The overarching goal of the proposed research is to develop predictive multiscale biophysical models of adaptive evolutionary dynamics. In earlier work we demonstrated for several cases of biomedical importance that fitness effect of genetic variation can be accurately predicted from a unique combination of molecular traits of the mutated protein. This finding transforms the concept of fitness landscape from an artful metaphor into a quantitative tractable tool to predict the genotype-phenotype relationship (GPR). Here we take these findings as a foundation to further extend our understanding of interplay between biophysical and population factors that determine the dynamics and outcome of adaptive evolution. We will apply microfluidics and automatic robotics setup along with protein engineering and genomic editing tools to explore evolutionary dynamics in laboratory experiments under conditions that allow tight control on all scales ? from molecules to populations. To that end, we carry out a set of evolution experiments with adapting populations of E. coli escaping from antibiotic stress and structural instability of the essential protein Dihydrofolate Reductase. We characterize on all scales ? genotyping, molecular traits, systems proteomics and population - multiple evolutionary paths to resistance and adaption of emerging bacterial strains and determine at which level of description (genotype, biophysical properties, systems responses) evolution becomes reproducible ? and by implication predictable. In parallel we model the evolutionary dynamics using multiscale models where cytoplasm of model cells is presented in a biophysically realistic manner, and fitness of model organisms is predicted from its molecular traits using experimentally derived GPR. Molecular traits of mutant forms are predicted using state of the art computational tools of molecular biophysics allowing reproducing and predicting complete evolutionary trajectories of adapting populations of model cells. A tight integration between theory and experiment will provide an opportunity to develop predictive evolutionary models of ever increasing accuracy and realism. Progress along these lines will transform our approaches to study evolutionary dynamics from descriptive into predictive and quantitative, which will be instrumental to the development of novel approaches to fight antibiotic resistance and, potentially, viral escape from stressors such as drugs and immune response.

Public Health Relevance

Through combination of experimentation and multiscale biophysical modeling this research will provide tools to predict antibiotic escape and other drug resistance dynamics. It will pave the path to develop new approaches to counter drug resistance in evolving pathogens.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM068670-16
Application #
9822975
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Lyster, Peter
Project Start
2004-04-01
Project End
2020-11-30
Budget Start
2019-12-01
Budget End
2020-11-30
Support Year
16
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Harvard University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code
02138
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Manhart, Michael; Shakhnovich, Eugene I (2018) Growth tradeoffs produce complex microbial communities on a single limiting resource. Nat Commun 9:3214
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Adkar, Bharat V; Manhart, Michael; Bhattacharyya, Sanchari et al. (2017) Optimization of lag phase shapes the evolution of a bacterial enzyme. Nat Ecol Evol 1:149
Rodrigues, João V; Bershtein, Shimon; Li, Anna et al. (2016) Biophysical principles predict fitness landscapes of drug resistance. Proc Natl Acad Sci U S A 113:E1470-8

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