Transcriptomics of epidermal mucus as a nonlethal method to compare gene expression variation among fish populations
Bibliographic record
Abstract
Although transcriptomic analysis of wild organisms is a powerful tool to understand molecular differences among populations, most methods require the use of lethal sampling. In fish, the use of epidermal mucus is a promising method for development of nonlethal sampling tools. Previous studies have shown that mRNA is dynamically regulated in fish epidermal mucus following stressor exposure, suggesting that mucus is reflective of molecular changes occurring within the organism in response to its environment. The aim of the study was to determine whether transcriptomics of mucus could discern molecular differences among populations of lake trout. In order to do so, mucus was collected and sequenced from four geographically-distinct lake trout (Salvelinus namaycush) populations at the IISD Experimental Lakes Area. Principal component analysis (PCA) and hierarchical clustering of read data showed that each lake trout population had unique transcriptomic profiles, suggesting that RNA sequencing of mucus is able to discern molecular differences among fish populations. Furthermore, differential gene expression analysis identified regulation of immune-related transcripts and viral gene expression transcripts among populations. PCA and a mixed linear model of water quality parameters indicated that environmental variables accounted for transcriptomic variation among populations. However, 32% of transcriptomic variance was unaccounted for by the mixed linear model, suggesting that other variables may influence transcription, such as epigenetics and presence of pathogens. Overall, results indicate that RNA sequencing of epidermal mucus is an effective, nonlethal method to study transcriptional differences among fish populations and may be especially useful for studies of endangered species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".