Protein-Protein Interaction Network Analysis Reveals Distinct Patterns of Antibiotic Resistance Genes

Antibiotic resistance genes (ARGs) are responsible for an increasing number of bacterial infections worldwide. ARGs are challenging to track within bacterial genomes as they are often subject to horizontal gene transfer via mobile genetic elements (MGEs). Complex protein-protein networks can reveal...

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Bibliographic Details
Published in2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 73 - 76
Main Authors Moumi, Nazifa Ahmed, Brown, Connor L., Vikesland, Peter J., Pruden, Amy, Zhang, Liqing
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.12.2022
Subjects
Antibiotics
ARG
Genomics
Microorganisms
Network analyzers
Pipelines
PPI
PPIN
Protein
Protein engineering
Proteins
Resistance
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Summary:Antibiotic resistance genes (ARGs) are responsible for an increasing number of bacterial infections worldwide. ARGs are challenging to track within bacterial genomes as they are often subject to horizontal gene transfer via mobile genetic elements (MGEs). Complex protein-protein networks can reveal proteins contributing to the spread and persistence. Here, we developed a pipeline that facilitates this process by analyzing features of a Protein-Protein Interaction Network (PPIN). This pipeline uses a random forest model to distinguish ARGs from non-ARGs and explores associations between ARGs and proteins with which they functionally interact. We tested the approach using the PPINs of Escherichia coli and Acinetobacter baumannii, two deadly organisms known to carry ARGs harbored by MGEs, and achieved a macro average accuracy of 85% in ARG identification. The approach also revealed that ARGs are disproportionately associated with MGEs and the neighbors (genes connected with only one edge to the ARGs) are likely to be less mobile.
DOI:10.1109/BIBM55620.2022.9995224