Molecular basis for the adaptive evolution of environment sensing by H-NS proteins
Shahul Hameed, Umar Farook
Remington, Jacob M
Radhakrishnan, Anand K
Arold, Stefan T.
KAUST DepartmentBiological and Environmental Science and Engineering (BESE) Division
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Structural Biology and Engineering
KAUST Grant NumberFCC/1/1976-25
AbstractThe DNA-binding protein H-NS is a pleiotropic gene regulator in gram-negative bacteria. Through its capacity to sense temperature and other environmental factors, H-NS allows pathogens like Salmonella to adapt their gene expression to their presence inside or outside warm-blooded hosts. To investigate how this sensing mechanism may have evolved to fit different bacterial lifestyles, we compared H-NS orthologs from bacteria that infect humans, plants, and insects, and from bacteria that live on a deep-sea hypothermal vent. The combination of biophysical characterization, high-resolution proton-less NMR spectroscopy and molecular simulations revealed, at an atomistic level, how the same general mechanism was adapted to specific habitats and lifestyles. In particular, we demonstrate how environment-sensing characteristics arise from specifically positioned intra- or intermolecular electrostatic interactions. Our integrative approach clarified the exact modus operandi for H-NS–mediated environmental sensing and suggests that this sensing mechanism resulted from the exaptation of an ancestral protein feature.
CitationZhao, X., Shahul Hameed, U. F., Kharchenko, V., Liao, C., Huser, F., Remington, J. M., … Li, J. (2021). Molecular basis for the adaptive evolution of environment sensing by H-NS proteins. eLife, 10. doi:10.7554/elife.57467
AcknowledgementsResearch by UH, VK, FH, AK, ML, LJ, and SA reported in this work was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline fund and the Award No FCC/1/1976-25 from the Office of Sponsored Research (OSR); XZ was partially supported by the ACS Petroleum Research Fund (58219-DNI); CL, JMR, and JL were partially supported by the National Institutes of Health award (R01GM129431). We acknowledge support from the KAUST Bioscience and Imaging core laboratories and the computational resources from the Vermont Advanced Compute Core (VACC) and the Anton supercomputer in Pittsburgh Supercomputing Center (PSC), and thank M. Cusack (KAUST Research Support Services) for editorial help.
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