Spleen gene expression is associated with mercury content in three-spined stickleback populations
Bibliographic record
Abstract
Abstract Mercury can be very toxic at low environmental concentrations by impairing immunological, neurological, and other vital pathways in humans and animals. Aquatic ecosystems are heavily impacted by mercury pollution, with evidence of biomagnification through the food web. We examined the effect of mercury toxicity on the spleen, one of the primary immune organs in fish, in natural populations of the three-spined stickleback ( Gasterosteus aculeatus Linnaeus, 1758). Our aim was to better understand adaptation to high mercury environments by investigating transcriptomic changes in the spleen. Three stickleback populations with mean Hg muscle concentrations above and three populations with mean Hg muscle concentrations below the European Biota Quality Standard of 20 ng/g wet weight were selected from the Scheldt and Meuse basin in Belgium. We then conducted RNA sequencing of the spleen tissue of 22 females from these populations. We identified 136 differentially expressed genes between individuals from populations with high and low mean mercury content. The 129 genes that were upregulated were related to the neurological system, immunological activity, hormonal regulation, and inorganic cation transporter activity. Seven genes were downregulated and were all involved in pre-mRNA splicing. The results are indicative of our ability to detect molecular alterations in natural populations that exceed an important environmental quality standard. This allows us to assess the biological relevance of such standards, offering an opportunity to better describe and manage mercury-associated environmental health risks in aquatic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".