Collateral Changes in Susceptibility of Burkholderia Multivorans

COLLATERAL CHANGES IN SUSCEPTIBILITY OF BURKHOLDERIA MULTIVORANS

Jerilyn Nicole Flanagan

A dissertation submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biology

Charlotte

2019

Approved by:

______Dr. Todd Steck

______Dr. Molly Redmond

______Dr. Adam Reitzel

______Dr. Jennifer Warner

______Dr. John Risley ii

iii ABSTRACT

JERILYN NICOLE FLANAGAN. Collateral changes in susceptibility of Burkholderia multivorans. (Under the direction of DR. TODD R. STECK)

Antibiotic resistant bacteria, particularly in chronic infections, are difficult and costly to treat, so research into detection, identification, and treatment options is critical.

Susceptibility measurement must accurately reflect the degree to which the strain will respond to antibiotics, so the first investigation was of parameters that affect antimicrobial susceptibility testing such as agar depth and hydration. Additionally, in order to study new ways in which bacteria may be vulnerable to antimicrobials, researchers must experimentally evolve bacteria to be drug-resistant. The second methodological investigation involved development of an easy, cost- and labor-efficient process for laboratory evolution of antibiotic-resistant bacteria.

Burkholderia multivorans, a member of the Burkholderia cepacia complex (Bcc), can cause chronic lung infections in vulnerable patients. A possible treatment for chronic infections arises from the existence of collateral sensitivity (CS): decreased resistance to a non-treatment antibiotic acquired along with resistance to the treatment drug.

Identifying CS patterns for bacteria may lead to sustainable treatment regimens that reduce development of multidrug resistant bacterial strains. CS has been found to occur in E. coli, P. aeruginosa, and S. aureus. The two latter studies report that B. multivorans exhibits antibiotic CS, as well as cross resistance (CR), describe CS and CR networks for six clinically-relevant antibiotics, and identify reciprocal CS drug pairs. Characterization of CS and CR patterns allow antibiotics to be separated into two clusters, which is a first step towards predicting antibiotic therapy strategies. iv

DEDICATION

There is no chance I would have completed this dissertation, much less my degree, without the staunch support of my favorite person, Robert Flanagan. It is impossible to put into words my love and appreciation without being trite and cheesy, so

I’ll simply say that when all my mirrors were angry, the enchanted burgundy heart- shaped medallion was perfect.

I love you. This dissertation (as well as anything good that ever comes from me) is for you.

v ACKNOWLEDGEMENTS

My deepest gratitude to Dr. Todd Steck for being my advisor.

I appreciate the help of my committee members – Drs. Molly Redmond, Adam

Reitzel, Jennifer Warner, and John Risley - throughout this process. I’m lucky to have people with genuine enthusiasm for science and academia as my mentors. I also gratefully acknowledge the assistance of Dr. James Oliver, my original advisor. To my labmates and fellow graduate students, I offer thanks for being a sounding board, and for sharing laughter, coffee, and my snacks. In particular, my utmost gratitude to Logan

Kavanaugh for doing the wretched sequencing stuff.

Without the help of people outside of academia, my accomplishments would never be. My Meghan is one of my favorite people, and I’m thankful she was always willing to say “bastard!” when necessary. I’m eternally grateful to Drs. Anthony

Patterson and Kyra Grosman for fixing my brain when it was horribly broken and teaching me that the remaining broken bits were manageable. I thank the microbiology department at Memorial Regional Hospital in Florida for showing me that a nerdy love of all things bacterial is, in fact, super cool. I send a special shout-out to SSC.

A special thanks goes to the billions of organisms that went unknowingly to their deaths to make my project work. I’ll always think of you when I smell bleach. vi TABLE OF CONTENTS

List of Tables viii

List of Figures ix

List of Abbreviations x

CHAPTER 1: Introduction 1

CHAPTER 2: The relationship between agar thickness and antimicrobial 7 susceptibility testing Citation and Abstract 7 Introduction 7 Materials and Methods 9 Results and Discussion 10 References 11 Tables and Figures 13-15

CHAPTER 3: Use of antibiotic disks to evolve drug-resistant bacteria 16 Citation and Abstract 16 Introduction 16 Materials and Methods 18 Results and Discussion 19 References 21 Tables and Figures 24

CHAPTER 4: Burkholderia multivorans exhibits antibiotic collateral 25 sensitivity Citation and Abstract 25 Introduction 26 Materials and Methods 28 Results 31 Discussion 34 References 38 Tables and Figures 44-47

CHAPTER 5: Reciprocal collateral sensitivity of Burkholderia 48 multivorans and genetic mutations involved Citation and Abstract 48 Introduction 49 Materials and Methods 51 Results 55 Discussion 61 References 65 vii Tables and Figures 76-83

CHAPTER 6: CONCLUSIONS 84

REFERENCES 87

APPENDIX A: EVOLVED STRAIN LIST 93

APPENDIX B: ANTIMICROBIAL SUSCEPTIBILITY TESTING 106 RESULTS SPREADSHEETS

viii LIST OF TABLES

CHAPTER 2

Table 1: Minimum inhibitory concentration and zone of inhibition 13 values on regular agar plates

Table 2: Minimum inhibitory concentration and zone of inhibition 14 values on dehydrated agar plates

CHAPTER 3

Table 3: Exposures needed to evolve resistant strains of bacteria to 24 one of six antibiotics

CHAPTER 4

Table 4: Antibiotics used for experimental evolution and testing 44

CHAPTER 5

Table 5: Antibiotics used for experimental evolution and testing 76

Table 6: Strains evolved and collateral changes observed 77

Table 7: Statistical significance of clustering within/between 79 antibiotics

Table 8: Mutations in strains with a treatment drug of levofloxacin 82 and collateral sensitivity to meropenem

Table 9: Mutations in strains with a treatment drug of meropenem 83 and collateral sensitivity to levofloxacin

ix LIST OF FIGURES

CHAPTER 2

Figure 1: Linear regression of minimum inhibitory concentration 15 (MIC) values compared to agar plate weight

CHAPTER 3

Figure 2: Image of bacterial strains before and after evolution of 24 antibiotic resistance

CHAPTER 4

Figure 3: Number of evolved strains exhibiting collateral 45 sensitivity and cross-resistance

Figure 4: Heat map of cross-resistance (CR) interactions 46

Figure 5: Network of collateral susceptibility changes 47

CHAPTER 5

Figure 6: Heat map of collateral resistance and sensitivity 78 interactions

Figure 7: Cross-resistance and collateral sensitivity interactions by 78 cluster

Figure 8: Change in minimum inhibitory concentration by non- 80 treatment drug

Figure 9: Reciprocal collateral resistance/sensitivity pair patterns 81

x LIST OF ABBREVIATIONS

AST antimicrobial susceptibility testing

B. multivorans Burkholderia multivorans

Bcc Burkholderia cepacia complex

βLA non-beta-lactam antibiotic

CHL chloramphenicol

CAZ ceftazidime

CF cystic fibrosis

CR cross-resistance

CS collateral susceptibility/sensitivity

MIC minimum inhibitory concentration

NON-βLA non-beta-lactam antibiotic

NTD non-treatment drug

MEM meropenem

LVX levofloxacin

MIN minocycline

SXT trimethoprim-sulfamethoxazole

TD treatment drug

ZOI zone of inhibition

1 CHAPTER 1: INTRODUCTION

Antimicrobial resistance is a critical problem facing our medical system due to

rapid evolution of resistant bacteria, over-exposure to antimicrobials leading to pan- resistance, and lack of new antibiotics. When bacteria can no longer be treated by an antibiotic to which it was sensitive, infections are harder to control, the risk of spread is increased, morbidity is prolonged with added financial cost, and the risk of death is greater (1). Monitoring bacteria for antimicrobial resistance, developing new antimicrobials or substances to boost efficacy of existing antibiotics, and investigating different ways to exploit bacterial weaknesses are all important components to the clinical counterstrike.

Antimicrobial susceptibility testing (AST) is an in vitro method of evaluating the

level of resistance of a bacterial strain to the drug of interest. Interpretative breakpoints

are established to predict treatment efficacy of an antimicrobial agent in an infected

patient based on in vitro testing (2). “Resistant” bacteria are defined as strains that are not

normally inhibited by usually achievable systemic concentration of an antimicrobial

agent with normal dosage schedule, which is correlated with a specific concentration of

the drug when tested in vitro. The “susceptible” or “sensitive” category includes isolates

that are inhibited by the antibiotic at a physiologically achievable concentration. The

“intermediate” designation is used to describe organisms that might respond to the

antibiotic, depending on the location of the infection and the drug dosage.

There are several methods of antimicrobial susceptibility testing, including broth

dilution and agar diffusion methods (3). For agar diffusion, an antibiotic-impregnated

sterile carrier material such as a paper disk or plastic strip is placed on the top of a solid 2 growth medium and the antibiotic diffuses out, creating a concentration gradient in the medium with the area closest to the disk containing the highest concentration of antibiotic. Inconsistency in test administration causes lower reproducibility of results (4,

5) so care must be taken in test performance. For my first published article, which is

Chapter 2 of this dissertation (6), I investigated the quantitative changes in minimum inhibitory concentration (MIC) at several agar depths.

Antibiotics are classified by target/mechanism of action, which can include inhibition of cell wall or folic acid synthesis, disruption of DNA gyrase activity, or binding to ribosomal subunits to inhibit protein synthesis (7). There are four main classifications of mechanisms of antibiotic resistance: modification of the bacterial target; enzymatic alteration of the drug; change in amount of drug per target by increased or decreased amount of the target, efflux pump-mediated removal of the drug from the intracellular space, and reduced cellular uptake or membrane permeability of the drug; and decreasing toxicity by bypassing the need for the involved pathway or changing cell functionality (8). Some of these mechanisms are specific to the antibiotic and/or target, such as alterations in penicillin-binding proteins, disallowing binding by the antibiotic, or enzymatic alteration of the beta-lactam moiety via cleavage or inactivating addition of a component to the antibiotic, resulting in resistance to cell wall synthesis inhibiting antibiotics. Other mechanisms can be non-specific, as seen with mutations in outer membrane porins or increased expression of efflux pumps resulting in a sub-inhibitory intracellular concentration of the drug.

In a classical paradigm of resistance development, the presence of an antibiotic, referred to as the treatment drug (TD), creates an environment that selects for mutants 3 resistant to that antibiotic; this is termed direct-resistance (9). This happens both naturally and experimentally. Previously used methods to experimentally evolve bacteria include a gradient plate method (10), sequentially-inoculated broths containing a range of drug concentrations (9), and a continuous culture device that adjusts concentration over time

(11). My second article (Ch. 3) resulted from developing an easy, cost-effective novel method to evolve antimicrobial-resistant bacterial strains using paper disks commonly used for AST (12).

The mutations that confer resistance to the treatment drug in antibiotic-adapted

strains may have pleiotropic effects including changed susceptibility to non-treatment

antibiotics (10). An increased resistance to non-treatment antibiotics is termed cross- resistance (CR) and a decreased resistance to non-treatment antibiotics is termed collateral susceptibility (CS) (9, 10, 13-27). This phenomenon has been reported to occur in Escherichia coli (9, 10, 16-20) Pseudomonas aeruginosa (21-24) and Staphylococcus aureus (25-27). Because CR and CS are assumed to result from the mutation that conferred resistance to the evolved strain, they are a fitness gain/cost of resistance acquisition which is detected in environments with the collaterally-involved antibiotics.

Collateral susceptibility interactions between two antibiotics can be either reciprocal, in which direct resistance to either drug in a pair leads to an increase in sensitivity to the other, or asymmetrical, in which direct resistance to Drug A leads to an increase in sensitivity to Drug B but the reverse does not occur (10, 24)

In chronic infections, periodic antibiotic use drives evolution of multidrug resistant bacteria (28), which complicates treatment by requiring antibiotics with greater toxicity and eliminating options of some antibiotic classes (29, 30). Antimicrobial 4 stewardship aims to reduce this process with judicious use and strategic selection of appropriate antibiotics in clinical treatment. Collateral susceptibility studies could help in this process and lead to sustainable treatment regimens that reduce development of multidrug resistant bacterial strains (23). If collateral susceptibility data for chronic infection-associated bacterial species are integrated into the antimicrobial stewardship paradigm, direct selection by the treatment drug could result in mutant with increased resistance to the treatment drug and increased sensitivity to other antimicrobials. This would reduce the development of multi- or pan-resistant clinical strains in chronic infections.

Chronic bacterial infections are associated with medical conditions such as non- healing wounds, indwelling medical devices, and genetic diseases such as chronic granulomatous disease and cystic fibrosis (CF). While some of those infections may be addressed by treating the underlying condition, as in the cases of wounds and devices, for

CF patients clinicians rely on periodic use of antibiotics which alleviate symptoms despite evidence that antibiotics do not necessarily eradicate or reduce in vivo bacterial populations (31).

Most people with CF have progressive lung dysfunction; respiratory deterioration due to chronic infection and inflammation is the leading cause of morbidity and death in this population (32). A hallmark of cystic fibrosis lung disease is inflammation, ineffective at eradicating bacteria yet damaging to the host’s own tissues. In fact, colonization by bacteria of the lung may be preceded by damage-causing inflammation

(33), allowing bacteria that are normally not associated with aggressive, invasive infections to infect the CF lung. Infections are typically polymicrobial, with complex 5 changes throughout the life of the patient. Organisms associated with CF lung infection

usually change over time, with Staphylococcus aureus and Haemophilus influenza

commonly isolated from young CF patients and Pseudomonas aeruginosa and

Burkholderia cepacia complex organisms found in older individuals (34).

Burkholderia multivorans is a member of the Burkholderia cepacia complex

(Bcc), a group of closely related Gram-negative bacterial species that are inherently

resistant to many antibiotics. Several Bcc species can cause chronic and debilitating lung

infections in CF patients (35); presence of a Bcc species is usually associated with advanced lung disease (36), likely both as a result and a contributor of lung damage.

Acquisition may be attributed to patient-to-patient transmission, via fomites such as contaminated medical devices, or from the natural environment (35). Transient colonization can occur (35, 37) but after establishment, Bcc infections are usually chronic and eradication is difficult. According to the Cystic Fibrosis Foundation 2016 Patient

Registry (34), just under 3% of CF patients are infected with Bcc organisms, and the genus is not commonly found in childhood or adolescent patients. Prevalence is low but the risks associated with Bcc infections are high including increased morbidity and mortality, and the potential for an often-fatal condition known as “cepacia syndrome”, characterized by fulminant, necrotizing lung infection and septicemia (35, 37). During a

pulmonary exacerbation, characterized by an increase in respiratory symptoms, a CF

patient may receive antibiotic treatment to alleviate symptoms despite evidence that, for

well-established infections, dominant and keystone bacterial taxa inhabiting the CF lungs

are relatively stable even with antimicrobial therapy (31). This therapy creates an 6 environment that selects for resistant mutants by eradicating sensitive strains, thereby reducing competition for resources.

My third (Ch. 4) and fourth (Ch. 5) articles report on my investigation of collateral changes of antibiotic susceptibility in B. multivorans. Chapter 4 discusses the presence of collateral susceptibility interactions in first-generation progeny from a clinical isolate and one putative mechanism of action to explain the direct resistance and collateral susceptibility. Chapter 5 describes the development of complex lineages of experimentally evolved strains from the same clinical isolate that was used for the first article, collateral interactions observed in evolved strains, and the existence of antibiotic pairs with reciprocal CS.

While I annotated genes based on references other than the National Center for

Biotechnology Information website and analyzed the sequencing results in the context of mutations observed, bioinformatics tasks including whole genome sequence analysis, annotation, and organization of results, was performed by Logan Kavanaugh, a member of the Steck Lab. Related results are found in Chapters 4 and 5.

7 CHAPTER 2

THE RELATIONSHIP BETWEEN AGAR THICKNESS AND ANTIMICROBIAL SUSCEPTIBILITY TESTING

Citation

Flanagan JN, Steck TR. 2017. The relationship between agar thickness and

antimicrobial susceptibility testing. Indian J Microbiol 57(4):503-506. Doi

10.1007/s12088-017-0683-z

Abstract

Antimicrobial susceptibility testing can be done using solid or liquid-based medium. Solid-based assays are easy and inexpensive; they are limited by not being as quantitative as liquid-based assays. Agar depth can inﬂuence the accuracy of plate-based assays and it is assumed the basis of this effect is antimicrobial agent diffusion. We tested this assumption by using ETEST to quantitate the relationship between agar depth and minimum inhibitory concentration and zone of inhibition.

Introduction

Antimicrobial susceptibility testing (AST) is routinely performed in clinical laboratories to guide selection of appropriate antibiotic treatment for patients with infections, and in research laboratories studying antibiotic resistance. AST methods can be separated based upon use of liquid or solid media, and whether results are qualitative or quantitative (1). 8 Liquid medium assays use deﬁned concentrations of antimicrobial agent to determine the minimum inhibitory concentration (MIC), which is the lowest concentration of the antimicrobial agent that inhibits growth in a deﬁned condition (1).

Multiple automated systems are available for susceptibility testing (1), though they require specialized equipment. Although quantitative, the concentration range examined in liquid based assays is not continuous, resulting in MIC accuracy being limited to the step differences in antimicrobial agent concentration between culture tubes.

The Kirby–Bauer disk diffusion method on 4 mm thick Mueller–Hinton agar is a common solid medium assay (2). Diffusion from a disc containing a known amount of the antimicrobial agent added to a plate seeded with bacteria results in formation of a zone of inhibition (zoi). The edge of that zone occurs where the antimicrobial agent concentration is insufﬁcient to prevent bacterial growth. Using CLSI guidelines (1, 3), the diameter of the zone allows a strain to be classiﬁed as sensitive, intermediate, or resistant to the drug. As described, this method provides categorical interpretative result only.

Because solid medium assays are easy and low cost, they are commonly used.

Established procedures for manual AST assays improve reproducibility of results and accuracy of interpretations. Slight deviations from these practices can impact results.

It is known that the zoi can be affected by the rate of bacteria growth and diffusion characteristics of the agent, but additional parameters, not recognized by many users of this technique, can affect results. These factors include the presence of multidrug resistance in tested strains (4), inconsistencies in inoculum preparation and application

(5), disk positioning (6), and media and materials (7, 8). The relevance of media and materials appears to be on agar thickness. Recommended solid medium used for diffusion 9 testing is standard or cation-adjusted Mueller–Hinton agar at a depth of 4 mm (3, 9).

Davis and Stout (7) showed amount of agar poured, nonplanar plate bottoms, location of plate within a poured plate stack, and wedge-shaped agar all impact accuracy. Woolfrey

et al. (8) identiﬁed Petri dish concavity as an accuracy variable. It is assumed that agar thickness and hydration impact zoi through inﬂuencing diffusion of the antimicrobial agent. To test this assumption requires having a quantitative gradient diffusion method.

That method exists in the ETEST®.

ETESTs are plastic strips with antibiotic on one side and an MIC reading scale on

the other; when placed onto solid media seeded with bacteria, the antimicrobial agent

creates a continuous gradient of antibiotic (10). The edge of bacterial growth next to the

strip corresponds to the MIC. ETESTs combine the advantages of liquid and solid-

medium based ASTs: ease of use, reduction in technical dilution errors, generation of

MIC, and interpretive results. ETESTs are also expensive.

Material and Methods

To examine the effects of variation of agar depth on AST determination, zois were correlated with agar thickness, as was done in previous studies, and then compared to MIC using ETESTs. Quantitative MIC results obtained from ETESTs allow more

accurate assessment of the impact of agar thickness on zoi than reported in previous

studies. AS149, a clinical strain of Burkholderia multivorans (11) was used to examine

ceftazidime (CAZ), meropenem (MEM), and minocycline (MIN). Mueller–Hinton plates

of 1.7% agar (Fisher BioReagents Agar) were poured to a depth of 4, 6, and 8 mm. The

gross weight of agar-containing plates was used to quantitate the amount of agar (the 10 weight of empty plates varied by less than 0.3% (12.56 ± 0.033 g)). Plate depth

measurements were taken on the day of inoculation. Results are shown in Table 1.

Results and Discussion

As agar plate depth/weight increases, the MIC also increases and the zone of

inhibition size decreases for ceftazidime and meropenem. For minocycline, the lowest

MIC and largest zoi is observed in the thinnest plate, but the 6 mm plate has a higher

MIC and lower zoi than does the 8 mm plate.

The effect of hydration on zone size and MIC was examined by using 4 mm and 8

mm plates for AST testing after being unstacked and closed on the bench top for 7 days.

A comparison of MIC and zoi values (Table 2) with those given in Table 1 shows MIC inversely correlates, and zoi positively correlates, with plate weight.

The relationship between agar depth and MIC/zoi can be modeled through linear regression analysis. Figure 1 shows this data for MIC (panels a, c, e) and zone of inhibition (panels b, d, f) for each of the three antibiotics. In all cases, there is a positive slope between agar depth and MIC, and a negative slope between agar depth and zone of inhibition. The absolute slope varies with different antibiotics with meropenem having a higher slope than minocycline or ceftazidime.

One factor that likely influences the effect of agar depth on MIC and zoi is the agar diffusion coefficient of the antimicrobial agent. While not measuring the diffusion coefficient directly, we compared the molecular weight of the three antibiotics used in this study: meropenem has a lower molecular weight (383.46 g/mol) that minocycline

(457.48 g/mol) or ceftazidime (546.58 g/mol). Because molecular weight correlates with 11 diffusivity, these observations suggest that the higher the agar diffusion rate for an agent,

the more susceptible is the resulting MIC and zone of inhibition value to agar depth.

Use of disk diffusion on solid medium to measure zone of inhibition remains a common AST tool. Past studies have identified that agar depth uniformity can affect the accuracy of this assay (7, 8), and CLSI guidelines call for agar to be at a depth of 4 mm.

Without knowing how variances from this depth affect results, assay users do not know

how rigorous they need to be when pouring plates. Here, we used MIC values obtained

from ETESTs to quantify the relationship between agar depth and zoi. We find an inverse

correlation between these two parameters and suggest that the smaller the agent under

study, the greater is this correlation.

Acknowledgements

This work was supported by NIH Grant (1R15HL126122-01) to T.R.S.

References

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and sensitivity in bacteria. J Appl Bacteriol 68:307–318

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http://www.microbelibrary.org/component/resource/laboratory-test/3189-kirby-bauer-

disk-diffusion-susceptibility-test-protocol. Accessed 6 Aug 2017

3. Clinical and Laboratory Standards Institute. 2013. Performance standards for

antimicrobial susceptibility testing; twenty-third informational supplement. Document

M100-S23. CLSI, Wayne 12 4. Brennan-Krohn T, Smith K, Kirby J. 2017. The poisoned well: enhancing the

predictive value of antimicrobial susceptibility testing in the era of multidrug

resistance. J Clin Microbiol 55:2304–2308. doi:10.1128/JCM.00511-17

5. Hombach M, Maurer F, Pfiffner T, Bottger E, Furrer R. 2015. Standardization of

operator-dependent variables affecting precision and accuracy of the disk diffusion

method for antibiotic susceptibility testing. J Clin Microbiol 53:3864–3869. doi:10.

1128/JCM.02351-15

6. Cunningham D, Flournoy DJ. 1983. The relationship of disc position to zone-of-

inhibition size in the disc-agar diffusion test. Methods Find Exp Clin Pharmacol

5:101–105

7. Davis WW, Stout TR. 1971. Disc plate method of microbiological antibiotic assay. I.

Factors influencing variability and error. Appl Microbiol 22:659–665

8. Woolfrey BF, Ramadei WA, Quall C. 1979. Petri dish concavity—a potential source

of error in antibiotic assay and agar diffusion antibiotic susceptibility tests. Am J

Clin Pathol 71:433–436

9. Coyle M (ed) .2005. Manual of antimicrobial susceptibility testing. American Society

for Microbiology, Washington

10. Biomerieux (2012) Etest antimicrobial susceptibility testing (package insert).

Biomerieux, Marcy-l’Etoile

11. Stokell JR, Gharaibeh RZ, Steck TR (2013) Rapid emergence of a ceftazaidime-

resistant Burkholderia multivorans strain in a cystic fibrosis patient. J Cyst Fibros

12:812–816

14 Table 2. Minimum inhibitory concentration and zone of inhibition values on dehydrated agar plates

Agar Plate weight (g) MEM MIN depth Prior to On day of MIC Zone Size MIC Zone Size (mm) drying inoculation µg/mL (mm) µg/mL (mm) 4 32.53 30.052 6 21.33 4 21.33 (31.40, (28.94, (6,6,6) (21,21,22) (4,4,4) (21,21,22) 32.93, 33.25) 30.42, 30.77) 8 52.629 50.056 18.67 15.33 8 17.33 (49.98, (47.46, (16,16,24) (14,16,16) (8,8,8) (16,18,18) 50.87, 57.04) 48.29, 54.42)

Minimum inhibitory concentration (MIC) and zone of inhibition values (zone size) are shown for meropenem (MEM) and minocycline (MIN) on dehydrated agar plates. Each value is the average of three replicates; the individual replicate values are given in parentheses. Plates were weighed after being poured (“prior to drying”) and then again after being left on the benchtop, unstacked and closed, at room temperature for 7 days (“on day of inoculation”). The “on day of inoculation” plates were used to generate the MIC and zone size results.

Figure 1. Linear regression of minimum inhibitory values compared to agar plate weight Linear regression of MIC values, given in µg/mL, for ETESTs and zone of inhibition values for antimicrobial susceptibility testing, compared to plate weight. A: Meropenem (MEM) and minocycline (MIN) ETEST MIC results. B: MEM and MIN disk diffusion results. C: MEM and MIN ETEST MIC results on plates left on the bench top to dry. D: MEM and MIN disk diffusion results on plates left on the bench top to dry. E. Ceftazidime (CAZ) ETEST MIC results. F: CAZ disk diffusion results.

16 CHAPTER 3

USE OF ANTIBIOTIC DISKS TO EVOLVE DRUG-RESISTANT BACTERIA

Citation

Flanagan JN, Steck TR. 2018. Use of antibiotic disks to evolve drug-resistant

bacteria. Antonie Van Leeuwenhoek 111(9):1719-1722. Doi 10.1007/s10482-

018-1055-3

Abstract

The methods used to generate antibiotic- resistant bacterial strains can be labour- intensive, costly, lengthy and/or prone to plate-to-plate variation. We propose a simple, inexpensive and easily replicated method to expose bacteria to a continuous gradient of antibiotic concentration, providing an environment of positive selective pressure for evolution of antibiotic-resistant strains.

Introduction

In the study of antibiotic resistance, cross-resistance and collateral sensitivity, multiple methods have been used to select for antibiotic resistance and to measure the level of resistance (1). Even though some of the methods to isolate antibiotic-resistant colonies have been in existence for decades (2), new approaches continue to be developed (3).

One common method is to make an agar plate containing a gradient of antibiotic concentration (4-5). Nutrient agar is poured into a tilted Petri dish and, after hardening, antibiotic-containing agar is poured on top of the leveled plate. Bacteria are added to the 17 surface of the plate and colonies from the end of the plate containing the deepest antibiotic-containing agar are selected. The method can be repeated until colonies classified as resistant are isolated. Advantages of this method include low cost and using a continuous antibiotic concentration gradient for selection. In favorable cases, as few as three platings were needed to evolve mutants with 100 times the resistance of the parental strain (5). Gradient plates also do not depend on the diffusion coefficient of the antibiotic, as do methods in which the antibiotic is added to the surface of hardened agar. However, square Petri dishes are desired and consistent inter-plate antibiotic concentration gradients require complete antibiotic homogenization in the medium and reproducible angling of the tilted plate.

Antibiotic resistant strains can also be obtained by serial transfer of cells in liquid cultures. Usually, the starting drug concentration is low enough to kill only a portion of the inoculated population and gradually increased to the desired level (6-7). Knowing the exact drug concentration in each culture is an advantage of this method, especially as the strength of the selective pressure can affect numbers and types of mutations (7).

Disadvantages include extensive time and supplies, trained labor for serial sub-culturing, the possibility of two independent mutants arise in the same culture tube, and loss of the entire evolved population due to the drug concentration being increased too much from one culture to the next (6).

The method developed by our laboratory to evolve antibiotic resistant bacteria uses commercially available antibiotic-impregnated paper disks of the type used to perform antimicrobial susceptibility testing (AST) to isolate resistant mutants. Zones of growth inhibition surrounding antibiotic-impregnated disks have been examined macro- 18 and microscopically to differentiate between resistant and susceptible isolates and describe morphological phenomenon such as lysis and colony size in the presence of antibiotics (8), but the disks have not previously been used as the source of the selective environment.

Materials and methods

Bacterial strain

A clinical isolate of Burkholderia multivorans, AS149, was used for assays (9).

Antibiotics

Drugs used for evolution of resistance were selected based on clinical relevance to

the organism and to represent different classes of antibiotic mechanism of action. They include drugs that target cell wall synthesis (ceftazidime (CAZ), meropenem (MEM)), polypeptide synthesis via inhibition of ribosome activity (minocycline (MIN), chloramphenicol (CHL), DNA synthesis (levofloxacin (LVX)), and folic acid synthesis

(trimethoprim/sulfamethoxazole (SXT)) (10). Paper antibiotic disks were from Becton

Dickinson & Company except for minocycline, which was from TCI America.

The concentration of SXT in the antibiotic disk, 1.25 µg trimethoprim/23.75 µg sulfamethoxazole, is lower than the MIC interpretative standard breakpoint for resistance, which is 4/76 µg. When a single SXT disk was used, an increase in resistance was achieved, but not to the level that would be considered clinically resistant. To achieve the

resistant breakpoint MIC, two disks were placed adjacent to each other on top of the inoculated lawn.

Selection 19 A turbid cell suspension of strain AS149 was used as the inoculum and spread

onto a Mueller–Hinton plate using a swab. After growth, the zone of inhibition (zoi) surrounding the disk was inspected. Any single colonies are present within the zoi were selected. Otherwise, the edge of the growth closest to the disk was collected using a cotton applicator and suspended in medium for use in the next exposure. This process was continued serially until single colonies in the inhibition zone were isolated or the edge of the growth zone reached the disk or entered the zoi within the ‘resistant’ category (11). A final test using standard AST practices on a single colony was performed to ensure the isolated cells are clonal and fully resistant (11-12).

Confirmation of antibiotic resistance

Resistance was confirmed by determining the MIC of isolated cells using

ETEST® (bioMerieux).

Results and discussion

The selection strategy is based upon a concentration gradient being created due to

diffusion from an antibiotic-infused disk when placed on a solid agar medium plate. When

the plate is first inoculated with a lawn of bacteria, cells are exposed to the wide range of antibiotic concentrations. We demonstrated the effectiveness of this method to evolve resistant mutants using AS149, a clinical strain of B. multivorans (9), and separate disks containing each of six antibiotics.

For all antibiotics examined, resistant colonies were obtained. After incubation, cells were collected from the edge of growth as described. After a given number of sequential platings, here referred to as “exposures” (see Table 1), the zone diameter 20 decreased (Fig. 1) to the breakpoint described for this species (13) or until there was

growth up to the disk.

Given that paper disks and plastic strips have similar methods of drug delivery, it

is reasonable to assume this method could be adapted to use ETESTs as the source of

antibiotic if research goals require knowing the concentration of antibiotic at which more resistant isolates arose. This assumption was not tested in development of our method because our goal was to develop a cost effective method and the expense of ETESTs limits their use.

While performing this procedure, it was noticed that the number of exposures required to obtain resistance varied for different antibiotics. To quantitate this phenomenon, selection for resistance was performed three times for each antibiotic and the average number of exposures needed for resistance acquisition determined. The minimum inhibitory concentration (MIC) was determined for each antibiotic prior to

(“pre-selection”) and after (“post-selection”) selection. The number of exposures required to select for resistance varied over a three-fold range, depending upon the antibiotic used

(Table 1).

To control for resistance arising independent of selective pressure, strain AS149 was serially plated for a total of 30 exposures on nutrient agar containing no antibiotic and tested periodically (every 5 exposures) for an increase in resistance to the drugs of interest.

No increase in resistance was observed at any point in the negative control.

Use of a solid medium, diffusion-based approach, to isolating drug-resistant bacterial allows evolutionary processes leading to resistance to be examined. Baym et al.

(3) describe a 120 x 60 cm MEGA-plate that allows bacteria to adapt as they migrate 21 across an antibiotic gradient on a single plate. Although our method is designed simply to obtain resistant mutants, it would be possible to examine intermediate cells by characterizing cells harvested after each exposure. We note that the zoi changes gradually over multiple exposures, suggesting intermediate resistant cells could be isolated.

Selection for antibiotic resistance variants of bacterial strains will remain a goal for numerous studies. To isolate such mutants, the agar-based, antibiotic gradient method described here is easy, cost-effective, reproducible (since each disk has a defined antibiotic concentration), and requires little technical skill.

Although one strain was examined, there is no reason to think the qualitative response to the antibiotic concentration gradient established using disks (i.e. acquired resistance) will be strain-specific. Hence, this method should be applicable to any bacterial species and strain.

Acknowledgements

We would like to thank the National Institutes of Health for supporting this research via a Grant (# 1R15HL126122-01) to T.R.S.

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24 Table 3. Exposures needed to evolve resistant strains of bacteria to antibiotics

Antibiotic MIC (pre-selection) MIC (post-selection) Exposures MEM 10 (12, 12, 6) 24* (16, >32, >32) 17.7 LVX 8 (8,8,8) 32* (>32, >32, >32) 21.3 SXT 0.38 (0.25, 0.38, 0.5) .83 (.75, .75, 1) 9 SXT** 0.38 (0.25, 0.38, 0.5) 6.3 (3, 8, 8) 15 CHL 58.7 (64, 64, 48) 256* (>256, >256, >256) 8.7 MIN 6 (6, 6, 6) 256* (>256, >256, >256) 27.7 CAZ 0.83 (1, 0.75, 0.75) 256* (>256, >256, >256) 10.7

Number of exposures needed to evolve antibiotic resistance and change in MIC are shown. Selection for resistance to ceftazidime (CAZ), minocycline (MIN), meropenem (MEN), trimethoprim-sulfamethoxazole (SXT), chloramphenicol (CHL), and levofloxacin (LVX) was done as described in the text. Each MIC value in bold is the average of two to three replicates (absolute values given in parentheses after the average). The average number of exposures required to obtain a strain whose resistance reached the breakpoint for B. multivorans is given. * The absolute MIC may be higher due to ETEST results that were “>” values being included in averages using the > value as the absolute value. **Two disks were used simultaneously to provide sufficient concentration to reach the resistance breakpoint MIC.

Figure 2. Image of bacterial strains before and after evolution of antibiotic resistance Antibiotic sensitive strain (a) evolved to be resistant (b) to ceftazidime (CAZ disk, 30 µg) after four exposures as described in the text.

25 CHAPTER 4: BURKHOLDERIA MULTIVORANS EXHIBITS ANTIBIOTIC COLLATERAL SENSITIVITY

Citation

Flanagan JN, Kavanaugh L, Steck TR. Burkholderia multivorans exhibits

antibiotic collateral sensitivity. Publication in progress.

Abstract

Burkholderia multivorans is a member of the Burkholderia cepacia complex

(Bcc) whose members are inherently resistant to many antibiotics and can cause chronic lung infections in patients with cystic fibrosis. A possible treatment for chronic infections arises from the existence of collateral sensitivity (CS) – acquired resistance to a treatment antibiotic results in a decreased resistance to a non-treatment antibiotic. Determining CS patterns for bacteria involved in chronic infections may lead to sustainable treatment regimens that reduce development of multidrug resistant bacterial strains. CS has been found to occur in E. coli, P. aeruginosa, and S. aureus. Here we report that B. multivorans exhibits antibiotic CS, as well as cross resistance (CR), describe CS and CR networks for six antibiotics (ceftazidime, chloramphenicol, levofloxacin, meropenem, minocycline, and trimethoprim-sulfamethoxazole), and identify candidate genes involved in CS. Characterization of CS and CR patterns allow antibiotics to be separated into two clusters based on the treatment drug to which the evolved strain developed primary resistance, identification of which is the first step towards predicting antibiotic therapy strategies.

26 Introduction

Chronic bacterial infections are associated with medical conditions such as non- healing wounds, indwelling medical devices, and genetic diseases such as cystic fibrosis.

While some of those infections may be addressed by treating the underlying condition, for cystic fibrosis patients clinicians rely on periodic use of antibiotics which alleviate symptoms despite evidence that antibiotics do not necessarily eradicate or reduce in vivo bacterial populations (1). Periodic antibiotic use drives evolution of multidrug resistant bacteria, which complicates treatment by requiring antibiotics with greater toxicity or eliminating options of some antibiotic classes.

Exposure to an antibiotic selects for mutants resistant to that antibiotic, but the mutation may have pleiotropic effects including changed susceptibility to non-treatment antibiotics. Resistance to an antibiotic due to selective pressure by that antibiotic is direct- resistance (2). An increased resistance to non-treatment antibiotics is termed cross- resistance (CR) and a decreased resistance to non-treatment antibiotics is termed collateral susceptibility (CS) (2-18). This phenomenon has been reported to occur in

Escherichia coli (2, 5-11), Pseudomonas aeruginosa (12-15), and Staphylococcus aureus

(16-18). The two Gram-negative pathogens had different and sometimes opposing collateral interactions (12), so collateral changes may be species specific. Even within species there can be different collateral reactions depending on the particular mutations that arise during experimental evolution (11, 19).

Because CR and CS are assumed to result from the mutation that conferred resistance to the evolved strain, they are a fitness gain/cost of resistance acquisition which is detected in environments with the collaterally-involved antibiotics. The fitness cost 27 theory of CS (7, 12) has at least three types of genetic changes that can result in CS: a

mutated gene product exerting direct pleiotrophic effects resulting in both the selected-for

resistance and collateral changes; a mutation in a regulatory gene (either cis- or trans-

acting); or coincidental acquisition of multiple mutations. Whole genome sequencing, to identify molecular mechanism of the phenomenon, and repeated testing, to decrease the likelihood of coincidental acquisition of uncorrelated mutations, is required to characterize the observed CS interactions.

Burkholderia multivorans is a member of the Burkholderia cepacia complex

(Bcc), a group of closely related species that cause chronic and debilitating lung infections in patients with cystic fibrosis (20) and are inherently resistant to many antibiotics. While Bcc prevalence is low in cystic fibrosis, the risks associated with Bcc infections are high, including the potential for “cepacia syndrome” (20, 21). While transient infections can occur (21), Bcc infections are often chronic once established.

Patients usually take antibiotics prophylactically and receive additional antibiotics in response to a pulmonary exacerbation. Because dominant bacterial taxa/species inhabiting cystic fibrosis lungs are relatively stable (1), antibiotic therapy creates an environment that selects for resistant mutants. These species are ideal for susceptibility research due to persistence of infections, inherent resistance, and potential for fatality.

Determining collateral susceptibility patterns for bacteria involved in chronic infections may lead to sustainable treatment regimens that reduce development of multidrug resistant bacterial strains. Elucidation of collateral susceptibility networks may allow treatment cycling regimens to become part of antimicrobial stewardship programs 28 for chronic infections. Here we report the first study on collateral resistance and

susceptibility in a Bcc species.

Materials and Methods

Strains, culture conditions, and antibiotics

B. multivorans strain AS149 (22), used to evolve all others in this study, is a clinical isolate from a patient with cystic fibrosis. Bacteria were grown on LB broth,

Miller (Fisher) with 1.5% agar (Fisher) for routine culturing and during experimental evolution. Mueller-Hinton broth 2 (Sigma-Aldrich) with 1.7% agar was used for antimicrobial susceptibility testing. All cultures were incubated at 37˚ in ambient air.

Antibiotics involved in this study, as listed in Table 1, were chosen due to their

inclusion in the CLSI (27) list of standard antibiotics tested against Burkholderia cepacia

and for having varied targets. BBL Sensi-Disc antimicrobial susceptibility test disks (BD) were used for all except minocycline, which were Oxoid antimicrobial susceptibility disks (Thermo Scientific). The minimum inhibitory concentration was determined using

ETEST gradient strips (bioMérieux). Antibiotics and abbreviations used, antimicrobial susceptibility testing, and the number of strains evolved for each treatment drug are given in Table 1. Clusters were determined post hoc based on result grouping, and were named

(βLA and non-βLA) based on antibiotic similarity.

Experimental evolution

Strains were evolved for resistance to a treatment drug using a previously described, plate-based method (28). In brief, we swabbed AS149 onto LB agar to create a lawn, then added an antibiotic-impregnated disk in the center. After 16-24 hours 29 incubation at 37˚C, the growth closest to the zone of inhibition was collected and used to

inoculate the next plate until resistance was achieved. Strain evolution was stopped when

antimicrobial susceptibility testing via disk diffusion on Mueller-Hinton agar plates exhibited a zone of inhibition (ZOI) that was considered ‘resistant’ by the CLSI breakpoints or there was growth up to the disk for the antibiotics that do not have disk diffusion breakpoints, i.e. LVX and CHL. At least eleven strains were independently evolved to be resistant to each drug as shown in Figure 1; we refer to all strains evolved to be resistant to a particular drug as the “resistance group” for that drug (8). To detect and exclude mutations that occur with only laboratory growth conditions as the selective pressure, we grew the parental strain on LB plates for 20 days as a negative control.

Antimicrobial susceptibility testing and interpretation

Disk diffusion testing was performed on strains selected for resistance to a treatment drug to determine susceptibility to five non-treatment drugs, as well as the treatment drug to ensure the strain was resistant and not a persister or cheater (29). The antibiogram of the evolved strain was compared to the antibiogram of the parental strain to determine collateral sensitivity (CS) and cross-resistance (CR). Any change in ZOI of

20% or greater for a non-TD reflects collateral changes in susceptibility, with an increase in ZOI indicating CS and a decrease in ZOI indicating CR.

To better quantitate collateral susceptibility (CS) interactions, an ETEST® was used to determine the minimum inhibitory concentration (MIC) on all strains having CS as indicated by 20% or greater increase in ZOI with disk diffusion testing. A decrease in

MIC, indicating increased sensitivity, was considered a positive collateral susceptibility interaction. 30 Statistical Analysis

GraphPad Prism was used to run observed versus expected binomial one tailed

tests for cross-resistance clustering. Numbers of cross-resistant interactions were used as

opposed to strains since strains could contain more than one interaction. Null hypothesis

is that any of the 5 non-treatment drugs had an equal chance to be the drug in the

observed interaction so expected values are 20% BLA (1 of 5) and 80% n-βLA (4 of 5)

when the treatment drug was a βLA; 40% βLA (2 of 5) and 60% n-βLA (3 of 5) when the

treatment drug was a n-βLA.

Genomic Analysis

Thirteen independently evolved Burkholderia multivorans isolates demonstrating

collateral susceptibility, three parental AS149 biological replicates, and eight negative

control biological replicates (2x five exposures, 10 exposures, 15 exposures, and 20

exposures) were subjected to whole genome sequencing. A single colony from each

strain and two morphologically distinct colonies from control plates were sent to Omega

Bioservices, where WGS was performed using 151 bp paired end reads with an Illumina

HiSeq 2500 platform.

Raw files were visualized in FastQC-0.11.8 (30) for quality. Adaptor reads and contigs >151 bp were trimmed and bases with quality reads falling below a phred score of 20 were trimmed in Trimmomatic-0.35 (31). Genomes were globally aligned to the reference genome Burkholderia multivorans ATCC BAA-247 (NCBI: Accession:

PRJNA264318) using Bowtie2-2.3.4.3 (32). Duplicate reads were marked and removed

using Picard-2.8.26 and INDELS realigned using GATK3 IndelAligner. Variants were

called in GATK3 HaplotypeCaller with a standard confidence call of 30 in Discovery 31 mode (33, 34). Variants below coverage less than 10X were filtered for the final variant

calls using VCFtools (35).

Python scripts and SAMtools were used to remove all called variants in parental

and control strains from individual CS strains to validate that all variants called in each

strain were induced in the evolution experiment (36). Final called variants (SNP/INDEL)

were annotated using snpEFF-4.3T and functional characterization was achieved through

NCBI, Burkholderia Database (Burkholderia.com), STRING database, and KEGG

database (37).

Results

Evolved strains and collateral changes in susceptibility

The parental strain of B. multivorans, which was a clinical isolate from sputum

of a cystic fibrosis patient (22), was exposed to one of six treatment antibiotics (at least

11 independent evolutionary procedures for each drug, see Materials and Methods), for a

total of 73 evolved strains. Of these, antimicrobial susceptibility testing documented that

16% exhibited collateral sensitivity (CS) and 79% exhibited cross-resistance (CR); 8% of the strains exhibited both CS and CR. When strains within a resistance group were analyzed, the percentage of strains with CS ranged from 0% (SXT) to 36% (MEM); the percentage of strains with CR ranged from 27% (MEM) to 100% (CHL and LVX). With

analysis of different parameters of collateral sensitivity and resistance (ex. number of

strains exhibiting CS or CR), the evolved strains grouped into two clusters (Table 1) with

the beta-lactam antibiotics making up one cluster (βLA), and the non-beta-lactam

antibiotics in the second (non-βLA). 32 Cross-resistance patterns

Cross-resistance to at least one of the five non-treatment drugs was present in the majority of strains in all resistance groups except for that of meropenem (Figure 1).

The combined percentages of strains exhibiting cross-resistance differed for βLA and non-βLA treatment drugs; 96% of 51 evolved strains had cross-resistance when a non-

βLA was the treatment drug, and 40% of 22 strains had cross-resistance when a βLA was the treatment drug.

The non-treatment drugs to which the cross-resistance-exhibiting strain had increased resistance clustered together within the non-βLA drugs; when the treatment drug was a non-βLA, more cross-resistance reactions to other non-βLA drugs than to βLA non-treatment drugs was observed (Figure 2a). For example, when the treatment drug was LVX (non-βLA), 100% of evolved strains demonstrated cross-resistance to all three of the other non-βLA drugs (CHL, MIN, and SXT), but only 27% demonstrated cross- resistance to a βLA drug. A similar pattern was observed for CAZ, but not MEM, within the βLAs. For example, when CAZ was the treatment drug (βLA), five of six evolved strains demonstrated cross-resistance to MEM (βLA), but only two to LVX, one to SXT, and none to CHL or MIN (non-βLA). When MEM was the treatment drug, there was no distinct pattern of clustering of the non-treatment drugs that showed increased resistance.

We performed an observed versus expected, one-tailed binomial statistical test (see

Materials and Methods); clustering was statistically significant for all treatment drugs except MEM with p-values of ≤ 0.0001 for CHL, LVX, MIN, and SXT and p = .0104 for

CAZ.

Collateral susceptibility patterns 33 Although within-cluster cross-resistance was common, all collateral sensitivity

(CS) interactions were between clusters (see Fig 3). The five LA strains exhibiting collateral sensitivity did so to non-LA drugs. One interesting pattern is seen with MEM, which had collateral sensitivity interactions as either the treatment drug or non-treatment drug. In the MEM resistance group, four of 11 evolved strains had a decrease in MIC to at least one non-treatment drug, and one of those four strains exhibited collateral sensitivity to three drugs (LVX, SXT, and MIN). All of the seven non-LA strains exhibiting collateral sensitivity did so to MEM (see Fig 1); CS was observed when the treatment drug was the non-βLA treatment drug LVX (3 of 11 strains), CHL (2 of 12 strains), or

MIN (2 of 13 strains). Of the 15 strains evolved to be resistant to SXT, none demonstrated collateral sensitivity.

Quantitative reduction in MIC for CS interactions

We considered any decrease in MIC for a non-treatment drug as measured by

ETEST® to be positive for collateral susceptibility. Since the MIC is a quantitative measurement, the amount of decrease in MIC reflects the degree of increased sensitivity the evolved strain had to the parental strain. For all strains demonstrating CS, we observed a range of decrease in MIC of 1.5-3.4 fold.

Patterns of CS reactions within antibiotic groups

If collateral sensitivity is to influence treatment regimes, strains that demonstrated CS need to do so in a predictable pattern, which was observed in resistance groups. Except for MEM, a consistency was seen for all treatment drugs; 100% of strains that exhibited CS had increased susceptibility to only one non-treatment drug. When the treatment drug was a non-βLA, 100% of all CS-exhibiting strains had increased 34 susceptibility to MEM, a βLA. When the treatment drug was a βLA, 80% (MEM) to

100% (CAZ) of the CS-exhibiting strains had increased susceptibility to MIN, a non-

βLA.

Genetic Determinants of CS

Whole genome sequencing and mutational analysis was conducted on 13 of the evolved strains, as well as the parental strain. Thirty-six genes accumulated nonsynonymous mutations in the evolved strains. The average mutation load was ~39.8 mutations/strain with range 13-61. Most mutations were found to be synonymous (~11.7; range 3-19) or in the intergenic region ~18.7; range 6-33). There were an average of 8.7 non-synonymous coding mutations/strain (range 4 to 16).

To identify candidate genes likely involved in direct resistance and collateral sensitivity, we focused on the mutations in two strains with similar phenotype: treatment drug of CAZ and CS to MIN. Based on known gene function, we identified two candidate genes. The first gene is penA; both strains contain a conservative in-frame deletion

[p.Thr180_Glu181del]. The second gene is mpl; a missense variation [p.Ala147Val] is seen in the L-alanyl-gamma-D-glutamyl-meso-diaminopimelate ligase protein Mpl in the

Mur_Ligase_C (CDD:332156) region in both strains.

Discussion

We have documented that B. multivorans exhibits both collateral resistance (CR) and collateral susceptibility (CS) in laboratory-evolved strains. This study focused on CS, which occurs when a treatment drug-adapted strain has a concomitant increase in sensitivity to a non-treatment drug. We chose the six antibiotics used (ceftazidime, 35 chloramphenicol, levofloxacin, meropenem, minocycline, and trimethoprim- sulfamethoxazole) because they varied by target and mechanism of action, and because they have defined antimicrobial susceptibility testing interpretations for resistant/intermediate/sensitivity using minimum inhibitory concentrations for

Burkholderia (27).

Our initial characterization of cross-resistance (CR) patterns allowed antibiotics to be separated into two clusters (Fig 2). If a single mutation is responsible for CR, we hypothesize that either there is likely a common target or underlying specific resistance mechanism between the treatment drug and the CR-exhibiting non-treatment drug, or resistance is due to a generalized resistance mechanism such as increased efflux or decreased permeability. The first hypothesis is supported by some of the data involving the βLAs meropenem and ceftazidime, which have a common target of cell wall synthesis inhibition and had a high number of CR responses. However, that CR interactions were often seen within non-βLA cluster members that have different mechanisms of action or cellular target is not consistent with the first hypothesis. The second hypothesis is currently being investigated.

Intriguingly, the observed CS pattern creates the same two clusters as is seen for

CR. However, while CR was most often seen within clusters, CS was always between clusters (see Fig 3). This is not surprising for the βLAs MEM and CAZ, which have a similar mechanism of action: inhibition of peptidoglycan cross-linking to disrupt cell wall synthesis. The between-cluster CS pattern was less expected when a non-βLA was the TD because they have a wider range mechanism of action. Interestingly, strains with CS to one β-lactam drug did not always exhibit increased sensitivity to the other β-lactam drug. 36 The lack of shared CS patterns may be due to the differences in the drugs’ resistance to

beta-lactamases, chemical structure, or other parameter of their activity. It is difficult to draw more firm conclusions from the number of strains exhibiting CS.

One strategy to elucidate the genetic mechanisms involved in CS as well as to allow prediction of CS is via mutation analysis. Towards this end, we analyzed genomic sequences of two strains with a similar phenotype: direct resistance to ceftazidime and collateral susceptibility to minocycline. Both strains had mutations in the penA, a class A beta-lactamase, and mpl genes. penA mutations have been implicated for ceftazidime resistance in Burkholderia pseudomallei (23). PenA is an essential enzyme for linking tripeptide L-alanyl-gamma-D-glutamyl-meso-diaminopimelic acid to UDP-N- acetylmuramate during peptidoglycan synthesis and is not required for survival (24). We propose that these two mutations cause disruptions within the peptidoglycan cross-linking pathway, resulting in increased resistance to ceftazidime and a compromised cell wall which allows increased diffusion of hydrophobic antibiotics such as minocycline (25).

Consistent with this hypothesis is that strains of P. aeruginosa that were experimentally adapted to penicillins or cephalosporins also had mutations in penicillin-binding protein and mpl genes (12).

Of the six antibiotics examined in this study and in strains evolved from AS149, meropenem exhibited unique collateral characteristics. Evolved strains in the MEM resistance group have the lowest percentage of strains exhibiting cross-resistance (3 of 11 total evolved strains) but the highest rate of collateral sensitivity (CS). MEM-evolved strains had increased sensitivity to multiple drugs (CHL, LVX, MIN, SXT) while every other resistance group had strains with collateral sensitivity to just one non-treatment 37 drug. If CS was observed in strains of the non-MEM resistance groups, MEM was most often the drug with increased sensitivity. Meropenem targets cell wall synthesis, which suggests an explanation for the observed characteristics as alterations of the cell wall may allow for increased intracellular accumulation of other drugs.

Although neither the genetic or biochemical mechanism involved in the observed interactions has been elucidated, such information is not required for these observations to impact treatment regimens. For example, if B. multivorans associated with a chronic infection became resistant to a βLA, selection of a non-βLA to treat the infection would still conform to the appropriate standard of care and may help avoid development of further resistance. Additional studies will increase the predictive value of collateral susceptibility interactions (11). One particular focus being pursued is identifying reciprocal collateral sensitivity patterns using two antibiotics, when use of one as the treatment drugs leads to collateral sensitivity in the non-treatment drug, and this pattern then continues when the initial non-treatment drug is subsequently used as the treatment drug. Such flipping patterns would allow long-term treatment of chronic infections to be limited to just two antibiotics with decreased concern of additional acquired antibiotic resistance.

Acknowledgments

This work was supported, in part, by funds provided by The University of North

Carolina at Charlotte and a National Institutes of Health grant 1R15HL126122-01 to

T.R.S.

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Figure 3. Number of evolved strains exhibiting collateral sensitivity and cross resistance The black bars indicate the total number of strains evolved to each treatment drug, shown on the X axis (CAZ = ceftazidime; MEM = meropenem; CHL = chloramphenicol; LVX = levofloxacin; MIN = minocycline; SXT = trimethoprim-sulfamethoxazole). The dark grey bars indicate the number of evolved strains exhibiting cross-resistance (CR). The light grey bars indicate the number of evolved strains exhibiting collateral susceptibility (CS). White bars indicate number of evolved strains exhibiting both CS and CR.

Figure 4. Heat map of cross-resistance (CR) interactions The number of strains that demonstrated CR is indicated in each box. The treatment drug used to evolve the strain is listed across the top and the non-treatment drug to which the strain exhibits CR is on the left. The heat map reflects percentage of evolved strains exhibiting CR to each non-treatment drug, with greater intensity indicating a higher percentage of reactions observed, and the absolute number of CR strains given in each box. Clustering is seen within the n-βLA group, with highest percentages of reactions seen within group members, as indicated by the box formed by dashed lines.

Figure 5. Network of collateral susceptibility changes Each sphere represents an antibiotic (CAZ = ceftazidime; MEM = meropenem; CHL = chloramphenicol; LVX = levofloxacin; MIN = minocycline; SXT = trimethoprim- sulfamethoxazole). Grey spheres are members of the β-lactamase antibiotic group (βLA) and black spheres are of the non-β-lactamase antibiotic group (non-βLA). Lines originate at a treatment drug and terminate at a non-treatment drug which had a collateral change in susceptibility. The line weight is proportional to the relative number of reactions. Panel A shows the cross-resistant network depicted with lines that end in a ball. Panel B shows the collateral susceptibility network depicted with lines that end in an arrowhead.

48 CHAPTER 5: RECIPROCAL COLLATERAL SENSITIVITY OF BURKHOLDERIA MULTIVORANS AND GENETIC MUTATIONS INVOLVED

Citation: Flanagan JN, Kavanaugh L, Steck TR. Reciprocal collateral sensitivity

of Burkholderia multivorans and genetic mutations involved. Publication in

progress.

Abstract:

Burkholderia multivorans, a member of the Burkholderia cepacia complex (Bcc), can cause chronic lung infections in patients with cystic fibrosis. A possible treatment for chronic infections arises from the existence of collateral sensitivity (CS) – acquired resistance to a treatment antibiotic resulting in a decreased resistance to a non-treatment antibiotic. Determining CS patterns for bacteria involved in chronic infections may lead to sustainable treatment regimens that reduce development of multidrug resistant bacterial strains. We previously reported that B. multivorans exhibits antibiotic CS as well as cross resistance (CR), and described CS and CR networks for six antibiotics

(ceftazidime, chloramphenicol, levofloxacin, meropenem, minocycline, and trimethoprim-sulfamethoxazole). Here we expand the network with additional collateral interactions, strengthening the previously noted clustering interactions between beta- lactam antibiotics and non-beta-lactam antibiotics. We also identify additional candidate genes involved in CS.

49 Introduction:

Burkholderia multivorans is a member of the Burkholderia cepacia complex

(Bcc), a group of closely related Gram-negative bacterial species that are inherently

resistant to many antibiotics. Several Bcc species can cause chronic and debilitating lung infections in cystic fibrosis patients (1); presence of a Bcc species is usually associated with advanced lung disease (2), likely both as a result and a contributor of lung damage.

Transient colonization can occur (1, 3) but after establishment, Bcc infections are usually

chronic and eradication is difficult. Just under 3% of cystic fibrosis patients are infected

with Bcc organisms, and the genus is not commonly found in childhood or adolescent

patients (4). Prevalence is low but the risks associated with Bcc infections are high

including increased morbidity and mortality, and the potential for an often-fatal condition

known as “cepacia syndrome” (1, 3). Antibiotic therapy use in CF patients is common,

both prophylactically and in response to pulmonary exacerbations, though bacterial taxa

profiles in the CF lungs may remain relatively stable (5). This therapy creates an

environment that selects for resistant mutants by eradicating sensitive strains.

Bacterial antimicrobial susceptibility profiles change in response to selective

pressure, including towards non-treatment drug antibiotics. Collateral changes in

susceptibility arise when a mutant has an increase or decrease in resistance to an

antibiotic to which the bacterium has not been exposed. Cross resistance (CR) is when a

strain has increased resistance to a non-treatment drug, and collateral susceptibility (CS) is when a strain has decreased resistance to a non-treatment drug (6-8). Experiments on

Escherichia coli (6, 8-13), Pseudomonas aeruginosa (14-17), and Staphylococcus aureus 50 (18-20) have demonstrated cross resistance and collateral susceptibility in these

pathogens.

Collateral susceptibility interactions between two antibiotics can be either

reciprocal, in which direct resistance to either drug in a pair leads to an increase in

sensitivity to the other, or asymmetrical, in which direct resistance to Drug A leads to an

increase in sensitivity to Drug B but the reverse does not occur (6, 17). Our laboratory

has identified collateral susceptibility and cross-resistance in a Burkholderia multivorans clinical isolated to six antibiotics (21). We determined that, using a small number of evolved strains, collateral resistance and sensitivity interactions generally occurred in patterns based on the treatment drug, forming two clusters we called beta-lactam antibiotics (βLA) and non-beta-lactam antibiotics (non-βLA). Here, we document reciprocal collateral susceptibility patterns in B. multivorans. Such drug-pair combinations allow a treatment strategy of switching between two antibiotics instead of using a sequential therapy regiment that leads to pathogens with expanding antibiotic resistance.

Molecular mechanisms of cross resistance have been reported and include upregulation or deregulation of efflux pumps (22) and mutations in a stress tolerance regulatory system component (8). Few molecular mechanisms of collateral sensitivity have been proposed. One involves decreased membrane permeability of aminoglycosides and decreased efflux of non-treatment drugs due to alteration of the membrane potential/proton motor force in E. coli (10, 13, 23). However, collateral sensitivity effects observed with these drugs that did not involve active proton motivation has also been 51 reported (24). Here we report evidence in support of candidate genes that can be further

investigated for a possible role in collateral sensitivity.

Materials and Methods:

Strains, culture conditions, and antibiotics

We used AS149, a strain of Burkholderia multivorans isolated from the sputum of a cystic fibrosis patient (25), as the ancestral parent. Bacteria were grown on LB broth,

Antibiotics involved in this study, listed in Table 1, were chosen due to their inclusion in the CLSI (26) list of standard antibiotics tested against Burkholderia cepacia and for having varied targets. BBL Sensi-Disc antimicrobial susceptibility test disks (BD) were used for all except minocycline, which were Oxoid antimicrobial susceptibility disks (Thermo Scientific). The minimum inhibitory concentration was determined using

ETEST gradient strips (bioMérieux).

Experimental evolution

Strains were evolved for resistance to one of six treatment drugs listed in Table 1 using a previously described, plate-based method (27). We considered an evolved strain to be adapted to the drug when antimicrobial susceptibility testing via disk diffusion on

Mueller-Hinton agar plates (26) exhibited a zone of inhibition that is considered

‘resistant’ by the CLSI breakpoints or there was confluent growth up to the disk edge for drugs without disk diffusion breakpoints, i.e. levofloxacin and chloramphenicol. 52 To create lineages, we used AS149 as the ancestral parent. A “lineage” includes

all progeny that started with one treatment drug (i.e. a MEM-lineage, CAZ-lineage, etc.).

Nomenclature and treatments used for all lineages is demonstrated for meropenem

(MEM): AS149 is exposed to MEM until resistant. The resulting strain is designated

Progeny1MEM-R. Disk diffusion testing on Progeny1MEM-R is performed using the

treatment drug, MEM, and five non-treatment drugs to ensure Progeny1 was resistant to

MEM and not a persister or cheater (28). If Progeny1MEM-R demonstrated a zone of

inhibition for any of the five non-treatment drugs, it was used to evolve a new strain that

would be resistant to the non-treatment drug. For example, if Progeny1MEM-R was

sensitive to CAZ, it was subjected to the above experimental evolution process to

produce a new evolved strain: Progeny2CAZ-R. That strain was then examined for changes in susceptibility to the five non-treatment drugs. If any collateral sensitivity interaction was observed, Progeny2 CAZ-R was subjected to the same treatment as was used with

Progeny1MEM-R to evolve progeny strains resistant to the CS non-treatment drug. Each of

the resulting Progeny 3 strains were tested as above and if CS was present, that

strain/non-treatment drug was used to evolve the next progeny. A lineage was terminated

for one of two reasons: 1) the terminal strain had no CS or 2) reciprocal CS had been

demonstrated twice.

Antimicrobial susceptibility testing and interpretation for collateral resistance and

sensitivity

Antimicrobial susceptibility testing (AST) was performed as in our previous study

(21): disk diffusion testing was performed on evolved strains selected for resistance to a

treatment drug to determine susceptibility to five non-treatment drugs, as well as the 53 treatment drug to ensure that the strain was truly resistant. The antibiogram of the

evolved strain was compared to the antibiogram of the immediate parental strain to

determine collateral sensitivity (CS) and cross-resistance (CR). Any change in zone of inhibition (ZOI) of 20% or greater for a non-treatment drug reflects collateral changes in susceptibility, with an increase in ZOI indicating CS and a decrease in ZOI indicating

CR.

To better quantitate collateral susceptibility interactions, an ETEST® was used to determine the minimum inhibitory concentration (MIC) on all strains having CS as indicated by 20% or greater increase in ZOI with disk diffusion testing. Any decrease in

MIC, indicating increased sensitivity, was considered a positive collateral susceptibility interaction.

Statistical Analysis

All statistical analyses were performed with GraphPad Prism. To determine statistical significance of clustering in cross-resistance (CR) and collateral sensitivity

(CS) regarding βLA and non-βLA clusters, we ran observed-versus-expected binomial one-tailed tests. Numbers of interactions were used, as opposed to strains, since strains could contain more than one interaction. We considered the null hypothesis to be that any of the 5 non-treatment drugs had an equal chance to be the drug in the observed interaction so expected values are 20% βLA (1 of 5) and 80% non-βLA (4 of 5) when the treatment drug was a βLA; 40% βLA (2 of 5) and 60% non-βLA (3 of 5) when the treatment drug was a non-βLA. We also analyzed decreases in MICs in strains exhibiting collateral sensitivity, as expressed by fold-changes for each non-treatment drug. Tests for 54 normality (Anderson-Darling and Kolmogorov-Smirnov) were negative for all, so

Kruskal-Wallis test and Dunn’s multiple comparisons test were run (Figure 3).

We subjected 68 independently evolved Burkholderia multivorans isolates, three

parental AS149 biological replicates, and eight negative control biological replicates (2x

five exposures, 10 exposures, 15 exposures, and 20 exposures) to whole genome

sequencing. A single colony from each strain and two morphologically distinct colonies

from control plates were sent to Omega Bioservices, where WGS was performed using

151 bp paired end reads with an Illumina HiSeq 2500 platform.

Raw files were visualized in FastQC-0.11.8 (29) for quality. Adaptor reads and

contigs >151 bp were trimmed and bases with quality reads falling below a phred score

of 20 were trimmed in Trimmomatic-0.35 (30). Genomes were globally aligned to the reference genome Burkholderia multivorans ATCC BAA-247 (NCBI:

Accession: PRJNA264318) using Bowtie2-2.3.4.3 (31). Duplicate reads were marked and removed using Picard-2.8.26 and INDELS realigned using GATK3 IndelAligner.

Variants were called in GATK3 HaplotypeCaller with a standard confidence call of 30 in

Discovery mode (32, 33)Variants below coverage less than 10X were filtered for the final variant calls using VCFtools (34).

Python scripts and SAMtools were used to remove all called variants in parental

and control strains from individual CS strains to validate that all variants called in each

strain were induced in the evolution experiment (35). Final called variants (SNP/INDEL)

were annotated using snpEFF-4.3T and functional characterization was achieved through

NCBI, Burkholderia Database (Burkholderia.com), STRING database, and KEGG

database (36). 55 To identify candidate genes likely involved in direct resistance and collateral

sensitivity, starting with the evolved strains of interest, we first eliminated mutations

from the ancestral parent, AS149. Then mutations in the negative control strains were

removed, as they are likely to be involved in adaptation to the laboratory growth

conditions. Then the results were organized by resistance group, i.e. all strains with the

same treatment drug were grouped together. Those strains without collateral sensitivity

were placed into their own file, and then strains with CS were organized by the non-

treatment drug(s) involved in the collateral interaction.

Results

Frequency of cross-resistance and collateral susceptibility

Of 279 evolved strains, 188 (67%) exhibited cross-resistance and 170 (61%) exhibited collateral susceptibility. The frequency for each of the six antibiotics is seen in

Table 2. The range of cross-resistance frequency for each treatment drug was 58%

(meropenem) to 90% (chloramphenicol). Collateral sensitivity frequencies ranged from

56% (ceftazidime and minocycline) to 70% (trimethoprim-sulfamethoxazole). As was observed previously (21), interaction patterns were observed based upon the treatment drug; the beta lactam antibiotics (βLA) forming one cluster (134 strains [48%]) and the non-beta lactam antibiotics (non-βLA) forming the other (145 strains [52%]).

Cross-resistance patterns

The majority of strains in each resistance group, i.e. all strains evolved with the

same treatment drug, exhibited cross-resistance (Table 2). To determine if there is a pattern to the interactions between different antibiotics, the ratio of CR-exhibiting strains 56 to total evolved strains was calculated and compared. For both βLA resistance groups, this ratio was lower (61%, 82 of 134) than for the four non-βLA resistance groups (73%,

106 with CR of 145 evolved). Another means to determine if there are CR patterns is to compare the number and type of CR interaction for each treatment drug. For all 188 CR- exhibiting strains, there as an average of 1.9 CR interactions per strain (366 total CR interactions). The range in this value for each treatment drug is presented in Fig 1A. If treatment drugs are separated into βLA and non-βLA clusters, the range for n-βLAs is

1.7-2.3 and for βLA is 1.7-1.8.

Although there was little difference in the percentage of evolved strains that exhibited CR, or the average CR interactions per strain, there was a difference in the type of CR observed. Most cross-resistance interactions were observed within clusters (Fig

1A). When the treatment drug was a non-βLA, if there was a CR interaction the non- treatment drug was more likely to be another non-βLA. The same pattern is seen in the

βLA cluster; >70% of CR-exhibiting strains with a βLA treatment drug had CR interactions occur in the other βLA. Between-cluster interactions were more common when the treatment drug was a βLA than a non-βLA (Figure 2A). We performed an observed-versus-expected, one-tailed binomial statistical test (see Materials and

Methods); clustering was statistically significant for all treatment drugs (Table 2).

Collateral susceptibility patterns

Of the 170 strains with collateral sensitivity, 45% had increased sensitivity to only one non-treatment drug (NTD) and the majority (86%) had either one or two interactions

(Table 2). The overall average is 1.8 interactions per CS-exhibiting strain, with meropenem having the highest average (2.1) and levofloxacin and minocycline having 57 the lowest (1.3). Two groups, trimethoprim-sulfamethoxazole and meropenem, had strains with 4 and 5 interactions. The absolute ratio of CS-exhibiting strains to total evolved strains did not significantly differ between these two clusters (59% for the βLA and 63% for the non-βLA).

The clusters identified from cross-resistance interaction patterns (βLA, non-βLA)

were also present in collateral sensitivity patterns. Unlike CR interactions that were

within clusters, most CS interactions were between clusters (Figure 1B and Figure 2B).

Again, we performed an observed-versus-expected, one-tailed binomial statistical test

(see Materials and Methods) and clustering was statistically significant for all treatment drugs (Table 3).

Quantitative decrease in MIC for collateral sensitivity interactions

CS interactions were classified based upon there being a certain minimum change in zone of inhibition (ZOI) resulting from antimicrobial susceptibility testing. A more accurate means of calculating the degree to which an evolved strain has decreased resistance to a non-treatment drug is by direct determination of minimum inhibitory concentration (MIC). Comparing ETEST results for an evolved strain to the immediate parent allows calculation of the fold decrease in MIC for all non-treatment drugs,

regardless of the treatment drug (Figure 3). Ceftazidime showed the greatest decrease,

with an average fold decrease of 86.1 (SD 204.2, range 1.3-1024); chloramphenicol

(mean 2.9, SD 2.2, range 1.3-8) and levofloxacin (mean 2.8, SD 2.0, range 1.3-12) had the smallest decreases.

Reciprocal CS between antibiotic pairs 58 Identification of two treatment drugs which lead to collateral sensitivity in the

other antibiotic, i.e. reciprocal CS, suggests a strategy to treat chronic infections.

Candidate antibiotic pairs were selected as follows: each member of the pair had a

relatively high percentage of strains with collateral sensitivity as the non-treatment drug

when the other was the treatment drug. For example, levofloxacin (LVX) and ceftazidime

(CAZ) were determined to be a reciprocal pair because, of the 62 strains evolved with

CAZ as the treatment drug, 26% had increased sensitivity to LVX (the non-treatment drug) and, of the 44 strains evolved with LVX as the TD, 30% had increased sensitivity to CAZ, the non-treatment drug. Pairs were ranked by their average percentage and the top five are given (Fig 4). Reciprocal pairs all had an average percentage of CS of at least

25%, and individually ranged from 21% to 60%. In order of the average percentages, we determined reciprocal pairs, with their respective individual and average percentages as the non-treatment drug, to be meropenem (MEM)/trimethoprim-sulfamethoxazole (SXT)

(non-treatment drug = MEM 60%, SXT 33%, average 46%), MEM/LVX (43, 32, 38),

CAZ/SXT (40, 29, 35), MEM/minocycline (MIN) (21, 43, 32), and CAZ-LVX (30, 26,

28).

Genes involved in CR-CS reciprocity.

Whole genome sequencing and mutational analysis was conducted on 68 of the evolved strains, as well as the parental strain. The average number of total mutations per strain was 50.23 (range: 13-120). The average number of intergenic mutations was 23.01 per strain (range: 1-67). For coding regions, there was an average of 13.92 (range: 2-31) synonymous mutations and an average of 13.29 (range: 4-25) nonsynonymous mutations. 59 When analyzing the mutation data after organization (see Materials and Methods),

we focused on the mutations in one set of reciprocal pairs: meropenem (MEM) and

levofloxacin (LVX). As seen in Figure 1b, meropenem was involved in many collateral

sensitivity interactions (3 of 5 CS-reciprocal pairs include MEM, and MEM had the highest average interactions per strain (2.1) as well as the most number of strains exhibiting CS (44)) so there was sufficient available data to analyze, so it was chosen as one member of the pair. We chose LVX as our second drug because MEM-LVX was the first trend noticed during the experimental evolution stage of data collection and because, unlike SXT, it had a manageable number of mutations to evaluate. Unfortunately, there are informational limitations in annotation and phenotypic effects of mutations for

Burkholderia multivorans; annotations did not always include the gene involved, but rather just a protein family, particularly for transcription factors which can have multiple

DNA binding sites based on their recognition motifs. For example, the annotation for a mutation included in both MEM and LVX lists is “TetR family protein”. There are many proteins included in this family and while they all presumedly act as transcription factors, without the gene annotation, we could not determine the gene(s) the transcription factor regulates. Additionally, most phenotypic effects of mutations have not been characterized in Bcc so it is difficult to correlate genotype with phenotype. We were unable to determine if there were genes that are responsible for both the direct resistance of the treatment drug and collateral sensitivity of the non-treatment drug, so we constructed a list of candidate genes that could be responsible for each of those phenotypes, which are in Table 4 and 5. 60 Mutations in strains with LVX as the treatment drug and exhibiting collateral

sensitivity to MEM are shown in Table 4. The mutations that seem most likely to be

responsible for resistance to LVX are in regulators of efflux pumps, a mechanism of

resistance to fluoroquinolones which has been reported in other studies (37, 38). We

observed mutations in a MarR protein, a transcription factor of the major facilitator

superfamily (39), and a TetR family transcriptional regulator (40), both of which have

been associated with multi-drug resistance due to efflux pumps (39, 40). Most reported

mutations leading to LVX resistance involved the gyrase gene and in efflux mechanisms

(38); while we did not see any mutations in gyrase genes, we did observe other mutations

affecting transport, which could contribute to LVX resistance which are also listed in the

table. The mutations that provide the most likely explanation for the CS to MEM are in

genes responsible for cell wall/membrane maintenance and integrity, including a

mutation in the coding region for penicillin-binding protein (PBP) 4 (41) and in outer membrane proteins, which have previously been associated with beta-lactam resistance

(42, 43).

Mutations in strains with MEM as the treatment drug and exhibiting collateral sensitivity to LVX are shown in Table 5. The mutations that seem most likely to be responsible for resistance to MEM are in genes responsible for cell wall integrity, including mrcA, which encodes PBP1a, responsible for polymerizing peptidoglycan and associated with carbapenem resistance (44), and lamB/yscF family (45-47). Like in the

LVX-adapted strains, a mutation in a TetR family regulator was observed. A previous study showed increased resistance to MEM with a loss of efflux pump regulation (22) so it could contribute to the resistance aspect of the phenotype or, if it had an opposite effect 61 that regulator mutations have been seen previously, an increase in sensitivity to LVX

(40). The observed mutation in dnaK could also contribute to the LVX-collateral sensitivity, as it has been associated with an increased sensitivity to fluoroquinolones

(48).

Discussion

In a previous study, we reported that experimentally evolved B. multivorans

strains exhibited collateral changes in susceptibility with a pattern based on the treatment

drug used to evolve the strains (21). This study quantitates the nature of the interactions

and existence of antibiotic clusters, and identifies reciprocal pairs occurring in B.

multivorans. The frequency of collateral susceptibility changes observed was higher (10)

and lower (6) than found previously with other organisms.

Clusters based on the treatment drug being a beta-lactam antibiotic (βLA) or not

(non-βLA) continue to the basis of patterns of collateral susceptibility changes (Fig 1A).

The majority of cross-resistance interactions are within each cluster, which was reported previously, however, here we observed more between cluster cross-resistance interactions

when the treatment was a βLA than is a non-βLA (Fig 2A). One explanation for the more even distribution in regards to cluster is that mutations leading to resistance to beta- lactam drugs may involve alterations in cell wall synthesis. Such alterations could impact growth rate (49), which could lead to either increased or decreased susceptibility changes to other substances.

Collateral sensitivity interactions were observed in 170 of 279 evolved strains, with the majority (55%) having more than one interaction. When present, these 62 interactions were usually seen between clusters (Fig 1B and 2B). A notable variation

from the cluster trend is seen with minocycline (MIN) as the treatment drug. Only one

MIN-adapted strain had resistance to chloramphenicol and none to levofloxacin, which is

consistent with the cluster trend, but the other interactions are somewhat evenly

distributed between trimethoprim-sulfamethoxazole (SXT) with 8 interactions, ceftazidime (9 interactions), and meropenem (7 interactions). Another noteworthy result is the rate of collateral sensitivity of meropenem when SXT was the treatment drug: 85% of all CS-exhibiting SXT-evolved strains had increased sensitivity to meropenem.

We examined change in the minimum inhibitory concentration (MIC) – a representation of the quantitative decrease in resistance of the CS-exhibiting non- treatment drug (Fig 3) - to determine the degree to which there is a collateral decrease in

resistance; this information could assist in elucidating mechanisms involved or be an

indication of usefulness of the CS interaction in clinical consideration. When assessing

these changes, we noted that ceftazidime stood out with a maximum of 1024-fold

decrease, 7 strains (12%) with a decrease of greater than 100-fold, and almost half (49%,

28 strains) with a decrease of >25-fold (data not shown). For all other non-treatment

drugs, the average fold decrease was 7.1 with a standard deviation of 11.8 (data not

shown). The

Identifying reproducible patterns of collateral interactions and antibiotic pairs that

exhibit reciprocal CS is a preliminary step in establishing recommendations for clinical

treatment cycling. The discovery of reciprocal pairs of antibiotics each of which, as the

NTD with CS when the other is the TD, arise in at least 25% of all evolved strains (Fig 63 4), is exciting for the potential impact on clinical decisions. Pairs include MEM-SXT,

MEM-LVX, CAZ-SXT, MEM-MIN, and CAZ-LVX.

Analysis of whole genome sequencing data to determine mechanisms of changes in antibiotic susceptibility was only partially successful, given the relative dearth of specific annotation (e.g., mutated gene annotated as “TetR”-type family transcription factor without the specific gene/transcription factor) and genomic information for this species. Many of the observed mutations were in general cell processes such as metabolism, transport, and wall/membrane integrity. Collateral sensitivity seen in B. multivorans may be due to a combination of specific and non-specific mutations. For example, collateral sensitivity to meropenem that we observed could have resulted from a combination of mutations in antibiotic-specific genes, such as beta lactamase or PBP genes, and non-specific processes, such as alterations in permeability of the cell wall or membrane (43, 50), which would combine to increase intracellular concentration of meropenem. The more genes/mutations that are involved in a phenotype, and the more combinations of multiple genes/mutations that could lead to the same phenotype, the more difficult it is to identify those genes. Mutations in efflux pumps could be contributing to either the direct resistance of the treatment drug, when the treatment drug was LVX or MEM, or collateral sensitivity of the non-treatment drug, depending on the nature of the mutation, ex. whether it is loss-of function or altered specificity, and substrate of the efflux pump. Adding another layer of complexity is that the mutations we observed were in the regulator of the efflux pump. Without knowing the direct and downstream effects of the mutation, we are unable to determine a correlation with phenotype. 64 There is a duplication of entries in the list of mutations seen in both LVX- and

MEM- adapted strains. For this pair, rpsL, which encodes the ribosomal protein S12, and araJ, an arabinose efflux permease, are in both list of mutations. This duplication may or may not have significance to the phenotype. For example, if the mutation listed in the levofloxacin treatment drug set had a cancelling effect on the mutation listed in the meropenem treatment drug set, then the duplication would be significant to collateral sensitivity. On the other hand, if the mutations had no effect on the other phenotype, then it could be that the mutation resulted from extended laboratory culture conditions or were cost-less in the environment. Further work is required to determine the similarity/dissimilarity of the genomic effects, such as deletions, insertions, or amino acid change, and phenotypic effects.

We have additional sequencing data to investigate for candidate genes for the other reciprocal CS pairs; we plan to conduct the analysis in the future. In addition, our lab plans to use gene editing or other recombinant techniques to elucidate the phenotypic effects of the candidate gene mutations observed in order to determine causation/correlation between mutations and changes in antibiotic susceptibility.

Acknowledgments

This work was supported, in part, by funds provided by The University of North

Carolina at Charlotte and a National Institutes of Health grant 1R15HL126122-01 to

T.R.S.

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77 Table 6. Strains evolved and collateral changes observed Results of experimental evolution are shown by treatment drugs: chloramphenicol (CHL), ceftazidime (CAZ), levofloxacin (LVX), meropenem (MEM), minocycline (MIN), and trimethoprim-sulfamethoxazole (SXT). Information for each treatment drug includes the total number of strains evolved, the percentage of strains showing cross- resistance (CR) and collateral sensitivity (CS), and the average number and range of CS interactions per CS-exhibiting strain.

Treatment Total # Percentage Percentage Avg # CS Range of CS Drug strains with CR with CS interactions interactions evolved per strain per strain w/CS CHL 20 90% 65% 1.6 1-3 CAZ 62 65% 56% 1.7 1-4 LVX 44 75% 59% 1.3 1-3 MEM 72 58% 61% 2.1 1-5 MIN 34 71% 56% 1.3 1-3 SXT 47 66% 70% 2.0 1-5

TOTALS 279 67% 61% 1.8 1-5

Figure 6. Heat map of cross-resistance (CR) and collateral sensitivity (CS) interactions For each treatment drug - chloramphenicol (CHL), ceftazidime (CAZ), levofloxacin (LVX), meropenem (MEM), minocycline (MIN), and trimethoprim-sulfamethoxazole (SXT) - used to evolve a strain (listed across the top) and the non-treatment drug to which the strain exhibits a change in sensitivity (on the left), the number of strains that demonstrated cross-resistance (Panel A) or cross-sensitivity (Panel B) is indicated in each box. The percentage of evolved strains exhibiting the reaction to each non-treatment drug are represented by the intensity of the cell. Groupings within the βLA and non-βLA clusters are bordered by dashed lines.

Figure 7. Cross-resistance (CR) and collateral sensitivity (CS) interactions by cluster The percentage of strains exhibiting CR (Panel A) or CS (Panel B) are grouped for each treatment drug based on the non-treatment drugs belonging to the βLA cluster, the non- βLA cluster, or non-treatment drugs from both clusters.

Table 7. Statistical significance of clustering within/between antibiotics P-values obtained by using observed-versus-expected binomial one-tailed tests are shown for treatment drugs within the non-beta-lactam antibiotics (non-βLA) cluster: chloramphenicol (CHL), levofloxacin (LVX), minocycline (MIN), and trimethoprim- sulfamethoxazole (SXT), and beta-lactam antibiotic (βLA) cluster: ceftazidime (CAZ), and meropenem (MEM). The null hypothesis that any of the 5 non-treatment drugs had an equal chance to be the drug in the observed collateral sensitivity or cross-resistance interaction; observed values for drugs within the cluster are shown above the observed values for each drug.

Cross-Resistance Collateral sensitivity Non-treatment drug non- non- cluster→ βLA P value βLA P value βLA βLA Treatment Drug ↓ Expected values for non-βLA treatment drug 40% 60% 40% 60% Observed values for non-βLA treatment drugs CHL 3% 97% <0.0001 76% 24% 0.0008 LVX 12% 88% <0.0001 91% 9% <0.0001 MIN 23% 76% 0.0063 64% 36% 0.0132 SXT 16% 83% 0.0002 70% 30% <0.0001 Expected values for βLA treatment drug 20% 80% 20% 80% Observed values for βLA treatment drugs CAZ 45% 55% <0.0001 5% 95% 0.0008 MEM 40% 60% <0.0001 10% 90% 0.0052

80 **** 300 *

250

200

150

100 * 50 **** Fold change (decrease in in MIC) (decrease change Fold **

0 CAZ MIN CHL LVX SXT MEM Non-treatment Drug

Figure 8. Change in minimum inhibitory concentration of non-treatment drug The minimum inhibitory concentration (MIC) of the non-treatment drug of evolved strains (regardless of treatment drug) with collateral susceptibility was measured with ETESTs and compared to the immediate parental strain. Error bars indicate standard deviation. Adjusted P-values: *<.02, **=.0023, ****<.0001.

Figure 9. Reciprocal cross-resistance/collateral sensitivity pair patterns Treatment drugs are shown in colored text around the outside of the circle with the βLA cluster on the left of the circle and the non-βLAs on the right. The non-treatment drugs exhibiting CS to that treatment drug are shown as the colored dots interior to the treatment drug (red = ceftazidime (CAZ), orange = meropenem (MEM), yellow = chloramphenicol (CHL), green = levofloxacin (LVX), blue = minocycline (MIN), violet = trimethoprim-sulfamethoxazole (SXT)). The location of the dot on the radial lines indicates the percentage of evolved strains for that treatment drug that show collateral sensitivity to that non-treatment drug. Lines between dots indicate the five reciprocal pairs with the highest average percentage; line weight corresponds to the relative average percentage. 82

84 CHAPTER 6: CONCLUSIONS

Scientific research does not always, or maybe ever, have a properly annotated, up

to date, well-proven roadmap with notes on how to get there most quickly, with few or no

detours. My original project was to establish a collateral susceptibility network and, while

that was accomplished, the journey resulted in additional tasks with resultant journal

articles on related subjects such as antimicrobial susceptibility testing parameters and

bacterial experimental evolution methods.

As a scientist, it is always my fervent hope that my work reflects the truth as closely as possible. Identifying the outcome of variation in test parameters is an important step to achieve this goal. My first article explicates the effects of different agar depths on results of antimicrobial susceptibility testing: there is an inverse relationship between agar depth and minimum inhibitory concentration (MIC) when using ETESTs, especially in the case of antibiotics with a small molecular weight. I applied this information to the collateral sensitivity project, which has a critical need for accuracy due to assessment of small concentration differences.

Experimental evolution of bacteria can yield important information by generating mutants which survive under the chosen selective pressure. Evolving bacteria can be difficult, time- and cost-intensive, fraught with potential for error, or require specialized equipment (11). As the collateral sensitivity project required many strains to be evolved to construct a sufficient network, I developed an easy, cost- and effort-effective, scheme to evolve antibiotic-resistant bacteria, which was published as a novel method.

Research into intelligently designed treatment regimens using antimicrobials already in existence has become imperative, as choices for effective treatment options for 85 bacterial infections decrease due to multi-drug resistance and increased number of

infections caused by opportunistic organisms (1). One aspect of future antimicrobial

stewardship includes investigating collateral changes in susceptibility which occur when,

as a side effect from direct resistance secondary to exposure to a treatment drug, a mutant

strain has changes in susceptibility to drugs to which it had no exposure. In particular, knowledge regarding cross-resistance (CR) and collateral susceptibility (CS) is crucial when treating chronic infections such as in the lungs of cystic fibrosis patients. I have demonstrated that experimentally evolved strains from a clinical isolate of B. multivorans, an organism associated with progressing lung deterioration due to chronic infection, exhibits CR and CS. While the resulting collateral changes are dependent on the particular mutation harbored by an individual strain and therefore are not always present in every evolved strain or identically organized, I detected patterns so that “best choice” options for treatment cycling using pairs of antibiotics are possible.

Future directions for this work include expanding the collateral interaction database by using strains evolved from other clinical isolates, which is in progress. We also hope to investigate CS in other members of the Burkholderia cepacia complex (Bcc) that have been implicated in cystic fibrosis lung infections. While there is a higher risk in working with this organism in the laboratory, I would be excited to investigate an attenuated strain of Burkholderia pseudomallei, the etiological agent of the human disease meliodosis, for CS interactions. In addition to the culture-dependent work to determine the patterns of collateral changes, continuing molecular work needs to be done to detect and confirm the genomic variations responsible for the phenotypes observed including whole genome sequencing (WGS), analysis of WGS results, and correlation of 86 the mutation with direct resistance and CS. Additionally, our lab plans to use recombinant DNA and knock-out techniques to confirm our hypotheses regarding mutation and phenotype correlation.

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93 APPENDIX A: EVOLVED STRAIN LIST

94 95 96 97 98 99 100 101 102 103 104 105

106 APPENDIX B: ANTIMICROBIAL SUSCEPTIBILITY TESTING RESULTS SPREADSHEETS

107 108 109 110 111 112 113 114 115

116 117

118

119 120 121 122 123

124

125

126 127 128 129 130 131 132