Profiling the immunome of little brown myotis provides a yardstick for measuring the genetic response to white‐nose syndrome
Bibliographic record
Abstract
Abstract White‐nose syndrome (WNS) has devastated populations of hibernating bats in eastern North America, leading to emergency conservation listings for several species including the previously ubiquitous little brown myotis (Myotis lucifugus). However, some bat populations near the epicenter of the WNS panzootic appear to be stabilizing after initial precipitous declines, which could reflect a selective immunogenetic sweep. To investigate the hypothesis that WNS exerts significant selection on the immunome of affected bat populations, we developed a novel, high‐throughput sequence capture assay targeting 138 adaptive, intrinsic, and innate immunity genes of putative adaptive significance, as well as their respective regulatory regions (~370 kbp of genomic sequence/individual). We used the assay to explore baseline immunogenetic variation in M. lucifugus and to investigate whether particular immune genes/variants are associated with WNS susceptibility. We also used our assay to detect 1,038 putatively neutral single nucleotide polymorphisms and characterize contemporary population structure, providing context for the identification of local immunogenetic adaptation. Sequence capture provided a cost‐effective, “all‐in‐one” assay to test for neutral genetic and immunogenetic structure and revealed fine‐scale, baseline immunogenetic differentiation between sampling sites <600 km apart. We identified functional immunogenetic variants in M. lucifugus associated with WNS susceptibility. This study lays the foundations for future investigations of rangewide immunogenetic adaptation to WNS in M. lucifugus and provides a blueprint for studies of evolutionary rescue in other host–pathogen systems.
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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 | 0.001 | 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) | 0.001 | 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".