Data from: Nonparallelism in MHC IIβ diversity accompanies nonparallelism in pathogen infection of lake whitefish (Coregonus clupeaformis) species pairs as revealed by next generation sequencing
Bibliographic record
Abstract
Major histocompatibility (MHC) immune system genes may evolve in response to pathogens in the environment. Because they also may affect mate choice, they are candidates for having great importance in ecological speciation. Here, we use next-generation sequencing to test the general hypothesis of parallelism in patterns of MHCIIβ diversity and bacterial infections among five dwarf and normal whitefish sympatric pairs. A second objective was to assess the functional relationships between specific MHCIIβ alleles and pathogens in natural conditions. Each individual had between one and four alleles, indicating two paralogous loci. In Cliff Lake, the dwarf ecotype was monomorphic for the most common allele. In Webster Lake, the skew in the allelic distribution was towards the same allele but in the normal ecotype, underscoring the nonparallel divergence among lakes. Our signal of balancing selection matched putative peptide binding region residues in some cases, but not in others, supporting other recent findings of substantial functional differences in fish MHCIIβ compared with mammals. Individuals with fewer alleles were less likely to be infected; thus, we found no evidence for the heterozygote advantage hypothesis. MHCIIβ alleles and pathogenic bacteria formed distinct clusters in multivariate analyses, and clusters of certain alleles were associated with clusters of pathogens, or sometimes the absence of pathogens, indicating functional relationships at the individual level. Given that patterns of MHCIIβ and bacteria were nonparallel among dwarf and normal whitefish pairs, we conclude that pathogens driving MHCIIβ evolution did not play a direct role in their parallel phenotypic evolution.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".