The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.

Mol Cell

Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Chan Zuckerberg Biohub, San Francisco, CA 94148, USA. Electronic address:

Published: May 2024

AI Article Synopsis

  • Mutations in transporters, specifically looking at the OCT1 liver transporter, can influence drug responses and contribute to various diseases.
  • The study analyzed over 11,000 variants in OCT1 using advanced techniques like saturation mutagenesis and multi-phenotypic screening, discovering that many variants mainly affect the protein's abundance rather than its function.
  • The research created detailed models of how these variants impact the transport cycle of OCT1, offering insights for predicting how genetic variations affect drug response and disease susceptibility.

Article Abstract

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11382353PMC
http://dx.doi.org/10.1016/j.molcel.2024.04.008DOI Listing

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