Maintaining functional major histocompatibility complex diversity under inbreeding: the case of a selfing vertebrate
Bibliographic record
Abstract
Major histocompatibility complex (MHC) genes encode proteins that present pathogen-derived antigens to T-cells, initiating the adaptive immune response in vertebrates. Although populations with low MHC diversity tend to be more susceptible to pathogens, some bottlenecked populations persist and even increase in numbers despite low MHC diversity. Thus, the relative importance of MHC diversity versus genome-wide variability for the long-term viability of populations after bottlenecks and/or under high inbreeding is controversial. We tested the hypothesis that genome-wide inbreeding (estimated using microsatellites) should be more critical than MHC diversity alone in determining pathogen resistance in the self-fertilizing fish Kryptolebias marmoratus by analysing MHC diversity and parasite loads in natural and laboratory populations with different degrees of inbreeding. Both MHC and neutral diversities were lost after several generations of selfing, but we also found evidence of parasite selection acting on MHC diversity and of non-random loss of alleles, suggesting a possible selective advantage of those individuals with functionally divergent MHC, in accordance with the hypothesis of divergent allele advantage. Moreover, we found that parasite loads were better explained by including MHC diversity in the model than by genome-wide (microsatellites) heterozygosity alone. Our results suggest that immune-related overdominance could be the key in maintaining variables rates of selfing and outcrossing in K. marmoratus and other mixed-mating species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".