Discovery of early life causes of autism spectrum disorder through retrospective metabolomics and proteome analysis of teeth

通过牙齿的回顾性代谢组学和蛋白质组分析发现自闭症谱系障碍的早期原因

• 批准号: 9809324
• 负责人: Syam S Andra
• 金额: $ 21.19万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9809324
• 关键词: 1 year old; Address; Affect; Biochemical; Biological; Biological Assay; Biological Markers; Blood; Chemicals; Child; Child Health; Childhood Acute Lymphocytic Leukemia; Clinical; Data; Development; Developmental Biology; Diagnosis; Direct Repeats; Disease; Early Diagnosis; Emerging Technologies; Environmental Exposure; Environmental Risk Factor; Etiology; Event; Exposure to; Eye Movements; Foundations; Genetic; Goals; Growth; Incidence; Individual; Inflammation; Investigation; Knowledge; Laboratories; Life; Link; Maps; Measurement; Measures; Metabolic; Metabolism; Metals; Methodology; Methods; Neonatal; Neurobiology; Neurologic Process; Nutrient; Onset of illness; Oxidative Stress; Participant; Pathway interactions; Population; Predisposition; Pregnancy; Prevalence; Process; Protein Analysis; Proteins; Proteome; Proteomics; Public Health; Questionnaires; Sample Size; Sampling; Second Pregnancy Trimester; Siblings; Statistical Methods; Stress; Sum; Techniques; Technology; Testing; Third Pregnancy Trimester; Time; Tooth Diseases; Tooth structure; Toxic effect; Trees; Twin Multiple Birth; Umbilical Cord Blood; Work; autism spectrum disorder; base; brain morphology; case control; cohort; design; disorder risk; early childhood; early life exposure; environmental chemical; fetal; high dimensionality; insight; interest; metabolome; metabolomics; metal metabolism; novel; postnatal development; postnatal period; prenatal; prenatal exposure; response; small molecule; temporal measurement; therapeutic target; treatment strategy

Summary/Abstract: Autism spectrum disorder (ASD) is a heterogeneous disease with an unknown etiology. The global increase in ASD incidence suggests that genetics alone is unlikely to be the major driver of ASD, but that the increased prevalence is likely due to altered exposures to environmental factors. In fact, we know that numerous environmental exposures (nutrients, chemicals, stress, etc.) impact child health, typically exerting their toxicity through either metabolites or perturbations in endogenous pathways, making metabolomics and protein analysis key emerging technologies to elucidate the relationships between these exposures and ASD. But how do we directly measure these early life exposures? Central to our study is the use of novel tooth matrix biomarkers, which take advantage of the incremental manner (similar to tree growth rings) of the developmental biology of teeth. The techniques that we have developed allow us to temporally distinguish exposure between the 2nd trimester, 3rd trimesters, and postnatal periods, enabling identification of the sensitive life stages for biological perturbations in fetal and neonatal development most strongly associated with ASD risk. For the present application, we will perform the first untargeted metabolomics analysis of ASD teeth to delineate unique alterations in corresponding autism and non-autism children. This will be supported by the first targeted highly multiplexed protein analysis of teeth (92 proteins) for delineating biological pathways of interest, including inflammation, oxidative stress and those associated with neurobiological processes. We will use novel statistical methodology, weighted quantile sum regression (WQS) that addresses time-varying effects of high-dimensional mixtures, and increases power when compared to traditional methods to discover biomarkers and biological pathways associated with ASD. Discovery will be performed on 40 ASD case-control sibling pairs, and replication on an independent population of 35 unrelated case-control pairs. Our method is a non-invasive advancement in technology to obtain direct and repeated measures of biomarkers associated with early life etiology of ASD.

摘要/摘要： 自闭症谱系障碍（ASD）是一种具有未知病因的异质疾病。全球增长 ASD发病率表明，仅遗传学不太可能是ASD的主要驱动力，但增加了 患病率可能是由于对环境因素的暴露变化所致。实际上，我们知道很多 环境暴露（营养，化学物质，压力等）影响儿童健康，通常会施加毒性 通过内源途径中的代谢产物或扰动，使代谢组学和蛋白质产生 分析关键的新兴技术以阐明这些暴露与ASD之间的关系。但是如何 我们是否直接衡量这些早期生活暴露？我们研究的中心是使用新颖的牙齿矩阵 生物标志物，它利用了增量方式（类似于树木生长环） 牙齿的发育生物学。我们开发的技术使我们能够在时间上区分 第二个三个月，第三个三物质和产后时期之间的暴露，可以鉴定 胎儿和新生儿发育中生物扰动的敏感生活阶段最密切相关 有ASD风险。对于本应用 牙齿描绘相应的自闭症和非自闭症儿童的独特变化。这将得到支持 通过对牙齿（92蛋白）的第一个靶向高度多路复用蛋白分析，用于描述生物学途径 感兴趣的，包括炎症，氧化应激以及与神经生物学过程相关的应激。我们 将使用新颖的统计方法论，加权分位数总回归（WQS），以解决时间变化 与传统方法相比，高维混合物的影响，并增加功率 生物标志物和与ASD相关的生物途径。发现将在40个ASD病例对照中进行 兄弟姐妹对，并在35个无关的病例对照对的独立人群中复制。我们的方法是 技术的非侵入性进步，以获得与生物标志物相关的直接和重复测量 与ASD的早期病因。