In vitro functional analysis of novel single nucleotide polymorphisms in OATP1B1
Bibliographic record
Abstract
The goal of our study is to functionally characterize six nonsynonymous single nucleotide polymorphisms (SNPs) in human organic anion transporting polypeptide 1B1 (OATP1B1) using in vitro transporter expression technologies. We hypothesize these SNPs will result in a decrease in transport activity compared to wild‐type. OATP1B1 cDNA packaged in pEF6/V5‐His TOPO was used as template, and 6 SNPs — 298G>;A (rs144508550), 419C>;T (rs147450830), 463C>;A (rs11045819), 1007C>;G (rs72559747), 1463G>;C (rs59502379), and 1738C>;T (rs71581941) — were introduced separately to wild‐type templates using QuikChange site‐directed mutagenesis. OATP1B1 variant cDNAs will then be transferred to pAd/CMV/V5‐DEST Gateway vector for further propagation as OATP1B1 variants expressed in adenovirus. We will determine the optimal titre of the OATP1B1‐ variant adenovirus for transporter expression using a monolayer cell lines such as HeLa. Subsequently, we will use prototypical substrates of OATP1B1 such as rosuvastatin to determine the relative affinity (K m ) and capacity (V max ) for uptake, followed by quantification of relative cell surface trafficking. Identification of new functional SNPs in OATP1B1 will give us further insights to genetic heterogeneity in OATP1B1 as a contributor to inter‐subject variation in response to OATP1B1 substrate drug therapy. Research is funded by the Canadian Institute for Health Research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".