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Functional genomics of OCTN2 variants informs protein-specific variant effect predictor for Carnitine Transporter Deficiency.

Megan L KoleskeGregory McInnesJulia E H BrownNeil ThomasKeino HutchinsonMarcus Y ChinAntoine KoehlMichelle R ArkinAvner SchlessingerRenata C GallagherYun S SongRuss B AltmanKathleen M Giacomini
Published in: Proceedings of the National Academy of Sciences of the United States of America (2022)
Genetic variants in <i>SLC22A5</i>, encoding the membrane carnitine transporter OCTN2, cause the rare metabolic disorder Carnitine Transporter Deficiency (CTD). CTD is potentially lethal but actionable if detected early, with confirmatory diagnosis involving sequencing of <i>SLC22A5</i>. Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date (<i>n</i> = 150) of OCTN2 variants identified in diverse ancestral populations, with the goals of furthering our understanding of the mechanisms leading to OCTN2 loss-of-function (LOF) and creating a protein-specific variant effect prediction model for OCTN2 function. Uptake assays with <sup>14</sup>C-carnitine revealed that 105 variants (70%) significantly reduced transport of carnitine compared to wild-type OCTN2, and 37 variants (25%) severely reduced function to less than 20%. All ancestral populations harbored LOF variants; 62% of green fluorescent protein (GFP)-tagged variants impaired OCTN2 localization to the plasma membrane of human embryonic kidney (HEK293T) cells, and subcellular localization significantly associated with function, revealing a major LOF mechanism of interest for CTD. With these data, we trained a model to classify variants as functional (&gt;20% function) or LOF (&lt;20% function). Our model outperformed existing state-of-the-art methods as evaluated by multiple performance metrics, with mean area under the receiver operating characteristic curve (AUROC) of 0.895 ± 0.025. In summary, in this study we generated a rich dataset of OCTN2 variant function and localization, revealed important disease-causing mechanisms, and improved upon machine learning-based prediction of OCTN2 variant function to aid in variant interpretation in the diagnosis and treatment of CTD.
Keyphrases
  • copy number
  • machine learning
  • endothelial cells
  • public health
  • protein protein
  • big data
  • genome wide
  • replacement therapy
  • smoking cessation
  • artificial intelligence