Transcriptional host–pathogen responses of <i>Pseudogymnoascus destructans</i> and three species of bats with white-nose syndrome
Bibliographic record
Abstract
Understanding how context (e.g., host species, environmental conditions) drives disease susceptibility is an essential goal of disease ecology. We hypothesized that in bat white-nose syndrome (WNS), species-specific host–pathogen interactions may partly explain varying disease outcomes among host species. We characterized bat and pathogen transcriptomes in paired samples of lesion-positive and lesion-negative wing tissue from bats infected with Pseudogymnoascus destructans in three parallel experiments. The first two experiments analyzed samples collected from the susceptible Nearctic Myotis lucifugus and the less-susceptible Nearctic Eptesicus fuscus, following experimental infection and hibernation in captivity under controlled conditions. The third experiment applied the same analyses to paired samples from infected, free-ranging Myotis myotis, a less susceptible, Palearctic species, following natural infection and hibernation (n = 8 sample pairs/species). Gene expression by P. destructans was similar among the three host species despite varying environmental conditions among the three experiments and was similar within each host species between saprophytic contexts (superficial growth on wings) and pathogenic contexts (growth in lesions on the same wings). In contrast, we observed qualitative variation in host response: M. lucifugus and M. myotis exhibited systemic responses to infection, while E. fuscus up-regulated a remarkably localized response. Our results suggest potential phylogenetic determinants of response to WNS and can inform further studies of context-dependent host–pathogen 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 | 0.000 | 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".