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

总结/摘要： 自闭症谱系障碍（ASD）是一种病因不明的异质性疾病。全球更多地 ASD发病率表明，遗传学本身不太可能是ASD的主要驱动因素，但增加的遗传因素可能是ASD的主要驱动因素。 流行可能是由于改变了对环境因素的接触。事实上，我们知道， 环境暴露（营养物质、化学物质、压力等）影响儿童健康，特别是发挥其毒性 通过代谢物或内源性途径的扰动，使代谢组学和蛋白质 分析关键的新兴技术，以阐明这些暴露与ASD之间的关系。但如何 我们是否直接测量这些早期生命的暴露量？我们研究的中心是使用新型牙基质 生物标志物，它利用的增量方式（类似于树木的年轮）的优势， 牙齿发育生物学我们开发的技术可以让我们在时间上区分 妊娠中期、妊娠晚期和产后期间的暴露，从而能够识别 胎儿和新生儿发育中生物学扰动的敏感生命阶段最强烈相关 ASD风险对于本申请，我们将进行ASD的第一个非靶向代谢组学分析 牙齿来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种蛋白质）的首次靶向高度多重蛋白质分析， 感兴趣的，包括炎症，氧化应激和那些与神经生物学过程。我们 将使用新的统计方法，加权分位数和回归（WQS），解决时变 高维混合物的影响，并增加了权力相比，传统的方法来发现 与ASD相关的生物标志物和生物途径。将对40例ASD病例对照进行发现 同胞对，并在35个无关的病例对照对的独立群体上复制。我们的方法是一个 无创技术的进步，以获得直接和重复测量的生物标志物相关 自闭症的早期病因

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.