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Using Complex Selection Dynamics to Reveal The USING COMPLEX SELECTION DYNAMICS TO REVEAL THE FITNESS LANDSCAPE AND CROSS EVOLUTIONARY VALLEYS by Barrett Steinberg A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland June 2015 Abstract Nature repurposes proteins via evolutionary processes. During evolution, the fitness landscapes of proteins change dynamically. Selection for new functionality leaves the protein susceptible to genetic drift in the absence of selective pressure for the former function. Drift is considered to be a driver of evolution, and functional tradeoffs are common during selection. We measured the effect on ampicillin resistance of ~12,500 unique mutants of alleles of TEM-1 β-lactamase along an adaptive path in the evolution of cefotaxime resistance. This series of shifting protein fitness landscapes provides a systematic, quantitative description of genetic drift and pairwise/tertiary intragenic epistasis involving adaptive mutations. Our study provides insight into the relationships between mutation, protein structure, protein stability, epistasis, and drift and reveals the tradeoffs inherent in the evolution of new functions. We further use principles of ruggedness and dynamic change in fitness landscapes to develop and evaluate novel directed evolution strategies using complex selection dynamics. Interestingly, the strategy that included negative selection relative to the original landscape yielded more highly active variants of β-lactamase than the other four selection strategies. We reconstructed evolutionary pathways leading to this highly active allele, confirmed the presence of a fitness valley, and found an initially deleterious mutation that serves as an epistatic bridge to cross this fitness valley. The ability of negative selection and changing environments to provide access to novel fitness peaks has important implications for applied directed evolution as well as the natural evolutionary mechanisms, particularly of antibiotic resistance. ii We finally applied principles of the influence of selection environment to a clinically relevant system by comparing the evolutionary pathways of cells evolving competitively and continuously to single antibiotics, a cocktail of two antibiotics, or alternating cycles of the two antibiotics. We find evidence for distinct evolutionary pathways between antibiotic strategies. Specifically, we suggest that cocktail strategies appear to select for “specialists” of varying activity while cycling more stringently selects for “generalists.” We hypothesize that this result is due to separately emerging populations to evolve in distinct niches during cocktail therapies. Our results have direct relevance to informing clinical antibiotic regimens. Advisor: Dr. Marc Ostermeier, Professor of Chemical and Biomolecular Engineering, Johns Hopkins University Readers: Dr. Michael Betenbaugh, Professor of Chemical and Biomolecular Engineering, Johns Hopkins University Dr. Robert Schleif, Professor of Biology, Johns Hopkins University iii Acknowledgments First and foremost, I would like to thank Kathleen January for her boundless support. I would not be here without both sharing help with her during harder times and joy with her at each small victory. I am relieved and ecstatic to finally see our greater plans begin to work out together. I also thank my parents and family for providing the building blocks towards my education. I couldn’t have finished without the support of members of the Ostermeier lab. Specifically, I couldn’t ask for a better coworker and friend than Nirav Shelat. Nirav, thank you for the science, pool, and whiskey. I’ve shared great camaraderie with Courtney Gonzalez, Nathan Nicholes, and Tina Xiong. Martin Kang, great job and best of luck. Outside the lab, both Dillon Nye and Joey Priola have shown to be excellent friends. I hope all of these connections are lasting, and wish you all the best of luck. I must directly thank Dr. Horacio Frydman for introducing me to proper scientific method and instilling the first true appreciation of science into me. Of course, I also thank Dr. Ostermeier for the freedom and critical feedback he has provided. He has given me the opportunity to try countless extra experiments, most of which have failed, but a few of which have succeeded. I have this freedom to thank for honing my scientific creativity, and these acknowledgements to thank for dealing with the many failures. I thank my committee members for providing feedback and support on this work. Finally, I cannot begin to acknowledge the great musicians, novelists, and scientists that have helped me. As Thomas Pynchon has helpfully summarized, “Life's single lesson: that there is more accident to it than a man can ever admit to in a lifetime and stay sane.” iv Table of contents Abstract .............................................................................................................................. ii Acknowledgments ............................................................................................................ iv Table of contents ............................................................................................................... v List of Tables .................................................................................................................. viii List of Figures ................................................................................................................... ix Chapter 1: Introduction and background ...................................................................... 1 The fitness landscape ........................................................................................ 1 Epistasis and the “tape of life” ......................................................................... 6 Genetic drift ..................................................................................................... 10 Directed Evolution .......................................................................................... 11 β-lactamase as a model gene in directed evolution ....................................... 14 Directed evolution informed by landscape ruggedness ............................... 19 Chapter 2: Shifting fitness and epistatic landscapes reflect tradeoffs along an evolutionary pathway ..................................................................................................... 21 Introduction ..................................................................................................... 21 Methods ............................................................................................................ 26 Mutagenesis .................................................................................................. 26 Selection ........................................................................................................ 26 Deep sequencing ........................................................................................... 26 Data analysis ................................................................................................. 27 v Results and Discussion .................................................................................... 32 Fitness landscapes along an adaptive pathway ............................................. 32 Structural map of epistasis ............................................................................ 40 Sign epistasis along an adaptive pathway ..................................................... 44 Reciprocal sign epistasis and ruggedness along an adaptive pathway ......... 46 Conclusions ................................................................................................... 47 Chapter 3: Environmental changes bridge fitness valleys .......................................... 49 Introduction ..................................................................................................... 49 Materials and Methods ................................................................................... 55 Plasmid conditions ........................................................................................ 55 Mutagenesis .................................................................................................. 55 Selection ........................................................................................................ 55 Deep sequencing and sequence analysis ....................................................... 56 Phylogenetic analysis .................................................................................... 57 Selection-weighted attractive graphing (SWAG) ......................................... 57 Calculation of epistasis ................................................................................. 57 Minimum inhibitory concentration testing ................................................... 58 Results and Discussion .................................................................................... 58 Chapter 4: Evaluation of antibiotic cycling and cocktail therapy on evolution of antibiotic resistance ........................................................................................................ 83 Introduction ..................................................................................................... 83 Methods ............................................................................................................ 86 Results .............................................................................................................
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