Plasma microRNA Profiles of Myotis lucifugus from a White-Nose Syndrome-affected Population
Bibliographic record
Abstract
White-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans (Pd), has devastated bat populations across North America by disrupting torpor, accelerating fat depletion, and causing severe winter mortality. Surviving populations of little brown bats (Myotis lucifugus) exhibit altered fat storage and adaptive physiological responses, suggesting potential mechanisms for WNS resistance or tolerance. MicroRNAs (miRNAs) are small, non-coding RNA molecules regulating gene expression that play critical roles in metabolic and immune pathways essential for hibernation physiology and pathogen defense. My thesis integrates insilico analyses and experimental validation to evaluate the role of miRNAs in hibernation physiology to establish a novel, non-lethal method for monitoring bat health. Using DIANA miRPath and a targeted literature review, I identified four miRNAs (miR-543, miR-27a, miR-92b, and miR-328) implicated in metabolic and immune pathways relevant to WNS, including lipogenesis, insulin signaling, and FOXO-mediated stress response. I quantified the presence and seasonal expression patterns of selected miRNAs using reverse transcription quantitative real-time PCR in plasma samples collected from WNS-affected bats during fall pre-hibernation and spring emergence. miR-27a-5p and miR-92b-5p showed increased expression in spring compared to fall, and miR-27a-5p correlated positively with Pd fungal load, suggesting its potential as a biomarker for WNS severity. miR-26a-5p was consistently stable across seasons and conditions and was used as a robust endogenous control for plasma-based miRNA studies. This study is the first to demonstrate stable detection and seasonal variation of circulating miRNAs in plasma from free-ranging little brown bats, and one of only a handful to quantify plasma miRNA levels in any of the nearly 1500 bat species, establishing a novel, non-lethal method for monitoring bat health. Future studies should validate gene targets and assess how miRNA expression varies with host physiological state and hibernation conditions. Importantly, this approach could guide targeted management interventions by enabling early identification of vulnerable populations or individuals. Ultimately, the ability to monitor bat health non-lethally, using plasma miRNAs, offers significant potential to enhance wildlife disease surveillance, guide conservation strategies, and contribute to the broader effort of mitigating WNS impacts across North America.
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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) | 0.000 | 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 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".