The influence of naturally occurring and in silico-informed mutations of MRP1/ABCC1 on the transport of arsenic triglutathione
Bibliographic record
Abstract
Chronic exposure of humans to arsenic causes skin, bladder, and lung tumours and is associated with multiple non-malignant diseases including atherosclerosis and diabetes mellitus. The multidrug resistance protein 1 (MRP1/ gene ABCC1 ) is an established cellular export pathway for arsenic metabolites including arsenic triglutathione (As(GS) 3 ). Little is known about the relationship between interindividual variation in susceptibility to arsenic-induced diseases and the highly polymorphic ABCC1 . Eleven naturally occurring mutants (C43S-, R230Q-, R433S-, R633Q-, G671V-, R723Q-, A989T-, C1047S-, R1058Q-, V1146I, and S1512L-MRP1) were tested for leukotriene C 4 (LTC 4 , prototypical MRP1 substrate) and As(GS) 3 transport using MRP1-enriched vesicles, prepared from human embryonic kidney 293T cells. Mutant-MRP1 levels and LTC 4 transport activity were similar to wild-type (WT)-MRP1, except R433S-MRP1 LTC 4 transport was reduced by 71%. As(GS) 3 transport by R230Q-, R433S- and A989T-MRP1-enriched membrane vesicles was reduced to 64 ± 9%, 30 ± 16%, and 44 ± 33% of WT-MRP1, respectively. The reduction in R230Q-, R433S-, and A989T-MRP1 As(GS) 3 transport activity was due to reduced V max values. Computational modeling indicated structural destabilization of these three mutants, and predicted multiple key As(GS) 3 -binding residues in WT-MRP1. Five of these residues were mutated and tested for As(GS) 3 transport activity. As(GS) 3 transport by W553A-, R593E-, and W1246A-MRP1 was 35 ± 9%, 34 ± 13%, and 48 ± 13% of WT-MRP1, respectively, while V554A- and E1089Q-MRP1 activity was similar to WT-MRP1. Thus, naturally occurring and in silico informed mutations negatively affect As(GS) 3 transport by MRP1. Individuals with R230Q-, R433S-, and A989T-MRP1 mutations may be more susceptible to arsenic-induced diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".