Identification of distinct gut microbiome communities and functional features associated with microbial profiles
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Bioinformatics
Other
Biomedical Informatics
Advisor Information
Hesham Ali
Location
MBSC Ballroom Poster # 202 - G (Doctoral)
Presentation Type
Poster
Start Date
24-3-2023 9:00 AM
End Date
24-3-2023 10:15 AM
Abstract
New biomedical technologies have allowed researchers to survey the genome of entire microbial communities. These trillions of small and invisible microbes are present within and on our body and play an essential role in human health. Recent research demonstrates that microbial community composition rapidly changes throughout a person’s life, however, it is not yet possible to identify distinct microbial compositions associated with health conditions. Classification of such communities would further the potential of microbial-based diagnostics, therapies, and the prevention of disease. In this study, we assess the microbial community composition of Inflammatory Bowel Disease (IBD) patients using a network-based approach to model microbial associations via co-occurrence patterns. By comparing these patterns, researchers can better associate specific health conditions with certain microbial profiles. Furthermore, the detection of highly cooccurred microbial communities can be used to predict their biological function. Our network model reveals the dynamic nature of microbial communities in response to health conditions and suggests new directions to explore how the identification of certain microbial profiles can be exploited to improve the health of their host.
Scheduling
9:15-10:30 a.m., 10:45 a.m.-Noon, 2:30 -3:45 p.m.
Identification of distinct gut microbiome communities and functional features associated with microbial profiles
MBSC Ballroom Poster # 202 - G (Doctoral)
New biomedical technologies have allowed researchers to survey the genome of entire microbial communities. These trillions of small and invisible microbes are present within and on our body and play an essential role in human health. Recent research demonstrates that microbial community composition rapidly changes throughout a person’s life, however, it is not yet possible to identify distinct microbial compositions associated with health conditions. Classification of such communities would further the potential of microbial-based diagnostics, therapies, and the prevention of disease. In this study, we assess the microbial community composition of Inflammatory Bowel Disease (IBD) patients using a network-based approach to model microbial associations via co-occurrence patterns. By comparing these patterns, researchers can better associate specific health conditions with certain microbial profiles. Furthermore, the detection of highly cooccurred microbial communities can be used to predict their biological function. Our network model reveals the dynamic nature of microbial communities in response to health conditions and suggests new directions to explore how the identification of certain microbial profiles can be exploited to improve the health of their host.