Compreendendo a resistência a antibióticos usando métodos experimentais e computacionais

segunda-feira, agosto 21, 2017

Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria

Paul A. Jensen 2,3, Zeyu Zhu 3, Tim van Opijnen 4,

2Present address: Department of Bioengineering and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

3These authors contributed equally

4Lead Contact

Open Access

Article Info

Publication History

Published: August 15, 2017 Accepted: July 23, 2017

Received in revised form: June 28, 2017 Received: March 10, 2017

User License

Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0) 


Phenotypic and transcriptional stress responses consist of distinct gene sets

• Metabolic network modeling reveals co-localization of stress-response gene sets

• Different stressors trigger responses indicative of their evolutionary history

• Separating expression and phenotype protects from erratic transcriptional behavior


Bacterial genes that change in expression upon environmental disturbance have commonly been seen as those that must also phenotypically matter. However, several studies suggest that differentially expressed genes are rarely phenotypically important. We demonstrate, for Gram-positive and Gram-negative bacteria, that these seemingly uncoordinated gene sets are involved in responses that can be linked through topological network analysis. However, the level of coordination is stress dependent. While a well-coordinated response is triggered in response to nutrient stress, antibiotics trigger an uncoordinated response in which transcriptionally and phenotypically important genes are neither linked spatially nor in their magnitude. Moreover, a gene expression meta-analysis reveals that genes with large fitness changes during stress have low transcriptional variation across hundreds of other conditions, and vice versa. Our work suggests that cellular responses can be understood through network models that incorporate regulatory and genetic relationships, which could aid drug target predictions and genetic network engineering.