Temporal stability of the mouse gut microbiota in relation to innate and adaptive immunity
Bibliographic record
Abstract
Gut microbial community properties of mammals are thought to be partly shaped by a combination of host immunity and environmental factors, but their relative importance is not firmly established. To address this gap, we first characterized the faecal bacteria of mice with a functioning immune system (wild-type, WT), mice with defective immune responses (CD45), mice lacking an adaptive immune system (RAG), and mice with both immune dysfunctions (45RAG). Using fingerprinting of 16S rRNA genes, we observed significant differences in gut microbiota composition across all mouse strains (P < 0.001) and identified several mouse strain-specific genera via pyrosequencing, including Turicibacter sp. (in WT mice) and Allobaculum sp. (in CD45-deficient animals). To define the role of the host immune system in constraining gut microbiota stability after perturbation, we cohoused CD45-deficient and WT mice and monitored gut bacterial community dynamics during 8 weeks. Cohousing caused the WT bacterial communities to become indistinguishable from those of CD45 mice (P > 0.05). Time-series analysis indicated that the communities of cohoused mice changed directionally as opposed to the relatively stable communities of non-cohoused controls. When we considered only taxonomic membership, it was the communities of CD45 non-cohoused mice that experienced the highest rate of change. Rather than be governed by fluctuations in the relative abundance of taxa, we suggest that CD45-regulated immune responses either are stimulated by the presence of bacteria per se or promote temporal stability by selecting for the occurrence of specific taxa.
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 | 0.000 |
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".