Microbiota regulate the induction and proliferation of revival intestinal stem cells through inflammatory cytokines
Bibliographic record
Abstract
Abstract Gut microbiota are critical mediators of inflammation and regeneration of the intestinal epithelium. Intestinal restitution is a coordinated response that involves the dedifferentiation of mature epithelial cell lineages, the proliferation of Lgr5+ intestinal stem cells, and the induction of Clu+ revival stem cells (RSCs). How the gut microbiota directly impacts this regenerative process remains unclear. Using irradiation as a model for small intestinal epithelium restitution, we demonstrate that microbiota regulate the induction and subsequent proliferation of Clu+ RSCs. Our results report that specific pathogen-free (SPF) mice induce greater RSCs 3 days post-IR in comparison to germ-free (GF) mice. This microbiota-dependent increase in RSCs was matched by an increase in BrdU+ proliferating cells and an increase in TUNEL+ apoptotic cells in SPF mice. Using intestinal organoids as an in vitro model of intestinal restitution, SPF and GF intestinal crypts demonstrated equal propensity to generate mature organoids. Transcriptional analysis of GF and SPF RSCs by single-cell RNA sequencing highlighted unique regenerative and inflammatory gene signatures. The altered regeneration kinetics observed in SPF mice was accompanied by an increase in Tnfa and Cxcl1 at the peak of RSC induction and an increase in Ifnγ post RSC induction. Altogether, these findings suggest that microbiota-dependent expression of inflammatory cytokines may be key regulators in facilitating the expansion and proliferation of RSCs to efficiently repair the intestinal epithelium following damage.
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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 | 0.001 |
| 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".