whole-genome analysis of mycobacterium tuberculosis ...rx917x53h/... · whole-genome analysis of...

Whole-genome analysis of Mycobacterium tuberculosis persister genes and investigation of the anti-

tubercular compound lassomycin

by Lauren E. Fitch

B.S. in Biology, Boston University

A.A. in Biology, Bunker Hill Community 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

October 13th

, 2015

Dissertation directed by

Kim Lewis

Distinguished Professor of Biology

ii

© 2015

Lauren E. Fitch

ALL RIGHTS RESERVED

Whole-genome analysis of Mycobacterium tuberculosis persister genes and investigation of the anti-

tubercular compound lassomycin

iii

ABSTRACT OF DISSERTATION

More people are killed each year by Mycobacterium tuberculosis (MTb) than any other bacterial

pathogen, due to the long and often ineffective treatment regimen required to eradicate the infection. This

treatment regimen is becoming increasingly fruitless as multi-drug resistant (MDR) and extensively-drug

resistant (XDR) MTb infections become more prevalent. MDR and XDR MTb pose a grave threat to global

health, and it is imperative that new drugs are developed to fight these infections. We conducted two

screens in order to identify new drug targets and compounds to combat MTb. First, transposon

mutagenesis and sequencing, or Tn-seq, was conducted. Tn-seq is a whole genome method of identifying

genes required for survival in a given environment. We challenged a library of transposon mutants with a

high dose of rifampicin and then identified, through sequencing of the transposon junctions, which genes

were under-represented in the surviving population. We found that genes involved in the plasma

membrane and cofactor metabolic processes were likely to be required for survival during rifampicin

treatment. Second, we conducted a natural products screen to identify compounds with activity against

MTb. One compound, initially called Novo23 and later renamed “lassomycin”, was identified. That in

vitro study had determined that lassomycin binds to the ClpC1 ATPase subunit of the ClpCP proteolytic

complex and both inhibits proteolysis and increases ATP hydrolysis. In order to determine lassomycin’s

mechanism of action, we conducted a proteomic analysis of MTb cells with and without lassomycin

treatment, and found that lassomycin treatment causes a shift in MTb’s proteome. We also measured ATP

concentration in cultures treated with lassomycin, and found that lassomycin significantly decreased ATP

concentration in comparison to untreated or rifampicin controls, similar to bedaquiline, a known ATP

synthase inhibitor. Lassomycin is an entirely new class of drug and reveals a heretofore unknown

mechanism of antibiotic action.

iv

ACKNOWLEDGMENTS

I would like to thank my dissertation advisor, Kim Lewis, for all of his helpful scientific insights over the

past five years, and for giving me the opportunity to learn so much. Thanks are also due to my dissertation

committee members Dr. Eric Stewart, Dr. Veronica Godoy Carter, Dr. Yunrong (Win) Chai, and Dr. Eric

Rubin for their perceptive comments and advice. I would also like to thank Dr. Iris Keren and Dr. Katya

Gavrish, former senior lab members who have provided me with guidance and supported me through my

initial years in the lab. I sincerely appreciate the efforts of Dr. Brian Conlon and Dr. Sarah Rowe, whose

advice and mentoring has been indispensable. This work would not have been possible without the help of

my collaborators: Drs. Andrew Camilli and David Lazinski at Tufts Medical Center, Drs. Joshua Adkins

and Gérémy Clair at Pacific Northwest National Laboratories, Dr. John D McKinney at École

Polytechnique Fédérale Lausanne, Dr. Christopher Sassetti at University of Massachusetts Medical

School, and The Broad Institute. I also wish to thank everyone at the BSL3 facility at Harvard School of

Public Health, particularly Larry Pipkin, Dr. Noman Siddiqi, and Jess Pinkham, for their assistance with

my BSL3 experiments. Jeff Bouffard and Ruxandra Sirbulescu trained me on the confocal microscope

and answered my many questions. Thank you to Dr. Heather Torrey for your exceptional mentorship and

help. Thank you to all of the current and past Lewis Lab members—it has been a blast working with you

for the past five years. Thank you to my parents, Robert Comeau and Kathleen Hill, and my siblings Nikki,

Alex, Emma, and Brian, for your love and support. Finally, many thanks to my husband, Britt Fitch: your

unwavering encouragement and support helped me to be brave enough to quit my job and follow my

dreams. Thank you.

v

TABLE OF CONTENTS

ABSTRACT…………………………………………………………………………………………………………………......... iii

ACKNOWLEDGEMENTS………………………………………………………………………………………………….. iv

TABLE OF CONTENTS…………………………………………………………………………………………..…………. v

LIST OF FIGURES………………………………………………………………………………………………..…………... vii

LIST OF TABLES……………………………………………………………………………………………..………………… ..viii

Chapter 1: Introduction……………………………………………………………………………..…………………….. 10

1.1 Mycobacterium tuberculosis is a continuing global health threat……………………............. 11

1.2 Bacterial drug tolerance………………………………………………………………………………………… 13

1.3 The need for novel antibiotics……………………………………………………………………………….. 17

1.4 Dissertation aims…………………………………………………………………………………………………. 18

Chapter 2: Whole genome screen for persister genes in Mycobacterium

tuberculosis……………………………………..............................………………………………………………………. 20

2.1 Introduction……………………………………………………….……………………………………………….. 21

2.2 Results…………………………………………………………………………………………………….…………. 24

Overall contribution to fitness per genes: Day 6……………………………………………….. 27

Overall contribution to fitness per genes: Day 1……………………………………………….. 28

Clustering analysis………………………………………………………………………………………… 29

Top hits, Day 6……………………………………………………………………………………………… 35

Top hits, Day 0…………………....................................................................................... 39

2.3 Discussion……………………………………………………………………............................................ 45

2.4 Materials and Methods………………………………………………………........................................ 49

Chapter 3: Lassomycin, a Novel Anti-Tubercular Compound, Depletes Intracellular ATP

and Modifies Protease Specificity…….......................................................................................... 57

3.1 Introduction…………………………………………………….…………………………….......................... 58

3.2 Results………………………………………………………………………............................................... 59

Lassomycin treatment results in a major alteration of the proteome………………..... 59

vi

Lassomycin treatment decreases ATP concentration……………................................. 62

Visualization of ATP depletion using ATP-sensitive FRET construct……….............. 64

3.3 Discussion……………………………………………………………………..............................................66

3.4 Materials and Methods…………………………………………...................................................... 68

3.5 Supplemental Materials………………………………………………………...................................... 73

Chapter 4: Discussion………………………………………………………………………………………………………. 85

References………………………………………………………………………………................................................ 89

vii

LIST OF FIGURES

Figure 1-1. A representative graph contrasting resistance and persistence………………………………….........3

Figure 1-2. Persister cells survive high doses of antibiotic………………………………………………………………..4

Figure 1-3. A proposed model for persister formation in E. coli……………………………………..…………………6

Figure 1-4. Most antibiotic classes were discovered in the 1950s and 1960s……………………………………….8

Figure 2-1. Number of MDR-TB cases estimated to occur among notified pulmonary TB cases, 2013…..12

Figure 2-2. Schematic diagram of the Tn-seq method………………………………………………………..……………15

Figure 2-3. Schematic for Tn-seq experiment…………………………………………………………………………………16

Figure 2-4. Analysis of fitness change……………………………………………………………………………………………17

Figure 2-5. Analysis of single gene contribution to fitness (Day 6 / Day 0)………………………………………..18

Figure 2-6. Analysis of single gene contribution to fitness (Day 1 / Day 0)……………………………..….……...19

Figure 2-7. Functional clusters enriched in Day 6 sample……………………………………………………….……….20

Figure 2-8. Functional clusters enriched in Day 1 sample………………………………………………………………..23

Figure 2-9. Functional clusters enriched in Day 1 unique genes..……………………………………………………..25

Figure 2-10. Time-dependent killing of MTb by rifampicin……………………………………….…………………….31

Figure 2-11. Comparison of 2-fold hits between Days 1 and 6…………………………………………………………..31

Figure 2-12. Growth curves of Msm mc2155 harboring pEXPR plasmid with or without 100 ng/mL anhydrous tetracycline (ATc) inducer………………………………………………………………………………………………33

Figure 2-13. Normalized fluorescence of 3 Msm mc2155 pEXPR::GFP strains and Msm mc2155 WT…...34

Figure 2-14. There were no observed significant differences in the growth or survival of the WT and transposon mutants tested……………………………………………………………………………………………………………..35

Figure 2-15: No significant difference was observed in survival between ΔRv3866 and CDC1551 WT when challenged with rifampicin during stationary phase………………………………………………………….………36

Figure 2-16. Mean fitness of TCA cycle genes on Day 6 compared to Day 0. Adapted from http://www.genome.jp/kegg-bin/show_pathway?mtu00020 and (Shan, Lazinski et al. 2015)……….…….39

Figure 3-1. Lassomycin treatment results in modified protein degradation………………..…………….……….52

Figure 3-2. Lassomycin treatment decreased ATP concentration……………………………………………………..54

Figure 3-3. Lassomycin rapidly depleted intracellular ATP………………………………………………………………56

Figure 3-4. Lassomycin rapidly depleted intracellular ATP in single cells………………………………………….57

http://www.genome.jp/kegg-bin/show_pathway?mtu00020

viii

LIST OF TABLES

Table 2-1. Genes from the identified Day 6 clusters………………………………………………..…….…………………22

Table 2-2. Genes from the identified Day 1 clusters……………………………………………………..………………….24

Table 2-3. Genes from the uniquely identified Day 1 clusters……………………………………………………..…….26

Table 2-4. Top 10 negative genes, day 6 / day 0…………………………………………………………………….………..26

Table 2-5. MoCo-dependent genes effect on rifampicin survival, Day 6/0…………………………………………29

Table 2-6. Top 5 unique negative genes, day 1 / day 0……………………………………………………………………..32

Table 2-7. Tn-Seq sequencing primers……………………………………………………………………………………………43

Table 2-8. List of genes cloned………………………………………………………………………………………………………44

Table 2-9. List of primers used in Gateway cloning…………………………………………………………………………44

Table 2-10. Transposon mutant strains used in validation studies……………………………………………………46

Supplementary Table 3-S1. Protein families decreased or increased in abundance in lassomycin-

treated samples………………………………………………………………………………………………………………….………….64

1

Chapter 1: Introduction

2

1.1 Mycobacterium tuberculosis is a continuing global health threat

Mycobacterium tuberculosis (MTb) kills more people each year than any other bacterial pathogen,

despite the availability of drugs that rapidly kill the bacterium in vitro (WHO 2010). MTb’s lethality is

partly due to its virulence: inhalation of fewer than 10 bacilli can cause infection (Nicas, Nazaroff et al.

2005). Approximately 10% of those infected with MTb will develop active tuberculosis during their

lifetime (Cole, Eisenach et al. 2005). The risk of developing active disease is increased by risk factors such

as low body weight, smoking, and depressed immune system, such as in cancer or HIV infection (Lawn

and Zumla 2011), with HIV-positive patients’ risk rising to 10% per year (Mainous III and Pomeroy 2010).

The lethality of MTb also stems from the long treatment duration required to cure tuberculosis (TB). The

current short course of treatment is rifampicin, isoniazid, pyrazinamide, and ethambutol for two months,

followed by rifampicin and isoniazid for an additional four months (WHO 2003). This long course of

treatment is necessary to prevent recurrent infections, but it is often very difficult for patients, especially

those in developing countries, to follow. Deviation from the recommended course of treatment is the

likely cause of the rise in multiple drug-resistant (MDR) and extensively drug-resistant (XDR) MTb

(O'Brien 1994). It is therefore crucial to develop new, shorter methods of treatment that reduce the risk of

bacterial drug resistance.

Previous work in the human pathogen Pseudomonas aeruginosa has shown that the recalcitrance of

chronic infections may be caused by persisters: drug-tolerant cells that lack resistance genes (Mulcahy,

Burns et al. 2010). Unlike genotypic drug resistance, persisters’ drug tolerance is phenotypic and is not

inherited by their progeny (Lewis 2010); when a bacterial culture is treated with antibiotic and the

surviving cells are grown in the absence of drug, the resulting culture exhibits similar sensitivity to the

same antibiotic (Keren, Kaldalu et al. 2004). If survival to an antibiotic is measured over time, resistant

cells will regrow, leading to an increase in colony forming units (CFU), but persister cells will reach a

plateau where no further growth or death is seen (Figure 1-1).

3

Figure 1-1: A representative graph contrasting resistance and persistence. After the addition of antibiotic at time 0, the sensitive cells that make up the bulk of the culture die off. Resistant cells will regrow, but persister cells will not. (Figure courtesy Marin Vulić).

Previous work by our lab has shown that MTb persister cells survive treatment with antibiotics of

different classes, including streptomycin, ciprofloxacin, isoniazid, and rifampin (Keren, Minami et al.

2011). These results are clinically relevant because isoniazid and rifampin form the front line of defense

against MTb, while fluoroquinolones like ciprofloxacin are used against MTb that is resistant to first line

treatment (WHO 2003). These findings emphasize the need for new MTb treatments that target

persistent, non-growing MTb.

After inhalation by the host, MTb is taken up by alveolar macrophages, where it replicates inside the

phagosome, despite the acidic and oxidative environment (Houben, Nguyen et al. 2006). The bacilli

possess a thick, waxy cell wall composed of peptidoglycan and mycolic acids, which protect it from the

toxic environment inside the phagosome (Hett and Rubin 2008). It is able to down-regulate its

metabolism in response to hypoxia, entering a dormant, drug-tolerant state (Wayne and Sohaskey 2001).

4

This persistent state may be responsible for MTb’s recalcitrance to treatment and subsequently, the

development of drug-resistant MTb (Gomez and McKinney 2004).

1.2 Bacterial drug tolerance

All bacterial species tested and reported on form persisters, phenotypically drug-tolerant cells that lack

classical resistance genes (Lewis 2010). The fraction of the population that is insensitive to antibiotics

ranges from less than 1% in exponentially growing Escherichia coli to nearly 100% in stationary phase

Staphylococcus aureus (Dorr, Vulic et al. 2010; Conlon, Nakayasu et al. 2013). Persistence is independent

of antibiotic concentration; survival is initially inversely correlated with drug concentration, but as

concentration increases, survival will reach a plateau where no additional killing can be achieved, even

with higher drug concentrations (Figure 1-2) (Lewis 2008).

Figure 1-2: Persister cells survive high doses of antibiotic. Unlike resistant cells, persisters are

not killed by high doses of antibiotic. Once a critical concentration is reached, no further killing is

achieved. Adapted from (Lewis 2008).

5

The presence of persisters has been known for over seventy years, nearly as long as antibiotics have been

in use. Joseph Bigger first discovered the phenomenon in 1944 when he noticed that some members of an

S. aureus culture survived treatment with penicillin, yet were not resistant (Lewis 2007). Persisters

remained largely unexplored until the 1980s, when Harris Moyed published a series of papers describing

hip (high persister) mutants in E. coli (Moyed and Bertrand 1983; Moyed and Broderick 1986; Scherrer

and Moyed 1988). Progress in the field slowed again, until the study of biofilms revealed that persisters

were responsible for their resistance to treatment (Lewis, Spoering et al. 2005; LaFleur, Kumamoto et al.

2006).

Much of the progress in understanding bacterial persisters has come from work done in E. coli. Several

persister genes have been identified, and a model has been proposed for persister formation in this

species (Figure 1-3) (Maisonneuve and Gerdes 2014). In this model, proteins ppGpp synthetase II SpoT

and ppGpp synthetase I RelA contribute to persister formation by producing (p)ppGpp (guanosine tetra-

and pentaphosphate) under stressful conditions. This molecule inhibits exopolyphosphatase PPX, leading

to an increase in PolyP (organic polyphosphate), which activates Lon. This cellular protease, once

activated, degrades antitoxin, freeing its bound toxin and allowing it to affect processes in the cell. The

mRNAse toxins in particular are believed to contribute to persister formation by degrading mRNA and

inhibiting protein synthesis, causing cell growth to slow down. This model also proposes a positive

feedback loop in which degradation of HipB antitoxin by Lon frees up HipA toxin to phosphorylate

glutamyl-tRNA synthetase GltX, resulting in accumulation of uncharged tRNA. These uncharged tRNAs

then induce RelA-dependent synthesis of (p)ppGpp.

6

Figure 1-3: A proposed model for persister formation in E. coli. Under stress conditions, the

protease Lon degrades antitoxin, leaving its cognate toxin free to affect cell growth. Endonuclease toxins

cleave mRNA, resulting in inhibition of protein translation and a slowdown of cell growth. Adapted from

(Gerdes and Maisonneuve 2012).

This model is based on work done in E. coli, and only with the antibiotics ampicillin and ciprofloxacin. It

does not account for the identification of other, non-Type II toxins as persister genes, such as TisB (Dorr,

Lewis et al. 2009; Dorr, Vulic et al. 2010). Upon treatment with ciprofloxacin or another fluoroquinolone

causing double-stranded DNA breaks, the SOS response is induced (Theodore, Lewis et al. 2013). This

pathway is intended to activate repair machinery to fix the DNA damage done by the antibiotic, but it also

activates transcription of TisB, a type I toxin. After induction by the SOS response, the small TisB protein

inserts itself into the cell membrane, resulting in a loss of proton motive force and a slowdown of growth.

This entrance into dormancy protects the cell from further damage by the antibiotic and allows the cell to

survive. ΔtisB mutants are more sensitive to ciprofloxacin than wild type, which is an unusual finding,

because most toxins have redundant functions, and single deletions do not usually display any persister

phenotype (Maisonneuve, Shakespeare et al. 2011). MTb is not closely related to E. coli, and it is currently

unknown whether MTb persisters form by these or other mechanisms. MTb does possess an active

7

stringent response, which is required for survival during latent infection (Klinkenberg, Lee et al. 2010);

however, its RelA-SpoT homologue Rv2583c has never been associated with drug-tolerance (Primm,

Andersen et al. 2000) and was down-regulated in a transcriptional study of persisters (Keren, Minami et

al. 2011). Although the MTb genome contains as many as 88 putative and at least 30 functional toxin-

antitoxin systems (Ramage, Connolly et al. 2009), it does not appear to contain a tisB homologue;

therefore, its survival to fluoroquinolones may be mediated differently than the response in E. coli.

It should be noted that persistence has a different meaning in the MTb field than in the rest of

microbiology: although persistence in other bacteria refers to survival of antibiotic treatment or other

induced stresses, in MTb it refers to the ability to establish a long-term infection, or persist, in vivo.

Therefore, so-called “persister genes” in MTb may actually refer to genes that are required for survival

during infection, regardless of antibiotic treatment (Zhang, Yew et al. 2012). In this work, “persister

genes” will be used as it is in the broader microbiology field, to identify genes that contribute to antibiotic

tolerance. Only a small number of persister genes have been definitively identified in MTb. The three

mRNA-degrading endonuclease RelE toxin homologues in MTb increase survival to rifampicin,

gentamicin, levofloxacin, and isoniazid when overexpressed, presumably due to their growth-arrest effects

on the cell. Knockout mutants in these genes had decreased survival; little effect was seen in ΔrelE1, but

the other two strains were more highly affected (Singh, Barry et al. 2010). Similarly, MazF3, another

mRNAse toxin, inhibits growth and increases drug tolerance in Msm (Mycobacterium smegmatis) (Han,

Lee et al. 2010). A PPK knockout was found in MTb to decrease persisters to isoniazid, levofloxacin, and

rifampicin, but not gentamicin (Singh, Singh et al. 2013). PPK is thought to contribute to persister

formation in E. coli by producing inorganic polyphosphate, which in turn activates the Lon protease and

induces it to degrade antitoxins (Germain, Roghanian et al. 2015). However, MTb is not known to contain

a Lon homologue, so it is unclear at this time how PPK increases persistence in this organism.

8

1.3 The Need for Novel Antibiotics

Bacterial drug resistance is a global and increasing problem (McKenna 2013). Drug resistance occurs in

both Gram-negative (Pseudomonas aeruginosa, Salmonella enterica, E. coli, Acinetobacter baumannii,

Klebsiella pneumoniae, Neisseria gonorrhoeae (Poole 2004)) and Gram-positive bacteria (S. aureus

(Bozdogan, Esel et al. 2003), Streptococcus pneumoniae (Albrich, Monnet et al. 2004), Enterococcus

faecalis (CDC Retrieved 9/1/2015), Clostridium difficile (Sebaihia, Wren et al. 2006), and MTb (LoBue

2009)). Drug-resistant bacteria cause 2 million infections and 23,000 deaths each year in the United

States (CDC 2013). One part of the strategy to combat drug-resistant pathogens is the development of new

antibiotics, yet the pace of antimicrobial drug discovery has slowed dramatically (Lewis 2012). Recently,

much of the research effort has been focused on broad spectrum antibiotics. These compounds are more

difficult to discover and also perturb the normal gut microbiota (Dethlefsen and Relman 2011), with

consequences ranging from increased obesity (Tilg and Kaser 2011) to C. difficile infection (Slimings and

Riley 2014). Therefore, there is an urgent need for the development of new species-specific antibiotics to

combat drug-resistant infections.

Figure 1-4: Most antibiotic classes were discovered in the 1950s and 1960s. A new antibiotic class, the diarylquinolines that target MTb, have recently been discovered. Bedaquiline was the first FDA-approved diarylquinoline (Mdluli, Kaneko et al. 2015). Adapted from (Lewis 2012).

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

β-lactams

Sulfadrugs

Aminoglycosides

Tetracyclines

Chloramphenicols

Macrolides

Glycopeptides

Oxazolidinones

Ansamycins

Quinolones

Streptogramins

Lipopeptides

Diarylquinolines

9

1.4 Dissertation Aims

This study aims to improve tuberculosis treatment by identifying genes responsible for persister

formation in M. tuberculosis, as we hypothesize these genes would be excellent drug targets. A whole-

genome screen for persister genes using transposon-insertion sequencing (Tn-seq) was conducted to

identify genes that enhanced survival to antibiotic treatment. A library of transposon mutants was

constructed, each mutant containing exactly one transposon insertion in a TA base pair site in its

chromosome. We pooled approximately 100,000 mutants, approximating full coverage of the 4,032 genes

in the MTb genome. This library was subjected to selection by rifampicin treatment, and the input and

output pools were Illumina sequenced. Insertions that were enriched in the output pool were identified as

potential genes that suppressed persister formation, while insertions that were depleted were identified as

potential genes that contributed to persister formation. This experiment produced a list of candidate

persister genes that were further validated using knockout and over-expression strains. Negative fitness

effects were enriched in genes involved in the plasma membrane and cofactor biosynthesis. Ten highly

effected genes were selected for further validation. Of particular interest were moeA1, encoding a

molybdopterin molybdenumtransferase, and vapC30, an mRNAse toxin. MoeA1 is required for effective

colonization and growth within macrophages (Rengarajan, Bloom et al. 2005), while other mRNAse

toxins have been shown in MTb and other species to promote persister formation (Han, Lee et al. 2010;

Singh, Barry et al. 2010; Maisonneuve, Shakespeare et al. 2011).

In addition to the whole genome screen for persister genes, we also worked to determine the mechanism

of action of lassomycin, a novel anti-tubercular compound. Lassomycin was previously found to bind in

vitro to ClpC1, an ATPase that partners with ClpP1P2 and activates its proteolytic activity. These previous

in vitro studies found that lassomycin treatment blocked ClpC1’s activation of ClpP1P2, while

simultaneously enhancing ClpC1’s ATPase activity. We sought to determine whether these effects

occurred in whole cells of MTb, and if so, which effect is responsible for lassomycin’s bactericidal effect.

We conducted proteomic analysis on lassomycin-treated cells and found that lassomycin treatment

10

caused a shift in the MTb proteome, with some proteins increasing and others decreasing in abundance.

Within these results, several protein families were particularly affected: transport, ribosomal, and

ESAT/ESX (early secretory antigenic target/ESAT 6-like) proteins, as well as antitoxins and

transcriptional regulators were all decreased, while toxins, redox, and proteins related to the glyoxylate

shunt and lipid metabolism were increased. The ClgR regulon, which includes ClpP1P2 and ClpC2, was

also increased. We hypothesize that the proteomic shift observed is due to two phenomena: first, that

lassomycin treatment altered proteome specificity, promoting the degradation of ClpXP substrates while

blocking the degradation of ClpCP substrates; and second, that transcriptional responses to lassomycin

treatment caused the changes in abundance seen in some genes. We are currently working to obtain a

transcriptome of lassomycin-treated cells to unravel these two possible causes.

Identifying genes required for antibiotic survival and revealing the bactericidal mechanism of action of

lassomycin will bring new insights to the problem of antibiotic tolerance and resistance. It is necessary to

have a clear understanding of the effects of an antibiotic before we can understand how a bacterium

survives it. Furthermore, this work will identify potential new targets for drug discovery by detecting new

persister genes and pathways. In time, this will lead to better treatments and outcomes for patients with

tuberculosis.

11

Chapter 2: Whole genome screen for persister genes in Mycobacterium tuberculosis

12

2.1 Introduction

Mycobacterium tuberculosis (MTb) kills more people each year than any other bacterial pathogen,

despite the availability of drugs that rapidly kill the bacterium in vitro (WHO 2010). The current short

course of treatment is rifampicin, isoniazid, pyrazinamide, and ethambutol for two months, followed by

rifampicin and isoniazid for an additional four months (WHO 2003). This long course of treatment is

necessary to prevent recurrent infections, but it is often very difficult for patients, especially those in

developing countries, to follow. Deviation from the recommended course of treatment is the likely cause

of the rise in multiple drug-resistant (MDR) and extensively drug-resistant (XDR) MTb (O'Brien 1994).

Although the overall rate of TB infection is dropping, MDR-TB and XDR-TB are becoming more prevalent

(Fitzpatrick and Floyd 2012). India and China both reported over 50,000 cases of MDR-TB in 2013

(Figure 2-1). It is therefore crucial to develop new, shorter methods of treatment that reduce the risk of

bacterial drug resistance.

Figure 2-1. Number of MDR-TB cases estimated to occur among notified pulmonary TB cases, 2013 (WHO 2014).

13

Previous work in the human pathogen Pseudomonas aeruginosa has shown that the recalcitrance of

chronic infections may be caused by persisters: drug-tolerant cells that lack resistance genes (Mulcahy,

Burns et al. 2010). Unlike genotypic drug resistance, persisters’ drug tolerance is phenotypic and is not

inherited by their progeny (Lewis 2010); when a bacterial culture is treated with antibiotic and the

surviving cells are grown in the absence of drug, the resulting culture exhibits similar sensitivity to the

same antibiotic (Keren, Kaldalu et al. 2004). If survival to an antibiotic is measured over time, resistant

cells will regrow, leading to an increase in colony forming units (CFU), but persister cells will reach a

plateau where no further growth or death is observed.

In previous work, our lab has shown that MTb persister cells survive treatment with antibiotics of

different classes, including streptomycin, ciprofloxacin, isoniazid, and rifampin (Keren, Minami et al.

2011). These results are clinically relevant because isoniazid and rifampin form the front line of defense

against MTb, while fluoroquinolones like ciprofloxacin are used against MTb that is resistant to first line

treatment (WHO 2003). The same study also found that ten toxin-antitoxin modules are over-expressed

in persisters, indicating that these operons may be important for persister formation or maintenance.

Toxin-antitoxin (TA) modules such as hipAB (Moyed and Bertrand 1983) and tisB/istR (Dorr, Vulic et al.

2010) have been shown to induce persister formation in Escherichia coli, and over-expression of RelE was

shown in MTb to increase multi-drug tolerance (Singh, Barry et al. 2010). It is likely that other TA

modules have a role in MTb persister formation, but their functions may be redundant. In a 2011 PNAS

paper (Maisonneuve, Shakespeare et al. 2011), Maisonneuve et al. created single and cumulative

knockouts of the 10 mRNAse toxins in E. coli. There was no difference in survival between the wild type

strain and any of the single knockouts, or any of the cumulative knockouts up to Δ4, but starting at Δ5,

each successive gene deleted led to fewer survivors to both ciprofloxacin and ampicillin. MTb contains at

least 65 TA systems (Ramage, Connolly et al. 2009); given the likely redundancy between mechanisms of

persister formation, screening these 65 TA modules would be unwieldy and unlikely to produce

14

meaningful data. We have therefore chosen to perform a whole genome screen for persister genes in MTb,

using the Tn-seq (transposon insertion sequencing) method.

Tn-seq has been used to identify essential genes in such species as MTb (Zhang, Ioerger et al. 2012) and

Streptococcus pneumoniae (van Opijnen, Bodi et al. 2009), and used to identify conditionally essential

genes in Vibrio cholerae (Pritchard, Chao et al. 2014), E. coli (Shan, Lazinski et al. 2015), and MTb

(Zhang, Ioerger et al. 2012). It is based on an earlier hybridization-based method called TraSH

(Transposon Site Hybridization) but has been updated to take advantage of recent advances in next-

generation sequencing. A library of transposon mutants is created in which each mutant contains exactly

one transposon insertion in a random TA base pair location. The library is then subjected to some form of

selection, for example, in vitro growth (for essential genes), or infection, antibiotic treatment, or growth

on alternative medium (for conditionally essential genes). The regions flanking the transposon insertion

are amplified and Illumina sequenced and the change in frequency for each insertion is compared

between the input and the output. Insertions in genes that are required for survival in the given condition

will decrease in the output pool, while insertions in genes that detract from fitness will increase (Figure 2-

2).

15

Figure 2-2. Schematic diagram of the Tn-seq method. A saturated library of transposon insertion mutants is created and plated on solid medium for selection. Genomic DNA is isolated from the input and output pools. The DNA is sheared and ligated with adapters to facilitate Illumina sequencing. The flanking regions are then amplified with primers analogous to the transposon ends and the Illumina adapter. This DNA is then sequenced and the read counts for each insertion are compared between the input and output pools. Adapted from (Zhang, Ioerger et al. 2012).

2.2 Results

In order to identify candidate persister genes in MTb, we constructed a saturated transposon insertion

library in mc26020 and subjected it to a high dose of rifampicin (333X MIC [minimum inhibitory

concentration]). We sampled the culture prior to addition of rifampicin (input pool) and at days 1 and 6

following treatment (output pools) (Figure 2-3).

16

Figure 2-3. Schematic for Tn-seq experiment. Samples were taken at days 0 (before rifampicin treatment), 1, and 6. DNA was extracted and Illumina sequenced. Read counts at each insertion site were compared and used to calculate relative fitness on day 1 and day 6.

After sequencing, we converted the aligned BAM (binary sequence alignment map) files received from The

Broad Institute (sequencing partner) to FASTQ (raw reads and quality scores) files using SAM to FASTQ

version 1.56.1 in order to be compatible with the Tufts Galaxy server requirements, which needed a

FASTQ file for processing. We mapped the raw reads to the H37Rv genome

(http://www.ncbi.nlm.nih.gov/nuccore/448814763) using Bowtie for Illumina version 1.1.2. After the

reads were mapped to the genome, we used custom scripts to aggregate these read counts per gene. We

removed genes with fewer than 7 TA sites in order to minimize false positives due to undersampling

(Zhang, Ioerger et al. 2012) (Figure 2-4). We also removed all genes previously identified as completely

essential by Zhang et. al (Zhang, Ioerger et al. 2012), as an insertion in a gene required for normal in vitro

growth could interfere with resuscitation or regrowth after removal of the antibiotic, but not be related to

tolerance of rifampicin. We then compiled a list of all genes with 2-fold or greater change between the

input and output pools, and these lists were used for clustering analysis and further investigation of top

hits.

http://www.ncbi.nlm.nih.gov/nuccore/448814763

17

Figure 2-4. Analysis of fitness change. A) Day 6: 4036 genetic regions were analyzed. At each step, regions were removed. Completely essentials genes per (Zhang, Ioerger et al. 2012) were removed giving 2926 regions. There were 194 genes with > 2-fold negative change and 144 genes with > 2-fold positive change. There were 32 genes with > 4-fold negative change and 61 genes with > 4-fold positive change. Genes that did not have 2 out of 3 replicates meeting the criteria were removed, as well as genes with standard deviation > mean fitness. B) Day 1: 4036 genetic regions were analyzed. At each step, regions were removed. Completely essentials genes per (Zhang, Ioerger et al. 2012) were removed giving 2974 regions. There were 154 genes with > 2-fold negative change and 384 genes with > 2-fold positive change. There were 15 genes with > 4-fold negative change and 226 genes with > 4-fold positive change. Genes that did not have 2 out of 3 replicates meeting the criteria were removed, as well as genes with standard deviation > mean fitness.

All genetic regions: 4036

> 7 TA sites: 3279

Non-essential genes: 2926

>2-fold: 194 + 337

>4-fold: 32 + 188

All genetic regions: 4036

> 7 TA sites: 3341

Non-essential genes: 2974

>2-fold: 154 + 384

>4-fold: 15 + 226

A B

18

Overall contribution to fitness per genes: Day 6

Figure 2-5. Analysis of single gene contribution to fitness (Day 6 / Day 0). A) Mean fitness versus gene rank. B) Genomic mean-adjusted log2 mean fitness versus gene rank. C) and D) Distribution of individual genes’ contribution to fitness. C) Negative gene contribution follows a normal distribution with a long tail comprising a small number of genes with a larger contribution to fitness. D) Positive gene contribution is randomly distributed.

To evaluate the overall contribution to fitness for each gene, those genes with fewer than 7 sites were

removed from the list (Zhang, Ioerger et al. 2012), the mean fitness was calculated from the three

replicates, and the genes were ranked in ascending order according to their mean fitness (Figure 2-5A). To

evaluate the positive and negative genes on the same scale, we calculated the log2 of the mean fitness and

normalized to the overall mean fitness (mean = 0.81825; Figure 2-5B). We then calculated each gene’s

individual contribution to fitness by dividing the adjusted log2 mean fitness by the sum of all of either the

05

101520253035404550

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Gene contribution

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19

positive or negative fitness values. The distribution of these individual contributions is shown in Figure 2-

5CD. The negative fitness values follow a normal right-skewed distribution, whose tail represents a small

number of genes with large contributions to fitness. The positive fitness values are randomly distributed

and display no obvious trend. We interpret these distributions to mean that the negative fitness genes

(those genes that, when disrupted, have a negative impact on fitness) represent biologically relevant data,

while the positive genes may not. Therefore, in the remainder of the investigation, we will focus our efforts

on the negative genes.

Overall contribution to fitness per genes: Day 1

Figure 2-6. Analysis of single gene contribution to fitness (Day 1 / Day 0). A) Mean fitness versus gene rank. B) Genomic mean-adjusted log2 mean fitness versus gene rank. C) and D) Distribution of individual genes’ contribution to fitness. C) Negative gene contribution follows a normal distribution with a long tail comprising a small number of genes with a larger contribution to fitness. D) Positive gene contribution is randomly distributed.

0

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ess

Gene rank

-6

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just

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fit

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01020304050607080

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20

Fitness, gene rank, and individual genes’ fitness contributions were calculated similarly to the Day 6

dataset (Figure 2-6). As in the Day 6 data, the negative fitness values follow a normal right-skewed

distribution, whose tail represents a small number of genes with large contributions to fitness. The

positive fitness values are randomly distributed and display no obvious trend. Similarly to the Day 6 data,

we interpret these distributions to mean that the negative fitness genes (those genes that, when disrupted,

have a negative impact on fitness) represent biologically relevant data, while the positive genes may not.

As with the Day 6 dataset, we will focus our efforts on the negative genes.

Clustering analysis

In order to determine whether any pathways or functions were specifically associated with rifampicin

tolerance, we performed clustering analysis using DAVID (The Database for Annotation, Visualization

and Integrated Discovery; https://david.ncifcrf.gov/) (Huang, Sherman et al. 2007). This tool uses

functional annotation of genes to identify functional clusters that are over-represented in a target list. We

used the >2-fold hits from day 1 and day 6 to perform this analysis.

Figure 2-7. Functional clusters enriched in Day 6 sample.

0.0001

0.0010

0.0100

0.1000

1.0000

0

0.5

1

1.5

2

2.5

3

3.5

p-v

alu

e

Fold

En

rich

men

t

https://david.ncifcrf.gov/

21

Genes involved in the plasma membrane and cofactor metabolic processes were overrepresented in the

Day 6 hits by approximately 2-and 3-fold respectively (p-values < 0.0005 and 0.005 respectively; Table 2-

1 and Figure 2-7; all clusters False Discovery Rate [FDR] < 5%). Rifampicin has previously been shown to

act synergistically with the cell membrane-acting antibiotic daptomycin (Rand and Houck 2004).

Daptomycin causes membrane depolarization by binding to the cellular membrane in a Ca2+-dependent

manner (Tally and DeBruin 2000). It is possible that one or more of the plasma membrane genes

identified here, for example potassium-transporting ATPase subunit A (KdpA), cause a similar effect

when rendered inactive by a transposon insertion.

The cofactor metabolic process cluster encompasses several distinct biosynthetic pathways. Two of the

genes identified are involved in folate biosynthesis: molybdopterin molybdenumtransferase 1 (MoeA1)

and dihydroneopterin aldolase. Although the antibiotic para-aminosalicylic acid (PAS) acts through the

folate biosynthesis pathway (Rengarajan, Sassetti et al. 2004), it is unclear how this pathway would

interact with rifampicin. Also part of this cluster are two genes involved in NAD+ biosynthesis, L-aspartate

oxidase (NadB) and glutamine-dependent NAD(+) synthetase (NadE). Rifampicin decreases total NAD+

(Boshoff, Xu et al. 2008), so it is possible that an insertion in either of these genes would have an additive

effect, reducing NAD+ levels in persisters to fatal levels. Finally, isocitrate dehydrogenase (Icd2), citrate

synthase 1 (GltA2), and potentially dephospho-CoA kinase (CoaE) are all part of the TCA (tricarboxylic

acid) cycle. Baek and colleagues previously reported that mutants in the triacylglycerol (TAG) pathway,

which funnels carbon away from the TCA cycle, are unable to switch to a dormant state under unfavorable

conditions, and that production of TAG promotes survival during antibiotic treatment, although they did

not report testing rifampicin (Baek, Li et al. 2011). However, this finding is in conflict with the current

study. Under the Baek model, shifting of metabolic resources towards triacylglycerol (TAG) production

resulted in greater tolerance. If this consequence held true for rifampicin treatment, we would expect to

see a similar effect with the TCA insertion mutants; however, the opposite was observed. A possible

explanation is that a single insertion is not sufficient to disrupt the TCA cycle in MTb, due to the presence

22

of enzymes with redundant functions, and that these mutants’ increased susceptibility to rifampicin acts

through another mechanism. In fact, the MTb genome does appear to have two citrate synthases

(Rv0889c citA citrate synthase 2 and Rv0896 gltA2 citrate synthase 1), as well as two isocitrate

dehydrogenases (Rv0066c isocitrate dehydrogenase and Rv3339c isocitrate dehydrogenase).

Table 2-1. Genes from the identified Day 6 clusters.

Plasma Membrane Cofactor Metabolic Process

two component sensor histidine kinase PrrB isocitrate dehydrogenase Icd2

transmembrane transport protein MmpL11 dephospho-CoA kinase CoaE

protein translocase subunit SecF L-aspartate oxidase NadB

potassium-transporting ATPase subunit A KdpA hypothetical protein

drug ABC transporter ATP-binding protein dihydroneopterin aldolase FolB

arabinosyltransferase A molybdopterin molybdenumtransferase 1 MoeA1

membrane protein MmpS1 citrate synthase 1 GltA2

transmembrane transport protein MmpL4 precorrin-6Y C(5,15)-methyltransferase CobL

oligopeptide ABC transporter permease OppB glutamine-dependent NAD(+) synthetase NadE

CDP-diacylglycerol--serine O-

phosphatidyltransferase PssA

phosphoserine/threonine phosphatase PstP

23

Figure 2-8. Functional clusters enriched in Day 1 sample.

Genes involved in peptide transport, nucleotide binding, enoyl-CoA hydratase activity, s-adenosyl-l-

methionine, cofactor metabolic process, amino acid metabolism, and oxidoreductases were over-

represented in the Day 1 sample (Figure 2-8 and Table 2-2; all clusters FDR < 5%). Many of the clusters

had relatively small enrichment factors, but the peptide transport cluster was enriched nearly 52-fold.

This cluster comprises oligopeptide ABC transporter permeases OppC and OppB. These genes modify the

cell surface by regulating the expression of many genes (Flores-Valdez, Morris et al. 2009).

0.00001

0.00010

0.00100

0.01000

0.10000

1.00000

0

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20

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Table 2-2. Genes from the identified Day 1 clusters.

Peptide

Transport

Nucleotide

Binding

Enoyl-CoA

Hydratase

Methyl-

transferases

Cofactor

Metabolic

Process

Amino Acid

Metabolism

GMC-

type

Oxido-

reductas

e

oligopeptide

ABC

transporter

permease

OppC

alanine/leucine

/valine-rich

protein

3-hydroxyl-

thioester

dehydratase

HtdZ

S-

adenosylmethionin

e-dependent

methyltransferase

8-amino-7-

oxononanoate

synthase

L-aspartate

oxidase

GMC-type

oxido-

reductase

oligopeptide

ABC

transporter

permease

OppB

two component

sensor histidine

kinase PrrB

enoyl-CoA

hydratase

EchA16

rRNA small

subunit

methyltransferase

H

dephospho-CoA

kinase CoaE

arginine-

succinate lyase

dephospho-CoA

kinase CoaE

enoyl-CoA

hydratase

EchA20

cyclopropane

mycolic acid

synthase

L-aspartate

oxidase

succinate-

semialdehyde

dehydrogenase

membrane

protein

hypothetical

protein

isocitrate lyase glutamate

synthase small

subunit

proteasome

accessory factor

PafA

hypothetical

protein

cyclic

pyranopterin

monophosphate

synthase

accessory protein

glutamine

synthetase

membrane

protein

precorrin-6Y

C(5,15)-

methyltransferase

dihydroneopterin

aldolase

50S ribosomal

protein L34

molybdopterin

molybdenum-

transferase 1

exonuclease V

subunit alpha

RecD

citrate synthase 1

hypothetical

protein

hypothetical

protein

ABC

transporter

ATP-binding

protein/permea

se

precorrin-6Y

C(5,15)-

methyltransferase

drug ABC

transporter

ATP-binding

protein

glutamine-

dependent

NAD(+)

synthetase

urease

accessory

protein UreG

hypothetical

protein

glutamine

synthetase

alkyl

hydroperoxide

reductase AphD

25

We also performed functional annotation clustering on the list of hits that was specific to Day 1. Because

the size of the hits list used for clustering was relatively small (36 genes), the list of genes in each cluster is

also small (five and two). We found that genes with a lyase function as well as those involved in glutamine

family amino acid biosynthesis were significantly enriched in our data by 8- and 26-fold, respectively

(Figure 2-9 and Table 2-3; p-value 5.20E-05 and 0.0047 respectively; all clusters <5% FDR).

Figure 2-9. Functional clusters enriched in Day 1 unique genes.

The genes in the lyase cluster are all involved in amino acid metabolism. 3-dehydroquinate synthase AroB

catalyzes a step in the shikimate pathway, a preliminary step to the synthesis of the amino acids

tryptophan, tyrosine, and phenylalanine. Argininosuccinate lyase ArgH is also involved in amino acid

synthesis; it converts N-(L-arginino)succinate to L-arginine. Enoyl-CoA hydratases EchA16 and Ech20

have many functions; they participate in fatty acid degradation, caprolactam degradation, and amino acid

degradation. Finally, O-acetylhomoserine sulfhydrylase MetC catalyzes the breakdown of O-acetyl-L-

0.00001

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30

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homoserine to L-homocysteine. Interestingly, the second unique Day 1 cluster also comprises amino acid

metabolism genes; in addition to ArgH, also a member of the lyase cluster, the glutamine family amino

acid biosynthesis cluster also includes glutamine synthase GlnA2, which converts ammonia to glutamine.

Therefore, it appears that amino acid metabolism contributes to rifampicin’s delayed bactericidal effect.

These experiments were conducted on 7H9/10 supplemented with casamino acids, which contains all of

the essential amino acids plus additional L-glutamic acid and glucose as the carbon source. Any mutant

with an insertion in an amino acid biosynthetic gene should not suffer from nutritional deficiency on this

medium; therefore these genes’ contribution to persister formation may act through another mechanism.

Table 2-3. Genes from the uniquely identified Day 1 clusters.

Lyase Glutamine Family Amino Acid Biosynthesis

3-dehydroquinate synthase AroB argininosuccinate lyase ArgH

argininosuccinate lyase ArgH glutamine synthetase GlnA2

enoyl-CoA hydratase EchA16

enoyl-CoA hydratase EchA20

O-acetylhomoserine sulfhydrylase metC

Top hits, Day 6

Table 2-4. Top 10 negative genes, day 6 / day 0.

Locus ProteinID Mean Fitness

Rv0019c FHA domain-containing protein FhaB 0.081546

Rv0018c phosphoserine/threonine phosphatase PstP 0.091558

Rv1480 hypothetical protein 0.111138

Rv3549c short-chain type dehydrogenase/reductase 0.11563

Rv3165c hypothetical protein 0.127967

Rv0994 molybdopterin molybdenumtransferase 1 0.1407

Rv1595 L-aspartate oxidase 0.157227

Rv2553c membrane protein 0.163545

Rv2691 TRK system potassium uptake protein CeoB 0.167779


27

Rv0019c FhaB and Rv0018c PstP

FhaB (forkhead-associated domain-containing protein) and PstP (phosphoserine/threonine protein

phosphatase) were the two biggest hits in the Day 6 screen, with a 0.08 and 0.09 mean fitness score,

respectively (Table 2-4). These genes are co-operonic and are co-transcribed (Fernandez, Saint-Joanis et

al. 2006). They are the second and third genes in a highly conserved 6-7 gene operon located close to the

origin of replication (Fernandez, Saint-Joanis et al. 2006). Multiple hits in an operon observed in a Tn-

seq screen are generally considered to be indicative of a robust screen, as an insertion in an upstream

gene may inactivate a downstream gene as well (van Opijnen, Bodi et al. 2009). However, this

phenomenon also obscures whether FhaB, PstP, or a gene product further down the operon are involved

in persister formation.

FhaB, also known as FipA (FtsZ-interacting protein), is a key member of the cell division pathway, where

it interacts with the Z-ring, providing stabilization and mediating interaction between FtsZ and FtsQ (cell

division proteins) (Kieser and Rubin 2014). Knockouts in this gene are more susceptible to oxidative

stress and have a longer shape due to inability to correctly form septa (Sureka, Hossain et al. 2010). The

FHA (forkhead-associated) domain binds to phosphorylated threonine on other proteins and facilitates

protein-protein interactions (Yaffe and Elia 2001). It is predicted to contain a domain essential for in

vitro growth (Zhang, Ioerger et al. 2012).

PstP dephosphorylates phospho-Ser/Thr substrates, including serine/threonine-protein kinase PknB, a

member of the same operon (Boitel, Ortiz-Lombardia et al. 2003). PstP is a transmembrane protein with

a C-terminal extracellular domain (Boitel, Ortiz-Lombardia et al. 2003), and is dependent on Mn2+ for its

phosphorylation activity (Pullen, Ng et al. 2004). This group of genes acts as signaling molecules to

regulate growth and division (Dasgupta, Datta et al. 2006).

28

Rv1480

This protein when BLASTed only matches within Mycobacterium, mostly slow-growing mycobacteria,

with the exception of M. abscessus. It has 3 EGF-like domains. These domains are found mainly in

secreted or in the extracellular portion of membrane-bound proteins, and are mostly found in animal

proteins (Bork, Downing et al. 1996). It also contains a Von Willebrand factor type C (VWFC) domain,

another type of domain usually found in eukaryotic proteins which assists in the formation of multi-

protein complexes (Bork 1991). Finally, the protein also features four ferredoxin-type iron-sulfur binding

regions. These regions participate in electron transfer during metabolic reactions (Bruschi and

Guerlesquin 1988). Rv1480 was found to be down-regulated after treatment with sulfamethoxazole, a

sulfonamide antibiotic (Macingwana 2014).

Rv3549c

Rv3549c is a short-chain type dehydrogenase/reductase found primarily within Mycobacteria, although

BLAST reveals homologues in Rhodococcus, Nocardia, Amycolatopsis, and Pseudomonas, among others.

Its transcription is controlled by two TetR-type regulators, KstR and KstR2 (Kendall, Burgess et al. 2010),

and it is involved in cholesterol metabolism, an essential process for in vivo survival (Kendall, Withers et

al. 2007).

Rv3165

Rv3165c is a hypothetical protein with no homologues outside Mycobacteria. Little data have been

published regarding this protein, however, it is one of the most abundant membrane proteins in MTb

(Poetsch and Wolters 2008). It has been suggested that this protein mediates apoptosis in MTb-infected

macrophages and that it is a membrane-bound sensor (Miller 2009).

29

Rv0994 MoeA1

MoeA1 (molybdopterin molybdenumtransferase 1) performs the last step in the biosynthesis of

molybdenum cofactor (MoCo) (Williams, Kana et al. 2011). It is found solely within Actinomycetales.

Molybdenum cofactor is used by molybdoenzymes to catalyze carbon, nitrogen and sulfur metabolism

reactions. A full list of enzymes dependent on MoCo can be found in Table 2-5 (Williams, Mizrahi et al.

2014), many of which are required for survival within macrophages (Rengarajan, Bloom et al. 2005). The

MoCo-dependent enzymes generally did not show a fitness defect when disrupted, despite MoeA1’s large

effect (mean fitness, 0.1407). A possible explanation is that any one MoCo-dependent enzyme is not

essential for survival during rifampicin treatment, but disruption of MoCo synthesis results in many or all

of the enzymes becoming nonfunctional, and causes an additive effect that lowers the bacterium’s ability

to survive. It is also possible that the disruption in this gene causes buildup of toxic precursors that

impede the bacterium’s ability to survive rifampicin treatment.

Table 2-5. MoCo-dependent genes effect on rifampicin survival, Day 6/0. Adapted from (Williams, Mizrahi et al. 2014).

Locus

designation

Gene

annotation

Function/proposed gene product Mean Fitness,

Day 6/0

Rv1736c narX Nitrate reductase-like protein NarX 0.71568

Rv3151 nuoG NADH dehydrogenase I (chain g) 1.83429

Rv0197 Possible oxidoreductase 0.93888

Rv1161 narG Nitrate reductase (alpha chain) 0.97989

Rv1442 bisC Probable biotin sulfoxide reductase 0.90554

Rv2900c fdhF Possible formate dehydrogenase H 0.86735

Rv0218 Probable conserved transmembrane protein, some

similarity with sulfite oxidases

1.26382

Rv0373c Carbon monoxide dehydrogenase (large chain) 0.95822

Rv1595 NadB

NadB is an L-aspartate oxidase found within the Actinomycetales that is part of the de novo NAD+

biosynthesis pathway (Vilcheze, Weinrick et al. 2010). It is upregulated in response to nitrogen stress,

probably because it is involved in converting aspartate to NAD+ (Williams, Jenkins et al. 2015). The de

30

novo pathway is not essential for in vitro growth because of the existence of a salvage pathway that can

recycle NAD+ through nicotinamide and nicotinate (Vilcheze, Weinrick et al. 2010).

Rv2553c

Rv2553c is a membrane protein found within the Actinomycetales. It is similar to the E. coli YceG protein

a predicted DNA-binding transcriptional regulator. No experimental data has been published about this

protein.

Rv2691 CeoB

CeoB (also referred to as TrkA) is a TRK (TRansport of K [potassium]) system potassium uptake protein

found within the Actinomycetales. It is upregulated in intraphagosomally-grown MTb compared to in

vitro MTb (Mattow, Siejak et al. 2006), and in macrophage infection (Rachman, Strong et al. 2006).

There may be a redundant potassium uptake system in MTb, as Cholo et al found that deletion of ceoB

and ceoC resulted in increased K+ uptake (Cholo, Boshoff et al. 2006). Interestingly, a study of

transposon mutants in Msm found that disruption of TrkA was associated with higher rifampicin

resistance (Castaneda-Garcia, Do et al. 2011); however, the opposite was found in this study: insertions in

Rv2691 were found to result in lower survival during rifampicin treatment. CeoB binds to INH

(isoniazid)-NAD(P) (nicotinamide adenine dinucleotide [phosphate]) with high affinity, although it is

unclear whether CeoB is affected by INH (Argyrou, Jin et al. 2006).

Rv3256c

Rv3256c is a conserved hypothetical protein found primarily with Mycobacterium. It lies between manA

(phosphomannose isomerase) and manB (phosphomannomutase) in the same direction. A study of

mannose donor biosynthesis genes found that Rv3256c is highly expressed early during macrophage

infection, but then expression decreases over time (Keiser, Azad et al. 2011).

31

Top hits, Day 0

Rifampicin kills with unusual bactericidal kinetics; rather than causing an immediate drop in CFU, as do

most bactericidal antibiotics, its killing effect is delayed by 2-3 days (Figure 2-10). We therefore took a

sample from the Tn-seq rifampicin experiment on day 1, hypothesizing that any genes that were required

for survival on Day 1 but not Day 6 would contribute to rifampicin’s delayed killing.

Figure 2-10. Time-dependent killing of MTb by rifampicin. Stationary phase MTb was treated with 10X MIC rifampicin. Samples were plated for CFU at each time point. Experiment was performed four times in triplicate; figure shows data from one representative trial.

We found that out of the 154 genes with a 2-fold or more decrease in fitness on day 1, 36 were unique to

day 1 and 118 were also required on day 6 (Figure 2-11). We therefore investigated the top unique hits

closely.

Figure 2-11. Comparison of 2-fold hits between Days 1 and 6. Genes were split nearly evenly between those that were required on both days (118) and those that were required on only one (112).

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0 1 2 3 4 5 6 7

Log 1

0 C

FU/m

L

Days

32

Table 2-6. Top 5 unique negative genes, day 1 / day 0.

Locus ProteinID Mean Fitness


Rv3481c integral membrane protein 0.106052

Rv3550 enoyl-CoA hydratase EchA20 0.119563

Rv0080 hypothetical protein 0.21798

Rv2538c 3-dehydroquinate synthase AroB 0.245275

Rv3046c

Rv3046c encodes a small protein of 124 amino acids and is found primarily within Mycobacterium. No

experimental data has been published for this gene.

Rv3481c

Rv3481c is an integral membrane protein sharing sequence homology with the Msm Gap protein, which

transports glycopeptidolipid to the cell surface (Seeliger, Holsclaw et al. 2012). It is found solely within

Mycobacterium. It is highly expressed after exposure to whole lung surfactant, indicating it may play an

important role during infection (Schwab, Rohde et al. 2009).

Rv3550 EchA20

Locus Rv3550 encodes enoyl-CoA hydratase EchA20. This enzyme breaks down fatty acids and produces

acetyl-CoA and NADH (Nelson and Cox 2005). It is induced in response to capreomycin and PA-824

relative to other drugs (Fu and Tai 2009). MTB possesses a large number of genes involved in lipid

synthesis and degradation, and is thought to primarily utilize fatty acids for energy in vivo (Cole, Brosch

et al. 1998).

Rv0080

Rv0080 encodes a hypothetical protein. Based on BLAST homology search, it appears to be a

Mycobacterium-specific gene. It is induced during hypoxia by DosR, a dormancy regulator (Park, Guinn

33

et al. 2003; Bacon, James et al. 2004). It was also found to be upregulated during THP-1 macrophage

infection (Fontan, Aris et al. 2008), however, its function is still unknown.

Rv2538c AroB

Rv2538c AroB encodes a 3-dehydroquinate synthase. This enzyme uses NAD+ to catalyze the first step in

the shikimate pathway, which produces aromatic amino acids (Herrmann and Weaver 1999). The

shikimate pathway has been proposed as a candidate drug target, because it is present in bacteria, algae,

and plants, but not in mammals (Ducati, Basso et al. 2007).

Top hit validation

We sought to validate the hits identified in our whole genome screen of transposon mutants by comparing

their survival to that of WT in controlled experiments. We began by constructing over-expression strains

of the top hits and testing them for growth defects.

Figure 2-12. Growth curves of Msm mc2155 harboring pEXPR plasmid with or without 100 ng/mL anhydrous tetracycline (ATc) inducer.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 6 12 18 24

OD

60

0

Hours

*Rv0019 FhaB uninduced*Rv1480 uninduced*Rv1915 AceAa uninduced*Rv0019 FhaB induced*Rv1480 induced

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 6 12 18 24

OD

60

0

Hours

*Rv3550 EchA20 uninduced

*Rv3865 EspF uninduced

*Rv3866 EspG uninduced

*Rv3550 EchA20 induced

34

Cultures were started from freezer stocks of over-expression strains and grown for 36-40 hours to

stationary phase, then diluted 1:100 into Msm 7H9 medium with or without ATc. Most strains did not

display a growth defect when the protein of interest was over-expressed (Figure 2-12), and the majority of

cultures reached a maximum OD of 0.6 at approximately 18-24 hours. Any differences in antibiotic

tolerance should therefore not be due to differences in growth rate.

Figure 2-13. Normalized fluorescence of 3 Msm mc2155 pEXPR::GFP strains and Msm mc2155 WT.

We confirmed that the pEXPR plasmid inducibly expresses protein by cloning in GFP (green fluorescent

protein) and measuring the fluorescence upon addition of ATc (Figure 2-13). All 3 GFP strains increased

in fluorescence over time only when inducer was added. The WT strain was not affected by ATc.

In order to validate the top day 6 or day 1 hits, we conducted growth-kill curves with several transposon

mutants. A growth-kill curve will reveal whether a mutant has a growth defect as compared to the wild

0

5000

10000

15000

20000

25000

30000

35000

40000

0 12 24 36 48 60

RFU

/OD

Time (hrs)

GFP11-1 no ATC

GFP11-2 no ATC

GFP11-3 no ATC

WT no ATC

GFP11-1 ATC

GFP11-2 ATC

GFP11-3 ATC

WT ATC

35

type as well as any differences in the rate of persister formation. Stationary cultures of CDC1551 WT,

ΔRv0994, and ΔRv1915 were diluted 1:100 into fresh medium. At each time point, samples were taken

and split in two: one sample was plated for CFU while the other was treated with rifampicin for 1 week,

then washed and plated. There were no observed significant differences in the growth or survival of the

WT and transposon mutants tested (Figure 2-14). It is possible that this result is due to differences

between this experiment and the initial Tn-seq screen: the initial screen was conducted in late (14 day)

stationary phase, while the growth-kill curve mainly measures survival during exponential and early

stationary phase (0-7 days).

Figure 2-14. There were no observed significant differences in the growth or survival of the WT and transposon mutants tested.

We attempted to determine if any of the mutants differed from WT when challenged with rifampicin

during stationary phase; however, one of the mutants (ΔRv1915) appeared to be contaminated (resulting

in the early termination of the previous experiment). This contamination appeared to be in the freezer

stocks as it repeated in subsequent experiments. ΔRv0018 never grew up from the shipped colonies; this

gene contains an essential domain (Zhang, Ioerger et al. 2012), so it is possible that this particular mutant

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

1.00E+09

0 1 2 3 4 5 6 7

CFU

/mL

Days

CDC1551 growth

Rv0994 growth

Rv1915 growth

CDC1551 kill

Rv0994 kill

Rv1915 kill

36

contains an insertion in or upstream of the essential domain. ΔRv0994 grew initially after receipt but

subsequently grew poorly and would not grow to a high enough density to conduct the rifampicin

challenge. Therefore, we only tested ΔRv3866. There was no significant difference observed in survival

between ΔRv3866 and CDC1551 WT when challenged with rifampicin during stationary phase (Figure 2-

15).

Figure 2-15: No significant difference was observed in survival between ΔRv3866 and CDC1551 WT when challenged with rifampicin during stationary phase.

2.3 Discussion

In this study, we applied the Tn-seq method of whole genome screening to find genes involved in

rifampicin tolerance in MTb. We found 194 genes with at least a 2-fold negative effect on fitness on Day 6,

and 154 genes with at least a 2-fold negative effect on fitness on Day 1. Of those, 36 were unique to Day 1.

Our finding that 194 genes, or 4.8% of the genome, have an effect on fitness during long term rifampicin

treatment indicates that persisters arise in diverse and redundant ways, consistent with previous findings

(Maisonneuve, Shakespeare et al. 2011; Shan, Lazinski et al. 2015). Genes associated with the plasma

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

1.00E+09

1.00E+10

1.00E+11

Day 0 Day 7

CFU

/mL WT

Δ Rv3866

37

membrane, cell division, and cofactor biosynthesis were all found to be significantly associated with lower

tolerance to rifampicin when disrupted.

Although validation of individual genes is still ongoing, we did find genes in the plasma membrane to be

over-represented in the genes with at least a 2-fold negative effect on fitness on Day 6; it is therefore

possible that many of these genes will have a real effect on persister formation. Rifampicin is hydrophobic

and may be able to diffuse through the lipid-rich mycobacterial cell wall (Lambert 2002). Therefore, it is

possible that some of these mutants have changes in the plasma membrane that allow additional

rifampicin into the cell. MICs should be measured for all candidate genes to determine whether they are

truly persister genes (no change in MIC) or whether they are resistance genes (change in MIC). If these

mutants do have changes in their plasma membrane that allow easier access to rifampicin, I would expect

to see lower MICs.

Genes involved in cofactor biosynthesis were also found to be enriched in the list of candidate genes. This

functional category included several specific pathways, such as folate biosynthesis, NAD+ biosynthesis,

and the TCA cycle. Trimethoprim and sulfamethoxazole, both inhibitors of the folate biosynthesis

pathway, synergize with rifampicin, increasing the bactericidal effect beyond either drug alone, and

preventing the rise of rifampicin resistance (Vilcheze and Jacobs 2012). It is possible that a disruption in a

folate biosynthesis gene would produce a similar synergistic effect. Further work should be completed to

determine whether these mutants have modified rifampicin MICs. We also found that genes in the NAD+

de novo biosynthesis pathway had a negative fitness effect when disrupted. Because rifampicin treatment

reduces NAD+ concentration, interruption of this pathway may lead to an unsurvivable drop in NAD+

level. If these genes are confirmed as having a fitness effect during 1:1 competition experiments, they

should be tested for their effect on NAD+ levels.

38

Based on the clustering result indicating that genes involved in the TCA cycle were overrepresented in the

list of hits, an analysis of all of the genes in the TCA cycle was conducted, similar to (Shan, Lazinski et al.

2015). Several of the TCA cycle genes are essential (Zhang, Ioerger et al. 2012), complicating the analysis;

however, we found that the enzymes from citrate synthase to isocitrate dehydrogenase tended to have a

negative impact on fitness when disrupted, while the enzymes from 2-oxoglutarate oxidoreductase to

malate dehydrogenase had a positive impact on fitness (Figure 2-16). Interestingly, both of the enzymes

that produce NADH, citrate synthase and isocitrate dehydrogenase, had greater than 4-fold decreases in

fitness (0.193 and 0.247, respectively). MTb uses a ferredoxin-dependent rather than NAD+-dependent α-

ketoglutarate dehydrogenase; therefore its conversion of α-ketoglutarate to succinyl-CoA does not

produce NADH (Baughn, Garforth et al. 2009). Three other enzymes in the TCA cycle, KorAB ferredoxin-

dependent α-ketoglutarate dehydrogenase, Rv0247c succinate dehydrogenase iron-sulfur subunit, and

Mdh malate dehydrogenase, had a positive effect on fitness when disrupted. Finally, insertions in the

glyoxylate bypass enzymes Icl1 isocitrate lyase and AceAa isocitrate lyase caused a fitness defect. All of

these hits should be validated with individual mutant 1:1 competition assays, as these findings may point

to a role for the TCA cycle in survival during rifampicin treatment.

39

Figure 2-16. Mean fitness of TCA cycle genes on Day 6 compared to Day 0. Adapted from http://www.genome.jp/kegg-bin/show_pathway?mtu00020 and (Shan, Lazinski et al. 2015).

Rifampicin has a delayed bactericidal effect (Figure 2-10). In order to better understand the cause of

rifampicin’s killing dynamics, we also examined genes with impaired fitness on Day 1. We reasoned that

any genes that were uniquely required for Day 1 but not Day 6 would be involved in this delayed effect. We

found 36 unique Day 1 genes, on which we performed functional annotation clustering analysis. Genes

involved in amino acid metabolism were enriched in our list of hits, indicating that this function

contributes to rifampicin’s delayed killing effect. It is unclear at this time how amino acid metabolism

would interact with rifampicin-mediated killing. Rifampicin’s target is RpoB, an RNA polymerase subunit.

Rifampicin binds to RpoB and prevents elongation of mRNA, thus inhibiting transcription. Translation

still occurs during rifampicin exposure; perhaps the requirement for these amino acid metabolism genes

http://www.genome.jp/kegg-bin/show_pathway?mtu00020

40

is due to a general housekeeping requirement for translation to remain active. These genes should be

validated in 1:1 competition assays and investigated further.

The current study does contain some drawbacks. Any essential genes that contribute to persister

formation would not be found by this method, both because we eliminate previously identified essential

genes from consideration, and because essential genes would not be able to grow in culture and would

therefore not be part of the input pool. We included genes with an essential domain; however, these genes

may also prove difficult to study, as a clean knockout of the gene may be difficult or impossible to grow, as

was the case with the ΔRv0018 transposon mutant. This study is not complete; the most promising

candidate genes for both Day 1 and Day 6 should be tested in 1:1 competition experiments, which will

require creating clean deletions for each gene. We also did not detect any significant negative effect from

disrupting the only described MTb persister genes, the RelE toxins. RelE1 and RelE2 had Day 6 fitness

ratios of 0.745 and 3.993, respectively, while the RelE3 toxin was eliminated from consideration due to

too few insertions. Rv3358 is a small gene consisting of only 238 amino acids, and it contains only 4 TA

sites, therefore it appears that its relatively low number of insertions is due to lack of insertion sites rather

than possible essentiality. Singh et al. previously reported that over-expression of RelE2 resulted in

increased survival during rifampicin treatment (Singh, Barry et al. 2010), however this study found the

opposite, that disruption of RelE2 caused increased persistence. These inconsistencies should be

investigated further. Two other toxins, VapC30 and VapC37, were found to have large fitness defects when

disrupted. Given toxins’ established role in persister formation in E. coli, these genes would also be

excellent candidates for further study.

2.4 Materials and Methods

Bacterial strains and culture conditions

Mycobacterium tuberculosis mc26020 was used for some experiments in this study. This auxotrophic

strain carries deletions in the lysA and panCD genes, rendering it non-pathogenic and suitable for study

41

in a BSL2+ laboratory (Sambandamurthy, Derrick et al. 2005). All chemicals were obtained from Sigma-

Aldrich except where indicated otherwise. Cultures were grown in Middlebrook 7H9 broth or on 7H10

agar (Difco) supplemented with 10% oleic acid-albumin-dextrose-catalase (OADC; Difco), 0.5% glycerol,

0.2% casamino acids (Difco), 0.05% tyloxapol (broth only), pantothenic acid (24 µg/ml), and lysine (80

µg/ml). Freezer stocks were diluted 1:100 in to 7H9 medium and grown in a shaking incubator at 37°C at

100 rpm. Antibiotics stocks were prepared and diluted as recommended

Transposon mutant library construction

A library of transposon mutants was constructed using mc26020 as the parent strain, following the

methods described in (Murry, Sassetti et al. 2008). Briefly, Mycobacterium tuberculosis mc26020 was

grown in a roller bottle containing 100 mL 7H9 medium supplemented as above until OD reached 0.8-1.0.

50 mL culture was spun down and washed with medium supplemented as above, without tyloxapol. Cell

pellet was resuspended in 5 mL wash medium and an aliquot removed as a control. Approximately 1011

phage was added to the resuspended cells and incubated for 4 hours at 37°C. The transduced cells were

frozen at -80°C. A frozen aliquot was thawed and plated in serial dilutions on 7H10 with 20 µg/mL

kanamycin. Transductants were plated on 15-cm 7H10 plates containing 20 µg/mL kanamycin.

Chromosomal DNA extraction

Colonies were harvested from 10 15-cm plates by scraping into 7H9 medium. The suspension was

centrifuged at 3300Xg for 10 minutes at room temperature. The supernatant was discarded and the pellet

resuspended in 5 mL 10 mM Tris‐HCl, 1mM EDTA at pH 9. The resuspended cells were mixed with an

equal volume of chloroform: methanol (2:1) and rocked for 5 minutes. The suspension was then

centrifuged at 3300xg for 10 minutes at room temperature. Both the aqueous and the organic phases were

collected into a 50 mL conical tube, and the solid bacterial mass was dried by leaving open in the biosafety

cabinet for 2 hours. 10mL TE containing 0.1M Tris-HCl at pH 9 was added to the pellet and vortexed to

resuspend. 0.01 volume of 10 mg/mL lysosome was added and the solution was incubated overnight at

42

37°C. After incubation, 1 mL 10% SDS was added, then proteinase K at a final concentration of 100

µg/mL. The samples were vortexed, and then incubated at 50°C for 3 hours. The viscous solution was

transferred into a clean tube containing an equal volume of phenol: chloroform 1:1, mixed well, and let to

stand for 30 minutes. The tube was then rocked at room temperature for 30 minutes and then centrifuged

at 12,000xg for 15 minutes. The upper aqueous phase was removed to a new tube with an equal volume of

chloroform and the centrifugation was repeated. The upper aqueous phase was removed to a new tube

with an equal volume of isopropanol and 0.1 volume of 3 M sodium acetate (pH 5.2). The DNA was

spooled out, washed with 70% ethanol, and dissolved in 500 µL TE.

DNA shearing and adapter ligation

5 µg of DNA was suspended in TE and placed in a Covaris microtube. DNA was sheared using the

following parameters on a Covaris acoustic sonicator: duty cycle 5%, intensity 3, cycles/burst 200, time

90 seconds.

The following components from the End Repair Kit (Epicentre) were mixed together: 34 µL DNA sample,

5 µL 10X End-Repair Buffer, 5 µL 10 mM dNTP mix, 5 µL ATP, 1 µL End Repair Enzyme Mix.

Components were mixed thoroughly, spun down, and incubated for 45 minutes at room temperature.

DNA was purified using a Reaction Clean-up Kit (Qiagen) and eluted in 35 µL DNase and RNase free

water.

The following components were mixed together: 32 µL end-repaired DNA, 5 µL 10X Taq buffer, 10 µL

dATP (100 mM), 3 µL Taq polymerase. Reaction was incubated at 72°C for 30 minutes. DNA was purified

using a Reaction Clean-up Kit (Qiagen) and eluted in 30 µL DNase and RNase free water, then vacuum

concentrated to 10 µL.

43

The following components were mixed together: 10 µL end-repaired, A-tailed DNA; 10X molar excess

adapters; 1.5 µL 10X ligase buffer; 1 µL T4 ligase; water to make up 15 µL. Reaction was incubated at room

temperature for 1 hour. Reaction mixture was spiked with 8 µL water, 1 µL T4 ligase, and 1 µL 10X ligase

buffer, then incubated at room temperature an additional 2 hours. Resulting DNA was run on a gel and

the 400-600 bp fragment was gel extracted using a DNA Gel Extraction Kit (Qiagen).

Amplification of transposon junctions

Transposon junctions were amplified by PCR using a primer that anneals to the transposon and a primer

that anneals to the adapter. The resulting PCR reaction was run on a 2% agarose gel and the 200-400 bp

smear was cut out and gel purified using a DNA Gel Extraction Kit (Qiagen).

Table 2-7. Tn-Seq sequencing primers.

Sequence Description

AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACAC

GACGCTCTTCCGATCCGGGGACTTATCAGCCAACC

Transposon out.

Attachment homology for Illumina chip;

Illumina Read 1 primer; transposon

homology

CAAGCAGAAGACGGCATACGAGATCGTGATGTGACTGGAGT

TCAGACGTGTGCTCTTCCGATCTATGATGGCCGGTGGATTT

GTG

Adapter primer.

Attachment homology for Illumina chip;

Index 1; Illumina Read2 primer; adapter

homology)

Rifampicin challenge

Three bottles containing 50 mL 7H9 were inoculated with 106 transposon mutants and were grown for 15

days. Three bottles containing 200 mL 7H9 media were then inoculated with 1 mL of the saturated 15-day

old cultures and grown for 2 weeks. The ODs reached 1.16 (A), 1.23 (B), and 1.23 (C). 1 mL aliquots from

each 2-week sample were diluted into 49 mL 7H9 and grown for 1 week. Genomic DNA was then

extracted from each sample as detailed above. The remaining 199 mL culture was challenged with

10ug/ml rifampicin. After 24 hours of challenge, another set of 1 mL aliquots were taken and diluted into

49 mL 7H9, grown for 1 week, and extracted for DNA. This process was repeated after 6 days of challenge.

44

Construction of over-expression strains

Table 2-8. List of genes cloned.

Locus Description Gene Day 1 or Day 6

Rv0018c phosphoserine/threonine phosphatase PstP pstP Both

Rv0019c FHA domain-containing protein FhaB fhaB Both

Rv1480 hypothetical protein Both

Rv0994 molybdopterin molybdenumtransferase 1 moeA1 Both

Rv0624 ribonuclease VapC30 vapC30 Both

Rv1915 isocitrate lyase AceAa aceAa Both

Rv2831 enoyl-CoA hydratase EchA16 echA16 Day 1

Rv3550 enoyl-CoA hydratase EchA20 echA20 Day 1

Rv3865 ESX-1 secretion-associated protein EspF EspF Day 1

Rv3866 ESX-1 secretion-associated protein EspG EspG Day 1

Table 2-9. List of primers used in Gateway cloning.

Primer ID Gene ID/Name Sequence

LEF-077 Rv0994 F MoeA1 GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATGTGCGT

TCTGTGGAGGAGCAG

LEF-078 Rv0994 R MoeA1 GGGGACCACTTTGTACAAGAAAGCTGGGTCAGCCGTGCTGAGCCAGGAA

LEF-079 Rv0019c F FhaB GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGCAG

GGGTTGGTACTGCAA

LEF-080 Rv0019c R FhaB GGGGACCACTTTGTACAAGAAAGCTGGGTCACGGGCGCAACTCGATTG

LEF-081 Rv0018c F PstP GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATGTGGCG

CGCGTGACCCTGGTC

LEF-082 Rv0018c R PstP GGGGACCACTTTGTACAAGAAAGCTGGGTCATGCCGCCGCCCGGCAGTC

LEF-089 Rv0624 F

VapC30

GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGGTG

ATCGACACGTCCGCG

LEF-145 Rv0624 R

VapC30

GGGGACCACTTTGTACAAGAAAGCTGGGTTAGGGCAGCGCGACCGTGG

LEF-091 Rv1480 F GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATGTGACC

GAATCCAAAGCGCCG

LEF-092 Rv1480 R GGGGACCACTTTGTACAAGAAAGCTGGGTCACTGGTGTCCCGCCAATGC

LEF-093 Rv1915 F AceAa GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGGCC

ATCGCCGAAACGGAC

LEF-094 Rv1915 R AceAa GGGGACCACTTTGTACAAGAAAGCTGGGTCAGGCCCGCGTCGTCCTC

LEF-095 Rv2831 F EchA16 GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGACC

GACGACATCCTGCTG

LEF-096 Rv2831 R EchA16 GGGGACCACTTTGTACAAGAAAGCTGGGTCTAACGCACCTGCGCGCG

LEF-097 Rv3550 F

EchA20

GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGCCG

ATCACCTCCACCACG

LEF-098 Rv3550 R

EchA20

GGGGACCACTTTGTACAAGAAAGCTGGGTCTATGACTTCTTCACAAAGG

C

LEF-099 Rv3865 F EspF GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGACC

45

GGATTTCTCGGTGTC

LEF-100 Rv3865 R EspF GGGGACCACTTTGTACAAGAAAGCTGGGTCAACCGAAAATCTTGTCGAT

AAC

LEF-101 Rv3866 F EspG GGGGACAAGTTTGTACAAAAAAGCAGGCTTAGGAGATATACATATGACG

GGTCCGTCCGCTGCA

LEF-102 Rv3866 R EspG GGGGACCACTTTGTACAAGAAAGCTGGGTCAGCCTCGGGAGGAGGCTTG

GWValidateF TACATCATTTCGACGCCGAGA

GWValidateR CCCCTCGAGGTCGACGGT

To construct MTb strains inducibly expressing our genes of interest, we used the Gateway cloning method

(Invitrogen-ThermoFisher Scientific) to insert the gene sequence into a plasmid backbone containing a

TetOR system (Carroll, Muttucumaru et al. 2005) and a kanamycin resistance cassette. Genes cloned into

this plasmid are conditionally expressed in the presence of anhydrous tetracycline. Genes were amplified

using GoTaq Green Master Mix (Promega) in a polymerase chain reaction (PCR), with primers from Table

2-9. These primers add attB sites that permit recombination with attP sites on an entry plasmid. The

entry plasmid is then recombined with the destination vector to produce the final expression vector.

The vector was transformed into OneShot Chemically Competent E. coli cells (Invitrogen-ThermoFisher

Scientific) according to manufacturer’s instructions and plated on LB agar containing 50 µg/mL

kanamycin. Colonies were restreaked for confirmation on the same medium, grown for 24 hours, then

picked into LB broth containing 50 µg/mL kanamycin. Plasmids were isolated from E. coli host cells using

a MiniPrep Kit (Qiagen). Candidate clones were confirmed by PCR with the primers GWValidateF and

GWValidateR and those candidates that produced bands were sequenced via Sanger sequencing.

Confirmed plasmids were transformed into Msm mc2155 by electroporation. Briefly, 50 mL cultures were

grown to OD600 0.8-1.0 and cultures were incubated on ice for 30 minutes. Cells were centrifuged at 5000

rpm for 10 minutes and supernatant was discarded and replaced with 25 mL cold 10% glycerol. This

process was repeated, halving the amount of glycerol each wash, until the cells were resuspended in 3 mL

10% glycerol. Cells were separated into 100 µL aliquots in microcentrifuge tubes and either frozen at -80

°C for later use or used immediately.

46

If frozen, tubes were thawed on ice for 10 minutes. DNA was added to cells and mixed gently, then

incubated for 10 minutes on ice. Cells were transferred to an electroporation cuvet and electroporated

using a BioRad GenePulser electroporation unit set to 2.5 kV, 1000 Ω, 25 µF. After pulsing, 1 mL 7H9

broth was added to the cuvet and cells were transferred to a 10 mL inkwell and recovered for 2 hours.

Cells were then plated on 7H10 agar plates containing 50 µg/mL kanamycin and incubated for 3 days.

Colonies were restreaked, then picked and confirmed by colony PCR.

Persister phenotyping of over-expression strains

Growth curves were conducted by diluting overnight cultures 1:100 in triplicate into 100 µL 7H9+Kan50

in a clear 96-well plate (Costar). Cultures were incubated in a BioTek Synergy H1 microplate reader for 48

hours at 37 °C with medium intensity double-orbital shaking.

Persister phenotyping of knockout strains

The following reagents were obtained through BEI Resources, NIAID, NIH:

Table 2-10. Transposon mutant strains used in validation studies.

Species Strain Mutant BEI Number

Mycobacterium tuberculosis CDC1551 MT0021/Rv0018c NR-14771

Mycobacterium tuberculosis CDC1551 MT1023/Rv0994 NR-18283



Growth-kill curves were conducted to determine whether any mutants had a growth defect, and whether

they were more or less tolerant to antibiotics than WT. Cultures were grown to stationary phase from

freezer stock by diluting 1:10 into 10 mL 7H9 liquid medium in 30 mL inkwell (ThermoScientific).

CDC1551 WT was a kind gift of Dr. Deepak Kaushal, Tulane University. Upon reaching stationary phase

(approximately 1 week), each strain was diluted 1:100 in triplicate into 20 mL 7H9 in 150 mL inkwell. A

47

200 µL aliquot was removed from each culture and centrifuged for 3 min at 3000 RPM. The supernatant

was discarded and the pellet was resuspended in phosphate buffered saline (PBS; MP Biomedicals). The

sample was serially diluted in PBS in a 96-well plate. Dilutions were spotted on 7H10 agar in 20 µL

volumes on rectangular single well plates (Thermo Scientific). Plates were incubated at 37°C for 3-4 weeks

until colonies became visible. Plates were counted and counts were used to calculate colony forming units

(CFU) per mL. For the kill portion, a 1 mL aliquot was removed from each sample and added to the wells

of a 12-well plate (Costar). Rifampicin was added to a final concentration of 10 µg/mL (stock solution of 1

mg/mL, 10 µL added to each well). The plates were sealed with Breathe-Easy film (USA Scientific) and

incubated with shaking at 37°C for 7 days. After 7 days, a 200 µL aliquot was removed from each sample

and washed, serially diluted, and plated as above for CFU.

To determine measure rifampicin tolerance in stationary phase, cultures were grown to stationary phase

from freezer stock by diluting 1:10 in triplicate into 10 mL 7H9 liquid medium in 30 mL inkwell

(ThermoScientific). Cultures were monitored by OD for entry into stationary phase. Once cultures had

reached OD 1.2 – 1.5, rifampicin was added to a final concentration of 10 µg/mL (stock solution of 1

mg/mL, 100 µL added to each inkwell vial). Washing, serial dilution, and plating for CFU counts were

performed as above.

48

Chapter 3: Lassomycin, a Novel Anti-Tubercular Compound, Depletes Intracellular ATP

and Modifies Protease Specificity in Mycobacterium tuberculosis.

49

3.1 Introduction

One-third of the world population is infected with Mycobacterium tuberculosis (MTb) and 10% of those

infected will develop active tuberculosis (TB). There were 1.5 million deaths from TB in 2013, and an

estimated 9 million people developed active TB. In addition, 480,000 people contracted multi-drug

resistant MTb (MDR-TB), and approximately 9% of those were found to have an extensively drug

resistant strain of TB (XDR-TB) (Fitzpatrick and Floyd 2012). Untreated TB is deadly; approximately 70%

of HIV-negative TB-positive patients will die within 10 years (WHO 2014). The current treatment is

burdensome and consists of six months’ treatment with four drugs: isoniazid, rifampicin, ethambutol and

pyrazinamide. The treatment regimen for drug-resistant TB is 20 months, and can drag on for 2 years or

longer (Quy, Lan et al. 2003). There is an urgent need for new drugs to kill MTb quickly and effectively,

yet the pace of discovery of new anti-tubercular compounds has slowed dramatically (Lewis 2012).

The pace of discovery of antitubercular compounds has been impeded by the focus on broad-spectrum

antibiotics, which are more difficult to find (Lewis 2012). However, whole-cell screens of natural products

have recently turned up several new compounds that target mycobacterial ClpC1, the ATP-dependent

subunit of the Clp protease (Schmitt, Riwanto et al. 2011; Gao, Kim et al. 2014; Gavrish, Sit et al. 2014).

The mycobacterial Clp complex comprises two proteolytic subunits, ClpP1 and ClpP2, which are

transcribed together and have unique substrate specificities (Personne, Brown et al. 2013). The AAA+

ATPases, ClpC1 and ClpX, regulate proteolysis by recognizing substrates and unfolding them for delivery

into the ClpP1P2 proteolytic core using ATP hydrolysis (Kirstein, Moliere et al. 2009). ClpP1 and ClpP2

form a heterotetradecameric structure, made up of two rings, each composed of seven units of either

ClpP1 or ClpP2. The ClpP1P2 double ring stacks asymmetrically with a hexameric ring of either ClpC1 or

ClpX, which facilitates substrate recognition and unfolding, and activates proteolysis by ClpP1P2 (Schmitz

and Sauer 2014; Leodolter, Warweg et al. 2015). ClpP1 and ClpP2 are both essential for in vitro growth, as

well as during infection (Raju, Unnikrishnan et al. 2012). ClpC1 is also essential for MTb’s in vitro growth

(Zhang, Ioerger et al. 2012), making it and the entire Clp proteolytic complex attractive drug targets

(Ollinger, O'Malley et al. 2011). Ecumicin, a cyclic peptide, targets ClpC1 and increases its ATPase activity,

50

while also inhibiting its activation of ClpP1P2 in vitro (Gao, Kim et al. 2015). Another cyclic peptide,

Cyclomarin A, binds to ClpC1 and kills both replicating and Wayne-model dormant cells (Schmitt,

Riwanto et al. 2011). Cyclomarin and the acyldepsipeptides (ADEPs) activate a non-specific proteolytic

activity of ClpP, causing excessive protein degradation (Brotz-Oesterhelt, Beyer et al. 2005; Kirstein,

Hoffmann et al. 2009; Conlon, Nakayasu et al. 2013). Bedaquiline, an inhibitor of the C subunit of ATP

synthase, was recently approved for use in the United States as a second-line drug to treat MDR-TB

(Worley and Estrada 2014). Bedaquiline’s sole mechanism of action is depletion of ATP, indicating that a

sufficiently severe drop in ATP level can be bactericidal.

We recently reported the discovery of lassomycin, a novel antitubercular compound (Gavrish, Sit et al.

2014). Lassomycin increases the ATPase activity of ClpC1 while simultaneously decreasing its activation of

ClpP1P2’s proteolytic activity in vitro, but its in vivo mechanism of action remained unknown. Here we

show through proteomic analysis that lassomycin treatment resulted in a markedly altered proteome with

over 300 proteins accumulating or diminishing in response to the antibiotic. We also show that

lassomycin caused a rapid, fatal drop in intracellular ATP levels.

3.2 Results

Lassomycin treatment results in a major alteration of the proteome. We previously showed

that lassomycin inhibits proteolysis in vitro by preventing ClpC1 from activating ClpP1P2’s proteolytic

activity (Gavrish, Sit et al. 2014). In order to determine whether lassomycin has a similar effect in vivo, we

performed proteomic studies on treated and untreated samples of MTb mc26020. Initial (T0) samples as

well as treated and untreated (T7) samples were analyzed in order to fully examine the scale of protein

degradation after lassomycin treatment. We identified 141,585 spectra attributed to 46,990 unique

peptides. 46,721 peptides where usable for quantification including 6,622 semi-tryptic peptides. Globally,

the number of proteolytic events seems to be unaffected. Indeed, 575 semi-tryptic peptides are increased

in abundance in the treated sample (p < 0.05 and log2(fold-change) > 0.6) while 559 semi-tryptic peptides

51

are decreased in abundance (p < 0.05 and log2(fold-change) < -0.6). We previously observed similar

results for a treatment with ADEP4 (11) at the semi-tryptic peptide level. But unlike ADEP4, the protein

abundance seems not to be strongly affected: while 172 proteins decreased in abundance in the

lassomycin-treated sample (p < 0.05 and log2(fold-change) < -0.6), 166 increased in abundance (p < 0.05

and log2(fold-change) > 0.6). Taken together (Figure 3-1A), these results suggest that unlike ADEP4,

lassomycin is not responsible for a global induction or inhibition of proteolysis, but a modification of

specificity due to the inhibition of ClpC1 unfoldase activity. Furthermore, we observed an increase in

members of the ClgR regulon, such as ClpB, Hsp, and ClgR itself (Estorninho, Smith et al. 2010). ClgR

and Hsp were the two most highly increased proteins, at 2.1 and 3.4 log2-fold increase, respectively. ClgR

is a direct positive regulator of the Clp complex that binds to a conserved sequence in the promoter region

and induces transcription of several genes, including ClpC1 and the ClpP1P2 operon (Estorninho, Smith et

al. 2010). ClpC1 and ClpP1 were both increased to a lesser degree (0.4 and 0.5 log2-fold), but that change

was statistically significant (p < 0.05 and 0.01, respectively).This manipulation of the Clp protease

machinery will have a marked effect on proteolysis events and substrate specificity. ClpX abundance was

not affected by lassomycin treatment (p ≥ 0.05, log2-fold change = 0.04).

52

Figure 3-1. Lassomycin treatment results in modified protein degradation. A) Global protein abundance is unaffected by the treatment by lassomycin. 166 proteins increased in abundance in the treated sample (red dots), while 172 decreased (green dots). The grey crosses represent proteins that do not pass the p-value cutoff (p > 0.05) and in black are those that do not pass the fold-change cutoff (ABS[log2(fold-change)] < 0.6). Comparison of genes/proteins in this study and DCS transcriptome. B) Genes/proteins that are down in one or both studies. C) Genes/proteins that are up in one or both studies.

In addition to the observation of the ClgR response to lassomycin, several interesting classes of proteins

were represented in both the increased and decreased populations (Supplemental Table 3-S1). Twenty-six

30S or 50S ribosomal proteins were decreased between 0.6 and 1.8 log2-fold. All of these genes were

previously found to be down-regulated in MTb treated with D-cycloserine, a cell wall biosynthesis

inhibitor (Keren, Minami et al. 2011). Also less abundant were members of the ESAT/ESX family of

virulence factors, including EsxBC, and these genes were also found to be down-regulated in the D-

cycloserine treated samples. In fact, 70 of the less abundant proteins found in the current proteome were

down-regulated in that study, which may indicate a common response to bactericidal antibiotic treatment

(Figure 3-1B-C). Several toxin-antitoxin (TA) systems were affected by lassomycin treatment: 15

antitoxins were decreased in the treated sample, while 7 toxins were increased. Most of these TA systems

53

were members of the VapBC TA family. The MTb genome contains 79 putative TA systems, an unusually

high number, with 50 of those VapBC TAs (Sala, Bordes et al. 2014). This family of type II TA system is

induced during stress conditions, where the VapC toxin targets RNA by various mechanisms and inhibits

translation (Ahidjo, Kuhnert et al. 2011). The components of the isocitrate lyase, AceAa and AceAb, also

increased after lassomycin treatment. This enzyme was previously shown to be required for MTb to adapt

to nutrient starvation by reducing its ATP level (Gengenbacher, Rao et al. 2010). Several genes involved in

fatty acid metabolism also increased, including acyl-CoA dehydrogenases FadE7 and FadE21.

Lassomycin treatment decreases ATP concentration. Based on the proteomics data showing

increased abundance of proteins involved in alternative metabolism and energy production in lassomycin-

treated samples, and previous work showing that lassomycin binds to ClpC1 and increases ATP hydrolysis

in vitro (Gavrish, Sit et al. 2014), we hypothesized that lassomycin might kill mycobacteria by decreasing

cellular ATP concentration. Therefore, we measured ATP concentration in cultures treated with a

bactericidal concentration (10X MIC) of lassomycin (10 µg/mL) or rifampicin (0.3 µg/mL) and compared

with an untreated culture. Rifampicin was included as a control as we did not expect treatment with this

drug to affect cellular ATP levels.

54

Figure 3-2. Lassomycin treatment decreased ATP concentration. A) Lassomycin killed nearly 20 times more than rifampicin. Day 7 p-value (untreated vs. lassomycin) < 0.001; p-value (rifampicin vs. lassomycin) < 0.05. B) Treatment of a stationary culture of M. tuberculosis mc26020 with lassomycin 10X MIC (10 µg/mL) significantly reduced ATP concentration compared to the untreated control (p < 0.001) and rifampicin 10X MIC (0.3 µg/mL) (p < 0.01). Student’s t-test was used for all comparisons.

A stationary phase culture of MTb mc26020 was treated with lassomycin, rifampicin, or left untreated,

and incubated at 37°C for seven days. Samples were removed daily for CFU counts and ATP measurement

using a luciferase-based assay. Lassomycin treatment was nearly 20 times more effective than rifampicin

(Figure 3-2A; p < 0.05, Student’s t-test). After an initial rise in ATP concentration in all samples, the

lassomycin-treated sample quickly decreased in ATP concentration while the rifampicin and untreated

cultures remained relatively constant (Figure 3-2B). By day 7, the ATP concentration in the lassomycin-

treated culture was nearly two orders of magnitude lower than the untreated control (p < 0.001; Student’s

t-test), and 28-fold lower than the rifampicin-treated culture (p < 0.01; Student’s t-test).

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0 1 2 3 4 5 6 7

Log1

0 C

FU/m

L)

Time (days)

Untreated

Lassomycin 10X MIC

Rifampicin 10X MIC

0.0001

0.001

0.01

0.1

1

0 1 2 3 4 5 6 7

ATP

Co

nce

nra

tio

n (

µM

)

Time (days)

Untreated

Lassomycin 10X MIC

Rifampicin 10X MIC

A B

55

Visualization of ATP depletion using ATP-sensitive FRET construct. We recently reported

(Maglica, Ozdemir et al. 2015) the use of the ATeam FRET-based ATP sensors (Imamura, Nhat et al.

2009) to measure changes in ATP concentration following antibiotic treatment in M. smegmatis. The

sensor is constructed of a CFP and YFP fluorophore linked by the ε subunit from Bacillus subtilis. When

the ATP concentration is low, the ε subunit is in a relaxed conformation, and the two components of the

FRET construct are not in proximity to one another; however, when the ATP concentration is high, the ε

subunit undergoes a conformational change, bringing CFP and YFP in close proximity to one another and

increasing the YFP/CFP emission ratio.

We challenged cultures of stationary phase M. smegmatis strain ΔfbiC_MA (medium affinity) with 10X

MIC of lassomycin, rifampicin, or bedaquiline and measured the CFP/YFP ratio at 6 hours after treatment

and compared it to control (Figures 3-3 and 3-4). Bedaquiline was included as a positive control; we

expected that treatment with this ATP synthase inhibitor would decrease the intracellular ATP

concentration. After 6 hours of incubation, the mean YFP/CFP ratio was 0.882 for untreated and 0.695

for lassomycin-treated cells (Figure 3-4A; p < 0.001). Examination of the distribution of FRET ratios

revealed that while the bedaquiline and untreated populations were highly divergent (Figure 3-4B; p < 10-

14), the lassomycin population resembled neither the bedaquiline nor untreated groups, representing a

population in transition from an ATP-rich to an ATP-poor state.

56

Figure 3-3. Lassomycin rapidly depleted intracellular ATP. Representative fluorescent images, 6 hour post-treatment. CFP and YFP channels overlaid to create composite image. A) Bedaquiline, B) Lassomycin, C) Rifampicin, D) Untreated.

57

Figure 3-4. Lassomycin rapidly depleted intracellular ATP in single cells. A) Mean YFP/CFP ratio, 6 hours post-treatment. Error bars represent standard error. All p-values are in comparison to the untreated control using 2-way ANOVA and Tukey’s multiple comparison test. B) Distribution of YFP/CFP ratio, 6 hours post-treatment.

3.3 Discussion

Prior to the approval of bedaquiline in 2012, there had not been a new drug approved to fight TB since

1967 (Long 1991), despite the rise in multi- and extensively-drug resistant TB (WHO 2014). Although the

approval of bedaquiline is an encouraging first step, the drug carries an increased risk of patient death

and is only used to treat MDR- or XDR-TB (Mahajan 2013). In previous work, we described lassomycin, a

novel lassopeptide synthesized by Lentzea kentuckyensis sp (Gavrish, Sit et al. 2014). Lassomycin targets

ClpC1 in vitro and simultaneously stimulates its ATP hydrolysis activity while also preventing its

activation of ClpP1P2’s proteolytic activity. ClpC1 and its associated protease, ClpP1P2, have recently been

shown to be promising drug targets (Parish 2014). Cyclomarin A, a cyclic peptide found through a whole-

cell screen of natural product extracts against MTb, binds to ClpC1 and increases protein degradation

(Schmitt, Riwanto et al. 2011). Although the exact mechanism of killing is not yet clear for cyclomarin A,

activation of proteolysis is the mechanism of another well-studied family of cyclic pepdtides, the ADEPs

(Kirstein, Hoffmann et al. 2009; Conlon, Nakayasu et al. 2013). Clearly, protein degradation is a crucial

process for TB, and it is not surprising that its disruption, whether through dysregulation, induction, or

inhibition, could lead to cell death.

A B

58

In this study, we found that treatment with lassomycin resulted in a specific stress response : (i) the

proteolytic events were strongly modified, possibly due to an alteration of Clp proteolysis; and (ii) the

abundance of several classes of proteins were significantly affected by the treatment. The ribosomal

proteins, ESAT/ESX proteins, and antitoxins all decreased in the treated samples while toxins, alternative

metabolism proteins, and members of the ClgR regulon increased. The ClgR-regulated proteins that

increased after lassomycin treatment included ClpP1, ClpC1, ClpB, Hsp, and ClgR itself. ClpP1 and ClpP2

are the two proteins that make up the subunits for the Clp protease. Together with an ATPase, either

ClpC1 or ClpX, they degrade misfolded or damaged proteins, and also regulate gene expression by

proteolysis of transcription factors like WhiB1 (Raju, Jedrychowski et al. 2014). The increased abundance

of ClpP1 and ClpC1 may be a response to the lowered proteolytic capability of the cell. The inhibition of

ClpC1 unfoldase activity by lassomycin and the upregulation of other Clp protease machinery may result

in an alteration of Clp substrate specificity. In support of this, the overall number of proteolytic events was

not affected; however, 166 proteins increased and 172 decreased in abundance in response to lassomycin.

Depletion of ClpP1P2 has been shown to be toxic in MTb, although the complex is nonessential in other

bacteria (Kirstein, Moliere et al. 2009; Raju, Jedrychowski et al. 2014). ClpP1P2 knockdown results in the

accumulation of many proteins, including transcriptional regulators WhiB1 and CarD (Raju,

Jedrychowski et al. 2014). In our proteomics data, we did not detect either of these proteins, which may

indicate that they are ClpX, rather than ClpC1, substrates. However, other transcriptional regulators were

affected by lassomycin, with 9 increasing and 20 decreasing. This change in the transcriptional

environment could explain a number of the proteomic changes we observed.

We previously showed that lassomycin increased ClpC1’s hydrolysis of ATP in vitro while also inhibiting

its activation of ClpP1P2. We hypothesized that this activity would lead to a drop in in vivo ATP

concentration. Lassomycin caused lower ATP levels in both MTb and M. smegmatis, although this drop

was not as large as that caused by the ATP synthase inhibitor bedaquiline. It is unclear at this time

59

whether lassomycin’s bactericidal effect is due to this decrease in ATP concentration, the alteration of

proteolytic events, a global proteome reorganization, or a combination of the three.

These in vivo studies highlight lassomycin’s novel mechanism of action; currently there are no approved

TB treatments that target cellular proteases or the proteasome (Zumla, Nahid et al. 2013). Given the

essential nature of this complex in mycobacteria, it presents an attractive target for further study and

optimization. Interestingly, a recent natural product screen has produced a second ClpC1-targeting

peptide, which its discoverers term ecumicin (Gao, Kim et al. 2014). This compound, like lassomycin,

activates the ATPase activity of ClpC1 while preventing its activation of ClpP1P2; however, it is not known

if ecumicin causes the same effects on ATP concentration and on the proteome. The two peptides have

very different structures and may bind different domains on ClpC1. Co-crystallization studies of these

drugs with ClpC1 should reveal how these structural differences affect their binding and activity.

3.4 Materials and Methods

Bacterial strains and culture conditions. Mycobacterium tuberculosis mc26020 was used for all

MTb experiments in this study. This auxotrophic strain carries deletions in the lysA and panCD genes,

rendering it non-pathogenic and suitable for study in a BSL2+ laboratory (Sambandamurthy, Derrick et

al. 2005). All chemicals were obtained from Sigma-Aldrich except where indicated otherwise. Cultures

were grown in Middlebrook 7H9 broth or on 7H10 agar (Difco) supplemented with 10% oleic acid-

albumin-dextrose-catalase (OADC; Difco), 0.5% glycerol, 0.2% casamino acids (Difco), 0.05% tyloxapol

(broth only), pantothenic acid (24 µg/ml), and lysine (80 µg/ml). M. smegmatis was grown in 7H9/7H10

supplemented with 10% albumin-dextrose-catalase, 0.5% glycerol, and 0.05% tyloxapol (broth only).

Freezer stocks were diluted 1:100 into 7H9 medium and grown in a shaking incubator at 37°C at 100 rpm.

Lassomycin was isolated from Lentzea kentuckyensis sp. as described in (Gavrish, Sit et al. 2014).

Bedaquiline was obtained from Janssen Pharmaceuticals. Antibiotics stocks were prepared and diluted as

recommended (Andrews 2001).

60

Bacterial cell enumeration. To determine viable cell counts, 200 µL of each culture was removed and

pelleted by centrifugation for 5 minutes at room temperature at 9,300 x g. The supernatant was removed

and an equal volume of phosphate buffered saline (PBS; MP Biomedicals) was added. The sample was

then sonicated seven times for 5 seconds at an amplitude of 4 on the Misonix Ultrasonic Liquid Processor

S-4000 (Qsonica), vortexed for 10 seconds, and serially diluted in PBS in a 96-well plate. Dilutions were

spotted on 7H10 agar in 20 µL volumes on rectangular single well plates (Thermo Scientific). Plates were

incubated at 37°C for 3-4 weeks until colonies became visible. Plates were counted and counts were used

to calculate colony forming units (CFU) per mL.

Minimum inhibitory concentration assay. Minimum inhibitory concentrations (MICs) of the

antibiotics used in this study (lassomycin, rifampicin, and bedaquiline) were determined using the

microplate-based Alamar Blue assay (MABA) as described (Franzblau, Witzig et al. 1998) with minor

modifications. A culture of MTb mc26020 was grown to exponential phase (OD600 0.3) and diluted 1:100

for the assay. Antibiotics were serially diluted two-fold in 100 µL volumes in a 96-well plate and an equal

volume of bacterial culture was added. Plates were sealed with Breathe-Easy® (3M Company) and

incubated at 37°C. After four days, 50 µl of a 1:1 mixture of 10X resazurin sodium salt and PBS with 0.05%

tyloxapol was added to each well and the plates were resealed and incubated for 24 – 48 hours. The colors

of all wells were recorded after visual inspection and plates were photographed. The MIC was determined

to be the lowest concentration of drug that did not result in a color change from blue to pink.

Proteomics sample preparation. To prepare samples for proteomics, an aliquot of mc26020 freezer

stock was diluted 1:100 and grown to stationary phase (2 weeks). This culture was then diluted 1:100 into

200 mL medium in duplicate and grown for 14 days to stationary phase. A 50 mL sample was removed

from each culture, centrifuged at 5000 rpm for 10 minutes and the supernatant was removed. The pellet

was frozen at -80°C until proteomics analysis. These were Day 0 (untreated) samples. An additional two

61

50 mL samples were removed from each culture; one was treated with 10X MIC lassomycin (10 µg/mL)

and one was untreated. These cultures were incubated at 37°C for 7 days, at which time they were pelleted

and frozen as described above. CFU counts were also performed at Days 0 and 7 to confirm the cell

numbers available for proteomics analysis. The proteins from each sample were extracted by bead beating

in 2.0-mL cryovial with 0.1-mm zirconia/silica disruption beads (BioSpec Products, Bartlesville, OK). The

proteins were treated with 8 M urea, 5 mM DTT for 30 min at 60 °C. The mixture was then diluted eight

times in order to obtain a final concentration of 1 M urea before a trypsin digestion for 3 h at 37 °C. The

resulting peptides were desalted using C18 SPE cartridges (Discovery C18, 1 mL, 50 mg, Sulpelco) and

labeled with TMTsixplex Label Reagents (Life Technologies) following the manufacturer

recommendations. The peptide concentration was measured by BCA assay (Thermo Scientific) and the

labeled samples were mixed in equal amount prior to being fractionated into 24 fractions as previously

described (Conlon, Nakayasu et al. 2013).

LC-MS/MS proteomics measurements. Each fraction was analyzed by reverse phase LC-MS/MS

using a Waters nanoEquityTM UPLC system (Millford, MA) coupled with a QExactive mass spectrometer

(Thermo Fisher Scientific; San Jose, CA). The LC was configured to load the sample first on a solid phase

extraction (SPE) column followed by separation on an analytical column. Analytical columns were made

in-house by slurry packing 3-µm Jupiter C18 stationary phase (Phenomenex; Torrence, CA) into a 70-cm

long, 360 µm OD x 75 µm ID fused silica capillary tubing (Polymicro Technologies Inc.; Phoenix, AZ). The

SPE column (360 µm OD x 150 µm ID) of 5 cm length was similarly made with 3.6-µm Aeries C18

particles. Mobile phases consisted of 0.1% formic acid in water (MP- A) and 0.1% formic acid in

acetonitrile (MP- B). Samples were made at a concentration of ~ 0.1 µg/µL and 5 µL were loaded on the

SPE column via a 5 µL sample loop for 30 minutes at a flow rate of 3 µL per minute and then separated by

the analytical column using a 110 minute gradient from 99% A to 5% A at a flow rate of 0.3 µL per minute.

Mass spectrometry analysis was started 15 minutes after the sample was moved to the analytical column

and mass spectra were recorded for 100 minutes. After the gradient was completed, column was washed

with 100% MP- B first and then reconditioned with 99% MP- A for 30 minutes. The effluents from the LC

column were ionized by ESI and analyzed with a QExactive hybrid quadrupole/Orbitrap mass

62

spectrometer operated in the data-dependent analysis mode. A voltage of 2.3 kV was used for electrospray

ionization and inlet capillary to the mass spectrometer was maintained at a temperature of 325oC for ion

de-solvation. A primary survey scan was performed in the mass range of 400 to 2000 Daltons at a

resolution of 70,000 (defined at mass 200) and automatic gain control (AGC) setting of 5 x 106 ions. The

top 10 ions from the survey scan were selected by a quadrupole mass filter for high energy collision

dissociation (HCD) in collision with nitrogen and mass analyzed by the Orbitrap at a resolution of 17500.

An isolation window of 2 Daltons was used with a collision energy of 28% was used for HCD with AGC

setting of 1 x 105 ions. Mass spectra were recorded for 100 minutes by repeating this process with a

dynamic exclusion of previously selected ions for 60 seconds.

Proteomic data analysis. Raw mass spectrometry data were converted to peak lists (DTA files) using

the DeconMSn (version 2.3.1.2, omics.pnl.gov/software) and searched with MS-GF+ (Kim and Pevzner

2014) against the M. tuberculosis H37Rv refseq database (4,003 sequences) and against bovine trypsin

and human keratin sequences (all in correct and reverse orientations, 8,138 total sequences). Searching

parameters included tryptic digestion of at least one of the peptide ends (partially tryptic), 20 ppm

peptide mass tolerance, methionine oxidation as variable modification, and N-terminus and lysine

labeling with TMT reagent as fixed modifications. The identified MS/MS spectra were filtered with an

MS-GF+ score of 9.533x10-10 resulting in a false-discovery rate (FDR) ≤ 0.2 % at the spectral level, a FDR

≤ 0.6% and a peptide and protein FDR ≤ 0.7%. For the quantitative analysis, the TMT reporter ion

intensities were extracted with MASIC (MS/MS Automated Selected Ion Chromatogram Generator,

version v2.6.5421, omics.pnl.gov/software/MASIC.php) (Monroe, Shaw et al. 2008). At the peptide level,

the TMT reporter intensities of the peptides fragmented various times were summed. At the protein level,

the reporter intensities of different fully tryptic peptides belonging to the same proteins were also

summed and only the proteins with two proteospecific peptides were conserved. Peptides and proteins

with missing data were excluded. In order to normalize the data the reporter ion intensity of each

peptide/protein was divided by the by the sum of all the corresponding reporter ion intensities. The data

were then log-transformed prior performing a two-tailed, homoscedastic Student’s t-test on Microsoft

63

Excel 2010 (V14.0.7145.5000) in order to determine the differentially abundant pathways, proteins and

peptides.

Luciferase assay. ATP concentration was measured using the BacTiterGlo Microbial Cell Viability

Assay (Promega) according to manufacturer’s instructions with minor modifications (Gengenbacher, Rao

et al. 2010). Briefly, 150 µL of each culture was heat-inactivated by heating at 100°C for 60 minutes. After

inactivation, 100 µL of each sample was added to a white flat-bottomed 96-well plate (Costar) and mixed

with an equal volume of BacTiterGlo Reagent. Plates were shaken in a double-orbital configuration for 30

seconds, and then incubated in the dark for 2 minutes before reading on a BioTek Synergy H1

luminometer. An ATP standard curve was constructed according to the manufacturer’s instructions and

these data were used to convert relative light units (RLUs) to ATP concentration (µM).

FRET microscopy and analysis. M. smegmatis strain ΔfbiC_MA (medium affinity) (Maglica,

Ozdemir et al. 2015) expressing the fluorescent ATeam1.03YEMK reporter (Imamura, Nhat et al. 2009) was

grown to stationary phase (36 hours) and then treated with lassomycin, rifampicin, or bedaquiline at 10X

MIC (lassomycin: 10 µg/mL; rifampicin: 320 µg/mL; 1.2 µg/mL) for 6 hours and imaged using a laser

scanning confocal microscope (LSM 710, Carl Zeiss Microscopy GmbH) using an inverted microscope

(Observer.Z1, Zeiss) and a 63X oil immersion (NA 0.3) EC Plan-Neofluor objective. Images were acquired

using a 427/10 excitation filter and two emission filters (CFP: 483/32; YFP: 542/27). Images were

acquired at 1840×1840 pixels and saved as 16-bit tagged image file format (TIFF) files. All image

processing was done using ImageJ (National Institutes of Health; Bethesda, MD,

USA; rsb.info.nih.gov/ij/) and the Image Montage plugin (National Institutes of Health; Bethesda, MD,

USA; imagej.nih.gov/ij/plugins/image-montage/index.html). At least 5 images were acquired per

condition with 5-10 cells analyzed per image. For each cell, the mean cell fluorescence in each channel was

calculated by subtracting the average background obtained from 3 cell-free areas in the same image. The

YFP/CFP ratio was then calculated for each cell. These ratios were compared using 2-way ANOVA and

Tukey’s multiple comparison test (GraphPad Prism 6).

64

3.5 Supplemental Materials

Supplementary Table 3-S1. Protein families decreased or increased in abundance in

lassomycin-treated samples.

ID

Pv

alu

e (

Stu

de

nt’

s t

-te

st)

log

2(D

7 L

as

so

my

cin

A/

D7

Un

tre

ate

d A

)

log

2(D

7 L

as

so

my

cin

B/

D7

Un

tre

ate

d B

)

Transport related protein

Rv0638 0.035 -1.7 -1.0 Probable preprotein translocase SecE1

Rv0732 0.000 -0.7 -0.7 Probable preprotein translocase SecY

Rv2587c 0.001 -0.8 -0.9 Probable protein-export membrane protein SecD

Rv2586c 0.001 -0.6 -0.6 Probable protein-export membrane protein SecF

Rv2127 0.029 -1.0 -0.6 L-asparagine permease AnsP1

Rv3783 0.024 -1.1 -1.0 Probable O-antigen/lipopolysaccharide transport integral membrane protein ABC transporter RfbD

65

Ribosomal protein

Rv1642 0.006 -1.6 -2.0 50S ribosomal protein L35 RpmI

Rv0634B 0.006 -2.0 -1.7 50S ribosomal protein L33 RpmG2

Rv0705 0.003 -1.8 -1.9 30S ribosomal protein S19 RpsS

Rv2056c 0.025 -1.1 -1.7 30S ribosomal protein S14 RpsN2

Rv0715 0.000 -1.6 -1.6 50S ribosomal protein L24 RplX

Rv2412 0.003 -1.6 -1.6 30S ribosomal protein S20 RpsT

Rv0709 0.003 -1.1 -1.2 50S ribosomal protein L29 RpmC

Rv2441c 0.021 -0.9 -1.4 50S ribosomal protein L27 RpmA

Rv0706 0.002 -1.2 -1.1 50S ribosomal protein L22 RplV

Rv0682 0.003 -1.2 -1.3 30S ribosomal protein S12 RpsL

Rv0979A 0.008 -1.0 -1.0 50S ribosomal protein L32 RpmF

Rv2055c 0.025 -0.8 -1.2 30S ribosomal protein S18 RpsR2

Rv2442c 0.009 -1.0 -0.9 50S ribosomal protein L21 RplU

Rv1298 0.021 -0.8 -1.1 50S ribosomal protein L31 RpmE

Rv0056 0.010 -1.0 -0.9 50S ribosomal protein L9 RplI

Rv3924c 0.031 -0.9 -1.3 50S ribosomal protein L34 RpmH

Rv2909c 0.001 -0.8 -0.8 30S ribosomal protein S16 RpsP

Rv2785c 0.016 -0.9 -0.6 30S ribosomal protein S15 RpsO

Rv0055 0.012 -0.6 -0.8 30S ribosomal protein S18-1 RpsR1

Rv0717 0.009 -0.6 -0.8 30S ribosomal protein S14 RpsN1

Rv0683 0.002 -0.7 -0.7 30S ribosomal protein S7 RpsG

Rv0641 0.006 -0.6 -0.8 50S ribosomal protein L1 RplA

Rv3456c 0.036 -0.7 -0.6 50S ribosomal protein L17 RplQ

Rv0710 0.008 -0.6 -0.6 30S ribosomal protein S17 RpsQ

Rv0700 0.003 -0.8 -0.8 30S ribosomal protein S10 RpsJ (transcription antitermination factor NusE)

Rv0722 0.010 -1.6 -1.4 50S ribosomal protein L30 RpmD

Rv2534c 0.020 -0.7 -1.0 Probable elongation factor P Efp

Rv1080c 0.038 -1.0 -0.6 Probable transcription elongation factor GreA (transcript cleavage factor GreA)

Rv2882c 0.015 -0.8 -0.6 Ribosome recycling factor Frr (ribosome releasing factor) (RRF)

Antitoxins (TA)

Rv2063 0.008 -2.5 -3.0 Antitoxin MazE7

Rv1721c 0.010 -1.8 -1.5 Possible antitoxin VapB12

Rv3407 0.005 -1.6 -1.3 Possible antitoxin VapB47


Rv1943c 0.021 -1.1 -1.6 Possible antitoxin MazE5





Rv1494 0.017 -1.0 -0.8 Possible antitoxin MazE4



Rv1177 0.005 -1.0 -1.0 Probable ferredoxin FdxC




66

ESAT/ESX proteins

Rv1037c 0.016 -0.6 -0.8 Putative ESAT-6 like protein EsxI (ESAT-6 like protein 1)

Rv0287 0.010 -1.0 -1.2 ESAT-6 like protein EsxG (conserved protein TB9.8)

Rv3891c 0.007 -1.2 -1.0 Possible ESAT-6 like protein EsxD

Rv3890c 0.002 -2.1 -1.8 ESAT-6 like protein EsxC (ESAT-6 like protein 11)

Rv1038c 0.038 -0.8 -0.7 ESAT-6 like protein EsxJ (ESAT-6 like protein 2)

Rv3865 0.002 -0.7 -0.6 ESX-1 secretion-associated protein EspF

Rv3880c 0.001 -0.8 -0.8 ESX-1 secretion-associated protein EspL

Rv3615c 0.020 -1.3 -1.7 ESX-1 secretion-associated protein EspC

Rv3874 0.000 -1.4 -1.3 10 kDa culture filtrate antigen EsxB (LHP) (CFP10)

Transcriptional Regulators

Rv3849 0.002 -1.3 -1.4 ESX-1 transcriptional regulatory protein EspR

Rv3687c 0.018 -0.5 -0.8 Anti-anti-sigma factor RsfB (anti-sigma factor antagonist) (regulator of sigma F B)

Rv1657 0.007 -1.2 -0.9 Probable arginine repressor ArgR (AHRC)

Rv3574 0.008 -0.7 -0.6 transcriptional regulatory protein TetR-family

Rv1846c 0.002 -0.8 -0.9 Transcriptional repressor BlaI

Rv0348 0.019 -0.8 -1.2 Possible transcriptional regulatory protein

Rv0081 0.008 -1.3 -1.1 Probable transcriptional regulatory protein

Rv1152 0.009 -0.8 -0.6 Probable transcriptional regulatory protein

Rv1626 0.016 -0.6 -0.8 Probable two-component system transcriptional regulator

Cold Shock protein

Rv3648c 0.002 -2.1 -2.2 Probable cold shock protein A CspA

Rv0871 0.005 -1.2 -1.2 Probable cold shock-like protein B CspB

Phage protein Rv1579c 0.015 -1.2 -1.7 Probable PhiRv1 phage protein

Rv1580c 0.004 -1.1 -1.0 Probable PhiRv1 phage protein

67

Unknown function

Rv3921c 0.001 -0.7 -0.7 Probable conserved transmembrane protein

Rv1075c 0.035 -0.6 -0.9 Conserved exported protein

Rv0810c 0.000 -2.7 -2.7 Conserved hypothetical protein

Rv1893 0.000 -1.9 -1.9 Conserved hypothetical protein

Rv1638A 0.002 -1.6 -1.5 Conserved hypothetical protein










Rv2525c 0.020 -0.9 -1.2 Conserved hypothetical protein. Secreted; predicted to be a substrate of the twin arginine translocation (tat) export system

Rv1002c 0.002 -0.7 -0.7 Conserved membrane protein

Rv0569 0.000 -3.0 -3.2 Conserved protein



Rv0566c 0.001 -1.3 -1.5 Conserved protein

Rv0787A 0.003 -1.1 -1.3 Conserved protein














Rv0814c 0.001 -0.9 -0.8 Conserved protein SseC2

Rv0577 0.014 -0.7 -0.6 Conserved protein TB27.3

Rv3208A 0.015 -0.8 -1.1 Conserved protein TB9.4

68

Rv0991c 0.042 -1.2 -1.0 Conserved serine rich protein

Rv0093c 0.023 -1.0 -0.7 Probable conserved membrane protein

Rv0361 0.007 -0.8 -0.7 Probable conserved membrane protein



Rv2219 0.001 -1.1 -1.0 Probable conserved transmembrane protein







Rv0634A 0.012 -1.5 -2.0 Unknown protein

Rv1192 0.006 -0.8 -0.8 Unknown protein

Rv3519 0.002 -0.8 -0.7 Unknown protein

Rv0108c 0.006 -0.9 -1.1 Hypothetical protein Rv0108c

Rv3788 0.007 -0.9 -0.8 Hypothetical protein Rv3788

Rv2237A 0.008 -0.8 -0.9 hypothetical protein Rv4001

Rv0901 0.010 -0.7 -0.5 Possible conserved exported or membrane protein

Rv0309 0.026 -0.8 -1.3 Possible conserved exported protein

Rv1024 0.010 -1.1 -1.1 Possible conserved membrane protein

Rv0007 0.005 -1.1 -1.0 Possible conserved membrane protein

Rv2700 0.027 -0.8 -0.5 Possible conserved secreted alanine rich protein

Rv0383c 0.003 -0.6 -0.7 Possible conserved secreted protein

Rv0206c 0.005 -0.8 -0.8 Possible conserved transmembrane transport protein MmpL3

69

Other function

Rv0636 0.006 -1.0 -0.9 (3R)-hydroxyacyl-ACP dehydratase subunit HadB

Rv3221c 0.033 -1.1 -0.7 Biotinylated protein TB7.3

Rv3493c 0.009 -0.6 -0.6 hypothetical Mce associated alanine and valine rich protein

Rv3597c 0.018 -0.7 -0.5 Iron-regulated H-NS-like protein Lsr2

Rv1636 0.007 -0.8 -0.7 Iron-regulated universal stress protein family protein TB15.3

Rv0341 0.006 -1.4 -1.7 Isoniazid inductible gene protein IniB

Rv2244 0.004 -1.2 -1.0 Meromycolate extension acyl carrier protein AcpM

Rv2533c 0.012 -1.2 -1.0 N utilization substance protein NusB (NusB protein)

Rv0832 0.035 -0.5 -0.9 PE-PGRS family protein PE_PGRS12

Rv0315 0.012 -1.1 -1.1 Possible beta-1,3-glucanase precursor

Rv3198A 0.014 -0.7 -0.5 Possible glutaredoxin protein

Rv1085c 0.047 -1.5 -0.8 Possible hemolysin-like protein

Rv0346c 0.013 -1.0 -0.7 Possible L-asparagine permease AnsP2 (L-asparagine transport protein)

Rv0088 0.000 -0.9 -0.9 Possible polyketide cyclase/dehydrase

Rv0426c 0.004 -1.4 -1.3 Possible transmembrane protein

Rv3144c 0.008 -0.7 -0.8 PPE family protein

Rv2072c 0.012 -0.7 -0.7 Precorrin-6Y C(5,15)-methyltransferase (decarboxylating) CobL

Rv0986 0.005 -0.8 -0.7 Probable adhesion component transport ATP-binding protein ABC transporter

Rv3682 0.003 -0.8 -0.7

Probable bifunctional membrane-associated penicillin-binding protein 1A/1B PonA2 (murein polymerase) [includes: penicillin-insensitive transglycosylase (peptidoglycan TGASE) + penicillin-sensitive transpeptidase (DD-transpeptidase)]

Rv2289 0.006 -0.9 -0.9 Probable CDP-diacylglycerol pyrophosphatase Cdh (CDP-diacylglycerol diphosphatase) (CDP-diacylglycerol phosphatidylhydrolase)

Rv2690c 0.000 -0.6 -0.6 Probable conserved integral membrane alanine and valine and leucine rich protein

Rv0176 0.018 -0.7 -0.8 Probable conserved Mce associated transmembrane protein

Rv1183 0.001 -0.6 -0.6 Probable conserved transmembrane transport protein MmpL10

Rv3084 0.004 -0.7 -0.6 Probable acetyl-hydrolase/esterase LipR

Rv3452 0.030 -0.6 -0.8 Probable cutinase precursor Cut4

Rv1653 0.029 -0.6 -0.8 Probable glutamate N-acetyltransferase ArgJ

Rv1140 0.011 -0.7 -0.8 Probable integral membrane protein

Rv2894c 0.004 -0.6 -0.7 Probable integrase/recombinase XerC

Rv3271c 0.027 -0.7 -0.6 Probable conserved integral membrane protein

Rv1343c 0.000 -0.7 -0.7 Probable conserved lipoprotein LprD

Rv2091c 0.005 -0.7 -0.7 Probable membrane protein

Rv2674 0.005 -0.6 -0.6 Probable peptide methionine sulfoxide reductase MsrB (protein-methionine-R-oxide reductase) (peptide met(O) reductase)

70

Rv2582 0.003 -0.9 -0.8 Probable peptidyl-prolyl cis-trans isomerase B PpiB (cyclophilin) (PPIase) (rotamase) (peptidylprolyl isomerase)

Rv1224 0.003 -0.7 -0.7 Probable protein TatB

Rv3317 0.010 -0.6 -0.6

Probable succinate dehydrogenase (hydrophobic membrane anchor subunit) SdhD (succinic dehydrogenase) (fumarate reductase) (fumarate dehydrogenase) (fumaric hydrogenase)

Rv2111c 0.025 -1.0 -0.8 Prokaryotic ubiquitin-like protein Pup

Rv1335 0.037 -0.8 -0.5 Sulfur carrier protein CysO

Ribosomal protein

Rv1644 0.016 0.5 0.8 Possible 23S rRNA methyltransferase TsnR

Rv3460c 0.012 0.6 0.8 30S ribosomal protein S13 RpsM

Rv2890c 0.002 0.6 0.7 30S ribosomal protein S2 RpsB

Rv0703 0.004 1.3 1.1 50S ribosomal protein L23 RplW

Transcriptional regulators

Rv2374c 0.007 0.8 0.8 Probable heat shock protein transcriptional repressor HrcA

Rv0353 0.010 0.7 0.7 Probable heat shock protein transcriptional repressor HspR (MerR family)

Rv2011c 0.002 0.8 0.9 Conserved hypothetical protein, probable transcription repressor

Rv1221 0.012 0.7 0.9 Alternative RNA polymerase sigma factor SigE

Rv2499c 0.002 0.9 0.8 Possible oxidase regulatory-related protein

Rv2710 0.007 1.5 1.8 RNA polymerase sigma factor SigB

Rv0981 0.010 0.8 0.9 two component response transcriptional regulatory protein MprA

Rv0485 0.009 0.7 1.0 Possible transcriptional regulatory protein

Rv0275c 0.028 1.3 2.0 Possible transcriptional regulatory protein (possibly TetR-family)

Rv3249c 0.020 0.9 0.7 Possible transcriptional regulatory protein (probably TetR-family)

Rv0465c 0.003 1.4 1.2 Probable transcriptional regulatory protein

Rv1460 0.015 1.5 1.2 Probable transcriptional regulatory protein

Rv3066 0.019 0.6 0.9 Probable transcriptional regulatory protein (probably DeoR-family)

Rv3334 0.001 1.1 1.1 Probable transcriptional regulatory protein (probably MerR-family)

Rv0232 0.000 1.2 1.2 Probable transcriptional regulatory protein (probably TetR/AcrR-family)

Rv2912c 0.034 0.8 0.6 Probable transcriptional regulatory protein (probably TetR-family)

Rv1395 0.009 0.6 0.7 Transcriptional regulatory protein

Rv3736 0.002 0.9 0.8 Transcriptional regulatory protein (probably AraC/XylS-family)

Rv0982 0.001 0.9 1.0 Two component sensor kinase MprB

Rv1364c 0.007 0.6 0.8 Possible sigma factor regulatory protein

Anti-toxin (TA) Rv1956 0.024 0.7 0.8 Possible antitoxin HigA

71

Toxins (TA)

Rv2527 0.010 0.7 0.7 Possible toxin VapC17

Rv0549c 0.019 0.7 1.0 Possible toxin VapC3

Rv2494 0.021 0.6 0.8 Possible toxin VapC38. Contains PIN domain




Rv0627 0.040 0.9 1.5 Possible toxin VapC5

ESAT/ESX proteins

Rv3868 0.003 0.8 0.9 ESX conserved component EccA1. ESX-1 type VII secretion system protein

Rv3884c 0.001 0.9 1.0 ESX conserved component EccA2. ESX-2 type VII secretion system protein. Probable CbxX/CfqX family protein

Rv3866 0.002 0.8 0.8 ESX-1 secretion-associated protein EspG1

Rv3879c 0.018 0.7 1.0 ESX-1 secretion-associated protein EspK. Alanine and proline rich protein

Peptide synthase

Rv2386c 0.000 0.8 0.8 Isochorismate synthase MbtI

Rv2380c 0.004 0.6 0.7 peptide synthetase MBTE (peptide synthase)

Redox related protein

Rv0575c 0.019 0.7 0.5 Possible oxidoreductase

Rv3725 0.002 0.8 0.7 Possible oxidoreductase

Rv3007c 0.011 0.8 0.7 Possible oxidoreductase

Rv0097 0.010 0.7 0.9 Possible oxidoreductase

Rv3673c 0.045 0.7 1.1 Possible membrane-anchored thioredoxin-like protein (thiol-disulfide interchange related protein)

Rv0303 0.009 0.7 0.6 Probable dehydrogenase/reductase

Transport related protein

Rv0507 0.007 0.7 0.6 Probable conserved transmembrane transport protein MmpL2

Rv2688c 0.006 0.8 1.0 Antibiotic-transport ATP-binding protein ABC transporter

Rv0724 0.011 0.6 0.7 Possible protease IV SppA (endopeptidase IV) (signal peptide peptidase)

Cytochrome related protein

Rv2266 0.008 0.8 0.7 Probable cytochrome P450 124 Cyp124

Rv1256c 0.032 0.6 1.1 Probable cytochrome P450 130 Cyp130

Rv0764c 0.008 0.6 0.7 Cytochrome P450 51 Cyp51 (CYPL1) (P450-L1A1) (sterol 14-alpha demethylase) (lanosterol 14-alpha demethylase) (P450-14DM)

Rv2874 0.010 0.9 1.2 Possible integral membrane C-type cytochrome biogenesis protein DipZ

72

Nucleic acid related protein

Rv3051c 0.000 0.9 0.9 Ribonucleoside-diphosphate reductase (alpha chain) NrdE (ribonucleotide reductase small subunit) (R1F protein)

Rv0233 0.048 0.9 0.5 Ribonucleoside-diphosphate reductase (beta chain) NrdB (ribonucleotide reductase small chain)

Rv1108c 0.023 0.7 1.0 Probable exodeoxyribonuclease VII (large subunit) XseA (exonuclease VII large subunit)

Rv1633 0.012 1.0 0.8 excinuclease ABC subunit B

Rv3211 0.004 1.0 1.1 Probable ATP-dependent RNA helicase RhlE

Rv2986c 0.017 0.7 0.5 DNA-binding protein HU homolog HupB (histone-like protein) (HLP) (21-kDa laminin-2-binding protein)

Rv2867c 0.009 0.7 0.7 GCN5-related N-acetyltransferase

Rv3225c 0.005 0.6 0.7 GCN5-related N-acetyltransferase, phosphorylase

Rv2792c 0.003 1.3 1.5 Possible resolvase

Rv0796 0.002 1.6 1.6 transposase IS6110

Rv2791c 0.002 0.9 0.8 Probable transposase

Glycoxylate cycle (by-pass

of the TCA)

Rv1915 0.015 1.4 1.0 Probable isocitrate lyase AceAa [first part] (isocitrase) (isocitratase) (Icl)

Rv1916 0.012 0.6 0.8 Probable isocitrate lyase AceAb [second part] (isocitrase) (isocitratase) (Icl)

Lipid related protein

Rv3016 0.006 1.2 1.0 Probable lipoprotein LpqA

Rv0220 0.009 0.7 0.9 Probable esterase LipC

Rv0099 0.009 0.7 0.6 Possible fatty-acid-CoA ligase FadD10 (fatty-acid-CoA synthetase) (fatty-acid-CoA synthase)

Rv0098 0.037 0.5 0.8 Probable fatty acyl CoA thioesterase type III FcoT

Rv2789c 0.001 0.7 0.7 Probable acyl-CoA dehydrogenase FadE21

Rv0400c 0.004 0.7 0.8 Acyl-CoA dehydrogenase FadE7

Rv2501c 0.007 0.5 0.7

Probable acetyl-/propionyl-coenzyme A carboxylase alpha chain (alpha subunit) AccA1: biotin carboxylase + biotin carboxyl carrier protein (BCCP)

Rv2351c 0.027 0.7 0.8 Membrane-associated phospholipase C 1 PlcA (MTP40 antigen)

Rv1814 0.021 1.2 0.9 Membrane-bound C-5 sterol desaturase Erg3 (sterol-C5-desaturase)

Rv0411c 0.002 0.7 0.8 Probable glutamine-binding lipoprotein GlnH (GLNBP)

Rv3842c 0.000 0.7 0.7 Probable glycerophosphoryl diester phosphodiesterase GlpQ1 (glycerophosphodiester phosphodiesterase)

Rv1130 0.007 0.6 0.7 Possible methylcitrate dehydratase PrpD

Phage protein Rv1577c 0.010 0.8 0.7 Probable PhiRv1 phage protein

Virulence related protein

Rv2108 0.001 1.2 1.3 PPE family protein PPE36

Rv3429 0.018 0.8 0.9 PPE family protein PPE59

Rv2875 0.016 0.8 0.6 Major secreted immunogenic protein Mpt70

Rv0589 0.022 0.7 0.7 Mce-family protein Mce2A

Rv0578c 0.006 1.9 2.3 PE-PGRS family protein PE_PGRS7

73

ClgR regulon

Rv0384c 0.001 0.7 0.7

myo-inositol-1-phosphate synthase Ino1 (inositol 1-phosphate synthetase) (D-glucose 6-phosphate cycloaldolase) (glucose 6-phosphate cyclase) (glucocycloaldolase)

Rv2745c 0.000 2.1 2.1 Probable inositol-monophosphatase ImpA (imp)

Rv0251c 0.001 3.3 3.6 Putative methyltransferase

Other function

Rv3138 0.012 1.1 0.9

Probable pyruvate formate lyase activating protein PflA (formate acetyltransferase activating enzyme) ([pyruvate formate-lyase] activating enzyme)

Rv0046c 0.005 0.7 0.6 Putative methyltransferase

Rv1604 0.014 1.1 0.8 Aminoglycoside 2'-N-acetyltransferase Aac (Aac(2')-IC)

Rv1405c 0.001 0.6 0.7 Possible magnesium chelatase

Rv2537c 0.039 0.9 1.0 Cadmium inducible protein CadI

Rv0262c 0.014 0.7 0.6 Glycosyltransferase MshA

Rv0958 0.026 0.6 0.8 Possible arylsulfatase AtsA (aryl-sulfate sulphohydrolase) (arylsulphatase)

Rv2641 0.019 1.6 1.2 Precorrin-6X reductase CobK

Rv0486 0.006 0.6 0.6 Probable 6-phosphogluconate dehydrogenase Gnd1

Rv0711 0.003 0.7 0.7 Probable acetaldehyde dehydrogenase (acetaldehyde dehydrogenase [acetylating])

Rv2070c 0.017 0.7 0.6 Probable bifunctional enzyme CysN/CysC: sulfate adenyltransferase (subunit 1) + adenylylsulfate kinase

Rv1844c 0.014 1.5 1.1 Probable branched-chain keto acid dehydrogenase E1 component, alpha subunit BkdA

Rv3535c 0.001 0.6 0.6 Probable GcpE protein

Rv1286 0.004 0.6 0.7 Probable L-aspartate oxidase NadB

Rv2497c 0.031 0.7 0.6 Probable L-lysine-epsilon aminotransferase Lat (L-lysine aminotransferase) (lysine 6-aminotransferase)

Rv2868c 0.015 0.7 0.6 Probable NADH dehydrogenase Ndh

Rv1595 0.001 0.7 0.7 Probable NrdI protein

Rv3290c 0.002 0.6 0.7 Probable PHOH-like protein PhoH1 (phosphate starvation-inducible protein PSIH)

Rv1854c 0.002 0.7 0.7 Probable polyketide synthase Pks8

Rv3052c 0.015 1.0 0.7 Probable sulfate adenylyltransferase subunit 2 CysD

Rv2368c 0.013 0.9 1.1 Probable polypeptide deformylase Def (PDF) (formylmethionine deformylase)

Rv1662 0.002 0.9 0.8 Probable monooxygenase

Rv1285 0.010 0.6 0.8 Hypothetical protein Rv1724c

Rv0429c 0.014 1.8 2.3 Hypothetical protein Rv1772

Rv3049c 0.030 0.8 0.6 Hypothetical protein Rv1907c

74

Unknown function

Rv1724c 0.013 0.8 1.0 Hypothetical protein Rv2016

Rv1772 0.001 0.8 0.8 Hypothetical protein Rv2633c

Rv1907c 0.003 1.0 0.8 Hypothetical protein Rv3403c

Rv2016 0.000 1.0 1.1 Hypothetical protein Rv3785

Rv2633c 0.047 0.5 0.9 Conserved 13E12 repeat family protein

Rv3403c 0.029 0.6 0.8 Conserved hypothetical membrane protein

Rv3785 0.003 0.8 0.9 Conserved hypothetical membrane protein

Rv0336 0.032 0.6 1.0 Conserved hypothetical protein

Rv1841c 0.022 0.7 0.6 Conserved hypothetical protein

















Rv2160A 0.047 0.6 1.1 Conserved hypothetical protein







Rv2390c 0.003 1.1 1.2 Conserved integral membrane protein YrbE1A

Rv3412 0.028 1.1 1.8 Conserved protein


75


Rv2004c 0.008 0.5 0.7 Conserved protein





Rv1322A 0.040 0.9 0.7 Conserved protein




Rv2749 0.001 1.1 1.0 Conserved protein (CPSA-related protein)

Rv1461 0.002 1.2 1.2 Conserved protein Acg

Rv0678 0.031 1.0 1.5 Probable conserved secreted protein TB22.2

Rv3267 0.004 0.8 0.9 Probable conserved transmembrane protein

Rv2032 0.037 0.5 0.8 Probable conserved transmembrane protein

Rv3036c 0.010 0.7 0.8 Probable conserved transmembrane protein

Rv0143c 0.006 0.7 0.9 Possible conserved membrane protein

Rv1072 0.003 0.9 0.9 Possible conserved membrane protein MmpS5

Rv0064 0.009 1.6 1.3 Probable endopeptidase ATP binding protein (chain B) ClpB (ClpB protein) (heat shock protein F84.1)

Rv0531 0.045 0.8 0.5 Transcriptional regulatory protein ClgR

Rv0677c 0.004 0.7 0.8 Heat shock protein Hsp (heat-stress-induced ribosome-binding protein A)

76

Chapter 4: Discussion

77

TB treatment failure due to persistent MTb remains a significant cause of death in many parts of the

world, yet we know little about how MTb is able to survive antibiotic treatment. A shorter treatment

regimen would go a long way towards solving the concomitant problems of persistence and resistance. In

these studies, we have described a novel anti-tubercular compound that targets a new pathway in MTb:

cytosolic proteolysis. We have also investigated the mechanisms of persister formation by conducting a

whole genome screen of transposon mutants, in order to identify genes that contribute to this process.

Lassomycin was previously described as a novel natural product lassopeptide that targeted ClpC1, the

ATPase adapter of the ClpP protease (Gavrish, Sit et al. 2014). It was found to bind to ClpC1 in vitro and

increase its hydrolysis of ATP, while simultaneously inhibiting its activation of ClpP. These results were

obtained biochemically with purified proteins. In this work, we sought to investigate whether these effects

occurred in whole cells of MTb, and whether either of these effects contributed to lassomycin’s

bactericidal effect. To determine how lassomycin treatment affected the proteome of MTb, we conducted

proteomic analysis in collaboration with Pacific Northwest National Laboratories, and found that

lassomycin treatment resulted in a shift in the proteome. Nearly equal numbers of proteins either

decreased or increased in abundance. Genes involved in lipid metabolism and the glyoxylate bypass, the

ClgR regulon, and mRNAse toxins were all increased, while ribosomal proteins, ESAT/ESX virulence

proteins, and antitoxins were all decreased. Many of these results overlapped with results from a previous

transcriptional study of MTb persisters (Keren, Minami et al. 2011). In order to determine which of these

changes are due to lassomycin’s effect on the protease and which are transcriptional responses, further

work should include transcriptional profiling of lassomycin treated cells. We also sought to characterize

the effect of lassomycin on ATP concentration in the cell. We used two experimental approaches: a

biochemical assay using luciferase enzyme to measure ATP concentration in culture, and an ATP-sensitive

FRET construct to measure changes in ATP concentration in single cells. Using the luciferase assay, we

found that lassomycin decreased ATP concentration compared to rifampicin or untreated control by 28-

and 77-fold, respectively. Lassomycin also killed a stationary phase culture 20-fold more effectively than

rifampicin. To be effective, MTb drugs must be able to kill non-growing cells, as a subpopulation of

bacteria may enter a dormant, persistent state during infection that is tolerant to drugs that target

78

actively-growing cells (Aldridge, Keren et al. 2014). We also found similar results with the single-cell

assay: the ATP concentration in the lassomycin cells was significantly lower than that of the untreated

cells, although not as low as that of the bedaquiline-treated cells. However, it is still unclear whether this

drop in ATP concentration is sufficient to explain lassomycin’s bactericidal activity. Titration experiments

should be performed to determine whether any concentration of lassomycin can induce an equivalent

drop in ATP to bedaquiline. In addition, during lassomycin’s initial investigation, resistant mutants were

obtained for target identification. These resistant mutants should be tested for their response in ATP

concentration to lassomycin treatment; if the resistant strains have a similar response in ATP

concentration as the wild type, then lassomycin probably kills through its effect on the proteome, but if

they are resistant to changes in ATP then it is probably the ATP concentration effects that cause

lassomycin to be bactericidal, as long as the mutant ClpC1 is still able to bind lassomycin.

In an independent project, we have also investigated the genetic basis for persister formation in MTb by

conducting a whole-genome screen of transposon mutants. This method has several advantages: the

mutant library is screened in a pool, eliminating the need for large numbers of laborious screening

experiments; each gene considered was represented by at least seven independent insertion mutants,

which is equivalent to repeating each experiment seven times; and a whole genome screen can reveal

previously unconsidered pathways that contribute to the persister phenomenon. We sequenced output

pools at both Day 1 and Day 6, with the intention of finding not only what genes contribute to long term

antibiotic survival, but also what causes rifampicin’s delayed bactericidal activity. We found that genes

involved in the plasma membrane and cofactor biosynthesis were over-represented in our list of hits of

Day 6. We also found that a small number of genes were uniquely required on Day 1, and that these genes

clustered in amino acid biosynthesis. These genes, as well as genes like MoeA1 and VapC30, which had

large fitness fold-changes but were not part of a cluster, should be validated using a 1:1 competition assay

against the wild type. Only a small amount of validation testing has been completed so far. Most mutants

tested so far do not have a growth defect; however, these mutants should also be tested for changes in

MIC which could account for the results observed in the Tn-Seq experiment. This type of screen does

suffer from its lack of ability to identify persister genes that are also required for in vitro growth, as seen

79

in the analysis of TCA cycle genes. This limitation should be carefully considered when functional

pathways are examined for their contributions to persister formation. These future efforts should provide

insights into MTb’s antibiotic survival mechanisms, and identify new candidate drug targets for novel

treatments of tuberculosis.

80

REFERENCES

Ahidjo, B. A., D. Kuhnert, et al. (2011). VapC toxins from Mycobacterium tuberculosis are ribonucleases that differentially inhibit growth and are neutralized by cognate VapB antitoxins. PLoS One 6(6): e21738.

Albrich, W. C., D. L. Monnet, et al. (2004). Antibiotic selection pressure and resistance in Streptococcus pneumoniae and Streptococcus pyogenes. Emerg Infect Dis 10(3): 514-517.

Aldridge, B. B., I. Keren, et al. (2014). The Spectrum of Drug Susceptibility in Mycobacteria. Microbiol Spectr 2(5).

Andrews, J. M. (2001). Determination of minimum inhibitory concentrations. J Antimicrob Chemother 48 Suppl 1: 5-16.

Argyrou, A., L. Jin, et al. (2006). Proteome-wide profiling of isoniazid targets in Mycobacterium tuberculosis. Biochemistry 45(47): 13947-13953.

Bacon, J., B. W. James, et al. (2004). The influence of reduced oxygen availability on pathogenicity and gene expression in Mycobacterium tuberculosis. Tuberculosis 84(3-4): 205-217.

Baek, S. H., A. H. Li, et al. (2011). Metabolic regulation of mycobacterial growth and antibiotic sensitivity. PLoS Biol 9(5): e1001065.

Baughn, A. D., S. J. Garforth, et al. (2009). An anaerobic-type alpha-ketoglutarate ferredoxin oxidoreductase completes the oxidative tricarboxylic acid cycle of Mycobacterium tuberculosis. PLoS pathogens 5(11): e1000662.

Boitel, B., M. Ortiz-Lombardia, et al. (2003). PknB kinase activity is regulated by phosphorylation in two Thr residues and dephosphorylation by PstP, the cognate phospho-Ser/Thr phosphatase, in Mycobacterium tuberculosis. Mol Microbiol 49(6): 1493-1508.

Bork, P. (1991). Shuffled domains in extracellular proteins. FEBS Lett 286(1-2): 47-54.

Bork, P., A. K. Downing, et al. (1996). Structure and distribution of modules in extracellular proteins. Q Rev Biophys 29(2): 119-167.

Boshoff, H. I., X. Xu, et al. (2008). Biosynthesis and recycling of nicotinamide cofactors in Mycobacterium tuberculosis. An essential role for NAD in nonreplicating bacilli. J Biol Chem 283(28): 19329-19341.

Bozdogan, B., D. Esel, et al. (2003). Antibacterial susceptibility of a vancomycin-resistant Staphylococcus aureus strain isolated at the Hershey Medical Center. J Antimicrob Chemother 52(5): 864-868.

Brotz-Oesterhelt, H., D. Beyer, et al. (2005). Dysregulation of bacterial proteolytic machinery by a new class of antibiotics. Nat Med 11(10): 1082-1087.

Bruschi, M. and F. Guerlesquin (1988). Structure, function and evolution of bacterial ferredoxins. FEMS Microbiol Rev 54: 155-178.

Carroll, P., D. G. N. Muttucumaru, et al. (2005). Use of a Tetracycline-Inducible System for Conditional Expression in Mycobacterium tuberculosis and Mycobacterium smegmatis. Applied and Environmental Microbiology 71(6): 3077-3084.

81

Castaneda-Garcia, A., T. T. Do, et al. (2011). The K+ uptake regulator TrkA controls membrane potential, pH homeostasis and multidrug susceptibility in Mycobacterium smegmatis. J Antimicrob Chemother 66(7): 1489-1498.

CDC (2013). Antibiotic Resistance Threats in the United States. CDC.

CDC. (Retrieved 9/1/2015). "Vancomycin-resistant Enterococci (VRE) and the Clinical Laboratory." Healthcare-Associated Infections, from http://www.cdc.gov/HAI/settings/lab/VREClinical-Laboratory.html.

Cholo, M. C., H. I. Boshoff, et al. (2006). Effects of clofazimine on potassium uptake by a Trk-deletion mutant of Mycobacterium tuberculosis. J Antimicrob Chemother 57(1): 79-84.

Cole, S. T., R. Brosch, et al. (1998). Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393(6685): 537-544.

Cole, S. T., K. D. Eisenach, et al. (2005). Tuberculosis And The Tubercle Bacillus (Hardcover), ASM press.

Conlon, B. P., E. S. Nakayasu, et al. (2013). Activated ClpP kills persisters and eradicates a chronic biofilm infection. Nature 503(7476): 365-370.

Dasgupta, A., P. Datta, et al. (2006). The serine/threonine kinase PknB of Mycobacterium tuberculosis phosphorylates PBPA, a penicillin-binding protein required for cell division. Microbiology 152(Pt 2): 493-504.

Dethlefsen, L. and D. A. Relman (2011). Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc Natl Acad Sci U S A 108 Suppl 1: 4554-4561.

Dorr, T., K. Lewis, et al. (2009). SOS response induces persistence to fluoroquinolones in Escherichia coli. PLoS Genet 5(12): e1000760.

Dorr, T., M. Vulic, et al. (2010). Ciprofloxacin causes persister formation by inducing the TisB toxin in Escherichia coli. PLoS Biol 8(2): e1000317.

Ducati, R. G., L. A. Basso, et al. (2007). Mycobacterial shikimate pathway enzymes as targets for drug design. Curr Drug Targets 8(3): 423-435.

Estorninho, M., H. Smith, et al. (2010). ClgR regulation of chaperone and protease systems is essential for Mycobacterium tuberculosis parasitism of the macrophage. Microbiology 156(Pt 11): 3445-3455.

Fernandez, P., B. Saint-Joanis, et al. (2006). The Ser/Thr protein kinase PknB is essential for sustaining mycobacterial growth. J Bacteriol 188(22): 7778-7784.

Fitzpatrick, C. and K. Floyd (2012). A Systematic Review of the Cost and Cost Effectiveness of Treatment for Multidrug-Resistant Tuberculosis. PharmacoEconomics 30(1): 63-80.

Flores-Valdez, M. A., R. P. Morris, et al. (2009). Mycobacterium tuberculosis modulates its cell surface via an oligopeptide permease (Opp) transport system. FASEB J 23(12): 4091-4104.

Fontan, P., V. Aris, et al. (2008). Global transcriptional profile of Mycobacterium tuberculosis during THP-1 human macrophage infection. Infect Immun 76(2): 717-725.

Franzblau, S. G., R. S. Witzig, et al. (1998). Rapid, low-technology MIC determination with clinical Mycobacterium tuberculosis isolates by using the microplate Alamar Blue assay. J Clin Microbiol 36(2): 362-366.

82

Fu, L. M. and S. C. Tai (2009). The Differential Gene Expression Pattern of Mycobacterium tuberculosis in Response to Capreomycin and PA-824 versus First-Line TB Drugs Reveals Stress- and PE/PPE-Related Drug Targets. Int J Microbiol 2009: 879621.

Gao, W., J. Y. Kim, et al. (2015). The cyclic peptide ecumicin targeting ClpC1 is active against Mycobacterium tuberculosis in vivo. Antimicrob Agents Chemother 59(2): 880-889.

Gao, W., J. Y. Kim, et al. (2014). Discovery and characterization of the tuberculosis drug lead ecumicin. Org Lett 16(23): 6044-6047.

Gavrish, E., C. S. Sit, et al. (2014). Lassomycin, a ribosomally synthesized cyclic peptide, kills Mycobacterium tuberculosis by targeting the ATP-dependent protease ClpC1P1P2. Chem Biol 21(4): 509-518.

Gengenbacher, M., S. P. S. Rao, et al. (2010). Nutrient-starved, non-replicating Mycobacterium tuberculosis requires respiration, ATP synthase and isocitrate lyase for maintenance of ATP homeostasis and viability. Microbiology 156(1): 81-87.

Gerdes, K. and E. Maisonneuve (2012). Bacterial persistence and toxin-antitoxin loci. Annu Rev Microbiol 66: 103-123.

Germain, E., M. Roghanian, et al. (2015). Stochastic induction of persister cells by HipA through (p)ppGpp-mediated activation of mRNA endonucleases. Proc Natl Acad Sci U S A 112(16): 5171-5176.

Gomez, J. E. and J. D. McKinney (2004). M. tuberculosis persistence, latency, and drug tolerance. Tuberculosis 84(1-2): 29-44.

Han, J. S., J. J. Lee, et al. (2010). Characterization of a chromosomal toxin-antitoxin, Rv1102c-Rv1103c system in Mycobacterium tuberculosis. Biochem Biophys Res Commun 400(3): 293-298.

Herrmann, K. M. and L. M. Weaver (1999). The Shikimate Pathway. Annu Rev Plant Physiol Plant Mol Biol 50: 473-503.

Hett, E. C. and E. J. Rubin (2008). Bacterial growth and cell division: a mycobacterial perspective. Microbiol Mol Biol Rev 72(1): 126-156.

Houben, E. N., L. Nguyen, et al. (2006). Interaction of pathogenic mycobacteria with the host immune system. Curr Opin Microbiol 9(1): 76-85.

Huang, D. W., B. T. Sherman, et al. (2007). The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8(9): R183.

Imamura, H., K. P. Nhat, et al. (2009). Visualization of ATP levels inside single living cells with fluorescence resonance energy transfer-based genetically encoded indicators. Proc Natl Acad Sci U S A 106(37): 15651-15656.

Keiser, T. L., A. K. Azad, et al. (2011). Comparative transcriptional study of the putative mannose donor biosynthesis genes in virulent Mycobacterium tuberculosis and attenuated Mycobacterium bovis BCG strains. Infect Immun 79(11): 4668-4673.

Kendall, S. L., P. Burgess, et al. (2010). Cholesterol utilization in mycobacteria is controlled by two TetR-type transcriptional regulators: kstR and kstR2. Microbiology 156(Pt 5): 1362-1371.

83

Kendall, S. L., M. Withers, et al. (2007). A highly conserved transcriptional repressor controls a large regulon involved in lipid degradation in Mycobacterium smegmatis and Mycobacterium tuberculosis. Mol Microbiol 65(3): 684-699.

Keren, I., N. Kaldalu, et al. (2004). Persister cells and tolerance to antimicrobials. FEMS Microbiol Lett 230(1): 13-18.

Keren, I., S. Minami, et al. (2011). Characterization and transcriptome analysis of Mycobacterium tuberculosis persisters. MBio 2(3).

Kieser, K. J. and E. J. Rubin (2014). How sisters grow apart: mycobacterial growth and division. Nat Rev Microbiol 12(8): 550-562.

Kim, S. and P. A. Pevzner (2014). MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun 5: 5277.

Kirstein, J., A. Hoffmann, et al. (2009). The antibiotic ADEP reprogrammes ClpP, switching it from a regulated to an uncontrolled protease. EMBO Mol Med 1(1): 37-49.

Kirstein, J., N. Moliere, et al. (2009). Adapting the machine: adaptor proteins for Hsp100/Clp and AAA+ proteases. Nat Rev Microbiol 7(8): 589-599.

Klinkenberg, L. G., J. H. Lee, et al. (2010). The stringent response is required for full virulence of Mycobacterium tuberculosis in guinea pigs. J Infect Dis 202(9): 1397-1404.

LaFleur, M. D., C. A. Kumamoto, et al. (2006). Candida albicans biofilms produce antifungal-tolerant persister cells. Antimicrob Agents Chemother 50(11): 3839-3846.

Lambert, P. A. (2002). Cellular impermeability and uptake of biocides and antibiotics in Gram-positive bacteria and mycobacteria. Journal of Applied Microbiology 92(Supplement S1): 46S-54S.

Lawn, S. D. and A. I. Zumla (2011). Tuberculosis. Lancet 378(9785): 57-72.

Leodolter, J., J. Warweg, et al. (2015). The Mycobacterium tuberculosis ClpP1P2 Protease Interacts Asymmetrically with Its ATPase Partners ClpX and ClpC1. PLoS One 10(5): e0125345.

Lewis, K. (2007). Persister cells, dormancy and infectious disease. Nat Rev Microbiol 5(1): 48-56.

Lewis, K. (2008). Multidrug tolerance of biofilms and persister cells. Curr Top Microbiol Immunol 322: 107-131.

Lewis, K. (2010). Persister cells. Annu Rev Microbiol 64: 357-372.

Lewis, K. (2012). Antibiotics: Recover the lost art of drug discovery. Nature 485(7399): 439-440.

Lewis, K., A. Spoering, et al. (2005). Persisters: specialized cells responsible for biofilm tolerance to antimicrobial agents. Biofilms, Infection, and Antimicrobial Therapy. J. Pace, M. E. Rupp and R. G. Finch. Boca Raton, London, New York, Singapore, Taylor & Francis: 241-256.

LoBue, P. (2009). Extensively drug-resistant tuberculosis. Curr Opin Infect Dis 22(2): 167-173.

Long, J. W. (1991). Essential Guide to Prescription Drugs 1992. New York, Harper Collins Publishers.

84

Macingwana, L. (2014). Investigation of the activity of sulfonamide anti-bacterial drugs in Mycobacterium tuberculosis and the role of oxidative stress on the efficacy of these drugs. PhD Dissertation, Stellenbosch University.

Maglica, Z., E. Ozdemir, et al. (2015). Single-cell tracking reveals antibiotic-induced changes in mycobacterial energy metabolism. MBio 6(1): e02236-02214.

Mahajan, R. (2013). Bedaquiline: First FDA-approved tuberculosis drug in 40 years. Int J Appl Basic Med Res 3(1): 1-2.

Mainous III, A. G. and C. Pomeroy (2010). Management of Antimicrobials in Infectious Diseases: Impact of Antibiotic Resistance, Springer.

Maisonneuve, E. and K. Gerdes (2014). Molecular mechanisms underlying bacterial persisters. Cell 157(3): 539-548.

Maisonneuve, E., L. J. Shakespeare, et al. (2011). Bacterial persistence by RNA endonucleases. Proc Natl Acad Sci U S A 108(32): 13206-13211.

Mattow, J., F. Siejak, et al. (2006). "roteins unique to intraphagosomally grown Mycobacterium tuberculosis. Proteomics 6(8): 2485-2494.

McKenna, M. (2013). Antibiotic resistance: the last resort. Nature 499(7459): 394-396.

Mdluli, K., T. Kaneko, et al. (2015). The Tuberculosis Drug Discovery and Development Pipeline and Emerging Drug Targets. Cold Spring Harb Perspect Med 5(6).

Miller, J. L. (2009). Molecular Mechanisms of the Inhibition of Host Cell Apoptosis by Mycobacterium tuberculosis. PhD Dissertation, University of Maryland, College Park.

Monroe, M. E., J. L. Shaw, et al. (2008). MASIC: a software program for fast quantitation and flexible visualization of chromatographic profiles from detected LC-MS(/MS) features. Computational Biology and Chemistry 32(3): 215-217.

Moyed, H. S. and K. P. Bertrand (1983). HipA, a newly recognized gene of Escherichia coli K-12 that affects frequency of persistence after inhibition of murein synthesis. J Bacteriol 155(2): 768-775.

Moyed, H. S. and S. H. Broderick (1986). Molecular cloning and expression of hipA, a gene of Escherichia coli K-12 that affects frequency of persistence after inhibition of murein synthesis. J Bacteriol. 166: 399-403.

Mulcahy, L. R., J. L. Burns, et al. (2010). Emergence of Pseudomonas aeruginosa strains producing high levels of persister cells in patients with cystic fibrosis. J Bacteriol 192(23): 6191-6199.

Murry, J. P., C. M. Sassetti, et al. (2008). Transposon site hybridization in Mycobacterium tuberculosis. Methods Mol Biol 416: 45-59.

Nelson, D. L. and M. M. Cox (2005). Lehninger Principles of Biochemistry. New York, W. H. Freeman and Company.

Nicas, M., W. W. Nazaroff, et al. (2005). Toward understanding the risk of secondary airborne infection: emission of respirable pathogens. J Occup Environ Hyg 2(3): 143-154.

O'Brien, R. J. (1994). Drug-resistant tuberculosis: etiology, management and prevention. Seminars in Respiratory Infections 9(2): 104-112.

85

Ollinger, J., T. O'Malley, et al. (2011). Validation of the essential ClpP protease in Mycobacterium tuberculosis as a novel drug target. J Bacteriol 194(3): 663-668.

Parish, T. (2014). Targeting Mycobacterial Proteolytic Complexes with Natural Products. Chemistry & Biology 21(4): 437-438.

Park, H. D., K. M. Guinn, et al. (2003). Rv3133c/dosR is a transcription factor that mediates the hypoxic response of Mycobacterium tuberculosis. Mol Microbiol 48(3): 833-843.

Personne, Y., A. C. Brown, et al. (2013). Mycobacterium tuberculosis ClpP proteases are co-transcribed but exhibit different substrate specificities. PLoS ONE 8(4): e60228.

Poetsch, A. and D. Wolters (2008). Bacterial membrane proteomics. Proteomics 8(19): 4100-4122.

Poole, K. (2004). Efflux-mediated multiresistance in Gram-negative bacteria. Clin Microbiol Infect 10(1): 12-26.

Primm, T. P., S. J. Andersen, et al. (2000). The stringent response of Mycobacterium tuberculosis is required for long-term survival. J Bacteriol 182(17): 4889-4898.

Pritchard, J. R., M. C. Chao, et al. (2014). ARTIST: high-resolution genome-wide assessment of fitness using transposon-insertion sequencing. PLoS Genet 10(11): e1004782.

Pullen, K. E., H. L. Ng, et al. (2004). An alternate conformation and a third metal in PstP/Ppp, the M. tuberculosis PP2C-Family Ser/Thr protein phosphatase. Structure 12(11): 1947-1954.

Quy, H. T., N. T. Lan, et al. (2003). Drug resistance among failure and relapse cases of tuberculosis: is the standard re-treatment regimen adequate? Int J Tuberc Lung Dis 7(7): 631-636.

Rachman, H., M. Strong, et al. (2006). Mycobacterium tuberculosis gene expression profiling within the context of protein networks. Microbes Infect 8(3): 747-757.

Raju, R. M., M. P. Jedrychowski, et al. (2014). Post-translational regulation via Clp protease is critical for survival of Mycobacterium tuberculosis. PLoS Pathog 10(3): e1003994.

Raju, R. M., M. Unnikrishnan, et al. (2012). Mycobacterium tuberculosis ClpP1 and ClpP2 function together in protein degradation and are required for viability in vitro and during infection. PLoS Pathog 8(2): e1002511.

Ramage, H. R., L. E. Connolly, et al. (2009). Comprehensive functional analysis of Mycobacterium tuberculosis toxin-antitoxin systems: implications for pathogenesis, stress responses, and evolution. PLoS Genet 5(12): e1000767.

Rand, K. H. and H. Houck (2004). Daptomycin synergy with rifampicin and ampicillin against vancomycin-resistant enterococci. J Antimicrob Chemother 53(3): 530-532.

Rengarajan, J., B. R. Bloom, et al. (2005). Genome-wide requirements for Mycobacterium tuberculosis adaptation and survival in macrophages. Proc Natl Acad Sci U S A 102(23): 8327-8332.

Rengarajan, J., C. M. Sassetti, et al. (2004). The folate pathway is a target for resistance to the drug para-aminosalicylic acid (PAS) in mycobacteria. Mol Microbiol 53(1): 275-282.

Sala, A., P. Bordes, et al. (2014). Multiple toxin-antitoxin systems in Mycobacterium tuberculosis. Toxins (Basel) 6(3): 1002-1020.

86

Sambandamurthy, V. K., S. C. Derrick, et al. (2005). Long-term protection against tuberculosis following vaccination with a severely attenuated double lysine and pantothenate auxotroph of Mycobacterium tuberculosis. Infect Immun 73(2): 1196-1203.

Scherrer, R. and H. S. Moyed (1988). Conditional impairment of cell division and altered lethality in hipA mutants of Escherichia coli K-12. J Bacteriol. 170: 3321-3326.

Schmitt, E. K., M. Riwanto, et al. (2011). The natural product cyclomarin kills Mycobacterium tuberculosis by targeting the ClpC1 subunit of the caseinolytic protease. Angew Chem Int Ed Engl 50(26): 5889-5891.

Schmitz, K. R. and R. T. Sauer (2014). Substrate delivery by the AAA+ ClpX and ClpC1 unfoldases activates the mycobacterial ClpP1P2 peptidase. Mol Microbiol 93(4): 617-628.

Schwab, U., K. H. Rohde, et al. (2009). Transcriptional responses of Mycobacterium tuberculosis to lung surfactant. Microb Pathog 46(4): 185-193.

Sebaihia, M., B. Wren, et al. (2006). The multidrug-resistant human pathogen Clostridium difficile has a highly mobile, mosaic genome. Nat Genet 38(7): 779-786.

Seeliger, J. C., C. M. Holsclaw, et al. (2012). Elucidation and chemical modulation of sulfolipid-1 biosynthesis in Mycobacterium tuberculosis. J Biol Chem 287(11): 7990-8000.

Shan, Y., D. Lazinski, et al. (2015). Genetic basis of persister tolerance to aminoglycosides in Escherichia coli. MBio 6(2).

Singh, R., C. E. Barry, 3rd, et al. (2010). The three RelE homologs of Mycobacterium tuberculosis have individual, drug-specific effects on bacterial antibiotic tolerance. J Bacteriol 192(5): 1279-1291.

Singh, R., M. Singh, et al. (2013). Polyphosphate deficiency in Mycobacterium tuberculosis is associated with enhanced drug susceptibility and impaired growth in guinea pigs. J Bacteriol 195(12): 2839-2851.

Slimings, C. and T. V. Riley (2014). Antibiotics and hospital-acquired Clostridium difficile infection: update of systematic review and meta-analysis. J Antimicrob Chemother 69(4): 881-891.

Sureka, K., T. Hossain, et al. (2010). Novel role of phosphorylation-dependent interaction between FtsZ and FipA in mycobacterial cell division. PLoS One 5(1): e8590.

Tally, F. P. and M. F. DeBruin (2000). Development of daptomycin for gram-positive infections. J Antimicrob Chemother 46(4): 523-526.

Theodore, A., K. Lewis, et al. (2013). Tolerance of Escherichia coli to fluoroquinolone antibiotics depends on specific components of the SOS response pathway. Genetics 195(4): 1265-1276.

Tilg, H. and A. Kaser (2011). Gut microbiome, obesity, and metabolic dysfunction. J Clin Invest 121(6): 2126-2132.

van Opijnen, T., K. L. Bodi, et al. (2009). Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat Methods 6(10): 767-772.

Vilcheze, C. and W. R. Jacobs, Jr. (2012). The combination of sulfamethoxazole, trimethoprim, and isoniazid or rifampin is bactericidal and prevents the emergence of drug resistance in Mycobacterium tuberculosis. Antimicrob Agents Chemother 56(10): 5142-5148.

87

Vilcheze, C., B. Weinrick, et al. (2010). NAD+ auxotrophy is bacteriocidal for the tubercle bacilli. Mol Microbiol 76(2): 365-377.

Wayne, L. G. and C. D. Sohaskey (2001). Nonreplicating persistence of Mycobacterium tuberculosis. Annu Rev Microbiol 55: 139-163.

WHO (2003). Treatment of Tuberculosis: Guidelines for National Programmes. Geneva, Switzerland, World Health Organization.

WHO (2010). Global tuberculosis control. Geneva, Switzerland, World Health Organization. 218.

WHO (2014). Global tuberculosis report 2014.

Williams, K. J., V. A. Jenkins, et al. (2015). Deciphering the metabolic response of Mycobacterium tuberculosis to nitrogen stress. Mol Microbiol.

Williams, M., V. Mizrahi, et al. (2014). Molybdenum cofactor: a key component of Mycobacterium tuberculosis pathogenesis? Crit Rev Microbiol 40(1): 18-29.

Williams, M. J., B. D. Kana, et al. (2011). Functional analysis of molybdopterin biosynthesis in mycobacteria identifies a fused molybdopterin synthase in Mycobacterium tuberculosis. J Bacteriol 193(1): 98-106.

Worley, M. V. and S. J. Estrada (2014). Bedaquiline: A Novel Antitubercular Agent for the Treatment of Multidrug-Resistant Tuberculosis. Pharmacotherapy.

Yaffe, M. B. and A. E. Elia (2001). Phosphoserine/threonine-binding domains. Curr Opin Cell Biol 13(2): 131-138.

Zhang, Y. J., T. R. Ioerger, et al. (2012). Global assessment of genomic regions required for growth in Mycobacterium tuberculosis. PLoS Pathog 8(9): e1002946.

Zhang, Y., W. W. Yew, et al. (2012). Targeting persisters for tuberculosis control. Antimicrob Agents Chemother 56(5): 2223-2230.

Zumla, A., P. Nahid, et al. (2013). Advances in the development of new tuberculosis drugs and treatment regimens. Nat Rev Drug Discov 12(5): 388-404.

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