Exploring and Enhancing Context-Dependent Beta-Lactam Antibiotic Efficacy

by Sarah Christine Bening

B.Bm.E., University of Minnesota (2015) Submitted to the Department of Biological Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biological Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2021 © Massachusetts Institute of Technology 2021. All rights reserved.

Author...... Department of Biological Engineering December 29, 2020

Certified by...... James J. Collins Professor of Biological Enginneering Thesis Supervisor

Accepted by ...... Katharina Ribbeck Professor of Biological Engineering Chair of Graduate Program, Department of Biological Engineering 2 Exploring and Enhancing Context-Dependent Beta-Lactam Antibiotic Efficacy by Sarah Christine Bening

Submitted to the Department of Biological Engineering on December 29, 2020, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biological Engineering

Abstract Antibiotics, such as beta-lactams, are essential medical tools for the treatment of bacterial infections. Unfortunately, clinical treatment efficacy is declining over time as bacteria adapt to and evade antibiotic treatment through mechanisms called antibiotic resistance, tolerance, and persistence. Antibiotic tolerance and persistence, in particular, are often context-dependent phenotypes: environmental factors can influence bacterial physiology and alter antibiotic efficacy. Optimal antibiotic use,as well as strategies to enhance antibiotic efficacy, can therefore be informed by studies of context-dependent antibiotic action. In this thesis, I present three vignettes about beta-lactam antibiotic efficacy and how environmental context alters in vitro treatment outcomes. First, I explore bacterial killing in multi-drug contexts, focusing on how different beta-lactams can have different effects in combination with antibiotics of other classes. Second, Ipresent a new counter-tolerance method using metabolic stimulation to sensitize tolerant, stationary phase bacteria to beta-lactam antibiotics. Third, I present an extension of this metabolic counter-tolerance strategy, now combining metabolic and target- specific stimulation to further enhance beta-lactam efficacy. I demonstrate that this combined approach, when coupled with beta-lactamase inhibitors, restores beta- lactam sensitivity to simultaneously tolerant and resistant cultures of clinically relevant pathogens. I conclude by discussing opportunities for future study into antibiotic context-dependence and the application of counter-tolerance approaches such as the one described in this thesis.

Thesis Supervisor: James J. Collins Title: Professor of Biological Enginneering

3 4 Acknowledgments

My parents made sure I loved reading. There were always books in the house for me to read. While I read a lot of books growing up, somewhere around high school or undergrad I stopped finding the time to read for fun. Thankfully, shortly after my thesis proposal, I started reading books again. There have been so many good books that have helped me relax and get through the tough parts of graduate school, so many opportunities to go on someone else’s adventure for a while. Unlike when I was younger, my reading habits during graduate school always started with a flip to the acknowledgments section. I wanted to see what circles the author ran in (and maybe find some new books to read). More importantly, the acknowledgments were a chance to hear the author’s voice, unfiltered by their characters. A chance to hear from the real actual human that created each story. You’d think after reading all those acknowledgment sections writing this would’ve been a little easier. Anyways, here’s my thank you to all the people who helped me on my graduate school adventure:

To Jim – when I started graduate school I knew next to nothing about how to study bacteria or about how antibiotics work. Thank you for giving me the opportunity to learn, the support and resources to make mistakes, and the freedom to pursue the projects that interested me.

To my committee, Katharina Ribbeck and Mike Laub – thanks for your support and encouragement, and thank you for taking the time and energy to really be present at my committee meetings. I always enjoyed talking science with you.

To the Collins lab – with no exceptions that I can think of (though there may very well be some), everything I know about microbiology research at the bench I learned from you all. I’m grateful for this massive and varied group of researchers that I got to work with and learn from every day.

To the group from my early days in the Collins lab – Prerna Bhargava, Rebecca Shapiro, Caroline Porter, Saloni Jain, and Meagan Hamblin – thank you for helping me get started. Thank you for helping me learn how to be a scientist, how to work hard, ask questions, and celebrate the good experiments. And thank you for your

5 friendship, for all the lunch chats, snack breaks, trips to the Muddy, and recently the never ending group chats. To the Collins lab graduate students – especially those at the Broad: Ian Andrews, Bernardo Cervantes, Meagan Hamblin (honorary graduate student), and Erica Zheng – you’ve been a great group to struggle through graduate school with. And I mean struggle in the best way: a good struggle’s okay. You’ve been a great group to learn with, work hard with, and fail miserably at making random dinner restaurant decisions with. To the group that are coauthors on the core of my thesis work – Ian Andrews, Meagan Hamblin, and Allison Lopatkin – thank you for being my closest collaborators. Thank you for the experiments you did, but more importantly, thank you for all the time we spent talking science and for helping me learn how to present this story. To the MIT BE community – especially BE 2015, the BE Grad Board, the BE Communication Lab, and the BE department staff – I don’t know how to say it better than "community." The different groups I’ve been part of have given methe opportunity to get to know and work with so many more people than the typical graduate student might, and I’ve benefitted so much from getting to know you all. I’m grateful to be part of a community that works hard and knows how to have fun. To my friends – classmates, roommates, coworkers, friends from before MIT and outside of MIT – thank you for all the time we had fun and weren’t stressed about science. For all the juggling, cookie balling, moth fighting, Bopping to the Top, Muddy-ing, Naco Taco-ing, and (unfortunately) Zooming times. Thanks for giving me the space to just be a human and stop thinking about PCR for a while. Thanks for your friendship and helping me get across the finish line. To my sister, Dr. Bening – thank you for always setting the bar high. You’re right: I definitely had it easy never having to take the bus to high school. Thankyou for being my best and longest teammate and friend. To my parents – thank you for everything. Growing up you made sure I had the opportunity to do everything I wanted to do. And all those things – all the sports, (recovering from) all the knee injuries, all the piano lessons and everything trombone,

6 and all the people I’ve gotten to know along the way – have made me who I am today. Thank you for your example of loving to read, learn, and work hard. Thank you for the environment you created at home, and for making sure school was never a scary thing. It’s because you encouraged me to keep chasing what I was interested in that I’m writing my PhD thesis acknowledgments.

7 8 Contents

1 Introduction 13 1.1 Overview of Thesis Chapters ...... 13 1.2 Antibiotic Failure Modes ...... 14 1.3 Antibiotic Efficacy and Context-Dependence ...... 15 1.4 The Beta-Lactam Antibiotics and Bacterial Peptidoglycan ...... 17

2 Characterization of Killing by Bactericidal Antibiotic Combinations 21 2.1 Introduction ...... 21 2.2 Synergy with Aminoglycosides is Common ...... 23 2.3 Concentration-Dependent Interactions Between Beta-Lactams and Amino- glycosides ...... 25 2.4 Exploring Non-Beta-Lactam Cell Shape Perturbations ...... 27 2.5 Discussion ...... 29 2.6 Methods ...... 31

3 A Metabolic Counter-Tolerance Strategy for Beta-Lactams 33 3.1 Introduction ...... 33 3.1.1 Targeting Metabolism as a Counter-Tolerance Strategy . . . . 34 3.1.2 Peptidoglycan Activity in Stationary Phase ...... 35 3.2 Stationary Phase Bacteria are Highly Tolerant to the Beta-Lactam Ampicillin ...... 35 3.3 Many Carbon Sources Restore Killing by High Ampicillin Concentrations 37 3.4 Cell Growth May Contribute to but Does Not Explain Sensitization . 40

9 3.5 Discussion ...... 45

4 Enhancing Beta-Lactam Counter-Tolerance Using D-Amino Acids 49 4.1 Introduction ...... 49 4.1.1 Stationary Phase Peptidoglycan ...... 50 4.1.2 L,D-Transpeptidases and Beta-Lactam Efficacy ...... 50 4.1.3 D-Amino Acids and Beta-Lactam Efficacy ...... 51 4.2 Testing Single D-Amino Acids ...... 52 4.3 Combining D-Amino Acids with Metabolic Stimulation by Carbon Sources ...... 55 4.4 Application to Other Organisms and 100% LB ...... 60 4.5 Discussion ...... 64

5 Future Directions and Conclusions 69 5.1 Antibiotic Discovery and Resistance ...... 69 5.2 Context-Dependence and Clinical Relevance ...... 70 5.3 Application of Counter-Tolerance Approaches ...... 72 5.3.1 Context-Dependent Carbon Source Efficacy ...... 72 5.3.2 Delaying the Evolution of Antibiotic Resistance ...... 73 5.3.3 Failure Modes of Counter-Tolerance Strategies ...... 74

A Methods for Sensitizing Tolerant Bacteria to Beta-Lactam Antibi- otics 91

10 List of Figures

1-1 Antibiotic Efficacy is a Systems-Level Process that is Sensitive to External Cues...... 16

2-1 Killing by Low Concentrations of Quinolones or Beta-Lactams in Combination with Sublethal Gentamicin...... 23 2-2 Synergy of Select Beta-Lactams at Low Concentrations with Additional Aminoglycosides...... 24 2-3 Effects of Aztreonam and Mecillinam Treatment on E. coli Cell Shape...... 25 2-4 Aztreonam and Gentamicin Synergy...... 26 2-5 Mecillinam and Gentamicin Antagonism...... 26 2-6 Concentration-Dependent Interactions of Ciprofloxacin and Penicillin with Gentamicin...... 27 2-7 Gentamicin Combinations with A22 or SulA Overexpression. 28

3-1 Stationary Phase E. coli are Highly Tolerant to the Beta- Lactam Ampicillin...... 36 3-2 Many Carbon Sources Sensitize Stationary Phase E. coli to Ampicillin...... 37 3-3 Ampicillin with Single Carbon Sources Has Limited Efficacy Against Stationary Phase E. coli...... 38 3-4 Sensitization to Ampicillin by Xylose Depends Upon Xylose Concentration...... 39

11 3-5 Carbon-Stimulated Growth in 1% LB is not Correlated with Effectiveness at Stimulating Lysis by Ampicillin ...... 41 3-6 Carbon Sources do not Stimulate Population-Level Growth in 100% LB...... 42 3-7 Fluorescence Dilution Measurement of Single-Cell Growth of Glucose or Xylose Treated Cultures...... 44

4-1 Most D-Amino Acids Alone Are Ineffective at Sensitizing Ampicillin Even in Low Density Cultures...... 53 4-2 D-Alanine Affects Ampicillin Sensitivity ...... 54 4-3 Combining Pyruvate with D-Alanine is Not Optimally Effec- tive in WT E. coli ...... 56 4-4 D-Amino Acids Potentiate Ampicillin when Combined with Pyruvate ...... 57 4-5 D-Methionine Specifically Potentiates Beta-Lactam Antibiotics. 59 4-6 D-Methionine Still Enhances Ampicillin Lethality in a ∆metNIQ Knockout...... 60 4-7 Xylose and D-Methionine Enable Killing by sub-100 휇g/ml Beta-Lactam Concentrations...... 61 4-8 Fluorescence Dilution Measurement of Single-Cell Growth of D-Methionine Treated Cultures...... 62 4-9 Sensitizing Stationary Phase K. pneumoniae to Ampicillin . 63 4-10 Sensitizing Stationary Phase M. smegmatis to Ampicillin . . 65 4-11 Effects of Non-Canonical D-Amino Acids (NCDAAs) onAs- pects of Peptidoglycan Maintenance ...... 67

12 Chapter 1

Introduction

1.1 Overview of Thesis Chapters

In this dissertation, I describe three vignettes about beta-lactam antibiotic efficacy, exploring both how beta-lactams work in different contexts and how we can manip- ulate environmental contexts to make beta-lactams more effective. In Chapter 1, I provide motivation for understanding and improving antibiotic efficacy as it relates to preserving the clinical usefulness of antibiotic therapy.I describe the recent work which reveals the complex, context-dependent aspects of antibiotic action, and provide relevant background on the beta-lactam antibiotics and their target, bacterial peptidoglycan. Beta-lactam antibiotics are not only a major class of antibacterials for clinical use, but have also played a significant historical role in experimental studies of antibiotic action. In Chapter 2, I describe work exploring the lethal outcomes of combinations of bactericidal antibiotics, featuring antibiotics from the beta-lactam, quinolone, and aminoglycoside classes. In particular, I focus on the beta-lactam antibiotics and explore how different beta-lactams can have different outcomes in combination with other drugs. In Chapter 3, I describe a metabolic strategy to enhance beta-lactam lethality against stationary phase E. coli through the addition of a single carbon source. I characterize the concentration-dependence of this strategy and describe how previous

13 studies did not observe beta-lactam lethality in similar conditions. In Chapter 4, I build upon the strategy described in Chapter 3 and describe a target-specific strategy to further enhance beta-lactam lethality against stationary phase bacteria. I provide evidence that D-amino acids enhance beta-lactam efficacy through interacting with bacterial peptidoglycan, and apply this combined strategy – carbon source and D-amino acid supplementation – to sensitize different bacteria in stationary phase to beta-lactam antibiotics. In Chapter 5, I discuss the current state of antibiotics research and future directions specific to understanding and utilizing context-dependence to improve antibiotic efficacy.

1.2 Antibiotic Failure Modes

Antibiotics are of great clinical importance for the treatment of bacterial infections. Unfortunately, antibiotic treatment can fail for multiple reasons, even when an antibiotic is able to reach an infection site. Specifically, bacteria can phenotypically evade antibiotic action using three major mechanisms: antibiotic resistance, tolerance, and persistence [16]. Antibiotic resistance is a phenotype – often driven by genetic changes – which enables bacteria to grow in high concentrations of an antibiotic. Antibiotic tolerance and persistence, in contrast, describe reduced killing efficacy of an antibiotic against a bacterial culture, resulting in a reduced population-level (tolerance) or sub-population (persistence) killing rate by the antibiotic. The clinical relevance of antibiotic resistance is well recognized [22]. We have es- tablished methods for quantifying the resistance level of infectious organisms, we know much about common mechanisms of resistance [33], and counter-resistance strategies such as antibiotic cycling or resistance-targeting adjuvants are being developed and deployed clinically [130, 37, 62, 107, 40]. In contrast, less is known about the mechanisms and clinical relevance of both antibiotic tolerance and persistence, though there is increasing evidence of these phe- nomena clinically in the form of chronic and recurrent infections [86, 47]. Treatment

14 methods aimed at increasing antibiotic lethality against tolerant or persistent bacteria are beginning to be developed [86]. Importantly, recent work has demonstrated how the reduced killing efficacy of antibiotics due to tolerance or persistence can facilitate the evolution of antibiotic resistance [128, 73, 75]. Collectively, these antibiotic failure modes threaten the current and future ability of antibiotics to treat bacterial infections while also pointing towards important avenues for research meant to preserve antibiotic efficacy. Studying mechanisms of antibiotic action – as well as mechanisms of resistance, tolerance, and persistence – can lead to interventions that improve and preserve antibiotic efficacy both for current antibiotics and those still being developed [18, 112]. In particular, antibiotic efficacy is context-dependent [131], and thus understanding of the environmental factors affecting treatment outcomes will inform both clinical stewardship programs and the development of new antibiotics or adjuvants targeting bacterial metabolism, stress responses, or other aspects of physiology in order to enhance antibiotic efficacy [65].

1.3 Antibiotic Efficacy and Context-Dependence

Antibiotic lethality requires downstream processes in addition to target inhibition. The primary targets of most bactericidal antibiotics are involved in essential cellular processes. Quinolone antibiotics inhibit the essential protein DNA gyrase, aminoglycosides bind to the ribosome and inhibit translation, and beta-lactam antibiotics inhibit penicillin binding proteins (PBPs) that are required for peptidoglycan synthesis. However, antibiotics do more than inhibit essential cellular processes (Figure 1-1). Quinolone antibiotics form toxic complexes containing broken DNA [82], and beta-lactam antibiotics lead to futile cycling of cell wall synthesis [25, 26]. Several studies have linked off-target cellular damage to antibiotic-mediated cell death, including the involvement of toxic reactive oxygen species (ROS) and DNA damage [39, 125, 6]. Broad metabolic [6, 135, 77, 36] and transcriptomic [27, 39, 90, 17] perturbations caused by antibiotics indicate that primary target inhibition is critically linked to bacterial physiology.

15 Figure 1-1: Antibiotic Efficacy is a Systems-Level Process that is Sensitive to External Cues. Antibiotics bind to and inhibit their primary targets (left) which contributes to cell death. Primary target inhibition also influences and is influenced by bacterial metabolic and transcriptomic states, which further impact cell death process and are also susceptible to influence from the extracellular environment.

Given these systems-level disruptions, it is apparent then that bacterial physiology can also impact antibiotic lethality, and thus external factors acting on the cell can alter antibiotic efficacy. In particular, external metabolic conditions can drastically alter antibiotic efficacy. Blocking protein translation or cellular respiration can inhibit the lethality of bactericidal drugs with different targets in both Escherichia coli and Staphlyococcus aureus [77]. TCA cycle activity enhances lethality of the aminoglycoside tobramycin in Psuedomonas aeruginosa, while flux through the glyoxylate shunt protects bacteria [87]. Additionally, external conditions can act through bacterial metabolism and alter cellular ATP levels. Multiple studies have shown, by varying culture conditions, that the lethality of many bactericidal drugs is dependent upon ATP, with increased lethality occuring at higher ATP levels [79, 136]. En- vironmental factors altering transcriptional states and stress response activity also impact antibiotic efficacy. For example, when antibiotics are used in combinations, transcriptional responses in the cell differ from what is observed with single drug treatment, impacting the efficacy of the combination treatment [14]. Continued study into

16 both downstream consequences of antibiotic target inhibition and context-dependent antibiotic efficacy will suggest new methods to improve antibiotic efficacy through altering bacterial physiology. Context-dependent antibiotic efficacy is particularly important for antibiotic tolerance and persistence. Resistance is predominantly a genetic phenotype [33], and genetic resistance can be assayed in clinical settings through growth-inhibition assays or sequence-targeted assays [8]. In contrast, environmental conditions such as starvation drastically influence antibiotic lethality [50, 117] and can lead to antibiotic tolerance and persistence. Unlike the stability of genetic changes, phenotypic or context-dependent antibiotic efficacy is challenging to assay in the clinic [86, 128]. Therefore, continued study of antibiotic efficacy and context-dependence is necessary to improve our ability to identify and combat antibiotic treatment failure due to tolerance and persistence.

1.4 The Beta-Lactam Antibiotics and Bacterial Pep- tidoglycan

Since the discovery of penicillin in 1929, the beta-lactam class of antibiotics has been expanded through both natural product discovery and chemical modifications to form a large group of antibiotics with varying spectra of activity [76]. The clinical significance of beta-lactam antibiotics is clear, as in 2009 over half of clinical spending on antibiotics in the United States was spent on beta-lactam antibiotics [54]. New beta-lactam derivatives are continuing to be developed and continue to form part of the preclinical pipeline of new antibiotics [112]. Thus, beta-lactams form an antibiotic class with great current and future clinical relevance, motivating continued study into their mechanisms of action and opportunities for improvement. Beta-lactam antibiotics inhibit synthesis of bacterial peptidoglycan [123, 103, 76]. Bacterial peptidoglycan is a common and effective antibiotic target [103], as peptidoglycan is a normally essential and bacterial-specific polymer [123]. There are many

17 enzymes active in building, maintaining, and degrading bacterial peptidoglycan, some of which will be discussed here and later in chapter 4 (see section 4.1). Importantly, following cytosolic synthesis of peptidoglycan precursor molecules, two key reactions insert new peptidoglycan: glycan polymerization by glycosyltransferases and peptide cross-linking by transpeptidases [123]. Beta-lactams inhibit this latter reaction, as the core beta-lactam structure mimics the terminal D-ala-D-ala dipeptide present in newly synthesized and uncross-linked peptidoglycan [114], and thus beta-lactams are able to bind to and inhibit the DD-transpeptidases known as penicillin-binding proteins (PBPs). As peptidoglycan plays a key role in the maintenance of cell shape [123], beta-lactam treatment causes dramatic changes in cell shape, specific to the cellular role of the PBPs targeted by a given drug [46].

Beta-lactams, in addition to their clinical significance, have been key to many scientific discoveries about bacterial physiology and, importantly, bacterial evasion strategies: resistance, tolerance, and persistence. The evolution of and clinical emer- gence of beta-lactam resistance mirrors that observed with other antibiotics: clinical observations of resistance rapidly following – or even preceeding – introduction of a new antibiotic into the clinic [28]. Indeed, penicillin resistance was observed in 1940 even before widespread use of penicillin to treat infections during World War II and afterwards [76]. Further, observations often cited as the first descriptions of antibiotic persistence occurred during the study of beta-lactam action [59, 10]. Re- cent advances in our understanding of tolerance and persistence featured beta-lactam antibiotics, such as early single-cell studies into persister mechanisms [3], demonstration of the evolution of antibiotic tolerance [44], and the demonstration that evolved tolerance can facilitate the evolution of antibiotic resistance [73]. Additionally, contributing to the study of bacterial persistence, beta-lactams are frequently used as a mechanism to isolate bacterial persisters. Methods include using a beta-lactam to lyse non-persistent bacteria and enable the collection of non-lysed persisters [64], and alternatively using a beta-lactam which induces filamentation to separate sensitive filamentous cells from small, non-growing persisters using size-based filtration [127]. Tolerance to beta-lactam antibiotics can also be used to isolate auxotrophic mutants,

18 thus contributing to the study of bacterial metabolism [34], and the PBP-specificities of beta-lactam antibiotics make beta-lactams useful chemical genetics tools for the study of bacterial peptidoglycan. As just one of many examples in E. coli, use of the PBP2-specific beta-lactam mecillinam led to the characterization of interactions between peptidoglycan-cleaving endopeptidases and peptidoglycan-synthesizing PBPs [67]. Collectively, these studies and many others demonstrate both the clinical and scientific significance of beta-lactam antibiotics. Continued study into the mechanisms of beta-lactam action and methods to enhance beta-lactam activity are thus expected to broadly impact future work in both the clinic and the lab.

19 20 Chapter 2

Characterization of Killing by Bactericidal Antibiotic Combinations

2.1 Introduction

Antibiotics are frequently used in combination clinically when the drugs are synergistic, when patients are critically ill and require treatment before completing susceptibility tests, or when prolonged treatment may select for resistance, thereby increasing the likelihood the infection is susceptible to at least one drug [71]. Results of recent studies have suggested current practices of using synergistic drugs are actually counterproductive in combating antibiotic resistance. These findings support the ar- gument that synergistic interactions increase selection pressure and drive acquisition of resistance, suggesting that antagonistic combinations may be better at preventing selection of resistance [94, 115, 88, 55]. Both of these potentially confounding needs – increasing treatment efficacy and limiting the spread of resistance – depend uponhow drugs interact, motivating further understanding of drug interaction mechanisms. Growth inhibition assays enable relatively rapid screening of antimicrobial interactions in vitro [23, 132]. While these methods have the benefit of being high throughput, this leaves differences in reported interactions between studies unexplained, as mechanistic followup must be limited. In one recent study, aminoglycosides in combination with either beta-lactam or quinolone antibiotics were antagonistic in E. coli

21 [23], while in another these combinations were synergistic [132]. These differences may be due to differences in growth conditions like media richness or the use of different methods to quantify interactions. Given the clinical acceptance of beta-lactam combinations with aminoglycosides [71], the inconsistencies in these screens is intriguing. Early studies of the lethality of beta-lactam/aminoglycoside combinations in the 60s, 70s, and 80s measured increased uptake of a radiolabeled aminoglycoside when combined with a beta-lactam [97, 91, 89]. However, it is unclear why some beta-lactams are antagonistic with aminoglycosides [92] or why synergy isn’t observed in some organisms [89]. Further mechanistic studies of these beta-lactam/aminoglycoside interactions may shed light into these discrepancies and could also inform clinical use of these antibiotic combinations.

Recent studies have moved beyond observing drug interactions and have taken a mechanism-oriented approach to study antibiotic combinations [77, 90, 23, 14, 24]. These studies used screens of single gene deletion libraries [23, 24], analysis of transcriptional responses [90, 14, 24], and perturbations of metabolic consequences [77] to characterize drug interactions. This focus on mechanism enabled testable predictions extending to other drugs or stressors [77, 90, 23, 24] and organisms [23]. Insights into downstream responses of bactericidal antibiotic treatment may similarly be useful to understand combinations of beta-lactam, aminoglycoside, and quinolone antibiotics. In particular, aminoglycoside uptake and activity can be stimulated by metabolites that enhance respiration and membrane potential [87, 2, 95], and bactericidal antibiotics of the quinolone and beta-lactam drug classes have been shown to stimulate respiration [77]. Together, this suggests respiration increases stimulated by quinolone or beta-lactam antibiotics could enhance uptake of aminoglycoside antibiotics. Studying how metabolic and transcriptional perturbations induced by bactericidal antibiotics affect outcomes of combination treatment may clarify interaction mechanisms and identify critical environmental factors that tune these interactions.

Here, we aim to characterize the killing efficacy of bactericidal drug combinations. By enumerating CFUs and evaluating antibiotic lethality, this work addresses a regime unavailable to high throughput growth inhibition screens.

22 2.2 Synergy with Aminoglycosides is Common

To explore combinations of bactericidal antibiotics, we began with antibiotic combinations with the aminoglycoside gentamicin, motivated by the clinical relevance of beta-lactam/aminoglycoside antibiotic combinations [71]. Uptake of aminoglycoside antibiotics, such as gentamicin, into the cell is dependent upon proton motive force (PMF) [110]. Based upon previous work that observed increased aerobic respiration rates in response to bactericidal antibiotics [77], we hypothesized that this antibiotic-induced electron transport chain activity could play a role in combinations of aminoglycosides with other antibiotics. Because norfloxacin-induced increases to bacterial respiration were maximal near the antibiotic’s minimum inhibitory concentration (MIC), doses of quinolone and beta-lactam antibiotics were selected to be near the MIC. All quinolones and three of the four beta-lactams tested showed synergy with gentamicin at these concentrations (Figure 2-1). Since gentamicin alone was

Figure 2-1: Killing by Low Concentrations of Quinolones or Beta-Lactams in Combination with Sublethal Gentamicin. Percent survival after three hours of treatment. Dotted line marks 100% survival. Quinolones: Ciprofloxacin (Cipro, 20 ng/ml), Levofloxacin (Levo, 30 ng/ml), Nalidixic Acid (Nal, 5 휇g/ml), Norfloxacin (Nor, 40 ng/ml). Beta-lactams: Penicillin (Pcn, 30 휇g/ml), Cefsulodin (Cef, 50 휇g/ml), Aztreonam (Azt, 500 ng/ml), Mecil- linam (Mec, 500 ng/ml). Bars represent the mean with error bars representing the standard error of the mean for 5 replicates, except mecillinam which is 2 replicates. non-lethal, these interactions can be classified as synergy because the lethality ofthe combination is more than that of the quinolone or beta-lactam alone. In contrast,

23 the combination of the beta-lactam mecillinam with gentamicin led to less killing than observed with mecillinam alone, indicative of antagonism, and consistent with previous studies which showed antagonism between mecillinam and aminoglycoside antibiotics [92]. Given the clinical relevance of beta-lactam/aminoglycoside combinations and frequently observed synergy between these drug classes, we also tested combinations of these beta-lactams with other aminoglycoside antibiotics, to see if the observed synergy was consistent in our experimental conditions. Here we did not include mecillinam, which was found to be antagonistic with gentamicin. In this panel of 4 beta-lactams and 3 aminoglycosides, we again found that synergy was common when combining beta-lactams near their MIC and sublethal aminoglycoside concentrations (Figure 2-2).

Figure 2-2: Synergy of Select Beta-Lactams at Low Concentrations with Additional Aminoglycosides. Percent survival after three hours of exposure. Beta-lactams: Penicillin (Pcn, 30 휇g/ml), Cefsulodin (Cef, 50 휇g/ml), Aztreonam (Azt, 500 ng/ml), Piperacillin (5 휇g/ml). Aminoglycosides: Gentamicin (Gent, 50 ng/ml), Kanamycin (Kan, 600 ng/ml), Streptomycin (Strep, 500 ng/ml). Bars represent the mean with error bars representing the standard error of the mean for 3 replicates.

24 2.3 Concentration-Dependent Interactions Between Beta-Lactams and Aminoglycosides

Additional concentrations were then tested for combinations of the beta-lactams aztreonam and mecillinam with gentamicin. These two antibiotics were chosen because they are known to inhibit complementary processes of peptidoglycan synthesis (Figure 2-3). Mecillinam inhibits PBP2, which is required for cell elongation, and

Figure 2-3: Effects of Aztreonam and Mecillinam Treatment on E. coli Cell Shape. thus mecillinam treatment induces sphere formation [104]. Aztreonam, in contrast, inhibits PBP3, which is required for cell division, and thus aztreonam treatment induces cell filamentation [51]. At the drug concentrations tested, combinations of aztreonam and gentamicin always led to increased killing (Figure 2-4). In contrast, lethal concentrations of mecillinam where antagonised by sublethal gentamicin (Figure 2-5, top) and lethal concentrations of gentamicin were antagonized by mecillinam (Figure 2-5, bottom). The findings with aztreonam and mecillinam establish a preliminary pattern: synergy between aminoglycosides and filamentation-inducing treatments, and antagonism between aminoglycosides and sphere-inducing treatments. In order to explore this pattern further, we first wanted to explore concentration-dependent drug interactions for other antibiotics with the aminoglycoside gentamicin. To do this, we used the quinolone antibiotic ciprofloxacin as well as the beta-lactam antibiotic penicillin, because both penicillin [48] and ciprofloxacin [84] are known to induce

25 Figure 2-4: Aztreonam and Gentamicin Synergy. Percent survival after three hours of exposure. The data presented in both figures is the same and serves to highlight gentamicin concentration-dependence (left) and aztreonam concentration-dependence (right). Data is presented as the mean of 3 replicates; error bars are standard error of the mean.

Figure 2-5: Mecillinam and Gentamicin Antagonism. Percent survival after three hours of exposure. The data presented in both figures is from the same experiment and are separated to highlight combinations with sublethal gentamicin (top) and lethal gentamicin (bottom). Data is presented as the mean of 3 replicates with individual replicates shown. dose-dependent changes in cell morphology. In our experimental conditions, both ciprofloxacin and penicillin were found to have dose-dependent interactions withgen-

26 tamicin (Figure 2-6), where low gentamicin concentrations antagonized killing by higher ciprofloxacin or penicillin concentrations, but lower ciprofloxacin or penicillin concentrations – concentrations near the MIC – were synergistic and showed increased killing with gentamicin. Given the known concentration-dependent morphological

Figure 2-6: Concentration-Dependent Interactions of Ciprofloxacin and Penicillin with Gentamicin. Percent survival after three hours of treatment. Left: gentamicin and ciprofloxacin, mean and standard deviation of two replicates. Right: gentamicin and penicillin, a single replicate. changes of both ciprofloxacin and penicillin, these results are supportive of apoten- tial role for morphology in combinations with aminoglycoside antibiotics. However, because concentration-dependent morphological effects were not verified in these experimental conditions, the link between morphology and aminoglycoside interactions is still limited.

2.4 Exploring Non-Beta-Lactam Cell Shape Pertur- bations

Because the concentration-dependent morphological effects of ciprofloxacin and penicillin complicate analysis without concurrent microscopy studies, we next sought out alternative methods to perturb cell shape which don’t have concentration-dependent effects on cell shape. Two such methods were identifiedFigure andtested( 2-7A). First, the small molecule A22 inactivates a protein other than PBP2 that is involved

27 in cell elongation, MreB [45]. Similarly to mecillinam, A22 inhibited killing by lethal concentrations of gentamicin (Figure 2-7B). Inducible overexpression of the protein

Figure 2-7: Gentamicin Combinations with A22 or SulA Overexpression. A) Schematic depicting expected cell shape changes caused by SulA overexpression and A22 treatment. B) Percent survival after three hours of treatment with gentamicin and A22 (single replicate, representative of additional experiments not show). C) Percent survival after three hours of treatment with gentamicin and ATC induction of WT or mutant L83R SulA (single replicate, representative of additional experiments not shown).

SulA was used as an alternative method to mimic the action of aztreonam and prevent cell division. SulA is induced by the SOS response to inhibit FtsZ polymerization, which is required for cell division [60]. SulA was cloned from the MG1655 genome into a plasmid and placed under the control of an inducible promoter responsive to anhydrotetracycline (ATC). As seen with aztreonam, inhibition of cell division by SulA led to decreased cell viability (Figure 2-7C), an effect not due to protein over-

28 expression, as indicated by the lack of interaction between gentamicin and a mutant form of SulA unable to bind FtsZ [60].

2.5 Discussion

Here I presented work characterizing concentration-dependent lethality of bactericidal antibiotic combinations. We find that synergy between both beta-lactam and quinolone antibiotics with sublethal aminoglycoside antibiotics is common, with the exception of the beta-lactam mecillinam. We then characterize dose-dependent interactions of various antibiotics with the aminoglycoside gentamicin, and suggest an initial hypothesis that changes in cell shape correlate with gentamicin interactions: that is, filamentation-inducing treatments synergize with gentamicin and sphere-inducing treatments are antagonistic with gentamicin. As a first step towards testing this hypothesis, we present data with two other cell-shape perturbations: antagonism with the MreB-inhibiting and sphere-inducing A22, and synergy with overexpression of the FtsZ-inhibitor and thus filamentation-inducing SulA. Further exploration of this shape-related aminoglycoside synergy should initially focus on three next steps. First, microscopy should be incorporated into these studies such that cell shape perturbations – particularly concentration-dependent effects as previously reported with ciprofloxacin [82] and penicillin [48] – can be directly correlated with killing phenotypes in the tested experimental conditions. Second, initial mechanistic studies using radiolabelled or fluorescently labelled aminoglycosides should be done, as increased aminoglycoside transport is frequently reported for beta-lactam/aminoglycoside synergy [97, 91, 89]. As with microscopy studies, aminoglycoside uptake studies would allow correlation of killing phenotypes with a potential mechanistic explanation – increased aminoglycoside transport – in identical experimental conditions. Finally, the third recommended next step is including additional methods of perturbing cell shape. This can include – but is not limited to – the following: single-gene knockouts with altered cell shape including the filamentous holD mutant and spherical rodZ mutant [43]; filamentation from shifts to non-permissive

29 temperatures for temperature sensitive mutants of cell division proteins; cells with altered dimensions due to titrated levels of key elongation (MreB) and division (FtsZ) proteins; plasmid-mediated over-expression of other proteins inhibiting FtsZ (for example CbtA [57], SlmA [7], or MinC [9]) or MreB (for example CbtA [57] or BolA [42]); and the inclusion of addition beta-lactams antibiotics [46].

While an attractively simple hypothesis that beta-lactam/aminoglycoside interactions can be classified according to the effects of the beta-lactam on cell shape, this hypothesis has limitations that should be explored further. First, beta-lactam and aminoglycoside synergy is also noted in spherical bacteria [89]. While beta- lactam treatment still affects cellular morphology in spherical bacteria, the extremes of filamentation and sphere-formation observed in the normally rod-shaped E. coli don’t directly translate to non-rod-shaped bacteria. Thus, extension of a cell shape as a determinant of beta-lactam/aminoglycoside synergy hypothesis to other bacteria by necessity requires more nuance. Two potential avenues to explore are as follows: to quantify cell surface area or volume rather than shape, and see if these metrics are correlated with aminoglycoside transport; beta-lactam treatment induces other physiologic changes (for example, to bacterial metabolism and stress responses [131]), occuring in parallel to changes in cell shape, and further characterization and perturbation of those physiological changes may be more directly correlated with aminoglycoside synergy than cell shape. A second outstanding question about shape- dependent beta-lactam/aminoglycoside interactions relates to drug interactions in conditions where beta-lactams induce shape changes but are not lethal. For example, both aztreonam and mecillinam induce shape changes in E. coli when grown in poor growth conditions such as minimal medium. If shape changes determine beta- lactam interactions with aminoglycosides, synergy and antagonism would still occur in these conditions. However, as aztreonam and mecillinam are not lethal in minimal medium, bactericidal processes downstream of primary target inhibition are likely altered in these conditions, and if these secondary physiological changes play a key role in beta-lactam/aminoglycoside interactions, drug interactions would change despite consistent changes in cell shape. Thus, in addition to testing additional cell shape

30 perturbations, testing combinations with gentamicin and mecillinam or aztreonam in minimal media or anaerobic conditions should be explored further. Finally, I note that much of this data is preliminary and exploratory, and additional replication and conditions must be completed.

2.6 Methods

Reagents: Antibiotics and PBS were purchased from Sigma-Aldrich. MOPS EZ Rich medium was purchased from Teknova. Strains and Plasmids: All experiments were done with E. coli MG1655 or MG1655 Pro (lab stock [39]) in MOPS EZ Rich Medium. SulA was amplified off of MG1655 genomic DNA and cloned into pZE21 using the KpnI and HindIII sites. The SulA L83R mutant was made using overlapping PCR and similarly cloned into KpnI and HindIII. Plasmids were transformed into MG1655 Pro to enable inducible expression; these cultures were grown with 50 휇g/ml kanamycin for plasmid maintenance. Killing Assays: For all experiments, overnight cultures from glycerol stocks were grown in MOPS EZ Rich Medium. In the morning, overnight cultures were diluted 1:5000 in fresh medium and grown to exponential phase, when antibiotics were added and treatment started (T=0, OD600=0.1). Percent survival reflects colony forming units (CFUs) relative to the measured density at T=0.

31 32 Chapter 3

A Metabolic Counter-Tolerance Strategy for Beta-Lactams

3.1 Introduction

Both antibiotic resistance and antibiotic tolerance allow bacteria to evade antibiotic action, limiting the effective treatment of bacterial infections [16, 86]. Many mechanisms of antibiotic resistance are well understood, and strategies such as adjuvants and multidrug combinations targeting these resistance mechanisms are being developed and deployed clinically [130, 37, 62, 107, 40]. In contrast, less is known about antibiotic tolerance — where genetically susceptible bacteria survive typically lethal antibiotic challenge [16]. Tolerant bacteria lead to chronic and costly infections in the clinic [86], and antibiotic tolerance was recently shown to facilitate the evolution of antibiotic resistance in vitro [73] and in the clinic [75]. In order to improve and preserve antibiotic efficacy, treatment strategies combating antibiotic tolerance are beginning to be developed [86]. Here, we aim to develop and characterize a metabolic counter-tolerance strategy for beta-lactams using E. coli. Bacterial metabolism is critically important for antibiotic efficacy, and metabolism has been successfully targeted to sensitize tolerant E. coli to other classes of antibiotics. Previously, direct translation of these approaches to beta-lactams have not worked with E. coli. Thus, this work aims to fill a gap for

33 the clinically important beta-lactam antibiotics.

3.1.1 Targeting Metabolism as a Counter-Tolerance Strategy

Antibiotics and bacterial metabolism interact bidirectionally [105, 131]. Antibiotic treatment leads to broad perturbations in bacterial metabolism, which has been observed in multiple species [6, 36, 135, 134]. Additionally, bacterial metabolic state impacts antibiotic efficacy, such that cellular respiration [77] and ATP levels [136, 79, 102, 29] tune antibiotic lethality. Collectively, this shows that metabolism is an important part of antibiotic efficacy. Bacterial metabolism, which is sensitive to environmental conditions and factors, is thus a logical target for countering antibiotic tolerance. This was first done in 2011, where stationary phase cultures were treated with the quinolone ofloxacin, and survivors – the persister fraction – were then resensitized to aminoglycoside antibiotics through stimulation by individual carbon sources [2]. This approach was found to be efficacious against E. coli and S. aureus [2], E. tarda [95], and P. aeruginosa [87]. This single-carbon strategy also showed efficacy against S. aureus persisters challenged with daptomycin [98]. Unfortunately, the usefulness of this approach was limited, and antibiotics from non-aminoglycoside classes, including quinolones and beta-lactams, were not sensitized in those initial studies [2, 95, 87]. Recently, a study found that sensitizing quinolones required a carbon source and an electron acceptor such as oxygen or fumarate [50]. This was found to work with E. coli, S. aureus, and M. smegmatis [50]. This two part approach with both a carbon source and electron acceptor still did not show efficacy with beta-lactam antibiotics and E. coli. However, this study does highlight that metabolic counter-tolerance strategies may be drug-specific; while the aminoglycoside-developed strategy did not work initially with quinolones, further study with quinolones led to a quinolone-developed strategy that did, suggesting that specific study with beta-lactams may lead to a metabolic counter-tolerance strategy that works with beta-lactam antibiotics.

34 3.1.2 Peptidoglycan Activity in Stationary Phase

Beta-lactam antibiotics have drastically reduced effectiveness against stationary phase bacteria, a physiological state commonly associated with phenotypic tolerance to many bactericidal drug classes. Despite high beta-lactam tolerance, peptidoglycan synthesis, maintenance, and turnover processes are still active in stationary phase. New peptidoglycan synthesis is drastically reduced in stationary phase, though it is measurably non-zero [11]. Further, evidence that peptidoglycan turnover and new synthesis are active come from studies of ldcA mutants. The ldcA gene encodes an L,D-carboxypeptidase which processes peptidoglycan turnover products which then are recycled for new insertion into peptidoglycan. The lcdA mutant lyses specifically in stationary phase due to reduced peptidoglycan cross-linking from incorporation of improperly recycled peptidoglycan fragments [111]. Importantly, the peptidoglycan recycling pathway of which LcdA is a part is not essential [93], only LcdA due to aberrant peptidoglycan synthesis in the absence of LcdA. Thus, deletion of lcdA does not lead to lysis because of uncontrolled degradation of peptidoglycan, but rather degradation accompanied by improper synthesis, again pointing towards non-zero peptidoglycan synthetic activity in stationary phase which can serve as a starting point for sensitizing stationary phase E. coli to beta-lactam antibiotics. Given the measurable activity of beta-lactam targets in stationary phase [11], the central role of metabolism in beta-lactam antibiotic lethality, and the presence of drug- and organism-specific differences in other metabolism-directed anti-tolerance strategies (see section 3.1.1), we sought to develop a metabolic strategy to sensitize tolerant stationary phase E. coli to beta-lactam antibiotics.

3.2 Stationary Phase Bacteria are Highly Tolerant to the Beta-Lactam Ampicillin

Our experimental protocol consisted of growing bacterial cultures for 24 hours to stationary phase, followed by 24 hours of treatment, after which we measured cul-

35 Figure 3-1: Stationary Phase E. coli are Highly Tolerant to the Beta-Lactam Ampicillin. A) Schematic depicting timeline of experiments and corresponding culture density in CFU/ml. B) Survival of stationary phase cultures of MG1655 E. coli grown in 100% (left) and 1% (right) LB and treated with 100 or 1000 휇g/ml ampicillin (Amp) for 24 hours. Data represents the mean of two replicates with individual replicates shown.

ture density (Figure 3-1A). Using this protocol, we confirmed that stationary phase MG1655 Escherichia coli grown in LB medium are tolerant to treatment with 100 휇g/ml ampicillin (Figure 3-1B), as we have observed previously [50]. We also confirmed that stationary phase E. coli are tolerant to 1000 휇g/ml ampicillin (Figure 3-1B), a high tolerance level which has previously been used to study peptidoglycan synthesis and modification in stationary phase [20].

Because beta-lactam efficacy is known to be effected by cell density [41, 63],we next wanted to test if stationary phase tolerance was due to culture density. To do this, we grew stationary phase cultures in LB medium diluted 1:100 in PBS (called 1% LB), reducing the stationary phase cell density by reducing the available nutrients in the culture medium. Cultures grown in 1% LB were also tolerant to 1000 휇g/ml ampicillin (Figure 3-1B). This suggests that tolerance of E. coli to beta-lactams in stationary phase is not due to culture density but rather growth state.

36 3.3 Many Carbon Sources Restore Killing by High Ampicillin Concentrations

Figure 3-2: Many Carbon Sources Sensitize Stationary Phase E. coli to Ampicillin. Survival of stationary phase cultures grown in 100% LB (top) or 1% LB (bottom) treated for 24 hours with 1000 휇g/ml ampicillin and 10 mM indicated carbon source. Bars are the mean of two replicates with replicates shown. NTC: no carbon control; Glc: glucose; Mann: mannitol; Fruc: frutose; Glyc: glycerol; Gluc: gluconate; Ara: arabinose; Rib: ribose; Xyl: xylose; Pyr: pyruvate; Fum: fumarate; Succ: succinate; Cit: citrate; Ace: acetate; Oxalo: oxaloacetate; Glyoxyl: glyoxylate; Prop: proprionate; Mal: malate.

We next tested a panel of carbon sources to see if they could restore killing of stationary phase E. coli by 1000 휇g/ml ampicillin. Unlike previous studies which tested one metabolic stimulus – optimized for a different antibiotic class – with only 100 휇g/ml ampicillin [2, 50], we reasoned that using a higher ampicillin concentration,

37 testing a panel of carbon sources, and also testing 1% LB would make it more likely we would find a carbon source that restored beta-lactam lethality. Indeed, wefound that in both 1% and 100% LB many carbon sources sensitized stationary phase E. coli to killing by 1000 휇g/ml ampicillin (Figure 3-2). The carbon sources which showed efficacy in 100% LB belong to glycolysis and the pentose phosphate pathway, whereas carbon sources from the TCA cycle also showed efficacy in 1% LB. Furthermore, a larger log-reduction in culture viability was observed in 1% LB compared to 100% LB. Overall, these results demonstrate that single carbon sources are able to sensitize tolerant stationary phase E. coli to beta-lactam antibiotics.

Figure 3-3: Ampicillin with Single Carbon Sources Has Limited Efficacy Against Stationary Phase E. coli. Survival of stationary phase cultures grown in 100% LB treated for 24 hours with 200 휇g/ml ampicillin (A, left) or 500 휇g/ml ampicillin (B, right) and 10 mM of the indicated carbon source. All individual replicates (6-7 for each treatment condition) are shown. NTC: no carbon control; Glc: glucose; Mann: mannitol; Fruc: frutose; Gly: glycerol; Gln: gluconate; Ara: arabinose; Xyl: xylose.

Having found that single carbon sources can sensitize tolerant stationary phase E. coli to 1000 휇g/ml ampicillin, we next sought to determine if these carbon sources could sensitize lower concentrations of ampicillin. To do this, we took the carbon sources which showed efficacy in 100% LB and tested their ability to sensitize stationary phase E. coli to a range of ampicillin concentrations. We found that one carbon source – xylose – was occasionally effective at 200 휇g/ml ampicillin (Figure 3-3A)

38 and most carbon sources showed occasional efficacy at 500 휇g/ml apmicillin (Figure 3-3B). These results are consistent with the failure of previous studies – which only tested a maximum concentration of 100 휇g/ml ampicillin – to observe sensitization of stationary phase E. coli to ampicillin by carbon source supplementation [2, 50]. As expected, metabolic counter-tolerance strategies are ampicillin-concentration- dependent (Figure 3-3). To understand the concentration-dependence of the carbon source stimulus, we next tested both 10 mM and 50 mM xylose. We found that 50 mM xylose sensitized lower ampicillin concentrations than 10 mM xylose (Figure 3-4). Additionally, at the ampicillin concentrations tested, the inter-replicate vari-

Figure 3-4: Sensitization to Ampicillin by Xylose Depends Upon Xylose Concentration. Survival of stationary phase E. coli grown in 100% LB and treated for 24 hours with ampicillin and no xylose (NTC), 10 mM, or 50 mM xylose. Thick lines and symbols denote the average of two replicates. Thin lines and shaded area denote individual replicates and their range for each condition.

ability observed with both 10 mM xylose and the other carbon sources (Figure 3-3) was not as pronounced with 50 mM xylose. Collectively, these results demonstrate that sensitizing tolerant E. coli to beta-lactam antibiotics is concentration-dependent in both the beta-lactam and the carbon source, and that previously tested conditions – such as 10 mM mannitol with 100 휇g/ml ampicillin – are just beyond the lower limit of effectiveness of this counter-tolerance strategy. Near this lower limit of effectiveness, inter-replicate variability leads to inconsistent results that mask the observation

39 that supplementation with single carbon sources is indeed able to sensitize tolerant stationary phase E. coli to beta-lactam antibiotics.

3.4 Cell Growth May Contribute to but Does Not Explain Sensitization

Next, we wanted to understand how cell growth contributes to sensitizing tolerant, stationary phase E. coli to ampicillin and other beta-lactams. It is commonly accepted that beta-lactam lethality requires cell growth and that beta-lactam killing rates are directly proportional to bacterial growth rates [69, 119], though recent studies have demonstrated that metabolic state and not growth rate determine beta- lactam lethality [79]. Therefore, we wanted to characterize cell growth in our experimental conditions to explore how cell growth contributes to restoring beta-lactam lethality. We first looked at 1% LB because at this low density we could rapidly quantify many different conditions using optical methods rather than CFU enumeration. For each carbon source, we tested a range of ampicillin concentrations and quantified the concentration at which lysis is observed (the lysis threshold) as well as any growth in the absence of ampicillin (Figure 3-5A). We would expect that if cell growth is key to beta-lactam sensitization that carbon sources that stimulate more growth would also stimulate lysis at lower ampicilin concentrations (Figure 3-5B). In contrast, we found a lack of correlation between cell growth and the metabolite-enabled ampicillin lysis threshold (Figure 3-5C). While these carbon sources did stimulate population-level growth as measured by biomass accumulation, the degree of biomass accumulation did not correlate with effectiveness in combination with ampicillin, suggesting thatin 1% LB cell growth alone does not differentiate carbon soucres, and perhaps specific metabolic fluxes tune efficacy as was observed previously with aminoglycosides [87]. We next wanted to quantify cell growth in 100% LB. To do this, we sampled culture density at multiple time points during treatment and enumerated CFUs. We

40 Figure 3-5: Carbon-Stimulated Growth in 1% LB is not Correlated with Effectiveness at Stimulating Lysis by Ampicillin A) Schematic depicting optical method of quantifying carbon source (CS) efficacy in 1% LB. For each carbon source, lysis threshold (where the curve crosses the No CS curve) and growth (ampicillin-free OD600) were quantified. B) Schematic depicting a possible correlation between growth and lysis threshold. Here, increased growth is correlated with increased effectiveness, given by a lower lysis threshold. C) Results for ampicillin. Individual data points represent individual replicates of the carbon sources in Figure 3-2.

measured population density of untreated cultures and cultures treated with two carbon sources, glucose and xylose, which showed differing effectiveness as single agents (Figure 3-6A). First, we found that final culture densities for glucose- and xylose- treated cultures were approximately the same (Figure 3-6B, C), suggesting that as in 1% LB, population-level growth does not differentiate between these metabolites. Additionally, we found that all three cultures – untreated, glucose supplemented, and xylose supplemented – decreased in density over the course of 24 hours, suggesting that in 100% LB, carbon sources do not sensitize stationary phase E. coli to ampicillin due to stimulating measurable population-level growth.

While these bulk culture CFU measurements did not show population-level growth in 100% LB cultures, these results do not rule out carbon sources such as glucose or xylose stimulating growth of a subpopulation of bacteria, a situation which could

41 Figure 3-6: Carbon Sources do not Stimulate Population-Level Growth in 100% LB. A) CFU/ml after 24 hours treatment with ampicillin and 10 mM glucose (left) or xylose (right). Thick lines present mean of 7 replicates; thin lines represent individual replicates; shading provides extremes of replicates at a given ampicillin concentration. B) CFU/ml over time of cultures treated with nothing (NTC), 10 mM glucose (Glc), 10 mM xylose (Xyl) in the absence of ampicillin. Thick lines present mean of 3 replicates; thin lines represent individual replicates; shading provides extremes of replicates at a given time. C) Data as in panel B showing only 24 hours.

explain why glucose and xylose treated cultures did not decrease in density as much as the untreated control (Figure 3-6C). To explore the possibility of subpopula- tions of growth bacteria in 100% LB, we used fluorescence dilution to assay single-cell growth and division [1, 56]. E. coli with a plasmid encoding ATC-inducible mCherry expression were grown to stationary phase with ATC induction. At time 0, these cultures were washed and resuspended in spent LB medium without ATC and treated with glucose or xylose. At 4, 8, and 24 hours, samples were washed, resuspended

42 in PBS, and single-cell mCherry fluorescence was measured using fluorescence microscopy. The distribution of mCherry levels in the untreated culture (grown with ATC induction, resuspended in spent LB without carbon source) decreased over the course of 24 hours (Figure 3-7A). This suggests some growth, division, and dilution occurs over the course of 24 hours even in the untreated control culture, but may also indicate degradation of the fluorescent protein over the course of the experiment. Because of the limited dynamic range of this experiment as performed (see section 3.5) and the decrease over time of the fluorescence of the control culture, differences between conditions at later time points will be inconclusive without future studies.

Two additional control cultures were used: after resuspension in spent LB without inducer, one culture was diluted 1:2 in fresh LB and another 1:5 in fresh LB. These cultures are expected to expand at least 2-5 fold due to the availability of fresh media and reduced culture density. Indeed, comparison of the mCherry fluorescence of these cultures with that of the control culture revealed reductions in mCherry levels corresponding to the available expansion potential of the culture: that is, the 1:2 diluted culture had lower fluorescence levels than the control, and the 1:5 diluted culture had fluorescence levels lower than the 1:2 diluted culture (Figure 3-7B). These dilution control cultures demonstrate that, at least at the 4 hour time point, this method can differentiate between cultures with single-cell growth and division characteristic of 2 to 5-fold expansion potential.

Next, we quantified single-cell fluorescence levels for cultures treated with 10mM of either glucose or xylose (Figure 3-7C). At 4 hours, these cultures have lower median fluorescence than the control, suggesting that relative to the control culture, glucose and xylose treated cultures do have some single-cell growth, division, and dilution of mCherry. The glucose and xylose treated cultures are similar throughout the course of the experiment, suggesting, as in previous experiments in 1% LB and through bulk measurements in 100% LB, that the effectiveness of glucose and xylose in sensitizing stationary phase E. coli to beta-lactams is not solely determined by differences in cell growth. Finally, in this preliminary experiment, we compared median fluorescence levels of the control culture at 0, 4, and 24 hours with the median

43 Figure 3-7: Fluorescence Dilution Measurement of Single-Cell Growth of Glucose or Xylose Treated Cultures. A) Distribution of single-cell mCherry fluorescence levels of control culture (induced growth to stationary phase, resuspended in spent medium without carbon source or inducer) at 0, 4, 8, and 24 hours. B) Median (solid line) and inter quartile range (shaded) mCherry fluorescence of the control culture (Control) and cultures diluted 1 to 2 or 1 to 5 in fresh medium. C) Median (solid line) and inter quartile range (shaded) mCherry fluorescence of the control culture (Control) and cultures treated with 10 mM glucose (Glc) or xylose (Xyl). D) Median (solid line) and inter quartile range (shaded) mCherry fluorescence of the indicated treatment conditions atthe indicated times.

44 fluorescence levels of the dilution controls and carbon-treated cultures at fourhours (Figure 3-7D). This comparison suggests that, at least at 4 hours, treatment with up to 50 mM glucose or xylose stimulates less growth and division than a 5-fold dilution into fresh medium. This comparison is limited, however, because the response to single carbon sources – here glucose or xylose – likely occurs on a different time scale than culture growth in fresh medium. Future studies with greater dynamic range, particularly at later time points, are warranted.

3.5 Discussion

In this chapter, we show that single carbon sources are sufficient metabolic stimulation to sensitize tolerant, stationary phase E. coli to beta-lactam antibiotics. In contrast to previous studies which suggested single carbon sources couldn’t restore beta-lactam killing against stationary phase E. coli, we found that higher beta-lactam and carbon-source concentrations are indeed effective. Notably, in our experimental conditions we found that xylose was the best single carbon source, despite inter- replicate variability in the minimum ampicillin concentration sensitized in 100% LB cultures. As previous studies with aminoglycosides [2] and quinolones [50] did not lead to cross-testing xylose with beta-lactam antibiotics, our work here reinforces the importance of searching for drug-specific strategies, rather than relying solely on translation of counter-tolerance strategies developed for other antibiotics. Overall, the identification of a metabolic counter-tolerance strategy reinforces the central roleof bacterial metabolism in antibiotic efficacy [105], and provides support for continued studies exploring metabolic counter-tolerance strategies for other antibiotic classes and in other organisms. Additional follow up studies improving single-cell growth and division measurements are warranted. This original fluorescence dilution experiment (Figure 3-7) had limited dynamic range, and thus limited resolution, particularly at later time points. Having collected initial data validating the method in stationary phase bacteria, transition from microscopy to flow cytometry would aid in both improving the

45 dynamic range of the assay and enabling easier sample collection, as collecting sufficient images for each sample was time consuming with the equipment used here. For this preliminary experiment, microscopy was used primarily due to easy access to a microscope and a desire to rapidly validate the method before involving collaborators or paying for core facility use.

Regardless, the population-level and single-cell growth measurements presented in this chapter suggest that metabolite-stimulated cell growth is not a key differentiating characteristic between carbon sources. This is in line with recent studies that suggest metabolic state and ATP levels, rather than growth, are key determinants of antibiotic lethality [79]. Thus, future study into metabolic changes induced by individual carbon sources may lead to an optimal stimulus which maximally sensitizes stationary phase E. coli while stimulating limited to no bacterial growth.

The key question remaining from this chapter is this: what differentiates the different carbon sources? Are differences between carbon sources related to differences in ATP generation [79]? Alternatively, is the effectiveness of a given carbon source related to levels of necessary metabolite-specific catabolic enzymes [133, 5], which may then determine ATP generation or activity in different metabolic pathways? In the case of aminoglycoside antibiotics, the relative efficacy of a given metabolite was linked to the ability to stimulate PMF and thus increase aminoglycoside transport into the cell [2, 87]. While PMF was not measured in this study, and PMF is not known to be as influential with beta-lactams as with aminoglycosides due to aminoglycoside transport requirements, because the relative efficacy of these carbon sources with beta-lactams differs from that measured previously with aminoglycosides [2], itis unlikely PMF generation distinguishes carbon source effectiveness for beta-lactams. Some initial studies which may begin to answer these questions are as follows. To assess the importance of ATP or other metabolic parameters, as well as a given carbon sources ability to stimulate ATP generation, various kits which quantify ATP or NAD levels could be used. To assess the importance of metabolite-specific enzyme availability, catabolic enzymes for different carbon sources could be constitutively expressed or have their expression titrated from a plasmid, thus bypassing variations

46 in enzyme expression due to catabolite repression, and allowing measurements of how carbon source preferences (determined by enzyme availability) affect the ability of different carbon sources to stimulate beta-lactam lethality. Similarly, combining these experiments with either a promoter-reporter library with promoters responsive to different carbon sources or using qPCR to assess the responsiveness of theculture to a given carbon source could provide insight into how transcriptional responses to a given carbon source affect relative efficacy at potentiating beta-lactam killing. Further exploration of what culture states are required for carbon source activity will guide further optimization of this counter-tolerance approach, both in vitro and in future attempts to translate this approach into in vivo settings.

47 48 Chapter 4

Enhancing Beta-Lactam Counter-Tolerance Using D-Amino Acids

4.1 Introduction

In chapter 3 we showed that single carbon sources are able to sensitize tolerant, stationary phase E. coli to beta-lactam antibiotics. In part, this was because we used higher ampicillin concentrations than were tested previously [2, 50], while also testing carbon sources specifically with beta-lactams. However, these single carbon sources still show limited effectiveness, requiring high ampicillin concentrations to be effective. We next sought to find a strategy that worked better, and couldsen- sitize stationary phase E. coli to lower beta-lactam concentrations. In the spirit of drug-specific strategies (see 3.1.1), we sought a strategy that specifically targeted peptidoglycan. This approach was informed by stationary phase-specific changes to bacterial peptidoglycan, which have been linked to antibiotic efficacy.

49 4.1.1 Stationary Phase Peptidoglycan

As discussed previously (see 3.1.2), despite high stationary phase tolerance of E. coli to the peptidoglycan-targeting beta-lactam antibiotics, peptidoglycan synthesis and maintenance processes are active during stationary phase [96]. Additionally, stationary phase peptidoglycan features a number of structural differences compared to exponential phase peptidoglycan which may inform strategies to restore beta-lactam sensitivity to stationary phase bacteria. Compared to exponential phase peptidoglycan, stationary phase peptidoglycan features more peptide cross-linking and increased levels of anhydromuropeptides indicative of shorter glycan chains [96]. Similar changes in peptidoglycan structure have been observed in low-growth conditions such as shifts to amino-acid starvation [120] or slow growth rates in chemostats [118], conditions like stationary phase which are associated with beta-lactam tolerance. Notably, changes in peptidoglycan structure that occur during the transition into stationary phase have been linked to antibiotic efficacy, as treatment in late exponential phase with beta-lactams such as piperacillin, inhibiting some peptidoglycan remodeling, reduces persister formation during overnight culture [1].

4.1.2 L,D-Transpeptidases and Beta-Lactam Efficacy

Increases in stationary phase peptidoglycan cross-linking are primarily due to increased abundance of 3-3 or mDap-mDap cross-links [96], an alternative peptide- linkage than the 4-3 or D-ala-mDap cross-links made by the D,D-transpeptidases that are the primary targets of most beta-lactams [123]. While only approximately 6% of muropeptides have an mDap-mDap cross-link in exponential phase, this rises to 10-12% in stationary phase; about 60% of the increase in cross-linking observed in stationary phase is thus due to increasd 3-3 cross-linking [96]. These alternative 3-3 cross-links are made by L,D-transpeptidases [80, 123]. Inter- estingly, laboratory evolution has revealed that in some organisms such as Enterococ- cus faecium, D,D-transpeptidases can be bypassed and the microbe can rely entirely on L,D-transpeptidases for peptidoglycan cross-linking, thus becoming beta-lactam

50 resistant [81]. In Mycobacterium tuberculosis, alternative 3-3 crosslinks are more common than in E. coli, and L,D-transpeptidase activity has been linked to beta-lactam efficacy. Deletion of a mycobacterial L,D-transpeptidase improves beta-lactam efficacy against M. tuberculosis [49], and targeting L,D-transpeptidases with carbapenem beta-lactams such as meropenem also shows efficacy against M. tuberculosis [61]. Collectively, these studies show that L,D-transpeptidases have increased importance in stationary phase, and suggest an opportunity to improve beta-lactam efficacy against stationary phase bacteria by targeting L,D-transpeptidases.

4.1.3 D-Amino Acids and Beta-Lactam Efficacy

Many bacterial species, including E. coli, can non-specifically incorporate NCDAAs (non-canonical D-amino acids) into their peptidoglycan [20, 21]. Notably, resting E. coli (starved or stationary phase) are capable of incorporating D-amino acids into their peptidoglycan [20, 66], and this occurs even in the presence of beta-lactams such as penicillin [116] or ampicillin [20] at high concentrations. Thus, in E. coli, high synthetic activity and incorporation of new peptidoglycan precursors is not required for D-amino acid modification of peptidoglycan. Previous studies have found that D-amino acids impact beta-lactam efficacy, though results have been inconsistent. An early study in Alcaligene fecalis found D-amino acids were synergistic with penicillin in inducing cell shape changes [68]. A later study, in contrast, observed antagonism between D-amino acids and beta-lactam lethality [122]. D-amino acid modifications to peptidoglycan are known to impact antibiotic sensitivity in other settings. Notably, changes to the peptide stem in enterococci confer resistance to vancomycin [103]. Additionally, introduction of D-serine to the fifth position of the S. aureus peptide stem is associated with sensitization of methicillin-resistant S. aureus (MRSA) to methicillin due to altered binding efficacy of the modified peptide stem to the mecA gene product and other PBPs [124]. While E. coli and S. aureus appear to primarily incorporate NCDAAs into peptidoglycan through different mechanisms – as E. coli primarily incorporates NCDAAs into the fourth stem position through L,D-transpeptidase action [21, 66] – this example with

51 MRSA supports the idea that D-amino acid supplementation can alter beta-lactam efficacy. Additional evidence suggesting addition of NCDAAs may alter stationary phase beta-lactam efficacy comes from study of beta-lactam antibiotics which have D-amino acids in their side chains. These beta-lactams show increased efficacy against bacteria in late exponential phase [116], against slowly growing bacteria in a chemostat [30], or against amino-acid starved bacteria [121, 117], all conditions where efficacy of beta- lactams like penicillin or ampillin is reduced, and peptidoglycan changes mirror those which occur upon entry into stationary phase (see section 4.1.1). In conclusion, previous studies suggest D-amino acids may specifically enhance beta-lactam efficacy in stationary phase. A key change in peptidoglycan structure in stationary phase is mediated by L,D-transpeptidases, enzymes which themselves are linked to beta-lactam efficacy in different organisms. While L,D-transpeptidases play a dominant role in NCDAA incorporation in E. coli [21, 66], NCDAAs have additional affects on peptidoglycan (discussed later, see section 4.5), which may mediate NCDAA-associated affects on beta-lactam efficacy. We thus sought to test theeffects of D-amino acids on beta-lactam efficacy against stationary phase E. coli.

4.2 Testing Single D-Amino Acids

To test the ability of D-amino acids to sensitize tolerant, stationary phase E. coli to beta-lactam antibiotics, we first looked at the ability of individual D-amino acids to restore ampicillin-induced lysis in cultures grown in 1% LB, using culture methods as described in chapter 3 (see Figure 3-1). We tested 9 different D-amino acids, chosen because they were soluble to at least 0.15 M in water. Of these D-amino acids, only two – D-alaine and D-serine – were able to sensitize tolerant cultures to 100 휇g/ml ampicillin, as measured by a decrease in optical density relative to the control culture, indicative of cell lysis (Figure 4-1A). We took these two D-amino acids then and tested their ability to restore killing by 1000 휇g/ml ampicillin to cultures grown in 100% LB. We found that neither D-alanine or D-serine alone were effective

52 at sensitizing full density (100% LB) cultures to this high concentration of ampicillin (Figure 4-1B)

Figure 4-1: Most D-Amino Acids Alone Are Ineffective at Sensitizing Ampi- cillin Even in Low Density Cultures. A) Optical density of E. coli cultures grown in 1% LB and treated for 24 hours with 100 휇g/ml ampicillin. D-amino acids were added at 10 mM. Bars represent the mean of at least 3 replicates, with individual replicates shown. B) Survival of stationary phase E. coli cultures grown in 100% LB and treated for 24 hours with 1000 휇g/ml ampicillin. D-amino acids were added at 10 mM. Bars represent the mean of at least 2 replicates, with individual replicates shown. No DAA/NTC: no D-amino acid; D-ALA/D-ala: D-alanine; D-SER/D-ser: D-serine; D-MET: D-methionine; D-THR: D-threonine; D-ILE: D-isoleucine; D-LEU: D-leucine; D-PRO: D-proline; D-VAL: D- valine; D-NLE: D-norleucine.

We next wanted to explore why only D-alanine and D-serine were effective at sensitizing cultures grown in 1% LB to ampicillin. Both D-alanine and D-serine are capable of acting as sole carbon sources for E. coli, unlike the other D-amino acids tested, suggesting that D-alanine and D-serine are able to impact central carbon metabolism in addition to any peptidoglycan-specific effects they may have. Indeed, both D-alanine and D-serine are catabolized into pyruvate by the enzymes DadA and DsdA, respectively [126, 12]. Therefore, we reasoned that stimulation of central carbon metabolism is necessary for sensitization. To test this hypothesis, we compared the ability of D-alanine and L-alanine – which can be converted to D-alanine by alanine racemases in the cell and then catabolized through DadA – to sensitize

53 ∆dadA cultures to ampicillin. Indeed, neither amino acid was able to sensitize 1% LB cultures of ∆dadA to ampicillin (Figure 4-2A). Next, rather than genetically

Figure 4-2: D-Alanine Affects Ampicillin Sensitivity A) ∆dadA cultures grown in 1% LB and treated for 24 hours without (top) or with (bottom) 10 mM pyruvate (PYR). Amino acids and pyruvate are added at 10 mM. Values represent the mean of at least three replicates with error bars showing standard deviation. Limit of detection is 100 CFU/ml. No AA: no alanine; D-ALA: D-alanine; L-ALA: L-alanine. B) dadA culture grown in 1% LB and treated for 24 hours. Carbon sources (vertical axis) and amino acids were both added at 10 mM. The fold change in the lysis threshold relative to the given carbon source alone is plotted on a log2 scale. Bars represent the mean of at least 3 individual replicates, also shown. GLC: glucose; PRV: pyruvate; FRC: fructose; MAN: mannitol; GLY: glycerol; RIB: ribose; GLN: gluconate; ARA: arabinose; XYL: xylose; SUC: succinate; PRP: proprionate. complementing the ∆dadA knockout, we metabolically complemented the knockout. That is, we added pyruvate separately and looked to see what effect D- and L-alanine

54 have independent of their direct effects on central carbon metabolism. Here we found that in the presence of metabolic stimulation from pyruvate, addition of D-alanine led to sensitization and killing at lower ampicillin concentrations, whereas L-alanine had a slight antagonistic effect with pyruvateFigure ( 4-2B). We repeated this metabolic complementation with a panel of different carbon sources to confirm it was not specific to pyruvate, and indeed found that D-alanine consistently improved sensitization in the ∆dadA strain whereas L-alanine had no or a negative effectFigure ( 4-2C). Collectively, these results suggest that D-alanine impacts WT E. coli through both central carbon metabolism and a second pathway, likely through interacting with peptidoglycan.

4.3 Combining D-Amino Acids with Metabolic Stim- ulation by Carbon Sources

Having found that D-alanine has both metabolic and non-metabolic effects, we next wanted to test this combined approach – D-amino acid plus carbon source. First, we tested the pyruvate and D-alanine combination in WT bacteria, still in 1% LB (Figure 4-3). While D-alanine alone was more effective than pyruvate alone, the combination of pyruvate and D-alanine was less effective than D-alanine alone. Swap- ping D-alanine for D-methionine, in contrast, was more effective than pyruvate or D-alanine alone at 10 mM or 20 mM concentrations. Thus, while D-alanine has some effect on it’s own, it is antagonistic in combination with pyruvate. Additionally, we see that the effectiveness of the pyruvate/D-methionine combination is not simply due to having 20 mM of added metabolites, as 20 mM pyruvate is much less affective than the pyruvate and D-methionine combination. Overall, this supports a two part approach to enhancing our counter-tolerance strategy: first, a carbon source that simulates bacterial metabolism, and second, a D-amino acid which can interact with bacterial peptidoglycan. We next tested this combined approach – D-amino acid and carbon source – with

55 Figure 4-3: Combining Pyruvate with D-Alanine is Not Optimally Effective in WT E. coli Optical density (OD600) of stationary phase cultures grown in 1% LB and then treated for 24 hours with ampicillin. Colorbar is scaled such that red indicates increases and blue decreases in optical density relative to the control (NTC). Carbon source and amino acid concentrations are listed on the vertical axis. The average of two replicates is shown. NTC: no carbon source or amino acid; Pyr: pyruvate; D-ala: D-alanine; D-Met: D-methionine. the rest of our D-amino acid panel to see which D-amino acids may be most effective. To test this, we quantified the ampicillin lysis threshold in 1% LB for pyruvate alone and in combination with 10 mM of a given D- or it’s corresponding L-amino acid. Similar to our findings with alanine in the ∆dadA knockout, the D-amino acids indeed caused a leftward shift in ampicillin lysis threshold, and the L-amino acids had no effect or less of an effect than the D-aminoFigure acids( 4-4A, B). One notable exception is D-proline, which had a minimal effect and was not differentiable from it’s L-isomer. In a 1992 study which included four of these amino acids, D-methionine, D-valine, and D-norleucine were all confirmed using HPLC analysis of peptidoglycan composition to be incorporated into peptidoglycan unlike their L-isomers [20]. In contrast, D-proline was not able to be incorporated into peptidoglycan [20]. Thus, these results provide support for the hypothesis that D-amino acids have peptidoglycan-

56 Figure 4-4: D-Amino Acids Potentiate Ampicillin when Combined with Pyruvate A) Optical density of 1% LB cultures 24 hour treatment. Thick lines and symbols represent the mean of at least three replicates. Replicates are given as thin lines. NTC: no addition; Pyr: 10 mM Pyruvate; D-Met: 10 mM D-Methionine. B) WT 1% LB culture treated for 24 hours with ampicillin and 10 mM pyruvate. Lysis threshold with 10 mM of the indicated amino acid relative to the lysis threshold for pyruvate alone is shown on a log2 scale. Bars represent the mean of at least 3 individual replicates, also shown. Shown below is the same data for the ∆dadA mutant with pyruvate and D- and L-alanine, which is the same data as shown previously in Figure 4-2. C) Change in optical density relative to the no treatment control. Cultures were grown to stationary phase in 1% LB and treated for 24 hours with the D-amino acids listed on the vertical axis without pyruvate (black) or with pyruvate (red). Bars represent the mean of at least 3 individual replicates, also shown. Shown below is the same data for the dadA mutant with D-alanine and with or without pyruvate. PRO: proline; LEU: leucine; ILE: isoleucine; THR: threonine; NLE: norleucine; VAL: valine; MET: methionine; ALA: alanine.

57 specific effects in our experimental system. Additionally, we looked at the effectsof D-amino acids, in the absence of ampicillin, to assess their impact on population- level cell growth, and we found that the D-amino acids with or without pyruvate did not stimulate increased growth as measured by biomass accumulation and optical density (Figure 4-4B). This further supports a mechanism by which D-amino acids act through a peptidoglycan-specific pathway rather than stimulating central carbon metabolism.

We next wanted to test if the ability of D-amino acids to enhance sensitization by carbon sources is a drug-specific effect. To do this, we tested the effect ofD- methionine in combination with pyruvate against the aminoglycoside antibiotic gentamicin and the quinolone antibiotic ciprofloxacin. We performed these experiments in 1% LB to be consistent with the ampicillin data collected thus far and because 10 mM of these metabolites has a larger effect size in 1% LB compared to 100% LB. We found that the carbon source pyruvate did indeed sensitize stationary phase E. coli to both gentamicin and ciprofloxacin (Figure 4-5B). For both gentamicin and ciprofloxacin, 10 mM D-methionine showed no effect on it’s own and showed aslight antagonistic effect (gentamicin) or small positive effect (ciprofloxacin) in combination with pyruvate. Compared with the 8-fold curve shift induced by D-methionine with pyruvate against ampicillin (Figure 4-5A), these small effect sizes suggest that D- methionine at 10 mM does not potentiate the aminoglycoside antibiotic gentamicin or the quinolone antibiotic ciprofloxacin. Collectively, these results suggest that the effect of D-methionine is specific to beta-lactams, at least at the concentrations of carbon source, D-amino acid, and antibiotics tested, and further supports a mechanism by which D-amino acids specifically interact with peptidoglycan to enhance beta-lactam antibiotic effcacy.

Finally, we wanted to test if D-methionine is acting in the periplasm and not the cytosol. To do this, we built a ∆metNIQ knockout strain which is unable to transport D-methionine into the cytosol [52, 85]. If D-methionine acts through the cytosol exclusively, deletion of MetNIQ should prevent D-methionine potentiation. If D-methionine potentiates ampicillin killing by interacting with peptidoglycan, this

58 Figure 4-5: D-Methionine Specifically Potentiates Beta-Lactam Antibiotics. A) CFU/ml of 1% LB culture after 24 hour treatment with ampicillin. B) CFU/ml of 1% LB cultures after 24 hour treatment with gentamicin (left) or ciprofloxacin (right). For all, thick lines and symbols represent the mean of at least three replicates. Replicates are given as thin lines. NTC: no pyruate or amino acid; Pyr: 10 mM pyruvate; D-Met: 10 mM D-methionine; L-Met: 10 mM L-methionine.

∆metNIQ strain will still D-methionine potentiation. Indeed, when combined with three different carbon sources – pyruvate, xylose, or glucose – D-methionine consistently improve ampicillin potentiation in the ∆metNIQ strain (Figure 4-6). This further supports a periplasmic, peptidoglycan-specific mechanism for D-amino acid sensitization of beta-lactam antibiotics.

59 Figure 4-6: D-Methionine Still Enhances Ampicillin Lethality in a ∆metNIQ Knockout. Optical density (OD600) of ∆metNIQ (left) or WT (right) cultures grown to stationary phase in 1% LB and treated for 24 hours with the indicated metabolites and ampicillin. Colorbar is scaled such that red indicates increases and blue decreases in optical density relative to the control (NTC). Average of two replicates. NTC: no metabolite or amino acid; D-Met: 10 mM D-Methionine; Pyr: 10 mM Pyruvate; Xyl: 10 mM Xylose; Glc: 10 mM Glucose.

4.4 Application to Other Organisms and 100% LB

Having shown that carbon sources sensitize tolerant, stationary phase E. coli to beta-lactam antibiotics, and having shown in 1% LB that D-amino acids enhance sensitization and make cultures sensitive to lower beta-lactam concentrations, we wanted to test this approach both in 100% LB to determine the efficacy of this counter- tolerance approach and against other organisms to determine the generalizability of this counter-tolerance approach. First, we returned to 100% LB. We decided to use xylose – the most effective single carbon source identified in chapter 3 of this thesis – in combination with D-methionine, which was the most effective D-amino acid in combination with pyruvate in 1% LB (Figure 4-4). In 100% LB, 10 mM xylose in combination with 10 mM D-methionine was able to sensitize stationary phase E. coli to sub-100 휇g/ml concentrations of the beta-lactams ampicillin and piperacillin (Figure 4-7A). When we repeated this experiment with 50 mM xylose, we observed

60 Figure 4-7: Xylose and D-Methionine Enable Killing by sub-100 휇g/ml Beta- Lactam Concentrations. CFU/ml of 100% LB cultures after 24 hour treatment with ampicillin (left) or piperacillin (right). A) 10 mM xylose. B) 50 mM xylose. NTC: no xylose or D- methionine. D-Met: 10 mM D-methionine. beta-lactam killing at even lower concentrations, as expected (Figure 4-7B). Thus, combining carbon source stimulation with D-amino acids was indeed able to improve the lower-limit of our counter-tolerance approach beyond that observed in chapter 3 of this thesis, reaching the low beta-lactam concentration ranges that previously limited the efficacy of counter-tolerance approaches against beta-lactam antibiotics [2, 50].

Next, we repeated fluorescence dilution measurements, as done in chapter 3(see Figure 3-7), with D-methionine to determine if D-methionine is affecting bacterial growth and division. In these preliminary experiments, the D-methionine treated culture was similar to the untreated control, and D-methionine in combination with

61 xylose was similar to xylose (Figure 4-8A, B). Despite the limitations of these

Figure 4-8: Fluorescence Dilution Measurement of Single-Cell Growth of D-Methionine Treated Cultures. A) Median (solid line) and inter quartile range (shaded) mCherry fluorescence of the control culture (Control) and cultures treated with 10 mM D-Methionine (D-Met), 10 mM Xylose (Xyl), or D-methionine and xylose. B) Median (solid line) and inter quartile range (shaded) mCherry fluorescence of the indicated treatment conditions at the indicated times. D-methionine (DM) is used at 10 mM and xylose (X) at 10 or 50 mM. intiail fluorescence dilution experiments (discussed in section 3.5), these results suggest D-methionine does not impact single-cell growth and division, providing further support for D-amino acids like D-methionine acting through a peptidoglycan-specific mechanism. Having shown this two part counter-tolerance method generalizes to beta-lactams in 100% LB, we next wanted to see if this approach generalizes to other bacteria. First, we tested if our approach could be applied to tolerant cultures of resistant clinical isolates grown in 1% LB for simplicity. We generated tolerant, stationary phase

62 cultures of a clinical isolate of Klebsiella pneumoniae which is resistant to ampicillin due to a plasmid-borne extended spectrum beta-lactamase (ESBL). ESBL enzymes can be inhibited by beta-lactamase inhibitors, such as sulbactam, which restore beta- lactam sensitivity for exponentially growing bacteria [37]. However, in simultaneously tolerant and resistant cultures, sulbactam alone was unable to restore sensitivity to ampicillin, just as our counter-tolerance approach alone was ineffectiveFigure ( 4- 9). Combining sulbactam with our counter-tolerance approach – here pyruvate and D-methionine – successfully restored ampicillin sensitivity to cultures of resistant K. pneumoniae in stationary phase (Figure 4-9). These results indicate that in the case of stationary phase tolerance and plasmid-borne beta-lactamase resistance, tolerance and resistance form a therapeutic AND gate, necessitating treatment strategies capable of addressing both to effectively treat simultaneously tolerant and resistant bacteria.

Figure 4-9: Sensitizing Stationary Phase K. pneumoniae to Ampicillin CFU/ml after 24 hours treatment with ampicillin and indicated counter-tolerance or -resistance strategies. Counter-Resistance: 10 휇g/ml sulbactam. Counter-Tolerance: 10 mM pyruvate and 10 mM D-methionine. Bars represent mean and standard deviation of three replicates.

Because our counter-tolerance approach was effective in combination with beta-

63 lactamase inhibitors against bacteria with acquired resistance, we next wanted to test our approach against Mycobacteria which are intrinsically resistant to beta-lactams due to genomically-encoded beta-lactamases. Increasing levels of multidrug resistant Mycobacterium tuberculosis has resulted in increased interest in using beta-lactams to treat Mtb infections [108]. Many beta-lactams, however, have limited efficacy against Mycobacteria. In addition to the expression of genomic beta-lactamases, Mycobac- teria also have altered peptidoglycan structure, with increased levels of crosslinks formed by L,D-transpeptidases [108]. Efficacy of beta-lactams, which primarily target D,D-transpeptidases, can be enhanced by simultaneously targeting these L,D- transpeptidases [61, 49]. Because L,D-transpeptidases are known to incorporate NC- DAAs in Mycobacterium smegmatis [4], we hypothesized that our approach with D-methionine would also be able to sensitize tolerant cultures of M. smegmatis to ampicillin. Indeed, D-methionine and glucose in combination with sulbactam sensitized stationary phase M. smegmatis to ampicillin (Figure 4-10), consistent with our observations with the resistant clinical isolate of K. pneumoniae. Thus, as beta- lactams continue to be explored for the treatment of Mtb, our method presents a potential metabolic adjuvant strategy for improving beta-lactam effectiveness against Mycobacteria.

4.5 Discussion

In this chapter, we show that D-amino acids can enhance sensitization of stationary phase bacteria to beta-lactam antibiotics when combined with stimulation of central carbon metabolism by a carbon source. While single D-amino acids such as D-alanine and D-serine – which can interact with both bacterial peptidoglycan and central carbon metabolism – showed limited effectiveness alone, these D-amino acids led to the development of a combined DAA/carbon source approach. We demonstrated using comparisons between D- and L-amino acids, testing D-amino acids with non-beta-lactam antibiotics, and preventing D-methionine transport to the cytosol that D-amino acids enhance beta-lactam efficacy through a peptidoglycan-specific

64 Figure 4-10: Sensitizing Stationary Phase M. smegmatis to Ampicillin CFU/ml after 48 hours treatment with ampicillin, 10 휇g/ml sulbactam, and 10 mM of the indicated metabolites. Bars represent mean with two replicates shown. NTC: no treatment control; Glc: 10 mM glucose; D-Met: 10 mM D-Methionine; D-Met Glc: 10 mM glucose and 10 mM D-Methionine. mechanism. Additionally, we show that this counter-tolerance strategy generalizes to other beta-lactam antibiotics and other organisms.

Together, chapters 3 and 4 present the following method of sensitizing tolerant bacteria to beta-lactam antibiotics: carbon source stimulation of central carbon metabolism in combination with a D-amino acid acting in a target-specific manner on bacterial peptidoglygcan. Furthermore, we demonstrated using M. smegmatis and a clinical, beta-lactamase producing isolate of K. pneumoniae that counter-tolerance and counter-resistance approaches can be combined, and when treating simultaneously tolerant and resistant bacteria, both counter-tolerance and counter-resistance approaches are needed.

In chapter 3 (see section 3.5) I highlighted outstanding questions related to identifying an optimal carbon source for use in this counter-tolerance approach. Now, it is possible that the optimal carbon source in combination with a D-amino acid differs from the optimal carbon source when used alone. Additionally, as with strain- or environment-specific carbon sources, the optimal D-amino acid may also have strain- and environment-specificity, as different organisms have different D-amino acidin-

65 corporation mechanisms [21, 66] and different D-amino acids impact peptidoglycan in different ways [19]. Thus, future work characterizing requirements for D-amino acids to affect beta-lactam efficacy and clarifying mechanism of action are requiredto identify optimal carbon source/D-amino acid combinations in different environments.

The most interesting outstanding question for this chapter is this: How do D- amino acids alter beta-lactam efficacy? In the introduction to this chapter, I presented evidence linking beta-lactam efficacy, changes in stationary phase peptidoglycan, L,D- transpeptidases, and D-amino acids, specifically in a way that suggested D-amino acids may partially inhibit L,D-transpeptidases and thus alter beta-lactam efficacy. However, potential ways D-amino acids alter beta-lactam antibiotic efficacy are more complicated than that, which I will review briefly here. Because of redundancy in peptidoglycan modifying enzymes and connectivity between different processes, it is likely both challenging to completely separate these affects and likely that multiple aspects contribute. Certain aspects of D-amino acid interaction with peptidoglycan are outlined in Figure 4-11, which is only partially complete.

It is known that in E. coli, non-canonical D-amino acids can be incorporated into peptidoglycan via L,D-transpeptidases [21, 80]. Recent evidence demonstrates that D,D-transpeptidases can also incorporate NCDAAs into the fifth position in the peptide stem, though D,D-carboxypeptidases usually cleave this bond, making it hard to detect this modified muropeptide [66]. Thus, one pathway through which D-amino acids may impact beta-lactam efficacy is through direct action and perhaps inhibition of both L,D- and D,D-transpeptidases, or as recently suggested, perhaps even activation and priming of these enzymes making them more sensitive to beta- lactam inhibition [66]. Once D-amino acids are incorporated into peptidoglycan they have other mechanisms of altering beta-lactam efficacy. One consequence of growth in the presence of NCDAAs in E. coli is reduced peptidoglycan cross-linking [20], which would weaken the peptidoglycan and make new 4-3 cross-links of increasing importance. Indeed, the converse is true: a recent study found that synthetically increasing cross-linking protected cells from beta-lactams [99]. Next, there is in vitro evidence that peptide stems containing D-amino acids are more sensitive to lytic

66 Figure 4-11: Effects of Non-Canonical D-Amino Acids (NCDAAs) onAs- pects of Peptidoglycan Maintenance transglycosylases [19, 70], and activity of lytic transglycosylases is known to contribute to beta-lactam-induced peptidoglycan futile cycling [25]. Finally, LcdA is less effective on recycled muropeptides containing a NCDAA [72], and LcdA activity is known to influence peptidoglycan stability in stationary phase [58, 111]. In summary, there are many potential explanations for why D-amino acids alter beta-lactam efficacy.

67 68 Chapter 5

Future Directions and Conclusions

5.1 Antibiotic Discovery and Resistance

The initial discovery of the beta-lactam antibiotics by way of penicillin in 1929 helped usher in what’s considered a golden age of antibiotic discovery [18]. While early screening platforms and medicinal chemistry approaches initially led to great success in identifying new antibiotics to treat bacterial infections, the success of these strategies eventually slowed, revealing clinical challenges coupled to the spread of antibiotic resistance [18]. Antibiotic use inevitably leads to resistance, notably through both endogenous mutation and acquisition of resistance elements through horizontal gene transfer [33]. As an example of the seemingly inevitable occurrence of antibiotic resistance, a beta-lactamase capable of degrading penicillin and thus conferring penicillin resistance was observed in 1940 even before widespread use of penicillin to treat infections during World War II and afterwards [76]. Indeed, the time between antibiotic deployment and resistance is short for beta-lactams and other antibiotic classes [28]. Combating antibiotic resistance requires multiple complementary approaches. In the clinic, antimicrobial stewardship seeks to optimize the use of antibiotics and limit selection for antibiotic resistance [35]. New antibiotics are also continuing to be dis- covered, leveraging technologies which enable culturing of previously-unculturable bacteria [74], mining previously un-screenable parts of microbial genomes [31], and using machine learning to identify promising antibiotics [106], as well as biological ap-

69 proaches such as phages, immune-targeted approaches, or antibiotic adjuvants [112]. A third complementary approach includes those described in this thesis: work that seeks to understand how antibiotics work and how to make them work better for eventually clinical translation. The emphasis on antibiotic resistance clinically is in part because of our accumu- lating knowledge of the genetics of antibiotic resistance as well as our ability to test for resistance clinically. We know many resistance mechanisms [33] and can design rapid, sequence-based methods to detect common genetic signatures for diagnostics [8]. Clinically, resistance measurements are common diagnostics [128], whereas quantification of either tolerance or persistence is both more technically challenging and recognized to be limited by experimental condition and context dependence [128, 86]. However, increasing evidence of the clinical importance of tolerance and resistance [86, 47]. Tolerance and persistence are also capable of facilitating evolution of resistance [128, 73, 75], further demonstrating the importance of studying and enhancing antibiotic lethality. In the remainder of this chapter, I further discuss opportunities within antibiotics research for future work that is relevant to the work presented in this thesis. In particular, I discuss recent work characterizing context-dependent antibiotic efficacy and potential strategies to continue improving the clinical relevance of in vitro antibiotics research, and I discuss important outstanding questions when it comes application and implementation of counter-tolerance approaches.

5.2 Context-Dependence and Clinical Relevance

Studying context-dependent antibiotic efficacy can take multiple forms. The work presented in this thesis explored multi-drug contexts and demonstrated a method to enhance beta-lactam antibiotic efficacy against stationary phase bacteria. Aswe continue studying antibiotic efficacy, here I want to briefly highlight two approaches that would further the work presented in this thesis and are also critical for future antibiotics research.

70 First, single-cell and quantitative methods are useful for proposing and testing mechanistic links between heterogeneity and antibiotic treatment outcomes. Two recent studies have linked natural variability in gene expression to heterogeneous outcomes following antibiotic treatment. In the first, natural variability in the levels of metabolic enzymes was quantified using flow cytometry and linked to differential ATP levels and corresponding differences in antibiotic sensitivity of bacteria within an otherwise homogeneous culture [133]. In the second, natural variability in promoter activity was tracked and linked to single-cell outcomes like survival after antibiotic challenge using time-lapse microscopy [100]. These studies demonstrate how single- cell and quantitative methods can be used to go beyond observing heterogeneous responses to antibiotic treatment and to start providing mechanistic explanations for variability with a bacterial population, knowledge that can then be used to predict treatment efficacy in other environments. Continued used of single-cell methods combined with leveraging predictive biology approaches [78] provide opportunities to further our understanding of context-dependent antibiotic mechanism of action. Of particular relevance to the work presented in this thesis, single-cell studies of gene expression or metabolic responses may be informative when exploring why some tolerant bacteria respond to metabolic stimuli and others do not. Such in vitro insight could inform optimal clinical translation of metabolic counter-tolerance approaches.

Second, the context-dependence of antibiotic action makes studying clinically relevant environments of increased importance, and clinically relevant experimental conditions are expected to help improve our understanding of antibiotic efficacy and clinical treatment outcomes [86]. Such approaches will not only inform our understanding of antibiotic tolerance and persistence, but also antibiotic resistance. While antibiotic resistance is often considered a genetic phenotype [33], resistance measurements such as minimum inhibitory concentrations are also sensitive to environmental context. For example, a recent study found that culture conditions drastically alter measured resistance to the beta-lactam mecillinam in the context of urinary tract infections (UTIs) [113]. Using isolates with a common resistance mutation in cysB, the authors demonstrate first that resistance levels are higher in common clinical lab-

71 oratory media compared with urine, and second that differences in urine metabolic composition between patients further alters resistance levels of this mutant strain [113]. Therefore, continued characterization of how environmental context alters antibiotic resistance, tolerance, and persistence, as well as continued characterization of infection environments, is expected to improve our ability to predict treatment outcomes and design optimal strategies to enhance antibiotic efficacy.

5.3 Application of Counter-Tolerance Approaches

Counter-tolerance strategies are expected to improve both short- and long-term antibiotic efficacy, first by improving treatment of chronic and recurrent infections [47, 86] and second by delaying the evolution of antibiotic resistance [128]. Here, I discuss three key questions about the application of counter-tolerance strategies, with an emphasis on metabolic counter-tolerance strategies: which carbon sources are effective clinically, will counter-tolerance strategies delay the evolution of resistance, and what are the failure modes of counter-tolerance strategies that emerge from repeated use and bacterial adaptation and evasion.

5.3.1 Context-Dependent Carbon Source Efficacy

There are multiple ways in which environmental factors – such as at an infection site – can alter the efficacy of different carbon sources used to sensitize tolerant bacteria. Thus, further studies into how different carbon sources work as counter-tolerance strategies and what conditions are required for effective metabolic stimulation can inform eventual clinical use of counter-tolerance approaches. First, a carbon source must reach the infection site. Initial in vivo mouse models demonstrated that carbon sources such as mannitol are able to alter outcomes of aminoglycoside treatment of UTIs [2], but the ability of supplemented carbon sources to reach and affect bacteria at other infection sites remains to be seen. Second, bacteria must be able to respond to and consume the given carbon source. Levels of metabolic enzymes vary naturally [133], and levels of these enzymes determine the ability of bacteria to respond to

72 and grow on a given carbon source [5]. Thus, how the infection environment affects bacterial metabolic state will impact how effective different carbon sources are as counter-tolerance adjuvants. Finally, even if enzymes are expressed such that a bac- terium can consume a given antibiotic, other environmental factors can alter efficacy. For example, while fumarate stimulation was effective at sensitizing stationary phase P. aeruginosa to aminoglycoside antibiotics, environmental context still altered efficacy such that exogenous glycoxylate altered metabolic fluxes and protected bacteria from antibiotic killing, even in the presence of tobramycin [87]. In conclusion, it is important to not only understand what determines carbon source efficacy in vitro, but also how a given infection environment allows or impacts relevant aspects of bacterial physiology.

5.3.2 Delaying the Evolution of Antibiotic Resistance

Multiple studies have shown that antibiotic tolerance facilitates the evolution of antibiotic resistance [73, 75, 129]. Evidence that the corollary is true, that countering tolerance can delay the evolution of resistance, is limited. Preliminary evidence in support of counter-tolerance strategies limiting selection for antibiotic resistance comes from a study which plated sensitive bacteria on agar containing the quinolone antibiotic ciprofloxacin, selecting for outgrowth of resistant mutants. When the plates also contained the carbon source mannitol, fewer colonies were selected in strains with mutations conferring high persistence phenotypes [129]. While fewer colonies were selected on plates with mannitol compared to plates without mannitol, it is unclear how resistance levels of selected colonies compare in identical environments, and if mannitol-containing plates merely led to selection of colonies with a higher resistance level when tested in a mannitol-free environment. Further studies are still warranted. Stationary phase provides a useful model system for tolerance that could be useful in future studies which assess the ability of counter-tolerance strategies to delay the evolution of resistance. In contrast to the study of high persistence mutants noted above [129], stationary phase provides a context-dependent form of tolerance. Sta- tionary phase antibiotic treatment – with our without counter-tolerance adjuvants –

73 would then be paired with outgrowth and resistance phenotyping in standard culture conditions. This can then directly compare temporal changes in population-level antibiotic resistance between cultures with or without counter-tolerance co-applied with antibiotic treatment. Additionally, for stationary phase E. coli, metabolic counter- tolerance methods have been developed for aminoglycosides [2], quinolones [50], and now beta-lactam antibiotics. Thus, stationary phase also provides an ability to compare similar, metabolic-based counter-tolerance strategies between antibiotic classes.

5.3.3 Failure Modes of Counter-Tolerance Strategies

Just as antibiotic use inevitably selects for resistance, bacteria are capable of adapting to non-antibiotic environments as well. For example, the E. coli Long-Term Evolu- tion Experiment (LTEE) demonstrates the capacity of E. coli to adapt to long-term culture, with alterations to core functions such as metabolism [13]. Additionally, bacteria adapt to non-antibiotic stressors [83]. Thus, one potential consequence of applying counter-tolerance strategies repeatedly – in the lab or in the clinic – is that bacteria may adapt to the counter-tolerance strategy specifically, leading to treatment failure. Studying failure modes of counter-tolerance strategies can take multiple forms, such as experimental evolution or screening of mutant strain libraries to explore how potential genetic adaptations alter counter-tolerance efficacy. In the case of metabolic counter-tolerance strategies, there’s some initial evidence of available mechanisms of counter-tolerance failure. First, for a carbon source to affect the cell, it must be trans- ported into the cell. In the case of fumarate sensitization of aminoglycosides against P. aeruginosa, there has been described a clinically-relevant rpoN mutation, altering the regulation of fumarate transport into the cell and thus rendering this counter- tolerance strategy ineffective [53]. Downstream of metabolite transport, in this thesis it was shown that a single gene deletion of dadA prevents D-alanine from sensitizing stationary phase E. coli as a single agent (Figure 4-2). Together, these examples suggest at least two potential failure modes of a given metabolic counter-tolerance strategy: mutations altering transport of the carbon source and mutations altering

74 consumption of the carbon source. Computational approaches such as genome-scale metabolic reconstructions [15] may be useful for characterizing available genetic adaptations altering transport and consumption of a given carbon source, similar to work done to characterize genomic and metabolic alterations accompanying the evolution of antibiotic resistance in P. aeruginosa [38]. In particular, computational approaches may be able to identify metabolite sets with overlapping or non-overlapping failure modes that could guide a carbon-rotation strategy similar to drug cycling protocols applied to combat cross resistance to antibiotics [62]. Additionally, for the beta-lactam approach presented in this thesis, the bacteria could evolve resistance to the D-amino acid used. Interestingly, an earlier study trying to select for D-methionine resistance on agar plates did not yield resistant mutants, though resistant mutants to other D-amino acids such as D-tryptophan emerged [19]. Another study found that deletion of three L,D-transpeptidases in E. coli does lead to increased D-methionine resistance [101]. Together, these studies suggest single- step or simple genetic alterations conferring D-methionine resistance may be unlikely. Additionally, D-tryptophan-selected mutants – while they did display cross resistance to other D-amino acids – did not display cross resistance to D-methionine [19], suggesting that even when resistance to one D-amino acid emerges, another may still be efficacious, allowing D-amino acids to be swapped as necessary to preserve treatment efficacy.

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89 90 Appendix A

Methods for Sensitizing Tolerant Bacteria to Beta-Lactam Antibiotics

The following methods apply to chapters 3 and 4. Reagents Antibiotics, all carbon sources except glucose, amino acids, and PBS were purchased from Sigma-Aldrich. Glucose was purchased from Fisher Scientific. LB Broth and 7H9 medium were purchased from Difco. MOPS EZ Rich medium was purchased from Teknova. Bacterial strains All experiments unless otherwise noted used Escherichia coli K12 strain MG1655. Fluorescence dilution experiments used MG1655 Pro carrying pAB224a. Where noted, other strains used for this study include E. coli BW25113 and M. smegmatis MC2155. The clinical isolates of Escherichia coli (isolate DICON- 146) and Klebsiella pneumoniae (isolate DICON-135) were from Duke Medical Center. E. coli genetic knockout ∆dadA was constructed by P1 transduction from the Keio collection into MG1655. E. coli genetic knockout ∆metNIQ was constructed using the Lambda red recombineering [32]. Colony PCR was used to verify deletion accuracy. Plasmid pAB224a expresses mCherry and was a generous gift from Ahmed Badran (Broad Institute). Culture conditions E. coli and K. pneumoniae were grown in LB medium. Where noted, E. coli was grown in MOPS EZ Rich Medium with 10 mM glucose. Overnight cultures of E. coli or K. pneumoniae were inoculated from glycerol stocks

91 and grown in non-baffled flasks for 24 hours° at37 C shaking at 300 RPM. Overnight cultures were grown in the final treatment medium. M. smegmatis was grown in Mid- dlebrook 7H9 supplemented with 0.05% oleic acid, 2% dextrose, and 0.004% catalase (OADC), 0.2% glycerol, and 0.05% tyloxapol. Overnight cultures of M. smegmatis were inoculated from glycerol stocks into 1% 7H9 medium (Supplemented 7H9 medium diluted 1:100 in PBS with 0.05% tyloxapol) and grown in non-baffled flasks for 48 hours at 37°C shaking at 300 RPM.

Killing assays Overnight cultures were dispensed into round bottom, 96-well plates with appropriate amounts of antibiotic and added metabolites. All metabolites were used at 10 mM unless otherwise noted. Plates were incubated for 24 hours (E. coli and K. pneumoniae) or 48 hours (M. smegmatis) at 37°C, 900 RPM, and 60% humidity. Plates were washed once in PBS before being diluted in PBS and plated on LB for CFU quantification. M. smegmatis was washed and diluted in PBS with 0.05% tyloxapol. Experiments with K. pneumoniae were done in 1% LB.

OD measurements Overnight cultures were dispensed into round bottom, 96- well plates with appropriate amounts of antibiotic and added metabolites. All of the metabolites were used at 10 mM unless otherwise noted. Plates were incubated for 24 hours at 37°C, 900 RPM, and 60% humidity. Optical density (OD) was measured at 600 nm on a SpectraMax M3 Microplate Reader spectrophotometer (Molecular Devices). Quantifications of growth (biomass accumulation, change in OD) arefrom samples with no antibiotic added.

Lysis Threshold Determination The lysis threshold for a given antibiotic and metabolite was defined by using a control culture that was not treated with metabolite to define a baseline, stationary phase OD. For a metabolite-treated culture, theOD dose-response for a given antibiotic was fit to a four-parameter logistic curve, and the resulting parameters used to identify the antibiotic concentration corresponding to the OD of the no-metabolite control culture which corresponds to no change in OD, or no growth or lysis of the culture. This concentration is denoted the lysis threshold. For measurements of threshold fold change, comparisons were made within the same treatment day. Curve fitting and calculations were done in Matlab R2018a

92 (Mathworks). CFU Growth Curve Overnight cultures were grown in 100% LB in flasks as for other experiments and at T=0 distributed into 96-well plates with appropriate concentrations of metabolites. Replicate plates were prepared for each time point. Plates were incubated at 37°C, 900 RPM, and 60% humidity and removed at the indicated time points. Plates were washed once in PBS before being diluted in PBS and plated on LB for CFU quantification. Fluorescence Dilution Overnight cultures of MG1655 Pro carrying pAB224a in 100% LB with 50 휇g/ml chloramphenicol were grown for 24 hours in flasks as for other experiments. Two cultures were grown: one with 100 ng/ml anhydrotetracycline for mCherry induction, and one culture with no inducer. After overnight growth, both cultures were washed once in PBS, and the spent medium from the un-induced culture was sterile filtered and used to resuspend the induced culture. This culture was dispensed into 96-well plates with appropriate concentrations of metabolites. Two dilution control cultures were also prepared, where the induced overnight was diluted 1 to 2 or 1 to 5 in fresh LB medium. Replicate plates were prepared for each time point. Plates were incubated at 37°C, 900 RPM, and 60% humidity and removed at the indicated time points. Plates were washed once in PBS and imaged on agarose pads. All fluorescence images were captured with matching exposure times. Image segmentation was done using Cellpose [109]. Image quantification was then done using Matlab R2018a (Mathworks). Only one replicate was completed.