Non-target analysis of maternal and cord blood samples: Advancing computational tools and discovering novel chemicals

母体和脐带血样本的非目标分析：改进计算工具并发现新型化学物质

基本信息

批准号：
10766529
负责人：
Dimitri Abrahamsson
金额：
$ 24.9万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-16 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10766529
关键词：
Algorithms Amino Acids Analytical Chemistry Area Award Bile Acids Binding Bioinformatics Biological Biological Monitoring Biometry Birth Blood specimen California Chemical Structure Chemicals Chemistry Chlorobenzene Clinical Data Complement Computing Methodologies Data Data Analyses Data Set Development Endocrine Disruptors Environmental Exposure Epidemiology Equilibrium Ethyl Ether Exposure to Flame Retardants Fundulus heteroclitus Goals Health Human Human Development Hydrocortisone Industrialization Knowledge Liquid Chromatography Low Birth Weight Infant Machine Learning Malignant Neoplasms Mass Spectrum Analysis Mentors Metabolic Diseases Methods Neurodevelopmental Disorder Octanols Organic solvent product Outcome Pathway interactions Persons Pesticides Plasticizers Poly-fluoroalkyl substances Postdoctoral Fellow Pregnancy Pregnancy Outcome Pregnant Women Premature Birth Property Race Reading Research Resolution Risk Assessment Sampling Science Stimulus Structure Sum Term Birth Time Toxicity Tests Training Umbilical Cord Blood Visualization Water Work advanced analytics apprenticeship cohort computational chemistry computer framework computerized tools dashboard data integration developmental toxicity environmental chemistry experience exposed human population feature detection fortification human disease in silico innovation insight large datasets machine learning algorithm metabolome metabolomics molecular mass novel prediction algorithm prenatal prenatal exposure psychologic reproductive toxicity response skill acquisition skills small molecule libraries stressor success time of flight mass spectrometry tool

项目摘要

PROJECT SUMMARY/ABSTRACT Non-targeted analysis (NTA) provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. Despite the recent advancements in NTA, the number of confirmed chemicals with analytical standards remains fairly small compared to the number of detected features. There is, thus, a need to further develop computational tools to derive more chemical structures and leverage the full potential of HRMS. Enhancing our ability to derive more chemical structures will enable the discovery of new industrial chemicals that humans are exposed to, especially in critical windows of development, such as pregnancy. It will also enable the discovery of endogenously produced metabolites that may be related to biological outcomes of importance, such as preterm birth. The objective of my proposal is to develop novel computational methods to significantly advance our ability to analyze and interpret non-targeted analysis data from high-resolution mass spectrometry (HRMS) and apply them to study prenatal exposures to industrial chemicals and endogenous metabolites in a large cohort of pregnant women from Northern California. My proposal builds on my expertise in analytical and environmental chemistry and my current postdoctoral experience in computational chemistry and applications in human exposure. I seek additional training to develop and apply innovative computational methods to better characterize the human exposome and in particular the exposome of preterm birth. The contribution of my proposal will be two-fold: (1) developing novel computational structure-prediction algorithms for HRMS datasets based on MS data and physicochemical properties (equilibrium partition ratios between organic solvents and water, e.g., octanol/water, chlorobenzene/water, diethyl ether/water etc.) (Aim 1) and apply them to derive potential structures for chemical features detected in a HRMS dataset from 340 maternal and 340 matched cord blood samples to complement the limited number of chemicals identified through MS/MS and analytical standards (Aim 2); and (2) study the interplay between the exposome and the metabolome in preterm birth using molecular interaction networks to visualize and compare how molecular interactions between industrial chemicals and endogenous metabolites differ between preterm and full-term birth (Aim 3). The K99 training will expand my prior research experience through coursework, research apprenticeship, and mentored reading, with specific training in: (1) advanced analytical skills including -omics data analysis, machine learning, and biostatistics; (2) epidemiology, risk assessment, human exposure to chemical stressors; and (3) human pregnancy and development. The skills acquired during this award are critical to my long-term goal to advance computational methods to better analyze and interpret non-targeted analysis data to support efforts to better characterize the human exposome. This work will produce new scientific knowledge to greatly advance the understanding of the influence of environmental exposures in the development of adverse health outcomes and in particular, preterm birth.

项目摘要/摘要非目标分析（NTA）提供了一种全面的方法来分析环境和生物学几乎所有化学物质的样品。尽管NTA最近取得了进步，但已确认的数量与检测到的特征数量相比，具有分析标准的化学品仍然相当小。那里因此，需要进一步开发计算工具来得出更多的化学结构并充分利用 HRMS的潜力。增强我们得出更多化学结构的能力将使新的发现人类接触的工业化学物质，尤其是在关键的发展窗口中，例如怀孕。它还将使可能与可能有关的内生产生的代谢产物发现重要性的生物学结果，例如早产。我的提议的目的是开发小说计算方法可显着提高我们分析和解释非目标分析数据的能力从高分辨率质谱（HRMS），并将其应用于研究产前暴露于工业来自北加州的大量孕妇中的化学物质和内源代谢物。我的提案基于我在分析和环境化学方面的专业知识以及目前的博士后的专业知识在人类暴露中具有计算化学和应用的经验。我寻求额外的培训开发和应用创新的计算方法，以更好地表征人类的展示体和特别是早产的揭示。我的提议的贡献将有两个方面：（1）发展小说基于MS数据和理化的HRMS数据集的计算结构预测算法特性（有机溶剂与水之间的平衡分区比率，例如辛醇/水，氯苯/水，二乙基醚/水等）（目标1），并将其应用于潜在的结构在HRMS数据集中检测到的化学特征，从340个母体和340个匹配的脐带血样本到补充通过MS/MS和分析标准鉴定的化学品数量有限（AIM 2）；和（2）研究使用分子相互作用在早产中的杂物体与代谢组之间的相互作用网络可视化和比较工业化学品与内源性之间的分子相互作用早产和完整分娩的代谢物不同（AIM 3）。 K99培训将扩大我以前的研究通过课程工作，研究学徒和指导阅读的经验，并在以下特定的培训中进行：（1）高级分析技能，包括 - 组数据分析，机器学习和生物统计学；（2）流行病学，风险评估，人类接触化学压力源；（3）人类的怀孕和发育。这在此奖项中获得的技能对于我的长期目标至关重要，以提高计算方法以改善分析和解释非目标分析数据，以支持努力以更好地表征人类的释放组。这项工作将产生新的科学知识，以极大地促进对影响的影响环境暴露在不良健康结果的发展中，尤其是早产。