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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：
10394398
负责人：
Dimitri Abrahamsson
金额：
$ 9.98万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
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 2023-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10394398
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 Ensure 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 Pregnancy Pregnancy Outcome Pregnant Women Premature Birth Property Reading Research Resolution Risk Assessment Sampling Science Stimulus Structure Sum Term Birth Time Toxicity Tests Training Umbilical Cord Blood Water Work advanced analytics apprenticeship base case control cohort computational chemistry computer framework computerized tools dashboard data integration developmental toxicity environmental chemistry experience exposed human population 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 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.

项目总结/摘要非靶向分析（NTA）提供了一种全面的方法来分析环境和生物几乎所有化学品的样品。尽管NTA最近取得了进展，但确认的人数与检测到的特征的数量相比，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，以获得更多的化学结构，并充分利用人力资源管理系统的潜力。提高我们推导出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，特别是在发展的关键时期，怀孕它还将使发现内源性产生的代谢物，可能与重要的生物学结果，如早产。我的建议的目的是开发新的计算方法，以显着提高我们的能力，分析和解释非目标分析数据高分辨率质谱（HRMS），并将其应用于研究产前暴露于工业来自北方加州的一个大的孕妇队列中的化学物质和内源性代谢物。我该提案基于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工作经验，在计算化学和人体暴露应用方面的经验。我寻求额外的培训，开发和应用创新的计算方法，以更好地描述人类的麻烦，特别是早产的烦恼。我的建议的贡献将是两方面的：（1）发展小说基于MS数据和物理化学的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）(Aim 1）并应用它们来推导潜在的结构，在来自340名母亲和340名匹配的脐带血样本的HRMS数据集中检测到的化学特征，补充通过MS/MS和分析标准确定的数量有限的化学品（目标2）;以及 (2)利用分子相互作用研究早产中代谢组和代谢组之间的相互作用网络可视化和比较工业化学品和内源性之间的分子相互作用代谢物在早产和足月分娩之间存在差异（目标3）。K99培训将扩展我之前的研究通过课程工作，研究学徒，和指导阅读的经验，与具体的培训：（1）先进的分析技能，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，人类暴露于化学应激源;（3）人类妊娠和发育。的在这个奖项获得的技能是至关重要的，我的长期目标，以推进计算方法，以更好地分析和解释非目标分析数据，以支持更好地表征人类疾病组的工作。这项工作将产生新的科学知识，大大促进对影响的理解。环境暴露对健康产生不利影响，特别是早产。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Large-Scale Implementation and Flaw Investigation of Human Serum Suspect Screening Analysis for Industrial Chemicals.

对工业化学品的人血清怀疑筛查分析的大规模实施和缺陷调查。

DOI：
10.1021/jasms.1c00135
发表时间：
2021-09-01
期刊：
Journal of the American Society for Mass Spectrometry
影响因子：
3.2
作者：
Jiang T;Wang M;Wang A;Abrahamsson D;Kuang W;Morello-Frosch R;Park JS;Woodruff TJ
通讯作者：
Woodruff TJ

Extending Nontargeted Discovery of Environmental Chemical Exposures during Pregnancy and Their Association with Pregnancy Complications-A Cross-Sectional Study.

DOI：
10.1289/ehp11546
发表时间：
2023-07
期刊：
Environmental health perspectives
影响因子：
10.4
作者：
通讯作者：

Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS).

DOI：
10.1038/s41370-022-00481-2
发表时间：
2022-11
期刊：
JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY
影响因子：
4.5
作者：
Abrahamsson, Dimitri;Siddharth, Adi;Robinson, Joshua F.;Soshilov, Anatoly;Elmore, Sarah;Cogliano, Vincent;Ng, Carla;Khan, Elaine;Ashton, Randolph;Chiu, Weihsueh A.;Fung, Jennifer;Zeise, Lauren;Woodruff, Tracey J.
通讯作者：
Woodruff, Tracey J.

Extracting Structural Information from Physicochemical Property Measurements Using Machine Learning─A New Approach for Structure Elucidation in Non-targeted Analysis.

DOI：
10.1021/acs.est.3c03003
发表时间：
2023-10-10
期刊：
ENVIRONMENTAL SCIENCE & TECHNOLOGY
影响因子：
11.4
作者：
Abrahamsson, Dimitri;Brueck, Christopher L.;Prasse, Carsten;Lambropoulou, Dimitra A.;Koronaiou, Lelouda-Athanasia;Wang, Miaomiao;Park, June-Soo;Woodruff, Tracey J.
通讯作者：
Woodruff, Tracey J.

Quantification of chemicals in non-targeted analysis without analytical standards - Understanding the mechanism of electrospray ionization and making predictions.

在没有分析标准的非靶向分析中对化学物质进行定量 - 了解电喷雾电离的机制并进行预测。