Principles of cellular adaptation
We study cellular adaptation―the constant attempt of cells to match their internal state to the endless variety of possible external conditions. In particular, we aim to understand how cells achieve adaptive gene-expression states, both during short-term physiological adaptation and long-term adaptive evolution. These are fundamental problems of basic interest and also major forces in pathogenesis, from infectious disease to cancer.
Historically, work on adaptation has focused on responses to specific stresses such as heat-shock or oxidative-stress. Despite the critical value of these efforts at elucidating specific mechanisms, they have not been as effective at revealing the organizing principles that underlie the evolution and operation of integrated networks of molecular interactions. Our approach is distinct because we seek to understand systems-level cellular responses with minimal prior assumptions. In general, we utilize large-scale global observations, such as gene-expression across hundreds of conditions, and use statistical-inference to identify the key components and determine their organization into regulatory and genetic networks1-11. This approach has been essential to our success in decoding genomic elements that drive transcriptional responses1,2,5,6,9,12-15 and revealing the genetic basis of adaptation to extreme environments3,4,8,10,16-19. Furthermore, our agnostic strategy allows the system, itself, to reveal the essential governing principles. This has been crucial to uncovering new phenomena such as the ability of microbial organisms to predict changes in their external environment10. Often these higher-level principles are obscured with approaches that focus only on a narrow slice of the cell's response. Our systems-level approach frequently requires global observations that are beyond the scale and resolution of existing methods. We have thus developed new enabling technologies with substantially higher throughput and resolution, for example: transposon-based fitness and epistasis profiling4, global in vivo protein-DNA interaction profiling20, global adaptive mutation mapping19, and functional surveys to discover post-transcriptional regulatory elements11. We make use of diverse experimental systems from bacteria to mammalian cell-lines in order to study general principles that operate across organismal taxa and complexity.
1. Beer MA, Tavazoie S. Predicting gene expression from sequence. Cell 2004;117:185-98. 2. Elemento O, Slonim N, Tavazoie S. A universal framework for regulatory element discovery across all Genomes and data types. Mol Cell 2007;28:337-50. 3. Girgis HS, Harris K, Tavazoie S. Large mutational target size for rapid emergence of bacterial persistence. Proc Natl Acad Sci U S A 2012;109:12740-5. 4. Girgis HS, Liu Y, Ryu WS, Tavazoie S. A comprehensive genetic characterization of bacterial motility. PLoS Genet 2007;3:1644-60. 5. Goodarzi H, Elemento O, Tavazoie S. Revealing global regulatory perturbations across human cancers. Mol Cell 2009;36:900-11. 6. Goodarzi H, Najafabadi HS, Oikonomou P, Greco TM, Fish L, Salavati R, Tavazoie S. Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature 2012;485:264-8. 7. Goodarzi H, Zhang S, Buss C, Fish L, Tavazoie S, Tavazoie SF. Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature 2014;(in press). 8. Hottes AK, Freddolino PL, Khare A, Donnell ZN, Liu JC, Tavazoie S. Bacterial adaptation through loss of function. PLoS Genet 2013;9:e1003617. 9. Kurdistani SK, Tavazoie S, Grunstein M. Mapping global histone acetylation patterns to gene expression. Cell 2004;117:721-33. 10. Tagkopoulos I, Liu YC, Tavazoie S. Predictive behavior within microbial genetic networks. Science 2008;320:1313-7. 11. Oikonomou P, Goodarzi H, Tavazoie S. Systematic identification of regulatory elements in conserved 3' UTRs of human transcripts. Cell reports 2014;7:281-92. 12. Chan CS, Elemento O, Tavazoie S. Revealing posttranscriptional regulatory elements through network-level conservation. PLoS Comput Biol 2005;1:e69. 13. De Renzis S, Elemento O, Tavazoie S, Wieschaus EF. Unmasking activation of the zygotic genome using chromosomal deletions in the Drosophila embryo. PLoS Biol 2007;5:e117. 14. Pritsker M, Liu YC, Beer MA, Tavazoie S. Whole-genome discovery of transcription factor binding sites by network-level conservation. Genome Res 2004;14:99-108. 15. Freckleton G, Lippman SI, Broach JR, Tavazoie S. Microarray profiling of phage-display selections for rapid mapping of transcription factor-DNA interactions. PLoS Genet 2009;5:e1000449. 16. Amini S, Hottes AK, Smith LE, Tavazoie S. Fitness landscape of antibiotic tolerance in Pseudomonas aeruginosa biofilms. PLoS Pathog 2011;7:e1002298. 17. Freddolino PL, Goodarzi H, Tavazoie S. Fitness landscape transformation through a single amino acid change in the rho terminator. PLoS Genet 2012;8:e1002744. 18. Goodarzi H, Bennett BD, Amini S, Reaves ML, Hottes AK, Rabinowitz JD, Tavazoie S. Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli. Mol Syst Biol 2010;6:378. 19. Goodarzi H, Hottes AK, Tavazoie S. Global discovery of adaptive mutations. Nat Methods 2009;6:581-3. 20. Vora T, Hottes AK, Tavazoie S. Protein occupancy landscape of a bacterial genome. Mol Cell 2009;35:247-53.
Department of Biochemistry and Molecular Biophysics
Initiative in Systems Biology