JAK-STAT Pathway Inhibition Partially Restores Intestinal Homeostasis in Hdac1- and Hdac2-Intestinal Epithelial Cell-Deficient Mice
Bibliographic record
Abstract
We have previously reported that histone deacetylase epigenetic regulator Hdac1 and Hdac2 deletion in intestinal epithelial cells (IEC) disrupts mucosal tissue architecture and barrier, causing chronic inflammation. In this study, proteome and transcriptome analysis revealed the importance of signaling pathways induced upon genetic IEC-Hdac1 and Hdac2 deletion. Indeed, Gene Ontology biological process analysis of enriched deficient IEC RNA and proteins identified common pathways, including lipid metabolic and oxidation–reduction process, cell adhesion, and antigen processing and presentation, related to immune responses, correlating with dysregulation of major histocompatibility complex (MHC) class II genes. Top upstream regulators included regulators associated with environmental sensing pathways to xenobiotics, microbial and diet-derived ligands, and endogenous metabolites. Proteome analysis revealed mTOR signaling IEC-specific defects. In addition to mTOR, the STAT and Notch pathways were dysregulated specifically in jejunal IEC. To determine the impact of pathway dysregulation on mutant jejunum alterations, we treated mutant mice with Tofacitinib, a JAK inhibitor. Treatment with the inhibitor partially corrected proliferation and tight junction defects, as well as niche stabilization by increasing Paneth cell numbers. Thus, IEC-specific histone deacetylases 1 (HDAC1) and 2 (HDAC2) support intestinal homeostasis by regulating survival and translation processes, as well as differentiation and metabolic pathways. HDAC1 and HDAC2 may play an important role in the regulation of IEC-specific inflammatory responses by controlling, directly or indirectly, the JAK/STAT pathway. IEC-specific JAK/STAT pathway deregulation may be, at least in part, responsible for intestinal homeostasis disruption in mutant mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".