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(http://www.niehs.nih.gov//portfolio/index.cfm?do=portfolio.grantdetail&&grant_number=R00ES032892&format=word)
| Principal Investigator: Abrahamsson, Dimitri | |
|---|---|
| Institute Receiving Award | New York University School Of Medicine |
| Location | New York, NY |
| Grant Number | R00ES032892 |
| Funding Organization | National Institute of Environmental Health Sciences |
| Award Funding Period | 16 Apr 2021 to 31 Mar 2026 |
| DESCRIPTION (provided by applicant): | 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. |
| Science Code(s)/Area of Science(s) |
Primary: 75 - Computational Biology/Computational Methods for Exposure Assessment Secondary: 03 - Carcinogenesis/Cell Transformation |
| Publications | No publications associated with this grant |
| Program Officer | Yuxia Cui |