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.

项目概要/摘要非靶向分析 (NTA) 提供了一种分析环境和生物的综合方法几乎所有存在的化学品的样品。尽管 NTA 最近取得了进展，但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少。那里因此，需要进一步开发计算工具来推导更多的化学结构并充分利用 HRMS 的潜力。增强我们获得更多化学结构的能力将使我们能够发现新的化学结构人类接触的工业化学品，特别是在关键的发展时期，例如怀孕。它还将能够发现可能与以下因素有关的内源性代谢物：重要的生物学结果，例如早产。我建议的目标是开发新颖的显着提高我们分析和解释非目标分析数据的能力的计算方法从高分辨率质谱 (HRMS) 中获取数据，并将其应用于研究产前工业暴露来自北加州的一大群孕妇的化学物质和内源代谢物。我的该提案建立在我在分析和环境化学方面的专业知识以及我目前的博士后的基础上计算化学和人体暴露应用方面的经验。我寻求额外的培训开发并应用创新的计算方法，以更好地表征人类暴露组和特别是早产的暴露。我的提案的贡献有两个：（1）开发新颖的基于 MS 数据和理化数据的 HRMS 数据集的计算结构预测算法性质（有机溶剂和水之间的平衡分配比，例如辛醇/水，氯苯/水、乙醚/水等）（目标 1）并应用它们推导潜在的结构在 HRMS 数据集中检测到 340 份母体和 340 份匹配脐带血样本的化学特征补充通过 MS/MS 和分析标准确定的有限数量的化学品（目标 2）；和 (2) 利用分子相互作用研究早产中暴露组和代谢组之间的相互作用网络来可视化和比较工业化学品和内源性分子之间的相互作用早产和足月出生的代谢物有所不同（目标 3）。 K99培训将扩展我之前的研究通过课程作业、研究学徒和指导阅读获得的经验，并接受过以下方面的具体培训：(1) 高级分析技能，包括组学数据分析、机器学习和生物统计学；（2）流行病学，风险评估、人体接触化学应激源； (3)人类怀孕和发育。这在这个奖项期间获得的技能对于我的长期目标至关重要，即推动计算方法更好分析和解释非靶向分析数据，以支持更好地表征人类暴露组的工作。这项工作将产生新的科学知识，极大地促进对影响的理解环境暴露会导致不良健康结果，特别是早产。