In Vitro Functional Characterization and in Silico Prediction of Rare Genetic Variation in the Bile Acid and Drug Transporter, Na<sup>+</sup>-Taurocholate Cotransporting Polypeptide (NTCP, <i>SLC10A1</i>)
Bibliographic record
Abstract
Na + -taurocholate cotransporting polypeptide (NTCP, SLC10A1 ) is a key hepatic uptake transporter for bile acids and drugs and is the main functional receptor for hepatitis B and D viruses. Next-generation sequencing has revealed that a large number of rare SLC10A1 variants exist in the population. Little data exist regarding head-to-head comparison of in silico algorithms to predict functional effects of pharmacogenetic variants when compared to direct in vitro functional assessment. This study aimed at characterizing rare SLC10A1 variants in vitro and to assess the performance of seven in silico algorithms to predict the observed functional impacts. Thirty-five previously uncharacterized, rare, missense SLC10A1 variants were transiently expressed in human embryonic kidney 293 type T (HEK293T) cells. NCTP protein expression as well as uptake of substrates taurocholic acid (TCA) and rosuvastatin were assessed. Substrate-specific effects were observed for NTCP G191R, with TCA and rosuvastatin transport observed at 89 and 8% of wild-type (WT) uptake, respectively. Significantly reduced transport of TCA and rosuvastatin was observed for 19 variants ( p < 0.05), with seven variants displaying decreased protein expression and marked reduction in transport of both substrates (0–13% of WT uptake, p < 0.0001). Performance of in silico algorithms to predict in vitro uptake, assessed using the area under the receiver operating characteristic curves (AUC ROC ), ranged from 0.69 to 0.97 and 0.72 to 0.84 for TCA and rosuvastatin uptake, respectively. In conclusion, we identified rare variants with significantly reduced NTCP expression and function. We demonstrated that no algorithm performed robustly enough to replace functional study in vitro, particularly given the broad substrate specificity of many pharmacogenes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".