A single cell survey of the microbial impacts on the mouse small intestinal epithelium
Bibliographic record
Abstract
The small intestinal epithelial barrier inputs signals from the gut microbiota in order to balance physiological inflammation and tolerance, and to promote homeostasis. Understanding the dynamic relationship between microbes and intestinal epithelial cells has been a challenge given the cellular heterogeneity associated with the epithelium and the inherent difficulty of isolating and identifying individual cell types. Here, we used single-cell RNA sequencing of small intestinal epithelial cells from germ-free and specific pathogen-free mice to study microbe-epithelium crosstalk at the single-cell resolution. The presence of microbiota did not impact overall cellular composition of the epithelium, except for an increase in Paneth cell numbers. Contrary to expectations, pattern recognition receptors and their adaptors were not induced by the microbiota but showed concentrated expression in a small proportion of epithelial cell subsets. The presence of the microbiota induced the expression of host defense- and glycosylation-associated genes in distinct epithelial cell compartments. Moreover, the microbiota altered the metabolic gene expression profile of epithelial cells, consequently inducing mTOR signaling thereby suggesting microbe-derived metabolites directly activate and regulate mTOR signaling. Altogether, these findings present a resource of the homeostatic transcriptional and cellular impact of the microbiota on the small intestinal epithelium.
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 | 0.001 | 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) | 0.001 | 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".