Emergingevidencesuggeststhathumanmicrobiome,composedofcollectivegenomesofasmanyas100trillion microorganisms, could be mediating disease-leading causal pathways initiated by environmental toxicants or otherfactorssuchasdrugusage.Prenatalarsenicexposurethroughdrinkingwater,forexample,couldinitiate perturbationofgutmicrobiome,andtherefore,childrencouldinheritperturbedmicrobiomecompositioniftheir mothershavearsenicexposureduringperinatalperiod.Theunhealthymicrobiomecompositioncould,inturn, induce children?s asthma, infection and allergy which could explain that arsenic exposure during pregnancy is relatedtochildren?sinfection.Takentogether,arsenicexposurecouldbetheinitiationofcausalpathwaysleading tochildren?sinfectionthroughperturbedmother?smicrobiomebeingpassedtochildren.Therearemanyother possibleinitiationfactorssuchasdiet,genemutation,deliverymodeandantibioticsleadingtodifferentchildren?s health outcomes. These mediations could happen through changes in particular microbial taxa or though the perturbation of microbiome population structure. While high-throughput sequencing technologies can characterizethetaxonomiccompositionofmicrobiomeinunprecedenteddetail,noneoftheexistingmediation analysis methods is adequate enough to model the mediation effects of microbiome due to the unique challenging features of microbiome data. Therefore, there is an urgent need to have appropriate mediation analysismethodsinplaceforestimatingandtestingthemediationaleffectsofhumanmicrobiome.Toaddress theseissues,wewilldeveloptwogeneralmediationanalysisframeworkstoidentifymediationthroughchanges inindividualmicrobialtaxaandmodelmediationthoughtheperturbationofoverallmicrobiomecomposition.The models will be tested with extensive simulations and cross validations. An R package and an interactive web applicationwillbedevelopedformodelimplementations.Intherealstudyapplications,wewillquantifyandtest the mediation effects of infant gut microbiome and breast-milk microbiome in the relations between prenatal exposures (e.g., arsenic exposure, maternal diet) and childhood infections and allerg/atopy in the first year of lifeusingtherichdatafromthelargeongoinglongitudinalmolecularepidemiologicNewHampshireBirthCohort study.Withtheapplicationsoftheproposedmodelsinacysticfibrosis(CF)study,wewillexaminewhetherCF transmembrane conductance regulator gene mutations lay the biological foundation for patterns in the developingmicrobiomeinthegutthatareassociatedwithCFexacerbationonsetinnewbornchildrenwithCF. ByanalyzingthedatafromtheInfantGrowthandMicrobiomeStudy,wewillanswerthekeyquestionoftherole playedbygutmicrobiomeinassociatingearlyantibioticexposure,deliveringmodeandfeedingmodewithinfant weightgain.

Public Health Relevance

The overarching goal of this project is to develop mediation modeling approaches and apply the approaches in four important ongoing studies 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 and an interactive web application will be developed to implement the proposed approaches to explore important 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 #
5R01GM123014-04
Application #
9985901
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2018-08-01
Project End
2022-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Florida
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
969663814
City
Gainesville
State
FL
Country
United States
Zip Code
32611