A key property of living cells is their ability to react to internal or external stimuli with specific biochemical responses. Bacteria have evolved to sense and rapidly adapt to environmental stimuli by changes in gene expression. Molecular details of the stress response networks regulating these responses have been uncovered for many model bacteria. However, we still lack network-level knowledge of these responses across species, which is necessary to obtain a deeper understanding of cellular functions and to best apply results obtained with model bacteria to pathogenic bacterial species that are poorly characterized or cannot be cultured in a laboratory. In this application we focus on two of the common basic building blocks of bacterial stress-response networks: two-component systems and alternative sigma-factor networks. Combining theoretical, computational and experimental approaches, our multi-disciplinary team will explore two important aspects of network organization - feedback loops due to transcriptional autoregulation and co-transcription of network genes in the same operon.
The Specific Aims are (SA1) To understand the relationship between co-expression of bacterial genes from a single operon and stochasticity in information processing of the corresponding networks and (SA2) To assess the role of feedback regulation in alternative sigma factor and two-component system networks. For each Specific Aim the research plan will involve three essential components: (1) formulation and analysis of biophysically realistic but analytically tractable models of master-regulation modules of stress-response networks, (2) simulations and analysis of detailed models of particular networks in the model organism Mycobacterium smegmatis, and (3) experimental tests of predictions in M. smegmatis. This will lead to iterative and synergistic feedback between theories/models and experiments. The results of the proposed work are expected to reveal novel evolutionary design principles characterizing relationships between network architecture and dynamical performance across bacterial species, which are essential for the manipulation of naturally occurring networks and for designing synthetic gene circuits.

Public Health Relevance

M. smegmatis is utilized as a model for pathogenic mycobacteria such as Mycobacterium tuberculosis, which still causes two million deaths a year worldwide. The networks under study have been associated with critical aspects of M. tuberculosis virulence -- susceptibility to antibiotics, persistence and dormancy. Thus, we expect to develop critical knowledge affecting our understanding of important human pathogens.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM096189-03
Application #
8310013
Study Section
Special Emphasis Panel (ZGM1-CBCB-5 (BM))
Program Officer
Lyster, Peter
Project Start
2010-08-16
Project End
2015-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
3
Fiscal Year
2012
Total Cost
$261,733
Indirect Cost
$27,089
Name
Rice University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
050299031
City
Houston
State
TX
Country
United States
Zip Code
77005
Narula, Jatin; Tiwari, Abhinav; Igoshin, Oleg A (2016) Role of Autoregulation and Relative Synthesis of Operon Partners in Alternative Sigma Factor Networks. PLoS Comput Biol 12:e1005267
Ascensao, Joao A; Datta, Pratik; Hancioglu, Baris et al. (2016) Non-monotonic Response to Monotonic Stimulus: Regulation of Glyoxylate Shunt Gene-Expression Dynamics in Mycobacterium tuberculosis. PLoS Comput Biol 12:e1004741
Chauhan, Rinki; Ravi, Janani; Datta, Pratik et al. (2016) Reconstruction and topological characterization of the sigma factor regulatory network of Mycobacterium tuberculosis. Nat Commun 7:11062
Datta, Pratik; Ravi, Janani; Guerrini, Valentina et al. (2015) The Psp system of Mycobacterium tuberculosis integrates envelope stress-sensing and envelope-preserving functions. Mol Microbiol 97:408-22
Castillo-Hair, Sebastian M; Igoshin, Oleg A; Tabor, Jeffrey J (2015) How to train your microbe: methods for dynamically characterizing gene networks. Curr Opin Microbiol 24:113-23
Lee, Jinho; Tiwari, Abhinav; Shum, Victor et al. (2014) Unraveling the regulatory connections between two controllers of breast cancer cell fate. Nucleic Acids Res 42:6839-49
Ray, J Christian J; Igoshin, Oleg A (2012) Interplay of gene expression noise and ultrasensitive dynamics affects bacterial operon organization. PLoS Comput Biol 8:e1002672
Tiwari, Abhinav; Igoshin, Oleg A (2012) Coupling between feedback loops in autoregulatory networks affects bistability range, open-loop gain and switching times. Phys Biol 9:055003
Tiwari, Abhinav; Ray, J Christian J; Narula, Jatin et al. (2011) Bistable responses in bacterial genetic networks: designs and dynamical consequences. Math Biosci 231:76-89
Ray, J Christian J; Tabor, Jeffrey J; Igoshin, Oleg A (2011) Non-transcriptional regulatory processes shape transcriptional network dynamics. Nat Rev Microbiol 9:817-28

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