The rise of antibiotic resistance in bacterial populations reflects inadequate counterselection by the antibiotic itself, by host immunity, or by fitness costs of the resistance mechanism. Our inability to control resistance stems from limited understanding of these three forces of selection and especially of the interplay between antibiotic dosage, how bacteria populations respond to antibiotics, and host immunity. This project maps these interactions in high definition for the bacterial pathogens Streptococcus pneumoniae and Acinetobacter baumannii, both of which are serious threats and can cause antibiotic-resistant pneumonia. Specifically, in this project: 1) RNA-seq is applied to construct transcriptional networks; 2) Two complementary, genome-wide methods of screening mutations are used to map the phenotypic networks that affect resistance. One screening method called Tn-seq screens all possible gene knockout mutations for their roles in antibiotic susceptibility and resistance. The other method, experimental evolution, enables mutants to arise naturally and compete for success in the antibiotic condition. These interactions will be studied in vitro, using standard culture and under resistance-inducing conditions, including biofilms on plastic surfaces that are often the source of nosocomial A. baumannii infections. Mutant responses to antibiotic selection will also be studied in vivo, in which bacterial populations infect mice that re treated with antibiotic but vary in their immune competence; 3) The state-of-the-immune-system (sIS) will be profiled during mouse infections to identify sIS fingerprints reflecting both immune success and failure, when the bacterial population evades both antibiotic and immune pressure. All of these methods will be applied to define the bacterial-adaptive and host-response pathways for 20 different clinically relevant antibiotics; 4) Ribo-seq is used to evaluate evolved strains and the manner in which their interactions with the immune system changes over time. Finally, all of these bacterial- networks, immune-states and responses will be integrated into a joint model that will be learnt using a novel plug-and-play toolbox for fast prototyping of data driven solutions. This model will be validated by testing the likelihood of the emergence of resistance through: 1) selection in the presence of five novel antibiotics, 2) selection in the presence of two different resistant pathogens, and 3) through selection in the presence of three different immunocompromised hosts. Therefore, the ultimate goal of this project is to design a `plug-and-play learning toolbox' that is able to forecast the likelihood of the emergence of antibiotic resistance and prioritize antibiotic and immune therapies that enable more rapid, effective treatment with minimal risk of treatment failure.
When antibiotic selection or host immunity break down, resistance may evolve for systematic but unclear reasons. This project comprehensively defines how bacterial genetic networks and host immunity interact to produce conditions that permit or prevent the evolution of antibiotic resistance and applies this knowledge to develop a novel `plug-and-play learning toolbox' that is able to forecast resistance and strategically identify effective treatments.
|Lucas, Aaron E; Ito, Ryota; Mustapha, Mustapha M et al. (2018) Frequency and Mechanisms of Spontaneous Fosfomycin Nonsusceptibility Observed upon Disk Diffusion Testing of Escherichia coli. J Clin Microbiol 56:|
|Margolis, Elisa; Rosch, Jason W (2018) Fitness Landscape of the Immune Compromised Favors the Emergence of Antibiotic Resistance. ACS Infect Dis 4:1275-1277|
|Cooper, Vaughn S (2018) Experimental Evolution as a High-Throughput Screen for Genetic Adaptations. mSphere 3:|
|Leung, Lisa M; Cooper, Vaughn S; Rasko, David A et al. (2017) Structural modification of LPS in colistin-resistant, KPC-producing Klebsiella pneumoniae. J Antimicrob Chemother 72:3035-3042|
|Geisinger, Edward; Isberg, Ralph R (2017) Interplay Between Antibiotic Resistance and Virulence During Disease Promoted by Multidrug-Resistant Bacteria. J Infect Dis 215:S9-S17|
|Honsa, Erin S; Cooper, Vaughn S; Mhaissen, Mohammed N et al. (2017) RelA Mutant Enterococcus faecium with Multiantibiotic Tolerance Arising in an Immunocompromised Host. MBio 8:|
|Jensen, Paul A; Zhu, Zeyu; van Opijnen, Tim (2017) Antibiotics Disrupt Coordination between Transcriptional and Phenotypic Stress Responses in Pathogenic Bacteria. Cell Rep 20:1705-1716|
|Lee, Nara; Le Sage, Valerie; Nanni, Adalena V et al. (2017) Genome-wide analysis of influenza viral RNA and nucleoprotein association. Nucleic Acids Res 45:8968-8977|
|McCoy, Katherine Maia; Antonio, Margaret L; van Opijnen, Tim (2017) MAGenTA: a Galaxy implemented tool for complete Tn-Seq analysis and data visualization. Bioinformatics 33:2781-2783|
|van Opijnen, Tim; Dedrick, Sandra; Bento, José (2016) Strain Dependent Genetic Networks for Antibiotic-Sensitivity in a Bacterial Pathogen with a Large Pan-Genome. PLoS Pathog 12:e1005869|