), which are shown to regulate expression of inflammatory response and immune-associated genes in PSC-UC patients exclusively. Differential methylation analysis between PSC-UC and UC demonstrated >2000 differentially methylated regions with a large proportion of them enriched in gene promoter and enhancer regions. We further show no difference in epigenetic age between PSC-UC and UC. Finally, we identify KLHL17 as hypomethylated and upregulated in PSC-UC patients. Conclusion: Our study, for the first time, identifies distinct gene expression and DNA methylation alterations that differentiate UC from PSC-UC at diagnosis in treatment-naïve pediatric patients. We show the gene expression differences observed between PSC-UC and UC are modulated by intricate molecular mechanisms involving master transcriptional regulator-mediated signaling through transcription factors. These findings suggest the potential utility of these molecular markers as predictive biomarkers for PSC development in UC at an early stage of development. Further validation in larger patient cohorts is warranted.
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 | 0.000 | 0.000 |
| 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".