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

摘要：串联质谱仪(MS/MS)用于新生儿筛查已经改变了我们的能力 对患有先天代谢障碍的婴儿进行早期、挽救生命的治疗。而MS/MS 筛查确定了大多数受影响的婴儿，它伴随着频繁的假阳性结果，这需要 采集血液和尿液样本进行额外的确证测试。虽然DNA测序已经成为一种 作为验证性测试的重要组成部分，新生儿干血斑(DBS)只能产生微小且高度可变的DNA 金额。迫切需要一种更有效的第二层国家统计局方法来确认所有筛查阳性 直接从出生时收集的星展银行卡中收集的病例。这对于有代谢风险的婴儿来说尤其重要。 在它们出生后的头几周就会患上疾病。这项提案的总体目标是将新的DNA测序和 应用质谱学技术诊断DBS先天代谢紊乱，并验证其临床应用 该方法用于二级筛查的可行性。为了实现这一目标，将实现以下具体目标 追求：(1)建立多重基因测序(RUSPseq)和10倍连锁阅读测序，以实现快速遗传 诊断不需要额外的亲代检测；(2)发展质谱学(Q-TOF/LC-MS)和 随机森林(RF)机器学习识别新的代谢标记，这些标记将被集成到一种新的 第二级筛查小组，以区分真阳性和假阳性病例；以及(3)展示临床和 这一方法更快地识别真阳性和假阳性病例的翻译可行性。我们会工作的 与公共NBS计划和NBSTRN的试点研究和实施工作组一起翻译这篇文章 进入二线国家统计局的综合方法。这些结果将通过减少诊断结果而产生重大影响 延迟和不确定性，并通过减少迭代测试回合及其相关成本，从而 减轻医疗系统以及患者及其家人的负担。

Abstract: Newborn screening (NBS) using tandem mass spectrometry (MS/MS) has transformed our ability to identify and provide early, lifesaving treatment to infants with inborn errors of metabolism. While MS/MS screening identifies most affected babies, it is accompanied by frequent false-positive results that require collecting blood and urine samples for additional confirmatory testing. While DNA sequencing has become an important part of confirmatory testing, newborn dried blood spots (DBS) yield only small and highly variable DNA amounts. There is an urgent need for a more efficient second-tier NBS approach for confirming all screen-positive cases directly from the 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 this proposal is to combine novel DNA sequencing and mass spectrometry 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, the following specific aims will be pursued: (1) Develop multiplex gene sequencing (RUSPseq) and 10X linked-read sequencing for rapid genetic diagnosis without the need for additional parental testing; (2) Develop mass spectrometry (Q-TOF/LC-MS) and Random Forest (RF) machine learning to identify novel metabolic markers, which will be integrated in a novel second-tier screening panel to separate true and false-positive cases; and (3) Demonstrate clinical and translational feasibility of this approach to more rapidly identify both true and false-positive cases. We will work with the public NBS program and NBSTRN’s Pilot Research and Implementation workgroup 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.