In Silico Analysis of Possible microRNAs Involved in the Pathogenesis of White-Nose Syndrome in Myotis lucifugus
Bibliographic record
Abstract
Since 2007, white-nose syndrome (WNS), caused by the fungus Pseudogymnoascus destructans, has killed millions of bats across North America by disrupting hibernation cycles, causing premature fat depletion and starvation. Little brown bats (Myotis lucifugus) from some populations persisting after WNS store larger pre-hibernation fat reserves than bats did before WNS, which may help bats survive winter starvation and mount an immune response to Pd in spring. MicroRNAs (miRNAs) are highly conserved, small, non-coding RNA molecules that regulate gene expression post-transcriptionally. Aberrant miRNA expression can affect metabolic pathways in mammals and has been linked to various diseases. If fat reserves and immune mechanisms influence survival from WNS, then miRNAs regulating metabolic and immune-related genes might affect WNS pathogenesis and bat survival. A previous study identified 43 miRNAs differentially expressed in bats with WNS. We analyzed these miRNAs for their roles in metabolism and immune-related pathways, using DIANA Tools and KEGG analysis, to determine a subset that could serve as biomarkers of pathophysiology or survival in WNS-affected bats. We identified miR-543, miR-27a, miR-92b, and miR-328 as particularly important because they regulate multiple pathways likely important for WNS (i.e., immune response, lipogenesis, insulin signaling, and FOXO signaling). As proof-of-concept, we used reverse transcription quantitative real-time PCR (RT-qPCR) to quantify the prevalence of these miRNAs in plasma samples of bats (n = 11) collected from a post-WNS population during fall fattening. All the selected miRNAs were detectable in at least some bats during fall fattening although prevalence varied among miRNAs. Future in vivo validation studies would help confirm functional roles and biomarker utility of these miRNAs for WNS-affected bats.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".