Using Transposon Sequencing to Identify Vulnerabilities in Staphylococcus Aureus

Using Transposon Sequencing to Identify Vulnerabilities in Staphylococcus aureus

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Citation Coe, Kathryn Ann. 2019. Using Transposon Sequencing to Identify Vulnerabilities in Staphylococcus aureus. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.

Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:42013046

Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Using Transposon Sequencing to Identify Vulnerabilities in Staphylococcus aureus

A dissertation presented by Kathryn Ann Coe to The Division of Medical Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Biological and Biomedical Sciences

Harvard University, Cambridge, Massachusetts

June 2019

Dissertation Advisors: Professor Suzanne Walker Kathryn Ann Coe Professor Yonatan Grad

Using Transposon Sequencing to Identify Vulnerabilities in Staphylococcus aureus

Abstract

Antibiotic resistant infections cost thousands of lives in the United States every year.

Resistance exists for every known antibiotic, making the development new antibiotics crucial to human health. One technique that has become instrumental in prioritizing targets for antibiotic development is transposon sequencing (Tn-Seq). Tn-Seq is a powerful high-throughput technology that connects bacterial genes with phenotypes and can be used to identify genes that are essential for bacterial survival. In the early years of Tn-Seq, results from a single representative strain were largely assumed to reflect the entire species. However, researchers are now showing that gene essentiality varies among members of a species and that this variability can have direct implications for antibiotic susceptibility. Here I describe the use of Tn-

Seq to more thoroughly characterize Staphylococcus aureus gene reliance. We have generated a compendium of core essential genes shared by five strains from across the S. aureus phylogeny, including three strains of methicillin-resistant S. aureus (MRSA), a leading cause of antibiotic resistance-associated mortality in the United States. To better understand antibiotic resistance, we have also developed a new analytical approach that uses Tn-Seq data to identify genes whose overexpression confers a fitness advantage in a given condition. We applied this method to a range of data from antibiotic-treated samples and recovered many clinically- reported mechanisms of resistance as well as new mechanisms that may provide insights into how antibiotics work and how bacteria overcome them. Given the baseline differences in gene

iii reliance between strains of S. aureus, we wondered whether antibiotic resistance factors would be shared across strains. We used daptomycin, a common antibiotic for the treatment of MRSA, as a case study to compare resistance factors and vulnerabilities across the five aforementioned strains of S. aureus. We found that the genes modulating antibiotic susceptibility were largely shared, even in cases where the strains relied on the genes to differing degrees in favorable growth conditions. Together, these studies provide the most nuanced characterization of S. aureus gene reliance to date and emphasize the utility of Tn-Seq for investigating antibiotic resistance.

Table of Contents

Chapter 1: Introduction to transposon sequencing and its growing import in Staphylococcus aureus bacteriology 1

Chapter 2: Comparative Tn-Seq reveals differences in gene essentiality between strains of Staphylococcus aureus 15

Chapter 3: Development of an analytical method to identify upregulation signatures in transposon sequencing data 46

Chapter 4: Comparative Tn-Seq reveals common daptomycin resistance determinants in Staphylococcus aureus despite strain-dependent differences in essentiality of shared cell envelope genes 72

Chapter 5: Conclusions and future directions 101

Appendix 108

Figures Figure 2.1: Tn-Seq can be used to identify essential genes. 19 Figure 2.2: Essential genes, while primarily shared, vary somewhat by strain. 32 Figure 2.3: The fitness of lipoteichoic acid pathway mutants varies by strain. 34 Figure 3.1: Adding an outward-facing promoter to a transposon makes insertion outcomes orientation-dependent. 48 Figure 3.2: Upregulation signatures in Tn-Seq data reveal fitness advantages through gene overexpression. 50 Figure 3.3: Single-strand tokens for HMM 2 were defined using read difference cutoffs. 56 Figure 3.4: Folate and DNA synthesis pathway genes have upregulation signatures in Tn-Seq data upon trimethoprim exposure. 63 Figure 4.1: Transposon sequencing supports previously reported daptomycin vulnerabilities in S. aureus and reveals new ones. 84 Figure 4.2: LTA loss sensitizes S. aureus to daptomycin. 85

Tables Table 2.1: We created complex transposon libraries of similar coverage in five diverse S. aureus strains. 31 Table 3.1: Emission probabilities for HMM 1. 54 Table 3.2: Emission probabilities for HMM 2. 58 Table 3.3: Known hits in three test files recaptured by each proposed analysis method. 61 Table 4.1: Daptomycin-exposed samples included in the multi-strain comparison of depleted, enriched, and upregulated genes. 79 Table 4.2: Upregulation signatures identify genes previously linked to reduced susceptibility to daptomycin. 86 Table 4.3: Genes enriched in reads in the presence of daptomycin are often slow growing. 88

Appendix Supplemental Table 1: Primers used in these studies. 109 Supplemental Table 2: Bacterial strains used in these studies. 110 Supplemental Table 3: Plasmids used in these studies. 111 Supplemental Table 4: Comparison of essential genes in S. aureus strains. 112 Supplemental Table 5: Core essential genes in S. aureus. 119 Supplemental Table 6: Gene ontology analysis reveals central dogma pathways to be universally essential in S. aureus. 125 Supplemental Table 7: Genes with upregulation signatures in the presence of various antibiotic compounds. 129 Supplemental Table 8: Genes with upregulation signatures in the presence of various antibiotic compounds, sorted by compound. 136 Supplemental Table 9: The number of antibiotics in the presence of which each gene has an upregulation signature. 145 Supplemental Table 10: Genes depleted of reads in the presence of daptomycin. 151 Supplemental Table 11: Genes with upregulation signatures in the presence of daptomycin. 154 Supplemental Table 12: Genes enriched in reads in the presence of daptomycin. 158 Supplemental Table 13: SNPs present in daptomycin-nonsusceptible isolates not found in paired susceptible isolates. 161

Acknowledgements

It takes a team to bring a dissertation to fruition. I am incredibly grateful for the mentorship Suzanne Walker has provided me throughout this process. She took me on even though I knew nothing about microbiology and has been a crucial advocate for me ever since.

When I was floundering in my third year, an aspiring bioinformatician in a decidedly noncomputational lab, she connected me with Yonatan Grad, who became my second advisor.

Yonatan has likewise been instrumental to my success here, encouraging me to approach all of my analyses with utmost rigor and making me a better scientist as a result. I also want to thank all of the people in Suzanne’s and Yonatan’s labs who have helped me along the way, providing both experimental and emotional support and making the experience enjoyable despite the toils.

Their passion for science has been inspiring and infectious. I am especially grateful to Marina

Santiago for all the work she did to set up the Tn-Seq analysis pipelines in the lab; Wonsik Lee for collaborating with me on all of my projects; Scott Olesen for his statistics and career mentorship; my bay-mates Anthony Hesser, Atsushi Taguchi, and Chris Vickery for providing so many laughs; Truc Do and Leigh Matano for helping me stay positive during rough patches; and

Jake Muscato and Sarah Potter for fearlessly taking over ordering and social planning, two of the most thankless lab jobs, so that I could focus on my research. I would also like to thank Tim

Meredith, Gloria Komazin-Meredith, Marina Santiago, and Leigh Matano for making the transposon libraries that were at the crux of all of my projects. I am appreciative of my preliminary qualifying exam committee (Ann Hochschild, Paula Watnick, and Marcia Goldberg), my dissertation advisory committee (Tom Bernhardt, Simon Dove, and David Hooper), and my dissertation examiners (Simon Dove, Tim van Opijnen, Steve Lory, and Dan Kahne) for their thoughtful advice and fruitful conversations and the Biological and Biomedical Sciences

Program and Microbiology Department for providing a richly intellectual and incredibly collaborative environment where I could thrive as a student.

vii

Last and most, I would like to thank my family. My parents, Larry and Diane Coe, have been my constant cheerleaders throughout my time here, ready with a drink and a pep talk during the rocky parts and a drink and a high-five to celebrate every success. If I ever needed anything, whether it was someone to talk to, a printer, help with a broken faucet, etc., they were always there for me in an instant. You would be hard-pressed to find anyone more loving and supportive than them, and I never could have done this without them. And of course, I would like to thank my husband, Sean Pearson, who has done everything in his power to keep me sane these last five years and has been my greatest source of happiness.

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Chapter 1: Introduction to transposon sequencing and its growing import in

Staphylococcus aureus bacteriology

1.1: Characterizing the genetic diversity of bacteria

Bacteria are amazingly diverse. They are found virtually everywhere on Earth and perform innumerable beneficial functions, contributing to processes like biogeochemical cycling, food production, waste degradation and bioremediation, and human health. They can also play an adversarial role, sometimes causing deadly infectious diseases. With this phenotypic diversity, it should be no surprise that bacteria also have immense genetic diversity. It has been estimated that two strains of a single species of bacteria differ as much as 35% in gene content

[1]. By contrast, if you compared the genome of any given human to a human reference genome, the difference in gene content would only be about 0.2% [2]. In acknowledgement of this remarkable diversity, the nascent field of bacterial functional genomics, which attempts to use high-throughput methods to understand how bacterial genotypes affect phenotypes, is quickly gaining traction.

Bacterial functional genomics is founded on bacterial genomics, a field whose explosive growth has been fueled by next-generation sequencing. Prior to the development of next- generation sequencing in 2008, sequencing a bacterial genome was prohibitively expensive, costing thousands of dollars per megabase. Since then, the price has fallen precipitously to the point where it now costs only a few cents per megabase. At this price, the technology is accessible to a much wider array of researchers, and it is feasible to sequence numerous bacterial strains at the same time. We now have a wealth of bacterial genomic information. As of January 2019, over 173,000 distinct bacterial strains had been sequenced and uploaded to the National Center for Biotechnology Information genome database, and its Sequence Read

Archive contains over 790,000 bacterial genome sequences [3]. Additional private and open access databases contain many more hundreds of thousands, if not millions, of isolate sequences. Bioinformaticians are using this sequencing data to answer a variety of important questions. Some researchers are characterizing the chemical reactions available in a mixed bacterial population and identifying the gene clusters used to make unique natural products [4].

Others are using the sequencing data to understand how bacteria have evolved over time [4, 5].

In the field of infectious disease, the data has been particularly helpful in understanding antibiotic resistance. Almost sixty percent of the bacteria in the NCBI genome database can colonize humans, and many hospitals have their own databases of sequenced bacterial isolates from infected patients. With this profusion of data, we can now achieve the statistical power to connect specific mutations or gene acquisition events to the development of antibiotic resistance [6]. The data has likewise been useful for identifying and tracking outbreaks of antibiotic-resistant bacteria and for assessing intervention strategies [7]. These data are so valuable that some scientists are calling for hospitals to sequence all bacterial samples from infected patients [8, 9]. Such a policy would afford health care providers more specific diagnostic and susceptibility information than traditional laboratory testing in a fraction of the time and, in cases of unexplained treatment failure, would give researchers the data needed to identify new markers of resistance or virulence, continually improving the system.

However, there is a hazard in over relying on bacterial genotyping in infectious disease without pairing it with phenotype data. The identity of a genetic change does not immediately indicate whether the effect is to overexpress, overactivate, underexpress, inactivate, change the temporal or spacial regulation of, or modulate the substrate specificity of an encoded protein.

Likewise, genotypes can be misleading when we do not fully understand the contextual elements needed for a resistant determinant to provide resistance. For example, although mecA is one of the main determinants of β-lactam resistance in Staphylococcus aureus, its presence is not sufficient for resistance, and some mecA-containing strains are susceptible to a subset of

β-lactams [10]. Conversely, resistance can sometimes be hard to characterized because it is multifactorial, in which case any of a set of individual mutations can increase the minimum inhibitory concentration of an antibiotic. A common example of this is daptomycin nonsusceptibility, for which numerous resistance factors have been identified [11]. Because different isolates have different combinations of the mutations, it can be difficult to identify which

3 mutations are most important using large genotype-based studies. Both of these problems can potentially be addressed by sequencing more diverse strains, as statistical power to identify resistant determinants and their compensatory mutations will naturally increase with the number of isolates sequenced. However, identifying the drivers of rare forms of resistance often requires thousands of isolate sequences, and in some cases that quantity of isolates simply is not available. It may be possible to identify potential resistance factors more quickly and with lower costs using other methods. Moreover, while helpful for understanding resistance mechanisms, these genomic data do not elucidate vulnerabilities that could be targeted with new antibiotics.

In these situations, functional genomic studies are often better suited to elucidate phenotypic implications of genetic differences.

1.2: Transposon sequencing: a high-throughput functional genomics method for prokaryotes

Just after the development of next-generation sequencing, researchers began devising new functional genomics approaches to pair genotypic data with phenotypic data on a genome- wide scale. In bacteria, this took the form of transposon sequencing (Tn-Seq), variations of which were developed independently by four different research groups in 2009 [12-15].

However, Tn-Seq was long in the making, built on the work of many groups going back to the

1970’s, so it is important to acknowledge the efforts that brought us to this point.

Transposons, or transposable elements, are mobile DNA segments that can relocate within genomes. They were first discovered in maize by Barbara McClintock and have since been shown to be ubiquitous in all domains of life, earning McClintock a Nobel Prize in 1983

[16, 17]. Transposons quickly became a favored tool in bacterial genetics [18]. Transposons were used for a variety of ends, including physical mapping, restriction site mapping, sequencing individual genes, introducing new restriction sites, finding cryptic promoters, and selecting for mutants with a particular phenotype, which would later become their primary use

[19]. However, until the 1990’s, the phenotypic experiments were low throughput, focusing only on a small number of mutants.

The first precursor of Tn-Seq was known as signature tagged mutagenesis (STM) [20].

STM shared a similar goal to Tn-Seq, namely to identify genes that become essential under a particular condition. For STM, transposons with unique nucleotide barcodes were inserted randomly into the genome, possibly disrupting genes if they landed within coding regions. The mutants were then both organized into an array and pooled to make a mutant library. The library was exposed to an experimental condition or left untreated. After growth in the experimental condition, the surviving mutants were harvested and their transposons were amplified by PCR, labeled, and hybridized to the arrayed mutants. Any arrayed mutant that hybridized to the initial pool but not the experimental condition pool was a candidate for follow up studies, as that mutant did not survive the experimental condition. The trouble with this technology was that the locations of the transposons within the genome were unknown, so each hit had to be characterized individually. One was also limited in how many mutants could be made. Each barcode had to be distinct enough that it would not hybridize with other transposons and had to be designed and created individually, making it difficult to make large enough transposon collections to obtain a saturating library. Moreover, because researchers did not know the locations of all of the library insertions and because the libraries were undersaturated, STM could not report on essential genes required for growth even under normal conditions, which are today defined by a paucity of transposon insertions within the gene.

These drawbacks were ameliorated by transposon footprinting methods like GAMBIT

(genomic analysis and mapping by in vitro transposition) once full bacterial sequences started becoming available [21, 22]. For GAMBIT, all mutants within the mutant library contained the same transposon, rather than barcoded transposons. The mutant library was be grown, the

DNA was be harvested, and PCR would be used to amplify regions of the genome, with one primer targeting a known genome locus and the other targeting the transposon sequence. PCR

5 products would be separated on a gel, creating a ladder of products. The length of each product indicated how far that mutant’s transposon was inserted from the genomic primer locus. If enough mutants were made, one could identify essential genes as areas in the genome that never contained a mutation. Then, conditionally essential genes could be identified by comparing the results from a pool grown in an experimental condition to the results from an untreated pool and seeing which PCR products were no longer present. This was the first technology to directly connect bacterial genotypes (i.e. characterized genetic knockouts) and phenotypes on a genome-wide scale. However, one major disadvantage of GAMBIT was its laboriousness; each PCR reaction could only report on a small subset of genes, so copious

PCR reactions needed to be run for every experiment.

Microarray-based approaches like TraSH (transposon site hybridization) sought to make genome-wide transposon mutagenesis studies more efficient [23-25]. For this approach, DNA from the transposon mutagenized pool was cut into small fragments using restriction enzymes, and fragments were size selected by gel electrophoresis. Adaptors were added to the cut ends of the DNA, and the fragments were amplified using primers targeting the adaptor and the transposon. The PCR products were then transcribed using a T7 promoter within the transposon, fluorophores or other tags were ligated to the transcribed RNA, and the RNA was hybridized to a DNA microarray containing DNA fragments from all open reading frames (ORFs) in known locations on the microarray. The microarray was imaged to identify which ORFs had fluorescent RNA attached, thereby locating the transposon insertions present in the library.

ORFs that never fluoresced were considered essential, and those that fluoresced when the library was left untreated but did not when the library was grown under an experimental condition were conditionally essential.

While this method was much less labor-intensive than prior methods and directly connected phenotypes to ORFs, it still had some room for improvement. First, it required that you create a unique microarray for each bacterial strain you wished to study, and the typical

6 microarray only had probes from ORFs, providing no information on intergenic regions [23, 24].

Moreover, the exact location of the insertion could not be identified, as each microarray probe could hybridize to upwards of ten different insertion mutants [23]. Later versions of the technology improved these facets, using unbiased probes from all areas of the genome and shorter probes that would hybridize to fewer mutants [26]. Still, the transposon insertions sites could only be estimated to within about 50 bp and microarrays were only available for well- studied bacteria. Additionally, researchers wanted to move away from a binary essentiality distinction towards a more nuanced assessment of the relative fitness of mutants. While this was possible to a certain degree with microarrays, it was difficult. In the microarrays, a genomic region that was more tolerant of insertions than another would theoretically fluoresce more brightly because more RNAs would hybridize to its probes. In practice, background hybridization and a fairly narrow dynamic range of detection made these determinations unreliable.

Tn-Seq capitalized on the newly developed next generation sequencing technologies to overcome the pitfalls of microarrays. Four groups published on Tn-Seq methodologies, each with a different name: Tn-Seq, TraDIS, HITS, and INSeq [12-15]. For all of these methodologies, the general workflow is 1) a large library of transposon mutants are made, such that each gene has multiple insertions, and the library is either exposed to a condition of interest or left untreated, 2) the DNA from the library is isolated and cut into fragments, 3) adaptors are ligated and the transposon-containing genome segments are PCR amplified, and 4) the PCR products are sequenced, mapped to the genome, and quantified [27]. The main difference between the methods is whether the genomic DNA is cut enzymatically (Tn-Seq and INSeq) or physically sheared, with or without subsequent size selection via gel electrophoresis (HITS and

TraDIS, respectively). Over time, the name Tn-Seq stuck, matching the nomenclature of other contemporaneous technologies like RNA-Seq, ChIP-Seq, etc. The methods are easier to implement than TraSH because no microarray is required, and the dynamic range and sensitivity are vastly improved. The number of sequencing reads mapping to a site directly

7 correlates with the number of surviving mutants with insertions at that location. The resolution is also better, as reads can be mapped to a precise nucleotide.

As a testament to its utility, Tn-Seq is still being widely implemented ten years after its introduction and is constantly improving. Transposon insertion methods have developed to the point that we can now make libraries with millions of unique insertions, and libraries have been reported in countless microbes. These include Gram-positive, Gram-negative, and mycobacteria; yeast; and archaea. The methodology has even been adapted to accommodate anaerobic bacteria [28]. The method used to make the library depends on the competency of the bacterium being studied and can take the form of in vitro, plasmid-based, phage-based, and electroporation-based transposition [29]. In addition, the transposons have been engineered to have new features, including outward-facing promoters that reduce polar effects, report on the fitness of upregulation mutants, and provide information on essential genes [30, 31]. With the methodologies established, the focus has shifted to finding new ways to apply Tn-Seq and new questions that it can answer.

Since its development, Tn-Seq has been used for a wide variety of research goals.

Researchers have applied it to identify the essential genes in a range of microbes, including

Gram-positive and Gram-negative bacteria and even archaea [29]. It has also been used to identify the genes required for a variety of in vitro growth conditions, including growth with specific primary nutrient sources, in animal-derived fluids, in the presence of antibiotics or pathway inhibitors, under ionizing radiation, and at different temperatures, and for infection of various in vivo models, including in tissue culture, wildtype animals, mutated animals, and germ- free or controlled microbiome animals [29]. Transposon libraries have also been made in gene knockout strains of bacteria to map the genetic interactions of the missing gene and to identify the genes that might be involved in particular pathways. The libraries have also been used to study non-lethal phenotypes, including sporulation, but this presupposes that a means exists to separate the mutants with and without the desired phenotype [32]. As more and more data for

8 the same species become available, we may soon be able to use machine learning approaches to gain even more insights from this data. In fact, our group has already used machine learning on Tn-Seq data from samples exposed to a variety of antibiotics to both predict the mechanism of action of new antibiotics and to identify genes that likely operate in the same pathways within the cell [33, 34]. The opportunities are endless, and new Tn-Seq applications are arising constantly.

1.3: Using Tn-Seq to characterize antibiotic vulnerabilities and resistance mechanisms in

Staphylococcus aureus

The focus of my project has been to apply Tn-Seq technologies to Staphylococcus aureus to better understand how S. aureus responds to antibiotics, how antibiotics function within the bacterial cell, and what vulnerabilities exist in S. aureus that we could exploit with new compounds in the future. We chose S. aureus as a model organism because of its clinical importance and the pervasiveness of antibiotic resistance in S. aureus. S. aureus causes over ten percent of hospital-acquired infections, and over 50% of those are resistant to the majority of

β-lactams, the primary antibiotic class used to treat S. aureus [35, 36]. These β-lactam resistant strains are called methicillin-resistant S. aureus, or MRSA. Even other antibiotics such as vancomycin, linezolid, and daptomycin are becoming ineffective in an increasing number of cases [37-39]. As a result, over 11,000 people die from antibiotic-resistant S. aureus in the

United States each year [40]. Because of the excessive mortality of S. aureus infections, its health care and community burden, and its high transmissibility, the World Health Organization considers antibiotic-resistant S. aureus a high research priority [35]. We urgently need new compounds that can either kill S. aureus outright or re-sensitize antibiotic-resistant isolates.

Throughout my research, we have used Tn-Seq to both prioritize targets for future antibiotic development and understand how antibiotics work and how S. aureus thwarts them.

First, we used Tn-Seq to characterize the essential genes that were needed by a diverse set of

S. aureus strains. These would be ideal candidates to investigate further for antibiotic development, as they are the pillars of S. aureus survival. The experiments also revealed some interesting phenotypic differences between the strains that could be leveraged to gain further insights into S. aureus biology.

However, we need to remain vigilant even after new antibiotics are developed, as antibiotic resistance can develop quickly. Ideally, we would have a comprehensive understanding of what types of mutations can provide resistance prior to using an antibiotic in the clinic so that we can have appropriate surveillance and diagnostic protocols in place. It has always been possible to identify genes whose loss can protect against an antibiotic using Tn-

Seq data. However, we wanted to extend our capabilities to also identify genes whose overexpression provides resistance. Transposon libraries containing outward-facing promoters that can upregulate nearby genes existed prior to my tenure in the Walker lab, but we lacked a statistically-valid analytic approach to reliably identify the selectively upregulated genes. Thus, a major portion of my work in the lab went into creating such an approach and it is now routinely applied to all new Tn-Seq data sets within our laboratory.

Once I had developed the upregulation analysis, we sought to delineate the extent to which antibiotic susceptibility and resistance profiles in S. aureus are conserved. We used daptomycin as a case study, as it is frequently used to treat MRSA infections in the clinic. To study the impact of genetic diversity on daptomycin susceptibility and resistance, we exposed transposon libraries in a disparate set of strains to daptomycin and compared which genes needed to be present for S. aureus to survive the antibiotic, what genes provided resistance when they were overexpressed, and what genes conferred a fitness cost in the presence of daptomycin. We found that resistance and susceptibility profiles are somewhat sensitive to changes in genetic background. However, there was a core set of genes that was important regardless of the genetic background. These results highlighted the importance of conducting

Tn-Seq not in just one strain and condition but several in order to prioritize hits for further investigation.

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39. Decousser J-W, Desroches M, Bourgeois-Nicolaos N, Potier J, Jehl F, Lina G, et al. Susceptibility trends including emergence of linezolid resistance among coagulase-negative staphylococci and meticillin-resistant Staphylococcus aureus from invasive infections. International journal of antimicrobial agents. 2015;46(6):622-30.

40. Centers for Disease Control and Prevention (CDC). Antibiotic resistance threats in the United States, 2013. 2013.

Chapter 2: Comparative Tn-Seq reveals differences in gene essentiality between strains of Staphylococcus aureus

A manuscript including much of this work, its text, and the associated figures has been submitted to PLOS Pathogens in collaboration with Dr. Wonsik Lee, Dr. Gloria Komazin-

Meredith, Dr. Timothy Meredith, Dr. Yonatan Grad, and Dr. Suzanne Walker. A preprint is available on bioRxiv [1]. Dr. Timothy Meredith and Dr. Gloria Komazin-Meredith created the transposon libraries. Dr. Wonsik Lee performed much of the validation. I performed all analyses, whole genome sequencing of strains, and lipoteichoic acid blotting.

2.1: Abstract

To develop effective antibiotics for the treatment of S. aureus, we need to know what the optimal targets would be. Ideally, any antibiotic would target a factor that is required for all strains of the species. However, we have yet to characterize the core essential genes in S. aureus. Thousands of S. aureus strains have been sequenced, annotated, and compared to categorize S. aureus genes as core (shared by all or most strains) or accessory (found in only a subset of strains). While these studies have been invaluable for understanding the chemical reactions available to S. aureus, they cannot reveal which genes S. aureus is most dependent upon for survival. Moreover, experiments identifying the essential genes of S. aureus have been limited primarily to a single branch of the phylogeny and have focused primarily on laboratory strains. By implementing Tn-Seq in five diverse strains of S. aureus, we aimed to separate the universally essential genes in S. aureus from those that are relied on by a subset of strains. We found that the core essential genes were enriched in DNA, RNA, and protein biosynthesis genes, while the variably essential genes were in myriad pathways. Several of the variably essential genes were in the lipoteichoic acid pathway, and we focused on those genes for validation and follow up.

2.2: Introduction

The Staphylococcus aureus phylogeny is remarkably diverse and has been extensively studied. At the time of publication, over 380 complete S. aureus genomes and over 5670 scaffolds had been deposited in the NCBI Genome database [2]. These data have been invaluable for characterizing the pangenome of S. aureus. The pangenome is the collection of all genes found in any member of a bacterial species. Some of the genes are found in all isolates, known as core genes, while others are only found in a subset of the isolates, deemed accessory genes. The first groups to attempt to categorize the S. aureus genes as core or accessory used microarrays with probes derived from one or more sequenced strains [3, 4]. If

DNA from a test isolate annealed to a microarrayed bait strain’s gene probe, that gene was present in the test isolate. Any microarrayed gene probes that did not bind any of the test DNA were absent from the test isolate’s genome. The limitation of these microarrays, of course, was that they were one-sided: they could determine which genes were absent in the test strains, but not which genes were absent in the probe strains.

Later groups characterized the pangenome by directly comparing annotated genomic sequences. Hall et al. compared the genes in fourteen different S. aureus strains and concluded that only about 45% of the pangenome was core, representing on average about 72% of each individual genome [5]. Moreover, approximately 13% of the genes were only found in a single strain, suggesting that more strains would need to be analyzed to fully characterize the S. aureus pangenome. Bosi et al. found that even after comparing sixty-four S. aureus genomes, they were still discovering new genes that had not been in any of the previous strains [6]. This led them to posit that the S. aureus pangenome is open, meaning that new genes are consistently being adopted by the species and that we will never be able to fully encapsulate its pangenome. It should be noted, however, that Bosi et al. included a combination of open and closed genomes in their analysis, which could lead to inaccuracies in their calculations.

The scientific community has learned a great deal from gene presence/absence studies, which can unveil the genes responsible for certain phenotype differences, such as increased

17 virulence. For example, researchers have been able to link the acquisition of the arginine catabolic mobile genetic element (ACME) to the emergence of the highly-prevalent community- acquired MRSA strain USA300 [7]. Meanwhile, Bosi et al. used gene content information to better characterize the chemistries available to individual strains [6]. They found that some

USA300 isolates are uniquely adapted to metabolize spermidine, a compound involved in wound healing, which may aid USA300 in infecting its host [8]. However, while these studies provide information about what a bug can do, they do not indicate what the bug must do, i.e. what genes are essential, which are the ideal targets for antibiotics. Even though on average

72% of the genes in a S. aureus isolate are core genes [6], roughly ten percent of genes are thought to be essential [9]. To understand what the genetic dependencies of S. aureus are, we need to pair genotype data with phenotype data.

Several research groups have used Tn-Seq to characterize the “essentialome” of S. aureus. Tn-Seq uses a collection of bacterial transposon mutants each with a single transposon inserted randomly into its genome (Figure 2.1). The library contains enough mutants that every gene that can tolerate insertions is represented by several individual mutants. To identify essential genes, the transposon-genome junctions in the library are sequenced and mapped to the genome; those genes harboring few or no insertions are considered essential. Researchers have used Tn-Seq and other related technologies to characterize the essential genes in the S. aureus strains SH1000, HG003, USA300-JE2, Newman, and ST398 [9-14]. SH1000 and

HG003 are both laboratory strains derived from the strain NCTC 8325 by making minor genetic alterations. For SH1000, those alterations included removing the Φ11, Φ12, and Φ13 prophages and repairing the rbsU gene, encoding a positive regulator of SigB [15]. In HG003, rbsU and a second virulence regulatory gene, tcaR, were repaired, but all of the prophages were maintained [16]. Newman was first isolated from a patient in 1952 and is an MSSA strain with no known antibiotic resistance markers [17, 18]. USA300 is the predominant community- acquired MRSA lineage in the United States and has been since 2004 [19]. While there are

Figure 2.1: Tn-Seq can be used to identify essential genes. To make a transposon library, transposons are inserted randomly into the genome. Essential genes are then identified by growing the library under favorable growth conditions, harvesting and sequencing its DNA, and mapping the locations of the inserted transposons. Genes lacking transposon insertions are essential, as the absence indicates that any bacteria harboring insertions in those genes did not survive. In the Walker lab, we pool six sub-libraries, each with a different transposon construct (right side) to achieve high-density libraries. Some of these constructs harbor outward-facing promoters that can reduce polar effects, a common pitfall of Tn-Seq essential gene analyses. Figure adapted from submitted manuscript [1]. differences between these strains, they all belong to the same clonal complex, CC8. ST398, on the other hand, is a MRSA strain from a distinct livestock-associated lineage of S. aureus [20].

Thus, we have heretofore only characterized the essential genes for S. aureus a narrow portion of the human-associated S. aureus phylogeny.

We had reason to suspect that different strains of S. aureus would differ in their gene dependencies. Researchers have recently moved toward performing Tn-Seq in multiple strains to determine the extent to which phenotypes are conserved, and all have found essential gene differences between the strains, often with direct clinical implications [21-23]. For example,

Carey et al. used Tn-Seq to compare mutant fitness in five clinical strains of Mycobacterium tuberculosis to a common laboratory strain [21]. They found that glcB, a gene whose product was targeted by a recently developed antibiotic [24], is only essential in the laboratory strain.

They then confirmed that those strains that did not require glcB for growth in the Tn-Seq experiment had high minimum inhibitory concentrations for the antibiotic. The gene essentiality differences also had consequences for the rate at which antibiotic resistance could be developed. They showed that katG, which is often inactivated to provide resistance to isoniazid

19 in laboratory settings [25-27], was more essential in the clinical strains. As a result, the clinical strains were not able to develop isoniazid resistance as quickly as the laboratory strain did.

Based on this and other studies, it is becoming clear that it is necessary to perform Tn-Seq in multiple strains to truly define the core essential genome of a bacterial species. In fact, in

Pseudomonas aeruginosa researchers have estimated that at least four strains are needed to obtain a reliable estimate of the core essential genes [23].

While it may be true that we now have transposon mutagenesis data for more than four strains of S. aureus, we felt that the current information was insufficient. As stated above, four of the five studies were performed in S. aureus strains belonging to CC8. While this is a common pathogenic lineage, it is not necessarily representative of the entire species. The four-strain requirement in P. aeruginosa to define the essential genome was estimated using strains from across its phylogeny [23]. It is also not clear how the putative openness of the S. aureus pangenome, as suggested by Bosi et al., will affect the number of strains that need to be studied before the core essential genes have been reliably characterized [6].

Another issue is that there is no way to directly compare across the studies that have been conducted. The transposon mutagenesis studies reported thus far in S. aureus have occurred over a period of time when technologies were rapidly developing. Because of this, the earlier studies had a much lower genomic insertion coverage than later studies did. Essential genes are statistically defined by their lack of insertions, so a lower baseline insertion rate can deeply influence the number of genes estimated to be essential. Our lab can be used as an example of this. In an early collaboration with Dr. Michael Gilmore’s group at the Massachusetts

Eye and Ear Infirmary, we helped conduct a Tn-Seq experiment in HG003 to identify its essential genes and identified four hundred fifty essential genes [10]. However, in the following year we developed a new transposition strategy that afforded much denser transposon libraries, with more than three times greater coverage of TA dinucleotide insertion sites. As a result, analysis of the new HG003 library led to the identification of only 261 essential genes [11]. With

20 this much variation between studies due to methodology differences, it would be impossible to compare across studies to determine which mutants show true biological variability in fitness between different strains.

To address these issues, we directly compared five different strains of S. aureus using

Tn-Seq, with Tn libraries created using the same method. The strains we chose were HG003,

USA300-TCH1516, MSSA476, MW2, and MRSA252. The HG003 library had already been created, as it was the strain in which we established a high-efficiency phage-based transposition method for generating S. aureus Tn libraries [11]. Using phage enabled us to circumvent the lack of competency in S. aureus to achieve robust coverage of the possible TA dinucleotide insertion sites. The library creation method is also unique because it involves pooling six sub- libraries, each made using a different barcoded transposon construct (Figure 2.1). Several constructs contain outward-facing promoters, which minimize polar effects due to upstream gene inactivation and can also upregulate nearby genes [28]. The next strain we made a library in was USA300-TCH1516, a methicillin-resistant (MRSA) strain from the same sequence type as HG003 (ST8). USA300 is currently the predominant community-acquired lineage in the

United States [29-31]. We also made libraries in MW2, the first reported United States community-acquired MRSA strain [32, 33], and the similar methicillin-sensitive (MSSA) strain

MSSA476 [34], both of which belong to ST1. Then, to capture a larger scope of the S. aureus phylogeny, we also included the European hospital-acquired strain MRSA252, which has attracted attention for its unusually large accessory genome [34].

We found that there were 200 genes that were essential in all five strains representing the core essential genome. However, we also identified genes that were uniquely essential in a given strain. Of these, we found particularly interesting the genes that were present in all five strains but were essential in only one or two. These cases implied that other genetic differences have made some strains more reliant on a conserved pathway. The lipoteichoic acid pathway is

21 one example of a conserved pathway that we found to be variably essential across S. aureus strains.

2.3: Methods

2.3.1: Transposon library construction

Construction of the transposon library in the laboratory strain background S. aureus

HG003 has been described [11]. A similar strategy was used by Dr. Timothy Meredith and Dr.

Gloria Komazin-Meredith at Pennsylvania State University to make libraries in other S. aureus genetic backgrounds. First, non-compatible DNA restriction systems and endogenous antibiotic resistance genes that would interfere with transposon library construction were removed. In the community acquired S. aureus MW2 and MSSA476 strains, the clonal complex CC1 hsdR Type

I restriction system in each background was first deleted using the temperature sensitive shuttle vector pKFC with ~1 kb DNA homology flanking arms. A second restriction system unique to

MSSA476 was deleted in a subsequent round using pKFC. Libraries were constructed in the S. aureus community acquired strain USA300-TCH1516 by first curing the endogenous plasmid pUSA300HOUMR through destabilizing replication via integration of pTM283. Cointegrated pUSA300HOUMR-pTM283 was passaged at 30°C for two rounds of outgrowth in 10 mL of tryptic soy broth (TSB) before streaking to single colonies. Colonies exhibiting kanamycin and erythromycin sensitivity (encoded on pUSA300HOUMR) were further checked by PCR to confirm loss of plasmid. The resulting strain, TM283, was used as host for transposon library construction. Transposon library in MSRA252 was made by first converting the hsdR

(SAR0196) Type I restriction gene deletion shuttle vector pGKM305 into a high frequency transduction vehicle by adding a φ11 DNA homology region (primers GKM422-423) into the SfoI site to make pGKM306. The plasmid was electroporated into RN4220 and transduced into wildtype MRSA252 using φ11-FRT with temperature permissive selection at 30°C [11]. The

SAR0196 gene was then deleted as described above. Both copies of the endogenous duplicated erythromycin resistance gene (SAR0050 and SAR1735) were likewise deleted in two rounds using pKFC_SAR0050, except plasmids were directly electroporated into the restriction negative parent strain GKM361. Due to endogenous aminoglycoside resistance, the transposase expressing plasmid pORF5 Tnp+ was converted into chloramphenicol resistant plasmids to enable selection in strain TXM369 and library construction. Once these modifications were made, the transposon libraries were created as previously described [11]. All strains, plasmids, and primers used in these studies are listed in Supplemental Tables 1, 2, and

3, respectively.

2.3.2: Transposon sequencing

To identify the essential genes in each of the S. aureus strains (HG003, USA300-

TCH1516, MSSA476, MW2, and MRSA252), I thawed library aliquots and diluted them to an

OD600 between 0.2 and 0.3 in 10 mL of cation-adjusted Mueller Hinton broth (MHBII) in duplicate. The cultures were then incubated shaking at 30°C until they reached an OD of approximately 0.4, roughly 1.5-1.75 hours. The cells were pelleted, and the DNA was extracted and prepared for Tn-Seq as previously described [11, 28]. Samples were then submitted to either the Harvard Biopolymers Facility or the Tufts University Core Facility for sequencing on a

HiSeq 2500 instrument.

2.3.3: Transposon sequencing data analysis

I performed all analyses of the transposon sequencing data. Transposon sequencing data was split by transposon and sample, trimmed, filtered, and mapped using the Galaxy software suite as previously described, and a workflow for the processing is provided on GitHub in the SuzanneWalkerLab/5SATnSeq repository [11, 28, 35, 36]. The resulting SAM files were converted into tab-delimited hop count files using Tufts Galaxy Tn-Seq software

(http://galaxy.med.tufts.edu/) or custom python scripts and then converted further into IGV- formatted files, as previously described [11, 28].

Chromosome nucleotide FASTA files for NCTC 8325 (NC_007795.1 - HG003 parent strain, as the HG003 genome sequence is not closed) [37], USA300-TCH1516 (NC_010079.1)

[30], MSSA476 (NC_002953.3) [34], MW2 (NC_003929.1) [32], and MRSA252 (NC_002952.2)

[34] were downloaded from the NCBI genomes database. The genomes were reannotated via

Prokka and the pangenome was aligned with Roary, splitting by homolog and using a 90% ID cutoff [38, 39]. Roary group names were then adjusted based on common S. aureus pangenome gene names found on AureoWiki [40]. I also mapped the Prokka annotations to the current NCBI locus tags for the strains and, when available, the old NCBI locus tags that were previously used for the strains (and are still used on AureoWiki). The list of corresponding locus tags is available in the SuzanneWalkerLab/5SATnSeq GitHub repository (MasterTagList.csv).

Genes in each strain were labeled as essential, non-essential, or uncertain using the

TRANSIT software Gumbel method [41]. Other methods, specifically the hidden Markov model- based software ARTIST and TRANSIT HMM were also considered [41, 42]. We decided to go forward with the Gumbel method both because it provided fairly consistent results when replicates were compared and because it gave a binary output (essential/nonessential) with an associated degree of confidence, where low-confidence genes were labeled uncertain or too small to evaluate. By contrast, the ARTIST method has essential, non-essential, and domain- essential categories and the TRANSIT HMM method has essential, growth advantage, growth defect, and non-essential categories. Because they were based on hidden Markov models, these latter two methods categorized all genes without providing the degree of confidence in the prediction. While the added categories could have lent additional nuance to our analyses, we were hesitant to use a software that did not provide clear statistical metrics to note which genes fell on the borderlines between categories. When applying the TRANSIT Gumbel method, only the transposon constructs with transcriptional terminators were included (four of six transposon

24 constructs). For each TRANSIT Gumbel run, data files for each transposon construct in both replicates (for a total of eight files) were submitted and the mean replicates parameter was used. We chose to use the mean of replicates rather than the sum because it gave more consistent results. For example, comparing Tn-Seq data from the all transposons for two replicates, summing the constructs provided 33 or 196 essential genes for the two replicates, whereas averaging the constructs provided 320 or 351 essential genes. We also chose to only use those transposons with transcriptional terminators because we have not yet determined the effects of inserting a non-terminating transposon in a gene. Eliminating the two transposons lacking terminators (erm and dual) had little effect on the number of genes found to be essential.

Once a list of essential genes was acquired for each strain, we then needed to determine whether there was a significant difference between the genes that were essential in one strain and not in others. Permutation tests with 20,000 permutations were conducted between each pair of strains for each gene to determine whether differences in essentiality were significant, using the sum of the same data sets used in the Gumbel analysis. Data were normalized by average reads per TA site with reads (non-zero means normalization) and the p- values from each file pair were corrected using the Benjamini-Hochberg method. A gene was considered significantly different between two strains if the q-value was less than 0.05. The list of genes essential for all five strains was then analyzed using the online gene ontology tool

PANTHER version 13.1, comparing the NCTC 8325 locus tags for the essential genes to the S. aureus reference gene list and using default settings for the overrepresentation test [43].

2.3.4: Whole genome sequence comparisons

To obtain the genomic sequences of the transposon library parent strains, I cultured the strains in MHBII at 37°C shaking overnight. The DNA was harvested using a Promega Wizard

Genomic DNA purification kit and cleaned using a Zymo DNA Clean Up Kit. DNA was tagmented using the Illumina Nextera DNA Library Prep kit but using 1/20th of the volume

25 recommended by the manufacturer and with starting DNA concentrations of 0.5, 0.75, 1, and 2 ng/μL. The tagged DNA fragments were then amplified via PCR. The PCR samples contained

11.2 μL of KAPA polymerase mix (Illumina), 4.4 μL each of the 5 μM column and row indexing primers, and 2.5 μL of tagmented DNA. The thermocycler settings were as follows: preincubation (3 min, 72 °C), polymerase activation (5 min, 98 °C), 13 amplification cycles

(denaturation at 98 °C for 10 sec, annealing at 62°C for 30 sec, and extension at 72°C for 30 sec), and termination (5 min, 72 °C).

To determine which starting DNA concentrations yielded the best fragment size, 3.75 μL of each amplified sample was mixed with 4 μL of 6x loading dye and run on a 1.5% agarose gel at 110 V. The sample with an average length closest to 500 bp was chosen for each strain.

Those samples were then cleaned as recommended by Illumina, except that we started with 15 uL of amplified tagmented DNA and 12 μL of AMPure XP beads. The concentrations of the resulting DNA samples were estimated using via Qubit Fluorometric Quantification (Thermo

Fisher Scientific) following the manufacturer’s instructions. The DNA was then diluted to 1 ng/μL and pooled, and a portion of it was submitted to the Harvard Biopolymer’s Facility for

TapeStation and qPCR quality control analysis. Upon passing the quality control step, the DNA was prepared and loaded into the sequencing cartridge as directed by the MiSeq Reagent Kit v3 with 150 cycles (Illumina), including a 1% PhiX control spike-in prepared according to manufacturer’s instructions (PhiX Control v3, Illumina), and paired-end sequenced using a

MiSeq instrument.

Genomic data was then analyzed to find SNPs, insertions, and deletions. MetaPhlAn2 was used to verify that the DNA was not contaminated [44]. All strains had 100% S. aureus

DNA except MRSA252, which had 0.014% Propionibacteriaceae DNA. Sequences were then aligned to the appropriate NCBI reference genome using the Burrow-Wheels Aligner (BWA-

MEM, v7.12) with default settings [45]. Note that NCTC 8325 was used for HG003, as the

HG003 genome on NCBI is not closed. Duplicate reads were marked with Picard, and Pilon

(v1.16) was used for variant calling, with a minimum depth of 1/10th of the average read depth and a minimum mapping quality of 30, unless the average read depth was less than 100, in which case the minimum mapping quality was set to 15 [46, 47]. The resulting VCF files were then examined to determine whether mutations existed in relevant areas of the genome (i.e. the

LTA pathway or known ltaS suppressors). We also compared the protein sequences for LTA pathway members and ltaS suppressors in the five strains using the online Clustal Omega tool from EMBL-EBI [48].

2.3.5: Gene deletion and complementation

Gene deletion and complementation strains were created by Dr. Wonsik Lee, and the primers, plasmids, and strains used are listed in Supplemental Tables 1, 2, and 3, respectively.

To make an anhydrotetracycline (Atet) inducible construct of ugtP, a fragment containing the ugtP and ltaA operon (SAOUHSC_00953-00952) including its ribosomal binding site was amplified from HG003 genomic DNA using ugtP-F and ugtP-R. The fragment was then cloned into pTP63 using KpnI and EcoRI to generate pTP63-ugtP, and the pTP63-ugtP was transformed into a wildtype RN4220 strain containing pTP44 [49, 50]. Next, the integrated inducible ugtP operon was transduced into wildtype HG003, MW2, and USA300-TCH1516 using φ11 phage to generate HG003-PAtet-ugtP, MW2-PAtet-ugtP, and USA300-TCH1516-PAtet- ugtP. To generate an inducible ugtP strains, the wildtype PAtet-ugtP strains were grown in TSB containing 0.3 mM Atet for 6 hours at 30°C, after which they were transduced with a transposon-inactivated ugtP marked with an erythromycin resistance gene [12]. The desired mutants were selected on TSB agar containing 5 μg/mL erythromycin and 0.3mM Atet and confirmed by PCR using the primers ugtP-CA and ugtP-CB.

To construct an Atet-inducible ltaS construct, the ltaS gene and its ribosomal binding site were PCR amplified using the primers ltaS-F and ltaS-R and cloned into pTP63 to make pTP63-

PAtet-ltaS. To make an inducible ltaS strain, pTP63-PAtet-ltaS and then a φltaS construct marked

27 with an erythromycin resistance gene were transduced into wildtype HG003, MW2, and

USA300-TCH1516 as described above [51]. The ltaS mutants were confirmed by PCR using the primers ltaS-CA and ltaS-CB.

2.3.6: Spot dilution assays

Spot dilution assays were performed by Dr. Wonsik Lee. Overnight cultures of the relevant strains were grown in TSB at 30°C until mid-log phase and diluted to an OD600 of 0.1.

Five 10-fold dilutions of the resulting cultures were prepared, and 5 μL of each dilution was spotted on TSB plates with or without 0.3 μM Atet inducer. Plates were imaged after approximately 16 hours of incubation. For the ltaS experiments, plates were incubated at 30°C,

37°C, and 42°C.

2.3.7: LTA western blot

I isolated LTAs from MW2, MW2-PAtet-ltaS, and 4S5, a strain derived from RN4220 known to not produce LTAs [51], and detected them via western blot using a procedure similar to that previously described [52]. Overnight cultures of each strain were grown shaking at 30°C in TSB supplemented with 7.5% salt, with the MW2-PAtet-ltaS grown both in the presence and absence of 0.4 μM Atet. Cultures were then diluted 1:50 in the same medium and incubated shaking at 30°C until the OD600 was between 0.6 and 0.75. The equivalent of 1 mL of OD600 0.8 was then harvested from each sample by centrifuging at 8000 x g for three minutes. The cell pellets were resuspended in 50 μL of a buffer composed of 50 mM Tris pH 7.5, 150 mM NaCl, and 200 μg/mL lysostaphin. Samples were incubated at 37°C for ten minutes. Then, 50 μL of 4x

SDS PAGE loading buffer was added and the sample was boiled for thirty minutes. After returning the sample to room temperature, 100 uL water and 0.5 μL of proteinase K (New

England Biolabs) was added to each sample, and samples were then incubated at 50°C for two

28 hours. After cooling samples to room temperature, 2 μL of AEBSF was added to each sample to quench the proteinase K. Ten microliters of each sample and 5 μL of Precision Plus Protein

Dual Xtra standard (BioRad) was loaded onto a 4-20% TGX precast gel (BioRad) and the gel was run for 30 minutes at 200 V using a running buffer that was 0.5% Tris, 1.5% glycine, and

0.1% SDS. Proteins were transferred to a methanol-activated PVDF membrane using a

TransBlot Turbo (BioRad) via the pre-installed mixed molecular weight setting. The membrane was rinsed with TBST and incubated rocking overnight at 4°C in 5% milk in TBST to block. After washing the membrane with TBST three times for five minutes each, the LTAs were bound with a 1:750 solution of a mouse anti-LTA antibody (Hycult Biotech) in TBST for 45 min. The membrane was then washed an additional three times for five minutes each before incubating in a 1:2000 dilution of anti-mouse horseradish peroxidase conjugated antibody (Cell Signaling

Technologies) in TBST. The membrane was washed a final five times for five minutes each and then exposed to ECL western blotting substrate (Pierce). The membrane was imaged on a

FluoroChem R gel doc (ProteinSimple) with a 2 min 40 sec exposure for luminescence to detect the LTAs and automatic exposure for red and green fluorescence channels to detect the protein standard.

2.4: Results

2.4.1: Creation of high-density S. aureus transposon libraries with both positive and negative modulation of gene expression

To characterize the functional diversity of S. aureus, we needed transposon libraries with similar coverage in the five strains chosen (HG003, USA300-TCH1516, MSSA476, MW2, and

MRSA252). For each strain, we used our phage-based transposition method to make six sub- libraries using different transposon constructs (Figure 2.1). One transposon construct contained the standard erythromycin resistance gene driven by its own promoter (Perm) and followed by a

29 transcriptional terminator; another lacked the transcriptional terminator to allow readthrough from the Perm promoter; three constructs contained the transcriptional terminator, but included an additional, outward-facing promoter that varied in strength; and the sixth construct lacked the transcriptional terminator and had an added promoter, allowing for bidirectional transcription.

The sub-libraries were made separately and then combined to make very high-density libraries that typically contained transposon insertions due to several different constructs at a given TA site. A unique barcode in each transposon construct made it possible to disambiguate which transposons inserted at a site. The constructs containing outward-facing promoters minimize polar effects by allowing expression of downstream genes when an operon is disrupted. Under some conditions, they also provide supplementary fitness information. For example, upon exposure to an antibiotic, gene upregulation can sometimes confer an advantage that allows the mutant to outcompete other mutants in the population. This presents in the data as a strand- biased enrichment of insertions due to promoter-containing constructs upstream of a gene, which we call an upregulation signature. We have previously shown that these upregulation signatures can identify molecular targets or mechanisms of resistance [28]. In Chapter 3, I describe an improved computational method to identify these biases, enabling us to routinely detect gain-of-function resistance mutations in the Tn-Seq data.

Tn-Seq analysis showed that the six different transposons together afforded high coverage of every genome (Table 2.1). Although there was a three-fold difference in the unique insertions (i.e., the sum of the insertions found in each of the six sub-libraries) between the library with the fewest insertions (MW2) and the library with the most insertions (USA300-

TCH1516), approximately 90% of the genes in each genome had high coverage of TA insertion sites. This gave us confidence that we could obtain a reliable estimate of gene essentiality differences across the strains.

Table 2.1: We created complex transposon libraries of similar coverage in five diverse S. aureus strains. High-density Tn libraries were made in five S. aureus strains. MSSA = methicillin-sensitive S. aureus; MRSA = methicillin-resistant S. aureus; ST = sequence type. Sites with insertions refers to the number of TA dinucleotide sites having insertions of at least one Tn construct. The “unique insertions” column counts TA insertions due to each transposon construct separately and then sums them. Coverage per gene is the average percent of TA sites within a gene that has Tn insertions, ± standard deviation. Low-coverage genes were defined as those that had less than half of the average gene coverage. Table from submitted manuscript [1].

2.4.2: Essential gene analysis reveals unexpected functional differences between strains

We performed Tn-Seq analysis on each transposon library to identify essential genes.

For the purposes of this study, we defined an essential gene as one whose loss precluded survival in competition with a heterogeneous bacterial population. We categorized genes as essential, non-essential, or indeterminate based on TA insertion site coverage and sequencing reads (Supplemental Table 4). Most essential genes were essential in all five strains, and these

200 genes represent the core essential genome of S. aureus (Figure 2.2, Supplemental Table

5). Gene ontology overrepresentation analysis showed that the ubiquitously essential genes were enriched in DNA, RNA, and protein metabolic processes (Supplemental Table 6).

Phospholipid and monosaccharide synthesis were also enriched pathways, reflecting the importance of fatty acids and other cell envelope precursors. Hypothetical genes were significantly underrepresented among the ubiquitously essential genes but dominated the group of essential genes that were only present in a subset of the genomes. Only two such genes were annotated: the antitioxin gene pezA found only in MW2 and a phage repressor found in

HG003, MW2, and MSSA476. The hypothetical genes that are essential in some strains and not

31 even found in the others are no doubt understudied because they are accessory genes, but their importance in some strains may be useful for determining what functions they perform.

Among annotated genes present in all five of the strains, there were notable differences in essentiality. Strains of the same lineage displayed as much variability in gene essentiality as strains of different lineages (Figure 2.2), suggesting it may not be possible to draw general conclusions about a clonal complex from a functional genomics analysis of one member.

Variably essential genes were found in a miscellany of pathways. For example, the uniquely essential genes in MRSA252 included sbcD, which is homologous to an E. coli gene encoding a

DNA hairpin cleavage enzyme [53, 54]; sagB, which encodes a β-N-acetylglucosaminidase [55]; and gdpP, which hydrolyzes the bacterial second messenger cyclic-di-AMP [56]. It is important to emphasize that essentiality in a Tn-Seq analysis does not imply that individual knockouts are

Figure 2.2: Essential genes, while primarily shared, vary somewhat by strain. The number of essential genes per strain (horizontal bars) and the number of genes found to be essential for all five or a subset of the strains (vertical bars, with black dots denoting strains for which the gene is essential). Note that gene essentiality is functionally defined as a lack of transposon insertions in a gene, but it may be possible to make knockouts of some of these genes under favorable conditions. Figure from submitted manuscript [1].

32 not viable; however, it does suggest that there are substantial differences in the fitness of mutants across strains and that it is worth considering the broader genomic context in evaluating the cellular roles of genes.

We expected pathways involved in constructing the cell envelope to be equally essential for all strains, given the importance of the cell envelope in protecting the cell from outside stressors. We were therefore surprised to find essentiality differences within the lipoteichoic acid

(LTA) biosynthesis pathway. LTAs are long ribitol or glycerol phosphate polymers anchored in the membrane of Gram-positive bacteria. In S. aureus, LTAs are important in cell division, autolysin regulation, and virulence, among other processes [57]. The LTA pathway consists of five genes: pgcA, gtaB, ugtP, ltaA, and ltaS (Figure 2.3A). The first three genes encode proteins involved in the biosynthesis of diglucosyl-diacylglycerol (Glc2DAG), the membrane anchor on which LTA is assembled. Glc2DAG is made inside the cell and flipped to the cell surface by

LtaA. The LTA synthase, LtaS, then assembles the polymer by sequential transfer of phosphoglycerol units from phosphatidylglycerol to Glc2DAG. If Glc2DAG is not available due to deletion of an upstream gene, LtaS can synthesize LTAs on the alternative membrane anchor phosphatidylglycerol. The four genes upstream of ltaS were previously found to be nonessential for S. aureus viability [58-60], but ltaS mutants were reported to be nonviable except at low temperature and under osmotically-stabilizing conditions unless suppressors are acquired [51,

61-63]. Our Tn-Seq analysis showed, however, that mutant fitness within the LTA pathway varied considerably by strain (Figure 2.3B). The ltaA gene was expendable in all strains, but the three genes responsible for synthesis of the Glc2DAG anchor, pgcA, gtaB, and ugtP (ypfP), were uniquely essential by Tn-Seq analysis for the USA300-TCH1516 strain (Figure 2.3C).

Conversely, ltaS was unexpectedly dispensable in MW2 and to a lesser degree in MSSA476

(Figure 2.3C).

We tested individual deletion mutants in the LTA pathway to confirm the Tn-Seq results.

We were able to delete ugtP from USA300-TCH1516, consistent with results in other USA300

Figure 2.3: The fitness of lipoteichoic acid pathway mutants varies by strain. (A) A schematic of the lipoteichoic acid pathway in S. aureus. (B) The number of reads in each LTA pathway gene for each transposon library, expressed as a percentage of the average number of reads per TA site in the coding regions of the libraries. There are strain-dependent differences in transposon insertions in LTA pathway genes, with USA300 appearing reliant on most genes in the pathway while MW2 is insensitive even to inactivation of ltaS. (C) Tn-Seq data for the LTA pathway genes, with reads from insertions in the plus strand in purple and minus strand in teal. The reads were calculated by summing two replicate Tn-Seq experiments, only including data from transposon constructs that contain a transcriptional terminator. The data were normalized to each other by the non-zero means normalization method prior to plotting. The y-axis is expressed on a log10 scale and is truncated to 500 reads. (D) Growth on agar plates for wildtype and ΔugtP strains confirms that USA300 is more sensitive to ugtP deletion than MW2 is. (E) Growth on agar plates of wildtype and ΔltaS strains confirms that ltaS is dispensable in MW2. (F) Western blot of LTAs produced by 4S5 (known to not produce LTAs), wildtype MW2, and MW2-PAtet-ltaS confirms that MW2-PAtet-ltaS does not produce LTAs unless expression of the exogenous copy of ltaS is induced. Figure adapted from submitted manuscript [1].

Figure 2.3 (Continued).

35 strain backgrounds, but a spot dilution assay showed that the mutant is less fit than the corresponding mutant in MW2 (Figure 2.3D). That said, in another USA300-TCH1516 strain,

USA300-JE2, a ΔugtP mutant shows no growth defect compared to the MW2 ΔugtP strain, highlighting that gene dependencies can vary even among closely-related strains (personal communication, Wonsik Lee). We also found that ltaS is dispensable in MW2, but essential in other strains (Figure 2.3E). A western blot using an anti-LTA antibody confirmed that the

MW2ΔltaS strain was not producing LTAs (Figure 2.3F). Although the MW2ΔltaS strain was more temperature-sensitive than MW2 wildtype, it grew even at 37°C. Because lethality due to ltaS deletion can be suppressed by deletion of gdpP, clpX, sgtB, [51, 61, 62] we sequenced the transposon library parent strain but found no nonsynonymous SNPs in sgtB or gdpP and only a single nonsynonymous SNP in a non-conserved region of the clpX gene. The clpX mutation was shared by MSSA476, which was not nearly as tolerant of loss of LTA in the Tn-Seq experiments. Thus, while the clpX mutation could contribute to the dispensability of ltaS in MW2, it does not fully explain the phenotype. Moreover, we did not find any evidence for a duplication of ltaS in MW2. The explanation for why LtaS is nonessential in MW2 must lie elsewhere in the genome and will need to be investigated further.

2.5: Discussion

With sequencing costs rapidly declining and efficient transposon mutagenesis methodologies available in a growing number of microbial species, the field of bacteriology is ready to probe phenotypes on a scale that was previously unattainable. Tn-Seq has become fairly commonplace, but experiments are still typically conducted in a single strain that is considered representative, and it is assumed that results from that strain can be extrapolated to other members of the species. For some questions, that is likely true, but for antibiotic susceptibilities, two recent studies have challenged this notion. One study involved Tn-Seq of eight clinical and one laboratory strain of Mycobacterium tuberculosis [21]. That work

36 demonstrated that GlcB, for which a new antibiotic was recently reported [24], was only essential in the laboratory strain. Thus, the MIC for the antibiotic was substantially higher for the clinical strains than the laboratory strain. Another gene, katG, which encodes a protein that converts the prodrug antibiotic isoniazid into its active form [25-27, 64], was more essential in the clinical strains, explaining the lower prevalence of loss-of-function resistance mutations in the clinic compared to laboratory studies. The researchers were unable to pinpoint specific genetic differences that caused the variability in essentiality for either gene. This study underscored that there is phenotypic variety among bacterial isolates, and, in some cases, that diversity may be consequential for antibiotic use and development.

In the present study, we reveal how complicated the concept of gene essentiality can be.

We were surprised to find a number of metabolism genes were essential despite growing the libraries in a rich medium that should provide most basic metabolites. We also found that the

LTA pathway, which has always been presumed to be essential for all S. aureus strains, was variably essential under normal growth conditions, ranging from required in USA300-TCH1516 to expendable in MW2. We suspect that intracellular Glc2DAG has an unidentified function that

USA300-TCH1516 depends upon for survival, given its requirement for the Glc2DAG biosynthesis genes (pgcA, gtaB, and ugtP) but not the flippase gene ltaA responsible for transporting the Glc2DAG to the outside of the cell. The reliance of USA300-TCH1516 on

Glc2DAG may provide a tool to interrogate what other roles the molecule has in the cell.

Likewise, having a strain like MW2 that can ostensibly survive without LTAs provides new opportunities for characterizing the functions of LTAs, as future investigations can interrogate what wildtype MW2 can do that an ltaS knockout cannot. Whether strains that can survive without LTA in vitro – due to the presence of known suppressors or to other uncharacterized genetic background differences – can survive in the more challenging environment of an animal infection needs to be assessed.

In summary, our comparison of Tn-Seq data across multiple strains provided a much more nuanced illustration of S. aureus genetic dependencies than we could have attained using just one strain. Through this research, we have generated the most comprehensive list of core essential genes in S. aureus to date and have identified myriad strain-dependent essential genes. The strain-to-strain gene essentiality differences under normal growth conditions provide a basis to more deeply interrogate S. aureus biology. If we had only performed Tn-Seq in one strain, we would have had no way of differentiating between the core vulnerabilities and those that exhibit strain-dependence. As sequencing continues to become more affordable, we envision that performing Tn-Seq in multiple strains will become routine and will lead us to a more comprehensive understanding of the scope of bacterial phenotypic diversity.

2.6: Future directions

While this project has brought us closer to understanding gene essentiality in S. aureus, we still have a long way to go. We currently do not have an explanation for the gene essentiality differences we observed between strains. The answer may lie in the accessory genes that are present in only a subset of strains or even in individual SNPs. Further wet lab investigations and in-depth genome-wide association studies are necessary to understand these phenotypic differences. We are also limited in that we only surveyed a small subset of the S. aureus phylogeny. As a result, it is unclear how common any of the peculiarities we observed are across the species. Expanding these studies to a wider variety of strains could allow us to begin to see informative patterns, leading to a better biological understanding of the selective forces of particular ecological niches. For example, it could be that hospital-acquired strains require a distinct subset of genes that are not as important for community-acquired strains or vice versa.

In the present study, we only included one hospital-acquired strain, MRSA252, and thus cannot draw any conclusions about the unique needs of hospital-acquired infections; any differences we observed may be quirks specific to MRSA252. Analyzing more strains will also refine the

38 core essential gene list generated in this study. As more data is added, it will add to our confidence in the list of reliably essential genes, which may then be evaluated as potential targets for future antibiotic development.

The study was also limited to protein-coding regions of the genome and did not consider any other genetic aspects. A growing body of evidence suggests that non-coding RNAs and epigenetic elements are crucial to bacterial homeostasis and can perform a wide variety of functions [65]. However, the vast majority of current studies focus only on intragenic insertions.

With a published list of all S. aureus transcripts now available [66], we should soon be able to determine which non-coding RNAs are essential for S. aureus and whether any exhibit variable mutant fitness between strains. Tn-Seq has been invaluable for understanding the functions of genes; it would presumably be equally effective in unveiling the roles of non-coding RNAs if we apply analyses that take those factors into account. Thus, even a decade after the advent of Tn-

Seq, there is still a lot to be done to understand the genetic dependencies of S. aureus and other bacteria.

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Chapter 3: Development of an analytical method to identify upregulation signatures in transposon sequencing data

A manuscript including some of this work and text has been submitted to PLOS

Pathogens in collaboration with Dr. Wonsik Lee, Dr. Gloria Komazin-Meredith, Dr. Timothy

Meredith, Dr. Yonatan Grad, and Dr. Suzanne Walker. A preprint of the manuscript is available in bioRxiv [1]. Dr. Timothy Meredith created the transposon library. Transposon sequencing data were generated by many current and former Walker lab members, including Dr. Mithila

Rajagopal, Dr. Mariana Santiago, Dr. Wonsik Lee, and Truc Do. I performed all analyses.

3.1: Abstract

Many groups have begun constructing Tn-Seq libraries with transposons containing outward-facing promoters. These promoter-donating transposons have primarily been used to reduce polar effects. However, we have found that they can also provide a new type of information: the fitness consequences of upregulating genes in a given condition. To optimally leverage the available data, we needed a method to evaluate transposon insertion patterns upstream of genes instead of only within genes. Prior work in the Walker lab had already established an analysis pipeline to identify cases in which gene upregulation conferred a fitness advantage. Here we describe our efforts to redesign the analysis to overcome the limitations of the prior version; namely that it relied on stringent cutoffs at individual insertion sites rather than statistical tests over a region, resulting in low sensitivity. After testing several options, we adopted a bootstrapping method that identifies genes with single-strand upstream read enrichments in an experimental condition compared to the control condition. The method is indeed more sensitive than the previous method, and we have applied the method to all Walker lab antibiotic Tn-Seq datasets collected to date, creating a compilation of genetic relationships to follow up on.

3.2: Introduction

Transposon sequencing (Tn-Seq) is a powerful technique for characterizing the fitness of gene knockout mutants on a genome-wide scale. It has been used for a variety of means, including finding factors required for growth in culture, for host infection, for survival under any number of stress conditions, and to carry out particular cellular processes [2]. It has also been used to probe genetic interactions like synthetic lethality either by using a compound known to inhibit a particular protein or by constructing a transposon library in a strain missing a gene.

Recently, we have also used the transposon sequencing knockout data to predict the mechanism of action of antibiotics using machine learning [3].

Often, Tn-Seq studies have relied on transposons comprising is a selection marker that is flanked by its own promoter and a transcriptional terminator (Figure 3.1). The transcriptional terminator is key; it ensures that there is no readthrough from the selection marker and that transcription is halted at the insertion site, effectively knocking out any gene harboring an insertion. Since 2011, groups have been reporting on the added utility of having a second promoter to the transposon, one that faces out from the transposon to allow expression of

Figure 3.1: Adding an outward-facing promoter to a transposon makes insertion outcomes orientation-dependent. The upper portion of the figure shows a standard transposon with no outward-facing promoter. Regardless of the direction of insertion, it will disrupt transcription of downstream genes. The lower portion of the figure shows a promoter- donating transposon. If the transposon inserts with the promoter facing a downstream gene, it can upregulate the gene. However, if the transposon inserts in the opposite orientation, it causes standard disruption.

48 genes downstream of the promoter (Figure 3.1) [4]. This addition makes the transposon asymmetrical. Inserted in one strand, the salient feature is the transcriptional terminator that obliterates expression of downstream features. Inserted in the other strand, the promoter facilitates expression of downstream features, though a gene harboring an insertion within its coding sequence is still disrupted. These promoter-donating transposons were initially used to characterize the essential non-coding regions of the genome in greater detail [4]. For example, the promoter for an essential operon could be identified as the area ahead of the operon that only had insertions in the upregulating orientation. However, most researchers now use the new transposons in exactly the same way they used the old transposons, focusing only on the protein-coding regions of the genome. The benefit of the promoter-donating transposons in this application is a reduction of false positives due to polar effects, as the new transposons can allow for downstream genes in polycistronic operons to be expressed despite insertions in an early gene in the operon [5]. In light of this, promoter-donating transposons have primarily served to refine the information we were already getting from the standard transposons.

Recently, we demonstrated that promoter-donating transposons can also report on gain- of-function antibiotic resistance mechanisms [3]. As with the earlier studies looking at essential promoter regions, we were specifically looking for strand-biased insertions upstream of genes.

The major difference was that we were comparing an experimental condition (e.g. antibiotic exposure) to a control condition so that we could identify cases where there was a strand- biased enrichment in reads in the experimental condition, implying a fitness advantage under the condition due to gene upregulation (Figure 3.2). Through an analytical method developed by

Dr. Marina Santiago, a former graduate student in the Walker lab, we were able to recover a number of known gain-of-function resistance mechanisms and, in many cases, the targets of the antibiotics [3, 6]. However, we were concerned that several expected hits were not recovered using the analysis and that several conditions had no hits, especially those conditions with only

Figure 3.2: Upregulation signatures in Tn-Seq data reveal fitness advantages through gene overexpression. In the Walker lab, transposon libraries are created by pooling six sub- libraries, each made with a different barcoded transposon construct. The standard construct has an erythromycin marker flanked by its own promoter and a transcriptional terminator. Another construct lacks the terminator, allowing readthrough from the ermR promoter. Three constructs maintain the terminator but also have an additional outward-facing promoter in the opposite direction, with promoters varying by strength. The final construct both lacks a terminator and has an outward-facing promoter. Libraries can then be exposed to a compound to find cases in which the outward-facing promoter confers a fitness advantage, seen as a single-strand enrichment in sequencing reads ahead of the gene in the compound-exposed sample compared to the control. We call these enrichments upregulation signatures (see red box). Figure adapted from submitted manuscript [1]. moderate selective pressure. We therefore wondered whether the analysis was too stringent and sought to develop a more sensitive method.

Dr. Santiago’s method used a series of cutoffs to identify upregulation signatures. Four read counts were considered: the reads in the plus strand in the experimental condition (pe), the reads in the minus strand in the experimental condition (me), the reads in the plus strand in the control condition (pc), and the reads in the minus strand in the control condition (mc). For each

푝푒+푚푒 TA insertion site, two ratios were calculated. The first was the total reads ratio: log⁡( ). The 푝푐+푚푐

푝푒 second was the directional bias ratio: log⁡( ). Note that one read was added to all counts prior 푚푒 to calculating the ratios to avoid having null denominators. TA insertion sites were highlighted if the total reads ratio was S standard deviations above the mean and the directional bias ratio

50 was S standard deviations above (plus strand bias) or below (minus strand bias) the mean, with

S set to the lowest integer value that would achieve no more than 150 TA insertion sites highlighted. The genome was then divided into 135 bp windows. If two neighboring windows together had three or more highlighted TAs with directional biases in the same strand with no intervening highlighted TAs with directional biases in the opposite strand, the region was considered a hit. Requiring at least three biased TA sites made the analysis robust to jackpots, or sequencing artifacts that result in an insertion site read count greatly exceeding typical values.

However, there were several potential issues with the method. First, very few TA sites in our libraries have reads in both strands and many TA sites will only have reads in either the compound-exposed or control sample due to bottleneck effects associated with how our samples are grown. We often use 2 mL of approximately 2x105 cells per mL for our Tn-Seq samples. We estimate that we have approximately 2x105 sites with insertions in our HG003 S. aureus library (see Table 2.1). Thus, we get at best close to 2x coverage of the insertion sites in each sample, but likely many mutants will not be represented because of stochasticity and the non-uniform fitness of mutants. The bottleneck is even more extreme if one considers unique mutants; if insertions of different types of transposon constructs are considered distinct, the

HG003 library has 4x105 unique mutants, so the coverage of unique mutants in each sample is fairly low. Any analysis using reads at individual TA sites as the primary metric will be problematic as a result. Consider a situation in which one TA site has many reads in the control but by chance is not present in the experimental sample while a neighboring TA site has many reads in the experimental sample but by chance is not present in the control sample; the former

TA site will be ignored while the latter TA site is highlighted as a hit, even though in reality the fitness of the region has not been impacted by the experimental condition. Moreover, choosing the most extreme 150 TA sites may exclude relevant data points, as only 0.075% of the library insertion sites will be considering. We will necessarily miss any promoter regions that have a

51 smaller but consistent bias in insertions and instead only detect cases where there are at least a few exaggerated read counts. Using windows of 135-270 bp was also arbitrary and does not allow for longer promoters, which in bacteria can be 500 bp or longer [7]. Given these limitations, I set out to devise a better approach.

Tn-Seq data can be difficult to work with. It is often sparse, and insertion sites with no reads are ambiguous. They can indicate that 1. the site cannot tolerate disruptions at all, 2. the site did not receive a Tn insertion by chance, 3. the sample taken from the Tn library did not include that mutant, or 4. the mutant did not survive well enough in competition with the other mutants to be detectable. The data also becomes sparser as the growth condition becomes more severe, which complicates normalization. To date, a consensus has yet to be reached regarding the proper way to normalize Tn-Seq data, though numerous methods have been suggested [8]. The data is also non-normal, approximating a geometric distribution. Thus, whatever analysis I devised would necessarily be more complicated than a simple t-test, though t-tests have been used by others for Tn-Seq analyses after implementing several data transformations to make the data appear more normal [9]. I tested several potential analytic methods and then chose to optimize a bootstrap-based method. The bootstrapping abolishes the need to normalize, making the method equally applicable to conditions with very high selective pressure and those that are closer to the control condition. The method is more sensitive than the earlier analysis method, and we were pleased to find several hits in our Tn-

Seq data that we had previously missed.

3.3: Methods

In developing a new upregulation analysis method, I compared two approaches: 1) using a hidden Markov model (HMM) to find context-independent upregulation signatures throughout the genome and 2) using a context-driven bootstrapping approach to find upregulation signatures upstream of genes. I define an upregulation signature as an area of the genome

52 where transposon insertions are biased toward a particular strand. For all method development,

I used existing HG003 Tn-Seq datasets from our lab.

3.3.1: HMM 1 – using a single sample to find intrinsic upregulation signatures

HMMs are defined by tokens, states, transition probabilities, and emission probabilities.

Tokens are labels given to each discrete entity in the dataset. In this case, we decided to make the model as simply as possible by assigning each TA dinucleotide site one of four tokens: n – no reads, p – reads only in the plus strand, m – reads only in the minus strand, and b – reads in both strands. States are labels given to a consecutive sequence of tokens and are what the model ultimately predicts. We chose to include four possible states: N – neutral, D – depleted of reads, P – enriched in the plus strand, and M – enriched in the minus strand. The P and M states would be characterized by an overabundance of p and m tokens, respectively, while the

D state would be defined by a preponderance of n tokens. The N state would represent the rest of the genome.

Emission probabilities represent the likelihood of having a particular token given a state.

Emission probabilities were estimated using existing Tn-Seq data. For N and D, nine control

HG003 files were used to calculate the expected probabilities of each of the tokens. The control files were analyzed using the python TRANSIT library Gumbel method to identify essential and non-essential genes, very similarly to the analysis described in Chapter 2 [10]. Non-essential genes were considered proxies for the N state, while essential genes were used to estimate the emission probabilities for the D state. TA sites within these genes were assigned tokens based on their read counts in the plus and minus strand for each of the nine control files, using either just the Ptuf transposon construct, which has the strongest outward-facing promoter, or the sum of Pcap, Ppen, and Ptuf (see Chapter 2, particularly Figure 2.1 and Table 2.1 for more information about our Tn-Seq libraries). The proportion of each token within the non-essential and essential genes was then calculated. See Table 3.1 for the emission probabilities. For the P and M

53 emission probabilities, I turned to the previously published upregulation signatures within our

Tn-Seq data [3]. I estimated the genomic range of each upregulation signature by eye, assigned a token to each TA site, and then found the proportions of each token in the plus strand and minus strand upregulation signatures.

Table 3.1: Emission probabilities for HMM 1.

Ptuf Only N D P M n 0.7 0.962 0.6 0.67 p 0.12 0.014 0.25 0.03 m 0.12 0.022 0.06 0.27 b 0.06 0.001 0.08 0.03 Pcap, Ppen, Ptuf N D P M n 0.51 0.926 0.49 0.57 p 0.16 0.026 0.3 0.04 m 0.17 0.043 0.08 0.34 b 0.16 0.005 0.14 0.06

A transition probability is the likelihood of transitioning from one state to another. I wanted it to be equally probable to transition between each of the states, as I had no prior expectations for what transitions would be most common. I estimated that an appropriate transition probability would give us states approximately 360 nucleotides long, on average, as this is about half the median length of an S. aureus gene. Converting this into TA sites, the median 360 bp region has roughly 34 TA sites. The transition probability for staying in the same state can then be calculated as 1 – 1/34, which is approximately 0.97. The transition probabilities for transitioning to any of the other states would be the remaining probability (0.03) divided by the number of possible states to transition to, which in this case would be three, yielding interstate transition probabilities of 0.01. The HMM was applied to Tn-Seq data using the R HMM library.

3.3.2: HMM 2 – comparing a control sample to compound-exposed sample to identify upregulation signatures

For the second HMM we developed, we wanted to compare an experimental condition to a control condition as a way of adding a greater depth of information to the model. The experimental sample data were normalized to the control data to ensure the total number of reads in the two datasets were the same. I used the sum of the Pcap, Ppen, and Ptuf transposon data for this model.

Here, the tokens were two letters, with the first representing the plus strand and the second representing the minus strand. The possible strand tokens were d, for depleted of reads in the experimental sample compared to the control; f, for slightly fewer reads; n, for no difference in reads; m, for slightly more reads; and e, for enriched in reads. The reason for having five designations rather than three (depleted of reads, no reads, enriched in reads) was to differentiate between sampling errors, experimental/biological variation, and meaningful differences. I noticed that sites that were not well-represented in the library had lower read variation from control sample to control sample than those that were well-represented. Sites that have low numbers of reads in some files and no reads in other files can either represent sequencing errors or sampling stochasticity. Read differences at sites that often had reads in the control files were more likely due to biological variation (along with some sampling stochasticity). Using five single-strand tokens, I could separate sites that were unlikely to have insertions (i.e. essential), those that had insertions but were not responsive to the expreimental condition (i.e. neutral), and those that were responsive to the experimental condition (i.e. conditionally depleted or enriched).

The read boundaries for the five single-strand tokens were defined by a lower (l) and upper (u) cutoff (Figure 3.3). To arrive at appropriate values for l and u, I divided the HG003 TA sites into two groups: TA sites that were rare in the library and sites that were more well- represented. TA sites that were rare were approximated as those without any reads in the control file with the greatest insertion density. All sites that had at least one read in the control file with the greatest insertion density were considered well-represented. I then gathered the read counts for the rare TA sites from all other control files available and defined l as the 99th percentile among the read counts, which was seven reads. For the upper cutoff, I calculated the read difference between the control file with the greatest insertion density and all other control files at the well-represented sites. After taking the absolute values of the read differences, I chose the 99th percentile of the read difference distribution to be the u cutoff, at a value of 111 reads. Thus, any TA site that had 111 fewer reads in the experimental condition in a given strand compared to the control condition was assigned a d, 111 more reads assigned an e, between 7 and 111 fewer assigned an f, between 7 and 111 more assigned an m, and between

7 fewer and 7 more assigned an n. As stated above, each TA insertion site was given a token for each strand for an ultimate two-letter token. Based on this system, there were twenty-five possible tokens indicating the direction and extent of the read difference for each strand when comparing the experimental condition to the control.

Figure 3.3: Single-strand tokens for HMM 2 were defined using read difference cutoffs. Read differences were calculated for each strand by subtracting control sample reads from the experimental conditon sample reads, after normalizing by total reads. The cutoff l was used to separate sequencing/sampling errors in rare mutants from growth variations in well-represented mutants, while the cutoff u was used to distinguish between normal growth variations between samples and biologically meaningful read differences.

Adding more read information than in HMM 1 engendered more informative states.

These were Z, for mostly zero reads or essential; D, for generally depleted of reads in both strands in the experimental compared to the control condition; N, for neutral in both strands; P, for plus-strand enriched; M, for minus-strand enriched; and E, for enriched in reads in both strands.

I also took a different approach for assigning emission probabilities. I was concerned from the results of HMM 1 that we were selecting to only find extreme hits by only including examples of upregulation signatures in the training data that we know are extreme. To address this problem, I manually annotating three compound-exposed sample files with what I perceived the state designations to be at each site. The files chosen were fosfomycin at 12 μg/mL, platensimycin at 0.5 μg/mL, and bacitracin at 8 μg/mL. All three of these had known hits based on Dr. Santiago’s previous work [3, 6]. Initial emission probability estimates were then calculated based on the proportion of each token for each type of state. However, I wanted the probabilities to be symmetrical for the strands (e.g. the probability of having an e in the plus strand of a P state TA site should be the same as the probability of having an e in the minus strand of an M state TA site). To do this, I averaged the ‘equivalent’ proportions from the initial emissions probabilities and used the averaged values as the final emission probabilities.

Emission probabilities can be found in Table 3.2. Transition probabilities were calculated the same as in HMM 1.

3.3.3: Bootstrap 1 – comparing to a distribution sampled from control data

The HMMs did not give any indication of how strong a hit was, but rather just gave the most probable sequence of states. As a possible alternative that would have a statistical metric to prioritize hits, we turned to bootstrapping, a method that relies on simulating a null distribution by sampling with replacement. In the first iteration of the bootstrap algorithm, we removed TA sites that had never been hit in any of the HG003 control files, treating the plus strand and

Table 3.2: Emission probabilities for HMM 2. The probability of having each token (rows) in each state (columns). Each row is formatted on a color scale from white (low) to blue (high) to indicate which state(s) each token is most prevalent in.

Z D N P M E dd 0.000009 0.001614 0.000241 0.000000 0.000000 0.000000 df 0.000013 0.003943 0.001164 0.000000 0.000211 0.000000 dn 0.000027 0.002033 0.000615 0.000000 0.002276 0.000091 dm 0.000000 0.000020 0.000507 0.000000 0.001397 0.000000

de 0.000000 0.000003 0.000082 0.000000 0.004226 0.000000

fd 0.000013 0.003943 0.001164 0.000211 0.000000 0.000000 ff 0.000367 0.065839 0.027155 0.011260 0.011260 0.002009 fn 0.001990 0.107751 0.057928 0.022677 0.070212 0.008593 fm 0.000009 0.000657 0.008183 0.000219 0.022504 0.002314 fe 0.000004 0.000082 0.000833 0.000000 0.050607 0.003383 nd 0.000027 0.002033 0.000615 0.002276 0.000000 0.000091 nf 0.001990 0.107751 0.057928 0.070212 0.022677 0.008593

nn 0.993248 0.699612 0.753515 0.615610 0.615610 0.619447

nm 0.001059 0.001784 0.035446 0.004887 0.103333 0.088848 ne 0.000085 0.000148 0.002088 0.000000 0.080737 0.043344 md 0.000000 0.000020 0.000507 0.001397 0.000000 0.000000 mf 0.000009 0.000657 0.008183 0.022504 0.000219 0.002314 mn 0.001059 0.001784 0.035446 0.103333 0.004887 0.088848 mm 0.000000 0.000083 0.004915 0.004222 0.004222 0.033513

me 0.000000 0.000003 0.000185 0.000211 0.003750 0.010376

ed 0.000000 0.000003 0.000082 0.004226 0.000000 0.000000 ef 0.000004 0.000082 0.000833 0.050607 0.000000 0.003383 en 0.000085 0.000148 0.002088 0.080737 0.000000 0.043344 em 0.000000 0.000003 0.000185 0.003750 0.000211 0.010376 ee 0.000000 0.000002 0.000112 0.001661 0.001661 0.031136 minus strand of the DNA separately. The read counts in the experimental condition sample were then normalized by a factor so that the control and experimental condition files would have the same number of total reads. I then annotated TA sites that landed within a promoter region, here defined as the 700 bases upstream of a gene in the strand relevant to that gene (e.g. if the gene was oriented in the forward direction, only the plus strand reads were included). For each gene, the number of TA sites n in the promoter region was counted. Then, two random samples of size n were taken with replacement from the collection of TA sites in the control file, only using data from the appropriate strand. The read counts for each sample were summed and the

58 ratio of the sums was recorded. This process was done many times to create a null distribution of promoter region read ratios, representing the distribution one would expect if the experimental and control data were equivalent. To save on computing time, the same distribution was used for all genes sharing the same n. The actual read ratio between the experimental condition and the control condition for a given gene’s promoter region was then compared to the distribution, calculating the percentage of the null promoter read ratios that exceeded the actual ratio to obtain a one-sided p-value. The p-values were then corrected using the Benjamini-Hochberg method.

3.3.4: Bootstrap 2 – comparing each promoter to a baseline difference between the experimental and control conditions

For a second iteration of the bootstrap method, I wanted to incorporate more information about the relationship between the experimental condition and control sample in the simulated distribution. For this method, sites that had no reads in both conditions were removed from the dataset, as these provided no information relevant to upregulation signatures. Outliers were reduced to 10-4 of the total reads in the control file (often approximately 300 reads) and then the read difference between the experimental and control sample was calculated for each TA insertion site. The dataset was then annotated with promoter region labels, with the 500 bp upstream of each gene used as an estimated promoter region. 500 bp is just under the 99th percentile for the length of the 5’ untranslated region of E. coli transcripts based on analysis of data from Gama-Castro, et al. [7]. For each gene, the number of TA sites n in the promoter region was counted. For each strand, n read differences were sampled from across the genome, summed, and recorded. This process was done 20,000 times to create a null distribution of expected promoter region read differences between the experimental condition and control samples. To save on computing time, the same distribution was used for all genes sharing the same n. The actual read difference for the given gene’s promoter region was then

59 compared to the null distribution, calculating the percentage of the null promoter read differences that exceeded the actual difference, to obtain a one-sided p-value. P-values for each strand were corrected using the Benjamini-Hochberg method. A promoter region was considered a hit if 1) the promoter region had more than 1 TA site with reads, 2) the q-value for one strand was less than 0.05, and 3) the read difference for the other strand is less than the

90th percentile for read differences in that strand.

3.3.5: Testing the analytic approaches

For each of the above described methods, the analysis was performed on three test files chosen from previously published HG003 Tn-Seq data [3]. The datasets chosen were fosfomycin at 12 μg/mL, platensimycin at 0.5 μg/mL, and bacitracin at 8 μg/mL. These were chosen because they had a range of saturation, with the fosfomycin sample having the strongest selective pressure and therefore lowest saturation and the bacitracin sample having comparatively few differences between it and the control. Only data for the Pcap, Ppen, and Ptuf transposons were included, and these data were summed to consider them jointly for all analyses. One representative control HG003 file was chosen from the many available. Hits that were found by the analyses were visually assessed using the genome browsing software

Artemis to evaluate whether they were plausible [11]. The latter analysis, Bootstrap 2, was then applied to all historic Tn-Seq data files, most of which were published along with Dr. Santiago’s prior upregulation analysis technique [3].

3.4: Results

3.4.1: Assessment of analytic methods

I used three test files to assess the four analytic procedures described in Section 3.3.

The files were chosen because they had known upregulation signatures based on Dr.

Santiago’s analysis and they represented a range of selection severity and therefore insertion density. The first evaluation for all methods was whether they could detect the previously published hits found in the files (Table 3.3) [3]. The first iteration of both methods (HMM 1 and

Bootstrap 1) each missed at least one known hit, while the latter methods recovered all known hits.

Table 3.3: Known hits in three test files recaptured by each proposed analysis method. Known hits were previously identified using Dr. Santiago’s analytical method [3]. Y indicates that the gene was listed as upregulated by the analytic method, whereas N indicates that the gene was not considered upregulated.

Antibiotic Locus Tag Gene HMM 1 HMM 2 Bootstrap 1 Bootstrap 2 Fosfomycin SAOUHSC_02337 murA1 Y Y Y Y Fosfomycin SAOUHSC_02365 murA2 N Y Y Y Fosfomycin SAOUHSC_02609 fosB Y Y Y Y Platensimycin SAOUHSC_02418 lmrS Y Y Y Y Platensimycin SAOUHSC_02630 emrK Y Y N Y Platensimycin SAOUHSC_02658 unknown Y Y Y Y Platensimycin SAOUHSC_02700 mdeA Y Y Y Y Bacitracin SAOUHSC_00691 uppP Y Y N Y

We also evaluated the plausibility of the other hits identified by the methods via data visualization. For both the first HMM method and the first bootstrap method, we noticed a high number of false positives. In HMM 1, the false positives were mostly in areas of the genome that had stretches of TA sites with small numbers of reads (i.e. fewer than five) that happened to be mostly in one strand. Because HMM 1 was based on insertion density rather than read counts, it is unsurprising that this type of false positive predominated. This was not a problem in HMM 2, which discretized TA sites based on read counts. However, HMM 2 had a number of hits that were labeled as either plus- or minus-strand enriched but were realistically enriched in both strands. Many of the HMM 2 hits were also in contexts that did not make sense. For example, there might be an upregulation signature labeled at the end of a gene rather than its beginning, with the promoters of the transposons facing toward the gene and no annotated genes nearby to explain it, despite its clear read bias. In both bootstrap methods, jackpots dominated the false

61 positives. Jackpots are rare, individual TA sites with sequencing artifacts that result in absurdly high read counts, often with thousands of reads. If not corrected for, the jackpots can drive the total read count for a promoter region up so that it appears statistically enriched in reads even if all other TA sites in the region are similar to those in the control file. Jackpots were corrected for in the second bootstrap method by truncating reads at a particular value, defined as a percentage of total reads. While Bootstrap 2 had fewer jackpots than Bootstrap 1, they remained a problem. The second bootstrap method was applied to all of our Tn-Seq antibiotic data sets. Among the labeled hits, we counted 399 genes that appeared to truly be upregulated and forty-seven false-positives. The false positives in this case were either weak (i.e. only a slight read difference between the control and the experimental condition sample, often found in conditions with low selective pressure), jackpots, generally enriched areas, and regions that did not appear to have enough bias for one strand in particular.

3.4.2: Application of upregulation analysis to historic Tn-Seq data

The Walker lab has amassed a large trove of antibiotic-exposed Tn-Seq data in the S. aureus strain HG003. Using the optimized bootstrapping method (Bootstrap 2) as the preferred upregulation analysis, I applied the method to all existing Tn-Seq antibiotic data in our laboratory (Supplemental Tables 7-8). As stated above, I visually assessed whether each hit seemed plausible or whether it seemed like a false positive. Ninety percent appeared to be accurate (399/445), while the remaining were either jackpots (27/445) or some other form of false positive (19/445). All previously identified upregulation signatures were recovered. Sixty- nine of the upregulation signatures were ahead of essential genes.

In addition, we recovered many formerly unappreciated hits. Among these were hits we had expected to see given the mechanism of action of the antibiotics. For example, pbp4 had an upregulation signature upon exposure to oxacillin, which targets the penicillin-binding proteins

(PBPs). PBP4 is the only core S. aureus PBP with low affinity for oxacillin and overexpression of

PBP4 has been reported as a potential mechanism of resistance to β-lactams [12, 13]. We also found upregulation signatures ahead of genes within the folate biosynthesis pathway in the presence of sulfamethoxazole and trimethoprim, both of which target genes in the pathway

(Figure 3.4) [14-16]. Surprisingly, the gene that most often had an upregulation signature was a hypothetical gene, SAOUHSC_01859 (Supplemental Table 9). It has no known homology or predicted function and is thought to encode a cytosolic protein. The other top ten upregulated genes were ddl, encoding a D-alanine-D-alanine ligase [17]; the antibiotic-resistance associated pump gene norA [18]; the ribosomal L28 subunit gene rpmB [19]; ytpP, which has homology to a thioredoxin gene in Bacillus subtilis [20]; murA2 and murJ, which encode proteins involved in peptidoglycan synthesis [21, 22]; the hypothetical genes SAOUHSC_00371 and

SAOUHSC_02149; and vraS, encoding a member of a multicomponent stress-response system

[23]. Many of the antibiotics that we have studied target the cell envelope, so it is not so surprising that many of the upregulated genes are involved in cell envelope processes.

Figure 3.4: Folate and DNA synthesis pathway genes have upregulation signatures in Tn- Seq data upon trimethoprim exposure. Upper part of the figure shows the Tn-Seq reads for both a trimethoprim-exposed sample and a control sample ahead of genes with upregulation signatures. The x-axis represents the location in the genome, while the y-axis is the sequencing reads truncated to 500. The lower part of the figure diagrams the folate biosynthesis pathway. Genes with upregulation signatures in the presence of trimethoprim are highlighted in green.

However, it is as yet unclear what role upregulation of rpmB, ytpP, or the hypothetical genes may play in relation to antibiotic resistance.

3.5: Discussion

A wide variety of techniques have been used to identify essential and conditionally essential genes using Tn-Seq data. Methods currently applied to Tn-Seq data include frequentist approaches, such as applying t-tests, Mann-Whitney tests, and permutation tests, and probabilistic approaches like hidden Markov models. The main challenge for developing an upregulation signature analysis was creating a method that was insensitive to the intensity of selective pressure applied to the Tn-Seq sample. Given the extreme difference in saturation between the Tn-Seq samples exposed to high concentrations of antibiotic and the control samples, we quickly decided that the standard frequentist tests would not suffice. Most frequentist statistical methods assume that the shape of the distribution is roughly the same between the two samples, relying on normalization to achieve this. However, proper normalization of Tn-Seq data remains a challenge. As a condition becomes more severe, the

Tn-Seq insertion density decreases and the read counts at each site increase. Some of the read count variation can be adjusted by factor-based normalization, but the sparseness is more difficult to adjust for. If one normalizes so that the control and compound-exposed sample have the same number of total reads, each TA site with reads in the experimental condition sample will have more reads on average than the control file. As a result, false positives are common; promoter regions with even just a few surviving mutants can have large read counts that exceed the typical control sample read counts. If one normalizes by non-zero means instead, such that the control and compound-exposed sample have the average reads per TA site with reads, the sparsity in the experimental condition data makes it very difficult for a region to acquire enough reads to be statistically enriched compared to the control sample, leading to false negatives. We generally circumvent this problem by excluding from analysis any overly harsh conditions.

However, it is hard to predict how much selective pressure a condition will exert on the Tn-Seq library a priori, and the Walker lab has amassed a large number of Tn-Seq samples with variable selective pressure. For many antibiotics, we only have data for concentrations that would be considered too high for standard analyses. With each sample costing hundreds of dollars, we were loath to write those datasets off as uninformative.

We were first drawn to hidden Markov models as a solution, as they have been successfully used in essential gene analyses of Tn-Seq data, as exemplified in the TRANSIT and ARTIST software packages [10, 24]. As described in the methods section, my first attempt at developing an upregulation signature HMM was minimalist; I decided not to compare the experimental sample to a control at all but to instead sought upregulation signatures intrinsic to the experimental condition data. I also focused only on whether a site had reads in each strand, instead of discretizing based on the number of reads in each strand. A major drawback of the approach was that a statistical test would have been necessary to determine whether the upregulation signatures in the experimental condition sample were significantly different from upregulation signatures found in the control sample if one wanted to show conditional upregulation. Thus, I used this more as a trial to determine whether a HMM could pick up on upregulation signatures at all and warranted further optimization. I was encouraged by the model’s ability to recover most of the hits previously identified using Dr. Santiago’s tool.

However, I noticed that there were many small windows being labeled as plus-strand or minus- strand enriched. These windows did indeed have a high density of plus or minus strand insertions, but the reads at those insertion sites were too low to be meaningful, typically with five or fewer reads. These were not the types of signatures I were looking to highlight, and as a result I decided to incorporate read data into the next version of the model.

Greater effort was spent on optimizing the second HMM version, this one directly comparing read counts between the experimental and control condition. The resulting model worked quite well by our initial assessments, recapturing all of the known upregulation

65 signatures in the three chosen test files and identifying several additional upregulation signatures, with fewer apparent false positives compared to the initial HMM attempt. I ultimately chose to abandon the HMM entirely, however, because of qualms about the development strategy and the ease of use of the method. The training strategy relied on manually annotating the states in a subset of files to approximate emission probabilities for the model. Manual annotation is prone to inconsistencies and can introduce bias but was used because we lacked sufficient validated training data. As for the ease of use, there were two major disadvantages.

The first was the lack of statistical metrics inherent in HMMs. While a region could be labeled as plus-strand enriched, the model would not indicate whether a true statistical difference existed between the experimental condition and control samples at that location or how strong the difference was. Probability metrics could be attained but would add additional computational time to an already fairly slow process. Without metrics regarding the strength of an upregulation signature, prioritizing hits becomes an issue. Another problem with the HMMs was that they were agnostic to gene context. As a result, many of the upregulation signatures identified were not located near an annotated gene that could be upregulated. While this type of hit may be interesting in the future when more is known about the intergenic regions of S. aureus, at the moment these hits mostly serve to distract from the hits we are looking for and require the researcher to spend time sifting through hits to find true upregulation signatures. To take a more statistics-driven, gene-targeted approach, I turned to bootstrapping.

Bootstrapping is a method whereby computational simulation is used to estimate a population distribution through random sampling with replacement. I thought that a bootstrap strategy would be particularly adept at addressing the disparity in insertion density among files, as I could form a null distribution that took into account the disparate shapes of the data distributions under different conditions. As with the first HMM, the first bootstrap I developed was minimalist and did not include enough information to perform well. However, it performed well enough to encourage me to proceed to develop the second bootstrapping method, which

66 was the analysis method we ultimately adopted. The second bootstrapping method, which strove to more directly incorporate the experimental data in the generation of the null distribution, performed admirably. It is easier to use than the HMMs, as it links upregulation signatures to specific genes and provides adjusted p-values to prioritize hits. It also recovered all formerly identified upregulation signatures and many new ones.

Applied in conjunction with methods that look for read depletions and enrichments within genes, the upregulation analysis will broaden our understanding of what genes are most important under a given condition. Unlike traditional Tn-Seq analysis, it can reveal possible gain-of-function mechanisms of resistance. The upregulation signatures can also be detected regardless of the essentiality of the gene, allowing us to obtain insights into the roles of a wider breadth of genes. This is one of the major advantages to examining upregulation signatures; in traditional Tn-Seq experiments looking only within genes, nothing can be learned about essential genes because they cannot tolerate insertions. However, many antibiotics work by targeting essential genes, so having a method that can report on those genes will be a valuable tool for understanding the mechanisms of action of antibiotics. Moreover, we have used the approach to analyze data from several different strains of S. aureus, suggesting that it is applicable beyond the HG003 library for which it was developed. This latter point will be discussed further in Chapter 4.

3.6: Future directions

While we are pleased that the second bootstrapping approach proved to be a more sensitive and more statistically-rigorous method for identifying upregulation signatures than the previous method our lab developed, more needs to be done to assess its quality. We have estimated the false positive rate by visual analysis, and literature precedent can confirm some of the hits. However, it remains unclear how many of the hits will experimentally validate. We also have no way of measuring false-negative rates. We often only follow up on a subset of the hits

67 found using the analysis, and often these are some of the strongest hits. Many more experimental validations would need to be conducted to determine how sensitive and specific the analysis truly is. It is no doubt more sensitive than the original method used by the Walker lab, with a tradeoff for specificity. The added sensitivity seems at this point to be worth making this compromise. That said, efforts toward reducing false positives, especially jackpots, would improve the tool. About sixty percent of the currently identified false positives are jackpots. In the current method, large read counts are reduced to a cutoff value defined as a percentage of total reads. However, it may be more effective to reduce the highest read count within each promoter region to the second highest read count, for example. It is also possible that certain

TA sites are more prone to jackpots, and those TAs could be omitted from the analysis. More work will be needed to find the best solution.

The bootstrapping approach is also computationally intensive. The more simulations one does, the more statistical power there is to detect biologically significant results. We need to have enough power such that the hits are not lost, even after correcting for the fact that thousands of statistical tests are being performed at once, but there is a direct tradeoff between computational time and number of simulations. One alternative approach would be to develop a statistical model for the distribution of read differences given the level of saturation of the experimental condition sample. The result would be an equation that could be used to calculate the p-value. This approach would be theoretically challenging but would ultimately require negligible computational resources for each analysis. Another alternative would be to continue to gather more known upregulation signatures and use them to train a machine learning algorithm to detect upregulation signatures, focusing on models that are computationally efficient during predictions.

Finally, the information provided by Tn-Seq studies relies heavily on genome annotations. For these efforts, I chose to use artificial promoter regions defined as the area ahead of genes. The method does not consider whether the gene is in an operon or whether

68 insertions in the promoter region might interfere with other genetic or epigenetic elements. By focusing on genes, we are also ignoring non-coding RNAs. The full transcriptome of at least one strain of S. aureus has been established [25], but it is unclear how conserved the non-coding

RNAs are across strains or how one would identify them in a genomic sequence without performing an extensive transcriptomics experiment. We suspect that some of these non-coding

RNAs may play an integral role in antibiotic response, as we have seen examples of directionally-biased Tn insertion enrichments in areas of the genome where there is no nearby gene to upregulate. Once these genetic elements are more defined, Tn-Seq will likely be instrumental in determining what roles they serve. There is also a large portion of genes of unknown function. Their unexplored roles make the relevance of their upregulation difficult to interpret, though they offer enticing opportunities for basic biology follow up work. The frequently upregulated genes SAOUHSC_01859, SAOUHSC_00371, and SAOUHSC_02149, for example, may warrant follow up studies because they seem to be involved in the response to a wide range of antibiotics. With so many researchers studying S. aureus in particular and bacteria as a whole, annotations will indubitably continue to improve, and Tn-Seq analysis will advance in stride.

3.6: References

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5. Deng J, Su S, Lin X, Hassett DJ, Lu LJ. A statistical framework for improving genomic annotations of prokaryotic essential genes. PLOS ONE. 2013;8(3):e58178.

6. Santiago MJ. New genomics tools and strategies for studying antibiotics and antibiotic- resistance in Staphylococcus aureus 2016.

7. Gama-Castro S, Salgado H, Santos-Zavaleta A, Ledezma-Tejeida D, Muniz-Rascado L, Garcia-Sotelo JS, et al. RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Research. 2016;44(D1):D133-43. doi: 10.1093/nar/gkv1156. PubMed PMID: 26527724; PubMed Central PMCID: PMC4702833.

8. DeJesus MA, Ioerger TR. Normalization of transposon-mutant library sequencing datasets to improve identification of conditionally essential genes. Journal of Bioinformatics and Computational Biology. 2016;14(03):1642004.

9. van Opijnen T, Bodi KL, Camilli A. Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nature Methods. 2009;6(10):767.

10. DeJesus MA, Ambadipudi C, Baker R, Sassetti C, Ioerger TR. TRANSIT - a software tool for Himar1 TnSeq analysis. PLOS Computational Biology. 2015;11(10):e1004401. doi: 10.1371/journal.pcbi.1004401. PubMed PMID: 26447887; PubMed Central PMCID: PMC4598096.

11. Carver T, Harris SR, Berriman M, Parkhill J, McQuillan JA. Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics. 2011;28(4):464-9.

12. Moisan H, Pruneau M, Malouin F. Binding of ceftaroline to penicillin-binding proteins of Staphylococcus aureus and Streptococcus pneumoniae. Journal of Antimicrobial Chemotherapy. 2010;65(4):713-6.

13. da Costa TM, de Oliveira CR, Chambers HF, Chatterjee SS. PBP4: a new perspective on Staphylococcus aureus beta-lactam resistance. Microorganisms. 2018;6(3). doi: 10.3390/microorganisms6030057. PubMed PMID: 29932109.

14. Hitchings GH. Mechanism of action of trimethoprim-sulfamethoxazole—I. Journal of Infectious Diseases. 1973;128(Supplement_3):S433-S6.

15. Burchall JJ. Mechanism of action of trimethoprim-sulfamethoxazole—II. Journal of Infectious Diseases. 1973;128(Supplement_3):S437-S41.

16. Zhu T, Pan Z, Domagalski N, Koepsel R, Ataai M, Domach M. Engineering of Bacillus subtilis for enhanced total synthesis of folic acid. Appl Environ Microbiol. 2005;71(11):7122-9.

17. Walsh CT. Enzymes in the D-alanine branch of bacterial cell wall peptidoglycan assembly. Journal of Biological Chemistry. 1989;264(5):2393-6.

18. Ubukata K, Itoh-Yamashita N, Konno M. Cloning and expression of the norA gene for fluoroquinolone resistance in Staphylococcus aureus. Antimicrobial Agents and Chemotherapy. 1989;33(9):1535-9.

19. Wittmann H. Components of bacterial ribosomes. Annual Review of Biochemistry. 1982;51(1):155-83.

20. Kouwen TR, Dubois J-YF, Freudl R, Quax WJ, van Dijl JM. Modulation of thiol-disulfide oxidoreductases for increased production of disulfide-bond-containing proteins in Bacillus subtilis. Applied Environmental Microbiology. 2008;74(24):7536-45.

21. Blake KL, O'Neill AJ, Mengin‐Lecreulx D, Henderson PJ, Bostock JM, Dunsmore CJ, et al. The nature of Staphylococcus aureus MurA and MurZ and approaches for detection of peptidoglycan biosynthesis inhibitors. Molecular Microbiology. 2009;72(2):335-43.

22. Sham L-T, Butler EK, Lebar MD, Kahne D, Bernhardt TG, Ruiz N. MurJ is the flippase of lipid-linked precursors for peptidoglycan biogenesis. Science. 2014;345(6193):220-2.

23. Kuroda M, Kuroda H, Oshima T, Takeuchi F, Mori H, Hiramatsu K. Two‐component system VraSR positively modulates the regulation of cell‐wall biosynthesis pathway in Staphylococcus aureus. Molecular Microbiology. 2003;49(3):807-21.

24. Pritchard JR, Chao MC, Abel S, Davis BM, Baranowski C, Zhang YJ, et al. ARTIST: high-resolution genome-wide assessment of fitness using transposon-insertion sequencing. PLOS Genetics. 2014;10(11):e1004782.

25. Mäder U, Nicolas P, Depke M, Pané-Farré J, Debarbouille M, van der Kooi-Pol MM, et al. Staphylococcus aureus transcriptome architecture: from laboratory to infection-mimicking conditions. PLOS Genetics. 2016;12(4):e1005962.

Chapter 4: Comparative Tn-Seq reveals common daptomycin resistance determinants in

Staphylococcus aureus despite strain-dependent differences in essentiality of shared cell envelope genes

A manuscript including much of this work, its text, and the associated figures has been submitted to PLOS Pathogens in collaboration with Dr. Wonsik Lee, Dr. Gloria Komazin-

Meredith, Dr. Timothy Meredith, Dr. Yonatan Grad, and Dr. Suzanne Walker. A preprint is available on bioRxiv [1]. Dr. Timothy Meredith and Dr. Gloria Komazin-Meredith created the transposon libraries. Dr. Wonsik Lee performed the transposon sequencing experiments and much of the validation. I performed all analyses and whole genome sequencing of strains.

4.1: Abstract

Staphylococcus aureus has developed resistance to a wide variety of antibiotics to date, including second- or even third-line antibiotics. Two major strategies to combat the ongoing development of antibiotic resistance are 1) to develop antibiotics targeting essential factors, which can be used in lieu of current antibiotics and 2) to develop compounds that, while not bactericidal on their own, can resensitize bacteria to our existing repertoire of antibiotics.

Chapter 2 of this thesis focused on identifying candidate targets for the development of new antibiotics. This chapter focuses on using transposon sequencing to identify factors shared among S. aureus strains that can be targeted to increase the potency of daptomycin, an important antibiotic for the treatment of methicillin-resistant S. aureus. We also examine the mechanisms by which S. aureus can become resistant to daptomycin, both through loss of function and gain of function mutations, with the latter identified using the upregulation signature analysis described in Chapter 3. We found that multicomponent stress-response systems and lipoteichoic acids play a large role in counteracting the effects of daptomycin and that slowing the growth rate of the bacteria confers a survival benefit.

4.2: Introduction

After characterizing the diversity of genetic dependencies among S. aureus strains under favorable growth conditions and expanding our repertoire of analytical tools for Tn-Seq

73 data, we wanted to extend our studies to understanding how much the genetic differences between strains of S. aureus impact their responses to an antibiotic stressor, daptomycin.

Daptomycin is a cyclic lipopeptide antibiotic used to treat Gram-positive bacterial infections, especially S. aureus infections [2]. It is most often used in cases in which the infection is resistant to or the patient is allergic to or otherwise cannot tolerate β-lactams or vancomycin. The precise mechanism of action of daptomycin is still somewhat obscure. It is thought to insert into the membrane with the support of calcium ions, changing in membrane curvature and permeability, slowly depolarizing the membrane, and mislocalizing key cell division and cell wall synthesis proteins [3-7]. Transcriptomics data shows overlap between the response to daptomycin and to both cell wall-perturbing antibiotics (e.g. vancomycin) and membrane disrupting antibiotics (e.g. carbonyl cyanide m-chlorophenylhydrazine, CCCP), pointing to a multifaceted mechanism of action that cannot be generalized as simply membrane depolarization [8]. Daptomycin is ineffective against Gram-negative bacteria, but, unlike most

Gram-positive-specific antibiotics, its specificity is not solely due to the presence of the outer membrane; even an E. coli mutant with a highly permeable outer membrane is resistant to daptomycin [9]. These results imply that daptomycin has a cellular target not present in Gram- negative organisms or requires specific cell envelope properties to work. Studies have shown that there is a connection between membrane composition and daptomycin activity [10-12].

However, how the membrane composition modulates activity is debated, as some studies find a positive and others a negative correlation between membrane fluidity and daptomycin resistance [11, 12]. More work is clearly needed to better understand the requirements for daptomycin to be effective.

Because daptomycin is a treatment for intractable, multidrug-resistant MRSA infections, daptomycin resistance is a serious concern. It is estimated that about 0.4% of S. aureus clinical isolates that have not been treated with daptomycin are non-susceptible, defined as having a minimum inhibitory concentration (MIC) of at least 2 ug/mL [13]. As of 2015 at least 62 cases of

74 daptomycin non-susceptible S. aureus infection had been reported [14]. Unlike many antibiotics, resistance to daptomycin develops stepwise, with individual mutations increasing the MIC a small amount [15]. Many of the resistance mechanism reported involve overactivation of genes related to the cell envelope. These are mprF (fmtC) that encodes a phosphatidylglycerol lysyltransferase [16, 17]; cls2, encoding one of two cardiolipin synthases in S. aureus [18, 19]; crtM, encoding a staphyloxanthin biosynthesis gene [20]; and the genes in the dlt operon whose encoded proteins decorate teichoic acids with D-alanine [16, 21]. Other mechanisms that have been confirmed to reducing daptomycin susceptibility include overactivation of the regulatory gene graR [22]; inactivation of the virulence gene agr [23]; inactivation of the phospholipid biosynthesis gene pgsA [19]; and acquirement of uncharacterized mutations in the multicomponent stress response system walKR (yycFG) [15, 24], the cell division gene ezrA

[25], or the RNA polymerase components rpoBC [15, 26, 27]. The most commonly reported clinical mechanism is overactivation of mprF, which is thought to decrease susceptibility by neutralizing the negative charge on the cell membrane and reducing the availability of phosphatidylglycerol, though support of these mechanisms is still lacking [28, 29]. With so many possible mechanisms of resistance, we are left wondering what determines the form of resistance adopted by a strain. Do mutations provide the same protection for all S. aureus strains, or does the protection achieved depend on the genetic background of the isolate?

Conversely, do all S. aureus strains share the same vulnerabilities?

We turned to Tn-Seq as a way of addressing this question. Tn-Seq is well-adapted to addressing this type of problem, as we can introduce mutations in every gene in the genome in a single experiment. Insertions within a gene can report on mechanisms of resistance via inactivation (presenting as a read enrichment in the compound-exposed sample compared to control) or intrinsic resistance factors that are required for surviving daptomycin (presenting as a read depletion compared to control). Meanwhile, insertions of promoter-donating transposons upstream of a gene can report on mechanisms of resistance via overactivation. These can be

75 identified using the analysis described in Chapter 3. A prior study, conducted in S. pneumoniae, emphasized the differences between the depleted and enriched genes in two strains [30]. The study demonstrated that only about half of the genes modulating daptomycin sensitivity in one strain were relevant in for daptomycin survival in the second strain. We were intrigued by this outcome and wondered whether the findings would generalize to S. aureus. We also wanted to extend the study to a larger number of strains, as it would enable us to determine which factors are most consistently important for S. aureus in the presence of daptomycin.

We performed Tn-Seq in the five S. aureus libraries described in Chapter 2, created in the strains HG003, USA300-TCH1516, MSSA476, MW2, and MRSA252, both with and without daptomycin to identify genes relevant to daptomycin susceptibility. In doing so, we found a number of conserved resistance mechanisms and vulnerabilities in the strains. In particular, we found that multicomponent stress-response systems and lipoteichoic acids are crucial for S. aureus to counteract the effects of daptomycin and that slowing growth provides a survival benefit upon exposure to daptomycin. Finally, several of the genes we found to modulate daptomycin susceptibility have never been previously reported. These genes would be particularly interesting to follow up on, both to better understand basic S. aureus biology and to further characterize the mechanism of action of daptomycin.

4.3: Methods

4.3.1: Materials, bacterial strains, plasmids, and oligonucleotides

All reagents were purchased from Sigma-Aldrich unless otherwise indicated. Bacterial culture media were purchased from BD Sciences. Restriction enzymes and enzymes for Tn-Seq preparation were purchased from New England Biolabs. Oligonucleotides and primers were purchased from Integrated DNA Technologies (IDT). DNA Concentrations were measured using a NanoDrop One Microvolume UV-Vis Spectrophotometer (Thermo Scientific). DNA sequencing

76 was performed by Eton Bioscience unless otherwise noted. KOD Hotstart DNA polymerase

(Novagen) was used for PCR amplification. S. aureus strains were grown at 30°C in tryptic soy broth (TSB) shaking or on TSB agar plates unless otherwise noted. For S. aureus strains, compounds for selection or gene induction were used at the following concentrations: 5 µg/mL chloramphenicol and erythromycin; 50 μg/mL kanamycin and neomycin; or 2.5 μg/mL tetracycline. The bacterial strains, plasmids, and oligonucleotide primers used in this study are summarized in Supplemental Tables 1-3.

4.3.2: Daptomycin transposon sequencing

Dr. Wonsik Lee sequenced samples of our S. aureus transposon libraries in the presence and absence of daptomycin. He used all five transposon libraries: HG003, USA300-

TCH1516, MW2, MSSA476, and MRSA252, which are described in detail in Chapter 2. The libraries were prepared by diluting thawed library aliquots to an OD600 of approximately 0.2 in either MHBII or RPMI+LB and grown for about 1.5 hours at 30°C, shaking, until they reached an

OD600 between 0.3 and 0.4. MHBII was prepared per manufacturer’s instructions. RPMI+LB was prepared by first preparing RPMI 1640 (Millipore-Sigma) from powder according to the manufacturer’s instructions and then supplementing with 10% v/v LB. Both media were filter sterilized. After reaching the desired OD600, libraries were diluted to an OD600 of 0.005 in 2 mL of media with a series of daptomycin concentrations (μg/mL): 0, 0.12, 0.25, 0.5, and 1. The cultures were then incubated at 37°C, shaking, and their ODs were monitored using a Genesys

20 spectrophotometer (Thermo Scientific). The 2 mL cultures were harvested by centrifugation when the OD600 reached 1.5. The cell pellets were stored at -80°C until processing for Tn-Seq.

The genomic DNA was extracted and prepared as described previously [31]. Samples were submitted to the Tufts University Core Facility for sequencing on a HiSeq 2500 instrument.

4.3.3: Transposon sequencing data analysis

I performed the analysis of our Tn-Seq data. Transposon sequencing data was split by transposon and sample, trimmed, filtered, and mapped using the Galaxy software suite as previously described, and a workflow for the processing is provided on GitHub

(http://github.com/SuzanneWalkerLab/5SATnSeq) [31-34]. The resulting SAM files were converted into tab-delimited hop count files using Tufts Galaxy Tn-Seq software

(http://galaxy.med.tufts.edu/) or custom python scripts (GitHub) and then converted further into

IGV-formatted files, as previously described [31, 34].

Chromosome nucleotide FASTA files for NCTC 8325 (NC_007795.1 - HG003 parent strain, as the HG003 genome is not closed), USA300-TCH1516 (NC_010079.1), MSSA476

(NC_002953.3), MW2 (NC_003929.1), and MRSA252 (NC_002952.2) were downloaded from the NCBI genomes database. The genomes were reannotated via Prokka and the pangenome was aligned with Roary, splitting by homolog and using a 90% ID cutoff [35, 36]. Roary group names were then adjusted based on common S. aureus pangenome gene names found on

AureoWiki [37].

Genes depleted or enriched in the presence of daptomycin were identified by normalizing the daptomycin-exposed sample data to the control sample data by non-zero means and performing a Mann-Whitney U test for each gene. For the normalization, the total read counts at each TA site in the daptomycin-exposed files were multiplied by a factor x so that the average reads per TA site with insertions in the daptomycin-exposed file equaled that of the control file. The equation for calculating x is below, where r is the sum of all reads in the file and n is the number of TA sites in the file with at least one sequencing read. 푟 𝑐𝑜𝑛푡푟𝑜𝑙⁄ 푛𝑐𝑜𝑛푡푟𝑜𝑙 푥 = 푟 𝑑푎𝑝푡𝑜𝑚푦𝑐푖𝑛⁄ 푛𝑑푎𝑝푡𝑜𝑚푦𝑐푖𝑛

The Mann-Whitney U p-values were corrected for multiple hypothesis testing by the Benjamini-

Hochberg method. A depleted gene was considered a hit if there were at least 100 reads in the control file, the q-value was less than 0.05, and the daptomycin:control read ratio was less than

0.1. An enriched gene was considered a hit if there were at least 100 normalized reads in the compound-exposed sample file, the q-value was less than 0.05, and the read ratio was greater than five. I then selected one representative daptomycin concentration for each strain in each medium, chosen to have a similar selective pressure (Table 4.1). Specifically, the highest daptomycin concentration with twenty-five or fewer depleted genes (according to cutoffs described above) was chosen for each strain and medium. I also identified genes with upregulation signatures under daptomycin exposure as described in Chapter 3 (Bootstrap 2).

Table 4.1: Daptomycin-exposed samples included in the multi-strain comparison of depleted, enriched, and upregulated genes. From submitted manuscript [1].

Strain Medium Concentration Depleted (μg/mL) Genes USA300-TCH1516 RPMI + LB 0.12 8 USA300-TCH1516 MHBII 0.5 12 MW2 RPMI + LB 0.25 18 MW2 MHBII 0.5 8 MSSA476 RPMI + LB 0.25 7 MSSA476 MHBII 0.5 8 HG003 RPMI + LB 0.25 9 HG003 MHBII 1 25 MRSA252 RPMI + LB 0.5 19 MRSA252 MHBII 1 18

4.3.4: Whole genome sequence comparisons

To determine whether any of the resistance mechanisms apparent in the Tn-Seq data matched those present in the daptomycin nonsusceptible isolates in the Cubist collection, I performed whole genome sequencing [16]. I prioritized strains that had unexplained nonsusceptibility, either having no missense mutation in mprF or the dlt operon or having a higher daptomycin MIC than other strains with an mprF mutation, and that were in sequence

79 types matching at least one of our Tn-Seq library strains. All nonsusceptible strains were paired with a similar susceptible isolate. The strain pairs (susceptible/nonsusceptible) chosen were

C3/C4, C7/C8, C13/C14, C15/16, and C34/C35. The strains were cultured in MHBII at 37°C shaking overnight. The DNA was harvested using a Promega Wizard Genomic DNA purification kit and cleaned using a Zymo DNA Clean Up Kit. DNA was tagmented using the Illumina

Nextera DNA Library Prep kit, using 1/20th of the volume recommended by the manufacturer and starting DNA concentrations of 0.5, 0.75, 1, and 2 ng/μL. The tagged DNA fragments were then amplified via PCR. The PCR samples contained 11.2 μL of KAPA polymerase mix

(Illumina), 4.4 μL each of the 5 μM column and row indexing primers, and 2.5 μL of tagmented

DNA. The thermocycler settings were as follows: preincubation (3 min, 72 °C), polymerase activation (5 min, 98 °C), 13 amplification cycles (denaturation at 98 °C for 10 sec, annealing at

62°C for 30 sec, and extension at 72°C for 30 sec), and termination (5 min, 72 °C).

Those samples were then cleaned as recommended by Illumina, starting with 15 uL of amplified tagmented DNA and 12 μL of AMPure XP beads. The concentrations of the resulting DNA samples were estimated using via Qubit Fluorometric Quantification (Thermo Fisher Scientific) following the manufacturer’s instructions. The DNA was then diluted to 1 ng/μL and pooled, and a portion of it was submitted to the Harvard Biopolymers Facility for TapeStation and qPCR quality control analysis. Upon passing the quality control step, the DNA was prepared and loaded into the sequencing cartridge as directed by the MiSeq Reagent Kit v3 with 150 cycles

(Illumina), including a 1% PhiX control spike-in prepared according to manufacturer’s instructions (PhiX Control v3, Illumina), and paired-end sequenced using a MiSeq instrument.

Genomic data was then analyzed to find SNPs, insertions, and deletions. MetaPhlAn2 was used to verify that the DNA was not contaminated [38]. All strains had 100% S. aureus

DNA. Sequences were then aligned to an appropriate NCBI reference genome using the

Burrow-Wheels Aligner (BWA-MEM, v7.12) with default settings [39]. Reference genomes were chosen based on the sequence type of the organism, determined by custom scripts created by

Mohamad Sater. All isolates were ST8 and were aligned to the genome of USA300-TCH1516.

Duplicate reads were marked with Picard [40], and Pilon (v1.16) was used for variant calling

[41], with a minimum depth of 1/10th of the average read depth and a minimum mapping quality of 30, unless the average read depth was less than 100, in which case the minimum mapping quality was set to 15. I used the resulting variant call files to identify missense or nonsense

SNPs present in the nonsusceptible strain but not in the susceptible strain.

4.3.5: Gene deletion and complementation

Dr. Wonsik Lee created ltaA deletion and complementation strains in MW2, HG003, and

USA300-TCH1516. To construct an Atet-inducible ltaA construct, the ltaA gene

(SAOUHSC_00952) and the ugtP-ltaA operon RBS were amplified from HG003 genomic DNA using primers ltaA-F and ugtP-R. The fragment was then cloned into pTP63 using KpnI and

EcoRI to generate pTP63-ltaA, and the pTP63-ltaA was transformed into a wildtype RN4220 strain containing pTP44 [42]. Next, the integrated inducible ltaA was transduced into wildtype

HG003, MW2, and USA300-TCH1516 using φ11 phage to generate HG003-PAtet-ltaA, MW2-

PAtet-ltaA, and USA300-TCH1516-PAtet-ltaA. The resulting strains were then transduced with a

ΔltaA construct marked with a kanamycin resistance gene to generate the inducible ltaA strains

[43].

4.3.6: Spot dilution assays

Dr. Wonsik Lee performed spot dilutions to validate Tn-Seq findings. Overnight cultures of the relevant strains were grown in TSB at 30°C until mid-log phase and diluted to an OD600 of

0.1. Five 10-fold dilutions of the resulting cultures were prepared, and 5 μL of each dilution was

81 spotted on TSB plates with or without 0.3 μM Atet and with or without 2.5 μg/mL daptomycin.

Plates were imaged after approximately 16 hours of incubation. Plates were incubated at 37°C.

4.3.7: Growth rate of Tn mutants

To determine whether genes enriched in reads under daptomycin exposure grew more slowly than other mutants, I compared the frequency of gene insertions in the Tn-Seq libraries before and after outgrowth. The three MRSA libraries were used for this analysis, each with two replicates. T0 time points, harvested after a brief wakeup period, were collected as described in

Chapter 2. For the T1 time points, collected after outgrowth, approximately 4x107 cells were inoculated into 100 mL of MHBII medium. Cultures were grown shaking at 37°C until they

8 reached an OD600 of approximately 1.0. The equivalent of 8x10 cells were collected by centrifugation. DNA was harvested, prepared for sequencing, and sequenced as described above. The same analytical workflow as described above was used to obtain read counts for all genes, summing the two replicates. TA sites with no reads were excluded from the analysis and outliers were subsequently reduced to the 99th read percentile. The relative growth rate for each gene was defined as the number of reads in that gene at T1 as a proportion of the total T1 reads divided by the number of reads in the gene at T0 as a proportion of the total T0 reads.

Genes were then assigned a ranked read percentile based on their growth rate, omitting genes that had fewer than ten TA sites with reads. Genes that were enriched in reads in any of the daptomycin-exposed library samples from the prior Tn-Seq experiment were highlighted, as were genes that were in the lower twentieth percentile (slow-growing). The number of slow- growing enriched genes was compared to a binomial distribution with a 0.2 probability to determine whether the enriched genes had a higher-than-expected representation of slow- growers.

4.4: Results

While antibiotics targeting essential gene are invaluable, it can also be advantageous to have a repertoire of compounds that resensitize bacteria to existing antibiotics, though they may not be antimicrobial on their own. To find suitable targets for such compounds, we chose daptomycin as a case study. We grew the five S. aureus transposon libraries described in

Chapter 2 in the presence and absence of daptomycin in both cation-adjusted Mueller Hinton broth and RPMI supplemented with 10% lysogeny broth. We found substantial overlap in the significantly depleted and upregulated genes in the two media, so the results are reported as a union of the results from the two media. For each strain, there were more hits shared with at least one other strain than there were hits unique to that strain (Figure 4.1A, Supplemental

Tables 10-11). Genes that were substantially depleted (>10-fold) in three or more strains are shown in Figure 4.1B. Several vulnerabilities cleared or were very close to the 10-fold cutoff in all five strains.

Many of the shared vulnerabilities to daptomycin are in genes related to the cell envelope. These include graRS/vraFG, encoding the multi-component signaling system that regulates cell envelope processes [44-46]; arlR, encoding a regulator of virulence genes [47,

48]; murA1, encoding an enzyme that catalyzes the first step in peptidoglycan biosynthesis [49,

50]; alr1, one of two genes encoding an alanine racemase that supplies D-alanine for cell wall synthesis [51]; mprF, encoding a phosphatidylglycerol lysyltransferase that modifies membrane charge [52]; and ltaA, encoding the Glc2DAG flippase gene in the LTA pathway [53]. The earlier

LTA pathway genes, gtaB, pgcA, and ugtP, were also significantly depleted in MW2, with the latter two also depleted in HG003 (Figure 4.2A). In addition, we identified three depleted cell division regulators, gpsB, ezrA, and noc [54-56]. There were also two hypothetical genes,

SAOUHSC_02149 and SAOUHSC_01050, both of which we have reported previously as important in the presence of daptomycin in HG003 [34, 57]. A number of these depleted genes have been previously associated with daptomycin susceptibility, including mprF [16, 17], graRS/vraFG [22, 45], and ezrA [25], but some that were substantially depleted in all five strains

Figure 4.1: Transposon sequencing supports previously reported daptomycin vulnerabilities in S. aureus and reveals new ones. (A) A comparison of the Tn-Seq hits under daptomycin exposure in five strains of S. aureus. All hits needed to have a q-value less than 0.05. Depleted genes had 10-fold fewer reads in the daptomycin-exposed sample compared to the control sample after normalization. Upregulated genes had a strand-biased enrichment of reads in the region upstream of the gene. The upper graph shows how many strains each hit was found in while the lower graph shows how many hits found in each strain were shared with at least one other strain and how many were unique to that strain. (B) Genes that were depleted of reads under daptomycin exposure in at least three of the transposon libraries, using the cutoffs described above. Hypothetical genes are listed according to their NCTC 8325 locus tag numbers. Genes meeting all cutoffs in a strain are indicated by dark pink. Those that have a significant q-value but are only depleted 5- to 10-fold are indicated in light pink. (C) Normalized Tn-Seq reads from daptomycin-exposed MSSA476 in RPMI+LB plotted against the reads from the control condition. Genes from above that were shared by at least 4 strains are highlighted. Figure from submitted manuscript [1].

84 have not been connected to daptomycin, including gpsB, aapA, and ltaA. We confirmed that deletion of ltaA sensitizes HG003, MW2, and USA300-TCH1516 to daptomycin and that the sensitivity can be reversed by ltaA complementation (Figure 4.2B). Therefore, Glc2DAG-LTA in the cell membrane contributes to S. aureus survival in the presence of daptomycin, and it may be possible to exploit this observation to overcome daptomycin nonsusceptibility or limit its development.

Figure 4.2: LTA loss sensitizes S. aureus to daptomycin. (A) Normalized Tn-Seq reads from daptomycin-exposed HG003 in RPMI+LB plotted against the reads from untreated HG003 in the same growth medium. Genes in the LTA pathway that were significantly depleted are highlighted. (B) Growth on agar plates of MW2, USA300-TCH1516, and HG003 wildtype, ΔltaA, and inducible ltaA complementation strains in the presence or absence of daptomycin and the inducer confirms that ltaA is required for S. aureus growth under daptomycin exposure. Figure adapted from submitted manuscript [1].

Our results also showed that there were more shared than unique upregulated genes for each strain, as judged by upregulation signatures (Figure 4.1A). Many genes whose upregulation was protective in multiple strains were previously implicated in daptomycin resistance in S. aureus either via gene overexpression or the acquisition of SNPs predicted to increase the encoded protein’s activity (Table 4.2). These genes included mprF [15, 17]; cls2, one of two cardiolipin synthases in S. aureus [21]; the staphyloxanthin biosynthesis gene, crtM

[20]; and two multicomponent signaling systems, graRS and walKR (yycFG) [15, 22]. Some of these resistance mechanisms have been reported for daptomycin resistance in other bacteria as well [58]. These hits affirm the utility of upregulation signatures for identifying clinically relevant mechanisms of resistance. The murA genes identified as important for withstanding daptomycin exposure in the depletion analysis also had upregulation signatures in some of the strains. The importance of MurA activity identified in this study is consistent with previous findings that fosfomycin sensitizes S. aureus to daptomycin [59-61]. We also found an upregulation signature ahead of the ugtP-ltaA operon in USA300-TCH1516, the strain found to

Table 4.2: Upregulation signatures identify genes previously linked to reduced

susceptibility to daptomycin. From submitted manuscript [1].

Gene Function

HG003 USA300 MSSA476 MW2 MRSA252 cls2 Cardolipin synthesis crtM Staphyloxanthin synthesis graRS Cell wall stress response mprF Lysyl phosphatidylglycerol synthesis murA1 Rate-limiting step in peptidoglycan murA2 precursor synthesis walKR Cell wall stress response 00969 Unknown 02149 Unknown

86 be most reliant on the LTA pathway (Chapter 2), and in MSSA476. This result further underlines the importance of LTAs for withstanding daptomycin stress.

Perhaps the most striking finding was that the only upregulation signatures shared across all five strains were upstream of the genes SAOUHSC_02149 and SAOUHSC_00969, genes we previously reported as daptomycin resistance factors in a transposon study of a single

S. aureus strain [34]. Both are small genes encoding single-membrane pass proteins of unknown function. We previously confirmed that overexpression of these genes conferred resistance to daptomycin while inactivation increased susceptibility, and the observation that they are important in all five S. aureus strains argues for a focus on their physiological roles. We performed whole genome sequencing on clinical pairs of daptomycin nonsusceptible and susceptible isolates to see whether any of the daptomycin nonsusceptible isolates harbored mutations within or upstream of these and other previously unreported daptomycin resistance factors found in our Tn-Seq experiments. None of the nonsusceptible isolates had missense mutations within mprF or SNPs upstream of the gene, and the causes of their nonsusceptibility were unclear. While we identified several SNPs in each nonsusceptible isolate compared to the paired susceptible isolate, none were in the genes discussed here (Supplemental Table 13)

In addition to upregulation signatures, we identified read enrichments within genes, but fewer of these were shared across strains. The most consistently enriched gene was prmC, encoding a protein homologous to the E. coli release factor methyl transferase HemK [62], but other genes enriched in a subset of strains include ktrA and ktrD, encoding a potassium transporter with homology to a system shown to be relevant for daptomycin survival in S. pneumoniae [30, 63], and cell envelope genes sgtB, encoding a monofunctional glycosyltransferase [64], and dltA and dltB, encoding proteins involved in adding D-alanine to teichoic acids (Supplemental Table 12) [65]. Deletions in these genes are known to slow growth, leading us to wonder whether the enriched genes are simply antibiotic tolerance mechanisms, providing protection to daptomycin by avoiding cell division and the defects caused by

87 daptomycin. We compared the growth rates of the mutants with transposons in the enriched genes to other mutants in the MRSA Tn-Seq libraries and found that the enriched genes had a statistically high representation of genes in the lower 20th percentile for mutant growth (Table

4.3). We also found one enriched gene that reinforced one of our upregulation analysis findings.

While walKR had an upregulation signature upon exposure to daptomycin, an operon encoding its putative negative regulators, walHI [66], was enriched in reads, highlighting the importance of this multicomponent stress-response system in the presence of daptomycin.

Table 4.3: Genes enriched in reads in the presence of daptomycin are often slow growing. The “Total” row quantifies the number of genes that were at least 5-fold enriched in reads in at least one strain and that had at least 10 TA sites with reads in the given strain. The “Slow Growing” row reports how many of those genes were within the lowest 20th percentile of growth in the given strain. A p-value was calculated using a binomial distribution with a probability of 0.2.

USA300 MW2 MRSA252 Total Slow Growing 12 23 25 60 Total 30 31 27 88 p-value 0.0031 9.94E-12 <0.1E-16 <0.1E-16

4.5: Discussion

This study represents the most thorough examination of strain-to-strain differences in daptomycin synergies in any bacterium to date. A study conducted by van Opijnen, et al. first demonstrated that the vulnerabilities and resistance factors for daptomycin can differ substantially across strains, using Streptococcus pneumoniae as the model organism [30]. That study compared two strains of S. pneumoniae and revealed that only half of the genes found to be significantly depleted or enriched of reads in the presence of daptomycin for one strain were also found to be significantly affected in the other strain. Performing a similar experiment in S. aureus and extending it to five strains showed a slightly different picture. We did find that there were differences in the depleted, enriched, and upregulated genes in the five strains, but our impression was that there were more commonalities than differences. In fact, approximately

88 seventy percent of genes that were depleted in one strain were depleted in at least one other strain.

We were particularly surprised to find that the LTA pathway was required for survival by all five strains in the presence of daptomycin, given that our essential gene comparison described in Chapter 2 demonstrated that not all of the strains rely on the LTA pathway under favorable growth conditions. The Glc2DAG flippase, ltaA, was significantly depleted of reads in all five of the daptomycin-exposed transposon libraries, while other LTA genes were significantly depleted of reads in HG003 and MW2. It is possible that the remaining LTA genes would have been more unanimously depleted if they were better represented in the Tn-Seq libraries. Even strains that could abide mutations in the pathway had low read depth in the LTA genes under favorable growth conditions, likely because disrupting the pathway yielded substantial growth defects (Figure 2.3B). It is difficult to obtain significant Tn-Seq depletions in genes do not have many reads to begin with. We were, however, able to recover upregulation signatures upstream of the ugtP/ltaA operon in a subset of the strains, reiterating the importance of this pathway under daptomycin stress. Given the available information, it is clear that the LTA pathway plays an important role in determining daptomycin susceptibility. The LTA pathway has long been thought to be involved in the S. aureus response to daptomycin. Researchers early on proposed that daptomycin targets the LTA pathway directly, as studies showed that LTA production was strongly inhibited by daptomycin in both cells and protoplasts [67, 68]. However, researchers at

Cubist Pharmaceuticals, the producers of daptomycin, were unable to replicate those results and instead proposed that daptomycin was toxic because it depolarizes the membrane [7, 69].

The present research reaffirms that a connection exists between daptomycin and LTAs and suggests that the LTA pathway could be a promising target for development of secondary antibiotics to resensitize daptomycin nonsusceptible strains.

An interesting difference that we noticed between the strains was which murA gene was depleted under daptomycin stress. All five strains had a murA gene that was depleted. For

MRSA252, murA2 (murZ) was required in the presence of daptomycin. For all four other strains, it was murA1 that was required (Figure 4.1). The proteins encoded by these genes are thought to perform the same function in the cell, catalyzing the first committed step in peptidoglycan biosynthesis, though their regulation appears to be different [50]. MurA1 has been shown to be the primary enzyme under normal conditions, while MurA2 expression is strongly induced when the cell is exposed to cell wall-targeting antibiotics. The two proteins are well-conserved among strains, with the five strains we studied having 99% sequence identity. However, the sequences of MurA1 and MurA2 are quite different from each other, with only about 45% sequence identity, leaving the potential for differences in function. MurA1 in MRSA252 has two differences compared to the other strains of S. aureus: a glutamate at position 278 instead of an aspartate and an aspartate at position 291 instead of glutamate. Meanwhile, MurA2 in MRSA252 has an arginine at position 292 instead of a glutamine. Fosfomycin, which has already been shown to have synergy with daptomycin [59-61], targets both MurA1 and MurA2, so there are not likely to be clinical implications for the difference in reliance under daptomycin exposure. However, it does reflect that strains have subtle differences in how they operate, even within conserved pathways.

The genes with upregulation signatures, identified using the new analytical approach described in Chapter 3, largely echoed the depleted gene list and were likewise predominantly cell envelope, cell division, and multicomponent stress response system genes. This makes intuitive sense, as one would expect that deleting a gene and upregulating it would have opposite effects on antibiotic susceptibility. A benefit of the upregulation signatures is that they can provide information on genes that are too essential to recover as depletions. For example, as stated above, ugtP had an upregulation signature in the presence of daptomycin in USA300-

TCH1516 even though it was found to be essential in that strain, and walR, an essential multicomponent signaling system gene, was upregulated in multiple strains. As validation for the approach, most of the genes that had upregulation signatures have been previously reported as

90 suspected gain-of-function resistance mechanisms for daptomycin. These include mprF, cls2, and crtM. With evidence that we recover clinically relevant antibiotic resistance mechanisms for daptomycin and other antibiotics using this approach, upregulation signatures could be hugely valuable for predicting mechanisms of resistance to new antibiotics before the antibiotics are even used in the clinic. The predicted mechanisms of resistance could then be used in surveillance platforms to quickly identify putative nonsusceptible outbreaks.

However, not all upregulation signatures will translate to resistant SNPs in the clinic. The most conserved upregulation signatures, found in all five of the strains tested, were ahead of the hypothetical genes SAOUHSC_00969 and SAOUHSC_02149. These genes are likely influential protectors against daptomycin, and we have previously confirmed that overexpression of these genes increases the MIC of daptomycin [34]. However, there are no reported cases of daptomycin nonsusceptible clinical isolates with mutations in these genes, and they were not mutated in the Cubist isolates we tested. There are many reasons a SNP that provides in vitro resistance may never be found in the clinic. For example, the genes may be required for survival in a host or for virulence. Of course, another possibility is that we simply have not sequenced enough daptomycin nonsusceptible strains to find all possible resistance mutations.

Unlike the depletion and upregulation hits, the genes that were enriched in reads in the presence of daptomycin were not centered around the cell wall or cell division. Intragenic read enrichments in Tn-Seq data report on loss-of-function resistance mechanisms or genes that have fitness costs in the presence of an antibiotic. Some of the enrichments did have direct connections to the depletion and upregulation hits. For example, while the walKR system had upregulation signatures in some strains under daptomycin stress, its putative negative regulators walH and walI were enriched in reads in HG003 and MSSA476 [70]. A few others were known to influence daptomycin susceptibility, namely dltA and dltB [16, 21]. However, most of the genes had no established connection to daptomycin, and some of our findings even diverged from what has been found in other organisms. For example, the potassium transporter

91 genes ktrA and ktrD were enriched in reads in MRSA252 and MSSA476, whereas van Opijnen et al. showed that potassium transporters were crucial for the survival of S. pneumoniae in the presence of daptomycin [30]. One commonality we noticed was that the mutants that were enriched upon daptomycin exposure grew more slowly than the average library mutant. While there could be a specific mechanistic link between these genes and daptomycin, the slow- growth trend suggests that some of the enriched genes represent tolerance mechanisms rather than resistance mechanisms. A tolerant mutant is one that does not have a different MIC from the wildtype strain but is killed at a slower rate than the wildtype strain [71]. Growing slowly in the presence of an antibiotic like daptomycin that causes cell division defects could delay the harm caused by the antibiotic and could allow those mutants to outcompete other mutants in the library.

4.6: Future directions

These experiments offer many avenues for follow up. Priority should be given to genes that were hits in all or most strains but were not previously associated with daptomycin, as these have the greatest potential for providing new insights. Among intrinsic resistance factors, these would be aapA, gpsB, and SAOUHSC_01050. Of course, the first step would be to validate that deleting these genes sensitizes S. aureus to daptomycin. Once that is confirmed, biochemical and genetic research opportunities could be explored. For example, aapA is annotated as a D- alanine, D-serine, and glycine permease. It may be possible to narrow down which amino acid becomes more important in the presence of daptomycin by creating media in which each of the three amino acids is selectively unavailable. One could hypothesize that S. aureus is more reliant on D-alanine transport when exposed to daptomycin, given that the dlt operon, encoding the proteins responsible for adding D-alanine to teichoic acids, has already been shown to influence daptomycin susceptibility [21]. As for SAOUHSC_01050 and gpsB, we now know that their encoded proteins interact (unpublished data from Dr. Ace Santiago in the Walker lab). Our

92 laboratory is actively researching the function of SAOUHSC_01050 and its relationship with gpsB. The gpsB gene encodes a cell cycle regulator, so biochemical reconstitution of its activity in vitro may be difficult. However, a combination of microscopy and genetics may provide insights into their roles. For example, one could investigate whether daptomycin causes mislocalization of GpsB or SAOUHSC_01050. We could also look for additional interacting partners. In fact, we have already performed Tn-Seq in a ΔSAOUHSC_01050 strain of HG003 and have found a number of genes that are synthetic lethal or antagonistic SAOUHSC_01050. graRS and graFG were both depleted in reads in that library, while gpsA, lytH, and srkA were all enriched in reads. We do not yet have any hypotheses to explain these relationships, though we suspect the genes may have a role in relaying information about the integrity of the cell envelope to the interior of the cell.

Among those genes with upregulation signatures in the presence of daptomycin, we are currently most interested in following up on SAOUHSC_02149. As stated above,

SAOUHSC_02149 encodes a hypothetical membrane-bound protein. We have found its upregulation to be protective for many types of antibiotic stress (see Chapter 3), which may indicate that it is involved in a stress response system. As a starting off point for determining what SAOUHSC_02149 does, it may be worthwhile to create a ΔSAOUHSC_02149 transposon library to identify its genetic relationships with other genes. A pull down to find directly interacting proteins would also be worthwhile. Together, these protein-protein interactions and genetic interactions may provide clues to what SAOUHSC_02149 does and how its upregulation protects the cell against daptomycin.

To continue to learn more about daptomycin resistance, we must move beyond Tn-Seq.

An unavoidable limitation of Tn-Seq is its lack of nuance. Standard transposons are designed to simply obliterate the function of the genes they land in. Resistance mutations, on the other hand, have a much wider range of possibilities. While they can completely inactivate a gene, they are also capable of partially reducing or increasing function, abolishing one function of a

93 gene while retaining another function (e.g. maintain a scaffolding function but reduce catalytic activity), reducing the binding affinity of a regulator to change the timing or extent of activation, etc. Having outward-facing promoters in the transposons can at least add resistance via upregulation as a detectable phenomenon in our Tn-Seq experiments, but transposon mutagenesis studies will never be able to mimic the full range of mutant variability found in nature. For example, rpoBC and pgsA were not found as hits in any of the transposon libraries exposed to daptomycin, even though mutations in these genes have been shown to reduce daptomycin susceptibility [15, 19, 26, 27]. These genes are essential in S. aureus (see Chapter

2) and thus are not well-represented in the Tn-Seq libraries.

If we wish to assemble a full compendium of daptomycin resistance mechanisms, we must continue to sequence daptomycin-nonsusceptible clinical isolates. The entire Cubist

Collection, a paired set of daptomycin susceptible and nonsusceptible isolates derived from patients during daptomycin clinical development, ought to be sequenced, and identified mutations should be evaluated for their effects on daptomycin susceptibility [16]. Thus far, the collection has only been sequenced at those sites known to harbor resistance mutations in other strains (e.g. mprF and the dlt operon), so we might be missing relevant mutations elsewhere in the genome. We reported here sequencing a few of the paired nonsusceptible and susceptible isolates, but we have not performed any more follow up experiments to determine which SNP(s) in each nonsusceptible strain caused the resistance phenotype. As more daptomycin- nonsusceptible strains are identified and sequenced, we may also be able to find compensatory mutations needed to develop particular forms of daptomycin non-susceptibility. Knowing the full range of mutations providing daptomycin nonsusceptibility affords avenues for further interrogations into the cellular effects of daptomycin as well as potential sets of diagnostic SNPs that clinicians can use to monitor for daptomycin nonsusceptible S. aureus. Should daptomycin nonsusceptibility arise on a larger scale in the future, having such diagnostic avenues available would allow clinicians to quickly identify and respond to the outbreak.

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44. Falord M, Karimova G, Hiron A, Msadek T. GraXSR proteins interact with the VraFG ABC transporter to form a five-component system required for cationic antimicrobial peptide sensing and resistance in Staphylococcus aureus. Antimicrobial Agents and Chemotherapy. 2012;56(2):1047-58.

45. Meehl M, Herbert S, Gotz F, Cheung A. Interaction of the GraRS two-component system with the VraFG ABC transporter to support vancomycin-intermediate resistance in Staphylococcus aureus. Antimicrobial Agents and Chemotherapy. 2007;51(8):2679-89. doi: 10.1128/AAC.00209-07. PubMed PMID: 17502406; PubMed Central PMCID: PMC1932546.

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47. Fournier B, Hooper DC. A new two-component regulatory system involved in adhesion, autolysis, and extracellular proteolytic activity of Staphylococcus aureus. Journal of Bacteriology. 2000;182(14):3955-64.

48. Fournier B, Klier A, Rapoport G. The two‐component system ArlS–ArlR is a regulator of virulence gene expression in Staphylococcus aureus. Molecular Microbiology. 2001;41(1):247- 61.

49. Bugg T, Walsh C. Intracellular steps of bacterial cell wall peptidoglycan biosynthesis: enzymology, antibiotics, and antibiotic resistance. Natural Product Reports. 1992;9(3):199-215.

50. Blake KL, O'Neill AJ, Mengin‐Lecreulx D, Henderson PJ, Bostock JM, Dunsmore CJ, et al. The nature of Staphylococcus aureus MurA and MurZ and approaches for detection of peptidoglycan biosynthesis inhibitors. Molecular Microbiology. 2009;72(2):335-43.

51. Kullik I, Jenni R, Berger-Bächi B. Sequence of the putative alanine racemase operon in Staphylococcus aureus: insertional interruption of this operon reduces D-alanine substitution of lipoteichoic acid and autolysis. Gene. 1998;219(1-2):9-17.

52. Peschel A, Jack RW, Otto M, Collins LV, Staubitz P, Nicholson G, et al. Staphylococcus aureus resistance to human defensins and evasion of neutrophil killing via the novel virulence factor MprF is based on modification of membrane lipids with L-lysine. Journal of Experimental Medicine. 2001;193(9):1067-76.

53. Gründling A, Schneewind O. Genes required for glycolipid synthesis and lipoteichoic acid anchoring in Staphylococcus aureus. Journal of Bacteriology. 2007;189(6):2521-30.

54. Eswara PJ, Brzozowski RS, Viola MG, Graham G, Spanoudis C, Trebino C, et al. An essential Staphylococcus aureus cell division protein directly regulates FtsZ dynamics. eLife. 2018;7:e38856.

55. Steele VR, Bottomley AL, Garcia‐Lara J, Kasturiarachchi J, Foster SJ. Multiple essential roles for EzrA in cell division of Staphylococcus aureus. Molecular Microbiology. 2011;80(2):542-55.

56. Pang T, Wang X, Lim HC, Bernhardt TG, Rudner DZ. The nucleoid occlusion factor Noc controls DNA replication initiation in Staphylococcus aureus. PLOS Genetics. 2017;13(7):e1006908.

57. Rajagopal M, Martin MJ, Santiago M, Lee W, Kos VN, Meredith T, et al. Multidrug intrinsic resistance factors in Staphylococcus aureus identified by profiling fitness within high- diversity transposon libraries. mBio. 2016;7(4):e00950-16.

58. Tran TT, Munita JM, Arias CA. Mechanisms of drug resistance: daptomycin resistance. Annals of the New York Academy of Sciences. 2015;1354(1):32-53.

59. Lingscheid T, Poeppl W, Bernitzky D, Veletzky L, Kussmann M, Plasenzotti R, et al. Daptomycin plus fosfomycin, a synergistic combination in experimental implant-associated osteomyelitis due to methicillin-resistant Staphylococcus aureus in rats. Antimicrobial Agents and Chemotherapy. 2015;59(2):859-63. doi: 10.1128/AAC.04246-14. PubMed PMID: 25403675; PubMed Central PMCID: PMCPMC4335895.

60. Miró JM, Entenza JM, Del Río A, Velasco M, Castañeda X, Garcia de la Mària C, et al. High-dose daptomycin plus fosfomycin is safe and effective in treating methicillin-susceptible and methicillin-resistant Staphylococcus aureus endocarditis. Antimicrobial Agents and

Chemotherapy. 2012;56(8):4511-5. doi: 10.1128/AAC.06449-11. PubMed PMID: 22644033; PubMed Central PMCID: PMC3421550.

61. Shaw Perujo E, Miró Meda JM, Puig-Asensio M, Pigrau C, Barcenilla F, Murillas J, et al. Daptomycin plus fosfomycin versus daptomycin monotherapy in treating MRSA: protocol of a multicentre, randomised, phase III trial. BMJ Open. 2015;5(3).

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63. Gries CM, Bose JL, Nuxoll AS, Fey PD, Bayles KW. The Ktr potassium transport system in Staphylococcus aureus and its role in cell physiology, antimicrobial resistance and pathogenesis. Molecular Microbiology. 2013;89(4):760-73.

64. Terrak M, Nguyen-Disteche M. Kinetic characterization of the monofunctional glycosyltransferase from Staphylococcus aureus. Journal of Bacteriology. 2006;188(7):2528-32.

65. Peschel A, Otto M, Jack RW, Kalbacher H, Jung G, Götz F. Inactivation of the dlt Operon in Staphylococcus aureus Confers Sensitivity to Defensins, Protegrins, and Other Antimicrobial Peptides. Journal of Biological Chemistry. 1999;274(13):8405-10.

66. Türck M, Bierbaum G. Purification and activity testing of the full-length YycFGHI proteins of Staphylococcus aureus. PLOS ONE. 2012;7(1):e30403.

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68. Boaretti M, Canepari P, Lleò MdM, Satta G. The activity of daptomycin on Enterococcus faecium protoplasts: indirect evidence supporting a novel mode of action on lipoteichoic acid synthesis. Journal of Antimicrobial Chemotherapy. 1993;31(2):227-35.

69. Laganas V, Alder J, Silverman JA. In vitro bactericidal activities of daptomycin against Staphylococcus aureus and Enterococcus faecalis are not mediated by inhibition of lipoteichoic acid biosynthesis. Antimicrobial Agents and Chemotherapy. 2003;47(8):2682-4.

70. Poupel O, Moyat M, Groizeleau J, Antunes LC, Gribaldo S, Msadek T, et al. Transcriptional analysis and subcellular protein localization reveal specific features of the essential WalKR system in Staphylococcus aureus. PLOS ONE. 2016;11(3):e0151449.

71. Balaban NQ, Helaine S, Lewis K, Ackermann M, Aldridge B, Andersson DI, et al. Definitions and guidelines for research on antibiotic persistence. Nature Reviews Microbiology. 2019:1.

100

Chapter 5: Conclusions and future directions

5.1: New Tn-Seq strategies add nuance to genome-wide phenotype assessments

It is not an exaggeration to say that transposon sequencing has revolutionized bacterial genetics. The ability to test the growth phenotypes of hundreds of thousands or even millions of mutants all at once in a single tube has exponentially accelerated our rate of biological discovery. Beyond that, new Tn-Seq strategies are continuously being developed to make an already powerful genomics tool even more useful. In this dissertation I discuss two such strategies: 1) comparing across strains of a species to achieve a more granular understanding of gene essentiality and 2) using intergenic insertions of promoter-donating transposons to identify fitness advantages gained by overexpressing genes.

Multi-strain Tn-Seq comparisons have also been carried by several other research groups in S. pneumoniae, M. tuberculosis, and P. aeruginosa [1-3]. Regardless of the bacterium, the overall picture is the same: while many of the genes that are essential or conditionally essential in one strain are shared by other strains, there are many variations in gene reliance across a species. We therefore cannot stress enough the importance of performing these sequencing analyses in multiple strains if one wishes to be able to generalize about gene essentiality in a bacterial species. Carey, et al. in particular demonstrated the risks we run in using individual laboratory strains to test antibiotic susceptibilities, as one may end up targeting a gene that is uniquely essential to the given strain [2]. Our findings even call into question the pervasive use of sequence types, groupings of S. aureus strains defined by allelic identity at a handful of sites. We saw as many gene essentiality differences between strains within the same sequence type as between strains of different sequence types, suggesting that the sequence type is not helpful for predicting strain phenotypes. That said, plenty of genes were essential for all of the S. aureus strains studied, whether it be under favorable growth

102 conditions or in the presence of daptomycin, and these genes could be leveraged to design more effective treatment regimens going forward.

We now also have a tool that can be used to more fully capture antibiotic resistance mechanisms via Tn-Seq. Our upregulation signature analysis can pinpoint genes that are protective in a given condition when they are over-expressed. The efforts described in this dissertation have largely focused on validating the approach using antibiotics with known mechanisms of action and known resistance mechanisms. The technique is able to recover many clinical resistance mechanisms, along with some that have never been reported in the clinic but that validate in vitro. We can also use this technique to learn about the relative importance of essential genes under various conditions, which has been impossible with standard transposons that are only able to obliterate gene function, not amplify it. Now that the initial validation is complete, we can apply the analysis to new antibiotics and in new ways. We have already shown that read depletions within genes in Tn-Seq data can be used to predict the mechanism of action of antibiotics [4]. Adding upregulation signatures to such an effort could make the predictions even more accurate. It would also provide insights for clinicians about what resistance mechanisms might be possible in patients and could inform surveillance efforts.

Rather than sequencing whole genomes of all patient isolates and trying to determine what

SNPs could be causing observed resistance, clinical labs could instead measure the abundance of a small panel of mRNAs to look for increases in gene expression that have been shown by

Tn-Seq to confer resistance.

We have also begun looking beyond antibiotic resistance toward more basic biology applications. For example, we have created several transposon libraries in gene knockout strains, such as HG003ΔSAOUHSC_01050. In that library, we found that SAOUHSC_00331 had an upregulation signature. SAOUHSC_00331 is another hypothetical protein but is suspected to be a transcriptional regulator. It could be that SAOUHSC_01050 positively regulates SAOUHSC_00331 and that SAOUHSC_00331 overexpression allows it to retain

103 activity in the absence of SAOUSHC_01050. Paired with the typical read depletion and enrichment outputs from Tn-Seq data, these upregulation signatures can help us piece together genetic interaction networks that will fill in gaps in our understanding of cellular processes.

5.2: The future of Tn-Seq

Given the strides that have already been made with Tn-Seq, it is exciting to ponder what its future might hold. Laboratories at the forefront of Tn-Seq development are already developing new methods to transcend the current limitations of Tn-Seq. For example, Thibault, et al. have developed a new microfluidics technique to assess the phenotypes of individual mutants separated into droplets instead of relying on the phenotypes of mutants in a pooled population [5]. Mutants defective in production of essential factors can sometimes survive in a heterogeneous population by freeloading off of products made by other members of the population, leading to false negatives in Tn-Seq analyses. By separating each mutant into its own media droplet, those mutants will no longer survive. This strategy will be especially helpful for annotating genes involve in extracellular processes, such as nutrient uptake, toxin-antitoxin systems, and quorum sensing.

Other laboratories are focusing their efforts on making Tn-Seq less laborious. One strategy that was developed several years ago but has yet to be widely adopted is random barcode Tn-Seq (RB-TnSeq) [6]. In RB-TnSeq, the transposons each harbor a unique barcode.

The library is fully sequenced once to map the barcodes to specific sites in the genome. Then, subsequent experiments can be performed by growing the library under a condition, amplifying the transposons with PCR, and submitting for sequencing. The barcode in each sequencing read is then assigned a genomic position based on the initial Tn-Seq experiment. The benefit of this approach is that it avoids the tedious enzymatic or mechanical DNA fragmentation and subsequent adaptor ligation steps involved in traditional Tn-Seq preparations. However, the strategy does not remove the difficulties associated with making the transposon libraries in the

104 first place, which can likewise be tedious and will need to be streamlined if transposon sequencing is to be used on a larger scale and in a wider variety of bacterial isolates. Beyond that, there are many bacteria for which our current methods simply do not work due to a lack of competence and other complications, so the field of bacteriology would benefit from continuing methods development for Tn-Seq library design. CRISPR screening has recently been proposed as an alternative to Tn-Seq, but it does little to solve the problems inherent in Tn-Seq and is likewise dependent upon competence [7].

We can also further expand the analyses performed on Tn-Seq data once it is acquired.

Tn-Seq analyses still focus predominantly on protein-coding genes. However, there is evidence that there are a variety of non-coding transcripts that are made in bacteria that have no known function. For example, a full transcriptome analysis of the S. aureus strain HG001 revealed over fifty independently transcribed non-coding RNAs [8]. Adding these annotations to Tn-Seq analyses could enable us to determine the roles of these RNAs. This would not necessarily even require new experiments; we could simply change the annotations in existing datasets and reanalyze them.

Another approach we have been considering is using Tn-Seq data to predict protein- protein interactions. We have observed that genes encoding proteins that interact often have similar changes in Tn-Seq read count under stress conditions. For example, GraRS and VraFG are known to interact and all four of their genes were depleted upon daptomycin exposure

(Chapter 4). The same can be said for SAOUHSC_01050 and GpsB, which we have recently discovered bind each other (Dr. Ace Santiago, unpublished data). We have also previously shown that members of the oxidative phosphorylation pathway in S. aureus have similar Tn-Seq read patterns when exposed to a panel of antibiotic conditions [9]. Having a systematic method for identifying interacting partners could be instrumental in determining the roles of hypothetical proteins in the cell. This type of analysis benefits from having data from a broad range of conditions and will become increasingly valuable as we continue to gather new data.

105

We are currently in a golden age for genomics. Sequencing costs are plummeting and as a result, an increasing number of researchers are turning to all forms of sequencing for biological insights. We still have a long way to go. In HG003, a well-studied strain of S. aureus, about a third of genes still cannot be annotated with a gene name or predicted function using current software. However, as we continue to build upon the utility of Tn-Seq and pair it with other new “-omic” technologies, we will slowly fill the gap and eventually achieve a more sophisticated understanding of bacterial biology.

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5.3: References

1. van Opijnen T, Dedrick S, Bento J. Strain dependent genetic networks for antibiotic- sensitivity in a bacterial pathogen with a large pan-genome. PLOS Pathogens. 2016;12(9):e1005869. doi: 10.1371/journal.ppat.1005869. PubMed PMID: 27607357; PubMed Central PMCID: PMC5015961.

2. Carey AF, Rock JM, Krieger IV, Chase MR, Fernandez-Suarez M, Gagneux S, et al. TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities. PLOS Pathogens. 2018;14(3):e1006939.

3. Poulsen BE, Yang R, Clatworthy AE, White T, Osmulski SJ, Li L, et al. Defining the core essential genome of Pseudomonas aeruginosa. Proceedings of the National Academy of Sciences. 2019:201900570. doi: 10.1073/pnas.1900570116.

4. Santiago M, Lee W, Fayad AA, Coe KA, Rajagopal M, Do T, et al. Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic. Nature Chemical Biology. 2018;14(6):601-8. doi: 10.1038/s41589-018-0041-4. PubMed PMID: 29662210; PubMed Central PMCID: PMC5964011.

5. Thibault D, Wood S, Jensen P, van Opijnen T. droplet-Tn-Seq combines microfluidics with Tn-Seq identifying complex single-cell phenotypes. bioRxiv. 2018:391045.

6. Wetmore KM, Price MN, Waters RJ, Lamson JS, He J, Hoover CA, et al. Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons. mBio. 2015;6(3):e00306-15.

7. Rousset F, Cui L, Siouve E, Becavin C, Depardieu F, Bikard D. Genome-wide CRISPR- dCas9 screens in E. coli identify essential genes and phage host factors. PLOS Genetics. 2018;14(11):e1007749.

8. Mäder U, Nicolas P, Depke M, Pané-Farré J, Debarbouille M, van der Kooi-Pol MM, et al. Staphylococcus aureus transcriptome architecture: from laboratory to infection-mimicking conditions. PLOS Genetics. 2016;12(4):e1005962.

9. Rajagopal M, Martin MJ, Santiago M, Lee W, Kos VN, Meredith T, et al. Multidrug intrinsic resistance factors in Staphylococcus aureus identified by profiling fitness within high- diversity transposon libraries. mBio. 2016;7(4):e00950-16.

107

Appendix

ATGGAAAGGTTCCTTTATATGCAAGATTC

CAAACTAACGGAGGGTGGCTATTA

TTATTTTTTAGAGTTTGCTTTAGGTCCT

TAATGAAATAATACTGTGTTTTATCTGCGA

ATTGAATTCAGGACCTAAAGCAAACTCTAAAAAATAA

GCGGGTACCATCTAAATAACGGGGGAAAGAATCAT

GGCGGTACC

ACAAAAGCTTCTAGCTTCAAACC

CTGAACCGCTCCTTTTTCTACAA

TTAGAATTCTTACTTAGCTTTTTCTCTATTTACTATAAAGT

CGAGGTACCCAAACTAACGGAGGGTGGCT

TGAATGGCGAATGGCGCATGCCATTCAAGAG

AATACCGCATCAGGCCAGACGCTTCTTCCA

GGGGATTATTTGTATCCTTAAGAAAATGATATAG

TGATTACGCCAAGCTCTTAATTTCAGTCGGAGCTGC

CAACCTCATTGTTGTTATTATATCTTCTTGTG

CCAAATTCAAAAATTATATGGAGATCTG

GCCCATCATTTCACTAATCTCTTTTATTCTCTG

TGATTACGCCAAGCTGTACTTAAACGCTAACG

CCTAAAGACACGCAAGTTACAAATTGTTTCAC

TTGCGTGTCTTTAGGGTTTTTCTGG

GACGGCCAGTGAATTAGGCGGTGTCTCGACTTC

CACGTTTCTGTGGGTCTTTAGCATTTGGTG

GAGACCTACGGTTCTTTTTATATAGAGCG

TGATTACGCCAAGCTGGCGCCCTTTATATACGC

GTATAAATATGGAACCGATGGCATTGAAAGAG

GTTCCATATTTATACACTCCTTG

GACGGCCAGTGAATTGTATTCTTTGCCGTATTG

GAAATGGGGATAGTTATCCAGAATTGTGTAC

CTTTTTGCTATGAGTGGGGGAACAACTGG

TGATTACGCCAAGCTGCCCACAGAGTTATCCAC

GATATAGAAGCGAGTCTACGATCACAAAAAGC

ACTCGCTTCTATATCTTGTTC

GACGGCCAGTGAATTGGGAATCATAAGGTCTTC

CAACCGTATATGTATTGTATGCTTC

GTGACGAATCAAGGTAGTCATAGTG

TGATTACGCCAAGCTTTACTCTCAGTTCCATAG

ACGCATTAGAAAATGTTGAATCTTTCGTGGCAG

CATTTTCTAATGCGTATTCACTTTG

GACGGCCAGTGAATTTGCAGTCAATTGCACAGG Sequence

operon RBS) for KpnI site

ltaA

ugtP

SAOUHSC_00952 primerSAOUHSC_00952 2 for EcoRI site

SAOUHSC_00952 primerSAOUHSC_00952 1 for KpnI site

site

ltaA ltaA

Primers used in these studies. these in used Primers

SAS25 check primerSAS25 check 2

SAS25 check primerSAS25 check 1

SAS25 SAS25 primer 4 for HindIII site

SAS25 SAS25 primer 3

SAS25 SAS25 primer 2

SAS25 SAS25 primer 1 for EcoRI site

SAR196 check SAR196 primercheck

SAR196 SAR196 primer 4 for HindIII site

SAR1735 check primerSAR1735 check

SAR50 primercheck 2

SAR50 primercheck 1

SAR50 primer 4 for HindIII site

SAR50 primer 3

SAR50 primer 2

SAR50 primer 1 for EcoRI site

USA300HOU_pUSAHOUMR0029 check primercheck USA300HOU_pUSAHOUMR0029 2

USA300HOU_pUSAHOUMR0029 check primercheck USA300HOU_pUSAHOUMR0029 1

USA300HOU_pUSAHOUMR0029 primer USA300HOU_pUSAHOUMR0029 4 for HindIII site

USA300HOU_pUSAHOUMR0029 primer USA300HOU_pUSAHOUMR0029 3

USA300HOU_pUSAHOUMR0029 primer USA300HOU_pUSAHOUMR0029 2

USA300HOU_pUSAHOUMR0029 primer USA300HOU_pUSAHOUMR0029 1 for EcoRI site

MW0169 check primercheck MW0169 2

MW0169 check primercheck MW0169 1

MW0169 primerMW0169 4 for HindIII site

MW0169 primerMW0169 3

MW0169 primerMW0169 2

MW0169 primerMW0169 1 for EcoRI site

SAOUHSC_00953 check primercheck SAOUHSC_00953 2

SAOUHSC_00953 check primercheck SAOUHSC_00953 1

SAOUHSC_00953 andSAOUHSC_00953

SAOUHSC_00952 primerSAOUHSC_00952 1 (bolded:

SAOUHSC_00728 check primercheck SAOUHSC_00728 2

SAOUHSC_00728 check primercheck SAOUHSC_00728 1

SAOUHSC_00728 primerSAOUHSC_00728 2 for EcoRI site

SAOUHSC_00728 primerSAOUHSC_00728 1 for KpnI site

11 11 homology primer 2 for SfoI

11 11 homology primer 1 for SfoI

ltaS

ltaA

ugtP ugtP

hsdR hsdR

ermR

hsdR2 hsdR2

hsdR hsdR

Description

ltaS-CR

ltaS-CF

ltaS-R

ltaS-F

ltaA-F

ugtP-CR

ugtP-CF

ugtP-R

ugtP-F

GKM423

GKM422

TM369

TM368

TM367

TM366

TM365

TM364

TM363

TM362

TM361

TM322

TM321

TM320

TM319

TM318

TM317

TM316

TM315

TM314

TM313

TM312

TM311

TM245

TM244

TM243

TM242

TM241

TM240 Primer Primer Name Supplemental Table 1: Table Supplemental 109

Supplemental Table 2: Bacterial strains used in these studies.

Strain Description Source Santiago, Marina, et al . "A new platform for ultra-high density Staphylococcus aureus transposon libraries." BMC genomics 16.1 TM226 S. aureus subsp. aureus HG003 f11::FRT; CC8 clonal complex; methicillin sensitive (2015): 252. S. aureus subsp. aureus MW2; CC1 clonal complex; heterogeneous methicillin MW2 resistance Dr. David Hooper, Massachusetts General Hospital S. aureus subsp. aureus MSSA476; ATCC BAA-1721; CC1 clonal complex; methicillin MSSA476 sensitive American Type Culture Collection (ATCC) S. aureus subsp. aureus USA300 (TCH1516); ATCC BAA-1717; heterogeneous USA300 methicillin resistance; CC8 clonal complex; ermR kanR American Type Culture Collection (ATCC) S. aureus subsp. aureus MRSA252 ATCC BAA-1720; CC30 clonal complex; MRSA252 homogeneous methicllin resistance; ermR kanR American Type Culture Collection (ATCC) S. aureus MW2 DMW0169; restriction system negative and compatable with DNA TM258 from S. aureus CC8 clonal complex; Tn library host This work TM257 S. aureus MSSA476 DSAS0170 This work S. aureus MSSA476 DSAS0170 DSAS0025; restriction system negative and TM278 compatable with DNA from S. aureus CC8 clonal complex; Tn library host This work S. aureus USA300 cured of pUSA300HOUMR using pTM283; erm and kan sensitive; TM283 Tn library host This work S. aureus MSRA252 DSAR0196; restriction system compatable with DNA from S. GKM361 aureus CC8 clonal complex This work GKM363 S. aureus MSRA252 DSAR0196 DSAR0050 This work

TXM369 S. aureus MSRA252 DSAR0196 DSAR0050 DSAR01735; erm sensitive; Tn library host This work Santiago, Marina, et al . "A new platform for ultra-high density Staphylococcus aureus transposon libraries." BMC genomics 16.1 f11-FRT f11 bacteriophage int attP ::FRT (2015): 252. WSL066 S. aureus HG003 ugtP ::TnErm with Atet-inducible copy of ugtP This work WSL068 S. aureus MW2 ugtP ::TnErm with Atet-inducible copy of ugtP This work WSL067 S. aureus USA300 ugtP ::TnErm with Atet-inducible copy of ugtP This work WSL054 S. aureus HG003 ΔltaA with Atet-inducible copy of ltaA; Kan resistant This work WSL199 S. aureus MW2 ΔltaA with Atet-inducible copy of ltaA; Kan resistant This work WSL198 S. aureus USA300 ΔltaA with Atet-inducible copy of ltaA; Kan resistant This work WSL107 S. aureus HG003 ΔltaS with Atet-inducible copy of ltaS; Erm resistant This work WSL108 S. aureus MW2 ΔltaS with Atet-inducible copy of ltaS; Erm resistant This work WSL109 S. aureus USA300 ΔltaS with Atet-inducible copy of ltaS; Erm resistant This work Pang, Ting, et al. "The nucleoid occlusion factor Noc controls DNA S. aureus RN4220 with pTP44, a plasmid encoding an integrase that allows for replication initiation in Staphylococcus aureus ." PLoS genetics 13.7 TD011 integration of pTP63; Tet resistant (2017): e1006908. Corrigan, Rebecca M., et al. "c-di-AMP is a new second messenger in Staphylococcus aureus with a role in controlling cell size and 4S5 S. aureus SEJ1 ΔltaS with suppressor mutations allowing growth without LTAs envelope stress." PLoS pathogens 7.9 (2011): e1002217.

110

Supplemental Table 3: Plasmids used in theses studies.

Plasmid Description Source Kato, Fuminori, and Motoyuki Sugai. "A simple method of markerless gene deletion in Staphylococcus aureus." Journal of pKFC E. coli -S. aureus shuttle vector with temperature sensitive replicon microbiological methods 87.1 (2011): 76-81. Santiago, Marina, et al . "A new platform for ultra- pWV01ts ori aphA-3 (kanR) Gram+ RBS HMAR1 C9 transposase with cI-like high density Staphylococcus aureus transposon pORF5 Tnp+ repressor ORF5 from Φ11 libraries." BMC genomics 16.1 (2015): 252. pKFC shuttle vector with ~1 kb DNA homology arms (primer pairs TM240/241 pTM252 and TM242/243) flanking hsdR (MW0169 or SAS0170) This work pKFC shuttle vector with ~1 kb DNA homology arms (primer pairs TM311/312 pTM278 and TM313/314) flanking hsdR2 (SAS0025) This work pKFC shuttle vector with ~1 kb DNA homology arms (primer pairs TM317/318 pTM283 and TM319/320) flanking ermR (USA300HOU_pUSAHOUMR0029) This work pKFC shuttle vector with ~1 kb DNA homology arms (primer pairs TM361/362 pGKM302 and TM363/364) flanking ermR (SAR0050 or SAR1735) This work pKFC shuttle vector with ~1 kb DNA homology arms (primer pairs TM240/241 pGKM305 and TM242/368) flanking hsdR (SAR0196) This work pGKM306 pGKM314 with ~1 kb of phi11 DNA for high frequency recombination This work pSHM054 pORF5 Tnp+ with kanR replaced by camR This work pTP63-ugtP pTP63 with HG003 ugtP and ltaA (SAOUHSC_00953-952) and their RBS Samir Moussa Pasquina, Lincoln, et al . "A synthetic lethal approach for compound and target identification Chloramphenicol resistance, integrative aTet-inducible expression vector for S. in Staphylococcus aureus." Nature chemical pTP63 aureus biology 12.1 (2016): 40. pTP63-ltaA pTP63 with HG003 ltaA (SAOUHSC_00952) and its RBS This work pTP63-ltaS pTP63 with HG003 ltaS (SAOUHSC_00728) and its RBS This work

111

Supplemental Table 4: Comparison of essential genes in S. aureus strains. A table of all genes that are essential for at least one strain, with essentiality designations for each gene in each strain for the TRANSIT Gumbel analysis and the subsequent permutation test which was used to determine whether differences between strains seen in the Gumbel results were significant. If differences were not significant, essential (E) and nonessential (NE) designations were converted to uncertain (U).

112

Supplemental Table 4 (Continued).

TRANSIT: Gumbel Permutation Name HG003 USA300 MW2 MSSA476 MRSA252 HG US MW MS MR HG US MW MS MR group_0884 USA300HOU_RS04240 E E accA SAOUHSC_01808 USA300HOU_RS09015 MW_RS08765 SAS_RS08640 SAR_RS09100 E E E E E E E E E E accB_1 SAOUHSC_01624 USA300HOU_RS08160 MW_RS07920 SAS_RS07795 SAR_RS08220 E E E E E E E E E E accD SAOUHSC_01809 USA300HOU_RS09020 MW_RS08770 SAS_RS08645 SAR_RS09105 E E E E E E E E E E ackA SAOUHSC_01820 USA300HOU_RS09075 MW_RS08830 SAS_RS08705 SAR_RS09160 E E E E NE U U U U U citB SAOUHSC_01347 USA300HOU_RS06870 MW_RS06645 SAS_RS06840 SAR_RS06950 NE E E NE NE NE E E NE NE acpP SAOUHSC_01201 USA300HOU_RS06195 MW_RS05990 SAS_RS06190 SAR_RS06170 E E E E E E E E E E acpS SAOUHSC_02306 USA300HOU_RS11185 MW_RS10880 SAS_RS10745 SAR_RS11250 E E E E E E E E E E addA SAOUHSC_00905 USA300HOU_RS04800 MW_RS04585 SAS_RS04475 SAR_RS04730 E E U NE E E E U NE E addB SAOUHSC_00904 USA300HOU_RS04795 MW_RS04580 SAS_RS04470 SAR_RS04725 E E U NE E E E U NE E adk SAOUHSC_02490 USA300HOU_RS12070 MW_RS11675 SAS_RS11540 SAR_RS12100 E E E E E E E E E E alaS SAOUHSC_01722 USA300HOU_RS08615 MW_RS08370 SAS_RS08245 SAR_RS08680 E E E E E E E E E E argS SAOUHSC_00611 USA300HOU_RS03230 MW_RS03125 SAS_RS03095 SAR_RS03165 E E E E E E E E E E aspS SAOUHSC_01737 USA300HOU_RS08675 MW_RS08435 SAS_RS08310 SAR_RS08740 E E E E E E E E E E sagB SAOUHSC_01895 USA300HOU_RS09425 MW_RS09170 SAS_RS09045 SAR_RS09530 NE NE NE NE E NE NE U NE E atpD_1 SAOUHSC_02341 USA300HOU_RS11360 MW_RS11050 SAS_RS10915 SAR_RS11425 NE NE NE E NE U NE U E U atpE SAOUHSC_02349 USA300HOU_RS11385 MW_RS11075 SAS_RS10940 SAR_RS11450 U NE NE E E U NE U U E bfmBAA SAOUHSC_01613 USA300HOU_RS08110 MW_RS07870 SAS_RS07745 SAR_RS08170 NE E E E E NE E E E E bfmBAB SAOUHSC_01612 USA300HOU_RS08105 MW_RS07865 SAS_RS07740 SAR_RS08165 NE E E E E NE E U U E birA SAOUHSC_01473 USA300HOU_RS07450 MW_RS07230 SAS_RS07425 SAR_RS07500 E E E E E E E E E E carA SAOUHSC_01169 USA300HOU_RS06040 MW_RS05835 SAS_RS06035 SAR_RS06015 E NE U E NE E NE U U NE carB SAOUHSC_01170 USA300HOU_RS06045 MW_RS05840 SAS_RS06040 SAR_RS06020 E E E E NE U U U U U papS SAOUHSC_01474 USA300HOU_RS07455 MW_RS07235 SAS_RS07430 SAR_RS07505 E E E E E E E E E E cdaA SAOUHSC_02407 USA300HOU_RS11665 MW_RS11335 SAS_RS11200 SAR_RS11725 NE E U U U U U U U U cdsA SAOUHSC_01238 USA300HOU_RS06355 MW_RS06150 SAS_RS06350 SAR_RS06330 E E E E E E E E E E accC_1 SAOUHSC_01623 USA300HOU_RS08155 MW_RS07915 SAS_RS07790 SAR_RS08215 E E E E E E E E E E clpP_1 SAOUHSC_00790 USA300HOU_RS10665 MW_RS03985 SAS_RS03955 SAR_RS10750 NE S U E E NE U U E U clpX SAOUHSC_01778 USA300HOU_RS08865 MW_RS08625 SAS_RS08500 SAR_RS08960 NE E E E E NE E E E E cmk SAOUHSC_01496 USA300HOU_RS07570 MW_RS07340 SAS_RS07535 SAR_RS07615 E E NE E E E E U E E coaBC SAOUHSC_01178 USA300HOU_RS06085 MW_RS05880 SAS_RS06080 SAR_RS06060 E E E E E E E E E E coaD SAOUHSC_01075 USA300HOU_RS05615 MW_RS05405 SAS_RS05605 SAR_RS05600 U E U E E U E U E E coaE SAOUHSC_01795 USA300HOU_RS08945 MW_RS08705 SAS_RS08580 SAR_RS09040 E E E E E E E E E E coaA SAOUHSC_02371 USA300HOU_RS11500 MW_RS11185 SAS_RS11050 SAR_RS11565 E E E E E E E E E E gatD_1 SAOUHSC_02106 USA300HOU_RS10265 MW_RS09980 SAS_RS09850 SAR_RS10345 E E U E NE U U U U U sucC SAOUHSC_01216 USA300HOU_RS06265 MW_RS06060 SAS_RS06260 SAR_RS06240 NE E NE E NE U U U U U sufS SAOUHSC_00849 USA300HOU_RS04525 MW_RS04320 SAS_RS04210 SAR_RS04465 E E E E E E E E E E cshA SAOUHSC_02316 USA300HOU_RS11235 MW_RS10930 SAS_RS10795 SAR_RS11300 E NE NE NE NE E NE U U NE cysE SAOUHSC_00510 USA300HOU_RS02785 MW_RS02680 SAS_RS02650 SAR_RS02770 E E E E E E E E E E cysS SAOUHSC_00511 USA300HOU_RS02790 MW_RS02685 SAS_RS02655 SAR_RS02775 E E E E E E E E E E dgkB SAOUHSC_02114 USA300HOU_RS10305 MW_RS10020 SAS_RS09890 SAR_RS10385 E E E E E E E E E E ddl SAOUHSC_02318 USA300HOU_RS11245 MW_RS10940 SAS_RS10805 SAR_RS11310 E E E E E E E E E E def_1 SAOUHSC_01038 USA300HOU_RS05445 MW_RS05235 SAS_RS05435 SAR_RS05430 E E E E E E E E E E engA SAOUHSC_01492 USA300HOU_RS07545 MW_RS07325 SAS_RS07520 SAR_RS07590 E E E E E E E E E E der SAOUHSC_01700 USA300HOU_RS08510 MW_RS08265 SAS_RS08140 SAR_RS08565 E E E E E E E E E E divlB SAOUHSC_01148 USA300HOU_RS05950 MW_RS05745 SAS_RS05945 SAR_RS05925 E E E E E E E E E E divlC SAOUHSC_00482 USA300HOU_RS02600 MW_RS02480 SAS_RS02450 SAR_RS02570 E E E E E E E E E E dltA SAOUHSC_00869 USA300HOU_RS04620 MW_RS04410 SAS_RS04300 SAR_RS04555 E E NE NE NE E E U NE NE dltC SAOUHSC_00871 USA300HOU_RS04630 MW_RS04420 SAS_RS04310 SAR_RS04565 E NE NE NE NE E U NE U U dnaA SAOUHSC_00001 USA300HOU_RS00005 MW_RS00005 SAS_RS00005 SAR_RS00005 E E E E E E E E E E dnaB SAOUHSC_01792 USA300HOU_RS08930 MW_RS08690 SAS_RS08565 SAR_RS09025 E E E E E E E E E E dnaC_1 SAOUHSC_00018 USA300HOU_RS00080 MW_RS00080 SAS_RS00080 SAR_RS00080 E E E E E E E E E E dnaD_1 SAOUHSC_01470 USA300HOU_RS07435 MW_RS07215 SAS_RS07410 SAR_RS07485 E E E E E E E E E E dnaE SAOUHSC_01811 USA300HOU_RS09030 MW_RS08780 SAS_RS08655 SAR_RS09115 E E E E E E E E E E dnaG SAOUHSC_01663 USA300HOU_RS08335 MW_RS08095 SAS_RS07970 SAR_RS08395 E E E E E E E E E E dnaI SAOUHSC_01791 USA300HOU_RS08925 MW_RS08685 SAS_RS08560 SAR_RS09020 E E E E E E E E E E dnaJ SAOUHSC_01682 USA300HOU_RS08420 MW_RS08180 SAS_RS08055 SAR_RS08480 NE NE NE E NE NE NE U E NE dnaK SAOUHSC_01683 USA300HOU_RS08425 MW_RS08185 SAS_RS08060 SAR_RS08485 NE NE NE E NE NE NE U E U dnaN SAOUHSC_00002 USA300HOU_RS00010 MW_RS00010 SAS_RS00010 SAR_RS00010 E E E E E E E E E E dnaX SAOUHSC_00442 USA300HOU_RS02415 MW_RS02295 SAS_RS02265 SAR_RS02380 E E E E E E E E E E holB SAOUHSC_00454 USA300HOU_RS02470 MW_RS02350 SAS_RS02320 SAR_RS02440 E E E E E E E E E E cbiO_1 SAOUHSC_02483 USA300HOU_RS12035 MW_RS11640 SAS_RS11505 SAR_RS12055 NE NE U E E NE U U U U cbiO_2 SAOUHSC_02482 USA300HOU_RS12030 MW_RS11635 SAS_RS11500 SAR_RS12050 NE NE NE E E U NE U E E ecfT SAOUHSC_02481 USA300HOU_RS12025 MW_RS11630 SAS_RS11495 SAR_RS12045 NE NE NE E E U U U U U engB SAOUHSC_01777 USA300HOU_RS08860 MW_RS08620 SAS_RS08495 SAR_RS08955 E E NE E E U U U U U eno SAOUHSC_00799 USA300HOU_RS04205 MW_RS04025 SAS_RS03995 SAR_RS04245 E E E E E E E E E E era SAOUHSC_01668 USA300HOU_RS08360 MW_RS08120 SAS_RS07995 SAR_RS08420 NE E E E NE U U U U U fabD SAOUHSC_01198 USA300HOU_RS06180 MW_RS05975 SAS_RS06175 SAR_RS06155 E E E E E E E E E E

113 Supplemental Table 4 (Continued).

TRANSIT: Gumbel Permutation Name HG003 USA300 MW2 MSSA476 MRSA252 HG US MW MS MR HG US MW MS MR fabF SAOUHSC_00921 USA300HOU_RS04880 MW_RS04665 SAS_RS04555 SAR_RS04815 E E U E E E E U E E fabG SAOUHSC_01199 USA300HOU_RS06185 MW_RS05980 SAS_RS06180 SAR_RS06160 E E E E E E E E E E fabH SAOUHSC_00920 USA300HOU_RS04875 MW_RS04660 SAS_RS04550 SAR_RS04810 E E E E E E E E E E fabI SAOUHSC_00947 USA300HOU_RS05010 MW_RS04795 SAS_RS04685 SAR_RS04970 E E E E E E E E E E fabZ SAOUHSC_02336 USA300HOU_RS11335 MW_RS11025 SAS_RS10890 SAR_RS11400 E E E E U E E E E U fbaA SAOUHSC_02366 USA300HOU_RS11470 MW_RS11160 SAS_RS11025 SAR_RS11540 U NE U E NE U NE U E U femA SAOUHSC_01373 USA300HOU_RS06995 MW_RS06770 SAS_RS06965 SAR_RS07080 E E E E U U U E U U femB SAOUHSC_01374 USA300HOU_RS07000 MW_RS06775 SAS_RS06970 SAR_RS07085 E E E E E E E E E E fmhB SAOUHSC_02527 USA300HOU_RS12240 MW_RS11845 SAS_RS11710 SAR_RS12265 E E E E E E E E E E ffh SAOUHSC_01207 USA300HOU_RS06220 MW_RS06015 SAS_RS06215 SAR_RS06195 E E E E E E E E E E fmt SAOUHSC_01183 USA300HOU_RS06110 MW_RS05905 SAS_RS06105 SAR_RS06085 E NE E E E E NE E E E fni SAOUHSC_02623 USA300HOU_RS12690 MW_RS12285 SAS_RS12150 SAR_RS12715 E E E E E E E E E E dfrA SAOUHSC_01434 USA300HOU_RS07290 MW_RS07065 SAS_RS07260 SAR_RS07350 E E U E E E E U E E folB SAOUHSC_00490 USA300HOU_RS02640 MW_RS02520 SAS_RS02490 SAR_RS02610 U NE U E E U U U U U folE2 SAOUHSC_00549 USA300HOU_RS02970 MW_RS02865 SAS_RS02835 SAR_RS02950 E E E E E E E E E E folK SAOUHSC_00491 USA300HOU_RS02645 MW_RS02525 SAS_RS02495 SAR_RS02615 E E E E E E E E E E folP SAOUHSC_00489 USA300HOU_RS02635 MW_RS02515 SAS_RS02485 SAR_RS02605 E E E E E E E E E E folC SAOUHSC_01766 USA300HOU_RS08805 MW_RS08565 SAS_RS08440 SAR_RS08900 E E E E E E E E E E frr SAOUHSC_01236 USA300HOU_RS06345 MW_RS06140 SAS_RS06340 SAR_RS06320 E E E E E E E E E E ftsA SAOUHSC_01149 USA300HOU_RS05955 MW_RS05750 SAS_RS05950 SAR_RS05930 E E E E E E E E E E ftsL SAOUHSC_01144 USA300HOU_RS05930 MW_RS05725 SAS_RS05925 SAR_RS05905 E E NE U E U U U U U ftsW SAOUHSC_01063 USA300HOU_RS05555 MW_RS05345 SAS_RS05545 SAR_RS05540 E E E E E E E E E E ftsY SAOUHSC_01205 USA300HOU_RS06210 MW_RS06005 SAS_RS06205 SAR_RS06185 E E E E E E E E E E ftsZ SAOUHSC_01150 USA300HOU_RS05960 MW_RS05755 SAS_RS05955 SAR_RS05935 E E E E E E E E E E fusA SAOUHSC_00529 USA300HOU_RS02875 MW_RS02770 SAS_RS02740 SAR_RS02860 E E E E E E E E E E mvaK1 SAOUHSC_00577 USA300HOU_RS03085 MW_RS02980 SAS_RS02950 SAR_RS03065 E E E E E E E E E E gapA SAOUHSC_00795 USA300HOU_RS04185 MW_RS04005 SAS_RS03975 SAR_RS04225 E E E E E E E E E E gatA SAOUHSC_02117 USA300HOU_RS10320 MW_RS10035 SAS_RS09905 SAR_RS10400 E E E E E E E E E E gatB_1 SAOUHSC_02116 USA300HOU_RS10315 MW_RS10030 SAS_RS09900 SAR_RS10395 E E E E E E E E E E gatC_1 SAOUHSC_02118 USA300HOU_RS10325 MW_RS10040 SAS_RS09910 SAR_RS10405 E E U U E E E U U E gdpP SAOUHSC_00015 USA300HOU_RS00070 MW_RS00070 SAS_RS00070 SAR_RS00070 NE NE NE NE E NE NE U NE E glmS SAOUHSC_02399 USA300HOU_RS11625 MW_RS11295 SAS_RS11160 SAR_RS11680 E E E E E E E E E E glmU SAOUHSC_00471 USA300HOU_RS02555 MW_RS02435 SAS_RS02405 SAR_RS02525 E E E E E E E E E E glnA SAOUHSC_01287 USA300HOU_RS06590 MW_RS06385 SAS_RS06585 SAR_RS06565 E E E E E E E E E E gltX SAOUHSC_00509 USA300HOU_RS02780 MW_RS02675 SAS_RS02645 SAR_RS02765 E E E E E E E E E E glyS SAOUHSC_01666 USA300HOU_RS08350 MW_RS08110 SAS_RS07985 SAR_RS08410 E E E E E E E E E E gmk SAOUHSC_01176 USA300HOU_RS06075 MW_RS05870 SAS_RS06070 SAR_RS06050 E E E E E E E E E E gnd SAOUHSC_01605 USA300HOU_RS08075 MW_RS07835 SAS_RS07710 SAR_RS08135 E E E E E E E E E E gph_2 SAOUHSC_01701 USA300HOU_RS08515 MW_RS08270 SAS_RS08145 SAR_RS08570 E E E E E E E E E E pgm SAOUHSC_00798 USA300HOU_RS04200 MW_RS04020 SAS_RS03990 SAR_RS04240 U NE NE NE E U NE U U E gpsA SAOUHSC_01491 USA300HOU_RS07540 MW_RS07320 SAS_RS07515 SAR_RS07585 E NE NE NE E E NE U U E greA SAOUHSC_01714 USA300HOU_RS08575 MW_RS08330 SAS_RS08205 SAR_RS08640 E NE U E E E NE U E E groEL SAOUHSC_02254 USA300HOU_RS10925 MW_RS10625 SAS_RS10490 SAR_RS10995 E E E E E E E E E E groES SAOUHSC_02255 USA300HOU_RS10930 MW_RS10630 SAS_RS10495 SAR_RS11000 U E E E U U E E E U group_1140 SAR_RS01845 E E group_1243 SAR_RS07985 E E group_0999 MW_RS04065 E E group_0887 USA300HOU_RS04440 E E group_0426 SAOUHSC_01721 USA300HOU_RS08610 MW_RS08365 SAS_RS08240 SAR_RS08675 E NE NE E NE U U U U U group_0445 SAOUHSC_01855 USA300HOU_RS09240 MW_RS09000 SAS_RS08875 SAR_RS09330 NE NE NE NE E U U U U U rpoY SAOUHSC_01036 USA300HOU_RS05435 MW_RS05225 SAS_RS05425 SAR_RS05420 E E NE NE E U U U U U group_0099 SAOUHSC_00444 USA300HOU_RS02420 MW_RS02300 SAS_RS02270 SAR_RS02385 NE NE E E E U U U U U mpsC SAOUHSC_00414 USA300HOU_RS02285 MW_RS02165 SAS_RS02135 SAR_RS02250 NE U NE E E U U U U U group_0135 SAOUHSC_00659 USA300HOU_RS03530 MW_RS03345 SAS_RS03315 SAR_RS03400 E E E E E E E E E E group_0168 SAOUHSC_00788 USA300HOU_RS04150 MW_RS03970 SAS_RS03940 SAR_RS04185 NE U NE E NE U U NE E NE group_0306 SAOUHSC_01338 USA300HOU_RS06835 MW_RS06605 SAS_RS06805 SAR_RS06905 NE E E E E U U U U U group_0429 SAOUHSC_01756 USA300HOU_RS08755 MW_RS08515 SAS_RS08390 SAR_RS08820 E E E E E E E E E E group_0272 SAOUHSC_01244 USA300HOU_RS06385 MW_RS06180 SAS_RS06380 SAR_RS06360 E U E E E E U E E E hemQ SAOUHSC_00573 USA300HOU_RS03070 MW_RS02965 SAS_RS02935 SAR_RS03050 E E U E E E E U E E group_0329 SAOUHSC_01477 USA300HOU_RS07470 MW_RS07250 SAS_RS07445 SAR_RS07520 E E E E E E E E E E mvaK2 SAOUHSC_00579 USA300HOU_RS03095 MW_RS02990 SAS_RS02960 SAR_RS03075 E E E E E E E E E E pmtD SAOUHSC_02151 USA300HOU_RS10480 MW_RS10195 SAS_RS10065 SAR_RS10560 E NE NE NE NE E NE NE NE NE dltD SAOUHSC_00872 USA300HOU_RS04635 MW_RS04425 SAS_RS04315 SAR_RS04570 E U U NE NE E U U NE NE holA SAOUHSC_01690 USA300HOU_RS08460 MW_RS08215 SAS_RS08090 SAR_RS08515 E E E E E E E E E E group_0216 SAOUHSC_00957 USA300HOU_RS05060 MW_RS04845 SAS_RS04735 SAR_RS05030 E E E E NE U U U U U group_0333 SAOUHSC_01488 USA300HOU_RS07525 MW_RS07305 SAS_RS07500 SAR_RS07570 U E NE U NE U U U U U group_0434 SAOUHSC_01782 USA300HOU_RS08885 MW_RS08645 SAS_RS08520 SAR_RS08980 NE E E E U NE E E E U

114 Supplemental Table 4 (Continued).

TRANSIT: Gumbel Permutation Name HG003 USA300 MW2 MSSA476 MRSA252 HG US MW MS MR HG US MW MS MR secDF SAOUHSC_01746 USA300HOU_RS08710 MW_RS08470 SAS_RS08345 SAR_RS08775 E U NE NE NE U U U U U lpdA SAOUHSC_01614 USA300HOU_RS08115 MW_RS07875 SAS_RS07750 SAR_RS08175 NE E E E E NE E U U E hemD SAOUHSC_01773 USA300HOU_RS08840 MW_RS08600 SAS_RS08475 SAR_RS08935 U E E E E U E E E E group_0487 SAOUHSC_02004 USA300HOU_RS10130 MW_RS09840 SAS_RS09715 SAR_RS10215 NE E NE E NE NE E U U NE mpsB SAOUHSC_00413 USA300HOU_RS02280 MW_RS02160 SAS_RS02130 SAR_RS02245 E E E E E E E E E E group_0659 SAOUHSC_02422 USA300HOU_RS11750 MW_RS11415 SAS_RS11280 SAR_RS11805 NE E NE NE NE NE E U NE NE group_0543 SAOUHSC_02075 USA300HOU_RS07900 E E E E group_0913 USA300HOU_RS07930 SAS_RS04765 E U U U grpE SAOUHSC_01684 USA300HOU_RS08430 MW_RS08190 SAS_RS08065 SAR_RS08490 NE NE NE E NE U NE U E U gtaB SAOUHSC_02801 USA300HOU_RS13545 MW_RS13125 SAS_RS12990 SAR_RS13535 NE E NE NE NE NE E NE NE NE guaA SAOUHSC_00375 USA300HOU_RS02070 MW_RS01925 SAS_RS01895 SAR_RS02060 NE NE E E E NE NE E U E gyrA SAOUHSC_00006 USA300HOU_RS00030 MW_RS00030 SAS_RS00030 SAR_RS00030 E E E E E E E E E E gyrB SAOUHSC_00005 USA300HOU_RS00025 MW_RS00025 SAS_RS00025 SAR_RS00025 E E E E E E E E E E hemA SAOUHSC_01776 USA300HOU_RS08855 MW_RS08615 SAS_RS08490 SAR_RS08950 E E E E E E E E E E hemB SAOUHSC_01772 USA300HOU_RS08835 MW_RS08595 SAS_RS08470 SAR_RS08930 U E E E E U E E E E hemC SAOUHSC_01774 USA300HOU_RS08845 MW_RS08605 SAS_RS08480 SAR_RS08940 E E E E E E E E E E hemE SAOUHSC_01962 USA300HOU_RS09795 MW_RS09510 SAS_RS09385 SAR_RS09885 E E E NE E U U E NE E hemH SAOUHSC_01961 USA300HOU_RS09790 MW_RS09505 SAS_RS09380 SAR_RS09880 E E U NE E U U U U E hemY SAOUHSC_01960 USA300HOU_RS09785 MW_RS09500 SAS_RS09375 SAR_RS09875 NE E NE NE E NE E E U E hepT SAOUHSC_01486 USA300HOU_RS07515 MW_RS07295 SAS_RS07490 SAR_RS07560 E E E E E E E E E E hisS SAOUHSC_01738 USA300HOU_RS08680 MW_RS08440 SAS_RS08315 SAR_RS08745 E E E E E E E E E E hprK SAOUHSC_00781 USA300HOU_RS04115 MW_RS03935 SAS_RS03905 SAR_RS04155 E U E E E E U E E E hup SAOUHSC_01490 USA300HOU_RS07535 MW_RS07315 SAS_RS07510 SAR_RS07580 U E E E E U E E E E citC SAOUHSC_01801 USA300HOU_RS08980 MW_RS08740 SAS_RS08615 SAR_RS09075 NE E E NE NE NE E E NE NE ileS SAOUHSC_01159 USA300HOU_RS05995 MW_RS05790 SAS_RS05990 SAR_RS05970 E E E E E E E E E E ilvE SAOUHSC_00536 USA300HOU_RS02910 MW_RS02805 SAS_RS02775 SAR_RS02895 NE E E E NE NE E E E NE infA SAOUHSC_02489 USA300HOU_RS12065 MW_RS11670 SAS_RS11535 SAR_RS12095 E S E E E E U E E E infB SAOUHSC_01246 USA300HOU_RS06395 MW_RS06190 SAS_RS06390 SAR_RS06370 E E E E E E E E E E infC SAOUHSC_01786 USA300HOU_RS08905 MW_RS08665 SAS_RS08540 SAR_RS09000 E E U E E E E U E E iscS SAOUHSC_01727 USA300HOU_RS08635 MW_RS08395 SAS_RS08270 SAR_RS08700 E E E E E E E E E E leuS SAOUHSC_01875 USA300HOU_RS09335 MW_RS09095 SAS_RS08970 SAR_RS09455 E E E E E E E E E E lexA_1 SAOUHSC_01333 USA300HOU_RS06815 MW_RS06585 SAS_RS06785 SAR_RS06885 E E NE E E U U U U U lexA_2 SAOUHSC_02235 MW_RS10545 SAS_RS10410 E E E E E E ligA SAOUHSC_02122 USA300HOU_RS10340 MW_RS10055 SAS_RS09925 SAR_RS10420 E E E E E E E E E E lipA SAOUHSC_00861 USA300HOU_RS04580 MW_RS04370 SAS_RS04260 SAR_RS04515 NE NE NE E NE U U U U U lipL SAOUHSC_00575 USA300HOU_RS03080 MW_RS02975 SAS_RS02945 SAR_RS03060 NE E E E E NE E E E E ltaS SAOUHSC_00728 USA300HOU_RS03880 MW_RS03695 SAS_RS03665 SAR_RS03920 E E NE NE E E E NE NE E lysP_1 SAOUHSC_01787 USA300HOU_RS08910 MW_RS08670 SAS_RS08545 SAR_RS09005 E NE NE NE NE E NE NE NE NE lysS SAOUHSC_00493 USA300HOU_RS02655 MW_RS02535 SAS_RS02505 SAR_RS02625 E E E E E E E E E E map_1 SAOUHSC_02102 USA300HOU_RS10240 MW_RS09950 SAS_RS09825 SAR_RS10325 E E E E E E E E E E menA SAOUHSC_00980 USA300HOU_RS05180 MW_RS04970 SAS_RS05170 SAR_RS05155 E E E E E E E E E E metS SAOUHSC_00461 USA300HOU_RS02505 MW_RS02385 SAS_RS02355 SAR_RS02475 E E E E E E E E E E metK SAOUHSC_01909 USA300HOU_RS09495 MW_RS09230 SAS_RS09105 SAR_RS09595 E E E E E E E E E E mnhA_1 SAOUHSC_00889 USA300HOU_RS04720 MW_RS04510 SAS_RS04400 SAR_RS04655 E NE NE NE E E NE U U E mnhB_1 SAOUHSC_00888 USA300HOU_RS04715 MW_RS04505 SAS_RS04395 SAR_RS04650 U U NE NE E U NE U NE E mnhC_1 SAOUHSC_00887 USA300HOU_RS04710 MW_RS04500 SAS_RS04390 SAR_RS04645 E NE NE NE E E NE U U E mnhD_1 SAOUHSC_00886 USA300HOU_RS04705 MW_RS04495 SAS_RS04385 SAR_RS04640 E NE NE E E E NE U E E mnhE_1 SAOUHSC_00885 USA300HOU_RS04700 MW_RS04490 SAS_RS04380 SAR_RS04635 E NE NE E U E NE U U U mnhF_1 SAOUHSC_00884 USA300HOU_RS04695 MW_RS04485 SAS_RS04375 SAR_RS04630 NE NE NE NE E U NE U NE E mnmA_A SAOUHSC_01726 USA300HOU_RS08630 MW_RS08390 SAS_RS08265 SAR_RS08695 E E E E E E E E E E mnmA_B SAOUHSC_01726 E E mraY SAOUHSC_01146 USA300HOU_RS05940 MW_RS05735 SAS_RS05935 SAR_RS05915 E E E E E E E E E E murB SAOUHSC_00752 USA300HOU_RS03995 MW_RS03815 SAS_RS03785 SAR_RS04030 E E E E E E E E E E murC SAOUHSC_01856 USA300HOU_RS09245 MW_RS09005 SAS_RS08880 SAR_RS09335 E E E E E E E E E E murD SAOUHSC_01147 USA300HOU_RS05945 MW_RS05740 SAS_RS05940 SAR_RS05920 E E E E E E E E E E murE SAOUHSC_00954 USA300HOU_RS05045 MW_RS04830 SAS_RS04720 SAR_RS05015 E E E E E E E E E E murT SAOUHSC_02107 USA300HOU_RS10270 MW_RS09985 SAS_RS09855 SAR_RS10350 E E E E NE E E U U NE murF SAOUHSC_02317 USA300HOU_RS11240 MW_RS10935 SAS_RS10800 SAR_RS11305 E E E E E E E E E E murG SAOUHSC_01424 USA300HOU_RS07245 MW_RS07015 SAS_RS07210 SAR_RS07300 E E U E E E E U E E murI SAOUHSC_01106 USA300HOU_RS05755 MW_RS05545 SAS_RS05745 SAR_RS05740 E E E E NE U U U U U murJ_1 SAOUHSC_01871 USA300HOU_RS09315 MW_RS09075 SAS_RS08950 SAR_RS09435 E E E E E E E E E E nadD SAOUHSC_01697 USA300HOU_RS08495 MW_RS08250 SAS_RS08125 SAR_RS08550 E E E E E E E E E E nadE SAOUHSC_02132 USA300HOU_RS10385 MW_RS10100 SAS_RS09970 SAR_RS10465 E E E E E E E E E E ppnK SAOUHSC_00943 USA300HOU_RS04990 MW_RS04775 SAS_RS04665 SAR_RS04950 E E E E E E E E E E nrdE SAOUHSC_00742 USA300HOU_RS03950 MW_RS03770 SAS_RS03740 SAR_RS03990 E E E E E E E E E E nrdF SAOUHSC_00743 USA300HOU_RS03955 MW_RS03775 SAS_RS03745 SAR_RS03995 E E E E E E E E E E nrdI SAOUHSC_00741 USA300HOU_RS03945 MW_RS03765 SAS_RS03735 SAR_RS03985 E E E E E E E E E E

115 Supplemental Table 4 (Continued).

TRANSIT: Gumbel Permutation Name HG003 USA300 MW2 MSSA476 MRSA252 HG US MW MS MR HG US MW MS MR mpsA SAOUHSC_00412 USA300HOU_RS02275 MW_RS02155 SAS_RS02125 SAR_RS02240 E E E E E E E E E E nusA SAOUHSC_01243 USA300HOU_RS06380 MW_RS06175 SAS_RS06375 SAR_RS06355 E E E E E E E E E E nusB SAOUHSC_01621 USA300HOU_RS08145 MW_RS07905 SAS_RS07780 SAR_RS08205 NE NE NE E NE U U U U U nusG SAOUHSC_00517 USA300HOU_RS02820 MW_RS02715 SAS_RS02685 SAR_RS02805 NE E U NE E U U U U U obgE SAOUHSC_01753 USA300HOU_RS08745 MW_RS08505 SAS_RS08380 SAR_RS08810 E E E E E E E E E E parC SAOUHSC_01352 USA300HOU_RS06895 MW_RS06670 SAS_RS06865 SAR_RS06975 E E E E E E E E E E parE SAOUHSC_01351 USA300HOU_RS06890 MW_RS06665 SAS_RS06860 SAR_RS06970 E E E E E E E E E E dltB SAOUHSC_00870 USA300HOU_RS04625 MW_RS04415 SAS_RS04305 SAR_RS04560 E NE NE NE NE E NE U NE NE pbp1 SAOUHSC_01145 USA300HOU_RS05935 MW_RS05730 SAS_RS05930 SAR_RS05910 E E E E E E E E E E pcrA SAOUHSC_02123 USA300HOU_RS10345 MW_RS10060 SAS_RS09930 SAR_RS10425 NE NE NE E NE NE NE U E NE bmfBB SAOUHSC_01611 USA300HOU_RS08100 MW_RS07860 SAS_RS07735 SAR_RS08160 U E E E E U E E E E pdhD SAOUHSC_01043 USA300HOU_RS05470 MW_RS05260 SAS_RS05460 SAR_RS05455 NE NE NE NE E U U U U U pdxS SAOUHSC_00499 USA300HOU_RS02730 MW_RS02625 SAS_RS02595 SAR_RS02710 NE NE E NE NE NE NE E NE NE pezA MW_RS07690 E E pfkA SAOUHSC_01807 USA300HOU_RS09010 MW_RS08760 SAS_RS08635 SAR_RS09095 E E E E E E E E E E pgcA SAOUHSC_02793 USA300HOU_RS13500 MW_RS13085 SAS_RS12950 SAR_RS13520 NE E NE NE NE NE E NE NE NE pgi SAOUHSC_00900 USA300HOU_RS04775 MW_RS04560 SAS_RS04450 SAR_RS04705 E E E E U E E E E U pgk SAOUHSC_00796 USA300HOU_RS04190 MW_RS04010 SAS_RS03980 SAR_RS04230 E E E E E E E E E E pgl SAOUHSC_02143 USA300HOU_RS10440 MW_RS10155 SAS_RS10025 SAR_RS10520 NE E NE U NE U U U U U pgsA SAOUHSC_01260 USA300HOU_RS06465 MW_RS06260 SAS_RS06460 SAR_RS06440 E E E E E E E E E E pheS SAOUHSC_01092 USA300HOU_RS05690 MW_RS05480 SAS_RS05680 SAR_RS05675 E E E E E E E E E E pheT_1 SAOUHSC_01093 USA300HOU_RS05695 MW_RS05485 SAS_RS05685 SAR_RS05680 E E E E E E E E E E plsC SAOUHSC_01837 USA300HOU_RS09165 MW_RS08920 SAS_RS08795 SAR_RS09250 E E E E E E E E E E plsX SAOUHSC_01197 USA300HOU_RS06175 MW_RS05970 SAS_RS06170 SAR_RS06150 E E E E E E E E E E plsY SAOUHSC_01350 USA300HOU_RS06885 MW_RS06660 SAS_RS06855 SAR_RS06965 E E E E E E E E E E pncB SAOUHSC_02133 USA300HOU_RS10390 MW_RS10105 SAS_RS09975 SAR_RS10470 E E E E E E E E E E polC_1 SAOUHSC_01241 USA300HOU_RS06370 MW_RS06165 SAS_RS06365 SAR_RS06345 E E E E E E E E E E pbp2 SAOUHSC_01467 USA300HOU_RS07420 MW_RS07200 SAS_RS07395 SAR_RS07470 E E E E E E E E E E ppaC SAOUHSC_02140 USA300HOU_RS10425 MW_RS10140 SAS_RS10010 SAR_RS10505 E E E E E E E E E E prfA SAOUHSC_02359 USA300HOU_RS11435 MW_RS11125 SAS_RS10990 SAR_RS11500 E E E E E E E E E E prfB SAOUHSC_00771 USA300HOU_RS04080 MW_RS03900 SAS_RS03870 SAR_RS04115 E E E E E E E E E E priA SAOUHSC_01179 USA300HOU_RS06090 MW_RS05885 SAS_RS06085 SAR_RS06065 E E E E E E E E E E proS SAOUHSC_01240 USA300HOU_RS06365 MW_RS06160 SAS_RS06360 SAR_RS06340 E E E E E E E E E E prs SAOUHSC_00472 USA300HOU_RS02560 MW_RS02440 SAS_RS02410 SAR_RS02530 E E E E E E E E E E eutD SAOUHSC_00574 USA300HOU_RS03075 MW_RS02970 SAS_RS02940 SAR_RS03055 E E E E E E E E E E pth SAOUHSC_00475 USA300HOU_RS02575 MW_RS02455 SAS_RS02425 SAR_RS02545 E E E E E E E E E E pykA SAOUHSC_01806 USA300HOU_RS09005 MW_RS08755 SAS_RS08630 SAR_RS09090 E E E E E E E E E E pyrB SAOUHSC_01166 USA300HOU_RS06030 MW_RS05825 SAS_RS06025 SAR_RS06005 E NE NE E NE U U U U U pyrC SAOUHSC_01168 USA300HOU_RS06035 MW_RS05830 SAS_RS06030 SAR_RS06010 NE NE U E NE U NE U U U pyrD SAOUHSC_02909 USA300HOU_RS14040 MW_RS13595 SAS_RS13460 SAR_RS14000 E E U E NE U U U U U pyrE SAOUHSC_01172 USA300HOU_RS06055 MW_RS05850 SAS_RS06050 SAR_RS06030 E NE U E E U U U U U pyrF SAOUHSC_01171 USA300HOU_RS06050 MW_RS05845 SAS_RS06045 SAR_RS06025 E U E E NE U U U U U pyrG SAOUHSC_02368 USA300HOU_RS11480 MW_RS11170 SAS_RS11035 SAR_RS11550 E E E E E E E E E E pyrH SAOUHSC_01235 USA300HOU_RS06340 MW_RS06135 SAS_RS06335 SAR_RS06315 E E E E E E E E E E rbfA SAOUHSC_01247 USA300HOU_RS06400 MW_RS06195 SAS_RS06395 SAR_RS06375 NE U NE E NE U U U U U rbgA SAOUHSC_01214 USA300HOU_RS06255 MW_RS06050 SAS_RS06250 SAR_RS06230 E E E E E E E E E E recG SAOUHSC_01194 USA300HOU_RS06165 MW_RS05960 SAS_RS06160 SAR_RS06140 E NE NE NE NE U U U U U recU SAOUHSC_01466 USA300HOU_RS07415 MW_RS07195 SAS_RS07390 SAR_RS07465 NE E E E U U U U U U relA SAOUHSC_01742 USA300HOU_RS08695 MW_RS08455 SAS_RS08330 SAR_RS08760 E E E E E E E E E E ribC SAOUHSC_01249 USA300HOU_RS06410 MW_RS06205 SAS_RS06405 SAR_RS06385 E E E E E E E E E E rimM SAOUHSC_01209 USA300HOU_RS06230 MW_RS06025 SAS_RS06225 SAR_RS06205 E E NE NE E U U U U U rnjA SAOUHSC_01035 USA300HOU_RS05430 MW_RS05220 SAS_RS05420 SAR_RS05415 E E E E E E E E E E rnjB SAOUHSC_01252 USA300HOU_RS06425 MW_RS06220 SAS_RS06420 SAR_RS06400 NE NE NE E E NE NE U U E rnpA SAOUHSC_03054 USA300HOU_RS14720 MW_RS14260 SAS_RS14125 SAR_RS14690 E E NE NE E U U U U U rny SAOUHSC_01263 USA300HOU_RS06480 MW_RS06275 SAS_RS06475 SAR_RS06455 E NE E NE U E U U NE U rnz SAOUHSC_01598 USA300HOU_RS08040 MW_RS07800 SAS_RS07675 SAR_RS08090 E E E E E E E E E E cfxE SAOUHSC_01189 USA300HOU_RS06140 MW_RS05935 SAS_RS06135 SAR_RS06115 E E E E E E E E E E rpiA SAOUHSC_02612 USA300HOU_RS12640 MW_RS12235 SAS_RS12100 SAR_RS12665 E E NE E E U U U U U rplA SAOUHSC_00519 USA300HOU_RS02830 MW_RS02725 SAS_RS02695 SAR_RS02815 E NE NE NE NE E NE U U U rplB SAOUHSC_02509 USA300HOU_RS12160 MW_RS11765 SAS_RS11630 SAR_RS12190 E E E E E E E E E E rplC SAOUHSC_02512 USA300HOU_RS12175 MW_RS11780 SAS_RS11645 SAR_RS12205 E E E E E E E E E E rplD SAOUHSC_02511 USA300HOU_RS12170 MW_RS11775 SAS_RS11640 SAR_RS12200 E E E E E E E E E E rplE SAOUHSC_02500 USA300HOU_RS12115 MW_RS11720 SAS_RS11585 SAR_RS12145 E E E E E E E E E E rplF SAOUHSC_02496 USA300HOU_RS12100 MW_RS11705 SAS_RS11570 SAR_RS12130 E E E E E E E E E E rplGA SAOUHSC_01245 USA300HOU_RS06390 MW_RS06185 SAS_RS06385 SAR_RS06365 E NE NE NE NE U U U U U rplGB SAOUHSC_00526 USA300HOU_RS02860 MW_RS02755 SAS_RS02725 SAR_RS02845 E E NE U E U U U U U rplJ SAOUHSC_00520 USA300HOU_RS02835 MW_RS02730 SAS_RS02700 SAR_RS02820 E E E E E E E E E E

116 Supplemental Table 4 (Continued).

TRANSIT: Gumbel Permutation Name HG003 USA300 MW2 MSSA476 MRSA252 HG US MW MS MR HG US MW MS MR rplK SAOUHSC_00518 USA300HOU_RS02825 MW_RS02720 SAS_RS02690 SAR_RS02810 NE E NE U E U U U U U rplL_1 SAOUHSC_00521 USA300HOU_RS02840 MW_RS02735 SAS_RS02705 SAR_RS02825 E U E E E E U E E E rplM SAOUHSC_02478 USA300HOU_RS12015 MW_RS11620 SAS_RS11485 SAR_RS12035 E E E E E E E E E E rplN SAOUHSC_02502 USA300HOU_RS12125 MW_RS11730 SAS_RS11595 SAR_RS12155 E U NE E E U U U U U rplO SAOUHSC_02492 USA300HOU_RS12080 MW_RS11685 SAS_RS11550 SAR_RS12110 E E NE U E U U U U U rplP SAOUHSC_02505 USA300HOU_RS12140 MW_RS11745 SAS_RS11610 SAR_RS12170 E E E E E E E E E E rplQ SAOUHSC_02484 USA300HOU_RS12040 MW_RS11645 SAS_RS11510 SAR_RS12070 E E E NE E U U U U U rplR SAOUHSC_02495 USA300HOU_RS12095 MW_RS11700 SAS_RS11565 SAR_RS12125 U E E E U U E E E U rplS SAOUHSC_01211 USA300HOU_RS06240 MW_RS06035 SAS_RS06235 SAR_RS06215 U E U E U U E U E U rplT SAOUHSC_01784 USA300HOU_RS08895 MW_RS08655 SAS_RS08530 SAR_RS08990 E E E E E E E E E E rplU SAOUHSC_01757 USA300HOU_RS08760 MW_RS08520 SAS_RS08395 SAR_RS08825 E E NE NE E U U U U U rplV SAOUHSC_02507 USA300HOU_RS12150 MW_RS11755 SAS_RS11620 SAR_RS12180 E E E E E E E E E E rplW SAOUHSC_02510 USA300HOU_RS12165 MW_RS11770 SAS_RS11635 SAR_RS12195 E E NE NE E U U U U U rplX SAOUHSC_02501 USA300HOU_RS12120 MW_RS11725 SAS_RS11590 SAR_RS12150 E E E E E E E E E E rpmA SAOUHSC_01755 USA300HOU_RS08750 MW_RS08510 SAS_RS08385 SAR_RS08815 E E U E E E E U E E rpmC SAOUHSC_02504 USA300HOU_RS12135 MW_RS11740 SAS_RS11605 SAR_RS12165 U U E E E U U E E E rpmD SAOUHSC_02493 USA300HOU_RS12085 MW_RS11690 SAS_RS11555 SAR_RS12115 E E E E E E E E E E rpmE2 SAOUHSC_02361 USA300HOU_RS11445 MW_RS11135 SAS_RS11000 SAR_RS11515 E E U E E E E U E E rpmI SAOUHSC_01785 USA300HOU_RS08900 MW_RS08660 SAS_RS08535 SAR_RS08995 E E E E NE U U U U U rpmJ SAOUHSC_02488 USA300HOU_RS12060 MW_RS11665 SAS_RS11530 SAR_RS12090 U E U U U U E U U U rpoA SAOUHSC_02485 USA300HOU_RS12045 MW_RS11650 SAS_RS11515 SAR_RS12075 E E E E E E E E E E rpoB SAOUHSC_00524 USA300HOU_RS02850 MW_RS02745 SAS_RS02715 SAR_RS02835 E E E E E E E E E E rpoC SAOUHSC_00525 USA300HOU_RS02855 MW_RS02750 SAS_RS02720 SAR_RS02840 E E E E E E E E E E rpsB SAOUHSC_01232A USA300HOU_RS06325 MW_RS06120 SAS_RS06320 SAR_RS06300 E E E E E E E E E E rpsC SAOUHSC_02506 USA300HOU_RS12145 MW_RS11750 SAS_RS11615 SAR_RS12175 E E E E E E E E E E rpsD SAOUHSC_01829 USA300HOU_RS09120 MW_RS08875 SAS_RS08750 SAR_RS09205 E E NE E E U U U U U rpsE SAOUHSC_02494 USA300HOU_RS12090 MW_RS11695 SAS_RS11560 SAR_RS12120 E E E E E E E E E E rpsF SAOUHSC_00348 USA300HOU_RS01935 MW_RS01795 SAS_RS01765 SAR_RS01820 E E E E E E E E E E rpsG SAOUHSC_00528 USA300HOU_RS02870 MW_RS02765 SAS_RS02735 SAR_RS02855 E E U E U E E U E U rpsH SAOUHSC_02498 USA300HOU_RS12105 MW_RS11710 SAS_RS11575 SAR_RS12135 E E NE E E U U U U U rpsI SAOUHSC_02477 USA300HOU_RS12010 MW_RS11615 SAS_RS11480 SAR_RS12030 U NE E E E U U U U U rpsJ SAOUHSC_02512a USA300HOU_RS12180 MW_RS11785 SAS_RS11650 SAR_RS12210 E E E E E E E E E E rpsK SAOUHSC_02486 USA300HOU_RS12050 MW_RS11655 SAS_RS11520 SAR_RS12080 E E E E E E E E E E rpsL SAOUHSC_00527 USA300HOU_RS02865 MW_RS02760 SAS_RS02730 SAR_RS02850 E E E E E E E E E E rpsM SAOUHSC_02487 USA300HOU_RS12055 MW_RS11660 SAS_RS11525 SAR_RS12085 E E E E E E E E E E rpsO SAOUHSC_01250 USA300HOU_RS06415 MW_RS06210 SAS_RS06410 SAR_RS06390 U U E E U U U E E U rpsP SAOUHSC_01208 USA300HOU_RS06225 MW_RS06020 SAS_RS06220 SAR_RS06200 E E U E E E E U E E rpsQ SAOUHSC_02503 USA300HOU_RS12130 MW_RS11735 SAS_RS11600 SAR_RS12160 E E NE E E U U U U U rpsR SAOUHSC_00350 USA300HOU_RS01945 MW_RS01805 SAS_RS01775 SAR_RS01830 E E E E U E E E E U rpsS SAOUHSC_02508 USA300HOU_RS12155 MW_RS11760 SAS_RS11625 SAR_RS12185 E E E E E E E E E E rpsU SAOUHSC_01678 USA300HOU_RS08400 MW_RS08160 SAS_RS08035 SAR_RS08460 E E U E U E E U E U rpsN SAOUHSC_02499 USA300HOU_RS12110 MW_RS11715 SAS_RS11580 SAR_RS12140 E E U NE E U U U U U rsgA SAOUHSC_01188 USA300HOU_RS06135 MW_RS05930 SAS_RS06130 SAR_RS06110 U E E E E U E E E E ksgA SAOUHSC_00464 USA300HOU_RS02520 MW_RS02400 SAS_RS02370 SAR_RS02490 NE NE NE E NE NE U U E NE ruvA SAOUHSC_01751 USA300HOU_RS08735 MW_RS08495 SAS_RS08370 SAR_RS08800 NE U NE E NE U U U U U ruvB SAOUHSC_01750 USA300HOU_RS08730 MW_RS08490 SAS_RS08365 SAR_RS08795 NE NE NE E U U U U U U sbcD SAOUHSC_01341 USA300HOU_RS06850 MW_RS06620 SAS_RS06820 SAR_RS06920 NE NE NE NE E NE NE U NE E secA SAOUHSC_00769 USA300HOU_RS04070 MW_RS03890 SAS_RS03860 SAR_RS04105 E E E E E E E E E E secE SAOUHSC_00516 USA300HOU_RS02815 MW_RS02710 SAS_RS02680 SAR_RS02800 U E U U U U U U U U secY SAOUHSC_02491 USA300HOU_RS12075 MW_RS11680 SAS_RS11545 SAR_RS12105 E E E E E E E E E E serS SAOUHSC_00009 USA300HOU_RS00045 MW_RS00045 SAS_RS00045 SAR_RS00045 E E E E E E E E E E sigA SAOUHSC_01662 USA300HOU_RS08330 MW_RS08090 SAS_RS07965 SAR_RS08390 E E E E E E E E E E slyA_2 SAOUHSC_02961 USA300HOU_RS14285 MW_RS13840 SAS_RS13705 SAR_RS14245 NE S E NE NE NE U E NE U smc_1 SAOUHSC_01204 USA300HOU_RS06205 MW_RS06000 SAS_RS06200 SAR_RS06180 U NE U E E U NE U U E smpB SAOUHSC_00804 USA300HOU_RS04230 MW_RS04050 SAS_RS04020 SAR_RS04270 E E E E E E E E E E spsB SAOUHSC_00903 USA300HOU_RS04790 MW_RS04575 SAS_RS04465 SAR_RS04720 E E U E E E E U E E spxA SAOUHSC_00934 USA300HOU_RS04945 MW_RS04730 SAS_RS04620 SAR_RS04905 E E E E U E E E E U srkA SAOUHSC_01866 USA300HOU_RS09290 MW_RS09050 SAS_RS08925 SAR_RS09410 U E E E E U E E E E ssb SAOUHSC_00349 USA300HOU_RS01940 MW_RS01800 SAS_RS01770 SAR_RS01825 E E E E E E E E E E sufD SAOUHSC_00848 USA300HOU_RS04520 MW_RS04315 SAS_RS04205 SAR_RS04460 E E E E E E E E E E sufB SAOUHSC_00851 USA300HOU_RS04535 MW_RS04330 SAS_RS04220 SAR_RS04475 E E E E E E E E E E sufC SAOUHSC_00847 USA300HOU_RS04515 MW_RS04310 SAS_RS04200 SAR_RS04455 E E E E E E E E E E sufU SAOUHSC_00850 USA300HOU_RS04530 MW_RS04325 SAS_RS04215 SAR_RS04470 E E NE NE E U U U U U tarG SAOUHSC_00642 USA300HOU_RS03455 MW_RS03270 SAS_RS03240 SAR_RS03325 E E E E E E E E E E tarH_1 SAOUHSC_00641 USA300HOU_RS03450 MW_RS03265 SAS_RS03235 SAR_RS03320 E E E E E E E E E E tarO SAOUHSC_00762 USA300HOU_RS04040 MW_RS03860 SAS_RS03830 SAR_RS04075 E E E E E E E E E E tarA SAOUHSC_00640 USA300HOU_RS03445 MW_RS03260 SAS_RS03230 SAR_RS03315 E E E E NE U U E E NE

117 Supplemental Table 4 (Continued).

TRANSIT: Gumbel Permutation Name HG003 USA300 MW2 MSSA476 MRSA252 HG US MW MS MR HG US MW MS MR tarB SAOUHSC_00643 USA300HOU_RS03460 MW_RS03275 SAS_RS03245 SAR_RS03330 E E E E E E E E E E tarD SAOUHSC_00645 USA300HOU_RS03470 MW_RS03285 SAS_RS03255 SAR_RS03340 E E U E E E E U E E tarF_1 SAOUHSC_00223 USA300HOU_RS01320 MW_RS01215 SAS_RS01190 SAR_RS01250 E E E E E E E E E E tarI SAOUHSC_00225 USA300HOU_RS01325 MW_RS01220 SAS_RS01195 SAR_RS01255 E E NE E E U U U U U tarJ SAOUHSC_00226 USA300HOU_RS01330 MW_RS01225 SAS_RS01200 SAR_RS01260 E E E U E E E E U E tarL SAOUHSC_00227 USA300HOU_RS01335 MW_RS01230 SAS_RS01205 SAR_RS01265 NE NE E E NE U U U U NE thiN SAOUHSC_01190 USA300HOU_RS06145 MW_RS05940 SAS_RS06140 SAR_RS06120 U E U E E U E U E E mvaD SAOUHSC_00578 USA300HOU_RS03090 MW_RS02985 SAS_RS02955 SAR_RS03070 E E E E E E E E E E thrS SAOUHSC_01788 USA300HOU_RS08915 MW_RS08675 SAS_RS08550 SAR_RS09010 E E E E E E E E E E thyA SAOUHSC_01435 USA300HOU_RS07295 MW_RS07070 SAS_RS07265 SAR_RS07355 E E E E E E E E E E tilS SAOUHSC_00484 USA300HOU_RS02610 MW_RS02490 SAS_RS02460 SAR_RS02580 E E E E E E E E E E tkt SAOUHSC_01337 USA300HOU_RS06830 MW_RS06600 SAS_RS06800 SAR_RS06900 E E E E E E E E E E tmk SAOUHSC_00451 USA300HOU_RS02455 MW_RS02335 SAS_RS02305 SAR_RS02425 E E E E E E E E E E topA SAOUHSC_01222 USA300HOU_RS06290 MW_RS06085 SAS_RS06285 SAR_RS06265 NE U NE E E NE U NE U E tpiA SAOUHSC_00797 USA300HOU_RS04195 MW_RS04015 SAS_RS03985 SAR_RS04235 E E E E E E E E E E trmD SAOUHSC_01210 USA300HOU_RS06235 MW_RS06030 SAS_RS06230 SAR_RS06210 E E E E E E E E E E trmK SAOUHSC_01661 USA300HOU_RS08325 MW_RS08085 SAS_RS07960 SAR_RS08385 E E E U E E E E U E trpS SAOUHSC_00933 USA300HOU_RS04940 MW_RS04725 SAS_RS04615 SAR_RS04900 E E E E E E E E E E trxA_1 SAOUHSC_01100 USA300HOU_RS05725 MW_RS05515 SAS_RS05715 SAR_RS05710 E U E E E E U E E E trxB SAOUHSC_00785 USA300HOU_RS04135 MW_RS03955 SAS_RS03925 SAR_RS04175 E E E E E E E E E E tsaB SAOUHSC_02279 USA300HOU_RS11045 MW_RS10745 SAS_RS10610 SAR_RS11110 E E E E E E E E E E gcp SAOUHSC_02277 USA300HOU_RS11035 MW_RS10735 SAS_RS10600 SAR_RS11100 E E E E E E E E E E tsaE SAOUHSC_02280 USA300HOU_RS11050 MW_RS10750 SAS_RS10615 SAR_RS11115 NE NE NE E E U U U U U tsf SAOUHSC_01234 USA300HOU_RS06335 MW_RS06130 SAS_RS06330 SAR_RS06310 E E E E E E E E E E tuf SAOUHSC_00530 USA300HOU_RS02880 MW_RS02775 SAS_RS02745 SAR_RS02865 E E E E E E E E E E tyrS SAOUHSC_01839 USA300HOU_RS09175 MW_RS08930 SAS_RS08805 SAR_RS09260 E E E E E E E E E E ugtP SAOUHSC_00953 USA300HOU_RS05040 MW_RS04825 SAS_RS04715 SAR_RS05010 NE E NE NE NE U E NE NE U uppS SAOUHSC_01237 USA300HOU_RS06350 MW_RS06145 SAS_RS06345 SAR_RS06325 E E E E E E E E E E valS SAOUHSC_01767 USA300HOU_RS08810 MW_RS08570 SAS_RS08445 SAR_RS08905 E E E E E E E E E E walK SAOUHSC_00021 USA300HOU_RS00105 MW_RS00105 SAS_RS00105 SAR_RS00105 E E E E E E E E E E walR SAOUHSC_00020 USA300HOU_RS00100 MW_RS00100 SAS_RS00100 SAR_RS00100 E E E E E E E E E E xerC SAOUHSC_01224 USA300HOU_RS06300 MW_RS06095 SAS_RS06295 SAR_RS06275 E NE NE NE NE E NE U U U xerD_1 SAOUHSC_01591 USA300HOU_RS08005 MW_RS07765 SAS_RS07640 SAR_RS08055 NE U NE U E U U U U U xseA SAOUHSC_01620 USA300HOU_RS08140 MW_RS07900 SAS_RS07775 SAR_RS08200 NE NE NE NE E U NE U U E ybeY SAOUHSC_01672 USA300HOU_RS08375 MW_RS08135 SAS_RS08010 SAR_RS08435 E E E E E E E E E E cbf1 SAOUHSC_01973 USA300HOU_RS09840 MW_RS09555 SAS_RS09430 SAR_RS09930 E E E E E E E E E E yhbY SAOUHSC_01698 USA300HOU_RS08500 MW_RS08255 SAS_RS08130 SAR_RS08555 NE NE E U E U U U U U yidC SAOUHSC_02327 USA300HOU_RS11290 MW_RS10980 SAS_RS10845 SAR_RS11355 E E E E E E E E E E yrrK SAOUHSC_01720 USA300HOU_RS08605 MW_RS08360 SAS_RS08235 SAR_RS08670 U NE NE E U U U U U U ywlC SAOUHSC_02357 USA300HOU_RS11425 MW_RS11115 SAS_RS10980 SAR_RS11490 E E E E E E E E E E pmtC SAOUHSC_02152 USA300HOU_RS10485 MW_RS10200 SAS_RS10070 SAR_RS10565 E NE NE NE NE E NE NE NE NE zwf SAOUHSC_01599 USA300HOU_RS08045 MW_RS07805 SAS_RS07680 SAR_RS08095 E E E E E E E E E E

118

Supplemental Table 5: Core essential genes in S. aureus. A list of the genes that were determined to be essential in all five S. aureus strains.

119 Supplemental Table 5 (Continued). Roary HG003 USA300-TCH1516 MW2 MSSA476 MRSA252 accA SAOUHSC_01808 USA300HOU_1686 MW1643 SAS1627 SAR1778 accB_1 SAOUHSC_01624 USA300HOU_1529 MW1480 SAS1466 SAR1605 accD SAOUHSC_01809 USA300HOU_1687 MW1644 SAS1628 SAR1779 acpP SAOUHSC_01201 USA300HOU_1169 MW1115 SAS1166 SAR1208 acpS SAOUHSC_02306 USA300HOU_2066 MW1995 SAS1976 SAR2159 adk SAOUHSC_02490 USA300HOU_2220 MW2148 SAS2120 SAR2314 alaS SAOUHSC_01722 USA300HOU_1618 MW1568 SAS1554 SAR1697 argS SAOUHSC_00611 USA300HOU_0613 MW0571 SAS0575 SAR0615 aspS SAOUHSC_01737 USA300HOU_1628 MW1580 SAS1566 SAR1710 birA SAOUHSC_01473 USA300HOU_1394 MW1346 SAS1399 SAR1467 cca SAOUHSC_01474 USA300HOU_1395 MW1347 SAS1400 SAR1468 cdsA SAOUHSC_01238 USA300HOU_1193 MW1144 SAS1195 SAR1237 cfiB_2 SAOUHSC_01623 USA300HOU_1528 MW1479 SAS1465 SAR1604 coaBC SAOUHSC_01178 USA300HOU_1149 MW1094 SAS1145 SAR1187 coaE SAOUHSC_01795 USA300HOU_1675 MW1631 SAS1616 SAR1767 coaW SAOUHSC_02371 USA300HOU_2119 MW2054 SAS2033 SAR2218 csd SAOUHSC_00849 USA300HOU_0873 MW0797 SAS0786 SAR0878 cysE SAOUHSC_00510 USA300HOU_0522 MW0484 SAS0486 SAR0532 cysS SAOUHSC_00511 USA300HOU_0523 MW0485 SAS0487 SAR0533 dagK SAOUHSC_02114 USA300HOU_1897 MW1839 SAS1820 SAR1989 ddl SAOUHSC_02318 USA300HOU_2078 MW2006 SAS1987 SAR2170 def SAOUHSC_01038 USA300HOU_1034 MW0974 SAS1026 SAR1065 der_2 SAOUHSC_01492 USA300HOU_1412 MW1364 SAS1416 SAR1484 der_3 SAOUHSC_01700 USA300HOU_1598 MW1548 SAS1534 SAR1674 divIB SAOUHSC_01148 USA300HOU_1124 MW1067 SAS1118 SAR1160 divIC SAOUHSC_00482 USA300HOU_0501 MW0462 SAS0464 SAR0508 dnaA SAOUHSC_00001 USA300HOU_0001 MW0001 SAS0001 SAR0001 dnaB SAOUHSC_01792 USA300HOU_1672 MW1628 SAS1613 SAR1764 dnaC SAOUHSC_00018 USA300HOU_0016 MW0016 SAS0016 SAR0016 dnaD SAOUHSC_01470 USA300HOU_1391 MW1343 SAS1396 SAR1464 dnaE SAOUHSC_01811 USA300HOU_1689 MW1646 SAS1630 SAR1781 dnaG SAOUHSC_01663 USA300HOU_1564 MW1514 SAS1500 SAR1639 dnaI SAOUHSC_01791 USA300HOU_1671 MW1627 SAS1612 SAR1763 dnaN SAOUHSC_00002 USA300HOU_0002 MW0002 SAS0002 SAR0002 dnaX_1 SAOUHSC_00442 USA300HOU_0477 MW0433 SAS0435 SAR0477 dnaX_2 SAOUHSC_00454 USA300HOU_0483 MW0439 SAS0441 SAR0485 eno SAOUHSC_00799 USA300HOU_0806 MW0738 SAS0742 SAR0832 fabD SAOUHSC_01198 USA300HOU_1166 MW1113 SAS1164 SAR1206 fabG SAOUHSC_01199 USA300HOU_1167 MW1114 SAS1165 SAR1207 fabH SAOUHSC_00920 USA300HOU_0941 MW0865 SAS0853 SAR0946 fabI SAOUHSC_00947 USA300HOU_0969 MW0892 SAS0880 SAR0978

120 Supplemental Table 5 (Continued). Roary HG003 USA300-TCH1516 MW2 MSSA476 MRSA252 femB SAOUHSC_01374 USA300HOU_1310 MW1262 SAS1315 SAR1388 femX SAOUHSC_02527 USA300HOU_2244 MW2180 SAS2152 SAR2346 ffh SAOUHSC_01207 MW1120 SAS1171 SAR1213 fni SAOUHSC_02623 USA300HOU_2327 MW2267 SAS2237 SAR2431 folE2 SAOUHSC_00549 USA300HOU_0560 MW0521 SAS0524 SAR0570 folK SAOUHSC_00491 MW0471 SAS0473 SAR0517 folP SAOUHSC_00489 USA300HOU_0508 MW0469 SAS0471 SAR0515 fpgS SAOUHSC_01766 USA300HOU_1654 MW1606 SAS1591 SAR1742 frr SAOUHSC_01236 MW1142 SAS1193 SAR1235 ftsA SAOUHSC_01149 MW1068 SAS1119 SAR1161 ftsW SAOUHSC_01063 USA300HOU_1051 MW0996 SAS1048 SAR1087 ftsY SAOUHSC_01205 USA300HOU_1172 MW1118 SAS1169 SAR1211 ftsZ SAOUHSC_01150 MW1069 SAS1120 SAR1162 fusA SAOUHSC_00529 MW0502 SAS0505 SAR0552 galK SAOUHSC_00577 USA300HOU_0583 MW0545 SAS0549 SAR0596 gapA1 SAOUHSC_00795 USA300HOU_0802 MW0734 SAS0738 SAR0828 gatA SAOUHSC_02117 USA300HOU_1901 MW1841 SAS1823 SAR1992 gatB_2 SAOUHSC_02116 USA300HOU_1900 MW1840 SAS1822 SAR1991 glmS SAOUHSC_02399 USA300HOU_2145 MW2080 SAS2055 SAR2242 glmU SAOUHSC_00471 MW0454 SAS0456 SAR0500 glnA SAOUHSC_01287 USA300HOU_1240 MW1192 SAS1242 SAR1284 gltX SAOUHSC_00509 USA300HOU_0521 MW0483 SAS0485 SAR0531 glyQS SAOUHSC_01666 USA300HOU_1566 MW1517 SAS1503 SAR1642 gmk SAOUHSC_01176 USA300HOU_1147 MW1092 SAS1143 SAR1185 gnd SAOUHSC_01605 USA300HOU_1512 MW1464 SAS1450 SAR1589 gph_2 SAOUHSC_01701 USA300HOU_1599 MW1549 SAS1535 SAR1675 groL SAOUHSC_02254 USA300HOU_2024 MW1953 SAS1935 SAR2116 group_1821 SAOUHSC_00659 USA300HOU_0674 MW0615 SAS0618 SAR0663 group_1966 SAOUHSC_01756 USA300HOU_1645 SAS1582 SAR1726 group_2135 SAOUHSC_01477 USA300HOU_1398 MW1350 SAS1403 SAR1471 group_2253 SAOUHSC_00579 USA300HOU_0585 MW0547 SAS0551 SAR0598 group_2428 SAOUHSC_01690 USA300HOU_1588 MW1538 SAS1524 SAR1664 group_3206 SAOUHSC_00413 USA300HOU_0458 MW0408 SAS0411 SAR0453 gyrA SAOUHSC_00006 USA300HOU_0006 MW0006 SAS0006 SAR0006 gyrB SAOUHSC_00005 USA300HOU_0005 MW0005 SAS0005 SAR0005 hemA SAOUHSC_01776 USA300HOU_1664 MW1616 SAS1601 SAR1752 hemC SAOUHSC_01774 USA300HOU_1662 MW1614 SAS1599 SAR1750 hepT SAOUHSC_01486 USA300HOU_1407 MW1359 SAS1411 SAR1479 hisS SAOUHSC_01738 USA300HOU_1629 MW1581 SAS1567 SAR1711 ileS SAOUHSC_01159 USA300HOU_1130 MW1076 SAS1127 SAR1169

121 Supplemental Table 5 (Continued). Roary HG003 USA300-TCH1516 MW2 MSSA476 MRSA252 infB SAOUHSC_01246 USA300HOU_1201 MW1152 SAS1203 SAR1245 iscS_1 SAOUHSC_01727 USA300HOU_1622 MW1572 SAS1558 SAR1702 leuS SAOUHSC_01875 USA300HOU_1749 MW1701 SAS1684 SAR1843 ligA SAOUHSC_02122 USA300HOU_1905 MW1845 SAS1827 SAR1996 lysS SAOUHSC_00493 MW0472 SAS0474 SAR0518 map SAOUHSC_02102 USA300HOU_1884 MW1828 SAS1810 SAR1978 menA SAOUHSC_00980 USA300HOU_0989 MW0925 SAS0977 SAR1015 metG SAOUHSC_00461 USA300HOU_0490 MW0445 SAS0447 SAR0491 metK SAOUHSC_01909 USA300HOU_1777 MW1728 SAS1711 SAR1870 mnmA SAOUHSC_01726 USA300HOU_1621 MW1571 SAS1557 SAR1701 mraY SAOUHSC_01146 USA300HOU_1122 MW1065 SAS1116 SAR1158 murB SAOUHSC_00752 USA300HOU_0764 MW0700 SAS0703 SAR0792 murC SAOUHSC_01856 USA300HOU_1730 MW1683 SAS1666 SAR1818 murD SAOUHSC_01147 USA300HOU_1123 MW1066 SAS1117 SAR1159 murE SAOUHSC_00954 USA300HOU_0976 MW0899 SAS0887 SAR0988 murF_2 SAOUHSC_02317 USA300HOU_2077 MW2005 SAS1986 SAR2169 murJ_2 SAOUHSC_01871 USA300HOU_1745 MW1697 SAS1680 SAR1839 nadD SAOUHSC_01697 USA300HOU_1595 MW1545 SAS1531 SAR1671 nadE SAOUHSC_02132 USA300HOU_1914 MW1853 SAS1836 SAR2005 nadK SAOUHSC_00943 USA300HOU_0964 MW0888 SAS0876 SAR0974 nrdE1 SAOUHSC_00742 USA300HOU_0755 MW0693 SAS0696 SAR0785 nrdF SAOUHSC_00743 USA300HOU_0756 MW0694 SAS0697 SAR0786 nrdI SAOUHSC_00741 USA300HOU_0754 MW0692 SAS0695 SAR0784 nuoL SAOUHSC_00412 USA300HOU_0457 MW0407 SAS0410 SAR0452 nusA SAOUHSC_01243 USA300HOU_1198 MW1149 SAS1200 SAR1242 obg SAOUHSC_01753 USA300HOU_1643 MW1594 SAS1580 SAR1724 parC SAOUHSC_01352 USA300HOU_1288 MW1242 SAS1295 SAR1367 parE SAOUHSC_01351 USA300HOU_1287 MW1241 SAS1294 SAR1366 pbpB SAOUHSC_01145 USA300HOU_1121 MW1064 SAS1115 SAR1157 pfkA SAOUHSC_01807 USA300HOU_1685 MW1642 SAS1626 SAR1777 pgk SAOUHSC_00796 USA300HOU_0803 MW0735 SAS0739 SAR0829 pgsA SAOUHSC_01260 USA300HOU_1215 MW1166 SAS1217 SAR1259 pheS SAOUHSC_01092 USA300HOU_1073 MW1021 SAS1072 SAR1111 pheT_1 SAOUHSC_01093 USA300HOU_1074 MW1022 SAS1073 SAR1112 plsC SAOUHSC_01837 MW1669 SAS1653 SAR1804 plsX SAOUHSC_01197 USA300HOU_1165 MW1112 SAS1163 SAR1205 plsY SAOUHSC_01350 USA300HOU_1286 MW1240 SAS1293 SAR1365 pncB2 SAOUHSC_02133 USA300HOU_1915 MW1854 SAS1837 SAR2006 polC_1 SAOUHSC_01241 USA300HOU_1196 MW1147 SAS1198 SAR1240 ponA SAOUHSC_01467 USA300HOU_1388 MW1340 SAS1393 SAR1461

122 Supplemental Table 5 (Continued). Roary HG003 USA300-TCH1516 MW2 MSSA476 MRSA252 ppaC SAOUHSC_02140 USA300HOU_1921 MW1860 SAS1843 SAR2012 prfA SAOUHSC_02359 USA300HOU_2106 MW2042 SAS2021 SAR2206 prfB SAOUHSC_00771 USA300HOU_0782 MW0716 priA SAOUHSC_01179 USA300HOU_1150 MW1095 SAS1146 SAR1188 proS SAOUHSC_01240 USA300HOU_1195 MW1146 SAS1197 SAR1239 prs SAOUHSC_00472 MW0455 SAS0457 SAR0501 pta SAOUHSC_00574 USA300HOU_0581 MW0543 SAS0547 SAR0594 pth SAOUHSC_00475 USA300HOU_0496 MW0457 SAS0459 SAR0503 pyk SAOUHSC_01806 USA300HOU_1684 MW1641 SAS1625 SAR1776 pyrG SAOUHSC_02368 USA300HOU_2115 MW2051 SAS2030 SAR2215 pyrH SAOUHSC_01235 USA300HOU_1191 MW1141 SAS1192 SAR1234 rbgA SAOUHSC_01214 USA300HOU_1174 MW1126 SAS1177 SAR1219 relA SAOUHSC_01742 USA300HOU_1632 MW1584 SAS1570 SAR1714 ribF SAOUHSC_01249 USA300HOU_1204 MW1155 SAS1206 SAR1248 rnj1 SAOUHSC_01035 USA300HOU_1031 MW0972 SAS1024 SAR1063 rnz SAOUHSC_01598 USA300HOU_1505 MW1458 SAS1444 SAR1581 rpe SAOUHSC_01189 USA300HOU_1159 MW1105 SAS1156 SAR1198 rplB SAOUHSC_02509 MW2166 SAS2138 SAR2332 rplC SAOUHSC_02512 MW2169 SAS2141 SAR2335 rplD SAOUHSC_02511 MW2168 SAS2140 SAR2334 rplE SAOUHSC_02500 USA300HOU_2229 MW2157 SAS2129 SAR2323 rplF SAOUHSC_02496 USA300HOU_2226 MW2154 SAS2126 SAR2320 rplJ SAOUHSC_00520 USA300HOU_0533 MW0494 SAS0497 SAR0544 rplM SAOUHSC_02478 USA300HOU_2209 MW2137 SAS2109 SAR2301 rplP SAOUHSC_02505 MW2162 SAS2134 SAR2328 rplT SAOUHSC_01784 MW1622 SAS1607 SAR1758 rplV SAOUHSC_02507 MW2164 SAS2136 SAR2330 rplX SAOUHSC_02501 USA300HOU_2230 MW2158 SAS2130 SAR2324 rpmD SAOUHSC_02493 USA300HOU_2223 MW2151 SAS2123 SAR2317 rpoA SAOUHSC_02485 USA300HOU_2215 MW2143 SAS2115 SAR2309 rpoB SAOUHSC_00524 USA300HOU_0536 MW0497 SAS0500 SAR0547 rpoC SAOUHSC_00525 USA300HOU_0537 MW0498 SAS0501 SAR0548 rpsB SAOUHSC_01232 USA300HOU_1188 MW1139 SAS1190 SAR1232 rpsC SAOUHSC_02506 MW2163 SAS2135 SAR2329 rpsE SAOUHSC_02494 USA300HOU_2224 MW2152 SAS2124 SAR2318 rpsF SAOUHSC_00348 USA300HOU_0387 MW0341 SAS0341 SAR0362 rpsJ SAOUHSC_02512a MW2170 SAS2142 SAR2336 rpsK SAOUHSC_02486 USA300HOU_2216 MW2144 SAS2116 SAR2310 rpsL SAOUHSC_00527 USA300HOU_0539 MW0500 SAS0503 SAR0550 rpsM SAOUHSC_02487 USA300HOU_2217 MW2145 SAS2117 SAR2311

123 Supplemental Table 5 (Continued). Roary HG003 USA300-TCH1516 MW2 MSSA476 MRSA252 rpsS SAOUHSC_02508 MW2165 SAS2137 SAR2331 secA_1 SAOUHSC_00769 USA300HOU_0780 MW0715 SAS0718 SAR0807 secY SAOUHSC_02491 USA300HOU_2221 MW2149 SAS2121 SAR2315 serS SAOUHSC_00009 USA300HOU_0009 MW0009 SAS0009 SAR0009 sigA SAOUHSC_01662 USA300HOU_1563 MW1513 SAS1499 SAR1638 smpB SAOUHSC_00804 USA300HOU_0811 MW0743 SAS0747 SAR0837 ssbA_1 SAOUHSC_00349 USA300HOU_0388 MW0342 SAS0342 SAR0363 sufB_1 SAOUHSC_00848 USA300HOU_0871 MW0796 SAS0785 SAR0877 sufB_2 SAOUHSC_00851 USA300HOU_0875 MW0799 SAS0788 SAR0880 sufC SAOUHSC_00847 USA300HOU_0870 MW0795 SAS0784 SAR0876 tagG SAOUHSC_00642 USA300HOU_0658 MW0600 SAS0604 SAR0648 tagH_1 SAOUHSC_00641 USA300HOU_0657 MW0599 SAS0603 SAR0647 tagO SAOUHSC_00762 USA300HOU_0774 MW0709 SAS0712 SAR0801 tarB SAOUHSC_00643 USA300HOU_0659 MW0601 SAS0605 SAR0649 tarF_1 SAOUHSC_00223 USA300HOU_0265 MW0230 SAS0231 SAR0251 thrB_1 SAOUHSC_00578 USA300HOU_0584 MW0546 SAS0550 SAR0597 thrS SAOUHSC_01788 USA300HOU_1669 MW1626 SAS1611 SAR1762 thyA SAOUHSC_01435 USA300HOU_1364 MW1317 SAS1370 SAR1440 tilS SAOUHSC_00484 USA300HOU_0503 MW0464 SAS0466 SAR0510 tkt SAOUHSC_01337 USA300HOU_1279 MW1229 SAS1282 SAR1352 tmk SAOUHSC_00451 USA300HOU_0481 MW0437 SAS0439 SAR0483 tpiA SAOUHSC_00797 USA300HOU_0804 MW0736 SAS0740 SAR0830 trmD SAOUHSC_01210 MW1123 SAS1174 SAR1216 trpS SAOUHSC_00933 USA300HOU_0954 MW0878 SAS0866 SAR0964 trxB SAOUHSC_00785 USA300HOU_0792 MW0726 SAS0729 SAR0818 tsaB SAOUHSC_02279 USA300HOU_2046 MW1975 SAS1956 SAR2138 tsaD SAOUHSC_02277 USA300HOU_2044 MW1973 SAS1954 SAR2136 tsf SAOUHSC_01234 USA300HOU_1190 MW1140 SAS1191 SAR1233 tuf SAOUHSC_00530 USA300HOU_0541 MW0503 SAS0506 SAR0553 tyrS SAOUHSC_01839 USA300HOU_1717 MW1671 SAS1655 SAR1806 uppS SAOUHSC_01237 USA300HOU_1192 MW1143 SAS1194 SAR1236 valS SAOUHSC_01767 USA300HOU_1655 MW1607 SAS1592 SAR1743 walK SAOUHSC_00021 USA300HOU_0019 MW0019 SAS0019 SAR0019 walR SAOUHSC_00020 USA300HOU_0018 MW0018 SAS0018 SAR0018 ybeY SAOUHSC_01672 USA300HOU_1571 MW1522 SAS1508 SAR1647 yhaM SAOUHSC_01973 USA300HOU_1834 MW1783 SAS1763 SAR1933 yidC SAOUHSC_02327 USA300HOU_2087 MW2013 SAS1994 SAR2179 ywlC SAOUHSC_02357 MW2040 SAS2019 SAR2204 zwf SAOUHSC_01599 USA300HOU_1507 MW1459 SAS1445 SAR1582

124

Supplemental Table 6: Gene ontology analysis reveals central dogma pathways to be universally essential in S. aureus. Red text highlights those processes mentioned in the main text. DNA, RNA, and protein metabolic processes are significantly over-represented among the genes essential for all five strains of S. aureus studied. Phospholipid and monosaccharide synthesis were likewise over-represented, while genes of unknown function were underrepresented.

125

Supplemental Table 6 (Continued).

Essential Expected REFLIST Genes Number of Over/ Fold PANTHER GO-Slim Biological Process (2889) (198) Genes Under Change p-value q-value metabolic process (GO:0008152) 698 114 47.84 + 2.38 5.98E-22 7.06E-20 Unclassified (UNCLASSIFIED) 2041 76 139.88 - 0.54 1.68E-19 9.92E-18 biosynthetic process (GO:0009058) 274 66 18.78 + 3.51 1.48E-18 5.83E-17 cellular process (GO:0009987) 559 93 38.31 + 2.43 5.64E-17 1.66E-15 translation (GO:0006412) 50 29 3.43 + 8.46 7.07E-16 1.67E-14 primary metabolic process (GO:0044238) 598 93 40.98 + 2.27 2.28E-15 4.48E-14 protein metabolic process (GO:0019538) 108 35 7.4 + 4.73 7.18E-13 1.21E-11 tRNA aminoacylation for protein translation (GO:0006418) 19 17 1.3 + 13.06 7.84E-12 1.16E-10 nucleobase-containing compound metabolic process (GO:0006139) 292 55 20.01 + 2.75 2.20E-11 2.89E-10 tRNA metabolic process (GO:0006399) 40 20 2.74 + 7.3 2.34E-10 2.76E-09 nitrogen compound metabolic process (GO:0006807) 342 57 23.44 + 2.43 8.98E-10 9.64E-09 RNA metabolic process (GO:0016070) 127 28 8.7 + 3.22 2.58E-07 2.53E-06 phosphate-containing compound metabolic process (GO:0006796) 172 31 11.79 + 2.63 3.55E-06 3.22E-05 coenzyme metabolic process (GO:0006732) 48 15 3.29 + 4.56 5.93E-06 5.00E-05 DNA replication (GO:0006260) 19 9 1.3 + 6.91 3.64E-05 2.86E-04 phospholipid metabolic process (GO:0006644) 11 7 0.75 + 9.29 6.97E-05 5.14E-04 cellular component organization or biogenesis (GO:0071840) 47 13 3.22 + 4.04 7.40E-05 5.14E-04 cellular component organization (GO:0016043) 29 10 1.99 + 5.03 1.16E-04 7.58E-04 cellular amino acid metabolic process (GO:0006520) 174 26 11.93 + 2.18 4.41E-04 2.74E-03 cellular component biogenesis (GO:0044085) 35 9 2.4 + 3.75 1.52E-03 8.95E-03 monosaccharide metabolic process (GO:0005996) 31 8 2.12 + 3.77 2.72E-03 1.53E-02 DNA metabolic process (GO:0006259) 57 11 3.91 + 2.82 3.45E-03 1.85E-02 mitochondrial translation (GO:0032543) 3 3 0.21 + 14.59 4.50E-03 2.21E-02 organelle organization (GO:0006996) 13 5 0.89 + 5.61 4.44E-03 2.28E-02 glycolysis (GO:0006096) 5 3 0.34 + 8.75 1.14E-02 5.19E-02 fatty acid biosynthetic process (GO:0006633) 5 3 0.34 + 8.75 1.14E-02 5.40E-02 pentose-phosphate shunt (GO:0006098) 8 3 0.55 + 5.47 2.92E-02 1.28E-01 polyphosphate catabolic process (GO:0006798) 3 2 0.21 + 9.73 3.60E-02 1.52E-01 carbohydrate metabolic process (GO:0005975) 122 15 8.36 + 1.79 4.66E-02 1.83E-01 protein transport (GO:0015031) 10 3 0.69 + 4.38 4.61E-02 1.88E-01 gluconeogenesis (GO:0006094) 4 2 0.27 + 7.3 5.17E-02 1.97E-01 lipid metabolic process (GO:0006629) 37 6 2.54 + 2.37 5.40E-02 1.99E-01 catabolic process (GO:0009056) 96 12 6.58 + 1.82 6.72E-02 2.40E-01 ion transport (GO:0006811) 48 0 3.29 - < 0.01 7.18E-02 2.49E-01 fatty acid metabolic process (GO:0006631) 13 3 0.89 + 3.37 7.84E-02 2.64E-01 generation of precursor metabolites and energy (GO:0006091) 47 7 3.22 + 2.17 8.16E-02 2.67E-01 acyl-CoA metabolic process (GO:0006637) 6 2 0.41 + 4.86 8.87E-02 2.83E-01 intracellular protein transport (GO:0006886) 7 2 0.48 + 4.17 1.09E-01 3.40E-01 pteridine-containing compound metabolic process (GO:0042558) 8 2 0.55 + 3.65 1.31E-01 3.87E-01 pyrimidine nucleobase metabolic process (GO:0006206) 8 2 0.55 + 3.65 1.31E-01 3.97E-01 mitochondrion organization (GO:0007005) 3 1 0.21 + 4.86 2.33E-01 6.25E-01 protein targeting (GO:0006605) 3 1 0.21 + 4.86 2.33E-01 6.39E-01 steroid metabolic process (GO:0008202) 13 2 0.89 + 2.24 2.50E-01 6.54E-01 lipid transport (GO:0006869) 3 1 0.21 + 4.86 2.33E-01 6.55E-01 amino acid transport (GO:0006865) 53 1 3.63 - 0.28 2.58E-01 6.61E-01

126 Supplemental Table 6 (Continued).

Essential Expected REFLIST Genes Number of Over/ Fold PANTHER GO-Slim Biological Process (2889) (198) Genes Under Change p-value q-value cellular component morphogenesis (GO:0032989) 3 1 0.21 + 4.86 2.33E-01 6.71E-01 localization (GO:0051179) 124 5 8.5 - 0.59 2.74E-01 6.87E-01 rRNA metabolic process (GO:0016072) 15 2 1.03 + 1.95 2.98E-01 7.34E-01 response to toxic substance (GO:0009636) 6 1 0.41 + 2.43 3.72E-01 7.83E-01 protein glycosylation (GO:0006486) 5 1 0.34 + 2.92 3.28E-01 7.91E-01 protein folding (GO:0006457) 6 1 0.41 + 2.43 3.72E-01 7.97E-01 cellular protein modification process (GO:0006464) 38 4 2.6 + 1.54 3.42E-01 8.08E-01 sulfur compound metabolic process (GO:0006790) 18 2 1.23 + 1.62 3.71E-01 8.10E-01 transport (GO:0006810) 123 5 8.43 - 0.59 3.53E-01 8.17E-01 carbohydrate transport (GO:0008643) 48 1 3.29 - 0.3 3.70E-01 8.23E-01 response to stress (GO:0006950) 45 1 3.08 - 0.32 3.64E-01 8.25E-01 anion transport (GO:0006820) 27 0 1.85 - < 0.01 4.14E-01 8.42E-01 protein localization (GO:0008104) 7 1 0.48 + 2.08 4.12E-01 8.53E-01 tricarboxylic acid cycle (GO:0006099) 9 1 0.62 + 1.62 4.85E-01 8.67E-01 intracellular signal transduction (GO:0035556) 9 1 0.62 + 1.62 4.85E-01 8.81E-01 oxidative phosphorylation (GO:0006119) 8 1 0.55 + 1.82 4.50E-01 8.85E-01 proteolysis (GO:0006508) 9 1 0.62 + 1.62 4.85E-01 8.95E-01 ferredoxin metabolic process (GO:0006124) 8 1 0.55 + 1.82 4.50E-01 9.00E-01 respiratory electron transport chain (GO:0022904) 30 3 2.06 + 1.46 4.67E-01 9.03E-01 regulation of translation (GO:0006417) 9 1 0.62 + 1.62 4.85E-01 9.09E-01 vitamin metabolic process (GO:0006766) 31 3 2.12 + 1.41 4.78E-01 9.09E-01 phosphate ion transport (GO:0006817) 42 1 2.88 - 0.35 5.22E-01 9.20E-01 cell cycle (GO:0007049) 12 1 0.82 + 1.22 5.78E-01 9.75E-01 cell communication (GO:0007154) 12 1 0.82 + 1.22 5.78E-01 9.89E-01 glycogen metabolic process (GO:0005977) 13 1 0.89 + 1.12 6.05E-01 9.92E-01 signal transduction (GO:0007165) 12 1 0.82 + 1.22 5.78E-01 1.00E+00 regulation of carbohydrate metabolic process (GO:0006109) 5 0 0.34 - < 0.01 1.00E+00 1.00E+00 regulation of biological process (GO:0050789) 13 1 0.89 + 1.12 6.05E-01 1.01E+00 response to stimulus (GO:0050896) 69 3 4.73 - 0.63 6.25E-01 1.01E+00 cholesterol metabolic process (GO:0008203) 8 0 0.55 - < 0.01 1.00E+00 1.01E+00 nucleobase-containing compound transport (GO:0015931) 6 0 0.41 - < 0.01 1.00E+00 1.02E+00 cation transport (GO:0006812) 9 0 0.62 - < 0.01 1.00E+00 1.03E+00 transcription from RNA polymerase II promoter (GO:0006366) 9 0 0.62 - < 0.01 1.00E+00 1.04E+00 regulation of transcription from RNA polymerase II promoter (GO:0006357) 9 0 0.62 - < 0.01 1.00E+00 1.04E+00 cellular amino acid catabolic process (GO:0009063) 21 2 1.44 + 1.39 6.56E-01 1.05E+00 fatty acid beta-oxidation (GO:0006635) 4 0 0.27 - < 0.01 1.00E+00 1.05E+00 secondary metabolic process (GO:0019748) 4 0 0.27 - < 0.01 1.00E+00 1.06E+00 protein acetylation (GO:0006473) 11 0 0.75 - < 0.01 1.00E+00 1.07E+00 RNA catabolic process (GO:0006401) 7 0 0.48 - < 0.01 1.00E+00 1.08E+00 vitamin biosynthetic process (GO:0009110) 25 2 1.71 + 1.17 6.91E-01 1.09E+00 protein lipidation (GO:0006497) 3 0 0.21 - < 0.01 1.00E+00 1.09E+00 protein complex biogenesis (GO:0070271) 3 0 0.21 - < 0.01 1.00E+00 1.10E+00 mitochondrial transport (GO:0006839) 3 0 0.21 - < 0.01 1.00E+00 1.11E+00 protein complex assembly (GO:0006461) 3 0 0.21 - < 0.01 1.00E+00 1.12E+00 purine nucleobase metabolic process (GO:0006144) 14 1 0.96 + 1.04 1.00E+00 1.13E+00

127 Supplemental Table 6 (Continued).

Essential Expected REFLIST Genes Number of Over/ Fold PANTHER GO-Slim Biological Process (2889) (198) Genes Under Change p-value q-value porphyrin-containing compound metabolic process (GO:0006778) 14 1 0.96 + 1.04 1.00E+00 1.15E+00 homeostatic process (GO:0042592) 12 0 0.82 - < 0.01 1.00E+00 1.16E+00 DNA repair (GO:0006281) 29 1 1.99 - 0.5 1.00E+00 1.17E+00 DNA recombination (GO:0006310) 19 1 1.3 - 0.77 1.00E+00 1.18E+00 regulation of nucleobase-containing compound metabolic process (GO:0019219) 45 2 3.08 - 0.65 7.67E-01 1.19E+00 polysaccharide metabolic process (GO:0005976) 23 1 1.58 - 0.63 1.00E+00 1.19E+00 biological regulation (GO:0065007) 23 1 1.58 - 0.63 1.00E+00 1.20E+00 transcription, DNA-dependent (GO:0006351) 60 3 4.11 - 0.73 7.96E-01 1.22E+00 prosthetic group metabolic process (GO:0051189) 2 0 0.14 - < 0.01 1.00E+00 1.22E+00 response to abiotic stimulus (GO:0009628) 2 0 0.14 - < 0.01 1.00E+00 1.23E+00 cytokinesis (GO:0000910) 2 0 0.14 - < 0.01 1.00E+00 1.24E+00 response to external stimulus (GO:0009605) 2 0 0.14 - < 0.01 1.00E+00 1.26E+00 DNA catabolic process (GO:0006308) 2 0 0.14 - < 0.01 1.00E+00 1.27E+00 protein phosphorylation (GO:0006468) 2 0 0.14 - < 0.01 1.00E+00 1.28E+00 reproduction (GO:0000003) 2 0 0.14 - < 0.01 1.00E+00 1.30E+00 protein methylation (GO:0006479) 2 0 0.14 - < 0.01 1.00E+00 1.31E+00 meiosis (GO:0007126) 2 0 0.14 - < 0.01 1.00E+00 1.33E+00 regulation of cellular amino acid metabolic process (GO:0006521) 1 0 0.07 - < 0.01 1.00E+00 1.34E+00 regulation of phosphate metabolic process (GO:0019220) 1 0 0.07 - < 0.01 1.00E+00 1.36E+00 regulation of catalytic activity (GO:0050790) 1 0 0.07 - < 0.01 1.00E+00 1.37E+00 developmental process (GO:0032502) 1 0 0.07 - < 0.01 1.00E+00 1.39E+00 nitrogen utilization (GO:0019740) 1 0 0.07 - < 0.01 1.00E+00 1.40E+00 vitamin catabolic process (GO:0009111) 1 0 0.07 - < 0.01 1.00E+00 1.42E+00 regulation of molecular function (GO:0065009) 1 0 0.07 - < 0.01 1.00E+00 1.44E+00 cyclic nucleotide metabolic process (GO:0009187) 1 0 0.07 - < 0.01 1.00E+00 1.46E+00 nitric oxide biosynthetic process (GO:0006809) 1 0 0.07 - < 0.01 1.00E+00 1.47E+00 vitamin transport (GO:0051180) 1 0 0.07 - < 0.01 1.00E+00 1.49E+00 cellular amino acid biosynthetic process (GO:0008652) 74 5 5.07 - 0.99 1.00E+00 1.51E+00

128

Supplemental Table 7: Genes with upregulation signatures in the presence of various antibiotic compounds. This is a full list of the hits obtained by applying the Bootstrap 2 method described in Chapter 3 to the Walker lab’s collection of compound-exposed Tn-Seq sample data. Included is the visual assessment of each hit and whether the gene is essential based on the work discussed in Chapter 2.

129 Supplemental Table 7 (Continued).

Plus Strand Minus Strand Antibiotic Conc. Locus Tag Gene Strand Ctrl Exp Dif qVal Ctrl Exp Dif qVal Visual Analysis Essentiality Ciprofloxacin 0.15 SAOUHSC_00007 nnrD - 752 674 -78 1.00E+00 394 1047 653 7.54E-03 Yes NE Gentamicin 0.5 SAOUHSC_00007 nnrD - 752 588 -164 1.00E+00 394 2286 1892 0.00E+00 Yes NE Polymyxin 100 SAOUHSC_00007 nnrD - 752 572 -180 1.00E+00 394 1250 856 1.20E-02 Yes NE Targocil 0.5 SAOUHSC_00070 sarS - 622 736 114 1.00E+00 966 2065 1099 1.02E-03 Yes NE Oxacillin 0.15 SAOUHSC_00253 group_0044 - 554 77 -477 1.00E+00 486 1285 799 2.95E-03 Yes NE Bacitracin 8(2) SAOUHSC_00335 group_0065 - 626 369 -257 1.00E+00 441 1038 597 2.27E-03 Yes NE Bacitracin 8 SAOUHSC_00335 group_0065 - 626 757 131 1.00E+00 441 1143 702 4.92E-03 Yes NE Daptomycin 1 SAOUHSC_00335 group_0065 - 626 67 -559 1.00E+00 441 922 481 3.36E-02 Yes NE Gentamicin 0.5 SAOUHSC_00335 group_0065 - 626 296 -330 1.00E+00 441 1314 873 1.59E-03 Yes NE Moenomycin 0.02 SAOUHSC_00335 group_0065 - 626 849 223 1.00E+00 441 2104 1663 0.00E+00 Yes NE Ciprofloxacin 0.3 SAOUHSC_00371 group_0079 - 478 23 -455 9.61E-01 130 1050 920 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_00371 group_0079 - 478 187 -291 1.00E+00 130 950 820 1.66E-02 Yes NE Lysobactin 0.5 SAOUHSC_00371 group_0079 - 478 295 -183 9.38E-01 130 1422 1292 0.00E+00 Yes NE Moxifloxacin 0.03125 SAOUHSC_00371 group_0079 - 478 444 -34 3.57E-01 130 4985 4855 0.00E+00 Yes NE NalidixicAcid 125 SAOUHSC_00371 group_0079 - 478 277 -201 7.59E-01 130 1778 1648 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_00473 SAOUHSC_00473 - 89 51 -38 9.75E-01 204 938 734 2.07E-02 Yes NE Trimethoprim 0.5 SAOUHSC_00549 folE2 - 97 11 -86 3.03E-01 169 809 640 1.34E-03 Yes E Sulfamethoxazole 18 SAOUHSC_00550 bshB2 - 326 18 -308 9.49E-01 425 858 433 1.47E-02 Yes NE Trimethoprim 0.5 SAOUHSC_00550 bshB2 - 326 11 -315 8.40E-01 425 2074 1649 0.00E+00 Yes NE Trimethoprim 0.5 SAOUHSC_00551 SAOUHSC_00551 - 589 16 -573 1.00E+00 264 1534 1270 0.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_00637 mntA - 383 596 213 8.93E-01 396 1330 934 1.01E-02 Yes NE Oxacillin 0.15 SAOUHSC_00646 pbp4 - 920 39 -881 1.00E+00 457 1326 869 7.56E-04 Yes NE Chloramphenicol 1(2) SAOUHSC_00663 rimI_1 - 700 313 -387 1.00E+00 989 1552 563 3.08E-02 Yes NE Chloramphenicol 1 SAOUHSC_00663 rimI_1 - 700 313 -387 1.00E+00 989 1552 563 2.41E-02 Yes NE Linezolid 0.25 SAOUHSC_00663 rimI_1 - 700 95 -605 1.00E+00 989 1737 748 3.08E-03 Yes NE Moenomycin 0.08 SAOUHSC_00663 rimI_1 - 700 676 -24 1.00E+00 989 1939 950 0.00E+00 Yes NE Bacitracin 16(2) SAOUHSC_00691 uppP - 426 44 -382 6.98E-01 517 2291 1774 0.00E+00 Yes NE Bacitracin 32 SAOUHSC_00691 uppP - 426 389 -37 1.00E+00 517 1890 1373 0.00E+00 Yes NE Bacitracin 8(2) SAOUHSC_00691 uppP - 426 133 -293 1.00E+00 517 4191 3674 0.00E+00 Yes NE Bacitracin 8 SAOUHSC_00691 uppP - 426 398 -28 1.00E+00 517 2475 1958 0.00E+00 Yes NE Daptomycin 1 SAOUHSC_00691 uppP - 426 62 -364 5.92E-01 517 12537 12020 0.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_00691 uppP - 426 475 49 1.00E+00 517 6745 6228 0.00E+00 Yes NE Moenomycin 0.32 SAOUHSC_00691 uppP - 426 1 -425 6.93E-01 517 3816 3299 0.00E+00 Yes NE Moenomycin 0.08 SAOUHSC_00752 murB - 764 435 -329 1.00E+00 896 1589 693 1.91E-02 Yes E TritonX100 4percent SAOUHSC_00760 dosC - 253 7 -246 1.00E+00 216 2760 2544 0.00E+00 Yes NE Linezolid 0.25 SAOUHSC_00792 group_0169 - 615 238 -377 1.00E+00 320 1198 878 0.00E+00 Yes NE Amphomycin 14.4 SAOUHSC_00846 group_0188 - 244 0 -244 8.67E-01 200 2562 2362 0.00E+00 Yes NE Amphomycin 8 SAOUHSC_00846 group_0188 - 244 75 -169 1.00E+00 200 1023 823 0.00E+00 Yes NE Amphomycin 9.6 SAOUHSC_00846 group_0188 - 244 17 -227 7.82E-01 200 2652 2452 0.00E+00 Yes NE Gramicidin 0.5 SAOUHSC_00846 group_0188 - 244 0 -244 8.53E-01 200 1218 1018 0.00E+00 Yes NE TritonX100 4percent SAOUHSC_00846 group_0188 - 244 113 -131 9.81E-01 200 1375 1175 3.31E-03 Yes NE NalidixicAcid 125 SAOUHSC_00940 yjbK - 1069 7 -1062 1.00E+00 247 1212 965 0.00E+00 Yes NE Moxifloxacin 0.0156 SAOUHSC_00980 menA - 108 112 4 9.33E-01 55 853 798 0.00E+00 Jackpot NE Amphomycin 9.6 SAOUHSC_00995 group_0228 - 363 76 -287 5.29E-01 216 972 756 0.00E+00 Yes NE CCCP 0.8 SAOUHSC_00997 ywtF - 456 94 -362 7.77E-01 468 2476 2008 0.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_01061 group_0239 - 410 416 6 1.00E+00 973 1704 731 5.96E-03 Yes NE CDFI 0.833 SAOUHSC_01073 group_0244 - 180 8 -172 9.47E-01 222 1176 954 8.09E-03 Yes NE Bacitracin 4 SAOUHSC_01076 group_0245 - 94 38 -56 2.85E-01 79 429 350 2.95E-02 Jackpot NE CDFI 0.833 SAOUHSC_01191 rpmB - 602 28 -574 1.00E+00 439 3655 3216 0.00E+00 Yes NE Cefaclor 0.5 SAOUHSC_01191 rpmB - 602 1 -601 1.00E+00 439 864 425 1.58E-02 Yes NE Daptomycin 2 SAOUHSC_01191 rpmB - 602 95 -507 1.00E+00 439 5060 4621 0.00E+00 Yes NE DMPI 1 SAOUHSC_01191 rpmB - 602 14 -588 1.00E+00 439 4059 3620 0.00E+00 Yes NE Linezolid 0.25 SAOUHSC_01191 rpmB - 602 156 -446 1.00E+00 439 2546 2107 0.00E+00 Yes NE NalidixicAcid 125 SAOUHSC_01191 rpmB - 602 9 -593 1.00E+00 439 3041 2602 0.00E+00 Yes NE Ciprofloxacin 0.3 SAOUHSC_01350 plsY - 61 4 -57 6.60E-01 46 417 371 2.03E-02 Yes E Moenomycin 0.02 SAOUHSC_01382 group_0311 - 422 436 14 1.00E+00 616 1472 856 4.17E-02 Yes NE Moenomycin 0.08 SAOUHSC_01391 cvfB - 111 209 98 9.13E-01 426 1006 580 2.98E-02 Jackpot NE TritonX100 4percent SAOUHSC_01420 arlR - 356 75 -281 1.00E+00 292 1572 1280 2.27E-03 Yes NE Trimethoprim 0.5 SAOUHSC_01434 dfrA - 206 4 -202 9.44E-01 92 727 635 0.00E+00 Yes E Sulfamethoxazole 18 SAOUHSC_01435 thyA - 527 15 -512 9.74E-01 444 778 334 1.47E-02 Yes E Trimethoprim 0.5 SAOUHSC_01435 thyA - 527 9 -518 8.57E-01 444 4369 3925 0.00E+00 Yes E Trimethoprim 1 SAOUHSC_01435 thyA - 527 22 -505 1.00E+00 444 1118 674 1.47E-02 Yes E Gramicidin 0.5 SAOUHSC_01450 steT - 321 0 -321 9.48E-01 82 395 313 4.05E-02 Jackpot NE General enrichment Gentamicin 0.5 SAOUHSC_01455 dynA - 625 587 -38 1.00E+00 249 1022 773 1.91E-03 of previous gene NE TritonX100 4percent SAOUHSC_01455 dynA - 625 50 -575 1.00E+00 249 1691 1442 0.00E+00 Yes NE General enrichment NalidixicAcid 25 SAOUHSC_01462 gpsB - 1253 617 -636 1.00E+00 151 480 329 2.76E-02 of previous gene NE Gentamicin 0.5 SAOUHSC_01488 group_0333 - 465 287 -178 1.00E+00 565 2827 2262 0.00E+00 Yes NE Targocil 0.125 SAOUHSC_01488 group_0333 - 465 421 -44 9.75E-01 565 1129 564 7.37E-03 Yes NE Targocil 0.5 SAOUHSC_01488 group_0333 - 465 797 332 1.00E+00 565 4117 3552 0.00E+00 Yes NE TritonX100 4percent SAOUHSC_01493 rpsA_1 - 453 189 -264 9.99E-01 580 2654 2074 0.00E+00 Yes NE Amphomycin 9.6 SAOUHSC_01655 zur - 424 275 -149 3.83E-01 202 1651 1449 0.00E+00 Yes NE Moxifloxacin 0.03125 SAOUHSC_01655 zur - 424 3 -421 9.59E-01 202 927 725 2.01E-03 Yes NE Ramoplanin 2 SAOUHSC_01655 zur - 424 375 -49 1.00E+00 202 1238 1036 7.97E-04 Yes NE Daptomycin 2 SAOUHSC_01678 rpsU - 484 393 -91 9.75E-01 533 4016 3483 0.00E+00 Yes E Linezolid 0.25 SAOUHSC_01678 rpsU - 484 183 -301 1.00E+00 533 1301 768 2.76E-03 Yes E Ramoplanin 2 SAOUHSC_01678 rpsU - 484 366 -118 1.00E+00 533 2961 2428 0.00E+00 Yes E Cycloserine 100(2) SAOUHSC_01701 gph_2 - 47 1 -46 3.50E-01 78 475 397 1.05E-02 Jackpot NE Cycloserine 100 SAOUHSC_01701 gph_2 - 47 0 -47 3.49E-01 78 456 378 1.64E-02 Jackpot NE Rifampicin 0.0375 SAOUHSC_01775 hemX - 44 4 -40 5.96E-01 32 396 364 2.83E-02 Jackpot NE Linezolid 0.25 SAOUHSC_01827 ezrA - 204 45 -159 8.59E-01 298 930 632 3.37E-03 Yes NE NalidixicAcid 125 SAOUHSC_01827 ezrA - 204 12 -192 7.67E-01 298 5892 5594 0.00E+00 Yes NE NalidixicAcid 25 SAOUHSC_01827 ezrA - 204 27 -177 5.45E-01 298 658 360 2.56E-02 Yes NE Gentamicin 1 SAOUHSC_01851 SAOUHSC_01851 - 527 4 -523 9.77E-01 267 1050 783 0.00E+00 Yes NE

130 Supplemental Table 7 (Continued).

Plus Strand Minus Strand Antibiotic Conc. Locus Tag Gene Strand Ctrl Exp Dif qVal Ctrl Exp Dif qVal Visual Analysis Essentiality Gramicidin 0.333 SAOUHSC_01858 pheT_2 - 40 0 -40 1.05E-01 31 400 369 4.42E-02 Jackpot NE CDFI 0.833 SAOUHSC_01859 group_0446 - 813 19 -794 1.00E+00 827 3058 2231 0.00E+00 Yes NE DMPI 1 SAOUHSC_01859 group_0446 - 813 11 -802 1.00E+00 827 2690 1863 1.18E-03 Yes NE Linezolid 0.25 SAOUHSC_01859 group_0446 - 813 235 -578 1.00E+00 827 1786 959 0.00E+00 Yes NE NalidixicAcid 125 SAOUHSC_01859 group_0446 - 813 9 -804 1.00E+00 827 1805 978 0.00E+00 Yes NE Oxacillin 0.15 SAOUHSC_01859 group_0446 - 813 63 -750 1.00E+00 827 2462 1635 0.00E+00 Yes NE Targocil 0.5 SAOUHSC_01859 group_0446 - 813 941 128 1.00E+00 827 1785 958 1.01E-02 Yes NE TritonX100 4percent SAOUHSC_01859 group_0446 - 813 7 -806 1.00E+00 827 1748 921 3.58E-02 Yes NE Tunicamycin 1 SAOUHSC_01859 group_0446 - 813 237 -576 1.00E+00 827 5671 4844 0.00E+00 Yes NE CDFI 0.833 SAOUHSC_01860 ytpP - 804 24 -780 1.00E+00 698 3580 2882 0.00E+00 Yes NE DMPI 1 SAOUHSC_01860 ytpP - 804 113 -691 1.00E+00 698 2515 1817 1.18E-03 Yes NE Linezolid 0.25 SAOUHSC_01860 ytpP - 804 239 -565 1.00E+00 698 1599 901 1.03E-03 Yes NE NalidixicAcid 125 SAOUHSC_01860 ytpP - 804 6 -798 1.00E+00 698 1552 854 1.32E-03 Yes NE Oxacillin 0.15 SAOUHSC_01860 ytpP - 804 58 -746 1.00E+00 698 1967 1269 0.00E+00 Yes NE Tunicamycin 1 SAOUHSC_01860 ytpP - 804 295 -509 1.00E+00 698 4803 4105 0.00E+00 Yes NE Tunicamycin 1 SAOUHSC_01861 pepA - 146 6 -140 9.29E-01 322 3734 3412 0.00E+00 Yes NE Mupirocin 0.15 SAOUHSC_01865 trmB - 104 27 -77 7.23E-01 8 395 387 0.00E+00 Jackpot NE CDFI 0.417 SAOUHSC_01871 murJ_1 - 480 219 -261 1.00E+00 483 4421 3938 0.00E+00 Yes E CDFI 0.833 SAOUHSC_01871 murJ_1 - 480 102 -378 1.00E+00 483 8152 7669 0.00E+00 Yes E CDFI 1.667 SAOUHSC_01871 murJ_1 - 480 51 -429 9.17E-01 483 6339 5856 0.00E+00 Yes E CDFI 3.333 SAOUHSC_01871 murJ_1 - 480 27 -453 8.71E-01 483 4295 3812 0.00E+00 Yes E Cycloserine 100 SAOUHSC_01871 murJ_1 - 480 1 -479 8.99E-01 483 1101 618 0.00E+00 Yes E DMPI 1 SAOUHSC_01871 murJ_1 - 480 301 -179 8.95E-01 483 5478 4995 0.00E+00 Yes E DMPI 2 SAOUHSC_01871 murJ_1 - 480 0 -480 8.93E-01 483 3997 3514 0.00E+00 Yes E Fosfomycin 100 SAOUHSC_01871 murJ_1 - 480 15 -465 8.72E-01 483 1616 1133 0.00E+00 Yes E Fosfomycin 12 SAOUHSC_01871 murJ_1 - 480 2 -478 9.19E-01 483 2270 1787 0.00E+00 Yes E Gentamicin 0.5 SAOUHSC_01871 murJ_1 - 480 216 -264 1.00E+00 483 1442 959 9.07E-04 Yes E Oxacillin 0.15(2) SAOUHSC_01871 murJ_1 - 480 0 -480 9.03E-01 483 1929 1446 0.00E+00 Yes E Fosfomycin 12 SAOUHSC_01901 tal - 455 245 -210 4.04E-01 437 1421 984 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_01917 group_0462 - 350 93 -257 1.00E+00 275 5178 4903 0.00E+00 Yes NE Vancomycin 1 SAOUHSC_01980 airR - 560 345 -215 1.00E+00 548 1468 920 0.00E+00 Yes NE Novobiocin 0.25 SAOUHSC_01981 airS - 446 370 -76 9.94E-01 726 1694 968 2.81E-03 Yes NE Platensimycin 0.4 SAOUHSC_02092 ampS - 1109 413 -696 1.00E+00 816 1575 759 0.00E+00 Yes NE Platensimycin 0.5 SAOUHSC_02092 ampS - 1109 70 -1039 1.00E+00 816 3491 2675 0.00E+00 Yes NE Platensimycin 0.4 SAOUHSC_02093 group_0556 - 896 437 -459 1.00E+00 740 1166 426 3.69E-02 Yes NE Platensimycin 0.5 SAOUHSC_02093 group_0556 - 896 73 -823 1.00E+00 740 3252 2512 0.00E+00 Yes NE Cefaclor 0.5 SAOUHSC_02099 vraS - 844 3 -841 1.00E+00 235 4350 4115 0.00E+00 Yes NE Cefaclor 1 SAOUHSC_02099 vraS - 844 8 -836 1.00E+00 235 534 299 1.84E-02 Yes NE Daptomycin 2 SAOUHSC_02099 vraS - 844 6 -838 1.00E+00 235 1468 1233 3.69E-04 Yes NE Fosfomycin 12 SAOUHSC_02099 vraS - 844 2 -842 1.00E+00 235 2476 2241 0.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_02099 vraS - 844 401 -443 1.00E+00 235 847 612 2.73E-02 Yes NE Lysobactin 0.25 SAOUHSC_02099 vraS - 844 421 -423 1.00E+00 235 2087 1852 0.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_02107 murT - 246 367 121 1.00E+00 646 1649 1003 1.37E-02 Yes E Vancomycin 1 SAOUHSC_02126 purB - 305 321 16 1.00E+00 374 906 532 1.90E-02 Jackpot NE Bacitracin 8 SAOUHSC_02133 pncB - 554 655 101 1.00E+00 1140 1743 603 4.55E-02 Yes E Cycloserine 50 SAOUHSC_02133 pncB - 554 625 71 8.36E-01 1140 2123 983 0.00E+00 Yes E Moenomycin 0.02 SAOUHSC_02133 pncB - 554 867 313 7.98E-01 1140 2220 1080 1.35E-02 Yes E Ramoplanin 2 SAOUHSC_02138 pfbA - 966 946 -20 1.00E+00 869 1860 991 4.58E-03 Yes NE Daptomycin 2 SAOUHSC_02149 group_0571 - 210 17 -193 1.00E+00 416 9308 8892 0.00E+00 Yes NE Moenomycin 0.08 SAOUHSC_02149 group_0571 - 210 246 36 1.00E+00 416 1444 1028 0.00E+00 Yes NE Polymyxin 100 SAOUHSC_02149 group_0571 - 210 169 -41 1.00E+00 416 1270 854 1.05E-02 Yes NE Ramoplanin 2 SAOUHSC_02149 group_0571 - 210 239 29 1.00E+00 416 1442 1026 7.97E-04 Yes NE Vancomycin 1 SAOUHSC_02149 group_0571 - 210 211 1 1.00E+00 416 1598 1182 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_02152 pmtC - 273 28 -245 1.00E+00 746 4518 3772 0.00E+00 Yes E Polymyxin 100 SAOUHSC_02152 pmtC - 273 325 52 9.97E-01 746 1687 941 1.20E-02 Yes E Daptomycin 2 SAOUHSC_02153 pmtB - 68 15 -53 9.75E-01 282 1753 1471 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_02154 pmtA - 132 71 -61 9.75E-01 117 1288 1171 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_02155 pmtR - 199 24 -175 1.00E+00 648 1866 1218 0.00E+00 Yes NE Targocil 0.5 SAOUHSC_02156 group_0573 - 250 422 172 1.00E+00 675 1554 879 3.84E-02 Yes NE Oxacillin 0.15 SAOUHSC_02161 eap_1 - 101 12 -89 7.40E-01 169 1191 1022 7.56E-04 Yes NE TritonX100 4percent SAOUHSC_02161 eap_1 - 101 11 -90 9.69E-01 169 1414 1245 7.56E-04 Yes NE CCCP 0.8 SAOUHSC_02304 mazE - 579 202 -377 8.86E-01 466 1155 689 1.41E-02 Yes NE Cycloserine 50 SAOUHSC_02308 group_0641 - 1053 28 -1025 1.00E+00 654 1399 745 6.55E-03 Yes NE Ramoplanin 2 SAOUHSC_02316 cshA - 626 214 -412 1.00E+00 414 1312 898 3.28E-03 Yes E Chloramphenicol 1(2) SAOUHSC_02318 ddl - 157 43 -114 1.00E+00 480 3183 2703 0.00E+00 Yes E Chloramphenicol 1 SAOUHSC_02318 ddl - 157 43 -114 1.00E+00 480 3183 2703 0.00E+00 Yes E Cycloserine 100(2) SAOUHSC_02318 ddl - 157 4 -153 3.26E-01 480 4018 3538 0.00E+00 Yes E Cycloserine 100 SAOUHSC_02318 ddl - 157 6 -151 3.49E-01 480 3895 3415 0.00E+00 Yes E Oxacillin 0.15(2) SAOUHSC_02318 ddl - 157 1 -156 4.28E-01 480 2444 1964 0.00E+00 Yes E Polymyxin 100 SAOUHSC_02318 ddl - 157 36 -121 1.00E+00 480 2427 1947 0.00E+00 Yes E Polymyxin 12 SAOUHSC_02318 ddl - 157 103 -54 9.67E-01 480 3351 2871 0.00E+00 Yes E Polymyxin 25 SAOUHSC_02318 ddl - 157 267 110 1.00E+00 480 2757 2277 0.00E+00 Yes E Polymyxin 50 SAOUHSC_02318 ddl - 157 135 -22 8.88E-01 480 3811 3331 0.00E+00 Yes E Rifampicin 0.00195 SAOUHSC_02318 ddl - 157 128 -29 8.04E-01 480 2289 1809 0.00E+00 Yes E Rifampicin 0.0375 SAOUHSC_02318 ddl - 157 6 -151 5.17E-01 480 2032 1552 0.00E+00 Yes E Sulfamethoxazole 1 SAOUHSC_02318 ddl - 157 42 -115 6.69E-01 480 2664 2184 0.00E+00 Yes E Tetracycline 0.6 SAOUHSC_02318 ddl - 157 146 -11 1.00E+00 480 2592 2112 0.00E+00 Yes E Tetracycline 1 SAOUHSC_02318 ddl - 157 31 -126 5.35E-01 480 3677 3197 0.00E+00 Yes E Tetracycline 2 SAOUHSC_02318 ddl - 157 8 -149 4.00E-01 480 2477 1997 0.00E+00 Yes E Fosfomycin 12 SAOUHSC_02337 murA1 - 581 5 -576 9.14E-01 356 2189 1833 0.00E+00 Yes NE Oxacillin 0.15 SAOUHSC_02337 murA1 - 581 31 -550 9.71E-01 356 5238 4882 0.00E+00 Yes NE Fosfomycin 12 SAOUHSC_02338 group_0646 - 513 3 -510 9.61E-01 231 608 377 3.16E-02 Yes NE Oxacillin 0.15 SAOUHSC_02338 group_0646 - 513 43 -470 1.00E+00 231 3306 3075 0.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_02354 glyA - 329 225 -104 1.00E+00 368 1491 1123 0.00E+00 Yes NE Polymyxin 100 SAOUHSC_02354 glyA - 329 14 -315 1.00E+00 368 1180 812 4.21E-03 Yes NE

131 Supplemental Table 7 (Continued).

Plus Strand Minus Strand Antibiotic Conc. Locus Tag Gene Strand Ctrl Exp Dif qVal Ctrl Exp Dif qVal Visual Analysis Essentiality Bacitracin 16(2) SAOUHSC_02365 murA2 - 659 101 -558 1.00E+00 303 2262 1959 0.00E+00 Yes NE Daptomycin 2 SAOUHSC_02365 murA2 - 659 169 -490 1.00E+00 303 2130 1827 0.00E+00 Yes NE Fosfomycin 12 SAOUHSC_02365 murA2 - 659 3 -656 1.00E+00 303 2967 2664 0.00E+00 Yes NE Oxacillin 0.15 SAOUHSC_02365 murA2 - 659 56 -603 1.00E+00 303 6847 6544 0.00E+00 Yes NE Oxacillin 0.78 SAOUHSC_02365 murA2 - 659 404 -255 1.00E+00 303 686 383 1.47E-02 Yes NE Ramoplanin 2 SAOUHSC_02365 murA2 - 659 524 -135 1.00E+00 303 2250 1947 0.00E+00 Yes NE TritonX100 4percent SAOUHSC_02365 murA2 - 659 622 -37 9.69E-01 303 2374 2071 0.00E+00 Yes NE Novobiocin 0.25 SAOUHSC_02379 deoC2 - 285 343 58 7.99E-01 590 1277 687 4.23E-02 Yes NE Linezolid 0.25 SAOUHSC_02383 group_0654 - 911 16 -895 1.00E+00 885 3497 2612 0.00E+00 Yes NE NalidixicAcid 125 SAOUHSC_02383 group_0654 - 911 11 -900 1.00E+00 885 3758 2873 0.00E+00 Yes NE Polymyxin 100 SAOUHSC_02383 group_0654 - 911 286 -625 1.00E+00 885 1910 1025 4.21E-03 Yes NE Platensimycin 0.5 SAOUHSC_02418 lmrS_1 - 585 13 -572 1.00E+00 819 6924 6105 0.00E+00 Yes NE Platensimycin 0.5 SAOUHSC_02420 sdrM_1 - 557 7 -550 1.00E+00 607 3323 2716 0.00E+00 Yes NE Linezolid 0.25 SAOUHSC_02435 group_0663 - 146 34 -112 8.51E-01 326 868 542 1.27E-02 Yes NE Bacitracin 8 SAOUHSC_02521 SAOUHSC_02521 - 799 923 124 1.00E+00 1050 1675 625 2.29E-02 Yes NE TritonX100 1percent SAOUHSC_02521 SAOUHSC_02521 - 799 487 -312 1.00E+00 1050 2176 1126 6.14E-03 Yes NE Gentamicin 0.5 SAOUHSC_02522 group_0681 - 323 193 -130 1.00E+00 402 3685 3283 0.00E+00 Yes NE Gentamicin 1 SAOUHSC_02522 group_0681 - 323 17 -306 7.45E-01 402 3572 3170 0.00E+00 Yes NE Polymyxin 100 SAOUHSC_02522 group_0681 - 323 424 101 9.97E-01 402 2269 1867 0.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_02527 fmhB - 1098 1292 194 1.00E+00 816 1922 1106 4.92E-03 Yes E TritonX100 1percent SAOUHSC_02534 SAOUHSC_02534 - 177 161 -16 9.63E-01 323 1419 1096 0.00E+00 Yes NE TritonX100 4percent SAOUHSC_02534 SAOUHSC_02534 - 177 32 -145 9.83E-01 323 1149 826 4.85E-02 Yes NE TritonX100 1percent SAOUHSC_02535 SAOUHSC_02535 - 198 173 -25 9.67E-01 351 1414 1063 5.36E-03 Yes NE TritonX100 4percent SAOUHSC_02536 moaA - 433 72 -361 1.00E+00 242 1642 1400 0.00E+00 Yes NE TritonX100 4percent SAOUHSC_02542 moeA - 193 27 -166 1.00E+00 223 1484 1261 7.56E-04 Yes NE TritonX100 4percent SAOUHSC_02546 modC - 242 59 -183 9.78E-01 525 1737 1212 6.28E-03 Yes NE TritonX100 1percent SAOUHSC_02549 modA - 648 846 198 9.09E-01 552 1569 1017 1.22E-02 Yes NE TritonX100 4percent SAOUHSC_02610 hutG - 769 224 -545 1.00E+00 536 2136 1600 7.56E-04 Yes NE Novobiocin 0.25 SAOUHSC_02630 emrK - 249 148 -101 1.00E+00 678 1624 946 2.21E-03 Yes NE Platensimycin 0.4 SAOUHSC_02630 emrK - 249 6 -243 1.00E+00 678 1476 798 0.00E+00 Yes NE Platensimycin 0.5 SAOUHSC_02630 emrK - 249 14 -235 9.50E-01 678 5278 4600 0.00E+00 Yes NE Platensimycin 0.4 SAOUHSC_02632 tcaB_1 - 709 280 -429 9.96E-01 667 1320 653 1.97E-03 Yes NE Platensimycin 0.5 SAOUHSC_02632 tcaB_1 - 709 35 -674 1.00E+00 667 6406 5739 0.00E+00 Yes NE Vancomycin 1 SAOUHSC_02636 - 317 231 -86 1.00E+00 781 1412 631 1.97E-02 Yes NE Platensimycin 0.5 SAOUHSC_02658 group_0716 - 413 189 -224 8.34E-01 1257 12113 10856 0.00E+00 Yes NE CCCP 0.8 SAOUHSC_02668 group_0720 - 279 12 -267 7.75E-01 276 886 610 2.21E-02 Yes NE Mupirocin 0.37 SAOUHSC_02700 mdeA - 887 743 -144 8.97E-01 608 1951 1343 0.00E+00 Yes NE Novobiocin 0.25 SAOUHSC_02700 mdeA - 887 851 -36 9.12E-01 608 2675 2067 0.00E+00 Yes NE Platensimycin 0.4 SAOUHSC_02700 mdeA - 887 332 -555 1.00E+00 608 1370 762 0.00E+00 Yes NE Platensimycin 0.5 SAOUHSC_02700 mdeA - 887 27 -860 1.00E+00 608 4964 4356 0.00E+00 Yes NE Sorangicin 0.04 SAOUHSC_02700 mdeA - 887 427 -460 9.92E-01 608 1191 583 0.00E+00 Yes NE Sorangicin 0.05 SAOUHSC_02700 mdeA - 887 561 -326 9.67E-01 608 2555 1947 0.00E+00 Yes NE Sorangicin 0.08 SAOUHSC_02700 mdeA - 887 495 -392 9.78E-01 608 7193 6585 0.00E+00 Yes NE Mupirocin 0.37 SAOUHSC_02701 SAOUHSC_02701 - 1120 959 -161 8.87E-01 595 1956 1361 0.00E+00 Yes NE Novobiocin 0.25 SAOUHSC_02701 SAOUHSC_02701 - 1120 1247 127 7.25E-01 595 2595 2000 0.00E+00 Yes NE Platensimycin 0.4 SAOUHSC_02701 SAOUHSC_02701 - 1120 433 -687 1.00E+00 595 1288 693 0.00E+00 Yes NE Platensimycin 0.5 SAOUHSC_02701 SAOUHSC_02701 - 1120 39 -1081 1.00E+00 595 3858 3263 0.00E+00 Yes NE Sorangicin 0.04 SAOUHSC_02701 SAOUHSC_02701 - 1120 512 -608 1.00E+00 595 1114 519 7.37E-03 Yes NE Sorangicin 0.05 SAOUHSC_02701 SAOUHSC_02701 - 1120 749 -371 9.67E-01 595 2473 1878 0.00E+00 Yes NE Sorangicin 0.08 SAOUHSC_02701 SAOUHSC_02701 - 1120 648 -472 9.87E-01 595 5968 5373 0.00E+00 Yes NE Ciprofloxacin 0.15 SAOUHSC_02756 group_0735 - 385 489 104 8.77E-01 243 691 448 4.62E-02 Not clearly biased NE TritonX100 4percent SAOUHSC_02768 group_0738 - 295 154 -141 9.79E-01 427 1800 1373 1.40E-03 Yes NE TritonX100 4percent SAOUHSC_02769 group_0739 - 685 142 -543 1.00E+00 553 2635 2082 0.00E+00 Yes NE TritonX100 1percent SAOUHSC_02770 dapF - 405 460 55 9.51E-01 501 1333 832 4.60E-02 Yes NE TritonX100 4percent SAOUHSC_02770 dapF - 405 116 -289 1.00E+00 501 1452 951 2.51E-02 Yes NE Platensimycin 0.5 SAOUHSC_02778 group_0744 - 806 2 -804 1.00E+00 978 1622 644 2.37E-02 Yes NE TritonX100 4percent SAOUHSC_02866 ydfJ - 347 462 115 9.58E-01 834 1990 1156 1.09E-02 Yes NE Daptomycin 2 SAOUHSC_02879 crtM_1 - 298 24 -274 1.00E+00 356 1731 1375 0.00E+00 Yes NE Moenomycin 0.08 SAOUHSC_02879 crtM_1 - 298 264 -34 1.00E+00 356 1007 651 1.37E-02 Yes NE Daptomycin 2 SAOUHSC_02880 crtQ - 231 15 -216 1.00E+00 772 1743 971 5.42E-03 Yes NE Gentamicin 0.5 SAOUHSC_02880 crtQ - 231 118 -113 1.00E+00 772 2034 1262 0.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_02880 crtQ - 231 353 122 1.00E+00 772 1563 791 3.72E-02 Yes NE Moenomycin 0.08 SAOUHSC_02880 crtQ - 231 238 7 1.00E+00 772 2094 1322 0.00E+00 Yes NE Vancomycin 1 SAOUHSC_02880 crtQ - 231 211 -20 1.00E+00 772 1349 577 8.04E-03 Yes NE Daptomycin 2 SAOUHSC_02881 crtP - 563 84 -479 1.00E+00 244 1525 1281 0.00E+00 Yes NE Moenomycin 0.08 SAOUHSC_02881 crtP - 563 541 -22 1.00E+00 244 880 636 1.25E-02 Yes NE Moenomycin 0.02 SAOUHSC_02924 group_0792 - 456 720 264 9.93E-01 834 1653 819 3.72E-02 Yes NE Polymyxin 25 SAOUHSC_02940 SAOUHSC_02940 - 698 882 184 1.00E+00 1538 2533 995 3.44E-02 Not clearly biased NE Moenomycin 0.02 SAOUHSC_02956 nsaR - 525 692 167 1.00E+00 633 1598 965 1.37E-02 Yes NE Novobiocin 0.25 SAOUHSC_02987 asp2 - 181 188 7 8.59E-01 296 1243 947 8.67E-04 Yes NE Novobiocin 0.25 SAOUHSC_02989 secY2 - 247 232 -15 9.21E-01 530 1104 574 4.23E-02 Yes NE Novobiocin 0.25 SAOUHSC_03014 hisG - 306 409 103 7.25E-01 361 1060 699 1.58E-02 Yes NE TritonX100 1percent SAOUHSC_03035 group_0812 - 442 517 75 9.47E-01 260 1278 1018 1.84E-03 Yes NE TritonX100 4percent SAOUHSC_03035 group_0812 - 442 329 -113 9.81E-01 260 2328 2068 0.00E+00 Yes NE Bacitracin 32 SAOUHSC_A01041 - 113 107 -6 1.00E+00 477 985 508 4.92E-02 Yes NE Trimethoprim 0.5 SAOUHSC_00001 dnaA + 183 669 486 3.93E-03 76 6 -70 2.39E-01 Yes E Moenomycin 0.08 SAOUHSC_00020 walR + 507 1661 1154 0.00E+00 415 276 -139 1.00E+00 Yes E Vancomycin 1 SAOUHSC_00020 walR + 507 2055 1548 0.00E+00 415 444 29 9.54E-01 Yes E Vancomycin 1 SAOUHSC_00023 walI + 78 540 462 2.53E-02 15 33 18 9.54E-01 Yes NE Linezolid 0.25 SAOUHSC_00035 cstA + 392 844 452 3.45E-02 250 96 -154 8.69E-01 Yes NE Moenomycin 0.08 SAOUHSC_00035 cstA + 392 1070 678 1.69E-02 250 314 64 1.00E+00 Yes NE NalidixicAcid 25 SAOUHSC_00035 cstA + 392 1084 692 0.00E+00 250 100 -150 4.97E-01 Yes NE Targocil 0.125 SAOUHSC_00064 norG + 538 960 422 1.47E-02 235 250 15 9.03E-01 Yes NE Targocil2 0.0625 SAOUHSC_00065 group_0015 + 1185 1533 348 1.77E-02 748 382 -366 9.43E-01 Yes NE Novobiocin 0.25 SAOUHSC_00075 sbnA + 491 2105 1614 0.00E+00 280 180 -100 9.91E-01 Yes NE

132 Supplemental Table 7 (Continued).

Plus Strand Minus Strand Antibiotic Conc. Locus Tag Gene Strand Ctrl Exp Dif qVal Ctrl Exp Dif qVal Visual Analysis Essentiality CCCP 0.8 SAOUHSC_00079 sbnE + 302 931 629 3.54E-02 534 18 -516 1.00E+00 Yes NE Novobiocin 0.25 SAOUHSC_00079 sbnE + 302 1180 878 1.18E-03 534 475 -59 8.73E-01 Yes NE Ciprofloxacin 0.15 SAOUHSC_00091 group_0018 + 827 1416 589 4.58E-02 460 595 135 7.68E-01 Weak NE Polymyxin 25 SAOUHSC_00091 group_0018 + 827 1700 873 4.42E-02 460 515 55 9.62E-01 Yes NE Novobiocin 0.25 SAOUHSC_00119 capF + 407 1852 1445 0.00E+00 287 233 -54 8.79E-01 Not clearly biased NE Moenomycin 0.08 SAOUHSC_00183 uhpT + 1475 2260 785 1.47E-02 1279 1218 -61 1.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_00222 tarL' + 1572 2246 674 3.36E-02 912 1241 329 7.75E-01 Not clearly biased NE Novobiocin 0.25 SAOUHSC_00223 tarF_1 + 436 1148 712 2.84E-02 1293 1320 27 8.21E-01 Yes E Novobiocin 0.25 SAOUHSC_00225 tarI + 576 2236 1660 0.00E+00 237 207 -30 8.35E-01 Yes NE Daptomycin 2 SAOUHSC_00226 tarJ + 4 906 902 3.93E-04 8 0 -8 7.84E-01 Yes E Novobiocin 0.25 SAOUHSC_00267 esxD + 518 1406 888 3.74E-03 446 510 64 8.06E-01 Not clearly biased NE TritonX100 1percent SAOUHSC_00331 group_0062 + 780 1710 930 1.62E-02 323 257 -66 9.39E-01 Yes NE TritonX100 4percent SAOUHSC_00331 group_0062 + 780 1605 825 4.92E-02 323 109 -214 9.90E-01 Yes NE TritonX100 4percent SAOUHSC_00333 ytrB + 453 2085 1632 1.43E-03 530 107 -423 1.00E+00 Yes NE Daptomycin 2 SAOUHSC_00355 group_0070 + 863 1513 650 4.81E-02 485 58 -427 1.00E+00 Yes NE TritonX100 4percent SAOUHSC_00375 guaA + 45 977 932 5.36E-03 11 4 -7 8.70E-01 Yes NE Bacitracin 8(2) SAOUHSC_00414 mpsC + 283 895 612 0.00E+00 188 50 -138 9.93E-01 Yes NE Moenomycin 0.02 SAOUHSC_00414 mpsC + 283 874 591 2.24E-02 188 258 70 1.00E+00 Yes NE Bacitracin 16(2) SAOUHSC_00416 group_0092 + 1092 11059 9967 0.00E+00 1145 112 -1033 1.00E+00 Yes NE Bacitracin 2 SAOUHSC_00416 group_0092 + 1092 1634 542 0.00E+00 1145 184 -961 1.00E+00 Yes NE Bacitracin 4 SAOUHSC_00416 group_0092 + 1092 1486 394 7.37E-03 1145 111 -1034 1.00E+00 Yes NE Bacitracin 8(2) SAOUHSC_00416 group_0092 + 1092 6252 5160 0.00E+00 1145 63 -1082 1.00E+00 Yes NE Bacitracin 8 SAOUHSC_00416 group_0092 + 1092 1848 756 0.00E+00 1145 625 -520 1.00E+00 Yes NE Cycloserine 50 SAOUHSC_00416 group_0092 + 1092 1817 725 0.00E+00 1145 564 -581 1.00E+00 Yes NE Daptomycin 1 SAOUHSC_00416 group_0092 + 1092 1651 559 0.00E+00 1145 32 -1113 1.00E+00 Yes NE Moenomycin 0.02 SAOUHSC_00416 group_0092 + 1092 3279 2187 0.00E+00 1145 760 -385 1.00E+00 Yes NE Platensimycin 0.5 SAOUHSC_00465 veg + 193 844 651 6.51E-03 179 160 -19 6.16E-01 Yes NE TritonX100 4percent SAOUHSC_00467 purR + 348 2987 2639 0.00E+00 2280 8 -2272 1.00E+00 Yes NE Novobiocin 0.25 SAOUHSC_00469 spoVG + 659 1667 1008 1.67E-03 1172 1311 139 6.63E-01 Jackpot NE Novobiocin 0.25 SAOUHSC_00472 prs + 568 1467 899 1.67E-03 11 13 2 8.21E-01 Yes E TritonX100 1percent SAOUHSC_00475 pth + 274 1281 1007 2.11E-03 338 533 195 9.11E-01 Jackpot NE TritonX100 4percent SAOUHSC_00475 pth + 274 1153 879 3.37E-02 338 71 -267 9.97E-01 Yes E General enrichment CDFI 0.833 SAOUHSC_00480 mazG + 508 3159 2651 0.00E+00 427 285 -142 7.65E-01 of previous gene NE Linezolid 0.25 SAOUHSC_00480 mazG + 508 1995 1487 0.00E+00 427 13 -414 1.00E+00 Yes NE TritonX100 4percent SAOUHSC_00480 mazG + 508 3055 2547 0.00E+00 427 3 -424 1.00E+00 Yes NE CDFI 0.833 SAOUHSC_00552 nagB_1 + 396 1158 762 2.53E-02 444 45 -399 1.00E+00 Yes NE Chloramphenicol 1(2) SAOUHSC_00557 vraA + 1378 1935 557 1.75E-02 916 862 -54 8.66E-01 Not clearly biased NE Chloramphenicol 1 SAOUHSC_00557 vraA + 1378 1935 557 2.03E-02 916 862 -54 8.67E-01 Not clearly biased NE Daptomycin 2 SAOUHSC_00577 mvaK1 + 482 2041 1559 0.00E+00 647 81 -566 1.00E+00 Yes E Ramoplanin 2 SAOUHSC_00577 mvaK1 + 482 1740 1258 0.00E+00 647 148 -499 1.00E+00 Yes E Novobiocin 0.25 SAOUHSC_00618 group_0130 + 328 1909 1581 0.00E+00 491 618 127 6.94E-01 Yes NE Novobiocin 0.25 SAOUHSC_00633 nhaK_1 + 471 1252 781 1.42E-02 716 342 -374 1.00E+00 Yes NE TritonX100 1percent SAOUHSC_00633 nhaK_1 + 471 1382 911 2.26E-02 716 483 -233 1.00E+00 Yes NE Moenomycin 0.08 SAOUHSC_00647 abcA + 1220 4322 3102 0.00E+00 448 238 -210 1.00E+00 Yes NE Polymyxin 100 SAOUHSC_00647 abcA + 1220 2741 1521 0.00E+00 448 38 -410 1.00E+00 Yes NE Targocil 0.5 SAOUHSC_00647 abcA + 1220 2977 1757 0.00E+00 448 400 -48 1.00E+00 Yes NE Ramoplanin 2 SAOUHSC_00664 graX + 625 2818 2193 0.00E+00 739 271 -468 1.00E+00 Yes NE Vancomycin 1 SAOUHSC_00664 graX + 625 2370 1745 0.00E+00 739 459 -280 1.00E+00 Yes NE General enrichment Targocil2 0.0625 SAOUHSC_00666 graS_1 + 534 940 406 1.77E-02 343 311 -32 5.91E-01 of previous gene NE Targocil 0.5 SAOUHSC_00684 group_0144 + 550 1570 1020 1.47E-02 250 429 179 1.00E+00 Jackpot NE Bacitracin 8(2) SAOUHSC_00703 norA + 491 1699 1208 0.00E+00 195 122 -73 6.45E-01 Yes NE Ciprofloxacin 0.15 SAOUHSC_00703 norA + 491 5067 4576 0.00E+00 195 287 92 9.42E-01 Yes NE Ciprofloxacin 0.3 SAOUHSC_00703 norA + 491 8376 7885 0.00E+00 195 4 -191 2.09E-01 Yes NE Gentamicin 1 SAOUHSC_00703 norA + 491 1172 681 8.85E-03 195 9 -186 4.18E-01 Yes NE Novobiocin 0.25 SAOUHSC_00703 norA + 491 2112 1621 0.00E+00 195 226 31 8.21E-01 Yes NE Polymyxin 25 SAOUHSC_00703 norA + 491 1612 1121 0.00E+00 195 181 -14 9.62E-01 Yes NE Tetracycline 0.6 SAOUHSC_00703 norA + 491 1828 1337 0.00E+00 195 301 106 9.28E-01 Yes NE Sulfamethoxazole 18 SAOUHSC_00722 pabA + 614 985 371 8.43E-03 280 194 -86 1.94E-01 Yes NE Trimethoprim 0.5 SAOUHSC_00722 pabA + 614 1915 1301 0.00E+00 280 19 -261 5.19E-01 Yes NE Moenomycin 0.08 SAOUHSC_00729 ettA + 475 1359 884 7.56E-04 566 529 -37 1.00E+00 Yes NE Rifampicin 0.00195 SAOUHSC_00729 ettA + 475 1022 547 6.32E-03 566 375 -191 9.70E-01 Yes NE TritonX100 4percent SAOUHSC_00729 ettA + 475 3400 2925 0.00E+00 566 70 -496 1.00E+00 Yes NE Vancomycin 1 SAOUHSC_00729 ettA + 475 1049 574 1.33E-02 566 404 -162 1.00E+00 Yes NE Moenomycin 0.08 SAOUHSC_00741 nrdI + 701 1423 722 1.86E-02 393 38 -355 1.00E+00 Yes E Moenomycin 0.08 SAOUHSC_00742 nrdE + 219 697 478 2.78E-02 215 23 -192 1.00E+00 Yes E Vancomycin 1 SAOUHSC_00762 tarO + 497 1008 511 4.77E-02 535 236 -299 1.00E+00 Yes E TritonX100 4percent SAOUHSC_00769 secA + 821 2066 1245 6.09E-03 416 9 -407 1.00E+00 Yes E TritonX100 1percent SAOUHSC_00784 bepA + 594 1360 766 4.94E-02 498 305 -193 1.00E+00 Yes NE CDFI 0.833 SAOUHSC_00788 group_0168 + 278 1410 1132 3.21E-03 319 6 -313 9.75E-01 Yes NE Oxacillin 0.15 SAOUHSC_00788 group_0168 + 278 7587 7309 0.00E+00 319 124 -195 6.88E-01 Yes NE TritonX100 4percent SAOUHSC_00788 group_0168 + 278 1309 1031 1.42E-02 319 1 -318 1.00E+00 Yes NE TritonX100 4percent SAOUHSC_00793 group_0170 + 728 3656 2928 0.00E+00 355 14 -341 1.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_00864 group_0195 + 866 1587 721 9.55E-03 228 159 -69 8.89E-01 Yes NE Platensimycin 0.4 SAOUHSC_00920 fabH + 465 1296 831 0.00E+00 219 88 -131 7.91E-01 Yes E Platensimycin 0.5 SAOUHSC_00920 fabH + 465 5941 5476 0.00E+00 219 13 -206 8.08E-01 Yes E Gentamicin 0.5 SAOUHSC_00941 group_0211 + 1196 2810 1614 0.00E+00 160 8 -152 1.00E+00 Yes NE Triclosan 0.005 SAOUHSC_00941 group_0211 + 1196 2187 991 0.00E+00 160 120 -40 8.69E-01 Yes NE Triclosan 0.01 SAOUHSC_00941 group_0211 + 1196 2533 1337 0.00E+00 160 87 -73 8.91E-01 Yes NE CCCP 0.8 SAOUHSC_00945 mgtE + 712 1390 678 1.20E-02 635 16 -619 1.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_00945 mgtE + 712 2669 1957 0.00E+00 635 178 -457 1.00E+00 Yes NE Triclosan 0.005 SAOUHSC_00945 mgtE + 712 3245 2533 0.00E+00 635 447 -188 9.40E-01 Yes NE Triclosan 0.01 SAOUHSC_00945 mgtE + 712 5511 4799 0.00E+00 635 368 -267 9.82E-01 Yes NE Gentamicin 0.5 SAOUHSC_00946 cpaA + 478 2212 1734 0.00E+00 260 59 -201 1.00E+00 Yes NE

133 Supplemental Table 7 (Continued).

Plus Strand Minus Strand Antibiotic Conc. Locus Tag Gene Strand Ctrl Exp Dif qVal Ctrl Exp Dif qVal Visual Analysis Essentiality Triclosan 0.005 SAOUHSC_00946 cpaA + 478 2708 2230 0.00E+00 260 145 -115 9.16E-01 Yes NE Triclosan 0.01 SAOUHSC_00946 cpaA + 478 4694 4216 0.00E+00 260 268 8 8.63E-01 Yes NE Gentamicin 0.5 SAOUHSC_00947 fabI + 577 2121 1544 0.00E+00 318 79 -239 1.00E+00 Yes E Triclosan 0.005 SAOUHSC_00947 fabI + 577 2220 1643 0.00E+00 318 155 -163 9.58E-01 Yes E Triclosan 0.01 SAOUHSC_00947 fabI + 577 3244 2667 0.00E+00 318 69 -249 9.93E-01 Yes E Daptomycin 2 SAOUHSC_00967 SAOUHSC_00967 + 529 6068 5539 0.00E+00 179 31 -148 9.22E-01 Yes NE Daptomycin 2 SAOUHSC_00968 group_0220 + 337 9737 9400 0.00E+00 254 205 -49 7.84E-01 Yes NE Daptomycin 2 SAOUHSC_00969 group_0221 + 475 9014 8539 0.00E+00 230 162 -68 7.87E-01 Yes NE Daptomycin 2 SAOUHSC_00970 group_0222 + 746 4773 4027 0.00E+00 501 152 -349 1.00E+00 Yes NE TritonX100 4percent SAOUHSC_00973 tarM + 273 1065 792 3.46E-02 470 342 -128 8.98E-01 Weak NE General enrichment Gentamicin 0.5 SAOUHSC_00998 fmtA + 423 1636 1213 0.00E+00 410 413 3 8.89E-01 of previous gene NE CCCP 0.8 SAOUHSC_01008 purE + 567 6226 5659 0.00E+00 159 26 -133 6.72E-01 Yes NE Novobiocin 0.25 SAOUHSC_01008 purE + 567 1358 791 1.51E-02 159 78 -81 9.93E-01 Yes NE Targocil 0.5 SAOUHSC_01008 purE + 567 1682 1115 3.10E-03 159 260 101 1.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_01031 cydA + 316 959 643 1.92E-02 149 52 -97 9.99E-01 Yes NE Gramicidin 0.333 SAOUHSC_01049 potD + 103 395 292 2.21E-02 152 0 -152 1.71E-01 Jackpot NE General enrichment Polymyxin 100 SAOUHSC_01066 ctaB + 212 1206 994 2.03E-03 242 377 135 9.23E-01 of previous gene NE TritonX100 4percent SAOUHSC_01066 ctaB + 212 1779 1567 0.00E+00 242 126 -116 8.91E-01 Yes NE Oxacillin 0.15(2) SAOUHSC_01067 ctaM + 13 371 358 4.24E-02 41 0 -41 1.17E-01 Jackpot NE Chloramphenicol 1(2) SAOUHSC_01096 zapA + 379 969 590 2.68E-03 388 286 -102 9.18E-01 Yes NE Chloramphenicol 1 SAOUHSC_01096 zapA + 379 969 590 6.81E-03 388 286 -102 9.19E-01 Yes NE Linezolid 0.25 SAOUHSC_01096 zapA + 379 2784 2405 0.00E+00 388 67 -321 1.00E+00 Yes NE Chloramphenicol 1(2) SAOUHSC_01097 group_0248 + 378 850 472 4.28E-02 316 28 -288 1.00E+00 Yes NE Chloramphenicol 1 SAOUHSC_01097 group_0248 + 378 850 472 4.20E-02 316 28 -288 1.00E+00 Yes NE Linezolid 0.25 SAOUHSC_01097 group_0248 + 378 2922 2544 0.00E+00 316 15 -301 9.31E-01 Yes NE Chloramphenicol 1(2) SAOUHSC_01098 polX + 370 1584 1214 0.00E+00 513 24 -489 1.00E+00 Yes NE Chloramphenicol 1 SAOUHSC_01098 polX + 370 1584 1214 0.00E+00 513 24 -489 1.00E+00 Yes NE Linezolid 0.25 SAOUHSC_01098 polX + 370 3322 2952 0.00E+00 513 12 -501 1.00E+00 Yes NE Chloramphenicol 1(2) SAOUHSC_01099 mutS2 + 329 844 515 1.75E-02 106 5 -101 9.49E-01 Yes NE Chloramphenicol 1 SAOUHSC_01099 mutS2 + 329 844 515 1.38E-02 106 5 -101 9.50E-01 Yes NE Linezolid 0.25 SAOUHSC_01099 mutS2 + 329 3083 2754 0.00E+00 106 2 -104 6.85E-01 Yes NE Bacitracin 4 SAOUHSC_01109 SAOUHSC_01109 + 286 598 312 0.00E+00 847 268 -579 1.00E+00 Yes NE Bacitracin 8(2) SAOUHSC_01109 SAOUHSC_01109 + 286 668 382 3.29E-02 847 390 -457 1.00E+00 Yes NE Oxacillin 0.39 SAOUHSC_01109 SAOUHSC_01109 + 286 715 429 0.00E+00 847 486 -361 1.00E+00 Yes NE Oxacillin 0.78 SAOUHSC_01109 SAOUHSC_01109 + 286 684 398 1.18E-02 847 428 -419 1.00E+00 Yes NE Rifampicin 0.00195 SAOUHSC_01109 SAOUHSC_01109 + 286 657 371 1.15E-02 847 452 -395 1.00E+00 Jackpot NE Linezolid 0.25 SAOUHSC_01146 mraY + 81 490 409 3.82E-02 8 2 -6 6.43E-01 Jackpot NE Mupirocin 0.37 SAOUHSC_01156 group_0262 + 305 1312 1007 0.00E+00 816 533 -283 1.00E+00 Yes NE Oxacillin 0.15 SAOUHSC_01186 stp1 + 148 624 476 4.51E-02 187 10 -177 7.85E-01 Yes NE Bacitracin 16(2) SAOUHSC_01187 pknB + 178 543 365 3.50E-02 350 8 -342 8.06E-01 Yes NE CCCP 0.8 SAOUHSC_01187 pknB + 178 2497 2319 0.00E+00 350 304 -46 5.61E-01 Yes NE Oxacillin 0.15 SAOUHSC_01187 pknB + 178 5168 4990 0.00E+00 350 90 -260 6.95E-01 Yes NE Ramoplanin 2 SAOUHSC_01187 pknB + 178 1956 1778 0.00E+00 350 188 -162 1.00E+00 Yes NE Chloramphenicol 1(2) SAOUHSC_01222 topA + 377 798 421 4.84E-02 230 227 -3 8.66E-01 Yes NE Chloramphenicol 1 SAOUHSC_01222 topA + 377 798 421 4.63E-02 230 227 -3 8.67E-01 Yes NE Amphomycin 14.4 SAOUHSC_01223 gid + 335 627 292 2.95E-02 800 423 -377 7.08E-01 Not clearly biased NE Platensimycin 0.5 SAOUHSC_01224 xerC + 225 1825 1600 0.00E+00 384 162 -222 7.28E-01 Yes E Platensimycin 0.5 SAOUHSC_01226 hslU + 150 1097 947 0.00E+00 118 1 -117 6.94E-01 Yes NE Platensimycin 0.5 SAOUHSC_01228 codY + 134 1382 1248 0.00E+00 21 0 -21 6.16E-01 Yes NE Moenomycin 0.08 SAOUHSC_01233 SAOUHSC_01233 + 43 417 374 3.07E-02 44 118 74 8.05E-01 Jackpot NE Moenomycin 0.08 SAOUHSC_01240 proS + 322 1410 1088 0.00E+00 16 55 39 1.00E+00 Yes E Vancomycin 1 SAOUHSC_01240 proS + 322 1088 766 0.00E+00 16 28 12 9.54E-01 Yes E NalidixicAcid 125 SAOUHSC_01244 group_0272 + 10 793 783 0.00E+00 0 0 0 1.00E+00 Yes E Oxacillin 0.15 SAOUHSC_01244 group_0272 + 10 536 526 1.16E-02 0 0 0 1.00E+00 Yes E General enrichment Amphomycin 9.6 SAOUHSC_01266 korA + 169 791 622 0.00E+00 106 59 -47 2.43E-01 of previous gene NE Amphomycin 9.6 SAOUHSC_01267 korB + 38 395 357 3.26E-02 117 0 -117 4.69E-01 Yes NE Bacitracin 8(2) SAOUHSC_01267 korB + 38 457 419 9.22E-03 117 100 -17 6.45E-01 Jackpot NE Ciprofloxacin 0.3 SAOUHSC_01267 korB + 38 395 357 2.11E-02 117 0 -117 4.14E-01 Jackpot NE Novobiocin 0.5 SAOUHSC_01267 korB + 38 415 377 1.11E-02 117 10 -107 6.01E-01 Jackpot NE Targocil2 0.0625 SAOUHSC_01267 korB + 38 416 378 2.95E-02 117 45 -72 6.27E-01 Jackpot NE Novobiocin 0.25 SAOUHSC_01351 parE + 95 580 485 4.32E-02 46 46 0 8.21E-01 Yes E Ciprofloxacin 0.3 SAOUHSC_01352 parC + 13 556 543 0.00E+00 128 35 -93 8.37E-01 Yes E Lysobactin 0.25 SAOUHSC_01354 alsT + 385 875 490 1.18E-02 1194 1151 -43 5.94E-01 Not clearly biased NE Daptomycin 2 SAOUHSC_01359 mprF + 125 3881 3756 0.00E+00 188 55 -133 9.19E-01 Yes NE Oxacillin 0.15 SAOUHSC_01361 lcpA + 391 831 440 4.33E-02 222 17 -205 6.65E-01 Yes NE Moenomycin 0.16 SAOUHSC_01840 sgtA + 144 1446 1302 0.00E+00 406 17 -389 9.79E-01 Yes NE Moxifloxacin 0.03125 SAOUHSC_01840 sgtA + 144 969 825 0.00E+00 406 6 -400 1.00E+00 Yes NE TritonX100 4percent SAOUHSC_01897 sigS_1 + 878 2189 1311 4.80E-03 538 592 54 8.70E-01 Yes NE Gentamicin 1 SAOUHSC_01964 traP_1 + 247 791 544 3.67E-02 257 19 -238 8.78E-01 Yes NE Daptomycin 1 SAOUHSC_02086 group_0552 + 107 405 298 2.11E-02 177 20 -157 7.18E-01 Jackpot NE Rifampicin 0.0375 SAOUHSC_02086 group_0552 + 107 401 294 3.79E-02 177 1 -176 6.77E-01 Jackpot NE Daptomycin 1 SAOUHSC_02087 group_0553 + 38 397 359 2.11E-02 38 3 -35 5.19E-01 Jackpot NE Moxifloxacin 0.0156 SAOUHSC_02087 group_0553 + 38 446 408 1.43E-02 38 54 16 9.00E-01 Jackpot NE Rifampicin 0.0375 SAOUHSC_02087 group_0553 + 38 399 361 3.79E-02 38 0 -38 4.08E-01 Jackpot NE Amphomycin 9.6 SAOUHSC_02164 SAOUHSC_02164 + 287 1185 898 0.00E+00 312 3 -309 5.66E-01 Yes NE Daptomycin 2 SAOUHSC_02164 SAOUHSC_02164 + 287 5063 4776 0.00E+00 312 96 -216 9.41E-01 Yes NE Moenomycin 0.08 SAOUHSC_02164 SAOUHSC_02164 + 287 1245 958 0.00E+00 312 161 -151 1.00E+00 Yes NE Moenomycin 0.32 SAOUHSC_02164 SAOUHSC_02164 + 287 3703 3416 0.00E+00 312 13 -299 5.39E-01 Yes NE Vancomycin 1 SAOUHSC_02164 SAOUHSC_02164 + 287 852 565 1.54E-02 312 255 -57 9.54E-01 Yes NE Moenomycin 0.32 SAOUHSC_02166 SAOUHSC_02166 + 240 1285 1045 0.00E+00 439 10 -429 7.39E-01 Yes NE TritonX100 4percent SAOUHSC_02262 agrD_1 + 627 1573 946 4.27E-02 4505 12 -4493 1.00E+00 Yes NE Chloramphenicol 1(2) SAOUHSC_02274 vga + 490 1429 939 0.00E+00 271 221 -50 8.70E-01 Yes NE

134 Supplemental Table 7 (Continued).

Plus Strand Minus Strand Antibiotic Conc. Locus Tag Gene Strand Ctrl Exp Dif qVal Ctrl Exp Dif qVal Visual Analysis Essentiality Chloramphenicol 1 SAOUHSC_02274 vga + 490 1429 939 0.00E+00 271 221 -50 8.71E-01 Yes NE General enrichment Moenomycin 0.16 SAOUHSC_02281 ilvD + 219 1873 1654 0.00E+00 755 281 -474 7.78E-01 of previous gene NE Bacitracin 8(2) SAOUHSC_02375 luxS + 662 1098 436 2.09E-02 398 190 -208 9.52E-01 Yes NE Targocil 0.5 SAOUHSC_02375 luxS + 662 2000 1338 0.00E+00 398 501 103 1.00E+00 Yes NE Targocil 0.5 SAOUHSC_02596 group_0699 + 449 1363 914 1.78E-02 267 295 28 1.00E+00 Yes NE Fosfomycin 12 SAOUHSC_02608 hutR + 186 795 609 5.90E-03 209 1 -208 6.67E-01 Yes NE Bacitracin 8(2) SAOUHSC_02609 fosB + 503 2201 1698 0.00E+00 714 519 -195 7.27E-01 Yes NE Fosfomycin 100 SAOUHSC_02609 fosB + 503 2688 2185 0.00E+00 714 14 -700 8.90E-01 Yes NE Fosfomycin 12 SAOUHSC_02609 fosB + 503 9732 9229 0.00E+00 714 184 -530 6.99E-01 Yes NE Rifampicin 0.00195 SAOUHSC_02609 fosB + 503 972 469 2.68E-03 714 383 -331 9.76E-01 Yes NE Rifampicin 0.0375 SAOUHSC_02609 fosB + 503 1988 1485 0.00E+00 714 18 -696 9.05E-01 Yes NE Daptomycin 2 SAOUHSC_02628 group_0707 + 226 2194 1968 0.00E+00 287 168 -119 8.91E-01 Yes NE Gentamicin 0.5 SAOUHSC_02628 group_0707 + 226 1147 921 9.51E-04 287 229 -58 8.89E-01 Yes NE Gentamicin 0.5 SAOUHSC_02669 sarZ + 346 2031 1685 0.00E+00 139 141 2 8.89E-01 Yes NE Polymyxin 100 SAOUHSC_02669 sarZ + 346 1817 1471 0.00E+00 139 69 -70 9.56E-01 Yes NE Daptomycin 2 SAOUHSC_02759 yehR + 216 909 693 1.98E-02 286 85 -201 1.00E+00 Yes NE Gentamicin 0.5 SAOUHSC_02795 group_0754 + 621 1776 1155 0.00E+00 1553 1344 -209 1.00E+00 Yes NE Vancomycin 1 SAOUHSC_02795 group_0754 + 621 1077 456 4.24E-02 1553 1427 -126 9.95E-01 Yes NE Amphomycin 14.4 SAOUHSC_02816 group_0760 + 435 2230 1795 0.00E+00 572 1 -571 9.95E-01 Yes NE Amphomycin 9.6 SAOUHSC_02816 group_0760 + 435 2485 2050 0.00E+00 572 10 -562 9.94E-01 Yes NE Oxacillin 0.15 SAOUHSC_02836 pitA_2 + 653 1350 697 3.69E-03 415 175 -240 6.54E-01 Yes NE Novobiocin 0.25 SAOUHSC_02935 opcR + 211 1210 999 0.00E+00 213 238 25 8.21E-01 Yes NE Moenomycin 0.08 SAOUHSC_A01912 + 420 1081 661 2.05E-02 11 15 4 1.00E+00 Yes NE

135

Supplemental Table 8: Genes with upregulation signatures in the presence of various antibiotic compounds, sorted by compound. This is a list of the hits obtained by applying the Bootstrap 2 method described in Chapter 3 to the Walker lab’s collection of compound-exposed Tn-Seq sample data. Only those hits that passed the visual assessment are included.

136 Supplemental Table 8 (Continued).

Compound Name Amphomycin korB Amphomycin SAOUHSC_00846 Amphomycin SAOUHSC_00995 Amphomycin SAOUHSC_02164 Amphomycin SAOUHSC_02816 Amphomycin zur Bacitracin fosB Bacitracin luxS Bacitracin mpsC Bacitracin murA2 Bacitracin norA Bacitracin pknB Bacitracin pncB Bacitracin SAOUHSC_00335 Bacitracin SAOUHSC_00416 Bacitracin SAOUHSC_01109 Bacitracin SAOUHSC_02521 Bacitracin uppP CCCP mazE CCCP mgtE CCCP pknB CCCP purE CCCP SAOUHSC_02668 CCCP sbnE CCCP ywtF CDFI murJ_1 CDFI nagB_1 CDFI rpmB CDFI SAOUHSC_00788 CDFI SAOUHSC_01073 CDFI SAOUHSC_01859 CDFI ytpP Cefaclor rpmB Cefaclor vraS Chloramphenicol ddl Chloramphenicol mutS2 Chloramphenicol polX Chloramphenicol rimI_1 Chloramphenicol SAOUHSC_01097 Chloramphenicol topA Chloramphenicol vga

137 Supplemental Table 8 (Continued).

Compound Name Chloramphenicol zapA Ciprofloxacin nnrD Ciprofloxacin norA Ciprofloxacin parC Ciprofloxacin plsY Ciprofloxacin SAOUHSC_00371 Cycloserine ddl Cycloserine murJ_1 Cycloserine pncB Cycloserine SAOUHSC_00416 Cycloserine SAOUHSC_02308 Daptomycin crtM_1 Daptomycin crtP Daptomycin crtQ Daptomycin mprF Daptomycin murA2 Daptomycin mvaK1 Daptomycin pmtA Daptomycin pmtB Daptomycin pmtC Daptomycin pmtR Daptomycin rpmB Daptomycin rpsU Daptomycin SAOUHSC_00335 Daptomycin SAOUHSC_00355 Daptomycin SAOUHSC_00371 Daptomycin SAOUHSC_00416 Daptomycin SAOUHSC_00473 Daptomycin SAOUHSC_00967 Daptomycin SAOUHSC_00968 Daptomycin SAOUHSC_00969 Daptomycin SAOUHSC_00970 Daptomycin SAOUHSC_01917 Daptomycin SAOUHSC_02149 Daptomycin SAOUHSC_02164 Daptomycin SAOUHSC_02628 Daptomycin tarJ Daptomycin uppP Daptomycin vraS Daptomycin yehR DMPI murJ_1

138 Supplemental Table 8 (Continued).

Compound Name DMPI rpmB DMPI SAOUHSC_01859 DMPI ytpP Fosfomycin fosB Fosfomycin hutR Fosfomycin murA1 Fosfomycin murA2 Fosfomycin murJ_1 Fosfomycin SAOUHSC_02338 Fosfomycin tal Fosfomycin vraS Gentamicin cpaA Gentamicin crtQ Gentamicin cydA Gentamicin fabI Gentamicin glyA Gentamicin mgtE Gentamicin murJ_1 Gentamicin nnrD Gentamicin norA Gentamicin SAOUHSC_00335 Gentamicin SAOUHSC_00864 Gentamicin SAOUHSC_00941 Gentamicin SAOUHSC_01061 Gentamicin SAOUHSC_01488 Gentamicin SAOUHSC_01851 Gentamicin SAOUHSC_02522 Gentamicin SAOUHSC_02628 Gentamicin SAOUHSC_02795 Gentamicin sarZ Gentamicin traP_1 Gentamicin vraS Gramicidin SAOUHSC_00846 Linezolid cstA Linezolid ezrA Linezolid mazG Linezolid mutS2 Linezolid polX Linezolid rimI_1 Linezolid rpmB Linezolid rpsU

139 Supplemental Table 8 (Continued).

Compound Name Linezolid SAOUHSC_00792 Linezolid SAOUHSC_01097 Linezolid SAOUHSC_01859 Linezolid SAOUHSC_02383 Linezolid SAOUHSC_02435 Linezolid ytpP Linezolid zapA Lysobactin SAOUHSC_00371 Lysobactin vraS Moenomycin abcA Moenomycin crtM_1 Moenomycin crtP Moenomycin crtQ Moenomycin cstA Moenomycin ettA Moenomycin fmhB Moenomycin mntA Moenomycin mpsC Moenomycin murB Moenomycin murT Moenomycin nrdE Moenomycin nrdI Moenomycin nsaR Moenomycin pncB Moenomycin proS Moenomycin rimI_1 Moenomycin SAOUHSC_00335 Moenomycin SAOUHSC_00416 Moenomycin SAOUHSC_01382 Moenomycin SAOUHSC_02149 Moenomycin SAOUHSC_02164 Moenomycin SAOUHSC_02166 Moenomycin SAOUHSC_02924 Moenomycin sgtA Moenomycin uhpT Moenomycin uppP Moenomycin walR Moxifloxacin SAOUHSC_00371 Moxifloxacin sgtA Moxifloxacin zur Mupirocin mdeA

140 Supplemental Table 8 (Continued).

Compound Name Mupirocin SAOUHSC_01156 Mupirocin SAOUHSC_02701 NalidixicAcid cstA NalidixicAcid ezrA NalidixicAcid rpmB NalidixicAcid SAOUHSC_00371 NalidixicAcid SAOUHSC_01244 NalidixicAcid SAOUHSC_01859 NalidixicAcid SAOUHSC_02383 NalidixicAcid yjbK NalidixicAcid ytpP Novobiocin airS Novobiocin asp2 Novobiocin deoC2 Novobiocin emrK Novobiocin hisG Novobiocin mdeA Novobiocin nhaK_1 Novobiocin norA Novobiocin opcR Novobiocin parE Novobiocin prs Novobiocin purE Novobiocin SAOUHSC_00618 Novobiocin SAOUHSC_02701 Novobiocin sbnA Novobiocin sbnE Novobiocin secY2 Novobiocin tarF_1 Novobiocin tarI Oxacillin ddl Oxacillin eap_1 Oxacillin lcpA Oxacillin murA1 Oxacillin murA2 Oxacillin murJ_1 Oxacillin pbp4 Oxacillin pitA_2 Oxacillin pknB Oxacillin SAOUHSC_00253 Oxacillin SAOUHSC_00788

141 Supplemental Table 8 (Continued).

Compound Name Oxacillin SAOUHSC_01109 Oxacillin SAOUHSC_01244 Oxacillin SAOUHSC_01859 Oxacillin SAOUHSC_02338 Oxacillin stp1 Oxacillin ytpP Platensimycin ampS Platensimycin codY Platensimycin emrK Platensimycin fabH Platensimycin hslU Platensimycin lmrS_1 Platensimycin mdeA Platensimycin SAOUHSC_02093 Platensimycin SAOUHSC_02658 Platensimycin SAOUHSC_02701 Platensimycin SAOUHSC_02778 Platensimycin sdrM_1 Platensimycin tcaB_1 Platensimycin veg Platensimycin xerC Polymyxin abcA Polymyxin ddl Polymyxin glyA Polymyxin nnrD Polymyxin norA Polymyxin pmtC Polymyxin SAOUHSC_00091 Polymyxin SAOUHSC_02149 Polymyxin SAOUHSC_02383 Polymyxin SAOUHSC_02522 Polymyxin sarZ Ramoplanin cshA Ramoplanin graX Ramoplanin murA2 Ramoplanin mvaK1 Ramoplanin pfbA Ramoplanin pknB Ramoplanin rpsU Ramoplanin SAOUHSC_02149 Ramoplanin zur

142 Supplemental Table 8 (Continued).

Compound Name Rifampicin ddl Rifampicin ettA Rifampicin fosB Sorangicin mdeA Sorangicin SAOUHSC_02701 SulfamethoxazolebshB2 Sulfamethoxazoleddl SulfamethoxazolepabA SulfamethoxazolethyA Targocil abcA Targocil luxS Targocil norG Targocil purE Targocil SAOUHSC_01488 Targocil SAOUHSC_01859 Targocil SAOUHSC_02156 Targocil SAOUHSC_02596 Targocil sarS Targocil2 SAOUHSC_00065 Tetracycline ddl Tetracycline norA Triclosan cpaA Triclosan fabI Triclosan mgtE Triclosan SAOUHSC_00941 Trimethoprim bshB2 Trimethoprim dfrA Trimethoprim dnaA Trimethoprim folE2 Trimethoprim pabA Trimethoprim SAOUHSC_00551 Trimethoprim thyA TritonX100 agrD_1 TritonX100 arlR TritonX100 bepA TritonX100 ctaB TritonX100 dapF TritonX100 dosC TritonX100 dynA TritonX100 eap_1 TritonX100 ettA

143 Supplemental Table 8 (Continued).

Compound Name TritonX100 guaA TritonX100 hutG TritonX100 mazG TritonX100 moaA TritonX100 modA TritonX100 modC TritonX100 moeA TritonX100 murA2 TritonX100 nhaK_1 TritonX100 pth TritonX100 purR TritonX100 rpsA_1 TritonX100 SAOUHSC_00331 TritonX100 SAOUHSC_00788 TritonX100 SAOUHSC_00793 TritonX100 SAOUHSC_00846 TritonX100 SAOUHSC_01859 TritonX100 SAOUHSC_02521 TritonX100 SAOUHSC_02534 TritonX100 SAOUHSC_02535 TritonX100 SAOUHSC_02768 TritonX100 SAOUHSC_02769 TritonX100 SAOUHSC_03035 TritonX100 secA TritonX100 sigS_1 TritonX100 ydfJ TritonX100 ytrB Tunicamycin pepA Tunicamycin SAOUHSC_01859 Tunicamycin ytpP Vancomycin airR Vancomycin crtQ Vancomycin ettA Vancomycin graX Vancomycin proS Vancomycin SAOUHSC_02149 Vancomycin SAOUHSC_02164 Vancomycin SAOUHSC_02795 Vancomycin tarO Vancomycin walI Vancomycin walR

144

Supplemental Table 9: The number of antibiotics in the presence of which each gene has an upregulation signature. Listed in descending count order and only included are genes that had an upregulation signature in the presence of at least one antibiotic.

145 Supplemental Table 9 (Continued).

Gene # Compounds SAOUHSC_01859 8 ddl 7 murA2 6 murJ_1 6 norA 6 rpmB 6 ytpP 6 SAOUHSC_00371 5 SAOUHSC_02149 5 vraS 5 crtQ 4 ettA 4 mdeA 4 pknB 4 SAOUHSC_00335 4 SAOUHSC_00416 4 SAOUHSC_02164 4 SAOUHSC_02701 4 abcA 3 cstA 3 fosB 3 mgtE 3 nnrD 3 pncB 3 purE 3 rimI_1 3 rpsU 3 SAOUHSC_00788 3 SAOUHSC_00846 3 SAOUHSC_02383 3 uppP 3 zur 3 bshB2 2 cpaA 2 crtM_1 2 crtP 2 eap_1 2 emrK 2 ezrA 2 fabI 2 glyA 2

146 Supplemental Table 9 (Continued).

Gene # Compounds graX 2 luxS 2 mazG 2 mpsC 2 murA1 2 mutS2 2 mvaK1 2 nhaK_1 2 pabA 2 pmtC 2 polX 2 proS 2 SAOUHSC_00941 2 SAOUHSC_01097 2 SAOUHSC_01109 2 SAOUHSC_01244 2 SAOUHSC_01488 2 SAOUHSC_02338 2 SAOUHSC_02521 2 SAOUHSC_02522 2 SAOUHSC_02628 2 SAOUHSC_02795 2 sarZ 2 sbnE 2 sgtA 2 thyA 2 walR 2 zapA 2 agrD_1 1 airR 1 airS 1 ampS 1 arlR 1 asp2 1 bepA 1 codY 1 cshA 1 ctaB 1 cydA 1 dapF 1 deoC2 1

147 Supplemental Table 9 (Continued).

Gene # Compounds dfrA 1 dnaA 1 dosC 1 dynA 1 fabH 1 fmhB 1 folE2 1 guaA 1 hisG 1 hslU 1 hutG 1 hutR 1 korB 1 lcpA 1 lmrS_1 1 mazE 1 mntA 1 moaA 1 modA 1 modC 1 moeA 1 mprF 1 murB 1 murT 1 nagB_1 1 norG 1 nrdE 1 nrdI 1 nsaR 1 opcR 1 parC 1 parE 1 pbp4 1 pepA 1 pfbA 1 pitA_2 1 plsY 1 pmtA 1 pmtB 1 pmtR 1 prs 1

148 Supplemental Table 9 (Continued).

Gene # Compounds pth 1 purR 1 rpsA_1 1 SAOUHSC_00065 1 SAOUHSC_00091 1 SAOUHSC_00253 1 SAOUHSC_00331 1 SAOUHSC_00355 1 SAOUHSC_00473 1 SAOUHSC_00551 1 SAOUHSC_00618 1 SAOUHSC_00792 1 SAOUHSC_00793 1 SAOUHSC_00864 1 SAOUHSC_00967 1 SAOUHSC_00968 1 SAOUHSC_00969 1 SAOUHSC_00970 1 SAOUHSC_00995 1 SAOUHSC_01061 1 SAOUHSC_01073 1 SAOUHSC_01156 1 SAOUHSC_01382 1 SAOUHSC_01851 1 SAOUHSC_01917 1 SAOUHSC_02093 1 SAOUHSC_02156 1 SAOUHSC_02166 1 SAOUHSC_02308 1 SAOUHSC_02435 1 SAOUHSC_02534 1 SAOUHSC_02535 1 SAOUHSC_02596 1 SAOUHSC_02658 1 SAOUHSC_02668 1 SAOUHSC_02768 1 SAOUHSC_02769 1 SAOUHSC_02778 1 SAOUHSC_02816 1 SAOUHSC_02924 1 SAOUHSC_03035 1

149 Supplemental Table 9 (Continued).

Gene # Compounds sarS 1 sbnA 1 sdrM_1 1 secA 1 secY2 1 sigS_1 1 stp1 1 tal 1 tarF_1 1 tarI 1 tarJ 1 tarO 1 tcaB_1 1 topA 1 traP_1 1 uhpT 1 veg 1 vga 1 walI 1 xerC 1 ydfJ 1 yehR 1 yjbK 1 ytrB 1 ywtF 1

150

Supplemental Table 10: Genes depleted of reads in the presence of daptomycin. Only included are those genes that are at least 10-fold depleted, with at least 100 reads in the control data and a q-value of less than 0.05.

151 Supplemental Table 10 (Continued).

Strain Medium Conc. Gene Roary SAOUHSC CtrlReads ExpReads Ratio qValue Description HG003 MHB2 1 SAOUHSC_01803 aapA SAOUHSC_01803 2714 31 0.0118 4.80E-23 D-serine/D-alanine/glycine transporter MRSA252 MHB2 1 SAR_RS09085 aapA SAOUHSC_01803 3238 252 0.0781 4.77E-16 D-serine/D-alanine/glycine transporter MRSA252 RPMI 0.5 SAR_RS09085 aapA SAOUHSC_01803 2347 71 0.0308 1.39E-12 D-serine/D-alanine/glycine transporter MW2 MHB2 0.5 MW_RS08750 aapA SAOUHSC_01803 7545 465 0.0617 1.42E-05 D-serine/D-alanine/glycine transporter MW2 RPMI 0.25 MW_RS08750 aapA SAOUHSC_01803 6003 195 0.0327 2.77E-13 D-serine/D-alanine/glycine transporter USA300 MHB2 0.5 USA300HOU_RS08990 aapA SAOUHSC_01803 4959 125 0.0254 1.76E-32 D-serine/D-alanine/glycine transporter HG003 MHB2 1 SAOUHSC_02305 alr SAOUHSC_02305 2789 87 0.0316 8.25E-16 Alanine racemase 1 MRSA252 MHB2 1 SAR_RS11245 alr SAOUHSC_02305 239 21 0.092 9.96E-05 Alanine racemase 1 MRSA252 RPMI 0.5 SAR_RS11245 alr SAOUHSC_02305 565 39 0.0708 9.85E-08 Alanine racemase 1 USA300 MHB2 0.5 USA300HOU_RS11180 alr SAOUHSC_02305 1283 52 0.0415 4.75E-24 Alanine racemase 1 HG003 MHB2 1 SAOUHSC_01420 arlR SAOUHSC_01420 856 76 0.0898 3.26E-04 Response regulator ArlR MSSA476 RPMI 0.25 SAS_RS07195 arlR SAOUHSC_01420 3669 39 0.0109 1.59E-12 Response regulator ArlR MW2 RPMI 0.25 MW_RS07000 arlR SAOUHSC_01420 466 20 0.0452 6.88E-05 Response regulator ArlR Signal transduction histidine-protein kinase MSSA476 RPMI 0.25 SAS_RS07190 arlS SAOUHSC_01419 3172 84 0.0267 7.07E-18 ArlS Signal transduction histidine-protein kinase MW2 RPMI 0.25 MW_RS06995 arlS SAOUHSC_01419 1081 98 0.0919 1.23E-03 ArlS MRSA252 MHB2 1 SAR_RS12375 bioY SAOUHSC_02552 1216 61 0.0512 7.16E-04 Biotin transporter BioY HG003 MHB2 1 SAOUHSC_01827 ezrA SAOUHSC_01827 413 17 0.0439 1.75E-07 Septation ring formation regulator EzrA MRSA252 RPMI 0.5 SAR_RS09195 ezrA SAOUHSC_01827 641 62 0.0977 3.18E-09 Septation ring formation regulator EzrA USA300 MHB2 0.5 USA300HOU_RS09110 ezrA SAOUHSC_01827 823 54 0.0673 2.22E-22 Septation ring formation regulator EzrA MRSA252 MHB2 1 SAR_RS13625 fbp SAOUHSC_02822 620 32 0.0526 2.94E-06 Fructose-1%2C6-bisphosphatase class 3 HG003 MHB2 1 SAOUHSC_00998 fmtA SAOUHSC_00998 2774 272 0.0984 1.98E-09 Putative penicillin-binding protein PbpX MRSA252 MHB2 1 SAR_RS11715 glmM SAOUHSC_02405 256 11 0.0449 1.95E-07 Phosphoglucosamine mutase HG003 MHB2 1 SAOUHSC_01462 gpsB SAOUHSC_01462 909 7 0.0091 1.54E-06 Cell cycle protein GpsB HG003 RPMI 0.25 SAOUHSC_01462 gpsB SAOUHSC_01462 1011 49 0.0499 3.20E-03 Cell cycle protein GpsB MRSA252 RPMI 0.5 SAR_RS07445 gpsB SAOUHSC_01462 272 13 0.0514 1.99E-02 Cell cycle protein GpsB MSSA476 MHB2 0.5 SAS_RS07370 gpsB SAOUHSC_01462 253 15 0.0642 4.61E-02 Cell cycle protein GpsB MW2 RPMI 0.25 MW_RS07175 gpsB SAOUHSC_01462 649 4 0.0071 6.26E-03 Cell cycle protein GpsB USA300 MHB2 0.5 USA300HOU_RS07395 gpsB SAOUHSC_01462 560 49 0.0891 8.59E-05 Cell cycle protein GpsB HG003 MHB2 1 SAOUHSC_00665 graR SAOUHSC_00665 1142 1 0.002 6.71E-12 Response regulator protein GraR HG003 RPMI 0.25 SAOUHSC_00665 graR SAOUHSC_00665 1567 28 0.0188 1.79E-09 Response regulator protein GraR MRSA252 MHB2 1 SAR_RS03435 graR SAOUHSC_00665 479 12 0.028 8.82E-07 Response regulator protein GraR MRSA252 RPMI 0.5 SAR_RS03435 graR SAOUHSC_00665 236 10 0.0447 3.44E-03 Response regulator protein GraR MSSA476 MHB2 0.5 SAS_RS03350 graR SAOUHSC_00665 502 7 0.0153 1.28E-08 Response regulator protein GraR MSSA476 RPMI 0.25 SAS_RS03350 graR SAOUHSC_00665 300 13 0.0466 1.53E-05 Response regulator protein GraR MW2 MHB2 0.5 MW_RS03380 graR SAOUHSC_00665 750 3 0.0049 1.10E-06 Response regulator protein GraR MW2 RPMI 0.25 MW_RS03380 graR SAOUHSC_00665 686 4 0.0077 2.19E-06 Response regulator protein GraR USA300 MHB2 0.5 USA300HOU_RS03565 graR SAOUHSC_00665 574 1 0.0036 1.18E-12 Response regulator protein GraR USA300 RPMI 0.12 USA300HOU_RS03565 graR SAOUHSC_00665 698 6 0.0104 8.84E-14 Response regulator protein GraR HG003 MHB2 1 SAOUHSC_00666 graS SAOUHSC_00666 2510 15 0.0062 1.97E-17 Sensor histidine kinase GraS HG003 RPMI 0.25 SAOUHSC_00666 graS SAOUHSC_00666 2936 67 0.0232 1.25E-15 Sensor histidine kinase GraS MRSA252 MHB2 1 SAR_RS03440 graS SAOUHSC_00666 1110 18 0.0173 7.86E-10 Sensor histidine kinase GraS MRSA252 RPMI 0.5 SAR_RS03440 graS SAOUHSC_00666 457 9 0.0216 1.02E-07 Sensor histidine kinase GraS MSSA476 MHB2 0.5 SAS_RS03355 graS SAOUHSC_00666 1068 67 0.0636 3.40E-11 Sensor histidine kinase GraS MSSA476 RPMI 0.25 SAS_RS03355 graS SAOUHSC_00666 588 36 0.0635 2.45E-08 Sensor histidine kinase GraS MW2 MHB2 0.5 MW_RS03385 graS SAOUHSC_00666 2967 139 0.0473 2.77E-09 Sensor histidine kinase GraS MW2 RPMI 0.25 MW_RS03385 graS SAOUHSC_00666 2568 71 0.0281 1.12E-06 Sensor histidine kinase GraS USA300 MHB2 0.5 USA300HOU_RS03570 graS SAOUHSC_00666 1266 20 0.0163 4.34E-22 Sensor histidine kinase GraS USA300 RPMI 0.12 USA300HOU_RS03570 graS SAOUHSC_00666 1383 11 0.0087 7.45E-22 Sensor histidine kinase GraS HG003 MHB2 1 SAOUHSC_00678 group_0141 SAOUHSC_00678 2828 175 0.0622 7.56E-08 hypothetical protein HG003 MHB2 1 SAOUHSC_01025 group_0231 SAOUHSC_01025 3104 47 0.0154 4.76E-26 hypothetical protein HG003 RPMI 0.25 SAOUHSC_01025 group_0231 SAOUHSC_01025 467 44 0.0971 8.77E-11 hypothetical protein MRSA252 MHB2 1 SAR_RS05365 group_0231 SAOUHSC_01025 2993 73 0.0247 9.49E-19 hypothetical protein MRSA252 RPMI 0.5 SAR_RS05365 group_0231 SAOUHSC_01025 1192 14 0.0129 6.62E-17 hypothetical protein HG003 MHB2 1 SAOUHSC_01050 group_0235 SAOUHSC_01050 2787 24 0.0089 1.62E-18 hypothetical protein MRSA252 RPMI 0.5 SAR_RS05490 group_0235 SAOUHSC_01050 824 66 0.081 1.47E-08 hypothetical protein MSSA476 MHB2 0.5 SAS_RS05495 group_0235 SAOUHSC_01050 668 44 0.0673 9.66E-09 hypothetical protein MW2 MHB2 0.5 MW_RS05295 group_0235 SAOUHSC_01050 1099 23 0.0215 4.19E-08 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS05505 group_0235 SAOUHSC_01050 583 49 0.0856 5.68E-14 hypothetical protein Putative membrane protein insertion USA300 RPMI 0.12 USA300HOU_RS09520 group_0461 SAOUHSC_01914 172 16 0.0967 2.17E-02 efficiency factor HG003 MHB2 1 SAOUHSC_02004 group_0487 SAOUHSC_02004 820 59 0.0736 1.34E-04 hypothetical protein HG003 MHB2 1 SAOUHSC_02149 group_0571 SAOUHSC_02149 903 44 0.05 3.66E-05 hypothetical protein MRSA252 MHB2 1 SAR_RS10550 group_0571 SAOUHSC_02149 1954 45 0.0236 5.12E-03 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS10470 group_0571 SAOUHSC_02149 1095 58 0.0536 3.19E-10 hypothetical protein UTP--glucose-1-phosphate MW2 RPMI 0.25 MW_RS13125 gtaB SAOUHSC_02801 285 19 0.0713 3.10E-02 uridylyltransferase HG003 MHB2 1 SAOUHSC_01361 lcpA SAOUHSC_01361 692 51 0.0748 3.97E-02 Regulatory protein MsrR MW2 RPMI 0.25 MW_RS06710 lcpA SAOUHSC_01361 1644 65 0.0399 2.48E-03 Regulatory protein MsrR HG003 MHB2 1 SAOUHSC_00782 lgt SAOUHSC_00782 874 81 0.094 3.62E-08 Prolipoprotein diacylglyceryl transferase HG003 MHB2 1 SAOUHSC_01162 lspA SAOUHSC_01162 499 37 0.076 2.13E-04 Lipoprotein signal peptidase HG003 MHB2 1 SAOUHSC_00952 ltaA SAOUHSC_00952 1919 66 0.0349 1.85E-13 putative glycolipid permease LtaA HG003 RPMI 0.25 SAOUHSC_00952 ltaA SAOUHSC_00952 2458 79 0.0324 1.89E-16 putative glycolipid permease LtaA MRSA252 RPMI 0.5 SAR_RS05005 ltaA SAOUHSC_00952 394 25 0.065 6.60E-11 putative glycolipid permease LtaA MW2 MHB2 0.5 MW_RS04820 ltaA SAOUHSC_00952 3277 190 0.0583 1.79E-05 putative glycolipid permease LtaA MW2 RPMI 0.25 MW_RS04820 ltaA SAOUHSC_00952 3024 250 0.083 5.99E-11 putative glycolipid permease LtaA USA300 RPMI 0.12 USA300HOU_RS05035 ltaA SAOUHSC_00952 882 39 0.0448 2.54E-20 putative glycolipid permease LtaA 2-succinyl-5-enolpyruvyl-6-hydroxy-3- MRSA252 MHB2 1 SAR_RS05165 menD SAOUHSC_00983 2631 151 0.0579 1.90E-13 cyclohexene-1-carboxylate synthase HG003 MHB2 1 SAOUHSC_01359 mprF SAOUHSC_01359 5972 182 0.0307 1.87E-40 Phosphatidylglycerol lysyltransferase MRSA252 MHB2 1 SAR_RS07005 mprF SAOUHSC_01359 2165 58 0.027 7.17E-24 Phosphatidylglycerol lysyltransferase

152 Supplemental Table 10 (Continued).

Strain Medium Conc. Gene Roary SAOUHSC CtrlReads ExpReads Ratio qValue Description MRSA252 RPMI 0.5 SAR_RS07005 mprF SAOUHSC_01359 1250 30 0.0249 7.85E-17 Phosphatidylglycerol lysyltransferase USA300 MHB2 0.5 USA300HOU_RS06925 mprF SAOUHSC_01359 3526 125 0.0358 2.49E-43 Phosphatidylglycerol lysyltransferase USA300 RPMI 0.12 USA300HOU_RS06925 mprF SAOUHSC_01359 3227 194 0.0605 5.14E-64 Phosphatidylglycerol lysyltransferase MW2 RPMI 0.25 MW_RS08530 mreC SAOUHSC_01759 1836 156 0.0854 4.19E-04 Cell shape-determining protein MreC UDP-N-acetylglucosamine 1- HG003 MHB2 1 SAOUHSC_02337 murA1 SAOUHSC_02337 1763 32 0.0185 1.62E-18 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- MSSA476 MHB2 0.5 SAS_RS10895 murA1 SAOUHSC_02337 917 17 0.0199 3.61E-14 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- MSSA476 RPMI 0.25 SAS_RS10895 murA1 SAOUHSC_02337 308 14 0.0472 3.35E-08 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- MW2 MHB2 0.5 MW_RS11030 murA1 SAOUHSC_02337 940 27 0.0299 1.48E-05 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- MW2 RPMI 0.25 MW_RS11030 murA1 SAOUHSC_02337 775 12 0.017 4.01E-07 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- USA300 MHB2 0.5 USA300HOU_RS11340 murA1 SAOUHSC_02337 2466 57 0.0233 4.75E-24 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- USA300 RPMI 0.12 USA300HOU_RS11340 murA1 SAOUHSC_02337 1644 102 0.0628 1.35E-21 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- MRSA252 MHB2 1 SAR_RS11405 murA2 SAOUHSC_02365 7365 697 0.0947 1.99E-06 carboxyvinyltransferase 2 UDP-N-acetylglucosamine 1- MRSA252 RPMI 0.5 SAR_RS11405 murA2 SAOUHSC_02365 3468 47 0.0137 7.52E-16 carboxyvinyltransferase 2 HG003 MHB2 1 SAOUHSC_03049 noc SAOUHSC_03049 420 13 0.0337 1.85E-05 Nucleoid occlusion protein HG003 RPMI 0.25 SAOUHSC_03049 noc SAOUHSC_03049 532 34 0.0664 9.43E-04 Nucleoid occlusion protein MRSA252 RPMI 0.5 SAR_RS14670 noc SAOUHSC_03049 579 51 0.0892 2.37E-02 Nucleoid occlusion protein MW2 RPMI 0.25 MW_RS14240 noc SAOUHSC_03049 1033 75 0.0739 3.27E-03 Nucleoid occlusion protein MRSA252 RPMI 0.5 SAR_RS08345 pbp3 SAOUHSC_01652 2007 55 0.0278 2.26E-13 Penicillin-binding protein H

MRSA252 RPMI 0.5 SAR_RS03345 pbp4 SAOUHSC_00646 1594 87 0.0552 5.15E-10 D-alanyl-D-alanine carboxypeptidase DacA

MW2 RPMI 0.25 MW_RS03290 pbp4 SAOUHSC_00646 4231 100 0.0238 1.02E-09 D-alanyl-D-alanine carboxypeptidase DacA Phosphoenolpyruvate carboxykinase MRSA252 MHB2 1 SAR_RS09600 pckA SAOUHSC_01910 279 24 0.0892 1.05E-05 (ATP) MW2 RPMI 0.25 MW_RS13085 pgcA SAOUHSC_02793 1463 29 0.0203 3.35E-07 Phosphoglucomutase Phosphoenolpyruvate-protein MRSA252 MHB2 1 SAR_RS05385 ptsI SAOUHSC_01029 9416 259 0.0276 4.18E-22 phosphotransferase HG003 MHB2 1 SAOUHSC_02362 rho SAOUHSC_02362 1753 157 0.0902 7.74E-09 Transcription termination factor Rho MRSA252 RPMI 0.5 SAR_RS11315 rodA SAOUHSC_02319 1821 86 0.048 2.30E-14 Peptidoglycan glycosyltransferase RodA MW2 RPMI 0.25 MW_RS10945 rodA SAOUHSC_02319 3633 160 0.0442 7.69E-08 Peptidoglycan glycosyltransferase RodA HG003 MHB2 1 SAOUHSC_01154 sepF SAOUHSC_01154 123 1 0.0134 2.67E-04 Cell division protein SepF MSSA476 MHB2 0.5 SAS_RS05970 sepF SAOUHSC_01154 129 11 0.0961 3.32E-03 Cell division protein SepF Processive diacylglycerol beta- HG003 RPMI 0.25 SAOUHSC_00953 ugtP SAOUHSC_00953 434 18 0.0428 2.23E-04 glucosyltransferase Bacitracin export ATP-binding protein HG003 MHB2 1 SAOUHSC_00667 vraF SAOUHSC_00667 2332 20 0.0089 1.09E-12 BceA Bacitracin export ATP-binding protein HG003 RPMI 0.25 SAOUHSC_00667 vraF SAOUHSC_00667 2541 78 0.0311 4.83E-08 BceA Bacitracin export ATP-binding protein MRSA252 RPMI 0.5 SAR_RS03445 vraF SAOUHSC_00667 467 26 0.0578 1.75E-07 BceA Bacitracin export ATP-binding protein MSSA476 MHB2 0.5 SAS_RS03360 vraF SAOUHSC_00667 819 28 0.0351 5.20E-09 BceA Bacitracin export ATP-binding protein MSSA476 RPMI 0.25 SAS_RS03360 vraF SAOUHSC_00667 406 29 0.0732 2.87E-06 BceA Bacitracin export ATP-binding protein MW2 MHB2 0.5 MW_RS03390 vraF SAOUHSC_00667 2106 39 0.019 7.21E-04 BceA Bacitracin export ATP-binding protein MW2 RPMI 0.25 MW_RS03390 vraF SAOUHSC_00667 1769 10 0.0062 2.54E-07 BceA Bacitracin export ATP-binding protein USA300 MHB2 0.5 USA300HOU_RS03575 vraF SAOUHSC_00667 991 16 0.0175 2.73E-14 BceA Bacitracin export ATP-binding protein USA300 RPMI 0.12 USA300HOU_RS03575 vraF SAOUHSC_00667 1209 13 0.0119 1.67E-19 BceA HG003 MHB2 1 SAOUHSC_00668 vraG SAOUHSC_00668 6227 34 0.0056 1.28E-44 Bacitracin export permease protein BceB HG003 RPMI 0.25 SAOUHSC_00668 vraG SAOUHSC_00668 7382 194 0.0264 1.21E-33 Bacitracin export permease protein BceB MRSA252 MHB2 1 SAR_RS03450 vraG SAOUHSC_00668 4597 84 0.0186 3.21E-30 Bacitracin export permease protein BceB MRSA252 RPMI 0.5 SAR_RS03450 vraG SAOUHSC_00668 1955 77 0.0398 2.83E-23 Bacitracin export permease protein BceB MSSA476 MHB2 0.5 SAS_RS03365 vraG SAOUHSC_00668 3098 112 0.0365 1.33E-29 Bacitracin export permease protein BceB MSSA476 RPMI 0.25 SAS_RS03365 vraG SAOUHSC_00668 1525 116 0.0765 3.91E-18 Bacitracin export permease protein BceB MW2 MHB2 0.5 MW_RS03395 vraG SAOUHSC_00668 8052 567 0.0705 2.14E-17 Bacitracin export permease protein BceB MW2 RPMI 0.25 MW_RS03395 vraG SAOUHSC_00668 7132 542 0.0761 8.89E-18 Bacitracin export permease protein BceB USA300 MHB2 0.5 USA300HOU_RS03580 vraG SAOUHSC_00668 3232 21 0.0067 1.80E-47 Bacitracin export permease protein BceB USA300 RPMI 0.12 USA300HOU_RS03580 vraG SAOUHSC_00668 3700 55 0.0152 9.20E-50 Bacitracin export permease protein BceB MRSA252 MHB2 1 SAR_RS10305 vraR SAOUHSC_02098 929 48 0.0526 1.80E-06 Response regulator protein VraR MRSA252 RPMI 0.5 SAR_RS10305 vraR SAOUHSC_02098 578 45 0.0787 1.05E-06 Response regulator protein VraR MW2 RPMI 0.25 MW_RS09930 vraR SAOUHSC_02098 1239 119 0.0964 6.20E-05 Response regulator protein VraR MRSA252 MHB2 1 SAR_RS10310 vraS SAOUHSC_02099 1381 121 0.0881 8.20E-07 Sensor protein VraS MRSA252 RPMI 0.5 SAR_RS10310 vraS SAOUHSC_02099 950 75 0.0796 5.86E-09 Sensor protein VraS MRSA252 MHB2 1 SAR_RS00110 walH SAOUHSC_00022 53188 5254 0.0988 1.19E-05 hypothetical protein HG003 MHB2 1 SAOUHSC_00483 yugI_2 SAOUHSC_00892 1346 55 0.0414 1.55E-05 General stress protein 13

153

Supplemental Table 11: Genes with upregulation signatures in the presence of daptomycin. Included are those genes considered a hit by the Bootstrap 2 method described in Chapter 3.

154 Supplemental Table 11 (Continued).

Plus Strand Minus Strand Strain Medium Conc. Locus Tag Gene SAOUHSC Strand Ctrl Exp q-Value Ctrl Exp q-Value Description MSSA476 MHB2 0.5 SAS_RS00410 group_0015 SAOUHSC_00065 + 218 472 2.77E-02 219 238 1.00E+00 hypothetical protein Sodium/proton-dependent MSSA476 RPMI 0.25 SAS_RS04695 acp SAOUHSC_00949 + 224 847 9.30E-03 177 450 8.79E-01 alanine carrier protein Beta-barrel assembly-enhancing HG003 MHB2 1 SAOUHSC_00784 bepA SAOUHSC_00784 + 314 977 6.40E-03 336 363 1.00E+00 protease Beta-barrel assembly-enhancing MSSA476 MHB2 0.5 SAS_RS03920 bepA SAOUHSC_00784 + 353 609 1.71E-02 364 422 1.00E+00 protease HG003 RPMI 0.25 SAOUHSC_02323 cls2 SAOUHSC_02323 + 297 749 2.69E-02 367 405 1.00E+00 Cardiolipin synthase MRSA252 RPMI 0.5 SAR_RS11340 cls2 SAOUHSC_02323 + 182 782 0.00E+00 199 199 9.90E-01 Cardiolipin synthase MSSA476 RPMI 0.25 SAS_RS10830 cls2 SAOUHSC_02323 + 367 1493 0.00E+00 90 355 6.99E-01 Cardiolipin synthase MW2 RPMI 0.25 MW_RS10965 cls2 SAOUHSC_02323 + 224 1191 1.50E-03 809 579 1.00E+00 Cardiolipin synthase

MSSA476 RPMI 0.25 SAS_RS05075 comK SAOUHSC_00961 - 143 267 1.00E+00 189 742 6.90E-03 Competence transcription factor HG003 MHB2 1 SAOUHSC_02879 crtM SAOUHSC_02879 - 184 200 1.00E+00 226 982 1.70E-03 Dehydrosqualene synthase MSSA476 RPMI 0.25 SAS_RS13320 crtM SAOUHSC_02879 - 108 209 1.00E+00 177 1176 0.00E+00 Dehydrosqualene synthase USA300 RPMI 0.12 USA300HOU_RS13905 crtM SAOUHSC_02879 - 364 404 1.00E+00 407 900 5.00E-03 Dehydrosqualene synthase HG003 MHB2 1 SAOUHSC_02881 crtP SAOUHSC_02881 - 334 303 1.00E+00 165 1304 0.00E+00 Diapolycopene oxygenase HG003 RPMI 0.25 SAOUHSC_02881 crtP SAOUHSC_02881 - 474 396 1.00E+00 277 1087 0.00E+00 Diapolycopene oxygenase MSSA476 RPMI 0.25 SAS_RS13330 crtP SAOUHSC_02881 - 187 308 1.00E+00 202 1572 0.00E+00 Diapolycopene oxygenase USA300 MHB2 0.5 USA300HOU_RS13915 crtP SAOUHSC_02881 - 287 288 9.56E-01 303 560 4.35E-02 Diapolycopene oxygenase USA300 RPMI 0.12 USA300HOU_RS13915 crtP SAOUHSC_02881 - 383 491 1.00E+00 392 1067 7.00E-04 Diapolycopene oxygenase 4%2C4'-diaponeurosporenoate HG003 MHB2 1 SAOUHSC_02880 crtQ SAOUHSC_02880 - 224 276 1.00E+00 418 1417 0.00E+00 glycosyltransferase MW2 MHB2 0.5 MW_RS02640 ctsR SAOUHSC_00502 + 321 816 1.62E-02 190 290 1.00E+00 Transcriptional regulator CtsR USA300 RPMI 0.12 USA300HOU_RS02745 ctsR SAOUHSC_00502 + 285 883 2.10E-03 183 235 1.00E+00 Transcriptional regulator CtsR ATP-binding/permease protein MW2 RPMI 0.25 MW_RS03515 cydD SAOUHSC_00692 + 673 1701 7.10E-03 583 542 9.97E-01 CydD ESAT-6 secretion system MSSA476 MHB2 0.5 SAS_RS01335 esxA SAOUHSC_00257 + 371 694 1.21E-02 285 257 1.00E+00 extracellular protein A ESAT-6 secretion system MSSA476 RPMI 0.25 SAS_RS01335 esxA SAOUHSC_00257 + 402 1201 0.00E+00 198 439 1.00E+00 extracellular protein A ESAT-6 secretion system USA300 RPMI 0.12 USA300HOU_RS01485 esxA SAOUHSC_00257 + 755 1376 2.50E-03 511 606 1.00E+00 extracellular protein A

Energy-dependent translational USA300 RPMI 0.12 USA300HOU_RS03890 ettA SAOUHSC_00729 + 441 840 2.97E-02 490 520 1.00E+00 throttle protein EttA Glycine cleavage system H-like HG003 MHB2 1 SAOUHSC_00305 gcvHL SAOUHSC_00305 + 181 1044 0.00E+00 176 372 1.00E+00 protein Glycine cleavage system H-like USA300 RPMI 0.12 USA300HOU_RS01725 gcvHL SAOUHSC_00305 + 377 805 1.00E-02 222 309 1.00E+00 protein PTS system maltose-specific USA300 RPMI 0.12 USA300HOU_RS12575 glvC SAOUHSC_02597 - 567 731 1.00E+00 911 1508 7.00E-03 EIICB component Response regulator protein HG003 MHB2 1 SAOUHSC_00665 graR SAOUHSC_00665 + 557 1216 6.10E-03 493 19 1.00E+00 GraR Response regulator protein MSSA476 MHB2 0.5 SAS_RS03350 graR SAOUHSC_00665 + 322 708 1.40E-03 355 65 1.00E+00 GraR Response regulator protein MSSA476 RPMI 0.25 SAS_RS03350 graR SAOUHSC_00665 + 319 1106 0.00E+00 190 22 1.00E+00 GraR Response regulator protein MW2 RPMI 0.25 MW_RS03380 graR SAOUHSC_00665 + 784 1854 3.00E-03 611 112 1.00E+00 GraR Response regulator protein USA300 MHB2 0.5 USA300HOU_RS03565 graR SAOUHSC_00665 + 371 1149 0.00E+00 419 45 1.00E+00 GraR Response regulator protein USA300 RPMI 0.12 USA300HOU_RS03565 graR SAOUHSC_00665 + 487 1337 0.00E+00 470 85 1.00E+00 GraR HG003 MHB2 1 SAOUHSC_00664 graX SAOUHSC_00664 + 521 1836 0.00E+00 498 220 1.00E+00 hypothetical protein HG003 RPMI 0.25 SAOUHSC_00664 graX SAOUHSC_00664 + 509 1662 0.00E+00 562 418 1.00E+00 hypothetical protein MSSA476 MHB2 0.5 SAS_RS03345 graX SAOUHSC_00664 + 349 977 0.00E+00 253 213 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS03345 graX SAOUHSC_00664 + 289 1938 0.00E+00 257 181 1.00E+00 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS03560 graX SAOUHSC_00664 + 247 996 0.00E+00 497 187 1.00E+00 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS03560 graX SAOUHSC_00664 + 385 1148 0.00E+00 492 419 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS10495 groES SAOUHSC_02255 - 156 311 1.00E+00 210 975 5.00E-04 10 kDa chaperonin USA300 RPMI 0.12 USA300HOU_RS14605 group_0809 SAOUHSC_03026 - 856 975 1.00E+00 1590 2178 2.90E-03 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS03335 group_0876 - 387 422 1.00E+00 338 857 2.90E-03 hypothetical protein HG003 MHB2 1 SAOUHSC_00331 group_0062 SAOUHSC_00331 + 597 1153 1.58E-02 266 366 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS04965 group_0357 SAOUHSC_01531 + 122 685 1.30E-03 122 271 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS11720 group_0685 SAOUHSC_02529 - 297 456 1.00E+00 509 1281 9.00E-04 hypothetical protein MW2 MHB2 0.5 MW_RS11855 group_0685 SAOUHSC_02529 - 425 455 1.00E+00 845 1410 3.64E-02 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS12250 group_0685 SAOUHSC_02529 - 415 445 1.00E+00 1375 1893 1.47E-02 hypothetical protein HG003 MHB2 1 SAOUHSC_01917 group_0462 SAOUHSC_01917 - 311 347 1.00E+00 189 931 7.10E-03 hypothetical protein MSSA476 RPMI 0.25 SAS_RS02145 group_0092 SAOUHSC_00416 + 386 1069 5.30E-03 350 596 1.00E+00 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS02295 group_0092 SAOUHSC_00416 + 784 1294 2.03E-02 669 786 1.00E+00 hypothetical protein putative ABC transporter ATP- HG003 MHB2 1 SAOUHSC_00970 group_0222 SAOUHSC_00970 + 659 2144 0.00E+00 376 486 1.00E+00 binding protein putative ABC transporter ATP- HG003 RPMI 0.25 SAOUHSC_00970 group_0222 SAOUHSC_00970 + 746 1357 9.40E-03 454 509 1.00E+00 binding protein putative ABC transporter ATP- MRSA252 RPMI 0.5 SAR_RS05105 group_0222 SAOUHSC_00970 + 261 910 0.00E+00 285 143 1.00E+00 binding protein putative ABC transporter ATP- MSSA476 RPMI 0.25 SAS_RS05125 group_0222 SAOUHSC_00970 + 428 2160 0.00E+00 393 584 1.00E+00 binding protein putative ABC transporter ATP- MW2 RPMI 0.25 MW_RS04925 group_0222 SAOUHSC_00970 + 290 1223 8.00E-03 734 390 1.00E+00 binding protein putative ABC transporter ATP- USA300 MHB2 0.5 USA300HOU_RS05135 group_0222 SAOUHSC_00970 + 555 1508 0.00E+00 517 425 9.67E-01 binding protein putative ABC transporter ATP- USA300 RPMI 0.12 USA300HOU_RS05135 group_0222 SAOUHSC_00970 + 719 1183 4.88E-02 503 614 1.00E+00 binding protein HG003 MHB2 1 SAOUHSC_02778 group_0744 SAOUHSC_02778 - 356 413 1.00E+00 534 1087 3.75E-02 putative oxidoreductase

155 Supplemental Table 11 (Continued).

Plus Strand Minus Strand Strain Medium Conc. Locus Tag Gene SAOUHSC Strand Ctrl Exp q-Value Ctrl Exp q-Value Description MSSA476 RPMI 0.25 SAS_RS07500 group_0333 SAOUHSC_01488 - 162 234 1.00E+00 251 1107 0.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS05370 group_0231 SAOUHSC_01025 + 312 890 1.74E-02 220 346 1.00E+00 hypothetical protein MW2 MHB2 0.5 MW_RS08645 group_0434 SAOUHSC_01782 - 9 13 1.00E+00 13 343 2.79E-02 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS08885 group_0434 SAOUHSC_01782 - 107 48 1.00E+00 34 370 4.10E-03 hypothetical protein HG003 MHB2 1 SAOUHSC_02149 group_0571 SAOUHSC_02149 - 128 232 1.00E+00 232 2284 0.00E+00 hypothetical protein HG003 RPMI 0.25 SAOUHSC_02149 group_0571 SAOUHSC_02149 - 160 215 1.00E+00 320 1185 0.00E+00 hypothetical protein MRSA252 RPMI 0.5 SAR_RS10550 group_0571 SAOUHSC_02149 - 96 82 1.00E+00 169 1114 0.00E+00 hypothetical protein MSSA476 MHB2 0.5 SAS_RS10055 group_0571 SAOUHSC_02149 - 55 122 8.90E-01 177 600 0.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS10055 group_0571 SAOUHSC_02149 - 77 71 1.00E+00 213 1681 0.00E+00 hypothetical protein MW2 MHB2 0.5 MW_RS10185 group_0571 SAOUHSC_02149 - 181 230 1.00E+00 79 539 3.61E-02 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS10470 group_0571 SAOUHSC_02149 - 189 103 1.00E+00 390 1742 0.00E+00 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS10470 group_0571 SAOUHSC_02149 - 129 199 1.00E+00 380 1205 0.00E+00 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS11835 group_0667 SAOUHSC_02442 - 1309 1212 1.00E+00 389 1135 0.00E+00 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS12715 group_0707 SAOUHSC_02628 + 452 1104 0.00E+00 352 429 1.00E+00 hypothetical protein HG003 MHB2 1 SAOUHSC_00969 group_0221 SAOUHSC_00969 + 325 2727 0.00E+00 196 309 1.00E+00 hypothetical protein HG003 RPMI 0.25 SAOUHSC_00969 group_0221 SAOUHSC_00969 + 426 1106 3.60E-03 240 251 1.00E+00 hypothetical protein MRSA252 RPMI 0.5 SAR_RS05100 group_0221 SAOUHSC_00969 + 240 1444 0.00E+00 153 125 1.00E+00 hypothetical protein MSSA476 MHB2 0.5 SAS_RS05120 group_0221 SAOUHSC_00969 + 326 769 0.00E+00 145 179 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS05120 group_0221 SAOUHSC_00969 + 259 2598 0.00E+00 211 278 1.00E+00 hypothetical protein MW2 MHB2 0.5 MW_RS04920 group_0221 SAOUHSC_00969 + 340 985 1.50E-03 289 326 1.00E+00 hypothetical protein MW2 RPMI 0.25 MW_RS04920 group_0221 SAOUHSC_00969 + 233 1461 0.00E+00 294 206 1.00E+00 hypothetical protein USA300 MHB2 0.5 USA300HOU_RS05130 group_0221 SAOUHSC_00969 + 439 1869 0.00E+00 293 256 8.98E-01 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS05130 group_0221 SAOUHSC_00969 + 408 1251 0.00E+00 354 366 1.00E+00 hypothetical protein MW2 MHB2 0.5 MW_RS09945 vraU SAOUHSC_02101 - 265 373 1.00E+00 483 890 4.09E-02 hypothetical protein MSSA476 RPMI 0.25 SAS_RS03790 group_0160 SAOUHSC_00753 - 256 337 1.00E+00 187 769 2.90E-02 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS12530 group_0694 SAOUHSC_02588 + 1093 1634 1.13E-02 850 1024 1.00E+00 hypothetical protein MSSA476 MHB2 0.5 SAS_RS13335 crtO SAOUHSC_02882 - 449 491 1.00E+00 210 466 2.83E-02 hypothetical protein MSSA476 RPMI 0.25 SAS_RS13335 crtO SAOUHSC_02882 - 390 545 1.00E+00 301 810 3.08E-02 hypothetical protein MSSA476 MHB2 0.5 SAS_RS05360 group_1083 + 535 811 3.71E-02 104 171 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS05360 group_1083 + 442 1121 1.00E-02 143 230 1.00E+00 hypothetical protein HG003 MHB2 1 SAOUHSC_00968 group_0220 SAOUHSC_00968 + 187 1781 0.00E+00 166 340 1.00E+00 hypothetical protein MRSA252 RPMI 0.5 SAR_RS05095 group_0220 SAOUHSC_00968 + 80 936 0.00E+00 102 136 9.90E-01 hypothetical protein MSSA476 MHB2 0.5 SAS_RS05110 group_0220 SAOUHSC_00968 + 253 941 0.00E+00 328 360 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS05110 group_0220 SAOUHSC_00968 + 206 2277 0.00E+00 132 408 1.00E+00 hypothetical protein MW2 MHB2 0.5 MW_RS04910 group_0220 SAOUHSC_00968 + 222 1430 0.00E+00 917 784 1.00E+00 hypothetical protein MW2 RPMI 0.25 MW_RS04910 group_0220 SAOUHSC_00968 + 258 1527 0.00E+00 630 605 9.97E-01 hypothetical protein USA300 RPMI 0.12 USA300HOU_RS05125 group_0220 SAOUHSC_00968 + 305 1024 0.00E+00 336 357 1.00E+00 hypothetical protein MW2 MHB2 0.5 MW_RS04915 group_1008 + 504 1382 0.00E+00 765 889 1.00E+00 hypothetical protein MW2 RPMI 0.25 MW_RS04915 group_1008 + 610 1600 7.10E-03 601 490 1.00E+00 hypothetical protein MSSA476 RPMI 0.25 SAS_RS06570 hflX SAOUHSC_01283 + 262 938 9.00E-04 130 388 7.35E-01 GTPase HflX USA300 RPMI 0.12 USA300HOU_RS06575 hflX SAOUHSC_01283 + 412 917 4.90E-03 355 496 1.00E+00 GTPase HflX

MW2 RPMI 0.25 MW_RS14100 hisG SAOUHSC_03014 - 413 441 1.00E+00 125 928 1.59E-02 ATP phosphoribosyltransferase HG003 MHB2 1 SAOUHSC_00067 lctP1 SAOUHSC_00067 + 492 1150 3.41E-02 533 616 1.00E+00 L-lactate permease Limonene 1%2C2- HG003 MHB2 1 SAOUHSC_00304 limB_1 SAOUHSC_00304 + 342 1088 3.60E-03 299 433 1.00E+00 monooxygenase Beta-lactam-inducible penicillin- USA300 RPMI 0.12 USA300HOU_RS00160 mecA - 1547 1679 1.00E+00 1324 1832 3.86E-02 binding protein Phosphatidylglycerol HG003 MHB2 1 SAOUHSC_01359 mprF SAOUHSC_01359 + 68 807 3.60E-03 107 161 1.00E+00 lysyltransferase Phosphatidylglycerol MSSA476 RPMI 0.25 SAS_RS06895 mprF SAOUHSC_01359 + 140 782 1.33E-02 111 153 1.00E+00 lysyltransferase Phosphatidylglycerol USA300 MHB2 0.5 USA300HOU_RS06925 mprF SAOUHSC_01359 + 401 1132 0.00E+00 331 289 8.92E-01 lysyltransferase Phosphatidylglycerol USA300 RPMI 0.12 USA300HOU_RS06925 mprF SAOUHSC_01359 + 351 985 2.50E-03 313 480 1.00E+00 lysyltransferase UDP-N-acetylglucosamine 1- MSSA476 RPMI 0.25 SAS_RS10895 murA1 SAOUHSC_02337 - 390 347 1.00E+00 272 1199 0.00E+00 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- MW2 RPMI 0.25 MW_RS11030 murA1 SAOUHSC_02337 - 558 216 1.00E+00 304 1177 2.90E-02 carboxyvinyltransferase 1 UDP-N-acetylglucosamine 1- HG003 RPMI 0.25 SAOUHSC_02365 murA2 SAOUHSC_02365 - 628 756 1.00E+00 244 744 3.64E-02 carboxyvinyltransferase 2 UDP-N-acetylglucosamine 1- MSSA476 RPMI 0.25 SAS_RS11020 murA2 SAOUHSC_02365 - 273 386 1.00E+00 109 863 0.00E+00 carboxyvinyltransferase 2 UDP-N-acetylglucosamine 1- MW2 RPMI 0.25 MW_RS11155 murA2 SAOUHSC_02365 - 583 560 1.00E+00 294 1712 1.80E-03 carboxyvinyltransferase 2 UDP-N-acetylglucosamine 1- USA300 RPMI 0.12 USA300HOU_RS11465 murA2 SAOUHSC_02365 - 505 655 1.00E+00 337 781 1.40E-02 carboxyvinyltransferase 2 UDP-N-acetylmuramoyl- tripeptide--D-alanyl-D-alanine MRSA252 MHB2 1 SAR_RS10350 murT SAOUHSC_02107 - 159 165 9.94E-01 467 938 2.77E-02 ligase UDP-N-acetylmuramoyl- tripeptide--D-alanyl-D-alanine MRSA252 RPMI 0.5 SAR_RS10350 murT SAOUHSC_02107 - 105 120 1.00E+00 266 1042 0.00E+00 ligase UDP-N-acetylmuramoyl- tripeptide--D-alanyl-D-alanine MSSA476 RPMI 0.25 SAS_RS09855 murT SAOUHSC_02107 - 79 175 1.00E+00 298 1070 9.00E-04 ligase HG003 RPMI 0.25 SAOUHSC_02005 mutY SAOUHSC_02005 - 914 1040 1.00E+00 694 1262 3.64E-02 Adenine DNA glycosylase MW2 RPMI 0.25 MW_RS09845 mutY SAOUHSC_02005 - 307 257 1.00E+00 391 1347 1.64E-02 Adenine DNA glycosylase USA300 RPMI 0.12 USA300HOU_RS03085 mvaK1 SAOUHSC_00577 + 1060 1633 2.57E-02 356 387 1.00E+00 Galactokinase ATP-dependent DNA helicase MSSA476 MHB2 0.5 SAS_RS09930 pcrA SAOUHSC_02123 - 163 237 1.00E+00 278 511 4.39E-02 PcrA Serine/threonine-protein kinase HG003 MHB2 1 SAOUHSC_01187 pknB SAOUHSC_01187 + 109 854 1.80E-03 367 187 1.00E+00 PrkC Serine/threonine-protein kinase MRSA252 RPMI 0.5 SAR_RS06105 pknB SAOUHSC_01187 + 49 415 3.10E-03 104 160 9.90E-01 PrkC

156 Supplemental Table 11 (Continued).

Plus Strand Minus Strand Strain Medium Conc. Locus Tag Gene SAOUHSC Strand Ctrl Exp q-Value Ctrl Exp q-Value Description 1-acyl-sn-glycerol-3-phosphate MRSA252 RPMI 0.5 SAR_RS09250 plsC SAOUHSC_01837 - 160 214 1.00E+00 278 764 1.22E-02 acyltransferase putative ABC transporter ATP- HG003 MHB2 1 SAOUHSC_02152 pmtC SAOUHSC_02152 - 181 156 1.00E+00 442 1968 0.00E+00 binding protein YxlF putative ABC transporter ATP- MSSA476 MHB2 0.5 SAS_RS10070 pmtC SAOUHSC_02152 - 326 328 1.00E+00 306 672 5.00E-03 binding protein YxlF putative ABC transporter ATP- MSSA476 RPMI 0.25 SAS_RS10070 pmtC SAOUHSC_02152 - 290 425 1.00E+00 281 903 1.78E-02 binding protein YxlF putative ABC transporter ATP- USA300 MHB2 0.5 USA300HOU_RS10485 pmtC SAOUHSC_02152 - 335 221 1.00E+00 434 705 4.29E-02 binding protein YxlF Spermidine/putrescine-binding MRSA252 RPMI 0.5 SAR_RS05485 potD SAOUHSC_01049 + 145 518 4.79E-02 234 209 1.00E+00 periplasmic protein MRSA252 RPMI 0.5 SAR_RS06125 rpmB SAOUHSC_01191 - 171 253 1.00E+00 196 893 1.00E-03 50S ribosomal protein L28 USA300 RPMI 0.12 USA300HOU_RS06150 rpmB SAOUHSC_01191 - 349 563 9.92E-01 718 1528 0.00E+00 50S ribosomal protein L28 HG003 MHB2 1 SAOUHSC_01678 rpsU SAOUHSC_01678 - 296 357 1.00E+00 328 1075 6.70E-03 30S ribosomal protein S21 Staphylococcal complement MRSA252 RPMI 0.5 SAR_RS10630 scn_1 SAOUHSC_02167 - 328 449 1.00E+00 425 977 0.00E+00 inhibitor MSSA476 MHB2 0.5 SAS_RS04235 tlyC SAOUHSC_00854 + 380 785 0.00E+00 227 292 1.00E+00 Hemolysin C MSSA476 RPMI 0.25 SAS_RS04235 tlyC SAOUHSC_00854 + 379 1843 0.00E+00 250 337 1.00E+00 Hemolysin C USA300 RPMI 0.12 USA300HOU_RS04550 tlyC SAOUHSC_00854 + 527 1031 3.92E-02 340 484 1.00E+00 Hemolysin C Processive diacylglycerol beta- MSSA476 RPMI 0.25 SAS_RS04715 ugtP SAOUHSC_00953 - 233 445 1.00E+00 119 1503 0.00E+00 glucosyltransferase Processive diacylglycerol beta- USA300 RPMI 0.12 USA300HOU_RS05040 ugtP SAOUHSC_00953 - 424 430 1.00E+00 326 1020 0.00E+00 glucosyltransferase putative ABC transporter ATP- MSSA476 RPMI 0.25 SAS_RS10585 vga SAOUHSC_02274 + 173 1271 0.00E+00 143 381 8.79E-01 binding protein YheS Transcriptional regulatory protein HG003 RPMI 0.25 SAOUHSC_00020 walR SAOUHSC_00020 + 506 1234 0.00E+00 392 433 1.00E+00 WalR Transcriptional regulatory protein MRSA252 RPMI 0.5 SAR_RS00100 walR SAOUHSC_00020 + 370 985 0.00E+00 169 179 9.90E-01 WalR Transcriptional regulatory protein MW2 MHB2 0.5 MW_RS00100 walR SAOUHSC_00020 + 314 1004 0.00E+00 345 362 1.00E+00 WalR Transcriptional regulatory protein USA300 MHB2 0.5 USA300HOU_RS00100 walR SAOUHSC_00020 + 497 1094 0.00E+00 239 208 8.92E-01 WalR Transcriptional regulatory protein USA300 RPMI 0.12 USA300HOU_RS00100 walR SAOUHSC_00020 + 702 1732 0.00E+00 369 492 1.00E+00 WalR putative amino acid permease MSSA476 MHB2 0.5 SAS_RS13535 yhdG_2 SAOUHSC_02923 + 219 460 4.86E-02 369 416 1.00E+00 YhdG MRSA252 RPMI 0.5 SAR_RS02575 yugI_1 SAOUHSC_00483 + 141 352 3.78E-02 3 24 9.90E-01 General stress protein 13 USA300 RPMI 0.12 USA300HOU_RS02605 yugI_1 SAOUHSC_00483 + 259 550 6.70E-03 77 84 1.00E+00 General stress protein 13

157

Supplemental Table 12: Genes enriched in reads in the presence of daptomycin. Included are genes that were at least five-fold enriched in reads in the presence of daptomycin, with at least 100 reads in the daptomycin-exposed sample data after normalization and a q-value less than 0.05.

158 Supplemental Table 12 (Continued).

Strain Medium Conc Locus Tag Gene SAOUHSC Ctrl Exp Ratio q-Value Description Adenine HG003 MHB2 1 HG003_01529 apt SAOUHSC_01743 543 4775 8.7802 1.81E-03 phosphoribosyltransferase Phospho-2-dehydro-3- HG003 MHB2 1 HG003_01621 aroA_2 SAOUHSC_01852 65 375 5.6979 6.20E-03 deoxyheptonate aldolase MSSA476 RPMI 0.25 A476_02095 asp23 SAOUHSC_02441 268 2208 8.2111 2.40E-03 Alkaline shock protein 23 GTP-sensing transcriptional MSSA476 RPMI 0.25 A476_01178 codY SAOUHSC_01228 207 1317 6.3373 9.29E-06 pleiotropic repressor CodY Protoheme IX farnesyltransferase MSSA476 RPMI 0.25 A476_01041 ctaB SAOUHSC_01066 68 1031 14.962 2.34E-04 2 D-alanine--poly(phosphoribitol) MSSA476 RPMI 0.25 A476_00795 dltA SAOUHSC_00869 20 1356 64.607 1.12E-13 ligase subunit 1

MSSA476 RPMI 0.25 A476_00796 dltB SAOUHSC_00870 42 473 11.03 7.99E-05 Peptidoglycan O-acetyltransferase HG003 MHB2 1 HG003_01462 era SAOUHSC_01668 24 177 7.1186 1.56E-03 GTPase Era ATP-dependent zinc MW2 RPMI 0.25 MW2_00460 ftsH SAOUHSC_00486 354 2508 7.0683 9.33E-04 metalloprotease FtsH Cyclic-di-AMP phosphodiesterase MW2 RPMI 0.25 MW2_00014 gdpP SAOUHSC_00015 303 1898 6.2475 1.91E-02 GdpP Aerobic glycerol-3-phosphate MRSA252 RPMI 0.5 MRSA252_01241 glpD SAOUHSC_01278 171 2496 14.515 6.64E-09 dehydrogenase Aerobic glycerol-3-phosphate MW2 MHB2 0.5 MW2_01182 glpD SAOUHSC_01278 221 1676 7.5549 3.52E-04 dehydrogenase MSSA476 RPMI 0.25 A476_00794 dltX SAOUHSC_00868 12 209 16.128 1.54E-02 hypothetical protein

MSSA476 RPMI 0.25 A476_00804 ndh2 SAOUHSC_00878 144 873 6.0291 5.32E-03 NADH dehydrogenase-like protein HG003 MHB2 1 HG003_00538 group_0130 SAOUHSC_00618 1489 7973 5.3515 9.89E-03 hypothetical protein MSSA476 RPMI 0.25 A476_00577 group_0130 SAOUHSC_00618 1171 6647 5.6721 4.48E-06 hypothetical protein MSSA476 RPMI 0.25 A476_01211 group_0277 SAOUHSC_01265 32 378 11.491 3.28E-07 hypothetical protein HG003 MHB2 1 HG003_00838 group_0212 SAOUHSC_00948 4212 30109 7.147 3.84E-10 hypothetical protein MSSA476 RPMI 0.25 A476_01569 group_0430 SAOUHSC_01761 673 4637 6.8817 9.40E-05 hypothetical protein MSSA476 RPMI 0.25 A476_01804 group_0488 SAOUHSC_02006 898 5367 5.9711 7.59E-06 hypothetical protein MSSA476 RPMI 0.25 A476_00957 group_1008 1862 9407 5.0497 4.06E-07 hypothetical protein GMP synthase [glutamine- HG003 MHB2 1 HG003_00330 guaA SAOUHSC_00375 40 216 5.2908 1.65E-04 hydrolyzing] Ktr system potassium uptake MRSA252 RPMI 0.5 MRSA252_01032 ktrA SAOUHSC_01034 155 2834 18.17 2.60E-04 protein A Ktr system potassium uptake MSSA476 RPMI 0.25 A476_01014 ktrA SAOUHSC_01034 80 1083 13.382 1.44E-06 protein A Ktr system potassium uptake MRSA252 RPMI 0.5 MRSA252_00964 ktrD SAOUHSC_00959 1412 12920 9.1443 4.56E-07 protein B Ktr system potassium uptake MSSA476 RPMI 0.25 A476_00947 ktrD SAOUHSC_00959 668 4133 6.1794 1.08E-11 protein B Ktr system potassium uptake MW2 RPMI 0.25 MW2_00907 ktrD SAOUHSC_00959 1249 15671 12.538 2.37E-02 protein B MSSA476 RPMI 0.25 A476_02234 lyrA SAOUHSC_02611 270 1540 5.6862 1.49E-05 Lysostaphin resistance protein A MSSA476 RPMI 0.25 A476_00813 mnhC_1 SAOUHSC_00887 13 164 11.754 7.76E-03 Na(+)/H(+) antiporter subunit C1 3-hydroxy-3-methylglutaryl- HG003 MHB2 1 HG003_02526 mvaA SAOUHSC_02859 12 117 9.0674 1.85E-05 coenzyme A reductase Polyribonucleotide MSSA476 RPMI 0.25 A476_01197 pnpA SAOUHSC_01251 59 461 7.7056 1.70E-04 nucleotidyltransferase Release factor glutamine HG003 MHB2 1 HG003_02094 prmC SAOUHSC_02358 432 3331 7.6959 1.48E-04 methyltransferase Release factor glutamine MRSA252 RPMI 0.5 MRSA252_02174 prmC SAOUHSC_02358 26 2432 90.129 7.73E-03 methyltransferase Release factor glutamine MSSA476 RPMI 0.25 A476_02029 prmC SAOUHSC_02358 44 2477 55.073 4.81E-03 methyltransferase Release factor glutamine USA300 MHB2 0.5 USA300_02124 prmC SAOUHSC_02358 33 255 7.5211 4.98E-06 methyltransferase Phosphoenolpyruvate-protein HG003 MHB2 1 HG003_00911 ptsI SAOUHSC_01029 345 1912 5.5278 4.27E-04 phosphotransferase Phosphoenolpyruvate-protein MSSA476 RPMI 0.25 A476_01010 ptsI SAOUHSC_01029 168 1799 10.652 8.73E-14 phosphotransferase Phosphoenolpyruvate-protein MW2 RPMI 0.25 MW2_00969 ptsI SAOUHSC_01029 340 2324 6.8191 6.32E-03 phosphotransferase Redox-sensing transcriptional MSSA476 RPMI 0.25 A476_01958 rex SAOUHSC_02273 303 2191 7.2103 1.18E-04 repressor Rex MSSA476 RPMI 0.25 A476_01156 rnc SAOUHSC_01203 21 255 11.63 2.05E-02 Ribonuclease 3 USA300 MHB2 0.5 USA300_01164 rnc SAOUHSC_01203 30 315 10.18 5.75E-07 Ribonuclease 3 MSSA476 RPMI 0.25 A476_01679 sagB SAOUHSC_01895 36 402 10.895 5.15E-05 Bifunctional autolysin MW2 MHB2 0.5 MW2_01578 secDF SAOUHSC_01746 522 7821 14.957 1.97E-02 hypothetical protein

159 Supplemental Table 12 (Continued).

Strain Medium Conc Locus Tag Gene SAOUHSC Ctrl Exp Ratio q-Value Description Monofunctional MSSA476 RPMI 0.25 A476_01809 sgtB SAOUHSC_02012 531 6285 11.815 5.73E-11 glycosyltransferase Monofunctional MW2 RPMI 0.25 MW2_01831 sgtB SAOUHSC_02012 1242 35342 28.434 4.06E-02 glycosyltransferase MSSA476 RPMI 0.25 A476_02422 srtA SAOUHSC_02834 103 1128 10.851 5.47E-07 hypothetical protein Staphylococcal secretory antigen HG003 MHB2 1 HG003_02279 ssaA_1 SAOUHSC_02571 2261 11528 5.0967 7.12E-04 ssaA2 MSSA476 RPMI 0.25 A476_00863 yjbH SAOUHSC_00938 247 9749 39.314 1.41E-14 hypothetical protein MW2 RPMI 0.25 MW2_00886 yjbH SAOUHSC_00938 3064 60889 19.866 6.12E-05 hypothetical protein Two-component system WalR/WalK regulatory protein HG003 MHB2 1 HG003_00022 walH SAOUHSC_00022 1243 15122 12.157 2.40E-06 YycH Two-component system WalR/WalK regulatory protein MSSA476 RPMI 0.25 A476_00022 walH SAOUHSC_00022 1602 17450 10.887 1.16E-09 YycH

Two-component system HG003 MHB2 1 HG003_00023 walI SAOUHSC_00023 853 7864 9.2101 2.54E-02 WalR/WalK regulatory protein YycI

Two-component system MSSA476 RPMI 0.25 A476_00023 walI SAOUHSC_00023 708 6164 8.6956 3.10E-07 WalR/WalK regulatory protein YycI

160

Supplemental Table 13: SNPs present in daptomycin-nonsusceptible isolates not found in paired susceptible isolates. Isolates come from the Cubist Collection. Ref: the nucleotide found in the USA300-TCH1516 reference genome. S: the nucleotide found in the susceptible isolate, with ‘.’ denoting that the nucleotide is the same as the reference. N: the nucleotide found in the nonsusceptible isolate. Loc: the nucleic acid location of the mutation within the gene. Len: Length of the gene in nucleic acids. Note that two genes are listed if the mutation falls in an area annotated as belonging to two genes.

161 Supplemental Table 13 (Continued).

Susceptible Nonsusceptible StrainPair Position Ref S N Depth Qual Depth Qual Tag Gene Change Loc Len 3-4 72015 A . G 103 60 95 60 USA300_00065; arcD_2 F>S; 959; 1422; 3-4 283033 A . G 111 60 96 60 USA300_00240; ldh1 D>G; 212; 954; 3-4 587538 A . G 109 60 127 60 USA300_00530; rpoB H>R; 1442; 3552; 3-4 1396341 C . T 85 60 67 60 USA300_01297; mprF P>L; 941; 2523; 7-8 1388352 C . T 52 60 94 60 USA300_01293; parC S>F; 239; 2403; 7-8 1646002 G . A 41 60 67 60 USA300_01530; pepQ2 silent; 651; 1062; 7-8 1911184 G . A 44 33 60 32 USA300_01771; hypothetical A>T; 163; 825; 7-8 2031621 C . T 48 60 26 37 Intergenic 7-8 2031624 T . A 50 60 25 37 Intergenic 13-14 16683 G . A 64 60 122 60 USA300_00012; metX S>N; 551; 969; 13-14 230962 A . G 122 60 132 60 USA300_00201; hsdR silent; 2121; 2790; 13-14 310907 T . C 132 60 126 60 USA300_00264; ptsG silent; 204; 792; 13-14 581390 T . C 214 60 260 60 Intergenic 13-14 587486 T . C 167 60 186 60 USA300_00530; rpoB S>P; 1390; 3552; 13-14 621920 C . T 133 55 150 55 USA300_00551; sdrE T>I; 2483; 3465; 13-14 633891 T . C 139 60 137 60 USA300_00562; vraA silent; 72; 1377; 13-14 662401 T . C 102 60 103 60 Intergenic 13-14 732442 T . C 84 60 97 60 USA300_00668; graX silent; 465; 924; 13-14 772387 T . C 90 60 113 60 USA300_00710; fruB M>T; 563; 921; 13-14 820203 T . C 106 60 160 60 USA300_00752; garK_1 silent; 405; 1125; 13-14 837619 C . T 130 60 142 60 Intergenic USA300_00909; leuA2; silent; 1131; 1146; 13-14 981498 T . C 71 60 120 60 USA300_00910; hypothetical M>T; 2; 642; 13-14 1063591 T . C 111 60 145 60 USA300_00989; ywtF I>V; 898; 1218; 13-14 1152575 T . C 101 60 127 60 USA300_01073; polX silent; 822; 1713; 13-14 1169791 T . C 81 60 116 60 USA300_01089; hypothetical H>R; 47; 234; 13-14 1172399 T . C 115 60 135 60 USA300_01093; hypothetical H>R; 188; 282; 13-14 1266274 C . T 78 60 89 60 USA300_01180; dprA P>S; 610; 873; 13-14 1332624 C . A 57 60 70 60 USA300_01234; hfq T>N; 68; 234; 13-14 1396815 C . A 98 60 158 60 USA300_01297; mprF T>K; 1415; 2523; 13-14 1480217 T . C 72 60 90 60 USA300_01371; ebh_1 silent; 21807; 31266; 13-14 1520082 T . C 69 60 99 60 USA300_01386; pbp2 silent; 1206; 2184; 13-14 1626878 T . C 94 60 92 60 Intergenic 13-14 1667010 T . C 90 60 100 60 USA300_01555; znuC silent; 480; 786; 13-14 1921903 T . C 114 60 165 60 USA300_01782; menE silent; 906; 1479; 13-14 1928224 T . C 62 60 71 60 Intergenic 13-14 1939090 A . T 82 41 140 42 USA300_01798; hsdM2_1 L>I; 52; 1557; 13-14 1974896 T . C 168 60 281 60 USA300_01838; smc_2 S>G; 2368; 2937; 13-14 2027965 T . C 198 60 271 60 USA300_01910; vraT D>G; 680; 702; 13-14 2052191 T . C 108 60 150 60 USA300_01929; pcrA silent; 102; 2193; 13-14 2137854 T . C 111 60 111 60 USA300_02033; hypothetical H>R; 257; 444; 13-14 2211259 T . C 112 51 141 52 Intergenic 13-14 2292242 T . C 163 60 172 60 Intergenic 13-14 2302679 T . C 168 60 188 60 USA300_02180; sdrM_1 M>V; 394; 1344; 13-14 2307056 T . C 216 60 293 60 USA300_02185; hypothetical D>G; 581; 1371; 13-14 2322669 T . C 182 60 207 60 Intergenic 13-14 2547771 G . C 141 60 134 60 USA300_02432; bioD L>V; 277; 1116; 13-14 2714388 A . G 95 60 96 60 USA300_02577; hypothetical F>L; 178; 1056; 15-16 677482 T . C 91 60 71 60 USA300_00611; hypothetical V>A; 359; 717; 15-16 1396440 T . G 98 60 64 60 USA300_01297; mprF M>R; 1040; 2523; 15-16 1413745 T . A 120 60 99 60 Intergenic

162 Supplemental Table 13 (Continued).

Susceptible Nonsusceptible StrainPair Position Ref S N Depth Qual Depth Qual Tag Gene Change Loc Len 15-16 1803990 A . T 47 39 38 36 USA300_01684; radC F>L; 723; 828; 15-16 2276591 T . C 182 60 178 60 USA300_02161; mtlR silent; 1944; 2133; 34-35 7282 C . T 129 60 82 60 USA300_00006; gyrA S>L; 251; 2664;

163