Exploring the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa | Akanksha Rajput

Interview

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Large data sets and machine learning are impacting a growing number of areas of research in the life sciences. Once the compendia of bacterial transcriptomes reached a critical size, we could use source signal extraction algorithms to find lists of co-regulated genes (called iModulons) associated with a transcription factor (TF) to them. The gene composition of iModulons and their condition-dependent activity levels constitute a quantitative description of the composition of bacterial transcriptomes.?This study shows how this approach can be used to reveal the responses of Pseudomonas aeruginosa to antibiotics and thus yield a deep regulatory understanding of pathogenicity properties. This study motivates the execution of similar studies for the other ESKAPEEs to yield a broad understanding of the role of Transcriptional Regulatory Networks (TRNs) in antibiotic responses to this urgent threat of bacterial pathogens.

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