The Influence of Selenium on Arsenic Hepatobiliary Transport
Notice bibliographique
Résumé
Over 200 million people worldwide are exposed to the proven human carcinogen arsenic at levels exceeding the World Health Organization guideline of 10 μg/L. In animal models arsenic and selenium are mutually protective via the formation and biliary excretion of the seleno‐bis ( S‐ glutathionyl) arsinium ion [SeAs(GS) 2 ] − , which allows for the fecal elimination of both compounds. Consistent with this, selenium deficiency in humans living in arsenic endemic regions is associated with an increased risk of arsenic‐induced disease. These observations have led to the initiation of human selenium supplementation trials. Despite these ongoing trials in arsenic endemic regions, the influence of selenium on human hepatic handling of arsenic is not adequately understood. Furthermore, supplementation trials have utilized different chemical forms of selenium with unknown influence on efficacy. In the liver, multidrug resistance protein 2 (MRP2/ ABCC2 ) transports arsenic metabolites, including [SeAs(GS) 2 ] − into bile, and the related MRP4 ( ABCC4 ) transports other arsenic metabolites into sinusoids. We hypothesized that selenium will increase the biliary excretion of arsenic from HepaRG cells, an immortalized cell line used as a surrogate for primary human hepatocytes. To test this hypothesis, we studied the influence of selenite (Se IV ), selenide (Se II ), methylselenocysteine (MSC) and selenomethionine (SeMet) on arsenic efflux from HepaRG cells. The expression of genes involved in hepatic arsenic methylation ( As3MT ) and export ( ABCC2 and ABCC4 ) were assessed. Crude membrane preparations subjected to immunoblots were used to evaluate the presence of MRP2 and MRP4 proteins. The influence of arsenic on the levels of these proteins was also assessed similarly. Fluorescence microscopy after treatment with 5(6)‐carboxy,2’,7’‐dichlorofluorescein (CDF) diacetate was performed to visualize the canalicular networks and assess MRP2 function. Transport across sinusoidal and canalicular membranes was measured after treatment of HepaRG cells with 1 μM 73 As III ± selenium (in the forms Se IV , Se II , MSC or SeMet) using B‐CLEAR® technology. Biliary excretion indices (BEIs) were calculated to quantify the extent of arsenic export into bile. ABCC2, ABCC4 , and As3MT are expressed by HepaRG Cells. MRP2 and MRP4 proteins were detected, and their expression was increased by the presence of As III . CDF accumulation in canalicular networks suggested that MRP2 was functional. At a 5 minute time point, the BEI of 73 As III alone was 17%. The addition of Se II increased biliary excretion of 73 As III to 30%. Biliary excretion of 73 As III was lost in the presence of Se IV , SeMet and MSC. Arsenic underwent biliary excretion in HepaRG cells and this was stimulated by Se II , and inhibited by Se IV , SeMet and MSC. These data have implications for the form of selenium utilized for selenium supplementation trials in arsenic endemic regions. Support or Funding Information Canadian Institutes of Health Research (CIHR)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».