Emerging evidence suggests that human microbiome, particularly the gut microbiome composed of collective genomes of as many as 100 trillion commensal, symbiotic and pathogenic microorganisms, could be mediating disease-leading causal pathways initiated by environmental toxicants or other factors such as drug usage. Arsenic exposure through drinking water could initiate perturbation of gut microbiome, and therefore, children could inherit perturbed microbiome composition if their mothers have arsenic exposure during perinatal period. The unhealthy microbiome composition could, in turn, induce children's asthma, infection and allergy, which could explain that arsenic exposure during pregnancy is related to children's infection. Taken together, arsenic exposure could be the initiation of causal pathways leading to children's infection through perturbed mother's microbiome being passed to children. There are many other possible initiation factors such as diet, gene mutation and antibiotics leading to different health outcomes. Despite this exciting evidence for microbiome mediating disease-leading causal pathways, it remains challenging to model mediating processes due to the complex nature of microbiome. These mediations could happen simply through changes in some individual microbes, though the perturbation of microbiome homeostasis, or through a mixture of both. Therefore, there is an urgent need to have appropriate mediation analysis methods in place for estimating and testing the mediational effects of human microbiome for these different situations. Although high-throughput sequencing technologies are able to quantify microbiome, none of the existing mediation analysis methods is adequate enough to model the mediational effects of microbiome due to the unique features of microbiome data despite the extensive developments of mediation methods in the literature. To address these issues, we will develop general mediation analysis frameworks to identify mediation through changes in individual bacterial taxa and model mediation though the perturbation of overall microbiome composition.

Public Health Relevance

The overarching goal of this project is to develop mediation modeling approaches to investigate the microbiome as a complex mediator in disease-leading causal pathways in children's health. This project will develop and evaluate two mediation analysis methods. A user-friendly R package will be developed to implement the proposed approaches for three studies to explore important research questions in children's health and facilitate the translation of research to medical practices.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM123014-01
Application #
9288447
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2017-09-12
Project End
2018-07-31
Budget Start
2017-09-12
Budget End
2018-07-31
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Dartmouth College
Department
Other Basic Sciences
Type
Schools of Medicine
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
03755