eLife Assessment: Genome Restructuring around Innate Immune Genes in Monocytes in Alcohol-associated Hepatitis
Bibliographic record
Abstract
Many inflammatory genes in the immune system are clustered in the genome. The 3D genome architecture of these clustered genes likely plays a critical role in their regulation and alterations to this structure may contribute to diseases where inflammation is poorly controlled. Alcohol-associated hepatitis (AH) is a severe inflammatory disease that contributes significantly to morbidity in alcohol associated liver disease. Monocytes in AH are hyper-responsive to inflammatory stimuli and contribute significantly to inflammation. We performed high throughput chromatin conformation capture (Hi-C) technology on monocytes isolated from 4 AH patients and 4 healthy controls to better understand how genome structure is altered in AH. Most chromosomes from AH and healthy controls were significantly dissimilar from each other. Comparing AH to HC, many regions of the genome contained significant changes in contact frequency. While there were alterations throughout the genome, there were a number of hotspots containing a higher density of changes in structure. A few of these hotspots contained genes involved in innate immunity including the NK-gene receptor complex and the CXC-chemokines. Finally, we compare these results to scRNA-seq data from patients with AH challenged with LPS to predict how chromatin conformation impacts transcription of clustered immune genes. Together, these results reveal changes in the chromatin structure of monocytes from AH patients that perturb expression of highly clustered proinflammatory genes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".