Snow crab (Chionoecetes opilio) hemocytes and hepatopancreas transcriptomes: identification, validation, and application of immune-relevant biomarkers of exposure to noise
Bibliographic record
Abstract
The snow crab (Chionoecetes opilio) fishery off the east coast of Newfoundland and Labrador, Canada, has been impacted by annual seismic oil and gas surveying in recent decades, and commercial crab harvesters are concerned that it could potentially affect this resource. A laboratory study was performed to investigate the effects of chronic noise exposure on the transcriptomes of snow crab hemocytes and hepatopancreas, which are important in immunity, metabolism and response to environmental stress. Snow crab were held in replicate control or experimental tanks; on alternating days, the experimental tanks were exposed to noise from an underwater speaker which played a recording of a seismic airgun firing every 10 sec in a continuous loop for ~ 22 weeks (chronic noise). RNA sequencing was used to identify candidate noise-responsive molecular biomarkers in both hemocytes and hepatopancreas. The quantified transcripts of individuals (i.e. n=10 from each of the four groups) were compared using DESeq2, identifying over 300 noise-responsive transcripts in each cell/tissue type. Real-time quantitative polymerase chain reaction (qPCR) assays were designed and run for 61 of these transcripts with significant BLASTx hits. One transcript (rgs2) was significantly (p < 0.05) higher expressed in both hemocytes and hepatopancreas in response to noise; five and seven other transcripts were either significant or showed a non-significant trend of differential expression (0.05 < p ≤ 0.1) in hemocytes (e.g. cfb upregulated, sacs and myof downregulated) and hepatopancreas (e.g. gpx3 and atf3 upregulated, sult1c4 downregulated), respectively. To further investigate the performance and utility of these candidate biomarkers in the field, expression levels of 25 selected transcripts were then examined in the hepatopancreas of snow crab that had been subjected to 2D and 3D seismic surveying using both qPCR and multivariate statistical analyses. Overall, few of these biomarkers showed consistent results in field-collected snow crab, highlighting the need to combine lab and field-based studies to fully evaluate the utility of biomarkers. Nonetheless, our study still identified noise-responsive biomarkers in both settings (including stress and immune relevant genes), providing valuable information for understanding the impact of ocean noise on snow crab physiology and health.
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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".