Principles of cellular adaptation
The major focus of our research is to understand cellular adaptation—the perpetual attempt of cells to optimize their internal state in response to a constantly changing world outside. 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 driving forces in disease pathogenesis, ranging from microbial antibiotic resistance to cancer progression. We study this inherently systems-level phenomenon across a range of timescales, from rapid transcriptional responses, to multi-generational epigenetic reprogramming, to long-term rewiring of signaling and regulatory networks over evolutionary timescales. 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.
A distinguishing feature of our approach is to study systems-level cellular behavior with minimal prior assumptions. For example, we apply machine learning to large-scale global observations, such as gene expression across thousands of conditions, to identify the critical regulatory components at the level of DNA, RNA, and protein, and to determine how they are organized into higher-level regulatory and genetic networks (Beer Cell, 2004; Elemento Molecular Cell, 2007; Goodarzi Nature, 2012). This unbiased strategy has been essential to our ability to decode genomic elements that drive transcriptional and post-transcriptional responses across diverse physiological, developmental, and pathological processes, including cancer initiation and progression (Goodarzi Molecular Cell, 2009; Goodarzi Nature, 2014).
The unbiased nature of our approach (global observations and minimally biased machine learning) allows the system, itself, to reveal the essential governing principles. This has been crucial to uncovering surprising new phenomena such as the ability of microbial regulatory networks to predict changes in their external environment, akin to the nervous systems of multicellular organisms (Tagkopoulos Science, 2008). Often these higher-level principles are obscured by approaches that focus only on a narrow slice of the cell's response.
We also utilize laboratory experimental evolution to probe the innate capacity of molecular networks to re-wire and adapt to novel challenging environments (Goodarzi MSB 2010). In addition to revealing general principles by which cells adapt to extreme environments (Hottes PLoS Genetics, 2013), these studies are identifying new mechanisms by which individual bacteria and populations develop clinically significant levels of antibiotic resistance and persistence (Girgis PNAS, 2012).
Our systems-level approach often requires global observations that are beyond the scale and resolution of existing methods. We thus develop new enabling technologies with substantially higher throughput and resolution, for example: global in vivo protein-DNA interaction profiling (Vora, Molecular Cell, 2009), transposon-based fitness and epistasis profiling (Girgis, PLoS Genetics, 2007), global adaptive mutation mapping (Goodarzi Nature Methods, 2009), and functional surveys to discover post-transcriptional regulatory elements (Oikonomou Cell Reports, 2014).
We are also exploring novel potential mechanisms of cellular adaptation based on first principles. For example, we wondered whether individual genes can establish optimal gene expression levels in the absence of dedicated sensory and regulatory control. We have shown that a stochastic gradient descent optimization process can achieve this by utilizing noisy gene expression, transcriptional memory, and the feedback of a global health signal. Our simulations show that this theoretical mode of adaptation, we have termed stochastic tuning, can simultaneously optimize the expression of thousands of genes. Our recent experimental work on engineered yeast cells has revealed a new mode of adaptation fully consistent with stochastic tuning (Freddolino et al. eLife, 2018). If conserved in mammalian cells, stochastic tuning could function in a variety of physiological and pathological cellular state transitions, including oncogenic transformation and chemotherapy resistance.
Active areas of research include:
Noise, phenotypic heterogeneity and adaptation to extreme environments
Adaptation through epigenetic reprogramming of gene expression
Antibiotic resistance and persistence
Predictive models of gene expression integrating transcriptional and post-transcriptional regulation
Post-transcriptional regulation by RNA-structural elements and RNA-binding proteins
Transcriptional and post-transcriptional perturbations contributing to cancer initiation and progression
Decoding regulation of gene expression in the nervous system
Next-generation technologies for mapping protein-DNA, protein-RNA, and protein-protein interactions
- Freddolino, P., Yang, J., Momen-Roknabadi, A., Tavazoie, S. Stochastic tuning of gene expression enables cellular adaptation in the absence of pre-existing regulatory circuitry. eLife 2018;7:e31867 DOI: 10.7554/eLife.31867
- Khare, A., Tavazoie, S. Multifactorial competition and resistance in a two-species bacterial system. PLoS Genetics 11(12):e1005715 (2015)
- Goodarzi, H., Zhang, S., Buss, C.G., Fish, L., Tavazoie, S., & Tavazoie, S.F. Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature 513(7517):256-60 (2014)
- Oikonomou, P., Goodarzi, H., Tavazoie, S. Systematic identification of regulatory elements in conserved 3’UTRs of human transcripts. Cell Reports 7(1): 281-92 (2014)
- Hottes, A.K., Freddolino, P.L., Khare, A., Donnell, Z.N., Liu, J.C., & Tavazoie, S. Bacterial adaptation through loss of function. PLoS Genetics 9(7):e1003617 (2013).
- Girgis, H., Harris, K., Tavazoie, S. Large mutational target-size for rapid emergence of bacterial persistence. PNAS 109(31):12740-5 (2012)
- Freddolino, P., Goodarzi, H., & Tavazoie, S. Fitness landscape transformation through a single amino acid change in the Rho terminator. PLoS Genetics 8(5):e1002744. (2012)
- Goodarzi, H., Najafabadi, H.S., Oikonomou, P., Greco, T.M., Fish, L., Salavati, R., Cristea, I.M., & Tavazoie, S. Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature 485, 264-268 (2012).
- Goodarzi, H., Bennett, BD., Amini, S., Reaves, ML., Hottes, AK., Rabinowitz, J., Tavazoie, S. Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli Molecular Systems Biology 6:378 (2010)
- Goodarzi, H., Elemento, O., Tavazoie, S. Revealing global regulatory perturbations across human cancers Molecular Cell 36:900-911 (2009)
- Goodarzi, H., Hottes, AK., Tavazoie, S. Global discovery of adaptive mutations Nature Methods 6(8):581-3. Epub 2009 Jul 13
- Vora, T., Hottes, AK., Tavazoie, S. Protein occupancy landscape of a bacterial genome. Molecular Cell 35(2):247-53 (2009)
- Amini, S., Goodarzi, H., Tavazoie, S. Genetic dissection of an exogenously induced biofilm in laboratory and clinical isolates of E. coli. PLoS Pathogens 5(4):e1000449 (2009)
- Tagkopoulos, I., Liu, Y., Tavazoie, S. Predictive behavior within microbial genetic networks. Science 320:1313-7 (2008)
- Elemento, O., Slonim, N., Tavazoie, S. A universal framework for regulatory element discovery across all genomes and data-types. Molecular Cell 28, 337-350 (2007)
- Girgis, H., Liu, Y., Ryu, W., Tavazoie, S. A comprehensive genetic characterization of bacterial motility. PLoS Genetics 3(9): e154 (2007)
- Slonim, N., Elemento, O., Tavazoie, S. Ab initio genotype-phenotype association reveals the intrinsic modularity of genetic networks. Mol. Syst. Biol. 2:2006.0005. Epub 2006 Jan. 31 (2006)
- Chan, S., Elemento, O., Tavazoie, S. Revealing posttranscriptional regulatory elements through network-level conservation. PLoS Comput. Biol. 1(7): 369 Epub 2005 Dec. 9 (2005)
- Kurdistani, S.K., Tavazoie, S., Grunstein, M. Mapping global histone acetylation patterns to gene expression. Cell 117: 721-733 (2004)
- Beer MA, Tavazoie S. Predicting gene expression from sequence. Cell 117:185-98 (2004)
- Jim K, Parmar K, Singh M, Tavazoie S. A cross-genomic approach for systematic mapping of phenotypic traits to genes. Genome Research 14:109-15 (2004)
- Pritsker, M., Liu, Y., Beer, M. and Tavazoie, S. Whole-genome discovery of transcription factor binding sites by network-level conservation. Genome Research 14: 99-108. Epub 2003 Dec 12 (2004)
- Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. & Church, G.M. Systematic determination of genetic network architecture. Nature Genetics 22: 281-285 (1999)
- Tavazoie, S., Church, G.M. Quantitative whole-genome analysis of DNA-protein interactions by in vivo methylase protection in E. coli. Nature Biotechnology 16: 566-571 (1998)