Subcellular Distribution of Cadmium and Nickel in Chronically Exposed Wild Fish: Inferences Regarding Metal Detoxification Strategies and Implications for Setting Water Quality Guidelines for Dissolved Metals
Bibliographic record
Abstract
The objective of this study was to investigate metal detoxification in chronically exposed juvenile yellow perch (YP: Perca flavescens) and to field test the commonly assumed threshold toxicity model. Fish were collected from lakes located along a cadmium (Cd) and nickel (Ni) concentration gradient. Ambient dissolved metal concentrations were measured to evaluate exposure and total hepatic metal concentrations were determined as a measure of metal bioaccumulation. Hepatic metal partitioning among potentially metal-sensitive fractions (heat-denatured proteins, organelles) and detoxified metal fractions (metallothionein) was determined after differential centrifugation of YP liver homogenates. Major proportions of hepatic Cd were found in the heat-stable cytosolic peptides and proteins fraction (HSP; including metallothioneins), whereas Ni was mainly found in the potentially metal-sensitive heat-denaturable proteins fraction (HDP). For these chronically exposed fish there was no threshold exposure concentration below which binding of Cd or Ni to the heat-denaturable protein fraction or the organelle fraction did not occur. Metal detoxification was clearly incomplete and P. flavescens was subject to some metal-related stress, as evidenced notably by endocrine perturbations. Similar subcellular partitioning results were obtained when juvenile yellow perch were transferred from a reference lake to a Cd-contaminated lake and Cd accumulation was followed over time; there was no accumulation threshold below which Cd binding to the putative metal-sensitive fractions (HDP and organelles) did not occur. The presence of Cd and Ni in these fractions, even for low exposure concentrations and low hepatic accumulation, contradicts the threshold toxicity model that underpins metal toxicology theory and that is implicitly used in setting water quality guidelines for metals. Chronically exposed YP appear to have settled for a tradeoff between the cost of turning on their detoxification apparatus at full capacity, to completely suppress metal binding to metal-sensitive sites, and the alternative cost of allowing some binding of inappropriate metals to metal-sensitive sites.
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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".