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Identifying Targets for Potentiators in S. Aureus Using Chemical Genetic Approaches

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Identifying Targets for Potentiators in S. Aureus Using Chemical Genetic Approaches Identifying targets for potentiators in S. aureus using chemical genetic approaches The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Rajagopal, Mithila. 2016. Identifying targets for potentiators in S. aureus using chemical genetic approaches. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:33840649 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Identifying targets for potentiators in S. aureus using chemical genetic approaches A dissertation presented by Mithila Rajagopal to The Department of Chemistry and Chemical Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Chemistry Harvard University Cambridge, Massachusetts June 2016 © 2016 Mithila Rajagopal All Rights Reserved Dissertation Advisors: Dr. Suzanne Walker and Dr. Daniel Kahne Mithila Rajagopal Identifying targets for potentiators in S. aureus using chemical genetic approaches Abstract Staphylococcus aureus is a highly feared Gram-positive pathogen. The rise in antibiotic resistance has made S. aureus infections intractable. To find new ways to treat S. aureus infections, it is important to understand how this organism protects itself from antibiotics. We probed S. aureus transposon libraries with different classes of antibiotics and used Tn-Seq to identify intrinsic resistance factors that are important in withstanding antibiotics. We identified and validated the importance of a number of previously known intrinsic resistance factors such as mprF, fmtA and the graRS/vraFG multi-component sensing system, as well as a number of novel factors whose involvement in antibiotic resistance has not been previously appreciated. In the course of this work, we realized that Tn-Seq data could be mined to predict antibiotic mechanism of action. We used a machine learning approach to predict that a class of anti-MRSA antibiotics that were thought to cause membrane damage actually bind to lipid II and inhibit cell wall synthesis. This predicted mechanism was validated. Finally, we report the identification of a class of disubstituted urea compounds against which the inactivation of mprF is protective. As the inactivation of mprF usually sensitizes to most antibiotics, these compounds might belong to a new class of inhibitors that could be used as tool compounds to further probe S. aureus cell biology. We have developed a strategy that could be useful in identifying the targets of such compounds. The results described here have opened several interesting avenues for a more in-depth understanding of S. aureus biology and antibiotic resistance. iii Acknowledgements I am very thankful to my advisors Dr. Daniel Kahne, and especially Dr. Suzanne Walker. She has been patient, encouraging and enthusiastic throughout my Ph.D. and I have been fortunate to have been able to learn from her for all these years. I am also thankful to all the members of both the Kahne and Walker labs, especially Marina and Wonsik, with whom I worked most closely. I hope I will be lifelong friends with the many of my labmates, as I treasure their influences in my life both scientific and otherwise. I feel very lucky to be supported in chasing whatever dreams I have by wonderful friends. It was reassuring to know that they always had my back. Finally, I would definitely not be here without my family. Their unconditional support, love, and their faith in me, even when I don't have faith in myself, is what enables me to accomplish anything at all. I owe my success to them: to their unwavering energy, enthusiasm and interest in the goals I pursue, their constant belief in me, and their inspiring trailblazing. For these and so many other reasons, I would not know what to do without them. iv Table of Contents Chapter 1: Envelope Structures of Gram-positive Bacteria ...................................... 1 1.1 Introduction ............................................................................................................ 1 1.2 Cell membrane ....................................................................................................... 2 1.3 Peptidoglycan ........................................................................................................ 6 1.3.1 Peptidoglycan structure ............................................................................... 7 1.3.2 Peptidoglycan biosynthesis ......................................................................... 9 1.3.3 Tailoring modifications of peptidoglycan ................................................... 15 1.4 Teichoic acids ...................................................................................................... 17 1.4.1 Wall teichoic acid structure ........................................................................ 18 1.4.2 Wall teichoic acid biosynthesis .................................................................. 19 1.4.3 Lipoteichoic acid structure ........................................................................ 21 1.4.4 Lipoteichoic acid synthesis ........................................................................ 22 1.4.5 Tailoring modifications of teichoic acids..................................................... 25 1.4.6 Roles of teichoic acids and their tailoring modifications in cell physiology and immune evasion ............................................................................... 27 1.5 Capsular polysaccharides .................................................................................... 31 1.5.1 Structural diversity of capsular polysaccharides ........................................ 32 1.5.2 Capsular polysaccharides, host immunity and vaccine development ........ 33 1.6 Exopolysaccharides and biofilm formation ........................................................... 34 1.7 Antibiotics targeting the cell envelope .................................................................. 35 v 1.8 The quest for novel antibiotic targets ................................................................... 39 Chapter 2: Validation of Tn-Seq approach to identify intrinsic resistance factors ............................................................................................................... 41 2.1 Why is understanding factors that contribute to antibiotic resistance important ... 41 2.2 Transposon libraries and their use in identifying intrinsic resistance factors ........ 43 2.3 Experimental approach and data analysis ........................................................... 47 2.4 Intrinsic factors that increase or decrease susceptibility to antibiotics.................. 53 2.5 Intrinsic factors that impact multiple classes of antibiotics ................................... 63 2.6 GraRS/VraFG is the single most important multi-component sensing system across tested antibiotics ..................................................................................... 64 2.7 Contribution of intrinsic resistance factors to antibiotic resistance can vary across strains ................................................................................................................. 69 2.8 Conclusions ......................................................................................................... 70 2.9 Methods ............................................................................................................... 71 Chapter 3: Genes that contribute to bacterial fitness in the presence of peptidoglycan biosynthesis inhibitors .......................................................... 76 3.1 Rationale for expanding the set of Tn-Seq derived fitness profiles ...................... 76 3.2 Peptidoglycan biosynthetic inhibitors tested ......................................................... 77 3.3 Identifying a core set of genes relevant under cell wall perturbation .................... 79 3.4 Previously characterized genes relevant under cell wall perturbation .................. 84 vi 3.5 Three previously uncharacterized genes important under cell wall stress ........... 86 3.6 Machine learning approach to suggest potential pathways genes of unknown function could be involved in .............................................................................. 93 3.7 Discussion on parameters to consider for library treatment ................................. 96 3.8 Conclusions ....................................................................................................... 100 3.9 Methods ............................................................................................................. 100 Chapter 4: Predicting the mechanism of action of antibiotics ............................. 106 4.1 The use of fitness profiles to cluster antibiotics by mechanism of action ........... 106 4.2 An expanded set of antibiotics were tested in Tn-Seq ....................................... 107 4.3 Hierarchical clustering of antibiotic fitness profiles ............................................. 111 4.4 K-nearest neighbors algorithm to classify antibiotics by mechanism of action ... 113 4.5 Predicting the mechanism of action of an unknown compound ......................... 120 4.6 General discussions on approaches to optimize
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