In the bacterial environment, temperature variation and temperature gradients are as ubiquitous as chemical variation and gradients. For bacteria, locating the optimal combination of temperature and nutrients is crucial for maximizing growth. Indeed, bacteria perform chemotaxis over a large range of temperatures and perform directed motion in temperature gradients, i.e. perform thermotaxis. In Escherichia coli, chemotaxis and thermotaxis both exploit the same well-characterized signaling network.
The specific aim of this research is to develop a predictive, quantitative understanding of the thermal properties of this network. Combining experiment with modeling will help answer several fundamental questions: What network features allow robust chemotaxis over a wide range of temperatures? How does the signaling network allow both chemotaxis and thermotaxis? How do multiple sensory stimuli such as attractants/repellants and temperature changes interact? Our preliminary results indicate that the individual steps of the chemotaxis pathway are temperature dependent, but that at the systems level, these dependencies compensate for one another. To quantify these observations, we will obtain temperature-dependent data on the individual steps of the E. coli chemotactic signaling pathway using single-tethered-cell measurements and multi-cell FRET studies. The existing theory for chemotaxis that we have helped develop will guide efficient data acquisition, and the same theory will be used as the basis for modeling. To determine the molecular mechanism(s) underlying E. coli thermotaxis, and to extend a preliminary thermotaxis model we have developed, the thermotactic response of E. coli will be systematically measured via tethered-cell and FRET studies. All experimental studies will exploit our large pre-existing collection of engineered mutant strains of E. coli. Our results are likely to have significance for many cellular signaling networks. Because temperature potentially affects all components of all signaling pathways, our results may reveal universal mechanisms used by cells to ensure faithful signaling over a range of temperatures. Also, since the chemotaxis network of bacteria has been implicated in infectivity, and is widespread and well conserved among bacteria but is not shared by humans, the pathway presents a potential target for the development of future antibiotics.
The aim of this research is to develop a deeper understanding of the network of proteins that allows bacteria to perform chemotaxis and thermotaxis, that is to sense and swim towards food or a preferred temperature, respectively. We propose to develop such an understanding through closely coupled experimental approaches and computational modeling. From a human health perspective, chemotaxis by bacterial pathogens has been implicated in infectivity and maintenance of disease, and thermotaxis may be implicated as well. Since the chemotaxis/thermotaxis network is widespread and well conserved among bacteria, but is not shared by humans, the pathway presents a potential target for the development of future antibiotics. In addition, as all signaling pathways within living cells must function in the context of fluctuating thermal environments, our efforts may reveal universal mechanisms by which cells ensure faithful signaling.
|Tareen, Ammar; Wingreen, Ned S; Mukhopadhyay, Ranjan (2018) Modeling evolution of crosstalk in noisy signal transduction networks. Phys Rev E 97:020402|
|Ali, Md Zulfikar; Wingreen, Ned S; Mukhopadhyay, Ranjan (2018) Hidden long evolutionary memory in a model biochemical network. Phys Rev E 97:040401|
|Li, Sophia Hsin-Jung; Li, Zhiyuan; Park, Junyoung O et al. (2018) Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions. Nat Microbiol 3:939-947|
|Paulick, Anja; Jakovljevic, Vladimir; Zhang, SiMing et al. (2017) Mechanism of bidirectional thermotaxis in Escherichia coli. Elife 6:|
|Taillefumier, Thibaud; Posfai, Anna; Meir, Yigal et al. (2017) Microbial consortia at steady supply. Elife 6:|
|Ni, Bin; Ghosh, Bhaswar; Paldy, Ferencz S et al. (2017) Evolutionary Remodeling of Bacterial Motility Checkpoint Control. Cell Rep 18:866-877|
|Posfai, Anna; Taillefumier, Thibaud; Wingreen, Ned S (2017) Metabolic Trade-Offs Promote Diversity in a Model Ecosystem. Phys Rev Lett 118:028103|
|Wasnik, Vaibhav; Wang, Hui; Wingreen, Ned S et al. (2017) Physical model of protein cluster positioning in growing bacteria. New J Phys 19:|
|Bitbol, Anne-Florence; Dwyer, Robert S; Colwell, Lucy J et al. (2016) Inferring interaction partners from protein sequences. Proc Natl Acad Sci U S A 113:12180-12185|
|Laganenka, Leanid; Colin, Remy; Sourjik, Victor (2016) Chemotaxis towards autoinducer 2 mediates autoaggregation in Escherichia coli. Nat Commun 7:12984|
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