Transport of selenium across the plasma membrane of primary hepatocytes and enterocytes of rainbow trout
Bibliographic record
Abstract
Transport of essential solutes across biological membranes is one of the fundamental characteristics of living cells. Although selenium is an essential micronutrient, little is known about the cellular mechanisms of chemical species-specific selenium transport in fish. We report here the kinetic and pharmacological transport characteristics of selenite and its thiol (glutathione and l-cysteine) derivatives in primary cultures of hepatocytes and isolated enterocytes of rainbow trout. Findings from the current study suggest an apparent low-affinity linear transport system for selenite in both cell types. However, we recorded high-affinity Hill kinetics (K(d)=3.61±0.28 μmol l(-1)) in enterocytes exposed to selenite in the presence of glutathione. The uptake of selenite in the presence of thiols was severalfold higher than uptake of selenite alone (at equimolar concentration) in both hepatocytes and enterocytes. Cellular accumulation of selenium was found to be energy independent. Interestingly, we observed a decrease in selenite transport with increasing pH, whereas selenite uptake increased with increasing pH in the presence glutathione in both cell types. The cellular uptake of selenite demonstrated a pronounced competitive interaction with a structurally similar compound, sulfite. The uptake of selenite as well as its thiol derivatives was found to be sensitive to the anion transport blocker DIDS, irrespective of the cell type. Inorganic mercury (Hg(2+)) elicited an inhibition of selenite transport in both cell types, but augmented the transport of reduced forms of selenite in hepatocytes. Based on the substrate choice and comparable pharmacological properties, we advocate that multiple anion transport systems are probably involved in the cellular transport of selenite in 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".