We propose to develop and apply a comprehensive set of experimental and computational methods for revealing the genetic basis of antibiotic tolerance in Escherichia coli. At the core of our approach is a microarray-based genetic footprinting technology that provides a global quantitative assessment of how each and every gene in the genome contributes to survival under antibiotic exposure. The identified genes will be placed within the context of genetic and regulatory networks through the application of a novel genome-wide epistasis analysis framework.
We aim to explore both mild resistance to sub-lethal antibiotic exposure and severe tolerance as expressed in the context of 'persistence'. Preliminary studies provide strong proof-of-principle evidence for the framework we propose. Application of our approach to E. coli chemotaxis identifies 95% of known loci on the time-scale of weeks, reveals the organization of these loci into functional sub-modules, and identifies signaling pathways that regulate the context-dependent expression of motility. Furthermore, in a phenotype that has been extensively explored for over thirty year, we find three dozen additional novel loci that contribute through diverse mechanisms including the Rcs signaling pathway and cyclic-di-GMP second messenger system. The application of our approach to mild and lethal antibiotic exposure has already revealed more than a dozen loci whose genetic perturbations dramatically increase antibiotic tolerance. The proposed work promises to significantly expand the number of genes involved, and through the adjunct use of epistasis, co-expression, and co-inheritance analysis, allow us to place these genes within the context of genetic and regulatory networks. We expect our findings to fundamentally advance the understanding of antibiotic resistance and to provide the biomedical community with well-characterized pathways that serve as the basis for the development of new drugs.
Antibiotic resistance is rapidly becoming a major health crisis around the world. We propose a comprehensive framework for studying the genetic basis of resistance across diverse drug classes. We expect the proposed research to lead to the discovery of novel pathways for combating antibiotic resistance.
| Khare, Anupama; Tavazoie, Saeed (2015) Multifactorial Competition and Resistance in a Two-Species Bacterial System. PLoS Genet 11:e1005715 |
| Freddolino, Peter L; Goodarzi, Hani; Tavazoie, Saeed (2014) Revealing the genetic basis of natural bacterial phenotypic divergence. J Bacteriol 196:825-39 |
| Hottes, Alison K; Freddolino, Peter L; Khare, Anupama et al. (2013) Bacterial adaptation through loss of function. PLoS Genet 9:e1003617 |
| Freddolino, Peter L; Goodarzi, Hani; Tavazoie, Saeed (2012) Fitness landscape transformation through a single amino acid change in the rho terminator. PLoS Genet 8:e1002744 |
| Freddolino, Peter L; Tavazoie, Saeed (2012) Beyond homeostasis: a predictive-dynamic framework for understanding cellular behavior. Annu Rev Cell Dev Biol 28:363-84 |
| Freddolino, Peter L; Amini, Sasan; Tavazoie, Saeed (2012) Newly identified genetic variations in common Escherichia coli MG1655 stock cultures. J Bacteriol 194:303-6 |
| Freddolino, Peter L; Tavazoie, Saeed (2012) The dawn of virtual cell biology. Cell 150:248-50 |
| Girgis, Hany S; Harris, Kendra; Tavazoie, Saeed (2012) Large mutational target size for rapid emergence of bacterial persistence. Proc Natl Acad Sci U S A 109:12740-5 |
| Khan, Zia; Amini, Sasan; Bloom, Joshua S et al. (2011) Accurate proteome-wide protein quantification from high-resolution 15N mass spectra. Genome Biol 12:R122 |
| Amini, Sasan; Tavazoie, Saeed (2011) Antibiotics and the post-genome revolution. Curr Opin Microbiol 14:513-8 |
Showing the most recent 10 out of 16 publications
