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

翻译后摘要：新生儿筛查（NBS）使用串联质谱（MS/MS）已经改变了我们的能力， 发现并为患有先天性代谢缺陷的婴儿提供早期、挽救生命的治疗。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， 采集血液和尿液样本进行额外的确证性检测。虽然DNA测序已经成为 作为确证性检测的重要组成部分，新生儿干血斑（DBS）仅产生小而高度可变的DNA 金额。迫切需要一种更有效的二级NBS方法来确认所有筛查阳性 直接来自出生时收集的DBS卡的病例。这对于有代谢风险的婴儿尤其重要。 在生命的最初几周里，该提案的总体目标是联合收割机新的DNA测序和 质谱技术诊断DBS的先天性代谢紊乱，并证明临床 这种方法用于第二层筛选的可行性。为实现这一目标，将实现以下具体目标： （1）开发多重基因测序（RUSPseq）和10 X连接阅读测序，用于快速遗传学分析。 诊断而无需额外的父母测试;（2）开发质谱法（Q-TOF/LC-MS）， 随机森林（RF）机器学习，以识别新的代谢标志物，这将被整合到一个新的 第二层筛选小组，以区分真阳性和假阳性病例;以及（3）证明临床和 这种方法的翻译可行性，以更快地识别真阳性和假阳性病例。我们将 与公共NBS计划和NBSTRN的试点研究和实施计划，以翻译这一点 第二层NBS的合并方法。这些结果将通过减少诊断， 延迟和不确定性，并通过减少迭代测试回合和与之相关的成本，从而 减轻医疗保健系统以及患者及其家庭的负担。

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.