Multiplex gene sequencing and metabolomics analysis from newborn dried blood spots to improve screening and diagnosis of metabolic disorders.

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Project summary: This application responds to PA-20-272 Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional). It will significantly contribute to our ability to accurately identify and provide early, lifesaving treatment to newborns with inborn errors of metabolism. While newborn screening (NBS) using tandem mass spectrometry (MS/MS) identifies most affected babies, it is accompanied by frequent false-positive results that require collecting blood and urine samples for additional confirmatory testing. There is an urgent need for a more efficient second-tier NBS approach for confirming all screen-positive cases directly from the newborn dried blood spot (DBS) cards collected at birth. This is especially critical for infants at risk for metabolic disease in their first weeks of life. The overall objective of our proposal is to combine novel DNA sequencing and metabolomics technology to diagnose inborn metabolic disorders from DBS, and to demonstrate the clinical feasibility of this approach for second-tier screening. To achieve this objective, we have developed multiplex gene sequencing (RUSPseq) for rapid genetic testing (Aim 1); and liquid chromatography tandem mass spectrometry (LC-MS/MS) and data mining (AI/ML) to identify novel metabolic markers that have been integrated in a novel second-tier screening panel to separate true and false-positive cases (Aim 2). The gene panel missed genetic variants in several confirmed metabolic cases, while the effectiveness for reducing false-positives using the metabolomics-AI/ML approach varied between the four metabolic disorders studied (range 51-100%). This supplement's goal is to perform genome sequencing of DBS samples from screen-positive cases to extend and strengthen the existing research described in Aim 3; and to enhance and refine the metabolomic-AI/ML algorithms to further improve the separation of true and false-positive cases. We will work with the public NBS program and NBSTRN to translate this combined approach into second-tier NBS. These outcomes will have significant impact by reducing diagnostic delays and uncertainties, and by reducing iterative testing rounds and the cost associated with them, thereby reducing the burden on the healthcare system as well as patients and their families.