Human Red Blood Cell Uptake and Sequestration of Arsenite and Selenite: Evidence for the Formation of a Protective Glutathione Conjugate
Bibliographic record
Abstract
Over 200 million people worldwide are exposed to the proven human carcinogen, arsenic, in contaminated drinking water. In laboratory animals, arsenic and the essential trace element, selenium, can undergo mutual detoxification through the formation of the seleno‐bis( S‐ glutathionyl) arsinium ion [(GS) 2 AsSe] − , which undergoes biliary and fecal elimination. [(GS) 2 AsSe] − , formed in animal red blood cells (RBCs), sequesters arsenic and selenium, and likely slows the distribution of both metalloids to the liver, and other organs susceptible to toxic effects. In human RBCs, the influence of arsenic on selenium accumulation, and vice versa, is largely unknown. The aims of this study were to characterize arsenite (As III ) and selenite (Se IV ) uptake by human RBCs, to determine if Se IV and As III increase the respective accumulation of the other in human RBCs, and ultimately to determine if this occurs through the formation and sequestration of [(GS) 2 AsSe] − . 75 Se IV accumulation was inhibited by 4,4′‐diisothiocyanatodihydrostilbene‐2,2′‐disulfonic acid (H 2 DIDS) (IC 50 1 ± 0.2 μM), suggesting uptake is mediated by the erythrocyte anion‐exchanger 1 (AE1 or Band 3, gene SLC4A1 ). HEK293 cells overexpressing AE1 showed concentration‐dependent 75 Se IV uptake. 73 As III uptake by human RBCs was temperature‐dependent and partly reduced by aquaglyceroporin 3 inhibitors. As III increased 75 Se IV accumulation (in the presence of albumin) and Se IV increased 73 As III accumulation in human RBCs. Near‐edge X‐ray absorption spectroscopy revealed the formation of [(GS) 2 AsSe] − in human RBCs exposed to both As III and Se IV . The sequestration of [(GS) 2 AsSe] − in human RBCs potentially slows arsenic distribution to susceptible tissues and could reduce arsenic‐induced disease. Support or Funding Information Canadian Institutes of Health Research, Canada Research Chairs program, Natural Science and Engineering Council of Canada
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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) | 0.002 | 0.001 |
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".