Application of XANES spectroscopy in understanding the metabolism of selenium in isolated rainbow trout hepatocytes: insights into selenium toxicity
Bibliographic record
Abstract
Selenium (Se) is an essential element, but causes toxic effects in fish at a slightly elevated level beyond the threshold. However, the degree of Se toxicity differs depending on the chemical forms of Se (e.g., organic vs. inorganic) to which fish are exposed to. The mechanisms of Se metabolism and toxicity in fish, particularly at cellular level, are poorly understood. The present study was designed to examine the metabolic fate of different seleno-compounds, both inorganic and organic, in isolated hepatocytes of rainbow trout (Oncorhynchus mykiss) in primary culture using XANES spectroscopy. In cells exposed to 100 μM of selenate and selenite for 6-24 h, elemental Se was found to be the primary metabolite. Whereas, selenocystine appeared to be the major metabolite in cells exposed to 100 μM seleno-L-methionine for 6-24 h. Interestingly, we recorded L-methionine-γ-lyase activity in S9 fraction of cell lysate-an enzyme that directly catalyzes selenomethionine into methylselenol. We also found concurrent reduction of glutathione (GSH) concentration following reaction of seleno-L-methionine with cellular S9 fraction. Moreover, we observed a rapid increase in cellular reactive oxygen species (ROS) generation with increasing seleno-L-methionine exposure dose (100-1000 μM). These findings indicated the rapid cellular metabolism of seleno-L-methionine into methylselenol at higher exposure dose (≥100 μM), and the occurrence of GSH mediated redox cycling of methylselenol--a process that is known to produce reactive oxygen species (ROS). Overall, our results suggest that inorganic and organic selenium are metabolized through different metabolic pathways in rainbow trout hepatocytes. The findings of our study have important implications for understanding the chemical species-specific differences in Se toxicity to fish.
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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".