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Bacterial Survival Guide: Metabolic Pathways Leading to Antibiotic Tolerance in Staphylococcus aureus
by Eliza Zalis
Bachelor of Arts, Providence College
A dissertation submitted to
The Faculty of the College of Science of Northeastern university
in partial fulfillment of the requirements for the degree of Doctor of Philosophy
April 10th, 2019
Dissertation directed by
Kim Lewis University Distinguished Professor of Biology
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Acknowledgements
I owe a debt of gratitude to my advisor Kim Lewis for his mentorship over the past five years. I
entered his lab with no microbiology or research experience and he allowed me to do
microbiology research anyway. I remain grateful for his trust and have benefitted from his insight
and leadership. Kim has shown me the importance of addressing questions that are both
important and interesting, which is a habit I hope to practice for the rest of my life.
I thank the members of my dissertation committee, including Veronica Godoy, Win Chai, Eddie
Geisinger, and Tim van Opijnen for their guidance. Their advice has shaped this project and it has
been a pleasure to learn from them.
I am also grateful for each member of the Lewis Lab, past and present. I have spent five years
doing research alongside excellent scientists and I thank them for their support and inspiration.
My labmates have helped me learn how to tackle big questions by focusing on the specifics while
still maintaining broad perspective. I thank in a special way Sarah Rowe, Brian Conlon, Austin
Nuxoll, Autumn Brown-Gandt, Yeva Yue Shan, Pooja Balani, Bijaya Sharma, and Phil Strandwitz
for their discussion and advice during my first year in lab. I also thank the Persister SistersTM,
especially Yeva Shan, David Cameron, Austin Nuxoll, Sylvie Manuse, Nadja Leimer, Jeff Quiqley,
Gabriel Fox, Samantha Nicolau, and Michael Gates for their insight and friendship. My labmates
have been incredible colleagues but have also become great friends. It has been a true joy to
work with them.
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I thank the teachers who have guided my education, from kindergarten to high school biology
and through my undergraduate and graduate study. I have pursued science because of their
instruction and encouragement.
I thank the Biology Department of Northeastern University for creating an environment of
productive learning. I am grateful for the friends I have made in the department and the support
of my fellow PhD students.
I have enjoyed serving on the board of Graduate Women in Science and Engineering at
Northeastern and the Boston Bacterial Meeting Organizing Committee. These experiences have
offered opportunities for service and outreach and have enriched my experience as a scientist in
Boston. Through these groups I have met new collaborators, mentors, and friends and I have
enjoyed every part of getting to know these people. I thank them for their dedication to
community engagement and science communication.
I also thank my friends outside of lab. They make my life better and I am grateful for their humor,
understanding, and love.
I am the person I am today because of my family. I am grateful for their endless support and love.
Any success I ever achieve is because of them. I thank my parents Sharon and Roy for encouraging
curiosity and creativity. I thank my siblings Eva, Joe, and Gretchen for their perspective and
humor. My grandmother Agnes has been a role model in education and in life and I am grateful
for her example. I thank my aunts, uncles, and cousins for their discussions about science and
beyond.
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Abstract of Dissertation
Persisters are rare cells in a bacterial population that are able to tolerate antibiotic treatment [1,
2]. Antibiotic tolerance is distinct from antibiotic resistance. Although both tolerance and
resistance result in antibiotic treatment failure, they do so by very different mechanisms which
must be understood separately in order to develop effective treatments for bacterial infections.
Antibiotic resistance occurs when bacteria acquire genetic mutations that allow them to grow in
the presence of antibiotics. Tolerance occurs when bacterial cells undergo a phenotypic switch
to a non-growing state [3]. Most antibiotics kill bacteria by binding to targets and interrupting
active cellular processes. Non-growing cells have fewer active targets for antibiotics to corrupt,
rendering antibiotics less effective. In clinical practice, antibiotics prescribed to treat bacterial
infections are often ineffective in completely eradicating a bacterial population. Surviving
persister cells can eventually resume growth, causing relapsing and recurrent infections.
Staphylococcus aureus is a notorious human pathogen responsible for pneumonia, endocarditis,
osteomyelitis, toxic shock syndrome, skin and soft tissue infections, and infection of implanted
devices. This research uncovers the mechanism by which S. aureus forms persister cells. We find
that low ATP levels lead to increased antibiotic tolerance. S. aureus is known to rely on the TCA
cycle and amino acids derived from host tissues to fuel growth during infection [4]. We show that
defects in the TCA cycle and catabolism of amino acids leads to low intracellular ATP and
increased antibiotic tolerance. We propose that within a population, natural fluctuation in gene
expression leads to phenotypic heterogeneity. We hypothesize that cells with low levels of
metabolic gene expression compared to the bulk of the population generate less ATP and are
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better able to survive antibiotic treatment. We sort individual cells with low levels of TCA gene
expression and show that they are indeed more tolerant to antibiotic treatment than the rest of
the population. We conclude that stochastic gene expression leads to heterogeneity in
phenotypic antibiotic susceptibility and gives rise to persister cells.
We next investigate persister cell resuscitation. We knew that low TCA cycle activity and low ATP
levels in S. aureus lead to high tolerance. We therefore expected that resuscitation from the
persister state would involve cellular metabolism. We conduct a broad screen of a library of S.
aureus mutants to see what genes are involved in growth resumption following antibiotic
treatment. We identify 262 genes implicated in resuscitation including genes involved in carbon
and nitrogen metabolism, nucleotide synthesis, and translation.
We also seek to understand the prevalence of metabolic gene defects in clinical isolates of S.
aureus. We assemble a collection of isolates from patients with endocarditis, osteomyelitis, skin
and soft tissue infections, and atopic dermatitis. We identify isolates with high tolerance
compared to wild type strains. We perform whole genome sequencing of each isolate and
compare sequences to a reference strain to identify variants. We expected that antibiotic usage
would select for mutations that facilitate tolerance and hypothesized that TCA cycle mutations
would be prevalent in clinical isolates. We indeed found multiple high-impact nucleotide
polymorphisms in TCA genes in S. aureus clinical isolates.
This work describes the mechanism of antibiotic tolerance in S. aureus. Defects in the TCA cycle
cause low intracellular ATP levels and high tolerance to multiple classes of antibiotics. We identify
mutations in TCA cycle genes in clinical isolates of S. aureus. We demonstrate that natural
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fluctuation in TCA cycle gene expression yields heterogeneity in phenotypic antibiotic
susceptibility.
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Table of Contents
Acknowledgements 2
Abstract of Dissertation 4
Table of Contents 7
List of Figures 11
List of Tables 12
Glossary of Terms 12
Chapter 1. Introduction 15
1.1 Persister Cells 15
1.2 Antibiotic Tolerance versus Resistance 15
1.3 Chronic and Recurrent Infection 16
1.4 Staphylococcus aureus: Commensal and Pathogen 17
Chapter 2. Persister formation in Staphylococcus aureus is associated with ATP depletion 20
2.1 Abstract 20
2.2 Results 21
2.3 Materials and Methods 33
2.3.1 Bacterial strains and growth conditions 33
2.3.2 Strain Constructions 34
2.3.3 Persister Assays 35
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2.3.4 Arsenate and rifampicin persister assays 35
2.3.5 Flow cytometry and FACS analysis using gfp reporters 35
2.3.6 Proteomic analysis 37
2.3.7 Real-Time qRT-PCR 37
2.3.8 ATP Assays 38
2.4 Contributions 38
2.5 Supplemental Information 39
Chapter 3. Stochastic variation in expression of the TCA cycle produces persister cells 45
3.1 Abstract 46
3.2 Introduction 47
3.3 Results 47
3.4 Discussion 55
3.5 Materials and Methods 57
3.5.1 Bacterial strains, culture conditions, and strain construction 57
3.5.2 Proteomic sample preparation 57
3.5.3 Proteomics and data analysis 58
3.5.4 Persister assays 59
3.5.5 ATP quantification of bulk culture 59
3.5.6 Construction of S. aureus HG003 expressing QUEEN2m 59
3.5.7 Microscopy 60
3.5.8 Single -cell ATP quantification using QUEEN 60
3.5.9 FACS analysis using GFP reporters 61
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3.6 Acknowledgements 61
3.7 Supplemental Information 62
Chapter 4. Persister Resuscitation 70
4.1 Abstract 70
4.2 Introduction 70
4.3 Results 71
4.3.1 Growth resumption 71
4.3.2 NTML screen 73
4.4 Discussion 81
4.5 Material and Methods 82
4.5.1 Strains and culture conditions 83
4.5.2 Resuscitation screen 83
4.5.3 Data analysis 84
Chapter 5. Clinical isolates of S. aureus harbor TCA cycle mutations 84
5.1 Abstract 85
5.2 Introduction 85
5.3 Results 86
5.4 Methods 92
5.4.1 S. aureus isolate sources 92
5.4.2 Whole genome sequencing and read mapping 92
5.4.3 MIC and persister experiments 93
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5.5 Discussion 93
5.6 Supplemental Information 96
Chapter 6. Dissertation conclusion and future directions 101
6.1 Summary 101
6.2 Ongoing research and future directions 103
6.2.1 Persister resuscitation 103
6.2.2 Noise-quenching 104
6.2.3 Eradicating persister cells 105
References Cited 106
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List of Figures
Figure 1: Model of Persistence.
Figure 2.1: Toxin-antitoxin modules and stringent response do not control persister formation in
S. aureus.
Figure 2.2: Activation of stationary markers is heterogeneous.
Figure 2.3: Persister sorting using stationary markers Pcap5A and ParcA.
Figure 2.4: Reduction in ATP induces persister formation and expression of stationary phase
markers.
Figure 3.1: TCA cycle enzyme abundance increases in stationary phase.
Figure 3.2: TCA cycle mutants have lower ATP levels than wild type.
Figure 3.3: S. aureus mutants lacking functional late TCA cycle genes exhibits increased antibiotic
tolerance.
Figure 3.4: Fluorescence-activated cell sorting after antibiotic treatment yields enrichment for
persister cells in populations expressing relatively low levels of TCA cycle genes.
Figure 4.1: Antibiotic killing and growth resumption after antibiotic inactivation.
Figure 3.2: Post-treatment persister resuscitation screen output
Figure 5.1: Variant type classified as deletion, insertion, nonsynonymous SNP, or synonymous
SNP.
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Figure 5.2: Biological subsystems implicated in genome variance analysis.
Figure 5.3: High-impact variants in TCA cycle genes.
List of Tables
Table 4.1: S. aureus mutants with significantly faster resuscitation after ciprofloxacin treatment
compared to the plate average.
Table 4.2: S. aureus mutants with significantly slower resuscitation after ciprofloxacin treatment
compared to the plate average.
Table 5.1: S. aureus clinical isolate source information and diagnosis.
Glossary of Terms
ADEP: acyldepsipeptide
Amp: ampicillin
Ars: arsenate
ATP: adenosine triphosphate
Cam: chloramphenicol
CA-MRSA: community-associated methicillin-resistant Staphylococcus aureus
CCCP: Carbonyl cyanide m-chlorophenylhydrazine
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CFU: colony forming unit
Cip: ciprofloxacin
Ctrl: control
Exp: Exponential
FACS: fluorescence-activated cell sorting
Gent: gentamicin
GFP: green fluorescent protein
KEGG: Kyoto Encyclopedia of Genes and Genomes
LBB/A: Lysogeny Broth/Agar
MHB/A: Mueller-Hinton Borth/Agar
MIC: Minimum Inhibitory Concentration
MOPS: 3-(N-morpholino) propanesulfonic acid
MRSA: Methicillin-resistant Staphylococcus aureus
MSSA: Methicillin-sensitive Staphylococcus aureus
NTML: Nebraska transposon mutant library
OD: optical density
Ox: oxacillin
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PATRIC: Pathosystems Resource Integration Center
PBS: phosphate buffered saline
PCR: Polymerase chain reaction
QUEEN: quantitative evaluator of cellular energy
RAST: Rapid Annotations using Subsystems Technology
Rpm: rotations per minute
Stat: Stationary
TA: Toxin-antitoxin
TCA: tricarboxylic acid cycle
Tn-seq: Transposon sequencing
TSB/A: Tryptic Soy Broth/Agar
Unk: unkown
WT: wild type
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Chapter 1: Introduction
1.1 Persister Cells
Persister cells are rare bacterial cells within a population that are able to tolerate antibiotic
treatment. Upon exposure to antibiotics, they survive but do not grow. Persister cells were
first described in 1944 by Joseph Bigger. Bigger found that treating a population of bacteria
with penicillin killed most of the cells, but a small portion were able to survive the antibiotic
treatment [1]. He observed resumed growth of the bacteria when penicillin was no longer
present in the culture. Bigger proposed that his failure to sterilize a culture in vitro might have
clinical implications; he proposed that these persister cells might contribute to antibiotic
treatment failure in patients with bacterial infections. This discovery of persister cells went
largely unexplored for fifty years, but in recent years has experienced a resurgence in the
fields of microbiology and infectious disease. Multiple microbial species have been shown to
produce persister cells, including Staphylococcus aureus, Escherichia coli, Candida albicans,
Pseudomonas aeruginosa, Mycobacterium tuberculosis, Salmonella Typhimurium, and
Borrelia burgdorferi [5-10]. Persister cells typically account for a small fraction of a
community but allow a population of bacteria to tolerate treatment with multiple types of
antibiotics.
1.2 Antibiotic Tolerance and Resistance
Antibiotic resistance is an important problem but it does not account for all cases of antibiotic
treatment failure. A patient can suffer from an infection caused by a pathogen that is drug-
susceptible by laboratory tests, but still experience treatment failure. The phenomenon of
resistance is separate from antibiotic tolerance. Resistance occurs when genetic mutations
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develop in a bacterial population that enable cells to grow even in the presence of that
antibiotic. Efflux pumps and genetic mutations that alter the structure of an antibiotic’s
molecular target can confer resistance [11]. Persister cells are phenotypic variants within a
population. These antibiotic tolerant cells are genetically susceptible to antibiotics but are
not killed by antibiotics. Figure 1 illustrates the phenomenon of antibiotic tolerance. It has
recently been shown that antibiotic tolerance facilitates the development of resistance. Since
tolerant cells are more likely to survive antibiotic treatment, they are more likely to develop
resistance mutations [12]. Antibiotic resistance and tolerance are fundamentally distinct but
result in the same outcome of antibiotic treatment failure.
1.3 Chronic Infection
The clinical implications of antibiotic tolerance are manifold. Persister cells that tolerate
antibiotic treatment can resume growth after antibiotics are no longer present in the
environment. In the clinical setting, resumed bacterial growth means repopulation of the
infection site and recurrent infection. A patient with a recurrent and relapsing infection would
be subject to multiple hospital visits and rounds of treatment.
Antibiotic tolerance has been implicated in multiple cases of infectious disease. Cystic fibrosis
is one example of persister cells in chronic infection. Patients with cystic fibrosis suffer from
Pseudomonas aeruginosa infections and undergo years of antibiotic treatment, but
treatment is not effective in eradicating the infection. When researchers tested the persister
levels of longitudinal isolates taken from the lungs of cystic fibrosis patients, they found that
samples collected during later stages of disease progression exhibited higher levels of
persister cells than those taken earlier [7], suggesting that selection for genetic mutations
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that promote tolerance occurs in vivo. Additionally, Mycobacterium tuberculosis, Candida
albicans, and Escherichia coli have been found to exhibit tolerance in cases of recurrent
infection [5, 13, 14].
Finding new treatments for chronic infections would decrease costs in health care and
improve patient outcomes. The aggregate cost of hospital-acquired infections in the United
States is estimated to be more than $15 billion each year [15, 16]. Staphylococcus aureus is
one of the most common human pathogens and is notorious for causing recalcitrant
infections, often associated with the formation of biofilms [17]. Hospital-associated
infections are frequently caused by S. aureus. Often, patients experience chronic and
recurrent S. aureus infections while battling other diseases that require hospital stays. Elderly
and immunocompromised individuals are especially likely to develop serious S. aureus
infections and often require complicated and costly treatment [15, 16].
1.4 Staphylococcus aureus: Commensal and Pathogen
Staphylococcus aureus is a human commensal and a pathogen. An estimated 30% of humans
are colonized asymptomatically, but S. aureus can cause endocarditis, osteomyelitis,
bacteremia, toxic shock syndrome, and pneumonia, in addition to skin and soft tissue
infections. Colonization is a risk factor for developing a S. aureus infection [18], as is
hospitalization [19, 20]. Staphylococcus aureus causes more than ten thousand deaths in the
U.S. each year [21] and even non-fatal cases impose a significant burden on health care
facilities. S. aureus is widely acknowledged as one of the most pernicious agents of healthcare
associated infections [22-24]. Patients often suffer from relapsing infection, especially in
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cases of indwelling medical devices. S. aureus is a notorious biofilm-former, capable growing
on cardiovascular implants, joint replacement devices, and catheters [25]. Biofilms contain
cells embedded in an exopolymeric matrix and provide protection from immune factors and
phagocytosis [26]. S. aureus biofilms are heterogeneous. Nutrients and signaling molecules
diffuse throughout the geography of the biofilm, promoting diverse phenotypes [27]. Cells
can disperse from a biofilm to establish infection at remote body sites.
Staphylococcus aureus employs various strategies to survive the host immune response.
Pathogenic S. aureus produces a number of toxins and virulence factors that interfere with
neutrophil activation, chemotaxis, and adhesion to epithelial cells [28, 29]. S. aureus also
evades targeted production of antimicrobial peptides and reactive oxygen species [30]. One
of the major weapons against invading S. aureus is the production of nitric oxide within
activated phagocytes. Unlike other Staphylococcal species, S. aureus is able to produce NAD+
to maintain glycolytic flux and redox balance by fermenting glucose via lactate
dehydrogenase and can therefore survive nitric oxide stress. [31-34] Increasing investigation
of the microenvironment of S. aureus infection has improved our understanding of the
complicated battle between pathogen and host.
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Figure 1: Model of Persistence. After addition of an antibiotic to a culture of
growing bacteria, most of the population will die (represented at 5 hours), but
persisters survive in the presence of antibiotics. The “persister plateau” represents
the relatively stable population of persisters, which can persist for long persiod of
treatment. If a population acquires resistance to an antibiotic, the population will
grow in the presence of the antibiotic (dashed grey line).
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Chapter 2: Persister formation in Staphylococcus aureus is associated with ATP depletion
This chapter includes work published in the following published article. [35]
Conlon, B. P., Rowe, S. E., Gandt, A. B., Nuxoll, A. S., Donegan, N. P., Zalis, E. A., ... & Lewis, K.
(2016). Persister formation in Staphylococcus aureus is associated with ATP depletion. Nature
microbiology, 1(5), 16051.
Antibiotic resistance is a major human health problem[36]. However, most pathogens that cause
hard to treat chronic infections are not drug resistant[2, 17, 37]. There is mounting evidence that
drug-tolerant persister cells contribute to this phenomenon [5, 7, 10, 13, 38]. Persister cells are
phenotypic variants that survive lethal doses of antibiotics and are genetically identical to their
drug susceptible kin. The mechanism of persister formation has been extensively studied in the
closely related Gram-negative organisms Escherichia coli and Salmonella Typhimurium [2, 39,
40]. In E. coli, isolated persisters express toxin/antitoxin (TA) modules[41], most of which code
for mRNA endonucleases called interferases [42]. While deletion of individual interferases has no
phenotype, a knockout of ten TAs produced a decrease in persisters in both a growing culture
and in stationary phase4. A small fraction of persisters forms in E. coli when cells stochastically
express the HipA toxin12. HipA is a protein kinase17 which phosphorylates glutamyl aminoacyl-
tRNA synthetase, inhibiting protein synthesis18,19. Selection for increased drug tolerance in
vitro led to the identification of a hipA7 mutant allele that produces up to 1000-fold more
persisters than the wild type6. We recently identified hipA7 strains among patients with chronic
urinary tract infections12. Similarly, hip mutants are common among isolates of P.
aeruginosa from patients with cystic fibrosis11, and from patients with chronic Candida
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albicans infections20. In S. Typhimurium, TA modules are responsible for a sharp increase in
persisters when the pathogen infects macrophages9. These findings provide a link between
persisters and clinical manifestation of disease.
Little is known about the mechanism of persister formation in Gram-positive species. We first
sought to examine the role of TAs in persister formation in S. aureus. There are three known type
II TAs in S. aureus- mazEF, and relBE homologues axe1/txe1, and axe2/txe2. These are the only
three TAs predicted in S. aureus 8325, the parental strain of HG001 and HG003. An additional
phage associated toxin-antitoxin was identified in S. aureus Newman using the TAfinder too but
overexpression of the potential toxin did not inhibit growth (Supplemental Figure 2.1). Therefore
we continued with analysis of the three active type II TAs. The toxins from all three modules are
RNA endonucleases[43]. We constructed a triple knockout in the TAs (Δ3TA), and examined the
strain’s ability to form persisters. Ciprofloxacin causes a characteristic biphasic killing of wild
type S. aureus with a subpopulation of surviving persisters (Figure 2.1). Unexpectedly, knockout
of all TAs had no effect on the level of persisters in exponentially growing or stationary phase
cells (Figure 2.1a). A similar result was obtained with oxacillin, vancomycin and rifampicin (Figure
2.1b). It remains possible that these TAs or as yet unannotated TAs play a role in persister
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formation under a specific environmental condition, but we see no evidence of a role for the TAs
we examined, in persister formation under regular growth conditions.
Figure 2.1: Toxin-antitoxin modules and stringent response do not control persister formation
in S. aureus. The contribution of (A-B) toxin-antitoxin modules, mazEF, axe1-txe1 and axe2-txe2,
in strain Newman and (C) the stringent response element rsh in strain HG001and d, the stringent
response regulator, codY, in strain SH1000 to persister formation in S. aureus. Strains were grown
for 4 hours to mid-exponential phase (exp) or overnight to stationary (stat) phase in MHB and
challenged with either ciprofloxacin (cip), vancomycin (vanc), oxacillin (ox) or rifampicin (rif) (10×
MIC). Aliquots were removed at indicated time points, washed and plated to enumerate
survivors. All experiments were performed in biological triplicates. Standard deviations (SD) are
indicated.
A B
C D
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It is known that S. aureus exhibits complete tolerance to many antibiotics at stationary state
which is another important distinction between this pathogen and E. coli[38, 44]. It appears
that S. aureus cells in a stationary state exhibit antibiotic tolerance similar to persisters. We
reasoned that persisters in exponential phase may be cells that have entered the stationary
phase early. To examine this we used two reporters of the stationary phase. The promoter of the
capsular polysaccharide operon, Pcap5A, has been shown to be activated in the stationary
phase[45, 46]. Increase in relative fluorescence of a strain carrying Pcap5A-GFP over time in a
growing culture confirmed the suitability of this promoter as a marker of the stationary phase
(Figure 2.2A-B). The promoter of the arginine deiminase pathway, ParcA, was used as a second
It is known that S. aureus exhibits complete tolerance to many antibiotics at stationary state
which is another important distinction between this pathogen and E. coli[38, 44]. It appears
that S. aureus cells in a stationary state exhibit antibiotic tolerance similar to persisters. We
reasoned that persisters in exponential phase may be cells that have entered the stationary
phase early. To examine this we used two reporters of the stationary phase. The promoter of the
capsular polysaccharide operon, Pcap5A, has been shown to be activated in the stationary phase
[45, 46]. Increase in relative fluorescence of a strain carrying Pcap5A-GFP over time in a growing
culture confirmed the suitability of this promoter as a marker of the stationary phase (Figure
2.2A-B). The promoter of the arginine deiminase pathway, ParcA, was used as a second marker,
since proteomic analysis showed that the ArcA protein accumulates specifically in the stationary
phase, increasing in abundance 10.5-fold relative to exponential phase. Analysis of ParcA fused
to gfp confirmed that this promoter is activated specifically in stationary phase (Supplemental
Figure 2.2). Real-time qRT-PCR analysis showed that transcript levels of cap5A and arcA increase
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3.88 and 25.38-fold in stationary phase, respectively. These promoters were inserted upstream
of gfpuvr in plasmid pALC1434 to yield Pcap5A::gfp and ParcA::gfp.
Figure 2.2: Activation of stationary markers is heterogeneous. (A) Growth (OD600) and (B) GFP
expression of HG003 Pcap5A::gfp over time. The blue lines represent entrance into stationary
A B
C D
E
E
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phase. Distribution of GFP signal in (C) Pcap5A::gfp and d, ParcA::gfp at hourly intervals. The cut-
off for the bright fraction is represented by a blue line. This cut-off represents the level of
expression in a stationary phase culture. (E) A subpopulation of stationary phase cells, defined as
cells with stationary phase levels of expression of ParcA and Pcap5A, is always present and
increases with population density. The blue line represents an estimation of the entrance into
stationary phase. All experiments were performed in biological triplicates. SD are indicated. C-D
are representative of one replicate.
Flow cytometry was then used to track cells expressing high levels of the stationary phase
markers (termed bright) at hourly intervals from early exponential to stationary phase (Figure
2.2C-D). We found that a subpopulation of cells expresses stationary markers in early exponential
phase, and their frequency increases with the rise in the density of the population (Figure 2.2E).
This suggests that stationary phase does not initiate in a uniform manner but is a heterogeneous
process.
We next sought to determine if the subpopulation of stationary phase cells in a growing culture
were in fact persisters. For this, we employed Fluorescence-Activated Cell Sorting (FACS). S.
aureus HG003 Pcap5A::gfp or HG003 ParcA::gfp were grown to mid-exponential or stationary
phase and analyzed by FACS (Figure 2.3A-B). In order to examine whether the bright cells were
persisters, the exponential phase culture was exposed to a lethal dose of ciprofloxacin (10× MIC)
for 24h. The culture was then re-analyzed by FACS, and cells were gated into bright,
middle and dim populations based on expression of Pcap5A::gfp, or ParcA::gfp (Figure 2.3A-B).
Cells were then sorted onto MH agar in 96 spots to enumerate survivors from each population
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(32 spots for each population: bright, middle, dim). The lethal dose of ciprofloxacin causes ~3 logs
of killing in the total culture, so cells were sorted onto MH agar plates at 1, 10, 100, 1000, and
5000 per spot to achieve viable counts for each population (representative plate, Figure 2.3C-D).
The bright population had 100–1000 fold more survivors than the middle and dim populations
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with both markers. We chose to compare only the middle and bright fractions for quantification
as the dim fraction had < 100% sorting efficiency (Figure 2.3E-F).
A B
C D
E
F
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Figure 2.3: Persister sorting using stationary markers Pcap5A and ParcA. Expression of (A)
Pcap5A::gfp or b, ParcA::gfp in exponential following ciprofloxacin challenge (grey peak) and
stationary phase (green peak) measured by FACS. Exponential phase cells were gated into 3
populations depending on expression of GFP - dim (pink peak), middle (orange peak) or bright
(red peak - cells expressing stationary phase levels of reporter in exponential phase). (C-D) Cells
were sorted based on dim, middle or bright GFP expression onto MHA plates at 1000 events/spot
for both Pcap5A::gfp and ParcA::gfp. Representative plates are shown. Survivors from each
population of HG003 or Δcap5A harboring (E), Pcap5A::gfp and (F), ParcA::gfp were counted
following incubation overnight at 37°C. The asterisks indicate statistical significance between
middle and bright populations, determined using Student’s t-test (P ** < 0.005 or P***<0.0005).
All experiments were performed in biological triplicates. SD are indicated. A-D are representative
of one replicate.
To determine if expression of capsular polysaccharide contributes to ciprofloxacin tolerance, we
transformed plasmid Pcap5A::gfp into a cap5A mutant strain and repeated the cell sorting
experiment. Disrupting the cap5A gene did not alter the expression profiles of
Pcap5A::gfp (Supplemental Figure 2.3). Similarly, the bright cells in a cap5A mutant also
exhibited a 100-fold enrichment for cells tolerant to ciprofloxacin in exponential phase compared
to the middle fraction showing that entry into stationary phase rather than levels of the CapA
protein affect persister formation (Figure 2.3E). We also examined persister formation in
an arcA mutant and found it to be similar to the wild-type strain (Supplemental Figure 2.4). As a
control for stationary phase reporters, we repeated the experiment using a promoter that is also
expressed in exponential phase (Pspa::gfp). In this case, the bright population had no enrichment
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of persisters compared to the middle of the population (Supplemental Figure 2.3). This shows
that expression of a stationary marker, rather than expression of GFP per se, determines whether
a cell is a persister.
We wanted to further examine any potential role for the stringent response and tested
expression of the persister markers in the rshsyn mutant background.
Neither cap5A nor arcA promoter activity were significantly affected by mutation
of rshsyn (Supplemental Figure 2.2). We reasoned that a decrease in the energy level of the cell
in stationary phase could lead to antibiotic tolerance. Killing by bactericidal antibiotics results
from corrupting active targets1. Aminoglycosides kill by causing mistranslation, which leads to
the production of toxic peptides [47]; fluoroquinolones inhibit the re-ligation step of DNA gyrase
and topoisomerase, causing double strand breaks [48], and β-lactams lead to a futile cycle of
peptidoglycan synthesis and autolysis [49]. A decrease in ATP would decrease the activity of ATP
dependent antibiotic targets such as gyrase, topoisomerase, and RNA polymerase, leading to
antibiotic tolerance, and ATP has previously been suggested to impact survival to antibiotics [50-
53].
We examined ATP levels of an exponential and stationary phase population and indeed found
that ATP levels decrease significantly in the stationary phase (Figure 2.4A). We then found that
emulating stationary phase ATP levels in an exponential phase population by decreasing it with
arsenate resulted in a 325-fold induction in persister formation (Figure 2.4B). ATP levels are
lowered by arsenate as it forms a rapidly-hydrolysable ADP-As, producing a futile cycle [54].
Interestingly, we found that stationary phase-specific promoters were also activated in response
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to arsenate (Figure 2.4C). Hence, these promoters are activated in the stationary phase as ATP
levels in the cells drop. The Pcap5A and ParcA promoters then enable single-cell detection of
ATP, linking a decrease in the energy level to antibiotic tolerance in individual persisters. It was
clear that cells with reduced ATP levels are antibiotic tolerant and express markers of this
phenotypic state. What remained unclear was whether a transcriptional response was necessary
for persister formation. To examine this, we again induced persister formation with arsenate,
however, we also included a 15 minute pre-incubation with rifampicin at 1× MIC, which was
sufficient to inhibit induction of stationary markers (Figure 2.4C) but did not cause cell death
(Supplemental Figure 2.5). Inhibition of transcription did not impede persister induction (Figure
2.4D) (Supplemental Figure 2.5). This shows that although a specific transcriptional response that
includes expression of Pcap5A and ParcA is induced in response to low ATP, this response is not
required for antibiotic tolerance. Rather, tolerance of both stationary populations and persisters
can be explained by a drop in ATP which will result in a decrease in the activity of drug targets.
To further test whether ATP levels determine persister formation, we examined killing in a
medium where ATP concentration is expected to increase. Supplementing TSB medium with
31
glucose increased ATP significantly and resulted in a 100 fold reduction in persisters
(Supplemental Figure 2.6).
Figure 2.4: Reduction in ATP induces persister formation and expression of stationary phase
markers. (A) Titering arsenate to produce stationary phase levels of ATP. Arsenate was added to
an exponential phase population of S. aureus for 15 minutes before measuring ATP. (B) Decrease
of ATP results in a 325-fold induction of persisters in exponential phase. On the x-axis, “–“
indicates cell count before addition of ciprofloxacin and “+” represents cell count after 24 hour
incubation in 10 × MIC of ciprofloxacin (0.4 µg/ml) C. Pcap5A::gfp and ParcA::gfp are induced by
depletion of ATP. (D) inhibition of transcriptional response by addition of 0.1 µg/ml rifampicin
15 minutes prior to ATP depletion (30 minutes of 1 mM arsenate), with results represented as
A B
C D
32
the Log % survival after 24 hours ciprofloxacin treatment. All experiments were performed in
biological triplicates. SD are indicated.
Promoters of arcA and cap5A are induced when ATP drops in stationary phase or in the presence
of arsenate. Cells expressing these markers are highly enriched for persisters. Low ATP can lead
to tolerance of a stationary culture, and explains antibiotic tolerance of a persister sub-
population. This work links the phenomena of population-wide tolerance and persister cell
tolerance. A growing population contains cells that enter into stationary state early, and these
become antibiotic tolerant persisters. Persisters form as cells lose ATP. The entrance into
stationary state is stochastic, with the frequency of persisters increasing with cell density. Our
measurements of ATP in single persister cells by FACS have been performed with two different
reporters, ParcA-GFP and Pcap5A-GFP. Both are ATP sensors, but the detection requires
transcription and translation of GFP. To establish direct causality, it would be interesting to
perform single cell detection of ATP in persisters more directly, such as with a FRET-based sensor
[55], once it is adapted to S. aureus.
Interestingly, tolerance to clinically relevant daptomycin was also observed in stationary phase
[56]. Also, a recent study shows that altered levels of inorganic phosphate and polyphosphate in
daptomycin tolerant cells, which could also be related to depletion of ATP39. A recent study
shows that population heterogeneity and capsular polysaccharide expressing sub-populations
also occur in vivo in persistent carriers of S. aureus [46]. The role of ATP levels in recalcitrance
of S. aureus infection should be examined and ATP levels of cells during infection may be an
important determinant of the outcome of infection.
33
Understanding how persisters form will improve our ability to control chronic infections. We
recently identified a compound capable of killing persisters, acyldepsipeptide (ADEP4). ADEP4
targets ClpP and converts it into a non-specific protease, which forces both growing and dormant
cells to self-digest [57]. Importantly, ADEP4 dissociates the protease from its ATP-dependent
chaperones and the dysregulated proteolysis does not require ATP. In combination with
rifampicin (to decrease resistance development), ADEP4 eradicated a biofilm both in vitro and in
a mouse model of a chronic S. aureus infection. This shows that persisters can be killed by a
compound which does not require an ATP-dependent target. In this regard, it is interesting to
note that stationary cells of S. aureus exhibit considerable tolerance to daptomycin, a
membrane-acting antibiotic [56, 57]. Why dormant cells would be tolerant to this compound is
an interesting problem that remains to be solved.
This study suggests that a new mechanism of persister formation, loss of energy leading to drug
tolerance, operates in S. aureus. It is possible that this is a general mechanism of tolerance which
governs persister formation in other bacteria as well.
2.3 Materials and Methods
2.3.1 Bacterial strains and growth conditions
S. aureus were cultured in Mueller-Hinton broth (MHB) or Tryptic Soy Broth (TSB) with or without
added glucose. TSB and TSB without glucose was buffered to pH 7.0 using 100mM MOPS. Bacteria
were routinely grown at 37°C at 225 rpm Media were supplemented with chloramphenicol 10
34
µg/ml to maintain plasmids where necessary. MSSA strains Newman, SH1000, and HG001 were
used to analyze the role of TA modules and stringent response as mutations of interest had
previously been constructed and characterized in these backgrounds [43, 58, 59]. The model
strain HG003 was used for all subsequent experiments. For E. coli experiments, growth of the
overexpression strain was compared to an empty vector control in a plate reader over 16 hours
at 37°C in LB medium supplemented with 0.2% arabinose.
2.3.2 Strain Constructions
For construction of reporter plasmids, primers Pcap5A_1 (gcgcgaattctctatctgataataatcatc)
Pcap5A_2 (ggcctctagactaatgtactttccattatt), Pspa_1 (gcaggaattctttccgaaattaaacctcagc) Pspa_2
(gcagtctagaattaataccccctgtatgta) and ParcA_1 (gcgcgaattcaaaatgtatattttgaccca) and ParcA_2
(ggcctctagatctatttcctccttttatct) flanked by restriction sites EcoRI and XbaI were used to amplify
predicted promoter sequences of cap5A, spa and arcA, respectively. The promoter regions were
cloned upstream of gfpuvr into the EcoRI and XbaI sites of plasmid pALC1434 [60]. A Newman
strain was created containing deletions for all three known type II toxin-antitoxin systems
(Newman ΔTA3). Using Newman ∆mazEF(ALC4072) as a starting strain,
the axe1/txe1 and axe2/txe2 operons were deleted by sequential allelic exchange using the
pMAD plasmids pALC6480 and pALC648143, respectively. Deletion of these genes was verified by
PCR analysis and chromosomal DNA sequencing. For hypothetical toxin overexpression, the
primers Ptox_1 gcgcgaattcatggaagaaactttaa and Ptox_2 gcgcggtaccttatgcaatttaaaaa were used to
amplify the toxin and the fragment was digested with EcoRI and KpnI and cloned into the pBAD33
35
vector upstream of an arabinose inducible promoter, digested with the same restriction
enzymes.
2.3.3 Persister Assays
Strains were grown to mid-exponential or stationary phase (~16h) in MHB in 14 ml round bottom
snap-cap culture tubes. Cells were plated for CFU counts and challenged with antibiotics
ciprofloxacin, rifampicin, vancomycin, gentamicin or oxacillin (4.0 µg/ml, 0.4 µg/ml, 10 µg/ml and
1.5 µg/ml respectively) At indicated times, an aliquot of cells was removed, washed with 1% NaCl,
and plated to enumerate survivors. All experiments were performed in biological triplicates.
Averages and standard deviations are representative of three biological replicates. Rifampicin
resistant mutants arise spontaneously at the frequency of ~2.3 × 10−8. Rifampicin killing in
exponential phase selected for the proliferation of rifampicin resistant mutants, which had
repopulated the exponential phase cultures by 24h (Supplemental Figure 2.5). For this reason,
levels of persisters tolerant to rifampicin were examined in stationary phase only.
2.3.4 Arsenate and rifampicin persister assays
Strains were grown to mid-exponential phase in MHB media. Where indicated, rifampicin 0.01
µg/ml was added for 15 minutes and/or arsenate 1 mM for 30 minutes prior to ciprofloxacin
challenge for 24h (10× MIC).
2.3.5 Flow cytometry and FACS analysis using gfp reporters
Fluorescent protein level was analyzed with a BD Aria II flow cytometer (BD Biosciences) with a
70-micron nozzle. Cell population was detected using forward and side scatter parameters (FSC
36
and SSC), and fluorescence was analyzed with emitting laser of 488 nm and bandpass filter of
525/15 nm, using a FACS ARIA II (Becton Dickinson, CA). Strains harboring plasmids Pcap5A::gfp,
ParcA::gfp or Pspa::gfp were grown to mid-exponential and stationary phase in MHB containing
10 µg/ml chloramphenicol. For growth curve construction, the population was gated so that over
90% of the stationary phase population were designated ‘bright’. These gates were applied to all
timepoints. At each timepoint, cfu was measured and the number of stationary phase cells was
calculated by multiplying the percentage of cells in the bright fraction by the total cell number.
An overnight culture was sub-cultured 1:100 into fresh MHB and grown for 3 hours. 300 µl of this
was added to 3 ml of MHB to begin the growth curve. This sub-culturing step removed any carry-
over of stationary phase cells from the stationary phase culture. For FACS analysis of persisters,
strains were exposed to ciprofloxacin for 24h. Before the challenge, an aliquot of the culture was
diluted and plated for cfu. Challenged cells were washed and plated to enumerate survivors. Cells
pre- and post- antibiotic challenge were analyzed by FACS. A gate was drawn based on stationary
phase expression of Pcap5A::gfp or ParcA::gfp. Exponential phase cells expressing stationary
phase levels of Pcap5A::gfp or ParcA::gfp were termed ‘bright’. Two gates were drawn within the
exponential phase Pcap5A::gfp expression peak and termed ‘middle’ and ‘dim’ respectively. To
calculate the percent survival of each population following antibiotic challenge, first, we
calculated the sorting efficiency from each population prior to antibiotic challenge. Events (cells)
from each population were sorted in a 96-well format with 32 spots for each population; dim,
middle and bright. 1 event per spot (for 32 spots) and colonies were counted following
incubation. For the middle and bright fractions we achieved 100% sorting efficiency (32 colonies),
however the sorting efficiency for the dim fraction was lower, ~90% or 29 colonies. This indicated
37
that not all events in the dim fraction were cells. For this reason we chose to focus on the
differences between the bright population and the middle or bulk of the population. Following
antibiotic challenge, cells (events) from each population were sorted onto MH agar plates in a
96-well format at 1, 10, 100, 1000, 5000 per spot (32 spots / population) to enumerate survivors.
A similar method was applied for all reporters. Ciprofloxacin treatment did not affect expression
of any reporters used in this study. A control experiment was performed where samples were
sonicated for 5 minutes in a sonicating water bath prior to cell sorting. Sonication had no impact
on the sorting results, confirming that cell aggregation was not influencing FACS experiments.
Cells were analyzed and sorted using FACS-Diva software. Figures were generated using FlowJo
software. Experiments were performed in triplicate. Error bars represent the standard deviations
of the means, and statistical significance was determined by the Student’s t test.
2.3.6 Proteomic analysis
Biological duplicates were grown in MHB and harvested in the mid-exponential and stationary
phase of growth. Samples were labelled and fractionated and mass spectrometry was performed
as previously described [38].
2.3.7 Real-Time qRT-PCR
RNA was isolated from exponential phase population after 4 hours of growth and stationary
phase after 16 hours of growth using a QIAGEN® RNA purification kit. Samples were treated with
Turbo DNase and RNA integrity was confirmed on a bioanalyzer. Reverse transcriptase was used
to convert to cDNA as per manufacturer’s instructions. Serial 10-fold dilutions of genomic DNA
were used to construct standard curves for each set of primers. qRT-PCR was performed using
38
SYBR® green enzyme. Fold change was calculated based on the cycle number required to achieve
a predesignated quantity of signal normalized to a 16S rRNA control.
2.3.8 ATP Assays
ATP levels of stationary and mid-exponential cultures with the addition of various concentrations
of arsenate were measured using a Promega BacTiter Glo kit according to the manufacturer’s
instructions.
2.4 Contributions
We would like to thank Dr. Christiane Wolz for the gift of the HG001, HG001 rshsyn and triple
mutant rshsyn, relP, relQ strains. We thank Dr. Richard Lee and Dr. Michael LaFleur for critical
discussions. This work was supported by NIH grant R01AI110578 to KL and by a Charles A. King
fellowship to BC.
39
2.5 Supplemental Information
Supplemental Figure 2. 1. Overexpression of the hypothetical phage associated toxin has no
effect on growth of E. coli. The gene encoding the hypothetical toxin, NWMN_0265 from S.
aureus Newman was overexpressed in E. coli MG1655 in vector pBAD33. The toxin was cloned
downstream of an arabinose inducible promoter and grown in the presence of 0.2% inducer
and growth was compared to that of an empty vector control at 37°C in Luria Bertoni (LB) broth.
Data represent biological triplicates. Error bars represent standard deviation.
A B
40
Supplemental Figure 2.2. Promoter activity in a rshsyn mutant background. (A) Promoters of
cap5A and arcA are specifically induced upon the onset of stationary phase with GFP expression
increasing as (B) growth ceases at the onset of stationary phase. Expression of Pcap5A and
ParcA is not affected by mutation of rshsyn. The blue line represents an estimation of the
entrance to stationary phase state. Data is an average of 3 biological replicates.
Supplemental Figure 2.3. Pcap5A activity is not affected by mutation of cap5A and cells
expressing Pspa::gfp in exponential phase are not enriched for persisters. (A) Pcap5A::gfp
expression in HG003 wild type (grey peak) and the cap5A mutant (blue peak). Strains were
grown to mid- exponential phase and analyzed by FACS. (B) S. aureus HG003 Pspa::gfp
expression in exponential phase following ciprofloxacin treatment (grey peak) and stationary
A B
C
41
phase (green peak) measured by FACS. Exponential phase cells treated with ciprofloxacin
were gated to dim (purple peak), middle (orange peak) and bright (red peak) expression of
GFP. (C) Survivors from each population were sorted onto MHA plates and enumerated
following incubation overnight at 37ºC. (A) and (B) are representative experiments. (C) is the
average of 3 biological replicates and error bars represent standard deviation.
Supplemental Figure 2.4. Mutation of arcA does not have an impact on persister formation
in S. aureus HG003. Wild-type and ∆arcA cells were grown to mid-exponential phase and
challenged with 10 x MIC of ciprofloxacin. Cfus were recorded at 24 and 48 hours. Data is an
average of 3 biological replicates and error bars represent standard deviation.
42
Supplemental Figure 2.5. Arsenate protects against killing by ciprofloxacin. Killing by
ciprofloxacin (4.0 µg/ml) in the presence of rifampicin (0.01 µg/ml) and/or arsenic acid (1 mM).
Rifampicin was added where indicated 15 minutes before the start of the experiment. Arsenate
was added, where indicated 30 minutes before the start of the experiment. Ciprofloxacin was
added where indicated at t = 0. Survivors were enumerated after 16 and 24h exposure. Data is
averaged from 3 biological replicates and error bars represent standard deviation.
43
Supplemental Figure 2.6. Increased ATP results in fewer persisters. (A) ATP levels were
measured after 3 hours of growth in TSB and TSB without glucose. (B) Survival of cells in TSB and
TSB without glucose after treatment with ciprofloxacin at 10 x MIC. Results are the average of 3
biological replicates and error bars represent standard deviation.
Supplemental Figure 2.7. Rifampicin resistance emerges in exponential phase. S. aureus
HG003 was grown to mid-exponential phase and rifampicin was added at t=0 to 10 x MIC (0.4
µg/ml). Cfus were counted at various timepoints. After an initial decline, the culture rebounded
over 24 hours due to a high frequency of resistance. Results are an average of 3 biological
replicates and error bars represent standard deviations.
44
Chapter 3: Stochastic variation in expression of the TCA cycle produces persister cells
This chapter contains work from the following manuscript, which has been submitted for publication:
Stochastic variation in expression of the TCA cycle produces persister cells
Eliza A. Zalis*, Austin S. Nuxoll*, Sylvie Manuse, Geremy Clair, Lauren C. Radlinski, Brian P. Conlon, Kim
Lewis
3.1 Abstract
Chronic bacterial infections are difficult to eradicate, though they are caused primarily by drug-
susceptible pathogens [2]. Antibiotic-tolerant persisters largely account for this paradox. In spite of their
significance in recalcitrance of chronic infections, the mechanism of persister formation is poorly
understood. We previously reported that a decrease in ATP levels leads to drug tolerance in Escherichia
coli, Pseudomonas aeruginosa, and Staphylococcus aureus [35, 61, 62]. We reasoned that stochastic
fluctuation in the expression of TCA cycle enzymes can produce cells with low energy levels. S. aureus
knockouts in glutamate dehydrogenase, 2-oxoketoglutarate dehydrogenase, succinyl CoA synthetase,
and fumarase have low ATP and exhibit increased tolerance to fluoroquinolone, aminoglycoside, and -
lactam antibiotics. FACS analysis of TCA genes shows a broad Gaussian distribution in a population, with
over three orders of magnitude difference in the levels of expression between individual cells. Sorted
cells will low levels of TCA enzyme expression have an increased tolerance to antibiotic treatment. These
findings suggest that fluctuation in expression of energy generating components serve as a mechanism
of persister formation.
3.2 Introduction
Persister cells are rare phenotypic variants that are able to survive antibiotic treatment [2]. Unlike
resistant bacteria, which have specific mechanisms to prevent antibiotics from binding to their targets,
45
persisters evade antibiotic killing by entering a tolerant non-growing state. Persisters have been
implicated in chronic infections in multiple species [5, 7, 10, 17] and growing evidence suggests that
persister cells are responsible for many cases of antibiotic treatment failure [17].
Toxin-antitoxin (TA) modules and the stringent response have been proposed as mechanisms of
antibiotic tolerance, based primarily on studies of E. coli. However, these findings have recently been
challenged [62, 63]. Similarly, we reported that knocking out all TA modules and (p)ppGpp synthases in
S. aureus had no effect on persister formation [35]. In search of an alternative mechanism, we found
that persister cells in a growing population express stationary cell markers, cap5A and arcA, coding for
capsular polysaccharide synthesis and arginine deiminase, respectively. Importantly, expression of
cap5A and arcA was induced by treatment with arsenate which depletes ATP through a futile cycle: ADP-
As – ADP + As (spontaneous hydrolysis) – ADP-As. This suggests that these markers actually respond to
ATP decrease, and the rare stationary-like cells in a growing population have low energy levels. These
cells had a considerably higher antibiotic tolerance after being sorted out from a growing population,
showing that they are persisters. Importantly, dropping ATP to stationary levels with arsenate treatment
in a growing culture recapitulates the persister level of a stationary population, showing that low energy
is sufficient for tolerance. If low ATP results in tolerance, then high ATP should have the opposite effect.
Indeed, supplementing TSB medium with glucose increased ATP significantly and resulted in a 100 fold
reduction in persisters. We made similar observations linking ATP and persisters in a study of E. coli [62].
Based on these findings, we proposed a “low energy” mechanism of persister formation. This hypothesis
provides a satisfactory explanation for the mechanism of drug tolerance. Bactericidal compounds kill by
corrupting active targets, and when ATP is low, cells become tolerant to antibiotics. In a stationary
population of S. aureus, ATP levels are indeed low. The entire population is highly tolerant to antibiotics
and is equivalent to persisters. However, how ATP levels may decrease in rare cells of a growing
46
population remained unknown. We reasoned that random fluctuations in the levels of energy-
generating components could lead to low-energy cells. Here we show that cells in a growing population
that have low levels of expression of TCA cycle enzymes are tolerant to killing by antibiotics.
3.3 Results
At late stages of growth, a population of S. aureus exhausts glucose and expression of TCA cycle
enzymes is upregulated [64]. These conditions emulate the nutrient environment during S. aureus
infection [64]. Proteome analysis confirms that this is the case in a stationary phase culture, where the
level of TCA cycle enzymes increases, while glycolytic enzyme abundance decreases. High levels of
enzymes responsible for incorporating amino acids in TCA cycle metabolism are also observed in
stationary phase (Figure 1). Stationary phase is also limited by oxygen which, combined with the lack of
glycolytic substrates would account for previously observed low ATP and high tolerance to antibiotics
[35]. In a late exponentially growing population where oxygen is available but glucose has been largely
exhausted, fluctuation in the levels of TCA cycle enzymes could then lead to a drug tolerant state. We
first sought to examine this in a model experiment by testing antibiotic tolerance of mutants with
knockouts in TCA cycle enzymes.
Figure 1: TCA enzyme abundance increases in late growth phase. (A) Heat map shows enrichment
analysis for proteins in exponential and stationary phase. (B) Map shows major steps in central
metabolism for which significant changes in enzyme abundance were detected between exponential
and stationary phase. Blue indicates decreased abundance in stationary phase; red indicates increased
abundance in stationary phase. Glutamate-catabolizing and TCA cycle enzymes were detected in higher
abundance in stationary phase, when glucose levels are known to be low. Four biological replicates were
analyzed to determine relative abundance for each condition. Gene Ontology and KEGG identifiers were
47
(A) (B)
extracted from UniprotKB. Protein abundance significance was determined using Student’s T-test.
Fisher’s exact tests were performed in R to identify the ontology groups enriched in the proteins
differentially expressed in the two conditions tested.
48
A B
E F
G H Oxacillin
Gentamicin
Ciprofloxacin
49
Figure 2: S. aureus lacking functional late TCA cycle genes exhibits increased antibiotic tolerance.
Antibiotic killing of mutants in TCA cycle genes gltA, sucA, sucC, fumC, and gudB (citrate synthase, 2-
oxogluterate dehydrogenase, succinyl coenzyme A synthetase, fumarase, and glutamate
dehydrogenase) over time compared to wild type (clear symbols) after 10X MIC antibiotic challenge in
TSB medium. Bar graphs represent percent survival of each strain after 96 or 120 hours, as indicated. (A-
B) Ciprofloxacin treatment. (E-F) Gentamicin treatment. (G-H) Oxacillin treatment. Error bars indicate
SEM. Asterisks indicate significance between a mutant and wild type as determined by Sidak’s multiple
comparisons test (*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001). Experiments were performed in
biological triplicate.
We constructed mutants lacking functional enzymes gltA (citrate synthase), gudB (glutamate
dehydrogenase), sucA (2-oxoglutarate dehydrogenase), sucC (succinyl-CoA synthetase), and fumC
(fumarate hydratase) by transducing insertions from the Nebraska Transposon Mutant Library into the
MSSA strain HG003, which is susceptible to antibiotics [65]. Strains were grown to late exponential
phase and challenged with 10x MIC of ciprofloxacin, gentamicin, or oxacillin, representing the main
classes of bactericidal antibiotics – fluoroquinolones, aminoglycosides, and -lactams. The TCA cycle and
amino acid catabolism are important for S. aureus growth in vivo, so we also investigated glutamate
dehydrogenase. Glutamate dehydrogenase fuels the TCA cycle in glucose-deplete conditions by
converting glutamate derived from the abundant proline in collagen into 2-oxoglutarate [4]. All TCA
cycle mutants as well as gudB have significantly higher survival than wild type upon treatment with
antibiotics, showing a multidrug tolerant phenotype characteristic of persisters (Figure 2). The highest
level of persisters is observed with sucA and fumC mutants, where nearly 10% of the population is
persisters.
50
We reasoned that strains with high persister levels should have low ATP. We measured ATP levels with
luciferase and observe that gudB, sucA, sucC, and fumC mutants indeed have significantly lower ATP
than wild type in late exponential phase (Figure 3B).
A B
citrate
isocitrate
2-oxoketoglutarate
succinyl-CoA
succinate
fumarate
malate
oxaloacetate
glutamate
gudB
C
51
Figure 3: TCA cycle mutants strains have lower ATP levels than wild type. (A) TCA cycle model, mutants
highlighted (B) ATP concentration per CFU of all strains. Cultures were grown to late exponential phase
and ATP was measured in the bulk population by luminescence assay. Mutants gudB, sucA, sucC, and
fumC have significantly lower ATP than wild type. Error bars represent standard error. Statistical
significance was determined using ANOVA followed by Sidak’s multiple comparisons test (*P<0.05,
****P<0.0001). (C) Frequency distribution of QUEEN signal for each TCA mutant and wild type strain.
Wild type S. aureus and TCA mutants were grown to early stationary phase in TSB without glucose at
30°C. Strains were grown with chloramphenicol 10 µg/ml to maintain the plasmid and QUEEN
expression was induced with 0.03% xylose. Single cells were analyzed by flow cytometry. Post-
acquisition analysis was performed in FlowJo Software. Ratio values were calculated by dividing
intensities from excitations at 405nm and 408nm (405ex/488ex).
In order to probe the heterogeneity of the population, we sought to examine ATP at a single cell level.
For this, we adopted the QUEEN ATP sensor [66]. QUEEN contains GFP fused to an ATP-binding subunit
of Bacillus PS3 F0F1 ATP synthase. The sensor absorbs at 405 nm and 488 nm and emits at 513 nm. At
higher levels of ATP, there is increased fluorescence from the 405 nm excitation, and decreased
fluorescence from the 488 nm excitation. A ratio between the two emission signals reports ATP
concentration. This ratio does not depend on the amount of the reporter, eliminating errors due to
variation in QUEEN levels among cells. We first cloned QUEEN in plasmid pEPSA5 under a xylose
promoter, but the fluorescence signal was weak. We then codon-optimized a QUEEN construct for
expression in S. aureus, which yielded an improved fluorescence signal (Supplemental Figure 1).
Using FACS analysis, we monitored ATP in single cells of wild type S. aureus and the gltA, gudB, sucA,
sucC, and fumC mutants. The frequency distribution of the ratios is shown in Figure 3C. A wide range of
52
ATP distribution among cells is evident both in the wild type population and in the TCA cycle mutants
(Figure 3C). As expected, the distribution of ATP in mutant populations is shifted to lower levels
compared to the wild type, consistent with an increase in persisters. Persisters tend to wake up during
sorting, which makes sorting prior to antibiotic treatment problematic [35, 62]. Sorting based on QUEEN
signal after antibiotic treatment would not report the ATP status of cells immediately prior to treatment.
Given that knockouts in TCA cycle had lower ATP, we used reporters of their expression to identify low
energy cells. Adding antibiotic prior to sorting provides a snapshot of a protein level at that point in
time.
We cloned the promoter regions of TCA cycle genes upstream of gfpuvr in plasmid pALC1434 [67] yielding
PgltA::gfp, PsucA::gfp, PsucC::gfp, and PfumC::gfp. These strains were grown to late exponential phase
and challenged with ciprofloxacin at 10x MIC. After 24 hours of antibiotic treatment, cells were analyzed
by FACS (Figure 5). Interestingly, there was a broad distribution of expression for each of these genes in
the population. Populations of low, intermediate, and high (“dim, middle, and bright”) TCA gene
expression were gated (Figure 5A-D) and sorted onto agar plates. Surviving cells formed colonies and we
quantified survival of each gated fraction of cells compared to the bulk of the population. We observed
a significant enrichment in persister cells in the dim fractions. In the case of sucA, sucC and fumC, there
was a close to 100 fold difference in persisters between the dim and bright fractions (Figure 4). In order
to test for a possible correlation between persister levels and a general decrease in transcription, we
performed an identical experiment with a strain containing a reporter of the constitutively expressed
sarR. There was no difference in persister levels among the dim, middle and bright populations of the
control. Taken together, these results suggest that random fluctuations in the levels of TCA cycle
enzymes cause a decrease in the energy level, producing drug-tolerant persisters.
53
Figure 5: Sorting of cells with low expression of TCA cycle enzymes enriches in drug tolerant persisters.
(A-E), GFP expression of PgltA::gfp, PsucA::gfp, PsucC::gfp, PfumC::gfp, PsarR::gfp. (F) Percent survival of
each gated fraction and the bulk of the unsorted population. Cells with low expression (Dim) of sucA, sucC,
and fumC exhibited significantly increased survival compared to cells with relatively high expression
(Bright). Sorting on the basis of constitutively expressed PsarR::gfp yields no significant enrichment in any
fraction. Asterisks indicate statistical significance as determined by two-way ANOVA multiple comparisons
(*P<0.05). Graph represents the mean of five biological replicates. A representative plate is shown in
Supplemental Figure 2.
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dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
-4
-3
-2
-1
0L
og
10
% s
urv
iva
lP s u c A ::G F P P s u c C ::G F P P fu m C ::G F P P s a rR ::G F PP g ltA ::G F P
* **
Br i
gh
t
Mid
dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
Br i
gh
t
Mid
dle
Dim
Bu
lk
-4
-3
-2
-1
0
Lo
g1
0 %
su
rv
iva
l
P s u c A ::G F P P s u c C ::G F P P fu m C ::G F P P s a rR ::G F PP g ltA ::G F P
* **
A B
C D
E
F
54
3.4 Discussion
Persisters were originally discovered in a study of S. aureus in 1944 [1], but understanding the
mechanism of their formation proved to be unusually challenging. This no doubt is due to the small size
of their population, and a temporary phenotype. Given that bactericidal antibiotics act by corrupting
their targets, we proposed that tolerance is caused by target inactivation [35, 62]. This concept is broad
enough to cover the two types of emerging persister formation mechanisms, specific and generalized.
Specialized toxins such as HipA and TisB govern specific mechanisms of persister formation. Selection for
high-persister mutants led to the identification of a gain-of-function allele in the hipA mutant in E. coli
[68] [68], and subsequent studies determined that it is a kinase [69] that inhibits translation by
phosphorylating glu-tRNA synthase [70, 71]. However, deletion of the hipBA locus has no phenotype,
and it does not appear that HipA plays a role in persister formation of wild type E. coli. At the same time,
we found that hipA7 high persister cells are present in E. coli isolates from patients with urinary tract
infection, a result of in vivo selection for drug tolerance [5]. Another E. coli toxin, TisB, provides an
example of a persister formation mechanism operating in wild type cells. Induced by fluoroquinolones
through the SOS DNA-damage response, TisB is an endogenous antimicrobial peptide that causes
tolerance by decreasing the pmf and ATP [52, 72]. These specific mechanisms however do not explain
how persisters are formed under regular growth conditions. For a while, the idea that RNA
endonuclease TAs such as RelBA or MazEF constitute the main mechanism of persister formation in
bacteria became the standard model [40], but several recent studies failed to find a connection between
these interferase toxins and persisters in E. coli [35, 62, 63]. In particular, a knockout of 10 interferase
TAs had no effect of persister formation [63].
In search of a general mechanism of persister formation, we identified a link between low energy,
specifically low ATP, and drug tolerance in both S. aureus and E. coli [35, 62]. Selectively decreasing the
55
level of ATP by arsenate is sufficient to produce persisters, and low energy cells sorted from a
population by monitoring transcriptional or translational ATP markers are tolerant to antibiotics [35, 62].
If ATP is low, target activity is diminished, providing a simple mechanism for drug tolerance. In the
current study, we sought to identify components that are responsible for producing persisters. Energy
generating components are a logical choice to consider, and not surprisingly we observe, in agreement
with previous observations [73] that knocking out TCA cycle components increases drug tolerance and
lowers ATP levels. The critical question however is whether natural fluctuation in expression of TCA
enzymes is sufficient to produce persisters. We find that sorting cells with low levels of expression of
several TCA cycle enzymes gltA, sucA, sucC, and fumC enriches in drug-tolerant persisters. Interestingly,
FACS analysis shows that noise in expression of these enzymes is considerable, over three orders of
magnitude, and largely follows a typical Gaussian distribution. This noise leads to formation of rare cells
with low levels of enzyme expression, decreased ATP, and drug tolerance.
In retrospect, the low energy hypothesis of persister formation is quite obvious – indeed, the simplest
way to inactivate all antibiotic targets is by lowering ATP, and the mechanism of the specialized TisB
toxin provides a precedent for this. However, noise in an energy generating component sufficient to
produce a drop in ATP is counterintuitive. It is commonly accepted that noise results from fluctuation in
sparsely expressed components. In a classical example, E. coli cells only have about 10 molecules of LacI
on average, and noise in expression produces rare cells with no repressor, resulting in full expression of
the lac operon in the absence of inducer [74]. By contrast, TCA cycle enzymes are abundant, and the
considerable level of noise in their expression we observe is unexpected.
56
The current study is the first step towards identifying components that can lead to a low energy state
and drug tolerance. Future studies will show how widely spread among bacteria is this general
mechanism of persister formation.
3.5 Methods
3.5.1 Bacterial strains, culture conditions, and strain construction
S. aureus strains were grown in either tryptic soy broth (TSB) (MP Biomedicals, USA), TSB without
glucose (Becton, Dickinson, and Company, USA), or Mueller Hinton Broth (MHB) as indicated. Cultures
were grown at 37oC shaking at 225 rpm. Strains encoding the QUEEN construct were grown on TSA
plates with chloramphenicol 10 µg/ml at 30oC or in TSB without glucose shaking at 225 rpm at 30oC with
chloramphenicol 10 µg/ml to maintain the pEPSA5 plasmid. QUEEN expression was induced with 0.03%
xylose. High concentrations of xylose led to a growth defect. The MSSA strain HG003 was used for these
studies and mutations for all TCA cycle genes in this background were transduced from the Nebraska
Transposon Mutant Library (NTML) using bacteriophage 80 or φ11. Mutations were subsequently
confirmed by amplifying from the beginning or end of the gene of interest to the transposon insertion as
previously described [65]. For construction of gfp reporters, promoter regions of gltA, sucAB, sucC, or
fumC were cloned upstream of gfp into the EcoRI and XbaI sites of pALC1434[67]. PsucAB was amplified
with 5’-gggcccgaattcgaaacctcatcaattcgaacaa-3’ and 5’-gggccctctagatttacaccctccacaaaaatgttgaaa-3’.
Escherichia coli DH5 was used to propagate plasmids. DH5 strains were grown in LB Broth, Miller
(Fisher BioReagents, USA) and ampicillin 100 µg/ml was used to maintain plasmids where necessary.
3.5.2 Proteomic sample preparation
Four replicates were prepared from either exponential or stationary phase cells. Cells were grown for 4
or 24 hours, respectively, after overnight cultures were diluted 1:1000 in 100 mL MHB. Cells were grown
in 500 mL baffled flasks at 37oC shaking at 225 rmp. Cells were pelleted at 5000 x g for 7 minutes at 4 oC,
57
washed twice in PBS, and pelleted again. Pellets were resuspended in 500 µL PBS, transferred to a 2 mL
tube, and washed a final time, then flash frozen with liquid nitrogen and stored at -80 oC. Bacterial
pellets were resuspended in 100 mM ammonium bicarbonate and lysed by vortexing 5 times for one
minute then with 0.1 mm zirconia/silica beads with resting periods of 30 s on ice. Samples were then
digested with trypsin as previously described [75] and desalted using C18 SPE cartridges (Discovery C18,
1 mL, 50 mg, Sulpelco). Peptide concentrations were measured by BCA assay (Thermo Scientific).
3.5.3 Proteomics and data analysis
For each sample 0.5 μg of peptides were separated using a 200 minute gradient on a Waters
nanoEquityTM UPLC system (Millford, MA) coupled with a QExactive HF (Thermo Fisher Scientific). MS
scans were recorded at a resolution of 35,000. The Top 12 ions from the survey scan were selected by a
quadrupole mass filter for high energy collision dissociation and mass analyzed by the Orbitrap. A
window of 2 m/z was used for the isolation of ions and collision energy of 28%. MS/MS spectra were
recorded with a resolution of 17,500. resulting data were processed using the MaxQuant V1.5.2.8. [76].
Proteins were identified with at least 2 peptides of lenght higher than 6 residues.
The RefSeq Staphylococcus aureus NCTC8325 database was used for the search (July 2017: 2,768
sequences). Match between run and MaxLFQ were used for quantification, other parameters were
conserved by default. Proteins only identified by site, reverse hits, and contaminants were removed.
Only protein groups with a measured LFQ intensity in at least 60% of one sample type were conserved
for further quantification. LFQ intensities were log2 transformed and median normalized. Statistics (e.g.
Student’s T.test, scaling, etc.) and normalization steps were performed in R using the packages Stat. The
mass spectrometry proteomics data has been deposited to ProteomeXchange Consortium with the
dataset identifier PXD013151.
58
3.5.4 Persister assays
Overnight cultures were diluted 1:1000 in 2 mL TSB (Fisher, MP Biomedicals) in a 14 mL capped culture
tube (VWR International), grown to late exponential phase and starting CFU were plated. Cultures were
challenged with ciprofloxacin, oxacillin, or gentamicin (4, 1.25, and 100 µg ml–1, respectively). To
enumerate survivors over time, 100 µL of culture was removed, pelleted by centrifugation, washed
with 1% NaCl, serial diluted and plated on TSA and CFU were counted after 24 h regrowth on TSA to
enumerate survivors. Experiments were performed in biological triplicates.
3.5.5 ATP quantification of bulk culture
ATP levels were measured using the Promega BacTiter-Glo Microbial Cell Viability Assay according to the
manufacturer’s instructions. A working volume of 100 µL was used. Aliguots from tubes were removed,
pelleted, and resuspended in 1% NaCl before reading luminescence. Experiments were performed in
biological triplicates.
3.5.6 Construction of S. aureus HG003 expressing QUEEN2m
pEPSA5-QUEEN2m
pEPSA5-QUEEN2m was created by amplifying QUEEN2m from pRsetB-his7-QUEEN2m [66] with primers
Q2m-F 5’-CGAGCTGAATTCTAGGGAGAGGTTTTAAACATGAAAACTGTGAAAGTGAATATAAC-3’ and Q2m-R
5’-CGAGCTGGTACCTCACTTCATTTCCGCAACGCTC-3’, and cloning it into the EcoRI/KpnI sites of pEPSA5
downstream of a xylose promoter [77]. Restrictions sites are underlined, and a RBS from sarA gene has
been added in Q2m-F primer (here in bold).
pEPSA5-QUEEN2m(opt)
59
QUEEN2m(opt) was codon-optimized from the original sequence by using JCat tool [78] and DNA
synthesized (Genewiz) with the same RBS used in pEPSA5-QUEEN2m in a custom plasmid. A fragment
containing QUEEN2m(opt) and its RBS was excised from this plasmid with EcoRI/KpnI and cloned into
the EcoRI/KpnI sites of pEPSA5 [77].
Those two plasmids have been transformed into S. aureus RN4220 and amplified from this background
before to be transformed in S. aureus HG003 background. Only the optimized version was transformed
into mutant strains.
3.5.7 Microscopy
S. aureus HG003-pEPSA5-QUEEN2m and HG003-pEPSA5-QUEEN2m(opt) were cultured at 30C in TSB
without glucose (complemented with chloramphenicol 10 µg/mL and xylose 0.03% for the pEPSA5
maintenance and the induction of QUEEN2m expression, respectively) to stationary phase, inoculated
into fresh medium of the same composition at 1:100, and cultured for 4.5 hours at 30°C. Samples were
washed with PBS, placed on top of a PBS agarose pad 1%, and observed under a ZEISS LSM 710 confocal
microscope using 63x oil immersion objective lens. The two fluorescent signals 405ex and 488ex were
sequentially collected. DIC image was recorded alongside the 405ex acquisition. Images were acquired
by Zen Software at a resolution of 1024 x 1024 and lane average of 8, and processed with the Fiji
software [79].
3.4.8 Single-cell ATP quantification using QUEEN
Flow cytometry was carried out using an Attune flow cytometer. Spatially separated violet and blue
lasers were used for excitation at 405 nm and 488 nm, respectively, to calculate the ratio of emission
at513 nm produced by each of these excitation wavelengths. To prepare samples, -80 oC stocks were
grown on TSA plates with chloramphenicol 10 µg/ml at 30oC. Single colonies were selected and
60
overnight cultures were inoculated in TSB without glucose plus chloramphenicol 10 µg/ml shaking at
225 rpm at 30 oC. Tubes were inoculated 24 hours later from overnight cultures and grown 25 hours
(HG003 empty vector, HG003 WT, gltA, sucA, and fumC mutants) or 26 hours (gudB and sucC mutants)
to reach comparable CFU/mL. Xylose 0.03% was used to induce expression of QUEEN. Strains used:
HG003 pEPSA5 empty vector, HG003 pEPSA5-QUEEN2m, gltA:: pEPSA5-QUEEN2m, gudB::
pEPSA5-QUEEN2m, sucA:: pEPSA5-QUEEN2m, sucC:: pEPSA5-QUEEN2m, and fumC::
pEPSA5-QUEEN2m. Three biological replicates were analyzed for each strain.
3.4.9 FACS analysis using GFP reporters
Cell sorting was carried out using a BD FACSAria II with a 70 micron nozzle. Briefly, 1:1000 dilutions of
overnight cultures were grown 5 hours to late exponential phase and challenged with ciprofloxacin (10x
MIC) for 24 hr. After 24 h, cells were diluted 1:100 and sonicated as previously described [80] to
disperse aggregates of S. aureus cells. FACS DIVA software was used in sorting setup; the initial
population of cells was gated by size using forward and side scatter (FSC and SSC) then on the basis of
gfp fluorescence (GFP-A). Gates were set to include the brightest 5% and dimmest 5%, in addition to a
middle ~30% of the population. Cells were sorted from each population onto TSA plates. Plates were
incubated at 37 oC for 24 h and colonies were counted. Percent survival was calculated from dim,
middle, and bright GFP fractions.
Acknowledgements
We would like to thank Hiromi Imamura for the QUEEN-2m coding plasmid. We thank Sarah Rowe and
David Cameron for critical discussions. This work was supported by NIH grant R01-AI110578-01
61
62
gene
symb
ol
refSeq ID Description UniprotID Locus pvalue
log2
(stat/ex
p)
Pathway
E.C.
numbe
r
ptsG YP_50130
5.1
PTS system
glucose-specific
transporter subunit
IIABC
PTU3C_STA
A8
SAOUHSC_02
848
0.0005
18 -0.7 Glycolysis
2.7.1.1
99
glcA YP_49875
4.1
PTS system
glucose-specific
protein
PTG3C_STA
A8
SAOUHSC_00
155
0.0002
18 -0.7 Glycolysis
2.7.1.1
99
ptbA YP_49995
5.1
PTS system
transporter subunit
IIA
Q2FYL0_STA
A8
SAOUHSC_01
430
0.0042
13 0.5 Glycolysis
2.7.1.1
99
pgi YP_49945
3.1
Glucose-6-
phosphate
isomerase
G6PI_STAA8 SAOUHSC_00
900
0.0001
44 -1.0 Glycolysis 5.3.1.9
pfkA YP_50031
2.1
6-
phosphofructokina
se
PFKA_STAA8 SAOUHSC_01
807
0.0000
41 -1.5 Glycolysis
2.7.1.1
1
FBPa
se
YP_50128
0.1
hypothetical
protein
SAOUHSC_02822
F16PC_STAA
8
SAOUHSC_02
822
0.9283
60 0.0 Glycolysis
3.1.3.1
1
fdaB YP_50137
9.1
fructose-1,6-
bisphosphate
aldolase
ALF1_STAA8 SAOUHSC_02
926
0.0065
61 -0.4 Glycolysis
4.1.2.1
3
fbaA YP_50084
2.1
fructose-
bisphosphate
aldolase
Q2FWD3_ST
AA8
SAOUHSC_02
366
0.0067
95 -0.4 Glycolysis
4.1.2.1
3
tpiA YP_49935
3.1
triosephosphate
isomerase TPIS_STAA8
SAOUHSC_00
797
0.0003
36 -1.0 Glycolysis 5.3.1.1
gap YP_49935
1.1
glyceraldehyde-3-
phosphate
dehydrogenase
Q2G032_STA
A8
SAOUHSC_00
795
0.0000
01 -1.3 Glycolysis
1.2.1.1
2
gapB YP_50029
8.1
glyceraldehyde 3-
phosphate
dehydrogenase 2
Q2FXP2_STA
A8
SAOUHSC_01
794
0.0508
71 -0.7 Glycolysis
1.2.1.1
2
pgk YP_49935
2.1
phosphoglycerate
kinase PGK_STAA8
SAOUHSC_00
796
0.0001
96 -1.1 Glycolysis 2.7.2.3
63
gpmA YP_50116
5.1
phosphoglyceromu
tase
GPMA_STAA
8
SAOUHSC_02
703
0.0006
28 -0.9 Glycolysis
5.4.2.1
1
pgmB YP_49894
9.1
phosphoglycerate
mutase family
protein
Q2G101_STA
A8
SAOUHSC_00
359
0.0949
29 -9.8 Glycolysis
5.4.2.1
1
pgm YP_49935
2.1
phosphoglycerate
kinase PGK_STAA8
SAOUHSC_00
796
0.0001
96 -1.1 Glycolysis
5.4.2.1
1
eno YP_49935
5.1
phosphopyruvate
hydratase ENO_STAA8
SAOUHSC_00
799
0.0000
14 -0.7 Glycolysis
4.2.1.1
1
pykA YP_50031
1.1 pyruvate kinase KPYK_STAA8
SAOUHSC_01
806
0.0001
70 -1.2 Glycolysis
2.7.1.4
0
lctE YP_49880
3.1
L-lactate
dehydrogenase LDH1_STAA8
SAOUHSC_00
206
0.0483
59 -11.5
Pyruvate/ace
tate
metabolism
1.1.1.2
7
ldh2 YP_50137
4.1
L-lactate
dehydrogenase LDH2_STAA8
SAOUHSC_02
922
0.0004
52 -0.6
Pyruvate/ace
tate
metabolism
1.1.1.2
7
ddh YP_50128
9.1
D-lactate
dehydrogenase
Q2FVA3_STA
A8
SAOUHSC_02
830
0.0003
00 -1.4
Pyruvate/ace
tate
metabolism
1.1.1.2
8
pycA YP_49961
0.1
pyruvate
carboxylase
Q2G2C1_ST
AA8
SAOUHSC_01
064
0.3906
06 -0.2
Pyruvate/ace
tate
metabolism
pdhA YP_49958
9.1
pyruvate
dehydrogenase
complex, E1
component subunit
alpha
Q2FZG4_STA
A8
SAOUHSC_01
040
0.0016
04 -1.2
Pyruvate/ace
tate
metabolism
1.2.4.1
pdhB YP_49959
0.1
pyruvate
dehydrogenase
complex, E1
component subunit
beta
Q2G2A5_STA
A8
SAOUHSC_01
041
0.0000
66 -0.8
Pyruvate/ace
tate
metabolism
1.2.4.1
pdhC YP_49959
1.1
branched-chain
alpha-keto acid
dehydrogenase
subunit E2
Q2G2A4_STA
A8
SAOUHSC_01
042
0.0000
75 -1.1
Pyruvate/ace
tate
metabolism
2.3.1.1
2
64
pdhD YP_49959
2.1
dihydrolipoamide
dehydrogenase
Q2G2A3_STA
A8
SAOUHSC_01
043
0.0005
17 -0.7
Pyruvate/ace
tate
metabolism
1.8.1.4
lpdA YP_50012
9.1
dihydrolipoamide
dehydrogenase
Q2FY51_STA
A8
SAOUHSC_01
614
0.0163
88 -0.8
Pyruvate/ace
tate
metabolism
1.8.1.4
pflB YP_49878
4.1
formate
acetyltransferase PFLB_STAA8
SAOUHSC_00
187
0.0084
47 -1.8
Pyruvate/ace
tate
metabolism
2.3.1.5
4
porA YP_49979
9.1
hypothetical
protein
SAOUHSC_01266
Q2FZ05_STA
A8
SAOUHSC_01
266
0.0152
38 -0.5
Pyruvate/ace
tate
metabolism
1.2.7.1
1
porB YP_49980
0.1
2-oxoglutarate
ferredoxin
oxidoreductase
subunit beta
Q2FZ04_STA
A8
SAOUHSC_01
267
0.0505
90 -0.3
Pyruvate/ace
tate
metabolism
1.2.7.1
1
eutD YP_49914
2.1
phosphotransacety
lase
Q2G0J0_STA
A8
SAOUHSC_00
574
0.0003
71 -1.2
Pyruvate/ace
tate
metabolism
2.3.1.8
acsA YP_50035
1.1
acetyl-CoA
synthetase
Q2G294_STA
A8
SAOUHSC_01
846
0.0024
62 1.5
Pyruvate/ace
tate
metabolism
6.2.1.1
AcsA2 YP_50138
2.1
acetyl-CoA
synthetase
Q2FV14_STA
A8
SAOUHSC_02
929
0.0043
96 -0.4
Pyruvate/ace
tate
metabolism
6.2.1.1
ackA YP_50032
5.1 acetate kinase
ACKA_STAA
8
SAOUHSC_01
820
0.0000
91 -1.2
Pyruvate/ace
tate
metabolism
2.7.2.1
acyP YP_49993
3.1 acylphosphatase
ACYP_STAA
8
SAOUHSC_01
406
0.0000
00 +inf
Pyruvate/ace
tate
metabolism
3.6.1.7
CidC YP_50130
6.1 pyruvate oxidase
Q2FV86_STA
A8
SAOUHSC_02
849
0.0000
04 -1.4
Pyruvate/ace
tate
metabolism
aldH YP_50063
2.1
aldehyde
dehydrogenase
Q2FWX9_ST
AA8
SAOUHSC_02
142
0.0139
02 0.9
Pyruvate/ace
tate
metabolism
1.2.1.3
65
aldA YP_50083
9.1
aldehyde
dehydrogenase ALD1_STAA8
SAOUHSC_02
363
0.9509
16 0.0
Pyruvate/ace
tate
metabolism
1.2.1.3
aldA2 YP_49873
2.1
aldehyde
dehydrogenase ALDA_STAA8
SAOUHSC_00
132
0.0280
68 0.4
Pyruvate/ace
tate
metabolism
1.2.1.3
adh1 YP_49917
1.1
alcohol
dehydrogenase ADH_STAA8
SAOUHSC_00
608
0.0011
73 -1.4
Pyruvate/ace
tate
metabolism
1.1.1.1
pckA YP_50041
1.1
phosphoenolpyruv
ate carboxykinase
PCKA_STAA
8
SAOUHSC_01
910
0.0065
73 0.9 TCA
4.1.1.4
9
pycA YP_49961
0.1
pyruvate
carboxylase
Q2G2C1_ST
AA8
SAOUHSC_01
064
0.3906
06 -0.2 TCA 6.4.1.1
citZ YP_50030
7.1
hypothetical
protein
SAOUHSC_01802
Q2FXN3_STA
A8
SAOUHSC_01
802
0.0055
31 0.8 TCA 2.3.3.1
citB YP_49987
5.1
aconitate
hydratase
Q2FYS9_STA
A8
SAOUHSC_01
347
0.0004
06 0.7 TCA 4.2.1.3
citC YP_50030
6.1
isocitrate
dehydrogenase
Q2FXN4_STA
A8
SAOUHSC_01
801
0.0227
20 0.4 TCA
1.1.1.4
2
sucA YP_49994
4.1
2-oxoglutarate
dehydrogenase E1
component
ODO1_STAA
8
SAOUHSC_01
418
0.0176
69 0.4 TCA 1.2.4.2
pdhD YP_49959
2.1
dihydrolipoamide
dehydrogenase
Q2G2A3_STA
A8
SAOUHSC_01
043
0.0005
17 -0.7 TCA 1.8.1.4
lpdA YP_50012
9.1
dihydrolipoamide
dehydrogenase
Q2FY51_STA
A8
SAOUHSC_01
614
0.0163
88 -0.8 TCA 1.8.1.4
odhB YP_49994
3.1
dihydrolipoamide
succinyltransferase
ODO2_STAA
8
SAOUHSC_01
416
0.0073
55 0.6 TCA
2.3.1.6
1
porA YP_49979
9.1
hypothetical
protein
SAOUHSC_01266
Q2FZ05_STA
A8
SAOUHSC_01
266
0.0152
38 -0.5 TCA 1.2.7.3
porB YP_49980
0.1
2-oxoglutarate
ferredoxin
oxidoreductase
subunit beta
Q2FZ04_STA
A8
SAOUHSC_01
267
0.0505
90 -0.3 TCA 1.2.7.3
66
sucD YP_49975
4.1
succinyl-CoA
synthetase subunit
alpha
Q2FZ36_STA
A8
SAOUHSC_01
218
0.0669
54 0.3 TCA 6.2.1.5
sucC YP_49975
3.1
succinyl-CoA
synthetase subunit
beta
SUCC_STAA
8
SAOUHSC_01
216
0.2682
54 0.2 TCA 6.2.1.5
sdhC YP_49964
7.1
succinate
dehydrogenase
cytochrome b-558
subunit
Q2FZC9_STA
A8
SAOUHSC_01
103
0.0091
17 1.1 TCA 1.3.5.4
sdhA YP_49964
8.1
succinate
dehydrogenase
flavoprotein
subunit
Q2FZC8_STA
A8
SAOUHSC_01
104
0.0002
64 0.8 TCA 1.3.5.4
sdhB YP_49964
9.1
succinate
dehydrogenase
iron-sulfur subunit
Q2FZC7_STA
A8
SAOUHSC_01
105
0.0014
24 0.5 TCA 1.3.5.4
fumC YP_50048
0.1
fumarate
hydratase
Q2FX94_STA
A8
SAOUHSC_01
983
0.0000
45 0.7 TCA 4.2.1.2
mqo1 YP_50110
9.1
malate:quinone
oxidoreductase
Q2FVQ5_ST
AA8
SAOUHSC_02
647
0.0026
38 0.6 TCA 1.1.5.4
mqo2 YP_50138
0.1
malate:quinone
oxidoreductase
Q2FV16_STA
A8 SAOUHSC_27
0.0196
18 -0.9 TCA 1.1.5.4
Supplementary Table 3.1: All enzymes represented in the map in Supplementary Figure 3.1.
P-value was determined by ANOVA.
67
Supplementary Figure 3.2: Optimization of QUEEN2m for expression in S. aureus.
Representative images of exponential phase cultures of S. aureus HG003-pEPSA5-QUEEN2m
and HG003-pEPSA5-QUEEN2m(opt) cultured at 30C in TSB without glucose (complemented
with chloramphenicol 10 µg/mL and xylose 0.03% for the pEPSA5 maintenance and the
induction of QUEEN2m expression, respectively). Samples were washed with PBS, placed on
the top of a PBS agarose pad 1%, and observed under the microscope. The two fluorescent
signals 405ex (false-coloured here in magenta) and 488ex (false-coloured here in green)
were sequentially collected one after the other. Scale bar, 5 µm.
68
Supplementary Figure 3.4: Representative photograph of colonies formed by surviving cells after antibiotic challenge and sorting. FACS of PsucA::gfp gated into dim, middle, and bright fractions is shown. Here, 1600 cells were sorted per fraction.
Dim Middle Bright
Supplemental Figure 3.3: ATP concentrations of wild type S. aureus and TCA cycle mutants. ATP concentrations in single cells was determined by FACS using the optimized QUEEN2m sensor. 50,000 events were analyzed for each sample. Experiment was performed in biological triplicate.
69
Chapter 4: Persister Resuscitation
4.1 Abstract
We have shown that low ATP induces persister cell formation [35]. Within a population of S.
aureus, individual cells with low TCA cycle activity are better able to survive antibiotic treatment
than the bulk of the population. It is likely that multiple factors contribute to maintenance of the
persister state, but this area is largely unexplored. Tolerance is a transient phenotype and
persister cells are able to exit the dormant state and resume growth [3]. This is believed to cause
chronic and relapsing infections. It is possible that persister resuscitation is stochastic or that
growth resumption is triggered by environmental cues [81-83]. We conducted a broad screen for
genes involved in persister resuscitation using the Nebraska Transposon Mutant Library [65] and
identify S. aureus mutants with significantly altered resuscitation kinetics following antibiotic
treatment. We identified 46 mutants with significantly faster resuscitation and 116 mutants with
significantly slower resuscitation compared to the bulk of the tested mutants.
4.2 Introduction
Most pathogens that cause difficult-to-treat infections are not resistant to antibiotics [17, 37]. In
cases of chronic and relapsing infection patients are typically prescribed antibiotics, which are
effective in killing most bacterial cells. Persister cells tolerate antibiotic treatment. When
antibiotics are no longer present in the environment, cells eventually resume growth and can
repopulate the infection site [84]. Neither the cause nor mechanism of persister resuscitation are
understood.
70
The phenomenon of resuscitation from the persister state is well-documented [3, 11, 12] but the
mechanism by which cells wake up from the persister state is poorly understood. Some groups
have proposed a microbial “scout” hypothesis, suggesting stochastic resumed growth among a
population of dormant cells [81]. Other groups have proposed that signaling molecules serve as
wake-up calls for dormant bacteria [82]. Others have observed different resuscitation kinetics
depending on the type of antibiotic treatment [83]. It is possible that bacteria have evolved
multiple strategies for emerging from dormant or tolerant states. Given that low ATP is
associated with persister formation and entrance into the tolerant non-growing state, we
hypothesized that metabolic genes would be active in resuscitation from the persister state.
We conduct a broad screen of nearly 2000 S. aureus mutants and identify candidate genes
involved in resuscitation. We treat growing cells with a lethal dose of ciprofloxacin and allow
surviving persisters to recover. We identify mutants that are fast or slow to resuscitate after
antibiotic treatment.
4.3 Results
4.3.1. Growth resumption
To demonstrate the principle of resuscitation following antibiotic treatment in vitro, we added
penicillinase to E.coli after ampicillin treatment and enumerated surviving cells every hour. We
hypothesized that penicillinase-mediated inactivation of ampicillin would permit bacterial
growth. E. coli MG1655 cells were grown to exponential phase and treated with ampicillin. The
addition of penicillinase after 1 hour of ampicillin killing resulted in rapid resumption of growth
(Figure 4.1). After three hours of permissive growth conditions, the penicillinase-treated cultures
71
reached nearly the same cellular density as the growth controls that had not been treated with
ampicillin. In vitro, removing antibiotics from growth conditions resulted in rapid bacterial
regrowth.
Figure 4.1: Antibiotic killing and growth resumption after antibiotic inactivation. E. coli
cultures were grown to exponential phase and treated with ampicillin at 0h where indicated
(red and purple symbols). After 1 hour, penicillinase was added where indicated (purple
symbols), resulting in resumed growth evident in the increase of CFU. Experiment was
performed in biological triplicate. Error bars represent standard error.
72
We sought to investigate the mechanisms of resuscitation from the persister state. S. aureus is
known to persist, sometimes for months or years, in a dormant state. Very little is known about
the emergence from dormancy. One recent study characterized the E. coli proteome during
growth resumption following antibiotic treatment [85] by quantifying the incorporation of stable
isotope labelled amino acids to peptides following treatment. Those results identified the
proteins that were synthesized by E.coli following tolerance induced by overproduction of the
membrane-depolarizing toxin TisB. Toxin-antitoxin systems have not been found to induce
persistence in S. aureus [35] and it is unclear how conserved resuscitation mechanisms are
conserved between species. We took a genetic approach and designed a library screen to identify
mutants with improved or decreased ability to resume growth after antibiotic treatment.
4.3.2. NTML screen
The Nebraska Transposon Mutant Library is a mariner-based transposon library in the USA300
strain background and contains roughly 2000 transposon mutants [65]. To screen this large
collection, we designed a 96-well plate-based assay to find mutants that had especially fast or
slow resuscitation after antibiotic treatment. Briefly, one mutant was inoculated per well in MHB
and grown overnight until the strains reached stationary phase. Mutants were inoculated into
fresh media in a new plate and strains were grown 3 hours to exponential phase then treated
with ciprofloxacin for 24 hours. Cells were pelleted and washed, then resuspended and diluted
into fresh MHB and post-treatment growth resumption was monitored in a plate reader for 21
hours. The OD600 of each well was recorded every 30 minutes. Output from this phase of the
experiment for a representative plate is shown in Figure 4.2. In all experiments, starting OD was
similar for all mutants, suggesting that a comparable number of cells were in the starting
73
inoculum. This experimental procedure was repeated until each mutant in the NTML had been
screen for resuscitation kinetics.
For each strain, the amount of time required to triple the starting OD was calculated. Strains were
classified as “fast” resuscitators if the time required to triple starting OD was more than 1.5
standard deviations higher than the mean for that plate. Strains were classified as “slow”
resuscitators if the time required to triple starting OD was less than 1.5 standard deviations lower
than the mean for that plate. Wherever possible, the emergence of each plate from stationary
phase was also monitored and the same analysis was performed. These mutants simply have a
long lag time before beginning exponential growth or a general growth defect and are not true
candidates for genes involved in resuscitation. They were therefore excluded from the list of hits
of genes implicated in persister resuscitation. Figure 4.2 shows a representative screening plate
of resuscitation following ciprofloxacin treatment. Fast resuscitators (here, SAUSA300_1208 and
SAUSA300_0585) are shown in green. Slow resuscitators (SAUSA300_0504, SAUSA300_2026,
SAUSA300_2024, SAUSA300_2393, SAUSA300_0444, SAUSA300_0844, SAUSA300_1473,
SAUSA300_1908, SAUSA300_1469) are shown in red.
74
Resuscitation candidate genes are listed in Table 4.1 and Table 4.2. Table 4.1 lists the NTML
mutants that resuscitated faster after ciprofloxacin treatment. These genes are expected to
impede resuscitation or potentially maintain a persister state. Table 4.2 lists the mutants that
resuscitated slowly relative to the bulk of the mutants screened. These genes are expected to
Figure 4.2: Post-treatment persister resuscitation screen output sample. One screening plate
(96 mutants) of the NTML are shown here. One mutant was inoculated per well. Cells were
grown to stationary phase and diluted into fresh media in a new plate. (A) Emergence from
stationary phase was monitored by measuring OD600 every 30 minutes. Following 24 hours of
ciprofloxacin treatment, cells were washed with PBS and inoculated into fresh MHB then grown
for 20 hours. (B) OD600 was measured every 30 minutes. Fast resuscitators were defined at
mutants that tripled their starting OD faster than the majority of mutants (T3OD >1.5 SDbulk) and
are colored in green. Slow resuscitators (T3OD <1.5 SDbulk) are colored in red. Mutants with the
same fast or slow phenotype during growth resumption out of stationary phase were excluded
from the list of candidate resuscitation genes.
A
B
75
facilitate resuscitation. Interestingly, the mutant purM which encodes
phosphoribosylaminoimidazole synthetase was slow to resuscitate in our screen. PurM was
recently shown to be highly synthesized during regrowth after antibiotic treatment [85],
suggesting that it is indeed important in the post-antibiotic resuscitation.
Gene Locus ID
putative surface protein SAUSA300_0883
DNA internalization-related competence protein ComEC/Rec2 SAUSA300_1547
phosphate transporter family protein SAUSA300_0650
phiSLT ORF401-like protein, integrase SAUSA300_1438
putative cobalamin synthesis protein SAUSA300_0424
putative membrane protein SAUSA300_2304
putative membrane protein SAUSA300_0881
lactose phosphotransferase system repressor SAUSA300_2156
3-hydroxyacyl-CoA dehydrogenase SAUSA300_0226
formate/nitrite transporter family protein SAUSA300_2349
putative membrane protein SAUSA300_1809
riboflavin synthase, alpha subunit SAUSA300_1714
conserved hypothetical protein SAUSA300_1485
conserved hypothetical protein SAUSA300_0872
iron compound ABC transporter, permease protein SirB SAUSA300_0116
acetyltransferase, GNAT family family SAUSA300_0943
putative glycosyl transferase SAUSA300_2583
FAD/NAD(P)-binding Rossmann fold superfamily protein SAUSA300_0394
galactose-6-phosphate isomerase subunit LacA SAUSA300_2155
capsular polysaccharide biosynthesis protein Cap5H SAUSA300_0159
hypothetical protein SAUSA300_0232
4-oxalocrotonate tautomerase SAUSA300_1258
hypothetical protein SAUSA300_2585
76
F0F1 ATP synthase subunit beta SAUSA300_2058
hypothetical protein SAUSA300_1486
manganese transport protein MntH SAUSA300_1005
serine protease SplC splC SAUSA300_1756
oligoendopeptidase F, PepF SAUSA300_0902
Oye family NADH-dependent flavin oxidoreductase SAUSA300_0322
oligopeptide ABC transporter, permease protein SAUSA300_2410
tandem lipoprotein SAUSA300_0411
Na+/H+ antiporter family protein SAUSA300_2273
thiol peroxidase, tpx SAUSA300_1659
phiPV083 ORF027-like protein SAUSA300_1952
aspartate kinase SAUSA300_1225
conserved hypothetical protein SAUSA300_1084
transcription antiterminator, glcT SAUSA300_1253
carbamoyl phosphate synthase large subunit carB SAUSA300_1096
amidohydrolase SAUSA300_0534
conserved hypothetical protein SAUSA300_0059
ATP-dependent RNA helicase, DEAD/DEAH box family SAUSA300_1518
tRNA modification GTPase TrmE SAUSA300_2646
glycerol kinase glpA SAUSA300_1192
hypothetical protein SAUSA300_2593
hypothetical protein SAUSA300_111
hypothetical protein SAUSA300_1208
hypothetical protein SAUSA300_0585
Table 4.1: S. aureus mutants with significantly faster resuscitation after ciprofloxacin treatment
compared to the plate average.
Gene Locus ID
conserved hypothetical protein SAUSA300_1560
putative tetracycline resistance protein SAUSA300_0139
excinuclease ABC, A subunit SAUSA300_0742
77
conserved hypothetical protein SAUSA300_0097
putative membrane protein SAUSA300_0351
isopropylmalate synthase-related protein SAUSA300_0879
multidrug resistance protein SAUSA300_2360
putative lipoprotein SAUSA300_2403
conserved hypothetical phage protein SAUSA300_1936
fibronectin binding protein A SAUSA300_2441
staphylococcal tandem lipoprotein SAUSA300_2428
methylenetetrahydrofolate dehydrogenase SAUSA300_0965
transporter gate domain protein SAUSA300_2520
conserved hypothetical protein SAUSA300_1902
conserved hypothetical protein SAUSA300_0595
aminotransferase, class V SAUSA300_1662
glycerol-3-phosphate dehydrogenase SAUSA300_1193
conserved hypothetical protein SAUSA300_0847
conserved hypothetical protein SAUSA300_2311
thymidine kinase SAUSA300_2073
lipoic acid synthetase SAUSA300_0829
putative exonuclease SAUSA300_1970
2,3-bisphosphoglycerate-dependent phosphoglycerate mutase SAUSA300_2362
branched-chain amino acid aminotransferase SAUSA300_0539
deoxyribose-phosphate aldolase SAUSA300_0140
DNA translocase FtsK SAUSA300_1169
putative membrane protein SAUSA300_0279
putative membrane protein SAUSA300_0917
conserved hypothetical protein SAUSA300_1871
phytoene dehydrogenase SAUSA300_2501
phosphoribosylformylglycinamidine synthase I SAUSA300_0970
putative D-isomer specific 2-hydroxyacid dehydrogenase SAUSA300_0179
ABC transporter, permease protein SAUSA300_2307
adenylosuccinate lyase SAUSA300_1889
78
conserved hypothetical protein SAUSA300_1537
conserved hypothetical protein SAUSA300_0780
succinyl-CoA synthetase, beta subunit SAUSA300_1138
amidophosphoribosyltransferase SAUSA300_0972
ATP synthase F1, alpha subunit SAUSA300_2060
4-diphosphocytidyl-2C-methyl-D-erythritol kinase SAUSA300_0472
UTP-glucose-1-phosphate uridylyltransferase SAUSA300_2439
cmp-binding-factor 1 SAUSA300_1791
conserved hypothetical protein SAUSA300_0655
5'-methylthioadenosine/S-adenosylhomocysteine nucleosidase SAUSA300_1558
quinol oxidase, subunit I SAUSA300_0962
magnesium transporter SAUSA300_0910
phosphoribosylaminoimidazole carboxylase, ATPase subunit SAUSA300_0967
fructose specific permease SAUSA300_0685
cytochrome oxidase assembly protein SAUSA300_1015
putative competence protein SAUSA300_0901
alpha,alpha-phosphotrehalase SAUSA300_0968
rod shape-determining protein MreD SAUSA300_1604
tyrosine recombinase xerC SAUSA300_1145
tRNA delta(2)-isopentenylpyrophosphate transferase SAUSA300_1195
alkyl hydroperoxide reductase subunit C SAUSA300_0380
ATP-dependent Clp protease proteolytic subunit SAUSA300_0752
ribosomal RNA small subunit methyltransferase B SAUSA300_1110
phosphoribosylaminoimidazole-succinocarboxamide synthase SAUSA300_0968
Na+/H+ antiporter family protein SAUSA300_0846
L-serine dehydratase, iron-sulfur-dependent, beta subunit SAUSA300_2470
multi drug resistance protein, norA SAUSA300_0680
hypothetical protein SAUSA300_2402
phosphoribosylaminoimidazole synthetase purM SAUSA300_0973
RNA polymerase sigma factor SigB rpoF SAUSA300_2022
cobalt transporter ATP-binding subunit cbiO SAUSA300_2176
79
putative lipoprotein SAUSA300_2355
hypothetical protein SAUSA300_0373
acetyl-CoA c-acetyltransferase, VraB SAUSA300_0560
arginine repressor, ArgR SAUSA300_0066
permease, LctP SAUSA300_0112
glyoxalase family protein SAUSA300_0338
phiSLT ORF527-like protein SAUSA300_1391
hypothetical protein SAUSA300_1484
hypothetical protein SAUSA300_1722
prephenate dehydrogenase SAUSA300_1260
hypothetical protein SAUSA300_0775
hypothetical protein SAUSA300_1543
glyceraldehyde 3-phosphate dehydrogenase 2, gap SAUSA300_1633
ATP-dependent DNA helicase RecG SAUSA300_1120
malate:quinone-oxidoreductase, mqo SAUSA300_2541
Leukocidin/Hemolysin toxin family protein SAUSA300_1974
phosphate starvation-induced protein, PhoH family SAUSA300_1531
gamma-hemolysin component A SAUSA300_2365
conserved hypothetical protein SAUSA300_0356
conserved hypothetical protein SAUSA300_0317
geranyltranstransferase SAUSA300_1470
anti-sigma-B factor, serine-protein kinase rsbW SAUSA300_2023
respiratory nitrate reductase, subunit delta narJ SAUSA300_2341
hypothetical protein SAUSA300_1745
ATP-binding Mrp/Nbp35 family protein SAUSA300_2125
glycine cleavage system protein H gcvH SAUSA300_0791
quinol oxidase, subunit III qoxC SAUSA300_0961
transcriptional repressor CodY SAUSA300_1148
dihydrolipoamide dehydrogenase lpdA SAUSA300_0996
mannose-6-phosphate isomerase manA SAUSA300_2096
30S ribosomal protein S1 rpsA SAUSA300_1365
80
branched-chain alpha-keto acid dehydrogenase subunit E2 SAUSA300_099
hypothetical protein SAUSA300_1213
hypothetical protein SAUSA300_1899
ABC transporter permease SAUSA300_2358
hypothetical protein SAUSA300_0841
hypothetical protein SAUSA300_1739
PemK family protein SAUSA300_2026
anti-sigma-B factor, antagonist rsbV SAUSA300_2024
glycine betaine/carnitine/choline ABC transporter ATP-binding protein opuCa SAUSA300_2393
LysR family regulatory protein gltC SAUSA300_0444
hypothetical protein SAUSA300_0844
transcription antitermination protein NusB SAUSA300_1473
hypothetical protein SAUSA300_1908
arginine repressor argR SAUSA300_1469
Table 4.2: S. aureus mutants with significantly faster resuscitation after ciprofloxacin treatment
compared to the plate average.
4.3 Discussion
In total we identified 46 mutants with significantly faster resuscitation and 116 mutants with
significantly slower resuscitation compared to the bulk of the tested mutants. Mutants with a
fast resuscitation phenotype (Table 4.1) represent genes that are expected to play a role in
maintaining the persister state or impede growth resumption following antibiotic treatment.
Mutants with a slow resuscitation phenotype (Table 4.2) represent genes that are expected to
promote resuscitation from the persister state.
81
Bacterial populations resuming growth after any period of slow or no growth undergo a lag
phase. The duration of lag phase depends on many environmental factors, including prior
stresses such as nutrient deprivation or growth inhibitors [86]. Diluting cells in a stationary state
into fresh media results in a short lag, then rapid exponential growth. It is likely that many of the
cellular pathways active during growth resumption from stationary phase are also active during
recovery from antibiotic treatment. We performed a counter-screen to identify mutants that
have simply have naturally slow or fast growth phenotypes. We focused on genes that play a role
specifically in growth resumption following antibiotic treatment.
We have shown that stochasticity in gene expression results in tolerant subpopulations; it is
possible that stochastic gene expression also launches individual cells into regrowth when
conditions are suitable. Our results show that mutants in metabolic genes are generally slower
to resume growth following antibiotic treatment. Possibly persister cells that resume growth
when conditions are favorable simply exhibit a fortunate combination of gene expression and
timing. The pathways necessary to resume growth after antibiotic treatment are not yet fully
understood, but it is likely that some of the processes necessary for recovery from the persister
state depend on the type of antibiotic used. For example, cells resuming growth after treatment
with DNA-damaging drugs likely need to activate DNA repair and nucleotide synthesis pathways.
Indeed, we see evidence of this in our screen; a purM mutant has a delayed resuscitation
phenotype. Resuscitation after treatment with cell-wall acting antibiotics might require increased
peptidoglycan synthesis. Further research is needed to describe the mechanism of persister cell
resuscitation.
82
4.5 Material and Methods
4.5.1 Strains and culture conditions
E. coli strain MG1655 was used for the initial proof of principle resuscitation experiment. Cultures
were grown in 14mL culture tubes at 37°C in 3mL LB shaking 220rpm. Overnight cultures were
used to inoculate in fresh LB and cultures were grown for 3 hours. Ampicillin 100g/mL was used
where indicated and penicillinase was added where indicated either at 0 hours (ctrl) or after 1
hour of killing with ampicillin. Time points were taken every hour and aliquots were taken from
cultures and diluted in PBS. Dilutions were plated on LBA for cfu.
4.5.2 Resuscitation Screen
The Nebraska Transposon Mutant Library was used to conduct the resuscitation screen [65].
Experimental screening plates (96-well, flat-bottom clear) containing 100uL of MHB per well
were stamped from the 384-well library plates. Plates were covered with a breathable membrane
and incubated 16 hours sharing at 220rpm at 37°C. Overnight plates were used to inoculate a
fresh 96-well plate 1:100. For full growth curves to monitor the emergence from stationary
phase, growth was monitored using a plate reader. For resuscitation experiments, overnight
plates were used to inoculate plates with fresh media and plates were incubated at 37°C for 3
hours. Mutants in each well were treated with ciprofloxacin 320ug/mL and incubated at 37°C for
24 hours. After treatment, plates were centrifuged for 10 minutes at 4000 rpm and media was
removed. Cells were resuspended in 100uL MHB. Resuspended cells were diluted 1:10 into fresh
MHB and growth was monitored in a plate reader. OD was recorded every 30 minutes.
83
4.5.3 Data analysis
Output OD600 values from each plate were recorded, with time points every 30 minutes. For each
well, the time required for the mutant to triple its starting OD was calculated. The mean of all 96
well was calculated, and those mutants with T3OD outside of 1.5 standard deviations from the
mean were considered screen hits. Fast resuscitators were T3OD >1.5 SDbulk and slow resuscitators
were T3OD <1.5 SDbulk.
Chapter 5. Clinical isolates of Staphylococcus aureus
5.1 Abstract
High-persister clinical strains of Staphylococcus aureus have been characterized in multiple
species [5, 7, 87]. We measure persister levels of isolates from patients with endocarditis,
osteomyelitis, skin and soft tissue infections, and atopic dermatitis. Isolates from patients
exhibited universally high persister levels compared to wild type lab strains. We perform whole
genome sequencing of isolates using Illumina Hi-Seq. Variation analysis was performed and high
confidence mutations were annotated and identified as insertions, deletions, or synonymous or
nonsynonymous single nucleotide polymorphisms (SNPs). All mutations predicted to be nonsilent
were mapped to a biological subsystem, resulting in a list of biological systems that are
potentially involved in antibiotic tolerance. The TCA cycle is a major driver of ATP generation for
S. aureus in vivo, so we expected that mutations in TCA cycle enzymes might contribute to high
antibiotic tolerance [4]. We perform multiple sequence alignments of all encoded TCA cycle
84
enzymes for each clinical isolate and find several amino acid substitutions in TCA cycle genes gltA,
acnA, icd, sucA, sucB, sucD, sucC, sdhA, sdhB, and fumC.
5.2 Introduction
Although an estimated 30% of humans are colonized with commensal Staphylococcus aureus, it
can cause a wide range of infections including bacteremia, endocarditis, osteomyelitis,
meningitis, toxic shock syndrome, and skin and soft tissue infections [20]. Colonization increases
an individual’s risk for infection. In a study of bacteremia, blood isolates were identical to
colonizing nasal strains in 82% of patients [88]. Methicillin-resistant S. aureus (MRSA) is the most
widely-known example of antibiotic-resistant S. aureus, but additional cases of resistant S. aureus
have emerged in recent years. The spread of vancomycin intermediate S. aureus (VISA) also adds
to the concerns about S. aureus infection [20, 89]. Although improvements in infection control
procedures have been effective in reducing the spread of resistant S. aureus, it remains a major
public health problem.
Community-associated S. aureus strains are more likely to be susceptible to antibiotics but the
sheer number of community-associated methicillin-resistant S. aureus (CA-MRSA) infections has
increased in recent years [90].
In the early 2000s, there was a sharp increase in the number of cases of MRSA infections that
were not associated with hospitalization [91, 92]. This spread was eventually attributed to the
USA300 North American clone. This clone has an expanded set of virulence genes and multiple
antibiotic resistance cassettes, which give strains an exceptional ability to establish and maintain
skin and soft tissue infections. USA300 strains are resistant to penicillin and often oxacillin and
85
erythromycin. Many strains exhibit decreased susceptibility to fluoroquinolones. The virulence
genes encoded by USA300 clones include lukS-PV/lukF-PV, sek, and seq [93]. The USA300 clone
has spread across North America, Europe, and Asia, making it one of the most widespread CA-
MRSA clones [93-96]. It remains a global health problem.
The majority of MRSA infections – approximately 80% - are healthcare-associated [25]. Invasive
surgery, medical device insertion, and immunodepression all increase the likelihood of
developing a healthcare associated S. aureus infection [97]. Infections on indwelling devices often
form biofilms, which further complicates treatment. Biofilm-associated infection are difficult to
treat and often persist over time. The epidemiology of S. aureus transmission and infection is not
well understood but increases in hospital contagion surveillance has resulted in more information
on the spread of the pathogen.
5.3 Results
The decreasing cost of DNA sequencing has improved our understanding of the epidemiology
and transmission of S. aureus [92, 98]. Very little work has been done using genomics to
understand host adaptation or the progression of chronic infection [99]. We use whole genome
sequencing to identify pathways and biological subsystems that harbor a high number of
mutations in clinical isolates of S. aureus.
The genetic basis of persister formation has been explored in several species [5, 52, 100-102].
Many of these investigations use Tn-seq, which allows for high-throughput in vitro screening of
a transposon library for genes involved in antibiotic tolerance. In a Tn-seq experiment, a
transposon mutant library is exposed to antibiotics and the survivors are cultured. Deep
86
sequencing of the surviving mutants allows for identification of mutants which did not survive
treatment; these mutations are in potential persister genes. Using a transposon library with
dense coverage means that each gene is represented with multiple insertions per coding or
promoter region. These experiments yield a robust list of genes contributing to antibiotic
tolerance. Tn-seq experiments in E. coli have shown that multiple pathways contribute to
persister formation. Genes involved in flagellar structure, amino acid metabolism, and the TCA
cycle were involved in tolerance to aminoglycosides [100]. Most Tn-seq experiments have been
conducted using E. coli mutant libraries but S. aureus has been studied recently [102].
We reasoned that mutations in metabolic pathways that impact ATP generation would affect
phenotypic susceptibility to antibiotics. Resistance mutations alter a strain’s inherent antibiotic
susceptibility by modifying the antibiotic target or enabling cells to pump out antibiotics [11].
High persister mutations permit the cells to tolerate treatment, but not to grow in the presence
of antibiotics. We expected that high persister strains of S. aureus would have mutations in
metabolic genes.
S. aureus isolates were taken from patients with pneumonia, osteomyelitis, or atopic dermatitis.
Clinical details of the isolates can be found in Table 5.1. We tested these patient isolates for
persister levels. Briefly, we determined the MIC of each strain to vancomycin, moxifloxacin, and
doxycycline (Supplemental Figure 5.1). We then treated isolates with 10x MIC and calculated
survival after 48 hours of treatment (Supplemental Figure 5.2). Some isolates had known
antibiotic resistance, and we excluded those drugs from consideration for experiments. Clinical
strains displayed generally increased antibiotic tolerance compared to a reference non-infecting
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strain from a matched clonal complex. Bearing in mind our previous finding that low ATP leads
to antibiotic tolerance, we wondered whether there were genetically encoded defects in
metabolic pathways that might contribute to increased persister levels in these clinical strains.
Table 5.1: S. aureus clinical isolate source information and diagnosis. Includes isolate number,
isolation source, clonal complex, indication of paired/non-paired isolates and hospital diagnosis.
Key source CC Paired Diagnosis
1 SPUTUM 5 A hospital-acquired pneumonia
8 SPUTUM 5 A hospital-acquired pneumonia
11 SPUTUM 5 hospital-acquired pneumonia
25 WOUND 5 osteomyelytis
26 WOUND 5 osteomyelytis
27 WOUND 5 osteomyelytis
30 NARES 5 colonization
15 NARES 8 H colonization
16 BLOOD 8 H colonization
21 NARES 8 colonization
48 SKIN 5 atopic dermatitis
64 SKIN 5 atopic dermatitis
31 SKIN 5 atopic dermatitis
24 SKIN 209 atopic dermatitis
23 SKIN 22 atopic dermatitis
44 SKIN unk atopic dermatitis
43 SKIN unk atopic dermatitis
40 SKIN 188 atopic dermatitis
88
Variance analysis was performed in parallel for ST5 and ST8. Variants were classified as either
deletions, insertions, nonsynonymous SNP, or synonymous SNP. For ST5 strains, 2369 total
variants were called. For ST8, 3047 total variants were called. Figure 5.1 shows the breakdown
by variant type. Synonymous SNPs were excluded from analysis since they were predicted to be
low-impact variants not affecting the amino acid code.
Identifiers of genes impacted by variants were classified by biological subsystem. Total variants
(InDels or nonsynonymous SNPs) per biological subsystem were quantified. Biological
subsystems are used to categorize proteins with functionally related roles (i.e. enzymes in single
biological pathway). Impacted subsystems are represented in Figure 5.2. Since previous work has
Figure 5.1: Variant type classified as deletion, insertion, nonsynonymous SNP, or
synonymous SNP. Isolates were divided between clonal complex groups for variance
analysis to prevent inclusion of variants that are simply a result of clonal evolutionary
divergence. Isolates from ST5 (A) and ST8 (B) are shown here.
A
B
89
shown that low levels of ATP induce antibiotic tolerance, we were specifically interested in
metabolic subsystems. We suspected that variants in genes encoding metabolic enzymes would
be prevalent in S. aureus isolates. Indeed, genes involved in pyruvate metabolism and the TCA
cycle harbored a high number of variants in clinical strains.
We suspected that mutations in TCA cycle genes would be detected in clinical isolates. Using the
Pathosystems Resource Integration Center, we compared variants in the TCA cycle between each
isolate. We found that the clinical isolates harbored a high number of high-impact mutations in
genes encoding TCA cycle enzymes. We considered variants predicted to alter amino acid
sequence with each coding region. We performed multiple sequence alignment of amino acid
sequence for TCA cycle proteins using the reference genome USA100. The heat map in Figure 5.2
shows the number of high-impact mutations in each TCA cycle gene. Specific amino acid
substitutions are described in Supplemental Table 5.3. Interestingly, we observed a high number
of mutations in late TCA cycle genes, especially fumC. FumC is responsible for converting
fumarate to malate. This step of the TCA cycle occurs after the glutamate portal into the TCA
cycle and mutation in fumC is likely to decrease ATP generation in cells using glutamate to drive
ATP generation via the TCA cycle. Future research is needed to determine the impact of these
mutations on ATP generation.
90
Figure 5.2: Biological subsystems implicated in genome variance analysis. Paired reads were mapped
against either S. aureus N315 (PATRIC Genome ID 158879.11) for ST5 (A) or S. aureus USA300_FPR3757
(PATRIC Genome ID 451515.3) for ST8 (B). Paired reads were aligned using BWA-mem and Freebayes
was used to call variants. Variants affecting amino acid sequence were grouped into biological
subsystems using Pathosystems Resource Integration Center (PATRIC) identifiers. Subsystems are listed
from those with the highest number of variants to lowest, with an arbitrary cutoff of 7
variants/subsystem.
A
B
91
5.4 Methods
5.4.1 S. aureus isolate sources
Isolates were collected from patients diagnosed with pneumonia, osteomyelitis, or atopic
dermatitis. Strains were provided by Bo Shopsin at NYU Langone Medical Center and Karen Acker
Primary cultures were grown in the laboratory and secondary stocks were made and frozen at -
80°C. Secondary stocks were used for all experiments.
5.4.2 Whole genome sequencing and read mapping
Whole genome sequencing was performed using Illumina Hi-Seq 4000. Three PCR amplification
cycles were performed in the DNA library preparation. Using the Pathosystems Resource
Integration Center (PATRIC) platform, paired-end reads were mapped against a reference
genome. Reads from samples in ST5 were mapped against Staphylococcus aureus N315 (PATRIC
Genome ID 158879.11) Reads from ST8 were mapped against Staphylococcus aureus
USA300_FPR3757 (PATRIC Genome ID 451515.3). Variance analysis was performed using
Burrows-Wheeler Aligner (BWA) with minimal exact matches. FreeBayes was used to call SNPs.
High confidence variants were used to compare mutations detected in clinical isolates compared
to reference strains.
Multiple sequence alignments as previously described [103, 104]. Coding sequences for specific
enzymes were aligned and amino acid sequences were compared. Variants were identified com
pared to reference genome USA100 of known clonal complex 5 (PATRIC Genome ID 1280.19099
). Comparisons were based on Rapid Annotations using Subsystems Technology (RAST) annotati
ons [105]. KEGG pathways [106] were used to identify protein families for comparison.
92
5.4.3 MIC assay and persister experiments
MIC assays were performed via broth microdilution assay in 96-well microtiter plates. Isolates
were grown in TSB with 100mM MOPS pH7. MICs were determined for each isolate for
moxifloxacin (Acros Organics, ThermoFisher Scientific, USA), vancomycin (Sigma-Aldrich, USA)
and doxycycline (ThermoFisher Scientific, USA).
For persister assays, strains were diluted 1:100 and grown to late exponential phase. Cultures
were grown in 2mL TSB shaking 220rpm at 37oC. Antibiotics were added at 10x MIC as indicated.
100uL aliquots were removed and washed with PBS. CFU was plated on TSA and recorded to
enumerate survivors.
5.5 Discussion
As previous work has shown, multiple factors can contribute to antibiotic tolerance [100]. Specific
gain-of-function mutations in hipA7 in E.coli leads to increased tolerance [5]. Null mutations in
the carB gene, encoding carbomyl phosphate synthetase leads to decreased tolerance in multiple
species [61]. In vitro work has shown that antibiotic treatment drives the evolution of antibiotic
tolerance [107]. Selection for tolerance mutations result in antibiotic treatment failure.
Sequencing has become more common in diagnosing infectious disease, but genetic screening
for mutations that enable bacteria to tolerate treatment are not part of standard care in most
settings.
Mutations that facilitate antibiotic tolerance are not necessarily advantageous under all
conditions. On the contrary, expression of toxins like hipA7 or mutations in important metabolic
genes often lead to growth defects or fitness disadvantages under some conditions. However,
93
we suspect that these mutations are adaptive. Situations of antibiotic challenge select for cells
with less active cellular processes. These tolerance mutations have been shown to precede the
development of resistance mutations in vitro [12] and in vivo [7].
Since antibiotic application drives the acquisition of tolerance mutations, it is useful to study
clinical bacterial isolates. Isolates are taken from patients during their course of treatment,
sometimes over the course of years. High-persister mutants of Pseudomonas aeruginosa,
Mycobacterium tuberculosis, Candida albicans, and Escherichia coli have been isolated from
patients with cystic fibrosis, tuberculosis, candidiasis, and urinary tract infections [5, 7, 8, 13]. We
test isolates of S. aureus from multiple patients for antibiotic tolerance and perform whole
genome sequencing to identify potential tolerance mutations. Our isolates represent multiple
types of infections and distinct clonal lineages. We show that metabolic pathways harbor high-
impact mutations in clinical isolates. Other groups have shown that mutations in the TCA cycle
are frequently found in clinical isolates [108]. Our results support this conclusion, but more
research on larger collections of clinical isolates is needed to draw conclusions about antibiotic
selection for persister mutations. We suspect that isolates from district sites of infection will
accumulate mutations in different metabolic pathways. For example, conversion of proline in
collagen to glutamate then 2-oxoglutarate is expected to drive ATP generation via the TCA cycle
[4] in a staphylococcal abscess.
94
Figure 5.3: High-impact variants in TCA cycle genes. Mutations affecting amino acid sequence 9f isolates (ID: isolate number, vertical axis) were identified per TCA cycle gene. Comparative pathways were analyzed in PATRIC, based on RAST annotation and multiple sequence alignment of TCA cycle genes.
95
5.6 Supplemental Information
moxifloxacin vancomycin doxycycline
1 16 4 0.125
8 4 1 0.125
11 1 8 0.25
15 4 8 4
16 1 0.125 0.125
21 1 8 0.25
25 4 4 0.25
26 8 8 0.25
27 4 8 0.125
30 16 4 0.25
PE001 0.0625 4 0.0078
PE002 0.25 4 0.25
PE003 0.125 4 0.25
PE004 0.25 8 0.25
PE005 0.125 8 0.25
PE007 0.0625 8 0.0078
PE008 0.125 8 0.25
PE010 0.0625 8 0.0078
HG WT 1 4 0.125
USA300 WT 8 4 0.25
96
Supplemental Figure 5.1: MIC for clinical isolates and wild type lab strains. Concentration
units are g/mL. MIC values were determined using broth dilution method in microtiter plates.
Figure 4.2: Percent survival after treatment with vancomycin. Clinical isolates were grown to
late exponential phase and treated with 10x MIC vancomycin. Blue represents S. aureus ST5
and orange represents ST8. CFU was counted after 72h treatment. Experiment was performed
in biological triplicate. Bars represent SEM.
97
gltA acnA icd sucA sucB sucD sucC sdhA sdhB fumC
1
8
11
15 S152N
L369F
E534D P176S
L56I
I122S
E177D
I211T
16 S152N
L369F
E534D P176S
L56I
I122S
E177D
I211T
21
P55S
E534D
P176S
L56I
I122S
E177D
I211T
25
D278N
26
27
98
30
D278N
48 A37T
64
N259K
R262K
S296A T203I
E134K
E177D
31
24 H88Y
N259K
E134K
E177D
23
Q863H N259K
A267S
G914R
E172K M365T
S411A
D435E
L440M
D488N
E3D
S5P
N8D
I122S
E134K
E177D
44
N259K E534D P176S E197K
S296A
L56I
I122S
E177D
I211T
43
E534D P176S
L56I
I122S
E177D
I211T
40
V609I
H550Y P166T S296A
E134K
99
5.7 Contributions
We thank Paul Planet, Karen Acker, and Bo Shopsin for providing clinical isolates and sequencing
these strains.
P802S P176S E177D
Supplemental Table 5.3: Amino acid substitutions in TCA cycle genes. Specific amino acid
substitutions in clinical S. aureus isolates. RAST annotation was used to identify TCA cycle
subsystem genes. MSA was performed based on Freebayes variant calling. Isolate numbers are
listed in the left-hand column with amino acid changes identified per gene. Only TCA cycle genes
are shown. USA100 was used as a reference genome.
100
Chapter 6. Conclusions and Future Directions
6.1 Summary
This work describes a general mechanism of persister formation mediated by low ATP. This
mechanism was first characterized in S. aureus and appears to be conserved among multiple
species [35, 61, 62]. It was previously believed that toxin-antitoxin systems mediated antibiotic
tolerance, but we showed that deletion of the known TA systems in S. aureus does not impact
tolerance. Rather, ATP depletion leads to increased tolerance.
We sought to understand the mechanism by which cells naturally enter the tolerant persister
state. We investigated the role of the TCA cycle in persister formation. We knew that the TCA
cycle and amino acid flux into the TCA cycle were major drivers of cellular growth in S. aureus in
vivo [4, 109] and expected that TCA cycle defects would impact ATP generation and therefore
antibiotic tolerance. We identified in vitro conditions that would mimic the metabolic state at an
infection site, where glucose concentrations are believed to be low and cells must rely on
secondary carbon sources. We assembled the full proteome of S. aureus during exponential
growth and stationary phase and showed that enzymes involved in the TCA cycle and amino acid
catabolism were more abundant during stationary phase than during exponential phase. We
therefore focused on the transition phase from exponential to stationary growth to investigate
the role of the TCA cycle in persister formation. We constructed mutants lacking functional TCA
components gltA (citrate synthase), gudB (glutamate dehydrogenase), sucA (2-oxoglutarate
dehydrogenase), sucC (succinyl-CoA synthetase), and fumC (fumarate hydratase). We studied the
gudB mutant in addition to the canonical TCA cycle genes because it catalyzes the conversion of
101
glutamate to 2-oxoglutarate. This point of entry into the TCA cycle is important in vivo, where
proline derived from collagen is converted to glutamate, which enters the TCA cycle as 2-
oxoglutarate, thus fueling energy generation. We found that these TCA cycle mutants had
decreased intracellular ATP compared to wild type S. aureus. Since ATP concentrations change
dynamically in single cells, we optimized the ATP biosensor QUEEN for expression in S. aureus to
quantify ATP in single cells with minimal perturbation. We used flow cytometry to quantify ATP
and found that TCA cycle defects caused a population shift where more single cells exhibited low
intracellular ATP. This confirmed our low-ATP phenotype in TCA mutants with a novel method.
These mutants also exhibited increased antibiotic tolerance after treatment with ciprofloxacin,
gentamicin, and oxacillin. These phenotypes could have been due to pleiotropic effects of
mutating major metabolic genes. We wanted to understand the role of the TCA cycle in native
persister cells within a population. We reasoned that natural fluctuation in gene expression gives
rise to phenotypic heterogeneity. We hypothesized that within a population, individual cells
expressing relatively low levels of TCA cycle enzymes would have low ATP and therefore be more
tolerant to antibiotic treatment. We made reporter strains using GFP to monitor TCA gene
expression and sorted single cells after antibiotic treatment. We found that the fraction of the
population expressing low TCA cycle genes were enriched for persister cells.
Persister cells are only interesting because they can resume growth. This is believed to cause
infection relapse. We demonstrated persister resuscitation in vitro. We also sought to identify
pathways involved in persister resuscitation and conducted a screen of nearly 2000 transposon
mutants to find genes implicated in resuscitation. We identified 46 mutants with significantly
faster resuscitation and 116 mutants with significantly slower resuscitation compared to the bulk
102
of the mutant screened. Many of these mutants were defective in metabolic pathways and
nucleotide repair. They offer a promising list of candidate genes for ongoing and future research.
We wondered whether there were genetic signatures of antibiotic tolerance in clinical isolates of
S. aureus. Persister mutations have been identified in other pathogenic species [5, 7, 14]. We test
a collection of clinical isolates for persister levels and find high persister levels in all isolates
compared to wild type lab strains. We performed whole genome sequencing of clinical isolates.
We focused specifically on the TCA cycle and found that mutations in the TCA cycle were
surprisingly common among clinical isolates from multiple types of infections. Interestingly, fumC
appeared to be the TCA cycle gene harboring the most high-impact variants. FumC is a late TCA
cycle enzyme that is responsible for converting fumarate to malate. Any defects in the TCA cycle
that occur in steps after the incorporation of glutamate are likely to decrease ATP generation.
6.2 Ongoing research and future directions
6.2.1 Persister resuscitation
Many groups are working to fill the gaps in our understanding of persister formation and
importantly, the role of persister cells in chronic infection. The phenomenon of resuscitation
from the persister state is well-documented [3, 11, 12] but the mechanism by which cells wake
up from a tolerant state is poorly understood. Some groups have proposed a microbial “scout”
hypothesis, suggesting that stochastic growth resumption among a population of dormant cells
causes persister resuscitation [81]. Other groups have proposed that signaling molecules serve
as wake-up calls for dormant cells [82]. Others have observed different resuscitation kinetics
103
depending on the type of antibiotic treatment [83]. It is possible that bacteria have evolved
multiple strategies for emerging from dormant or tolerant states, but these strategies are not
well described.
This work shows that metabolic defects enable cells to survive antibiotic treatment. Resumed
metabolic activity and ATP generation are therefore likely to correspond with persister
resuscitation. Recent work used incorporation of isotope-labelled amino acids to identify
proteins that are synthesized during post-antibiotic recovery [85]. This characterization of the
proteome during post-treatment gives clues about the processes involved in resuscitation.
Nutrient uptake and nucleotide repair emerged from that work and from our screen as important
processes in persister resuscitation. PurM stands out as one candidate resuscitation factor in S.
aureus. Future work will explore specific pathways involved in the emergence from dormancy.
6.2.2. Noise-quenching
Biological populations are naturally heterogeneous. Bacterial phenotypic heterogeneity can be
influenced by many factors including chemical gradients of signaling factors, access to nutrients,
population density, and noisy gene expression [110]. This work shows that natural stochasticity
in metabolic gene expression results in subpopulations that are more tolerant to antibiotics. This
phenotypic heterogeneity can be manipulated by noise quenching. Overexpression of metabolic
genes is expected to increase metabolic flux and increase ATP levels. Bacterial metabolism is
complex and highly regulated and thus quenching noisy gene expression requires identification
of bottlenecks in ATP synthesis. This work identified the TCA cycle and amino acid metabolism as
potential targets for noise-quenching experiments. Ongoing research is aimed at controlling
104
carbon input to direct metabolic flux through known pathways then overexpressing bottleneck
genes to maximize ATP production. We predict that this will result in improved killing by
antibiotics and eradication of persister cells. Further research will be needed to identify the
bottlenecks in bacterial metabolism in vivo. Preliminary evidence suggests that proline and
glutamate catabolism, which this work identified as important for ATP generation in vitro, is
required for bacterial growth in vivo. The S. aureus genome encodes multiple proteases that can
be used to degrade host proteins [111]. It is also known that host-derived proteases are induced
during staphylococcal abscess formation [112, 113]. It is likely that S. aureus uses its own and
host-derived proteases to break down collagen to liberate proline, which is then converted to
arginine and glutamate [4]. Glutamate flux into the TCA cycle serves as a potential noise-
quenching target that could spike intracellular ATP and increase susceptibility to antibiotic
treatment.
6.2.3. Eradicating persister cells
The goal of studying antibiotic tolerance is to eradicate bacterial infections. Infectious disease is
a major cause of death worldwide. The spread of antibiotic-resistant bacteria is expected to
contribute to increasing cases of fatal infections. New antibiotics and careful and treatment are
needed to combat this crisis. Treatment failure due to tolerant persister cells must also be
addressed in the future of infectious disease treatment. This work shows that bacteria with low
ATP can survive antibiotic treatment. Antibiotics with ATP-independent targets offer a potential
solution to the problem of antibiotic tolerance. One such antibiotic is aceyldepsipeptide 4
(ADEP4), which is capable of killing persister cells. ADEP4 activates the ClpP protease, causing
105
unregulated extensive proteolysis. ADEP4 dissociates ClpP from its ATP-dependent chaperones,
resulting in ATP-independent killing by proteolysis. ADEP4 is eradicates S. aureus biofilms in vitro
and in a mouse model [38]. ADEP4 is effective in killing persister cells because it does not rely on
ATP to cause cell death. Compounds that are effective in killing non-replicating bacteria have
been also been identified in M. tuberculosis [114]. New antibiotics that kill in an ATP-independent
manner offer promise for eradicating dormant persister populations.
In addition to discovering new antibiotics, adjuvant therapies offer another option for treatment
of chronic or recurring infections. Metabolite adjuvants have been proposed as an effective way
to eliminate persister populations. Other groups have observed metabolite-mediated eradication
of aminoglycoside-tolerant populations. Although this phenomenon appears restricted to
aminoglycosides, metabolic stimulation was effective in potentiating aminoglycoside killing in a
mouse model and has possible for clinical impact [115, 116]. This is a promising avenue for
treating recurrent infections because metabolites to be used in combination with existing
antibiotics face fewer regulatory hurdles compared to entirely new antibiotics. Fumarate, for
example, is a potentially appealing tobramycin adjuvant and has already been approved by the
Food and Drug Administration for treating asthma [117]. Further research is needed to identify
metabolic stimulants that are effective in combination with existing antibiotics.
106
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