Constructing a network graph of correlations between microbial taxa and fecal metabolites in the gut of captive marmosets
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Biology
Other
Biology
Advisor Information
(jclayton@unomaha.edu) Jonathan Clayton, University of Nebraska Omaha
Location
MBSC Ballroom Poster # 1209 - G (Doctoral)
Presentation Type
Poster
Start Date
24-3-2023 2:30 PM
End Date
24-3-2023 3:45 PM
Abstract
Jordan B. Hernandeza,b, Shivdeep S. Hayera,b, Sophie Alvarezc, Anne Fischerc, Haley R. Hassenstaba,d, Jeffery A. Frenchb,d,e, Katherine Cooperf , Zahraa W. Alsafwanif , Andrew K. Bensonb,g, Mallory J. Suhr Van Hauteb,g, and Jonathan B. Claytona,b,d,g,h,i,*
aDepartment of Biology, University of Nebraska at Omaha, Omaha, NE, USA
bNebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, NE, USA
cProteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, USA
dCallitrichid Research Center, University of Nebraska at Omaha, Omaha, NE, USA
eNeuroscience Program, University of Nebraska at Omaha, Omaha, NE 68182, United States
fSchool of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
gDepartment of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, USA
hDepartment of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
iPrimate Microbiome Project, University of Nebraska-Lincoln, Lincoln, NE, USA
The composition of the gut microbiome changes naturally over time as a product of age, diet, and numerous other factors. The gut microbiome’s dynamic nature makes it susceptible to dysbiosis when resident flora begin outcompeting each other, resulting in decreased microbial diversity and opening the door to further health complications. Although much emphasis in microbiology literature has been placed on the diagnosis and treatment of gut dysbiosis in relation to healthy controls, the natural fluctuation of the microbiome over time is a topic that is comparatively sparse. A thorough understanding of how gut flora behave under normative conditions can aid in the prediction of dysbiosis by providing a “noise” profile of microbe-microbe, metabolite-metabolite, and microbe-metabolite interactions that occur over time in multiple individuals. Interactions that occur in many individuals are more likely to be essential for proper microbiome function, and can be used as a baseline to find interactions specific to a certain condition, similar to housekeeping genes in genetics studies or the default mode network in neuroimaging studies. In this study, we characterize interactions in the gut microbiome of the common marmoset by calculating the Pearson correlation coefficient between 16S genera abundance and targeted metabolomics data over 3 time points spanning a period of 40 days. We then visualize significant correlations in association network graphs to find genera and metabolites that exhibit a high degree of associations, marking them as highly influential in the microbiome. Clostridiaceae Clostridium sensu stricto 1 was among the highest-degree genera, being significantly associated with a large number of metabolites and other genera. It also had a high relative betweenness centrality and a negative correlation with the highly microbially connected genus Prevotellaceae Paraprevotella, indicating that it could be part of an “information bottleneck” within the microbe-microbe interaction network. Among the high-degree metabolites were the bile acids and several amino acids including γ-Aminobutyric acid (GABA), Taurine, and Lysine. Metabolites shared the strongest degree of correlation with other metabolites, giving credence to the interpretation of correlation as reflecting true covariation over time. Together, these influential genera and metabolites are likely candidates for being essential to microbiome health.
Scheduling
10:45 a.m.-Noon, 1-2:15 p.m., 2:30 -3:45 p.m.
Constructing a network graph of correlations between microbial taxa and fecal metabolites in the gut of captive marmosets
MBSC Ballroom Poster # 1209 - G (Doctoral)
Jordan B. Hernandeza,b, Shivdeep S. Hayera,b, Sophie Alvarezc, Anne Fischerc, Haley R. Hassenstaba,d, Jeffery A. Frenchb,d,e, Katherine Cooperf , Zahraa W. Alsafwanif , Andrew K. Bensonb,g, Mallory J. Suhr Van Hauteb,g, and Jonathan B. Claytona,b,d,g,h,i,*
aDepartment of Biology, University of Nebraska at Omaha, Omaha, NE, USA
bNebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, NE, USA
cProteomics and Metabolomics Facility, Nebraska Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, USA
dCallitrichid Research Center, University of Nebraska at Omaha, Omaha, NE, USA
eNeuroscience Program, University of Nebraska at Omaha, Omaha, NE 68182, United States
fSchool of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA
gDepartment of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, USA
hDepartment of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
iPrimate Microbiome Project, University of Nebraska-Lincoln, Lincoln, NE, USA
The composition of the gut microbiome changes naturally over time as a product of age, diet, and numerous other factors. The gut microbiome’s dynamic nature makes it susceptible to dysbiosis when resident flora begin outcompeting each other, resulting in decreased microbial diversity and opening the door to further health complications. Although much emphasis in microbiology literature has been placed on the diagnosis and treatment of gut dysbiosis in relation to healthy controls, the natural fluctuation of the microbiome over time is a topic that is comparatively sparse. A thorough understanding of how gut flora behave under normative conditions can aid in the prediction of dysbiosis by providing a “noise” profile of microbe-microbe, metabolite-metabolite, and microbe-metabolite interactions that occur over time in multiple individuals. Interactions that occur in many individuals are more likely to be essential for proper microbiome function, and can be used as a baseline to find interactions specific to a certain condition, similar to housekeeping genes in genetics studies or the default mode network in neuroimaging studies. In this study, we characterize interactions in the gut microbiome of the common marmoset by calculating the Pearson correlation coefficient between 16S genera abundance and targeted metabolomics data over 3 time points spanning a period of 40 days. We then visualize significant correlations in association network graphs to find genera and metabolites that exhibit a high degree of associations, marking them as highly influential in the microbiome. Clostridiaceae Clostridium sensu stricto 1 was among the highest-degree genera, being significantly associated with a large number of metabolites and other genera. It also had a high relative betweenness centrality and a negative correlation with the highly microbially connected genus Prevotellaceae Paraprevotella, indicating that it could be part of an “information bottleneck” within the microbe-microbe interaction network. Among the high-degree metabolites were the bile acids and several amino acids including γ-Aminobutyric acid (GABA), Taurine, and Lysine. Metabolites shared the strongest degree of correlation with other metabolites, giving credence to the interpretation of correlation as reflecting true covariation over time. Together, these influential genera and metabolites are likely candidates for being essential to microbiome health.