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

项目摘要：本申请响应PA-20-272现有NIH赠款的行政补充 和合作协议（家长管理支持临床试验可选）。这将大大有助于我们 能够准确地识别和提供早期，挽救生命的治疗，新生儿与先天性缺陷， 新陈代谢.虽然使用串联质谱法（MS/MS）的新生儿筛查（NBS）可以识别大多数 受影响的婴儿，它是伴随着频繁的假阳性结果，需要收集血液和尿液 样本进行额外的确认性检测。迫切需要一个更有效的二级国家统计局 直接从新生儿干血斑（DBS）卡确认所有筛查阳性病例的方法 出生时收集。这对于在出生后的第一周内有代谢疾病风险的婴儿尤其重要。的 我们计划的总体目标是将新型DNA测序和代谢组学技术联合收割机结合起来， 从DBS诊断先天性代谢紊乱，并证明这种方法的临床可行性， 二级筛选为了实现这一目标，我们开发了多重基因测序（RUSPseq） 用于快速基因检测（目标1）;和液相色谱串联质谱（LC-MS/MS）和数据 挖掘（AI/ML），以识别已整合到新型二级筛选中的新型代谢标志物 面板以区分真阳性和假阳性病例（目标2）。基因小组遗漏了几个基因变异 确认代谢病例，而使用代谢组学AI/ML减少假阳性的有效性 方法在研究的四种代谢紊乱之间变化（范围51-100%）。本补充的目的是 对筛查阳性病例的DBS样本进行基因组测序， 目标3中描述的研究;并增强和完善代谢组学AI/ML算法，以进一步提高 区分真假阳性病例。我们将与公共NBS计划和NBSTRN合作， 将这种组合方法转化为二级NBS。这些成果将产生重大影响， 减少诊断延迟和不确定性，并通过减少迭代测试轮次和相关成本 因此，我们必须与他们合作，从而减轻医疗系统以及患者及其家庭的负担。

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.