Comprehensive pathogen diagnostics in wild fish populations using blood-based molecular strategies: an Atlantic herring case study
Bibliographic record
Abstract
Climate change affects marine ecosystems by promoting pathogens that threaten key fish populations. To protect these, monitoring programs must adapt to manage threats and sustain fisheries. Here, we combined traditional PCR methods and transcriptomic analysis from a single drop of blood stored on FTA cards to determine the prevalence of erythrocytic necrosis virus (ENV) and the Ichthyophonus parasite in the Atlantic herring population. Across 2023–2024, 33% of individual blood samples tested positive for ENV and 10% for Ichthyophonus by PCR, with ENV-positive fish more frequently found in estuarine and coastal areas. Spatial analyses revealed a clustered distribution for ENV and a more sporadic occurrence of Ichthyophonus. RNA-Seq detected viral RNA fragments in ENV PCR-positive fish, revealing high levels of viral transcripts consistent with active viral replication. However, no significant changes were observed in the host blood transcriptome between infected and uninfected individuals, suggesting that ENV replication may proceed with limited systemic host transcriptional response under subclinical conditions. Overall, our study provides the first comprehensive baseline on the prevalence and molecular activity of ENV and Ichthyophonus in Atlantic herring, demonstrating the power of FTA-based RNA-Seq diagnostics to uncover hidden infections and informing future surveillance and management of wild fish populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".