Super-enhancer signature reveals key mechanisms associated with resistance to non-alcoholic steatohepatitis in humans with obesity
Bibliographic record
Abstract
Abstract The molecular underpinnings of non-alcoholic steatohepatitis (NASH) development in patients are poorly understood. Active enhancer landscapes are known to determine cell states and behaviors. Super-enhancers, in particular, have helped reveal key disease drivers in several cancer types; however, they remain unexplored in human NASH. To define the enhancer signature of NASH-prone (NP) and NASH-resistant (NR) phenotypes in humans with obesity, we performed chromatin run-on sequencing (ChRO-seq) analysis on liver biopsies of individuals with obesity who were stratified into either NP or NR. We first demonstrated that NP and NR groups exhibit distinct active enhancer signatures. The subsequent identification of NP- and NR-specific super-enhancers revealed the specific genes that are likely the most critical for each of the phenotypes, including HES1 for NP and GATM for NR. Integrative analysis with results from genome-wide association studies of NAFLD and related traits identified disease/trait-loci specific to NP or NR enhancers. Further analysis of the ChRO-seq data pointed to critical roles for serine/glycine metabolism in NASH resistance, which was corroborate by profiling of circulating amino acids in the same patients. Overall, the distinct enhancer signatures of human NP and NR phenotypes revealed key genes, pathways, and transcription factor networks that promote NASH development.
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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 | 0.000 |
| 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) | 0.003 | 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".