Inflammasomes make the case for littermate-controlled experimental design in studying host-microbiota interactions
Bibliographic record
Abstract
Several human diseases are thought to evolve due to a combination of host genetic mutations and environmental factors that include alterations in intestinal microbiota composition termed dysbiosis. Although in some cases, host genetics may shape the gut microbiota and enable it to provoke disease, experimentally disentangling cause and consequence in such host-microbe interactions requires strict control over non-genetic confounding factors. Mouse genetic studies previously proposed Nlrp6/ASC inflammasomes as innate immunity regulators of the intestinal ecosystem. In contrast, using littermate-controlled experimental setups, we recently showed that Nlrp6/ASC inflammasomes do not alter the gut microbiota composition. Our analyses indicated that maternal inheritance and long-term separate housing are non-genetic confounders that preclude the use of non-littermate mice when analyzing host genetic effects on intestinal ecology. Here, we summarize and discuss our gut microbiota analyses in inflammasome-deficient mice for illustrating the importance of littermate experimental design in studying host-microbiota interactions.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".