Precision environmental health monitoring by longitudinal exposome and multi-omics profiling
- Peng Gao1,3,
- Xiaotao Shen1,3,
- Xinyue Zhang1,
- Chao Jiang2,
- Sai Zhang1,
- Xin Zhou1,
- Sophia Miryam Schüssler-Fiorenza Rose1 and
- Michael Snyder1
- 1Department of Genetics, Stanford University School of Medicine, Stanford, California 94304, USA;
- 2Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310058, China
-
↵3 These authors contributed equally to this work.
Abstract
Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.
Footnotes
-
[Supplemental material is available for this article.]
-
Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.276521.121.
-
Freely available online through the Genome Research Open Access option.
- Received December 20, 2021.
- Accepted April 18, 2022.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
