Freshwater mussels (Unionidae) brought into captivity exhibit up-regulation of genes involved in stress and energy metabolism
Bibliographic record
Abstract
Approximately two thirds of freshwater mussel species in the United States and Canada are imperiled, and populations are declining rapidly. Translocation and captive management are commonly used to mitigate losses of freshwater mussel biodiversity, but these conservation tools may result in decreased growth and increased mortality. This study uses RNA-Seq to determine how translocation into captivity affects gene expression in Amblema plicata. Mussels were collected from the Muskingum River in Ohio, USA and brought into a captive holding facility. RNA was extracted from gill tissue 11 months post translocation from mussels in captivity and the Muskingum River on the same day. RNA was sequenced on an Illumina HiSeq 2500, and differential expression analysis was performed on de novo assembled transcripts. More than 1200 transcripts were up-regulated in captive mussels, and 246 were assigned functional annotations. Many up-regulated transcripts were involved in energy metabolism and the stress response, such as heat shock proteins and antioxidants. More than 500 transcripts were down-regulated in captive mussels, and 41 were assigned functional annotations. We observed an over-representation of down-regulated transcripts associated with immune response. Our work suggests that A. plicata experienced moderate levels of stress and altered energy metabolism and immune response for at least 11 months post translocation into captivity.
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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 | 0.001 | 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".