AI Article Synopsis

  • Genetic variants in the OCTN2 gene, which encodes a carnitine transporter, lead to a serious condition called Carnitine Transporter Deficiency (CTD) that can be life-threatening but manageable if caught early.
  • Researchers characterized 150 OCTN2 variants to understand how they affect its function, finding that 70% reduced carnitine transport significantly, and 25% had severe impacts on function.
  • They developed a predictive model that surpassed previous methods in identifying whether variants were functional or loss-of-function, aiding in the interpretation of variants for diagnosing and treating CTD.

Article Abstract

Genetic variants in , 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 . Interpretation of missense variants of uncertain significance (VUSs) is a major challenge. In this study, we sought to characterize the largest set to date ( = 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 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 (>20% function) or LOF (<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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674959PMC
http://dx.doi.org/10.1073/pnas.2210247119DOI Listing

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