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MOMMI-MP: Database for Integrated Analysis of Metabolic and Microbiome Profiling of Mouse Pregnancy

thesis
posted on 2025-04-01, 21:56 authored by Kaustubh Kishor Pachpor
Pregnancy is a dynamic state involving multiple metabolic changes. Insulin resistance during the later time periods is a normal physiologic response in pregnancy. Gestational diabetes mellitus (GDM), a form of diabetes that appears during pregnancy, develops if metabolic aberrations occur above the normal insulin resistance. Multi-omics is a powerful approach for uncovering the mechanisms driving metabolic change in different physiologic and pathologic states. A recent study demonstrated that the gestational gut microbiome mediates pregnancy metabolic adaptations through effects on gut indoleamine-2,3 dioxygenase 1 activity and kynurenine production. This study collected pregnancy-specific physiological and metabolic profiles, 16S rRNA microbiome, and plasma untargeted LC-MS metabolome data from 3 genetically diverse strains of mice (C57BL/6J, CD1, and NIH-Swiss) over 6-time points during the gestational (days 0, 10, 15, and 19 during gestation) and postpartum (days 3 and 20 after delivery) states, totaling 180 samples for each strain. To facilitate the utilisation of these data by other researchers, we developed MOMMI-MP, a database that provides an easy-to-use platform to browse and search differential abundant microbial taxa, metabolites, metabolic pathways, predicted micro-metabolite interactions using an array of state-of-art statistical and machine learning models. The computational results are presented in various tables, plots, and organized in MOMMI-MP to empower exploratory analyses by other researchers. In conclusion, MOMMI-MP is a resource to facilitate the investigation of novel mechanisms governing metabolic changes during pregnancy.<p></p>

History

Language

  • en

Advisor

Dai, YangLayden, Brian

Chair

Dai, Yang

Department

Biomedical Engineering

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Masters

Degree name

MS, Master of Science

Committee Member

P e n g , Z h a n g l i

Submitted date

August 2023

Thesis type

application/pdf

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