Major <scp>H</scp>istocompatibility <scp>C</scp>omplex class <scp>II</scp>b polymorphism influences gut microbiota composition and diversity
Bibliographic record
Abstract
Animals harbour diverse communities of symbiotic bacteria, which differ dramatically among host individuals. This heterogeneity poses an immunological challenge: distinguishing between mutualistic and pathogenic members of diverse and host-specific microbial communities. We propose that Major Histocompatibility class II (MHC) genotypes contribute to recognition and regulation of gut microbes, and thus, MHC polymorphism contributes to microbial variation among hosts. Here, we show that MHC IIb polymorphism is associated with among-individual variation in gut microbiota within a single wild vertebrate population of a small fish, the threespine stickleback. We sampled stickleback from Cedar Lake, on Vancouver Island, and used next-generation sequencing to genotype the sticklebacks' gut microbiota (16S sequencing) and their MHC class IIb exon 2 sequences. The presence of certain MHC motifs was associated with altered relative abundance (increase or decrease) of some microbial Families. The effect sizes are modest and entail a minority of microbial taxa, but these results represent the first indication that MHC genotype may affect gut microbiota composition in natural populations (MHC-microbe associations have also been found in a few studies of lab mice). Surprisingly, these MHC effects were frequently sex-dependent. Finally, hosts with more diverse MHC motifs had less diverse gut microbiota. One implication is that MHC might influence the efficacy of therapeutic strategies to treat dysbiosis-associated disease, including the outcome of microbial transplants between healthy and diseased patients. We also speculate that macroparasite-driven selection on MHC has the potential to indirectly alter the host gut microbiota, and vice versa.
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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 | 0.000 |
| 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".