Differential interactions within the gut microbiome of marmosets treated with antibiotics as revealed by computational network analysis
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Bioinformatics
Author ORCID Identifier
0009-0000-9824-959X
Advisor Information
(jclayton@unomaha.edu) Jonathan Clayton , University of Nebraska at Omaha
Location
CEC RM #127
Presentation Type
Oral Presentation
Start Date
22-3-2024 10:30 AM
End Date
22-3-2024 11:45 AM
Abstract
Antibiotics are an important tool in modern medicine that can be used to control the otherwise unmitigated growth of pathogenic bacteria, but their administration can have unintended consequences within the gut microbiome by pushing resident gut bacteria into an unhealthy state known as dysbiosis. While gut dysbiosis following antibiotic treatment is a well-documented phenomenon, the specifics of how the gut microbiota are affected – and how the microbiota affect their host in turn – is poorly understood. In this study we sought to answer these questions by using 16S sequencing to measure how bacterial abundance changes in marmoset monkeys treated with a broad-spectrum antibiotic cocktail for 28 days, followed by a 14-day recovery period. Results from k-means clustering performed on 16S data revealed the existence of 2 distinct subgroups within the antibiotic-treated marmosets: an antibiotic medium-resistance group that was less affected by antibiotics overall, and a low-resistance group that was more affected. Network analysis comparing the subgroups to each other and to vehicle-treated controls further revealed that the bacterial genus Acinetobacter had a high number of interactions (degree centrality) with other genera in antibiotic-treated marmosets, while the genus Roseburia had high degree centrality in control marmosets, suggesting that these genera play an influential role within the microbiomes of their respective treatment groups. Moreover, the genus Faecalibacterium had high degree centrality in the antibiotic medium-resistance subgroup, suggesting that this genus contributed to microbiome stability under antibiotic stress. Differentially abundant metabolites were detected in both subgroups, indicating a corresponding change in the fecal metabolome.
Differential interactions within the gut microbiome of marmosets treated with antibiotics as revealed by computational network analysis
CEC RM #127
Antibiotics are an important tool in modern medicine that can be used to control the otherwise unmitigated growth of pathogenic bacteria, but their administration can have unintended consequences within the gut microbiome by pushing resident gut bacteria into an unhealthy state known as dysbiosis. While gut dysbiosis following antibiotic treatment is a well-documented phenomenon, the specifics of how the gut microbiota are affected – and how the microbiota affect their host in turn – is poorly understood. In this study we sought to answer these questions by using 16S sequencing to measure how bacterial abundance changes in marmoset monkeys treated with a broad-spectrum antibiotic cocktail for 28 days, followed by a 14-day recovery period. Results from k-means clustering performed on 16S data revealed the existence of 2 distinct subgroups within the antibiotic-treated marmosets: an antibiotic medium-resistance group that was less affected by antibiotics overall, and a low-resistance group that was more affected. Network analysis comparing the subgroups to each other and to vehicle-treated controls further revealed that the bacterial genus Acinetobacter had a high number of interactions (degree centrality) with other genera in antibiotic-treated marmosets, while the genus Roseburia had high degree centrality in control marmosets, suggesting that these genera play an influential role within the microbiomes of their respective treatment groups. Moreover, the genus Faecalibacterium had high degree centrality in the antibiotic medium-resistance subgroup, suggesting that this genus contributed to microbiome stability under antibiotic stress. Differentially abundant metabolites were detected in both subgroups, indicating a corresponding change in the fecal metabolome.