Antifungal Potential of the Skin Microbiota of Hibernating Big Brown Bats (Eptesicus fuscus) Infected With the Causal Agent of White-Nose Syndrome
Bibliographic record
Abstract
Little is known about skin microbiota in the context of the disease white-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans (Pd), that has caused enormous declines of hibernating North American bats over the past decade. Interestingly, some hibernating species, such as the big brown bat (Eptesicus fuscus), appear resistant to the disease and their skin microbiota could play a role. However, a comprehensive analysis of the skin microbiota of E. fuscus in the context of Pd has not been done. In January 2017, we captured hibernating E. fuscus, sampled their skin microbiota, and inoculated them with Pd or sham inoculum. We allowed the bats to hibernate in the lab under controlled conditions for 11 weeks and then sampled their skin microbiota to test the following hypotheses: 1) Pd infection would not disrupt the skin microbiota of Pd-resistant E. fuscus; and 2) microbial taxa with antifungal properties would be abundant both before and after inoculation with Pd. Using high-throughput 16S rRNA gene sequencing, we discovered that beta diversity of Pd-inoculated bats changed more over time than that of sham-inoculated bats. Still, the most abundant taxa in the community were stable throughout the experiment. Among the most abundant taxa, Pseudomonas and Rhodococcus are known for antifungal potential against Pd and other fungi. Thus, in contrast to hypothesis 1, Pd infection destabilized the skin microbiota but consistent with hypothesis 2, bacteria with known antifungal properties remained abundant and stable on the skin. This study is the first to provide a comprehensive survey of skin microbiota of E. fuscus, demonstrating potential associations between the bat skin microbiota and resistance to the Pd infection and WNS. These results set the stage for future studies to characterize microbiota gene expression, better understand mechanisms of resistance to WNS, and help develop conservation strategies.
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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".