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1 i Elongation Factor P-Dependent Translation and Metabolic Phenotypes of Salmonella by Steven Jeremy Hersch A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Molecular Genetics University of Toronto © Copyright by Steven J. Hersch (2016)

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i

Elongation Factor P-Dependent Translation and

Metabolic Phenotypes of Salmonella

by

Steven Jeremy Hersch

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Department of Molecular Genetics

University of Toronto

© Copyright by Steven J. Hersch (2016)

ii

Elongation Factor P-Dependent Translation and

Metabolic Phenotypes of Salmonella

Steven Jeremy Hersch

Doctor of Philosophy

Department of Molecular Genetics

University of Toronto

2016

Abstract

The facultative intracellular pathogen Salmonella has acquired adaptations contributing

to virulence, including pathogenicity islands and regulatory alterations. Elongation factor P (EF-

P) is a universally conserved translation factor, and deletion of efp in Salmonella results in

pleiotropic phenotypes, including hyperactive metabolism, reduced stress resistance, and

avirulence. Crystal structures demonstrated that EF-P adopts the shape of a tRNA and binds to

the ribosome to stimulate peptide bond synthesis. Preliminary analyses suggested that EF-P is

not a general translation factor but rather influences a specific subset of proteins. In this thesis I

investigate the mechanism underlying EF-P’s impact on translation and how its deletion leads to

the observed phenotypes. I present the Salmonella EF-P-dependent proteome, and particular

proteins offer explanations for efp mutant phenotypes. Meanwhile, other groups discovered that

EF-P rescues ribosomes stalled at polyproline motifs. I expand upon their work and show that

polyproline motifs are not strictly necessary or solely sufficient to render a protein dependent on

EF-P. I demonstrate that residues upstream of the polyproline motif significantly influence stall

strength. Furthermore, translation initiation rate greatly impacts EF-P-dependence. I present a

model wherein elongation stalls only reduce protein synthesis if they are more rate-limiting than

translation initiation.

iii

While examining efp mutants, I discovered a novel phenotype of wild-type Salmonella.

When dicarboxylates are the sole carbon source, Salmonella represses its growth using the

general stress response sigma factor RpoS, the RpoS-stabilizer IraP, and repression of the

dicarboxylate transporter DctA. Moreover, E. coli does not exhibit this phenotype and the E. coli

dctA promoter is sufficient to induce Salmonella growth. This divergence suggests a potential

role in virulence, and I examine the accumulation of succinate and itaconate in activated

macrophage. I demonstrate that the dicarboxylate phenotype does not influence intracellular

survival, and I propose novel hypotheses of why Salmonella has evolved this trait.

iv

Acknowledgments

Many people have influenced my graduate school experience and deserve thanks. All the

people who have donated strains, plasmids, ideas and time; the people who ask questions at

seminars because sometimes even those dreaded questions that you can’t answer and make you

think end up leading to a new figure in a paper; and the people who don’t necessarily ask

questions but still say “nice talk” afterward – you’re noticed and greatly appreciated!

Specifically I’d like to thank a few people from the Molecular Genetics department including

Ryan Gaudet for donating a significant amount of his time to teaching me how to culture and

infect macrophage. Joe Bondy-Denomy and Vanessa Tran for always being willing to discuss

cool science and get as excited as I do about microbiology conferences! I would also like to

thank my supervisory committee members Dr. Leah Cowen and Dr. Jack Greenblatt who have

always offered their time, advice and knowledge to support me over the course of my degree.

Their mentorship and motivation have been invaluable!

I also want to say thank you to all the members of the Navarre lab that I’ve interacted

with over the years: Former members Hazel Soto-Montoya, Emily Beckett, Grace Tong, and

Jeremy Soo for their support and ideas; Sabrina Ali and Andrea Leung for their guidance and for

creating such a positive working environment in the lab; and current members Vivian Cheung,

Kamna Singh, Erin Wong and Sinwan Basharat for listening to my ramblings and bouncing ideas

back and forth with me. Primarily I would like to thank Betty Zou, who not only established the

foundation for my project and was always willing to discuss EF-P, but moreover hugely

contributed to training, supporting and guiding me through much of my graduate career. All of

these people have not only been great colleagues but also great friends.

My family has always supported me in so many ways. My brothers Mike and Rob are my

closest friends and are always interested not just in hearing about my work, but in really learning

what it is and means despite it being in a completely different field than their own very exciting

careers. My parents, Ann and Edwin, whose high regard for education, discussion and doing

what I find interesting allowed me to pursue this career! They’ve contributed so much support in

everything from advice to finance to just being cool people that I like to hang out with.

Predominantly I want to thank them for raising me to be the person that I am today and for their

guidance and love, always. Furthermore I want to thank my fiancée Annie Walton who is always

there for me and has kept me sane over the years. She’s my favourite person in the world.

v

Finally I would like to thank my supervisor Dr. William Navarre. Will has been a

fantastic mentor and without him graduate school would have been nowhere near as exciting,

enlightening and successful. He gave me the freedom to pursue my crazy ideas, but was always

willing and able to figure out problems and guide me through them. He has also been an

extraordinary source of ideas and leadership. I will always remember the ‘best lab meeting ever’

when he saw a bunch of my odd data and on the spot compiled it into the primary model of my

thesis that made everything make sense! Beyond that he’s been a great person to work and get

along with as not just a mentor and supervisor, but as a friend.

Thank you everyone once again.

…And now on to science!!

vi

Table of Contents

Abstract .......................................................................................................................................... ii

Acknowledgments ........................................................................................................................ iv

Table of Contents ......................................................................................................................... vi

List of Tables ................................................................................................................................. x

List of Figures ............................................................................................................................... xi

List of Appendices ...................................................................................................................... xiii

1 Introduction ........................................................................................................................... 1

1.1 A brief overview of bacterial metabolism and response to stress ................................... 1

1.2 Salmonella....................................................................................................................... 4

1.2.1 Overview ..................................................................................................................................4

1.2.2 Virulence traits and divergence from E. coli ...........................................................................4

1.3 Bacterial translation ........................................................................................................ 6

1.3.1 Overview ..................................................................................................................................6

1.3.2 The role of ribosome stalling and translation rates during elongation ...................................9

1.4 PoxA, YjeK and EF-P, the ‘PYE pathway’ .................................................................... 9

1.4.1 Identification and overview .....................................................................................................9

1.4.2 Post-translational modification of EF-P ...............................................................................12

1.4.3 PYE mutants display pleiotropic phenotypes.........................................................................12

1.4.4 EF-P does not appear to be a general translation factor ......................................................12

1.5 Thesis rationale ............................................................................................................. 13

2 Methods ................................................................................................................................ 14

2.1 Bacterial strains ............................................................................................................. 14

2.1.1 Salmonella strain background and efp mutants ....................................................................14

2.1.2 Chromosomal replacement of the dctA promoter ..................................................................14

2.1.3 SGSC and ECOR collections .................................................................................................15

2.2 Plasmids ........................................................................................................................ 15

2.2.1 GFP translational fusion plasmids ........................................................................................15

2.2.2 DctA overexpression plasmid ................................................................................................16

2.2.3 STM3120 transcriptional fusion plasmid ..............................................................................17

2.3 Experimental Methods .................................................................................................. 17

2.3.1 N-Phenyl-1-Naphtylamine accumulation assay .....................................................................17

vii

2.3.2 Ethidium Bromide (EtBr) Accumulation Assay .....................................................................18

2.3.3 Stable isotope labelling of amino acids in cell culture (SILAC) ............................................18

2.3.4 Mass spectrometry and proteomic data analysis ...................................................................19

2.3.5 DAVID analysis .....................................................................................................................20

2.3.6 Translational fusion assay .....................................................................................................20

2.3.7 Reverse transcriptase quantitative PCR (RT-qPCR) .............................................................21

2.3.8 Immunoblotting ......................................................................................................................21

2.3.9 Curve Fitting ..........................................................................................................................22

2.3.10 Growth using dicarboxylates as a sole carbon source ......................................................22

2.3.11 Catalase assay ...................................................................................................................23

2.3.12 Acidified media survival assay ..........................................................................................23

2.3.13 Promoter induction fluorescence assay.............................................................................24

2.3.14 Macrophage survival assay ...............................................................................................25

2.3.15 Fluorescence microscopy ..................................................................................................25

2.3.16 Identification of STM3121-STM3117 orthologs in Salmonella serovars ..........................26

3 Analysis of the Salmonella efp mutant proteome ............................................................. 27

3.1 Overview ....................................................................................................................... 28

3.2 Results ........................................................................................................................... 29

3.2.1 Salmonella efp mutants are selectively more permeable to NPN ..........................................29

3.2.2 Deletion of yfcM has no effect on NPN permeability ............................................................31

3.2.3 Deletion of kdgM partially complements the permeability defect .........................................31

3.2.4 Identification of EF-P regulated proteins by SILAC .............................................................34

3.2.5 EF-P rescues ribosomes stalled at polyproline motifs ..........................................................36

3.2.6 Polyproline motifs are not always necessary or sufficient to confer EF-P-dependence .......36

3.2.7 Functional annotation analysis of EF-P-dependent proteins ................................................39

3.3 Discussion ..................................................................................................................... 40

4 Factors that influence EF-P-dependence .......................................................................... 42

4.1 Overview ....................................................................................................................... 43

4.2 Results ........................................................................................................................... 43

4.2.1 PoxB requires EF-P activity for its efficient translation .......................................................43

4.2.2 PoxB is dependent on EF-P due to a novel six amino acid motif, GSCGPG ........................47

4.2.3 AtpD and AtpA as model proteins to study polyproline motifs ..............................................50

4.2.4 Upstream residues influence EF-P-dependence in AtpD and AtpA ......................................54

4.2.5 The 5’ UTR plays a significant role in EF-P dependence .....................................................56

viii

4.2.6 The 5’ UTR regions that include the SD sequence and a potential translation enhancer play

a significant role in EF-P dependence ...............................................................................................58

4.2.7 Mutations that affect translation initiation influence EF-P dependence ...............................61

4.2.8 Initiation rate and stall strength correlate with EF-P dependence .......................................64

4.2.9 Modeling the interplay between translation initiation rate and elongation stalls .................67

4.2.10 Fate of stalled peptides in vivo ..........................................................................................68

4.3 Discussion ..................................................................................................................... 70

5 Salmonella evolved to suppress its uptake of dicarboxylic acids during stress ............. 75

5.1 Overview ....................................................................................................................... 76

5.2 Results ........................................................................................................................... 76

5.2.1 Wild-type but not efp or rpoS mutant Salmonella displays delayed growth using

dicarboxylic acids as a sole carbon source ........................................................................................76

5.2.2 Many Salmonella but few E. coli strains delay growth using succinate ................................79

5.2.3 The RpoS stabilizer IraP contributes to growth shutdown in succinate media .....................82

5.2.4 Supplementation with proline or citrate induces diauxic growth using succinate and

subsequent repression requires the stringent response ......................................................................85

5.2.5 Growth lag appears to be due to repression of succinate import ..........................................88

5.2.6 The E. coli dctA promoter is sufficient to induce Salmonella growth using succinate ..........88

5.3 Discussion ..................................................................................................................... 91

6 Salmonella versus macrophage-produced dicarboxylic acids......................................... 96

6.1 Overview ....................................................................................................................... 97

6.2 Results ........................................................................................................................... 97

6.2.1 Hypothesis and rationale that Salmonella may repress uptake of dicarboxylic acids to

improve survival in activated macrophage .........................................................................................97

6.2.2 Constitutive expression of dctA from a plasmid reduces Salmonella survival in acidified

succinate and in macrophage ...........................................................................................................100

6.2.3 Chromosomal E. coli dctA promoter does not reduce Salmonella survival ........................102

6.2.4 Salmonella contains genes that can degrade itaconate .......................................................104

6.2.5 The itaconate degradation operon is induced by STM3121 in the presence of itaconate ...106

6.2.6 Deletion of the itaconate degradation operon does not reduce Salmonella survival ..........108

6.2.7 The itaconate degradation operon is induced in J774 but not THP-1 macrophage............110

6.3 Discussion ................................................................................................................... 112

7 Discussion and Future Directions .................................................................................... 117

ix

7.1 Summary and Discussion ............................................................................................ 117

7.2 Future Directions ........................................................................................................ 121

7.2.1 Regulation of EF-P and its potential regulatory activity ....................................................121

7.2.2 Characterize the repression of the Salmonella dctA promoter ............................................122

7.2.3 Elucidate the role of Salmonella dctA repression in macrophage, .....................................123

7.2.4 Investigate alternate itaconate defence mechanisms ...........................................................124

7.2.5 Scrutinize the induction of itaconate synthesis in human and mouse macrophage .............125

7.3 Conclusions ................................................................................................................. 126

References .................................................................................................................................. 127

x

List of Tables

Table 1: Constructs used in Figure 22 ......................................................................................... 66

xi

List of Figures

Figure 1: Regulation of rpoS at transcriptional and post-transcriptional levels ............................. 3

Figure 2: Translation in bacteria ..................................................................................................... 8

Figure 3: Structure and modification of EF-P............................................................................... 11

Figure 4: Salmonella efp mutants are more permeable to NPN but not EtBr. .............................. 30

Figure 5: Deletion of yfcM has no effect on permeability to NPN ............................................... 32

Figure 6: Deletion of kdgM partially complements the efp mutant permeability defect .............. 33

Figure 7: A subset of proteins show significantly altered abundance in Δefp Salmonella ........... 35

Figure 8: Comparison of proteins identified in SILAC and those with EF-P-dependent

polyproline motifs. ........................................................................................................................ 38

Figure 9: PoxB-sfGFP translation is impaired in PYE mutants .................................................... 45

Figure 10: PoxB mRNA levels are similar in wild-type and ∆efp strains .................................... 46

Figure 11: The GSCGPG motif of PoxB renders it dependent on EF-P ...................................... 48

Figure 12: PoxB dependence on EF-P is determined by the amino acid sequence ...................... 49

Figure 13: ATP Synthase components show altered abundance in the efp mutant. ..................... 51

Figure 14: AtpD but not AtpA requires EF-P for its synthesis ..................................................... 52

Figure 15: AtpD but not AtpA shows decreased fluorescence throughout the growth curve of efp

mutant Salmonella. ....................................................................................................................... 53

Figure 16: Upstream residues influence EF-P-dependence .......................................................... 55

Figure 17: The 5’ UTR plays a role in EF-P dependence ............................................................. 57

Figure 18: The 5’ UTR regions affecting EF-P dependence include the ribosome binding site and

correlate with expression levels .................................................................................................... 59

Figure 19: GFP levels determined by immunoblotting follow similar trends as levels determined

by fluorescence. ............................................................................................................................ 60

Figure 20: The effect of the 5’ UTR on EF-P dependence requires an intact stall motif. ............ 62

Figure 21: Single base mutations in the SD sequence or start codon alter expression and EF-P

dependence .................................................................................................................................... 63

Figure 22: Translation initiation and elongation stall strength influence protein level ................ 65

Figure 23: Peptide abundance ratios do not change significantly before and after APP, PPG or

PPP motifs ..................................................................................................................................... 69

Figure 24: Model of the interplay between translation initiation rate and elongation stalls ......... 74

xii

Figure 25: Wild-type Salmonella displays an extended lag phase using dicarboxylic acids as a

sole carbon source ......................................................................................................................... 78

Figure 26: E. coli grows earlier than Salmonella using succinate ................................................ 81

Figure 27: Deletion of the RpoS stabilizer IraP results in early growth on succinate .................. 84

Figure 28: Proline or citrate induce growth using succinate and subsequent repression involves

the stringent response .................................................................................................................... 87

Figure 29: Expression of dctA induces growth using succinate ................................................... 89

Figure 30: Working model of Salmonella’s response to succinate ............................................... 94

Figure 31: Alignment of Salmonella and E. coli dctA promoters ................................................. 95

Figure 32: Dicarboxylic acids can act as proton shuttles in acidified conditions ......................... 99

Figure 33: Overexpression of DctA from a plasmid decreases survival in macrophage due to

toxicity ........................................................................................................................................ 101

Figure 34: Replacement of the Salmonella dctA promoter does not influence survival ............. 103

Figure 35: The itaconate degradation operon ............................................................................. 105

Figure 36: The STM3120 promoter is inducible by itaconate and STM3121 is necessary and

sufficient for this induction ......................................................................................................... 107

Figure 37: Deletion of the Salmonella itaconate degradation operon or its regulator does not

influence survival in acidified itaconate or macrophage ............................................................ 109

Figure 38: The STM3120 promoter is induced in mouse but not human macrophage .............. 111

Figure 39: Salmonella serovars Typhi, Paratyphi A and Agona uniquely lack the itaconate

degradation operon...................................................................................................................... 116

xiii

List of Appendices

Hersch_Steven_J_201611_PhD_datatable1.xlsx

Hersch_Steven_J_201611_PhD_datatable2.xlsx

1

1 Introduction

1.1 A brief overview of bacterial metabolism and response to stress

Bacteria must constantly adapt to changing environmental conditions by sensing their

surroundings and integrating signals to initiate rapid growth in nutrient rich situations or instigate

defence mechanisms and metabolic hibernation in response to stress1. Bacteria have evolved

many different means of sensing and responding to environmental cues, but many of the best

studied are those of E. coli. For instance, E. coli can respond to the presence or absence of the

rich carbon source glucose by means of a process called catabolite repression2-7. In brief, the

absence of glucose leads to the phosphorylation of the CyaA protein, which synthesizes the

second messenger molecular cyclic AMP (cAMP). cAMP is bound by the cAMP receptor

protein (CRP) allowing it to bind to DNA and regulate gene expression to allow for import,

degradation and use of alternate carbon sources. In addition to CRP-cAMP, many other response

systems also modulate bacterial metabolism. For instance the catabolite repressor activator, Cra

(also known as FruR), is able to sense metabolic flux to alter gene expression pertaining to

central metabolic pathways1,8-10. These include glycolysis, wherein glucose is degraded into

pyruvate, and the tricarboxylic acid (TCA) cycle, which further converts pyruvate to carbon

dioxide to produce the reduced intermediates NADH and FADH2 (Ecocyc, described in

reference 11, is a very useful database and tool for examining metabolic pathways of E. coli).

These intermediates can then be employed by the electron transport chain to produce energy via

ATP Synthase12.

Metabolic modulation is important during conditions of stress as exemplified by the

bacterial stringent response, wherein the RelA protein senses amino acid starvation and produces

the second messenger molecule, guanosine 5’-disphosphate 3’-diphosphate (ppGpp)13-16. This

second messenger is also produced by the SpoT protein in response to other cellular stress cues

to influence transcription of amino acid biosynthesis genes, transfer RNA (tRNA), and the

translation apparatus, including ribosomal and translation factor genes (amongst others). Bacteria

can also alter gene expression in response to stress using alternative sigma factors that associate

with the RNA polymerase holoenzyme to guide transcription. A particularly well studied

instance is the general stress response sigma factor RpoS (σS). RpoS can be activated in response

to a variety of different conditions including starvation, hyper-osmolarity and oxidative stress,

2

and can be regulated at all levels of synthesis from transcription to protein degradation17 (Figure

1). Moreover, RpoS has been linked to virulence in a number of pathogenic bacteria, likely by

contributing to survival within an infected host18. Indeed, pathogenic bacteria have further

adapted their mechanisms of sensing and reacting to their environment such that they are

especially equipped to survive and replicate within their particular host niche. A useful approach

to study differences in pathogenic and commensal bacteria is comparing the genomes and

phenotypes of two well characterized enterobacteria, E. coli and Salmonella. These bacteria are

closely related, yet Salmonella has acquired a number of adaptations in regulatory pathways,

metabolism and its response to stress that accommodate its virulent lifestyle19.

3

Figure 1: Regulation of rpoS at transcriptional and post-transcriptional levels. Figure is

shown as it appears in reference 17 and the publisher states that they allow for its use in this thesis

without special permission. Regulation of rpoS transcription (A), translation (B), activity (C),

and stability (D). The stringent response second messenger ppGpp and the antiadaptor IraP,

which binds to RssB to stabilize RpoS are particularly relevant in the later chapters of this thesis.

4

1.2 Salmonella

1.2.1 Overview

Salmonella are gram negative pathogenic bacteria, and the genus contain two species, S.

enterica and S. bongori. The S. enterica species contains thousands of serovars (serological

variants) that are grouped into six subspecies (numbered I to VI with group VII representing S.

bongori)20. Subspecies I, also known as subsp. enterica, contains the majority of serovars that

cause disease in humans, including the most studied Salmonella enterica subsp. enterica serovar

Typhimurium (non-typhoidal; causes gastroenteritis in humans) and the most lethal Salmonella

enterica subsp. enterica serovar Typhi (typhoidal; causes typhoid fever in humans)21. Salmonella

can invade the gut epithelium and non-typhoidal strains induce an inflammatory response

resulting in gastroenteritis. In contrast, Typhoid strains do not induce a prominent inflammation

response in the gastrointestinal tract but instead cause a systemic disease called typhoid fever

characterized by fever and abdominal pain22. Most Salmonella serovars can infect animal hosts

as well as (or instead of) humans, but serovars Typhi and Paratyphi A are human specific23,24.

Globally per annum, Salmonella is estimated to cause almost 100 million cases of gastroenteritis

resulting in about 150 000 deaths, and an estimated 22 million cases of typhoid fever resulting in

about 200 000 deaths25,26. Thus Salmonella does not only represent a useful model organism that

can be genetically manipulated in a lab setting, but also has a significant global disease burden,

and research on its virulence could have a significant impact on human health.

1.2.2 Virulence traits and divergence from E. coli

The study of how Salmonella evolved to be a pathogen has been greatly facilitated by

comparing it to the closely related bacteria Escherichia, including E. coli. Both genera are

members of the family Enterobacteriaceae; they diverged from their last common ancestor

approximately 100-160 million years ago, and many of the adaptations acquired by Salmonella

since then pertain to its virulent lifestyle19. The Salmonellae have acquired a number of genomic

islands by horizontal gene transfer called Salmonella pathogenicity islands (SPI). For instance,

all Salmonella including S. bongori have SPI-1, a cluster of more than 40 genes encoding a type

three secretion system and associated virulence factors that allow the bacteria to inject effector

5

proteins into host epithelial cells for the purpose of invasion and intracellular growth27. This

differs from most strains of E. coli (with the exception of enteroinvasive E. coli and Shigella

spp., which are now classified as variants of E. coli) which do not invade the epithelium and

remain in the gut lumen28. Over 20 SPIs have been identified in various serovars that each play

specific roles in Salmonella virulence21. For instance SPI-2, which is found in all S. enterica

isolates but not S. bongori, encodes a second type three secretion system and is involved in

intracellular survival and replication within host cells such as macrophage29. This is another

instance where E. coli and Salmonella differ – survival within host macrophage is important for

Salmonella virulence30,31. The induction of SPI-2 and other survival mechanisms within host

cells is therefore critical for Salmonella’s pathogenic lifestyle.

Salmonella has adapted its regulatory networks to recognize and react to environments

that it encounters during infection, such as the phagosome of macrophage. For example, E. coli

and Salmonella both encode a ‘two-component’ signalling system known as PhoPQ. The PhoPQ

system of Salmonella, however, has evolved to respond to conditions within a macrophage

phagosome, including low pH, low magnesium concentration, and the presence of antimicrobial

peptides32-35. Activation of the PhoPQ system contributes to induction of virulence genes,

reorganization of the Salmonella outer membrane to defend against the innate immune system,

and stimulation of the stress response via RpoS36-38.

In addition to regulatory adaptations like that of PhoPQ, Salmonella has also acquired

subtle but important metabolic differences from E. coli. An interesting example is Salmonella’s

distinctive ability to employ tetrathionate as a terminal electron acceptor using the ttrRSBCA

gene cluster in SPI-239. Though tetrathionate is not normally present in the gut, the inflammation

induced by Salmonella invasion of host cells leads to an oxidative burst and the oxidation of

readily available thiosulfate to tetrathionate40. This provides Salmonella in the anaerobic gut

lumen with a terminal electron acceptor allowing it to respire under conditions where E. coli

cannot. Salmonella can thereby use alternative carbon sources such as ethanolamine that require

respiration to provide energy, and ultimately out-compete other microbes in the gut41. Thus, over

the course of a non-typhoidal infection, Salmonella “wants” to induce inflammation in order to

acquire tetrathionate and provide it with a fitness edge over the normal microbiome, allowing it

to replicate and disseminate efficiently.

6

The identification and characterization of the factors and adaptations that enable

Salmonella to be a pathogen is ongoing, and novel discoveries continue to be made. During his

post-doctoral work, my supervisor, Dr. William Navarre, identified mutations in two poorly

characterized genes, poxA and yjeK, that render Salmonella avirulent. As discussed below, these

were later identified to play a role in translation and so the next section will provide an overview

of protein synthesis in bacteria.

1.3 Bacterial translation

1.3.1 Overview

Cellular protein levels are the sum of transcription, translation and mRNA and protein

degradation rates, and all of these incorporate their own regulatory mechanisms. During

translation, one protein is produced for each ribosome that successfully recognizes and

assembles on an mRNA transcript (initiation), constructs a polypeptide (elongation) and releases

the full-length amino acid chain (termination). The process of decoding an mRNA transcript to

synthesize a polypeptide that can fold into a protein is complex, and bacteria have evolved a

number of translation factors to catalyze the process. The ribosome itself is a complex enzyme

consisting of large (50S) and small (30S) subunits totalling 3 ribosomal RNAs (rRNA) and about

50 proteins that combine to form the complete 2.5 megadalton (70S) ribosome that catalyzes

peptide bond formation (Figure 2A)42.

The ribosome first assembles on the 5’ untranslated region (UTR) of an mRNA transcript

with the help of three initiation factors (IF1-3) and usually an initiator tRNA charged with a

methionine amino acid corresponding to the ATG start codon (although other codons, including

GTG, can act as start codons for certain proteins). Elongation of the nascent peptide then

progresses in cycles as the elongation factor EF-Tu recruits amino acyl-charged tRNAs to the

‘acceptor’ (A) site of the ribosome. ATP hydrolysis by EF-Tu ensures that only tRNAs that

properly match the mRNA codon remain bound. Following this decoding event, EF-Tu releases

the tRNA, which rocks forward toward the tRNA in the ‘peptidyl’ (P) site of the ribosome. At

this point the ribosome catalyzes the formation of a peptide bond between the amino acid residue

bound to the P site tRNA and the new residue in the A site. Thus the amino acid and any

7

polypeptide chain attached to it are transferred to the A site tRNA. Another elongation factor,

EF-G then contributes to translocation wherein the A site tRNA (with the growing polypeptide

chain) shifts to the P site, and the former P site tRNA (now uncharged) shifts to the ‘exit’ (E) site

for release from the ribosome. This process repeats until the mRNA encodes a stop codon, which

is recognized by release factor (RF) 1 or 2 to release the new protein and (with the aid of RF-3)

dissociate the subunits of the ribosome for reuse on other transcripts.

Decreased efficiency of any of these phases (initiation, elongation or termination) reduces

the number of ribosomes able to complete translation and, hence, lowers total protein production.

To maximize the efficient use of resources, the rate-limiting step usually occurs at the point of

initiation and extensive work has made it clear that the strength of ribosome binding greatly

affects the amount of protein produced from an mRNA transcript43. By altering elements of the

ribosome binding site (RBS) translation rates can be controlled in order to fine tune gene

expression at the post-transcriptional level44-46. One such element is the Shine-Dalgarno (SD)

sequence of canonical eubacterial mRNA, which is complementary to a region of the 16S

ribosomal RNA that recruits the 30S ribosome to the mRNA transcript. Thus the strength of

binding between the SD sequence and the 16S rRNA is directly related to translation initiation

rate. Other elements of the 5’ UTR can also play a role in translation initiation including mRNA

secondary structure, small RNA (sRNA) binding sites, the start codon, and translation enhancers

– AT-rich regions in proximity to the SD sequence that improve small subunit binding47-49.

8

A)

B)

Figure 2: Translation in bacteria. A) Detailed overview of translation in bacteria. Figure is

shown with permission as it appears in reference 42. B) Simplified cartoon depicting key aspects

of bacterial translation that are particularly relevant for this thesis.

Start codon Stop codon

Open reading frame (ORF)

5’ UTR

mRNA

Initiation Elongation Termination(Rate limiting step)

70S5’ 3’

3’ UTR

50S

30SRibosome

9

1.3.2 The role of ribosome stalling and translation rates during elongation

Though initiation is typically the primary regulatory step in translation, slower

progression through elongation can also influence protein synthesis. This has been studied

extensively for rare codons that slow the elongation rate due to lower cellular tRNA

concentration, and starvation conditions that restrict the amount of aminoacylated tRNA50-54. In

these instances ribosomes must pause until a properly charged tRNA accurately decodes at the

A-site, and the impact of this delay on protein levels can be influenced by rare codon clustering,

repetition, or position within an open reading frame (ORF)55-59.

Elongation stalls have been shown to regulate mRNA structures affecting ribosome

binding, influence transcription of downstream genes via impeding the transcriptional terminator

Rho, potentially fascilitate proper protein folding, or directly mediate protein production60-65. For

instance, during translation of secM, ribosomes pause at a particular amino acid sequence until

the N-terminal peptide is recognized by the SecA secretion apparatus – thus directing secM

expression66,67. Interaction between the nascent polypeptide and the ribosome exit tunnel has

been implicated to mediate this and other stalls in elongation or termination68-71. Though these

stall motifs bear little sequence similarity, many involve prolines, presumably because of their

unique characteristics and relatively poor peptide bonding capability72,73.

1.4 PoxA, YjeK and EF-P, the ‘PYE pathway’

1.4.1 Identification and overview

During his post-doctoral work, Dr. Navarre identified mutations in two genes, poxA and

yjeK, that decreased Salmonella growth, increased their resistance to the nitric oxide (NO)-

donating compound S-Nitrosoglutathione (GSNO), and rendered them avirulent in a mouse

model of infection74. Very little was known about either gene other than that PoxA was required

for the efficient expression of the pyruvate oxidase (poxB) gene75,76. Bioinformatic analysis of

genomic location, synteny, and species distribution of both genes revealed that poxA and yjeK

are genetically linked to a third gene, efp, encoding elongation factor P (EF-P). This was a poorly

understood translation factor that had only been demonstrated to modestly stimulate peptide

bond formation in vitro77,78. Crystal structures of EF-P, both alone and in complex with the

10

ribosome, revealed that EF-P adopts the size and shape of a tRNA and that it binds to the

ribosome between the P and E sites and can align itself adjacent to the P site tRNA (Figure 3A

and B)79-81. Moreover, EF-P is universally conserved in all bacteria, and eukarya and archaea

also encode homologs called initiation factor 5A (e/aIF-5A), strongly suggesting that it plays a

critical role in translation82.

11

Figure 3: Structure and modification of EF-P. A) EF-P is similar in size and shape to a tRNA.

Arrows indicate sites of amino acylation and β-lysylation for tRNA and EF-P respectively.

Figure is shown as it appears in reference 80 and the publisher states that they allow for its use in

this thesis without special permission. B) EF-P occupies a unique position between the peptidyl

(P) and exit (E) sites. Figure is reproduced with permission from reference 79. PTC, peptidyl

transferase center. I, II and III indicate domains of EF-P. C) Modification of EF-P by YjeK and

PoxA. Figure is shown as it appears in reference 83 and the publisher states that they allow for its

use in this thesis without special permission.

A.

B.

C.

12

1.4.2 Post-translational modification of EF-P

In collaboration with Dr. Michael Ibba at the Ohio State University, Dr. Navarre and a

former graduate student in the lab, Dr. Betty Zou, determined that YjeK and PoxA coordinate to

post-translationally modify EF-P74,83-85. Specifically, YjeK synthesizes (R)-β-lysine, which PoxA

attaches to lysine 34 of EF-P to form a unique lysyl-(R)-β-lysine moiety (Figure 3C).

Importantly, this modification extends towards the peptidyl transferase centre of the ribosome to

modulate peptide bond formation. This unique β-lysyl modification is critical for the function of

EF-P in E. coli and Salmonella. Recently it was discovered that the YfcM protein further

hydroxylates the β-lysyl moiety of modified EF-P, however the role of this hydroxylation is

unclear, as it appears to be dispensable for EF-P activity under all conditions tested thus far86,87.

1.4.3 PYE mutants display pleiotropic phenotypes

The Navarre lab has collectively termed PoxA, YjeK and EF-P the “PYE pathway”. As

might be expected, individual Salmonella PYE mutants phenocopy one another and display a

wide variety of pleiotropic phenotypes74,84. Of particular note are the results of Biolog Phenotype

MicroarraysTM 88, which show that Salmonella strains containing defects in the PYE pathway

display misregulated metabolism and increased respiration when growing on certain nutrient

sources, and yet these mutants are hypersusceptible to a range of cellular stressors, including

hypo-osmolarity, detergents, and many pharmacologically unrelated antibiotics. PYE mutants

also display reduced motility and virulence. Analysis of membrane proteins revealed that efp

mutant Salmonella greatly overexpresses kdgM encoding an outer membrane porin84. This may

influence the permeability of PYE mutants and proposes the hypothesis that they are

hypersusceptible to stress due to increased permeability of their membranes.

1.4.4 EF-P does not appear to be a general translation factor

The question of what role EF-P plays in translation remained unresolved until very

recently, and most of the progress studying this factor occurred in our lab and others during the

course of my time as a student. Evidence including the analysis of membrane proteins mentioned

13

above suggested that EF-P is not a general translation factor but rather plays a role in the

translation of specific proteins. The notion that EF-P affects the production of a limited set of

proteins was further supported by two-dimensional difference in gel electrophoresis (2D-DIGE)

analysis of a Salmonella poxA mutant74. In this method, total cellular proteins from wild type and

poxA mutant Salmonella are labeled with fluorescent dyes, mixed in equal amounts, and then

separated by 2D-polyacrylamide gel electrophoresis (PAGE) prior to analysis by mass

spectrometry. Using this technique, Betty Zou was able to determine that only a relatively small

subset of proteins are affected by perturbations in the PYE pathway – a finding in agreement

with earlier work on the efp mutant of Agrobacterium89. However, she could only

unambiguously identify a small number of these proteins due to crowding on the 2D gel. This

warranted further investigation into the EF-P-dependent proteome by more sensitive, quantitative

and comprehensive methods.

1.5 Thesis rationale

When I began my doctoral research, EF-P was known to be a universally conserved

translation factor that resulted in a range of pleiotropic phenotypes when deleted from a number

of bacteria, including Salmonella. However, the mechanisms underlying these phenotypes were

uncharacterized. Data demonstrating the overexpression of outer membrane porins implicated a

potential role for increased permeability in the hyper-susceptibility of PYE mutants to stress. As

well, the increased respiration when growing on specific nutrient sources suggested that PYE

mutants may also misregulate their metabolism, potentially limiting their ability to adapt to

stress. Furthermore, preliminary proteomic analyses suggested that EF-P is not a general

translation factor but rather impacts the synthesis of a specific subset of proteins. Yet the

mechanism of how it influences translation and why only specific proteins require it remained

unknown. In light of these observations, I began my graduate work in January of 2011 with the

goal of characterizing the EF-P-dependent proteome of Salmonella and determining the

mechanism by which it influences the translation of a specific subset of proteins. Moreover I

aimed to ascertain the basis of PYE mutant phenotypes and the role of EF-P in Salmonella

metabolism, stress resistance, and virulence.

14

2 Methods

2.1 Bacterial strains

2.1.1 Salmonella strain background and efp mutants

(Used in all chapters)

With the exception of the SILAC experiment and the SGSC collection, all Salmonella

strains are derivatives of Salmonella enterica subsp. enterica serovar Typhimurium (S.

Typhimurium) strain 14028s. Lambda red recombination was used to generate all of the gene

knockout mutants in this background84,90. Two efp mutant strains were generated by Dr. Betty

Zou: the original mutant, WN934, is a deletion for the entire efp open reading frame including

part of the adjacent yjeK promoter, effectively creating a double mutant. In contrast, WN1405 is

a deletion of the majority of the efp open reading frame (basepairs 145 to 424) that leaves the

yjeK promoter intact. In this thesis, WN934 is the efp mutant in the original permeability assays

(Figure 4). For all other data (with the exception of SILAC) the Salmonella efp mutant is

WN1405.

For SILAC I employed strain WN1269, an arginine and lysine auxotroph (ΔargH ΔlysA)

derivative of S. Typhimurium strain SL1344 that was utilized in an earlier study and was a

generous gift from Dr. Brian Coombes and Dr. Colin Cooper91. I confirmed the auxotrophy by

testing growth in minimal media in the absence of lysine or arginine. I transferred the efp

mutation from WN934 into SL1344 by transduction using the HT105/1 int-201 derivative of

phage P22 to generate strain WN130892.

2.1.2 Chromosomal replacement of the dctA promoter

(Used in Chapters 5 and 6)

I generated chromosomal swaps to replace the dctA promoter in the Salmonella

chromosome with the E. coli dctA promoter. I first inserted the 500bp upstream of the E. coli

dctA start codon into the pXG10sf plasmid upstream of the full length Salmonella dctA ORF and

downstream of the chloramphenicol resistance cassette used to maintain the plasmid93. I then

used PCR to amplify from the end of the dctA ORF to the end of the chloramphenicol resistance

15

cassette and including overhangs homologous to the Salmonella genome. I then inserted the

fragment into the Salmonella chromosome by lambda red recombination and subsequent

transduction using p22HT phage.

2.1.3 SGSC and ECOR collections

(Used in Chapter 5)

The Salmonella genetic stock centre (SGSC) collection contains a range of Salmonella

strains isolated from a variety of sources and maintained by the University of Calgary SGSC. I

employed three subgroups: The SARA collection contains 72 strains from 5 serovars within

subspecies I (including Salmonella Typhimurium) allowing for a more thorough analysis with

many strains from each serovar94. The SARB collection contains 37 strains including a more

broad range of serovars from subspecies I95. Finally the SARC collection contains two isolates

from each Salmonella enterica subspecies and also of Salmonella bongori96. The E. coli

reference (ECOR) collection is a collection of 72 E. coli strains from various sources that are

commonly used as a sample spanning the genetic diversity of E. coli97. E. coli K12 is a common

lab strain and specifically BW25113 was the strain used as the genetic background for the

generation of the Keio collection98.

2.2 Plasmids

2.2.1 GFP translational fusion plasmids

(Used in Chapter 4)

I generated plasmids used for the PoxB translational fusion assay by PCR amplification

of the 5’ UTR (28 base pairs upstream of the start codon) and full-length (excluding the stop

codon) or C-terminally truncated poxB gene using S. Typhimurium strain 14028s genomic DNA

as a template. I inserted these amplicons into the NsiI and NheI sites of pXG10sf to generate

translational fusions to “super-folder” green fluorescent protein (sfGFP)93,99. The plasmid

employs the constitutively active PLtet0-1 promoter and the tightly controlled pSC101 low copy-

number origin of replication to ensure minimal variation in transcript levels. I generated point

16

mutations by site directed mutagenesis of the full-length PoxB construct. Plasmids are named

according to the truncation site in poxB (i.e. the PoxB 68 construct is truncated after codon 68 of

poxB) or the location of a specific point mutation in full-length poxB (e.g. PoxB P76L contains a

proline to leucine mutation at codon 76). LacZ186, included as a control, consists of the first 186

codons of the E. coli lacZ gene inserted into the NsiI and NheI sites of the pXG10sf plasmid,

similar to the PoxB fusions.

I constructed the plasmids used for the AtpD and AtpA translational fusion assay in a

similar manner as the PoxB fusion plasmids with one main exception: Since AtpD and AtpA are

translated form a single mRNA encoding all eight ATP Synthase subunits, I did not include the

entirety of the upstream mRNA in the plasmid. Instead I included only the 74 or 75 base-pairs

(for AtpD and AtpA respectively) upstream of the ATG start codon as a 5’ UTR. I generated

mutations and swaps using Gibson isothermal assembly cloning with overlapping primers100.

Plasmids are named according to the regions that are swapped from AtpA into AtpD-sfGFP or

vice versa (i.e. the ‘AtpD - AtpA UTR’ construct encodes full-length AtpD-sfGFP with the 5’

UTR from atpA).

2.2.2 DctA overexpression plasmid

(Used in Chapter 5 and 6)

I made the constitutive dctA expression plasmid by inserting the full length dctA 5’ UTR

and ORF into pXG10sf under the control of the constitutively active PLtet0-1 promoter. I

generated the IraP complementation plasmid by inserting the iraP ORF and the upstream 300bp

into pXG10sf. Since the plasmid and the original dctA and iraP mutant strains employ

chloramphenicol resistance, the chloramphenicol cassette was first flipped out of the dctA and

iraP mutants using the pCP20 plasmid encoding FLP recombinase101. The heat-unstable pCP20

plasmid was eliminated from the strain by passaging overnight at 42°C.

17

2.2.3 STM3120 transcriptional fusion plasmid

(Used in Chapter 6)

To test the induction of the itaconate degradation operon (STM3120 to STM 3117) I

generated a plasmid construct with the STM3120 promoter (P3120) driving expression of sfGFP.

Specifically I inserted P3120 (the 333bp upstream of the STM3120 start codon and thereby

including the 5’ UTR and promoter to 25bp upstream of the predicted -35 box) into pXG10sf to

replace the PLtet0-1 promoter and drive expression of LacZ186-sfGFP93. For the STM3121-P3120

construct, I extended the same promoter to 1570bp upstream of the STM3120 start codon to

include the entire STM3121 ORF and a predicted transcriptional terminator following it.

For fluorescence microscopy I modified the pXG10sf plasmid by inserting constitutively

expressed (PLtet0-1 promoter) mCherry into a transcriptionally independent region of the

plasmid. I designated this plasmid ‘independent constitutive mCherry’ or pICM. To generate

pICM-P3120, I inserted the constitutive mCherry into the pXG10sf-P3120 plasmid in the same way.

The same was true for the pICM-STM3121-P3120 plasmid. Thus for the pICM plasmids, mCherry

is constitutively expressed, whereas sfGFP levels are driven by the promoter in question.

2.3 Experimental Methods

2.3.1 N-Phenyl-1-Naphtylamine accumulation assay

(Used in Chapter 3)

This protocol was adapted from others previously established to measure the intrinsic

permeability of bacterial membranes102-104. LB broth containing appropriate antibiotics was

inoculated from overnight cultures of the test strains. The cells were grown to early stationary

phase (10 hours), centrifuged (13,000 rpm for 1 min) and washed twice in the assay buffer (5

mM HEPES pH 7.2, 137 mM NaCl). The cells were resuspended in buffer and the OD600 was

adjusted to 1.0. Fractions from these suspensions were diluted and plated in triplicate to acquire

CFU counts. 100 μl were also loaded into wells of a black 96-well fluorescence microplate

(Greiner Bio-One). Controls containing only buffer were included and three technical replicates

were conducted. A 20 μM solution of NPN (Sigma-Aldrich) was prepared in the assay buffer just

prior to experimentation and 100 μl was added to appropriate wells of the microplate, yielding a

18

final bacterial OD600 of 0.5 and a final NPN concentration of 10 μM. Controls were included in

which buffer was added instead of dye. Immediately after the addition of the dye, the plate was

inserted into a Tecan Infinite M200 microplate reader (a delay of approximately 15s) and was

read at excitation and emission wavelengths of 355 nm and 402 nm, respectively. Readings were

taken every 25 seconds for 10 minutes. Analysis of the fluorescence values was conducted using

Microsoft Excel. Background fluorescence (NPN in buffer only) was subtracted from the raw

values and the result was divided by the number of CFU in the corresponding sample. Finally,

the fluorescence value of wild-type Salmonella at time 0 was defined to be 100% and all other

values were normalized accordingly.

2.3.2 Ethidium Bromide (EtBr) Accumulation Assay

(Used in Chapter 3)

EtBr uptake was measured very similarly to NPN uptake with several differences: The

buffer used was 50 mM KH2PO4 pH 7.0, 137 mM NaCl. As well, cells were diluted to an OD600

of 0.4 (final OD600 of 0.2 after addition of EtBr). The concentration of the stock EtBr solution

was 12 μM (final concentration of 6 μM after adding to cells). EtBr fluorescence was read at

excitation and emission wavelengths of 545 nm and 600 nm, respectively. Finally, since wild-

type fluorescence values were at background levels, the value for wild-type at time 0 was zero

for some trials. The values could therefore not be represented as a percentage of wild-type at

time 0. The data is instead presented relative to the acrAB mutant at time 0.

2.3.3 Stable isotope labelling of amino acids in cell culture (SILAC)

(Used in Chapter 3)

For SILAC, I supplemented MOPS minimal media with amino acids at the

concentrations previously described91 and used 0.2% (w/v) glycerol as a carbon source. For

heavy isotope labeled samples arginine and lysine were replaced with 13C6-Arg and 2H4-Lys

isotopes at the equivalent molar concentration. WN1269 (wild-type) was grown in heavy

19

arginine and lysine isotopes and WN1308 (Δefp) was grown in light isotopes in this media for

16h to ensure complete labeling of all proteins. Strains were subsequently subcultured 1:200 into

the same media and grown to an optical density (600nm) of 0.5 (mid-log phase) at which point

the cells were harvested by centrifugation. The pellets were subjected to lysis by heating to 99°C

for 5 minutes in fresh lysis buffer (1% deoxycholate [DOC] in 50mM ammonium bicarbonate

[NH4HCO3] at pH 8). Cell debris was removed by centrifugation at 13,000 x g for 15 minutes

and the supernatant was frozen at -80°C until used.

2.3.4 Mass spectrometry and proteomic data analysis

(Used in Chapter 3)

The mass spectrometry and data analysis described here were conducted by Kyung-Mee

Moon and Dr. Leonard Foster. I have included the method in this thesis as background to clarify

how the SILAC analysis data was produced. For analysis of the isotope-labeled lysates, 30μg of

protein from each of WN1269 (heavy) and WN1308 (light) were combined, fractionated into 12

pieces by gel electrophoresis and in-gel trypsin digested following a previously outlined

procedure105. The resulting peptides were subjected to liquid chromatography coupled to tandem

mass spectrometry using an Orbitrap XL (Thermo Scientific) as described previously106. Data

analysis was conducted using the MaxQuant software107 to generate an average normalized

heavy / light ratio over three biological replicates and Significance B values were calculated

using Perseus108. To determine significance, we used the cutoff of a Significance B score of less

than 0.01 in at least one trial. This statistic measures significance within a single trial even for

proteins that were only identified in one the three biological replicates. When we assess

significance using the average of Significance B scores across all three trials (excluding scores

where the protein of interest was not identified in a given trial), we obtain similar percentages of

proteins containing EF-P target motifs that show significantly altered abundance. For example,

using an average Significance B score of less than 0.05, we identified 107 proteins with

significantly altered abundance. 61 of these are greater than two-fold less abundant in the efp

mutant and 25 of these 61 contain a PPP, PPG or APP motif.

20

2.3.5 DAVID analysis

(Used in Chapter 3)

Lists of proteins affected by EF-P were generated based on the SILAC data and/or on the

presence of putative EF-P target motifs in Salmonella proteins. The proteins identified in SILAC

had gene identification numbers corresponding to the SL1344 strain used. I identified each

corresponding homolog gene identification number in the S. Typhimurium strain LT2 so that I

could upload the gene lists to the Database for Annotation, Visualization and Integrated

Discovery (DAVID) online analysis software109. The program compares a list of genes with

functional annotation databases including GO terms, KEGG pathways and SP-PIR keywords

amongst others. I employed the Functional Annotation Tools to determine which functional

groups were overrepresented amongst my query genes. DAVID employs a one-tailed Fisher

Exact Probability Test to calculate p-values of individual annotation groups and cluster p-values

are generated as the geometric mean of the p-values of all constituent groups. Functional

annotation clusters with p-values less than 0.05 are shown in figures.

2.3.6 Translational fusion assay

(Used in Chapter 4)

Wild-type (S. Typhimurium str. 14028s) and the isogenic Δefp mutant carrying plasmids

encoding poxB, atpD or atpA translational fusions to sfGFP were grown in MOPS minimal

media supplemented with 0.2% (w/v) glucose and 20μg/mL chloramphenicol. Growth was

conducted for 16 hours at 37°C in a TECAN Infinite M200 microplate reader with constant

aeration. Optical density (OD600nm) and GFP fluorescence readings (excitation and emission

wavelengths of 475nm and 511nm respectively) were taken every 15 minutes. For both OD600

and GFP, background values taken from no-cell controls were subtracted from all readings. For

clarity the wild-type / Δefp fluorescence ratios are displayed as GFP fluorescence per OD600 unit

at 10 hours of growth. Results obtained when cultures were measured at 6, 8, 12, or 16 hours of

growth or in mid-log phase were similar to those at 10 hours110.

21

2.3.7 Reverse transcriptase quantitative PCR (RT-qPCR)

(Used in Chapter 4)

Wild-type and efp mutant Salmonella containing the translational fusion plasmids were

grown to an OD600 of 0.5 (OD600 ≈ 0.2 as read in TECAN Infinite M200) in MOPS minimal with

0.2% glucose as a carbon source and supplemented with chloramphenicol (20μg/mL). 1.5 ml of

each sample was pelleted in RNA Protect Bacterial Reagent (Qiagen) according to

manufacturer’s instructions and stored at -80°C. Subsequent RNA preparations were performed

using the Aurum Total RNA Mini kit (BioRad). Reverse transcription was performed using the

iScript cDNA synthesis kit (BioRad) using random hexamer primers. The cDNA generated was

used for quantitative PCR analysis using iQ SYBR Green Mix (BioRad) according to the

manufacturer’s protocol. To measure poxB-sfGFP mRNA levels at two points on the transcript I

used primers specific to either poxB or sfGFP. The transcript of 16S rRNA was used as an

internal standard for normalization. Since the strains also encode poxB in their genomes, the

mRNA levels obtained for the lacZ186 construct with the poxB-specific primers was subtracted

as background from all other strains. Under the conditions tested this background poxB mRNA

level was negligible (greater than 300 fold lower) compared to any of the strains expressing

PoxB-GFP from a plasmid.

2.3.8 Immunoblotting

(Used in Chapter 4)

Salmonella strains were grown in MOPS minimal media with 0.2% glucose as the carbon

source to mid log phase (OD600 ~ 0.5), washed twice in wash buffer (1 mM Tris pH 8.0, 5 mM

magnesium acetate) and lysed by sonication in lysis buffer (9.32 M urea, 2.67 M thiourea, 40

mM Tris, 86.78 mM CHAPS, pH 8.5). 10μg of total cell lysate was mixed with 2x SDS loading

buffer and boiled for 10 min at 95ºC. Proteins were separated by SDS-PAGE and transferred

(semidry) to a nitrocellulose membrane. Following 1 h blocking at room temperature in 5% milk

in TBST (1x Tris-buffered saline, 0.05% Tween 20), immunoblotting was conducted overnight

in TBST +5% milk at 4ºC using a mouse anti-DnaK antibody (1:50,000; Enzo Life Sciences) and

a mouse monoclonal antibody specific for the beta subunit of E. coli ATP Synthase (1:1000;

22

MitoSciences) or a mouse monoclonal antibody specific for GFP (1:1000; Santa Cruz

Biotechnology). Blots were washed and subsequently incubated for 1 h at room temperature with

HRP-fused goat anti-mouse antibody (1:10,000 in TBST +5% milk) for ECL imaging (Thermo

Scientific). Quantification of AtpD protein levels relative to DnaK was done using Image Lab

software (Bio-Rad Laboratories).

2.3.9 Curve Fitting

(Used in Chapter 4)

Fluorescence data was fit to a ‘1-exp’ curve of the form 𝑦 = 𝐴(1 − 𝑒−𝐵𝑥) by the

minimum sum of chi-squares method: For each observed wild-type fluorescence value (x-

coordinate), I calculated chi-square values by comparing the corresponding observed ∆efp

fluorescence to the y-value calculated by the equation. The chi-squares for all constructs with the

same ORF were summed and the values of A and B yielding the minimal sum of chi-squares was

solved using Microsoft Excel’s ‘Solver’ function. Coefficient of determination (R2) comparing

observed ∆efp data and y-values predicted by the solved 1-exp equation was calculated using

Excel’s ‘RSQ’ function.

2.3.10 Growth using dicarboxylates as a sole carbon source

(Used in Chapter 5)

Strains were grown overnight in LB media inoculated from single colonies. The next day

the cultures were centrifuged, the LB was removed and the cells were resuspended in MOPS

minimal media with no carbon source to an optical density (OD600) of approximately 1.75. This

suspension was used to inoculate (1/200 dilution) MOPS minimal media containing 0.2% (unless

otherwise indicated) succinate as the sole carbon source. For some experiments, carbon sources

other than succinate were used and these are indicated. Growth was conducted in a TECAN

Infinite M200 plate reader at 37ºC with shaking and OD600 was read every 15 minutes. Of note,

for salts and hydrates of carbon sources the final concentration reflects the percent of the carbon

23

source itself; for example 0.2% succinate was made as 0.47% sodium succinate dibasic

hexahydrate.

The SGSC and ECOR collections screen was conducted by testing 47 strains per run in a

96-well plate such that each strain had two technical replicates and wild-type and rpoS mutant

Salmonella could be included on every plate as quality controls. Each strain was tested on at

least three separate days. To identify the first timepoint for each technical replicate where the

OD600 surpassed 0.1, I employed Excel’s Match and Index functions and values from at least two

technical replicates were averaged for each biological replicate.

2.3.11 Catalase assay

(Used in Chapter 5)

For each replicate of the SGSC and ECOR collections screen, I tested each strain for

catalase activity as an analog for RpoS function111. Following inoculation of the LB overnight

cultures used as inoculum, I spotted 10μl of each culture onto an LB plate. The next day these

spots were tested for catalase activity by the addition of 10μl of hydrogen peroxide. Bubbling

was scored compared to wild-type (catalase positive) and rpoS mutant (catalase negative)

Salmonella.

2.3.12 Acidified media survival assay

(Used in Chapter 6)

LPM media was made as described previously91 and succinate or itaconate were added to

either 0.2% or 0.4% as indicated in figures. The pH of the media was then adjusted to 4.4. For

the assay, 0.1 OD units (the equivalent of 100μl of culture at an OD600 of 1) of an LB overnight

culture was centrifuged and the supernatant was removed. The pellet was resuspended in 1ml of

acidified LPM media by vortexing for 15 seconds in a 1.5ml tube. The tube was then incubated

in a 37ºC water bath. At time points the tube was vortexed for 10 seconds and 5 spots of 10μl

were plated for colony forming units (CFU). An additional 10μl was also serially diluted and

24

plated for CFU (5 spots of 10μl at each dilution). To allow for multiple test strains while

maintaining exact timepoints, tubes were staggered by 1 minute to allow for the sampling of one

strain prior to the next sample reaching the same timepoint. For strains including a plasmid,

antibiotics were included in the overnight culture used as an inoculum but were not included in

the challenge media. Since I found that residual chloramphenicol from the overnight culture

increased survival in this assay, I generated ampicillin resistant versions of the plasmids as

described in Bacterial Strains and Plasmids. All plasmid-containing strains used in this assay

contained the ampicillin resistant version of the plasmid.

2.3.13 Promoter induction fluorescence assay

(Used in Chapter 6)

I tested induction of the Salmonella STM3120 promoter using a transcriptional fusion of

P3120 to sfGFP in either the pXG10sf or pICM plasmid. Data from the two plasmids were

combined as the inducible region is identical and the plasmids only differ in the constitutively

active mCherry expressed independently from pICM. Overnight LB cultures were used to

inoculate (1/200 dilution) either LB, LPM, or MOPS minimal media containing 0.2% of the

indicated carbon source. Supplements for testing fluorescence induction were added to a

concentration of 0.2%. Of note, for salts and hydrates of carbon sources the final concentration

reflects the percent of the carbon source itself; for example 0.2% succinate was made as 0.47%

sodium succinate (dibasic) hexahydrate. Growth was conducted in a TECAN Infinite M200 plate

reader at 37ºC with shaking and OD600 and GFP fluorescence (475nm and 511nm excitation and

emission wavelengths respectively) were read every 15 minutes. For clarity, bar graphs show

fluorescence at 16h post inoculation. Chloramphenicol was included in all media at a

concentration of 20μg/ml to maintain the plasmids.

25

2.3.14 Macrophage survival assay

(Used in Chapter 6)

The THP-1 human monocyte cell line and the J774 mouse macrophage cell line were

maintained in RPMI Medium 1640 (with L-glutamine) supplemented with 10% FBS and 1%

Glutamax, and grown at 37ºC and 5% CO2. For infection assays, THP-1 cells were seeded in 96-

well plates at 50,000 per well with 50nM PMA (phorbol 12-myristate 13-acetate) added to the

media to induced them to become adherent macrophage. After 48h, the media was replaced with

normal growth media (no PMA) overnight. For infections with J774 macrophage the cells were

seeded in 96-well plates at 50,000 per well overnight in normal growth media. Salmonella were

added onto seeded cells in RPMI growth media at a multiplicity of infection (MOI) of

approximately 20 bacteria to 1 macrophage and centrifuged for 10 minutes at 1000rpm for

maximum cell contact. After centrifuging the plate was placed at 37ºC (5% CO2) and this was

called ‘time 0’. After 30 minutes, non-adherent Salmonella were washed off by three washes

with PBS followed by replacement with fresh media containing 100 μg/ml gentamicin to kill

extracellular Salmonella. At 2 hours the media was replaced with media containing gentamicin at

10 μg/ml. At timepoints, media was replaced with PBS containing 1% Triton X-100 and

incubated at room temperature for 5 minutes. Following resuspension by vigorous pipetting,

samples were serially diluted. For each dilution, five 10μl spots were plated for CFU counting.

Each sample included three separate wells as technical replicates and CFU for that biological

replicate was the average from each well (a total of 15 x 10μl spots counted per sample).

2.3.15 Fluorescence microscopy

(Used in Chapter 6)

Fluorescence microscopy was conducted similarly to the macrophage survival assay with

some exceptions: Cells were seeded in 24-well plates containing glass coverslips at 125,000 per

well. Bacteria were infected at an MOI of approximately 100 to maximize the instance of

macrophage that contain bacteria. At timepoints the media was removed and cells were washed

three times with PBS. They were then fixed for 10 minutes at room temperature in PBS

containing 4% paraformaldehyde (PFA). Following three more PBS washes the cells were

26

permeabilized for 10 minutes in PBS + 0.2% Triton X-100 + 1% BSA. Coverslips were washed

again with PBS, dipped in water, and edges dabbed to remove excess liquid. They were then

mounted on slides using 3μl mounting media containing DAPI and allowed to dry overnight in

the dark. Coverslips were then sealed to the slide with nail polish and allowed to dry for 1h in the

dark. Slides where viewed using a Zeiss Observer.z1 microscope using a 100x oil immersion

objective and the Zeiss Zen microscopy software. Images were taken with a Zeiss Axiocam 506

mono camera mounted on the microscope. For viewing, a positive control constitutively

expressing GFP and mCherry was used to establish exposure times for all fluorophores and the

same exposure time was employed for all samples. For the pICM-PSTM3120 experiment presented

here I employed a 2 second exposure for mCherry and 1 second exposure for sfGFP. For

quantification, ImageJ software was employed to calculate fluorescence intensities in the red and

green channels and a ratio was generated for greater than 125 bacteria per test condition across

three biological replicates.

2.3.16 Identification of STM3121-STM3117 orthologs in Salmonella serovars

(Used in Chapter 6)

To explore the presence of the itaconate degradation operon (STM3120-STM3117) and

its regulator (STM3121) across many serovars of Salmonella, I employed the Prokaryotic

Genome Analysis Tool (PGAT)112. Specifically I used the Ortholog Search Tool to identify the

presence or absence of the operon in the 69 Salmonella strains in the database. The Synteny

Mapper was used to align the operon region of select genomes.

27

3 Analysis of the Salmonella efp mutant proteome

Primary references

Much of the data presented in this chapter is published work reproduced with permission from:

Zou SB, Hersch SJ, Roy H, Wiggers B, Leung AS, Buranyi S, Xie JL, Dare K, Ibba M,

Navarre WW. Loss of elongation factor P disrupts bacterial outer membrane integrity. J

Bacteriol. 2012;194(2):413-425. 84

Bullwinkle TJ, Zou SB, Rajkovic A, Hersch SJ, Elgamal S, Robinson N, Smil D,

Bolshan Y, Navarre WW, Ibba M. (R)-β-lysine-modified elongation factor P functions in

translation elongation. J Biol Chem. 2013;288(6):4416-4423. 87

Hersch SJ, Wang M, Zou SB, Moon K-M, Foster LJ, Ibba M, Navarre WW. Divergent

protein motifs direct elongation factor P-mediated translational regulation in Salmonella enterica

and Escherichia coli. mBio. 2013;4(2). 110

Acknowledgements

Dr. Betty Zou generated the efp mutant strains used throughout this work as well as the

yfcM mutant. She also identified that kdgM was overexpressed in the efp mutant, generated the

Δefp ΔkdgM double knockout strain and conducted experiments assessing the role of KdgM in

stress susceptibility. I conducted the experiments assessing the permeability of these strains.

I conducted the SILAC experiment in collaboration with Dr. Leonard Foster and Kyung-

Mee Moon who performed the LC-MS/MS and initial analysis to generate protein abundance

ratios from the mass spectrometry output data. I prepared the labelled Salmonella protein lysates

and later conducted further data analysis.

Mengchi Wang in the lab of our collaborator Dr. Michael Ibba conducted the analysis of

the SILAC dataset that identified APP as an additional tripeptide polyproline motif showing EF-

P-dependence in vivo.

28

3.1 Overview

PYE mutants display similar pleiotropic phenotypes, including reduced growth in rich

media, reduced motility, altered metabolism on certain nutrient sources and hypersusceptibility

to an array of cellular stressors and antimicrobial compounds74,84. Previous proteomic analyses of

poxA and efp mutants suggested that EF-P acts in a targeted manner rather than as a global

translation factor, implying that EF-P is required for the efficient translation of specific

transcripts and that failure to efficiently produce these proteins in an efp mutant could underlie

the phenotypes observed74,84,89,113. In this chapter I present data showing that efp mutant

Salmonella demonstrates increased permeability that is partially due to kdgM overexpression.

However, concomitant work conducted by Dr. Betty Zou indicated that deletion of kdgM does

not affect other Δefp phenotypes such as increased stress susceptibility – implicating a role in

these phenotypes for additional unidentified EF-P-dependent proteins.

I conducted an unbiased in vivo analysis of the Salmonella efp mutant using stable-

isotope labeling of amino acids in cell culture (SILAC). This revealed a subset of proteins with

significantly altered abundance in the efp mutant, and I further conducted a functional annotation

analysis highlighting ontological groups that were overrepresented amongst these proteins. Some

of the proteins and functional groups showing altered steady state levels in the efp mutant can

provide a parsimonious explanation for the observed PYE mutant phenotypes. Shortly after the

completion of this SILAC dataset, two publications from other labs employed in vitro techniques

to demonstrate that EF-P rescues the translation of ribosomes that are stalled at particular

tripeptide motifs, specifically Proline-Proline-Proline (PPP) or Proline-Proline-Glycine

(PPG)114,115. These findings established that EF-P functions to rescue ribosomes stalled at

specific motifs and enabled me to compare the presence of these proline containing motifs with

my in vivo SILAC dataset of EF-P-dependent proteins. Interestingly, my analysis reveals that the

presence of a polyproline motif is not always necessary or sufficient to render a protein

dependent on EF-P for its efficient translation in vivo.

29

3.2 Results

3.2.1 Salmonella efp mutants are selectively more permeable to NPN

The hypersensitivity of PYE mutants to a broad range of cellular stressors suggests that

they may have an altered cell envelope resulting in increased membrane permeability. I tested

the permeability of the S. Typhimurium (Str. 14028s) efp mutant (WN934) by measuring the

uptake of the fluorescent probes ethidium bromide (EtBr) and N-phenyl-1-naphthylamine

(NPN), two dyes that fluoresce upon entering the cell but for different reasons103,104,116. EtBr will

not fluoresce unless it permeates both the inner and outer membranes to intercalate into nucleic

acid. In contrast, NPN is a nonpolar dye that will fluoresce strongly in a hydrophobic

environment such as the lipid bilayer of the inner membrane or the inner leaflet of the outer

membrane. The outer leaflet of the outer membrane of gram negative bacteria is composed of

lipolysaccharide (LPS), which forms a tightly packed semicrystalline structure that effectively

excludes both hydrophilic and hydrophobic compounds, including NPN103,117.

I found that uptake of NPN was significantly higher in the efp mutant than in wild-type

Salmonella or a complemented strain expressing exogenous efp from a plasmid (Figure 4A). This

uptake happened rapidly and equilibrium was almost achieved within the 10 to 15 seconds

between adding the dye and measuring fluorescence. In contrast, I observed no significant

difference in permeability to EtBr between wild-type and efp mutant Salmonella (Figure 4B).

This was not a systematic error of the method, as an isogenic strain lacking the AcrAB multidrug

efflux pump served as a positive control. While the strain lacking AcrAB is expected to exhibit

the same rate of dye influx as wild-type Salmonella, a decrease in the cell’s efflux capability

results in the accumulation of the dye and subsequent fluorescence. As expected, this acrAB

mutant showed increased uptake of both NPN and EtBr, but notably achieved equilibrium at a

slower rate than the efp mutant. These kinetics suggest that, although the efp strain contains a

functional acrAB efflux system, increased membrane permeability enhances its rate of dye influx

such that it exceeds the capacity of the efflux system.

30

Figure 4: Salmonella efp mutants are more permeable to NPN but not EtBr. Fluorescent dye

uptake for NPN (A) or EtBr (B) by the S. Typhimurium 14028s wild-type, efp mutant (Δefp

WN934), and complemented (Δefp pEF-P pYjeK) strains. The isogenic ΔacrAB strain was

included as a positive control. Dyes were added at time 0 with a 10 to 15 second delay while the

plate was inserted into the plate reader. Data shows fluorescence per CFU normalized to the

wild-type strain at time 0 (for NPN) or the ΔacrAB strain at time 0 (for EtBr). Values are the

average of four biological replicates. p-values indicated in the legend were calculated using a

two-tailed t-test (assuming unequal variance) comparing strains to the wild-type at 10 minutes (*

p < 0.05, ** p < 0.01, *** p < 0.001). Figure is as it appears in Zou, Hersch et al. (2012), J

Bacteriol 84.

31

3.2.2 Deletion of yfcM has no effect on NPN permeability

A third EF-P-modifying protein called YfcM was recently identified that acts after PoxA

and YjeK to hydroxylate modified EF-P in E. coli86. However despite its role in post-

translationally modifying EF-P, and in contrast to the PYE mutants, no phenotypes have yet been

observed for yfcM deletion mutants in either Salmonella or E. coli87. Indeed I find that in contrast

to efp, deletion of the Salmonella yfcM gene has no effect on NPN permeability (Figure 5). These

data suggest that the hydroxylation modification performed by YfcM is not necessary for EF-P

function.

3.2.3 Deletion of kdgM partially complements the permeability defect

By examining altered protein levels in membrane fractions Dr. Betty Zou identified

specific proteins showing altered abundance in an efp mutant. In particular, an outer membrane

porin called KdgM was much more highly expressed in the efp mutant than in wild-type

Salmonella. To address the possibility that the increased permeability that I observed for efp

mutants was due to the overexpression of kdgM, I tested the NPN permeability of the kdgM

mutant and efp kdgM double mutant. I observed a partial complementation of the efp mutant

permeability defect when the kdgM gene was also deleted (Figure 6). This finding supports the

hypothesis that the increased permeability of the efp mutant is at least partially due to the

overexpression of kdgM. In parallel, Betty tested the efp kdgM double mutant under stress

conditions, such as sensitivity to the aminoglycoside antibiotic gentamicin and the zwitterionic

detergent lauryl-sulfobetaine. Despite the role that I found for kdgM in permeability, Betty

observed no change in the double mutant compared to the efp single mutant for resistance to

either compound or for any of the other phenotypes tested. Cumulatively these data suggest that

overproduction of KdgM partially accounts for the increased permeability of the efp mutant but

the mechanisms underlying the other PYE mutant phenotypes cannot be attributed to it and likely

involve other EF-P-dependent proteins.

32

Figure 5: Deletion of yfcM has no effect on permeability to NPN. As in Figure 4A,

fluorescent dye uptake for NPN by the S. Typhimurium 14028s wild-type, efp mutant (Δefp),

yfcM mutant (ΔyfcM) and efp yfcM double knockout (Δefp ΔyfcM) strains. The isogenic ΔacrAB

strain was included as a positive control. Dyes were added at time 0 with a 10 to 15 second delay

while the plate was inserted into the plate reader. Data shows NPN fluorescence in arbitrary units

(AU) normalized to one million colony forming units (CFU). Values are the average of four

biological replicates and error bars showing one standard deviation are shown at 0, 2.5, 5 and 10

minutes. Figure is as it appears in Bullwinkle, Zou, Rajkovic, Hersch et al. (2013) J Biol

Chem87.

33

Figure 6: Deletion of kdgM partially complements the efp mutant permeability defect. As in

Figure 4A, fluorescent dye uptake for NPN by the S. Typhimurium 14028s wild-type, efp mutant

(Δefp), kdgM mutant (ΔkdgM) and efp kdgM double knockout (Δefp ΔkdgM) strains. The

isogenic ΔacrAB strain was included as a positive control. Dyes were added at time 0 with a 10

to 15 second delay while the plate was inserted into the plate reader. Data shows fluorescence

per CFU normalized to the wild-type strain at time 0. Values are the average of four biological

replicates. p-values indicated in the legend were calculated using a two-tailed t-test (assuming

unequal variance) comparing strains to the efp mutant at 10 minutes (* p < 0.05, ** p < 0.01, ***

p < 0.001).

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

0 100 200 300 400 500 600

% F

luo

resc

en

ce

Time (seconds)

NPN

WT

efp-

1131-

efp- 1131-

acrAB-

WT **

Δefp

ΔkdgM ***

Δefp ΔkdgM *

ΔacrAB *

34

3.2.4 Identification of EF-P regulated proteins by SILAC

Previous proteomic analysis of a Salmonella poxA mutant using 2D-DIGE suggested that

only a relatively small subset of proteins were affected by perturbations in the PYE pathway – a

finding in agreement with earlier work on the efp mutant of Agrobacterium74,89. However, only a

limited number of proteins could be unambiguously identified due to crowding on the 2D gels.

To gain a more comprehensive view of the effect of EF-P on protein levels, I collaborated with

the lab of Dr. Leonard Foster to conduct stable isotope labeling of amino acids in cell culture

(SILAC) in conjunction with quantitative mass spectrometry-based proteomics to examine the

proteome of an efp mutant strain of Salmonella enterica Sv. Typhimurium strain SL1344 (strain

WN1308). I generated heavy isotope-labelled samples and we were able to detect, quantify and

identify a total of 1517 proteins, or approximately 34% of the 4514 proteins predicted to be

encoded in the S. Typhimurium strain SL1344 genome (Supplementary DataTable1). Using

Microsoft Excel, I identified proteins showing greater than two-fold altered abundance in the efp

mutant and further screened for a significant difference using a Significance B cutoff of 0.01

(Figure 7). By this criterion, 87 proteins showed changes of two-fold or greater and 28 displayed

a change of greater than ten-fold. Of the 87 proteins with significantly altered abundance, 49

showed a decrease in steady-state levels in the efp mutant strain and are more likely to be

directly dependent on EF-P owing to its characterized stimulatory effect on translation.

35

Figure 7: A subset of proteins show significantly altered abundance in Δefp Salmonella.

Histogram outlining the distribution of protein expression ratios identified in SILAC comparing

the efp mutant (WN1308) with the isogenic wild-type background strain (WN1269) such that

proteins showing increased abundance in the efp mutant are further left and those with decreased

abundance in the efp mutant are further right. Columns indicate the number of proteins with

average expression ratio between two neighbouring x-axis values. Note that underlined values in

the x-axis indicate a change in scale. The inset table shows the number of SILAC hits

demonstrating a greater than two-fold difference in protein level between the efp+ and ∆efp

strains. The second column further indicates the proteins with a Significance B value of less than

0.01 in at least one trial. Expression ratios shown are the average normalized heavy/light ratios

of three biological replicates. Figure is shown as it appears in Hersch et al. (2013), mBio 110.

36

3.2.5 EF-P rescues ribosomes stalled at polyproline motifs

Shortly after the completion of my SILAC proteomic analysis of efp mutant Salmonella,

two papers were published in Science demonstrating that EF-P rescues ribosomes stalled at

sequences of consecutive prolines114,115. Specifically, these articles identified two tripeptide

motifs, three consecutive prolines (PPP) or two prolines followed by a glycine (PPG), that

require EF-P for their efficient translation in vitro. They went on to demonstrate that the

ribosome stalls at these motifs and stalling is dependent on the amino acid combinations rather

than on specific codons. Furthermore they showed that in the absence of EF-P the ribosome fails

to efficiently make a peptide bond between the second and third amino acids of the motif,

resulting in the stall.

Their findings were supported by a computer script written by Mengchi to search my

SILAC data for over-represented tripeptide motifs within the ten percent of proteins showing the

greatest reduction of abundance in the efp mutant relative to wild-type. He identified PPP and

PPG motifs as the two most prominent tripeptide motifs amongst this group110. Moreover he also

identified Alanine-Proline-Proline (APP) as the third most prominent motif and the only other

polyproline-containing tripeptide in his top ten hits. This suggests that APP is another

polyproline motif that is dependent on EF-P for its efficient translation.

3.2.6 Polyproline motifs are not always necessary or sufficient to confer EF-P-dependence

As described earlier, 1517 proteins were conclusively identified by the SILAC analysis

and 49 showed significantly (Significance B score < 0.01) reduced abundance in the efp mutant

relative to wild-type (Figure 7). With the subsequent publications showing that EF-P is required

for the efficient translation of polyproline motifs, I began a comparison of the presence of these

motifs with EF-P-dependent expression in vivo. I generated a Venn diagram overlapping the 49

proteins that showed significantly reduced abundance in the efp mutant with the 422 Salmonella

proteins that contain at least one of the polyproline motifs PPP, PPG or APP as annotated in the

Salmonella Typhimurium str. SL1344 genome (Figure 8A) (GenBank ID: FQ312003.1). I found

that of the 49 proteins that showed reduced expression in the efp mutant in vivo, 20 of them

contained a polyproline motif – as might be expected based on the two Science papers114,115.

37

However, there remained 29 proteins that were conclusively identified in SILAC and showed

reduced expression in the efp mutant yet did not contain a characterized EF-P-dependent motif.

While it is possible that many of these are indirect effects, some of these proteins may contain a

novel EF-P-dependent motif, suggesting that polyproline motifs may not always be necessary to

render a transcript dependent on EF-P for its efficient translation.

My analysis also led to the discovery that polyproline motifs may not in-and-of

themselves be sufficient to confer EF-P-rescuable ribosome stalling in vivo. Specifically, 100

proteins containing a characterized polyproline motif (PPP, PPG or APP) were conclusively

identified in my SILAC data but only 20 of those showed significantly reduced abundance in the

efp mutant. Examples include ZipA, SseA and YtfM, which were identified to have average

wild-type/efp mutant expression ratios of 1.07, 0.74 and 0.84 respectively with Significance B

scores that were far from significant in all three replicates. Though they did not show

significantly altered abundance in the efp mutant, ZipA has two distinct APP motifs as well as an

APPP motif, SseA has an APPG motif, and YtfM contains a PPP motif. Even a more

conservative analysis of proteins with SILAC ratios of less than two without regard for the

significance score found that there were 45 proteins identified in SILAC that did not show

reduced abundance in the efp mutant yet have a PPP, PPG or APP motif (12, 22 and 16 proteins

respectively contain each motif and 5 proteins have two of the three motifs). This demonstrates

that a large percentage of proteins that contain a putative EF-P-dependent motif are not produced

in lower abundance in the efp deletion strain, suggesting that additional factors beyond short

polyproline motifs dictate whether a transcript will induce an EF-P-rescuable ribosome

stall114,115. I have made significant progress towards elucidating the factors governing whether a

polyproline motif will or will not induce an EF-P-rescuable ribosome stall and I will discuss this

work in detail in Chapter 4.

38

Figure 8: Comparison of proteins identified in SILAC and those with EF-P-dependent

polyproline motifs. A) Venn diagram outlining the overlap of the 1517 proteins conclusively

identified in SILAC (red), the 49 proteins with significantly reduced abundance in the efp mutant

(green), and the 422 Salmonella proteins containing a PPP, PPG or APP motif (blue). B) DAVID

analysis of the 49 SILAC hits that showed significantly reduced abundance in the efp mutant.

Functional annotation clusters showing significant overrepresentation (p-value < 0.05) are

shown. C) DAVID analysis showing the most significantly overrepresented clusters amongst the

422 proteins that contain a known EF-P-dependent polyproline motif. For clarity, only groups

with p < 0.001 are shown. D) DAVID analysis showing the only significantly overrepresented

cluster (p-value < 0.05) amongst the 20 proteins that belong to all three categories. Figure was

adapted from Hersch et al. (2013), mBio 110.

39

3.2.7 Functional annotation analysis of EF-P-dependent proteins

I subjected my SILAC data to a functional annotation analysis using the Database for

Annotation, Visualization and Integrated Discovery (DAVID) software package109,118,119. Upon

analysis of the 49 proteins with significantly lower abundance in the efp mutant in SILAC, I find

that four clusters demonstrate overrepresentation with a cluster p-value of less than 0.05 (Figure

8B). Most prominent amongst these groups are two-component regulatory systems, with

particular emphasis on proteins involved in chemotaxis and motility. Furthermore, metabolic

proteins also show reduced abundance in the efp mutant as annotated by functions in nucleotide

binding, oxidative phosphorylation, or proteolysis.

I subsequently examined the 422 ORFs in the S. Typhimurium SL1344 genome encoding

a PPP, PPG or APP motif (112, 195, and 185 ORFs, respectively, with 70 proteins containing

more than one motif). An obvious caveat of this analysis is that the presence of a polyproline

motif is not always sufficient to confer dependence on EF-P in vivo, and so many of these

proteins will not show altered levels in an efp mutant despite containing a polyproline motif. My

DAVID cluster analysis of these genes found that the overrepresented functional groups were

similar to those identified amongst proteins that were significantly depleted in the efp mutant in

SILAC including those involved in motility, two-component systems and nucleotide binding

proteins (Figure 8C).

I identified twenty proteins that both contain a polyproline motif and showed

significantly reduced abundance in the efp mutant in SILAC. Upon analysis using DAVID, seven

of these were identified as nucleotide binding proteins – the only overrepresented category in this

subset (Figure 8D).

40

3.3 Discussion

In this chapter I demonstrated that an efp mutant is more permeable than wild-type

Salmonella to the fluorescent dye NPN but not to ethidium bromide. Since NPN can fluoresce

upon bypassing the outer membrane whereas EtBr must also bypass the inner membrane, these

data suggested that efp mutants have a more permeable outer membrane that may underlie their

increased susceptibility to cellular stressors such as antibiotics74,84. In support of this I showed

that deletion of the kdgM gene partially complemented the NPN permeability defect. However,

Dr. Betty Zou demonstrated that deletion of kdgM in the efp mutant did not rescue any of the

other phenotypes, suggesting that the permeability defect (at least the portion of it conferred by

kdgM overexpression) was not the sole mechanism underlying the hyper-susceptibility of the efp

mutant to cellular stressors. Many of the proteins identified in SILAC that contain a

PPP/PPG/APP motif are predicted membrane proteins, suggesting a potential role in

permeability. Proteins involved in DNA repair were also overrepresented suggesting a potential

role for EF-P in translating proteins involved in resistance to DNA damaging agents.

Some proteins identified as EF-P-dependent by SILAC or by the presence of a

polyproline motif can provide a parsimonious explanation for previously described phenotypes

of Salmonella strains lacking PoxA or EF-P. For example, gamma-glutamyl transferase (Ggt) is

present at a level approximately 16-fold lower in the efp mutant compared to wild-type

Salmonella (Supplementary DataTable1). Ggt contains a PPP motif at residues 291-293 and its

strongly reduced synthesis in the efp mutant likely explains why Salmonella strains lacking EF-

P, PoxA, or YjeK are simultaneously unable to utilize -glutamyl-glycine as a nitrogen source

and are resistant to the compound GSNO (S-nitrosoglutathione)74,84. Finally, the impaired

synthesis of proteins involved in motility and chemotaxis, such as CheA, which is depleted

approximately four-fold in the efp mutant, likely contributes to the observed motility defect in

PYE mutant strains84.

Some of the EF-P-dependent proteins are involved in central metabolism and may

contribute to efp mutant phenotypes, including impaired growth: AtpD is the catalytic subunit of

the FOF1 ATPase and was previously identified as downregulated in the proteome of poxA

mutants74. PfkB is a phosphofructokinase that functions in glycolysis. Three of the other EF-P-

dependent nucleotide binding proteins (HflB/FtsH, HslU, and ClpB) play a role in protein

41

stability and turnover. The function of the polyproline motifs in these proteins is unlikely to be

universally conserved. For example, in AtpD120, ClpB121, and PfkB (PDB ID: 3UMP) the

putative EF-P dependent motifs are not proximal to the region of the protein that interacts with

ATP, whereas in HflB/FtsH the putative EF-P dependent motif (GPPG) makes contact with

AMP122.

Following the discovery that EF-P rescues ribosomes stalled at particular tripeptide

motifs, I probed the proteomic data for the presence of such sequences in proteins showing

reduced levels in the efp mutant. I discovered that these tripeptide polyproline motifs may not

always be necessary or solely sufficient to confer EF-P-dependence in vivo. This is reinforced by

the fact that the tripeptide motifs can be relatively common in the proteome and yet the overall

number of proteins affected strongly by the loss of EF-P is considerably smaller. Indeed, of the

100 polyproline motif-containing proteins identified in SILAC, only twenty showed significantly

reduced abundance in the efp mutant. Thus, EF-P-dependence in vivo may depend on additional

factors as well.

It is clear that not all ribosomal stalls are created equal and that many subtle factors will

influence when translation will pause. For example, many peptides known to stall during

elongation will stall despite the presence of EF-P. Multiple groups have reported on proteins that

instigate a translational stall via not only a particular sequence in the vicinity of the PTC but also

upstream residues in the nascent polypeptide chain that interact with the ribosomal exit tunnel.

These stalls may be dependent on external factors such as the antibiotic erythromycin in the case

of ermAL1 and tryptophan for TnaC, or may be self-mediated as for SecM and MifM61-64,123,124.

The laboratory of Allen Buskirk identified a number of stall sequences containing polyproline

tracts near the PTC that also require upstream residues for efficient stalling70,125. In Chapter 4 I

will present evidence elucidating additional factors influencing EF-P-dependence including a

role for the amino acids upstream of a proline-rich motif and a significant impact of the rate of

translation initiation.

42

4 Factors that influence EF-P-dependence

Primary references

Much of the data presented in this chapter is published work reproduced with permission from:

Hersch SJ, Wang M, Zou SB, Moon K-M, Foster LJ, Ibba M, Navarre WW. Divergent

protein motifs direct elongation factor P-mediated translational regulation in Salmonella enterica

and Escherichia coli. mBio. 2013;4(2). 110

Hersch SJ, Elgamal S, Katz A, Ibba M, Navarre WW. Translation initiation rate

determines the impact of ribosome stalling on bacterial protein synthesis. J Biol Chem.

2014;289(41):28160-28171. 126

Elgamal S, Katz A, Hersch SJ, Newsom D, White P, Navarre WW, Ibba M. EF-P

dependent pauses integrate proximal and distal signals during translation. PLoS Genet.

2014;10(8):e1004553. 127

Acknowledgements

Dr. Michael Ibba at the Ohio State University and members of his lab have been close

collaborators on this project. Ribosome profiling and computational modelling were conducted

by Sara Elgamal and Dr. Assaf Katz and some of their findings are mentioned in this chapter.

Further details about their data and methods can be found in Elgamal, Katz, Hersch et al. (2014)

PLoS Genet127 (for ribosome profiling) and in Hersch et al. (2014) J Biol Chem126 (for

computation modelling of translation).

The pXG10sf plasmid used for generating translational fusions was a generous gift from

Dr. Jörg Vogel93.

43

4.1 Overview

Recent work demonstrated that three amino acid proline-containing motifs require EF-P

for their efficient translation in vitro114,115. Comparing my SILAC proteomic data to the presence

of polyproline motifs revealed that a three amino acid polyproline motif is not always necessary

or sufficient to induce EF-P-dependent protein levels in cells lacking EF-P. In this chapter I

identify a novel six amino acid EF-P-dependent motif that contains only a single proline in

pyruvate oxidase (PoxB), which was the first protein in the literature to demonstrate PYE

pathway-dependent expression75,76. I also employ the model proteins AtpA and AtpD, which

each contain a PPG motif and are both encoded on the same mRNA transcript, yet AtpD

consistently exhibits lower levels in efp mutants whereas AtpA abundance is unaffected by the

loss of EF-P128,129. Using these model proteins I demonstrate that residues up to three positions

upstream of the PPG motif can influence the degree of stalling in efp mutants. Furthermore, I

show that the component most critical for the difference in EF-P dependence is within the 5’

untranslated region (UTR). Further mutagenesis and modeling studies demonstrate a relationship

between the translation initiation rate of a transcript and the impact of an EF-P-rescuable stall on

protein production. Taken together these data indicate that not all polyproline motifs induce

stalling to equal degrees and that EF-P dependence is only observed when a given polyproline

sequence impacts the rate of protein synthesis more than translation initiation.

4.2 Results

4.2.1 PoxB requires EF-P activity for its efficient translation

Levels of pyruvate oxidase (PoxB) are reduced five- to eight-fold in E. coli poxA

mutants, implicating it as a protein dependent on EF-P for its synthesis75,76. The PoxB protein,

however, lacks any of the proline-rich sequences (e.g. PPG) implicated to be EF-P-dependent

motifs, which leaves open the question as to whether the PoxB protein is indeed a target of EF-P

and, if so, what particular sequence renders it dependent on EF-P for its synthesis. I was unable

to detect PoxB in the SILAC assay presented in Chapter 3, likely due to the particular growth

conditions employed. To circumvent this issue, I constructed a reporter vector wherein the PoxB

5’ UTR and open reading frame was fused to a “super-folder” variant of green fluorescent

44

protein (sfGFP)99. A constitutive PLtet0-1 promoter drives transciption of the chimeric gene,

enabling me to measure translation independently of transcription. In a Salmonella background

the full-length PoxB protein fused to sfGFP show a marked and reproducible dependence on EF-

P for synthesis (Figure 9). I observed this decrease in fluorescence not only in the efp mutant but

also in poxA and yjeK mutants. In contrast, I found no significant difference in synthesis of the

PoxB-sfGFP fusion construct in a strain lacking the YfcM protein. These results are consistent

with previous data showing that YfcM-mediated hydroxylation of EF-P is not critical for its

function87.

To ensure that this difference was due to an effect on translation, I measured mRNA

levels using reverse transcriptase quantitative PCR. Results indicated that transcription of the

chimeric PoxB-sfGFP fusion construct was similar between the mutant and wild-type strains

(Figure 10). Interestingly, though similar poxB transcript levels indicate that transcription is not

altered, gfp mRNA levels were decreased in strains expressing EF-P-dependent constructs. Since

previous in vitro data measured the effect of EF-P in the absence of nucleases114,115,125, this

degradation is likely not the cause of decreased GFP fluorescence. Instead it is likely that

ribosome stalling at EF-P-dependent motifs leads to reduced ribosome-mediated nuclease

protection of the 3’ end of the poxB-gfp transcript. This supports the concept that the altered

expression of poxB-sfGFP in PYE mutants reflects PoxB requiring EF-P during translation rather

than an artefact of mRNA stability.

45

Figure 9: PoxB-sfGFP translation is impaired in PYE mutants but not in the yfcM mutant.

Fluorescence of sfGFP fusion constructs in mutant strains lacking an EF-P-modifying protein.

Mutant strains are indicated in the top row of the x-axis, the sfGFP fusion constructs are

indicated in the bottom row of the x-axis. LacZ indicates the first 186 amino acids of LacZ,

PoxB FL indicates full-length PoxB (572 codons), and PoxB 21 indicates the first 21 residues of

PoxB are fused to sfGFP. Values were taken at 10 hours post-inoculation and are shown in

arbitrary fluorescence units (AU) normalized to optical density at 600nm (OD600). The ‘no

plasmid’ controls do not contain a sfGFP fusion plasmid and were grown in the absence of

chloramphenicol. All values are the average of at least 3 biological replicates. Error bars show

one standard deviation.

0

1

2

3

4

5

6

7

8

9

WT Δefp WT Δefp ΔyfcM ΔpoxA ΔyjeK WT Δefp ΔyfcM ΔpoxA ΔyjeK WT Δefp ΔyfcM ΔpoxA ΔyjeK

no plasmid LacZ PoxB FL PoxB 21

GFP

Flu

ore

sce

nce

(1

00

00

AU

)

Construct in pXG10sf and Salmonella strain

46

Figure 10: PoxB mRNA levels are similar in wild-type and ∆efp strains for both EF-P

dependent and independent constructs. GFP fluorescence normalized to optical density (black),

poxB mRNA transcript levels (light grey) and sfGFP mRNA transcript levels (dark grey) were

assayed at mid-log phase. Transcript levels were assessed by RT-qPCR using gene-specific

primers. Since the strains used encode poxB in their genomes, the mRNA levels obtained for

lacZ186 with the poxB-specific primers was subtracted as background from all other values. This

background poxB mRNA level was negligible (greater than 300 fold lower) compared to any of

the strains expressing PoxB-GFP from a plasmid. All values are shown as the ratio of the efp

mutant relative to the wild-type strain expressing the same construct. All values are the average

of at least 3 biological replicates with the exception of those for the LacZ186 construct, which

are the average of 2 biological replicates. For the replicate where LacZ186 was not included,

background poxB mRNA levels were obtained from another strain expressing a non-poxB

construct from the same plasmid. Error bars show one standard deviation. Figure is shown as it

appears in Hersch et al. (2013), mBio 110.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

LacZ186 PoxB FL PoxB 68 PoxB 82 P76L

Δef

p/

WT

rati

o

GFP fluorescence

poxB mRNA

sfGFP mRNA

47

4.2.2 PoxB is dependent on EF-P due to a novel six amino acid motif, GSCGPG

To identify the region of PoxB that rendered it dependent on EF-P I systematically

generated C-terminal truncations of the poxB sequence fused to sfGFP such that each construct

encoded the poxB 5’ UTR followed by varying lengths of the poxB ORF (Figure 11A). By

comparing fluorescence of these constructs in wild-type and efp mutant Salmonella, I was able to

narrow down the critical region to a 14 amino acid stretch between residues 69 and 82 with the

sequence: VCAGSCGPGNLHLI (Figure 11B and 8C). Furthermore I confirmed that it is the

amino acid (as opposed to the nucleotide) sequence in this region that is critical for EF-P

dependence by generating a construct with this region shifted to the +1 reading frame and

observing an abolition of the requirement for EF-P (Figure 12). Mutating the proline codon to

any of the other three proline codons also had no effect (Figure 12).

Residues 69 to 82 of PoxB contain a GPG motif that I hypothesized could, like PPG,

mediate the EF-P dependence of the PoxB protein. To test this, I performed site directed

mutagenesis on the GPG motif in the full-length PoxB-sfGFP construct as well as on additional

upstream and downstream residues, converting each residue to a leucine. I chose leucine because

of its absence from the top scoring motifs predicted by Mengchi Wang’s tripeptide motif analysis

(See Chapter 3). I found that mutation of any residue in the GPG motif or the three amino acids

upstream (sequence: GSC) restored GFP fluorescence in the efp mutant (Figure 11D). In

contrast, mutating residues further downstream or upstream of the GSCGPG motif had negligible

effect on the synthesis of the reporter construct. This novel EF-P dependent motif of PoxB is

considerably larger than the previously described tripeptide motifs and can cause a protein to be

dependent on EF-P for proper synthesis in vivo. Notably, in Salmonella the GSCGPG sequence

is only found in PoxB.

48

Figure 11: The GSCGPG motif of PoxB renders it dependent on EF-P. A) Outline of

translational fusion constructs expressing full-length (FL) PoxB or C-terminal truncations fused

to sfGFP. Construct designations are shown in bold at left as a figure key and indicate the length

in codons of the truncated poxB gene. Arrow indicates the transcriptional start site under the

control of the PLtet0-1 constitutively active promoter and fMet indicates the ATG start codon of

PoxB. Numbers indicate the number of codons from the poxB start codon to the C-terminal

truncation and fusion to sfGFP. For clarity only a selection of constructs are illustrated. Amino

acids 69-82 are highlighted. B) Sequence of amino acids 69-82 of PoxB. The GSCGPG motif is

emboldened and underlined. C) Relative GFP fluorescence of PoxB truncations. Values were

taken at 10 hours post-inoculation, were normalized to optical density at 600nm (OD600) and are

shown as a ratio of the efp mutant relative to the wild-type strain expressing the same construct.

The same plasmid encoding the first 186 codons of lacZ instead of poxB is included as a control.

D) As in (C) but showing single residue mutations to leucine in the full-length PoxB construct.

All values are the average of at least 3 biological replicates. Error bars show one standard

deviation. Figure was adapted from Hersch et al. (2013), mBio 110.

C)

0.00.20.40.60.81.01.21.41.61.8

GFP

Flu

ore

sce

nce

(∆

efp

/ W

T)

Construct in pXG10sf

D)

0.00.20.40.60.81.01.21.41.61.8

GFP

Flu

ore

sce

nce

(∆

efp

/ W

T)

Construct in pXG10sf

A)

B)

49

Figure 12: PoxB dependence on EF-P is determined by the amino acid sequence as opposed

to nucleotides. A) Nucleotide (upper line) and amino acid (lower line) sequences of PoxB FL and

PoxB Shifted constructs are shown from residues 66-85. The PoxB Shifted construct was

generated by shifting codons 69-82 (emboldened) of full-length PoxB to the +1 reading frame by

introducing an upstream single base-pair insertion (underlined adenine) and downstream deletion

(struck-out cytosine). B) Relative GFP fluorescence values taken at 10 hours post-inoculation,

normalized to optical density at 600nm (OD600) and shown as a ratio comparing expression in the

efp mutant relative to wild-type Salmonella expressing the same construct. P76::CCX constructs

are mutated at proline 76 to an alternate proline codon. LacZ186, PoxB FL, PoxB 82 and PoxB

68 from Figure 11 are shown for comparison. All values are the average of at least 3 biological

replicates. Error bars show one standard deviation. Figure was adapted from Hersch et al.

(2013), mBio 110.

0.000

0.200

0.400

0.600

0.800

1.000

1.200

1.400

1.600

1.800

2.000

GFP

Flu

ore

sce

nce

(∆

efp

/WT)

Construct in pXG10sf

PoxB FL… GAG CTG GCA GTG TGC GCC GGT TCA TGT GGA CCG GGC AAC CTG CAC CTG ATC AAT GGC CTG …… E L A V C A G S C G P G N L H L I N G L …

PoxBShifted

… GAG CTG GCA AGT GTG CGC CGG TTC ATG TGG ACC GGG CAA CCT GCA CCT GAT AAT GGC CTG …… E L A S V R R F M W T G Q P A P D N G L …

Positionɪ

70ɪ

75ɪ

80ɪ

85

A)

B)

50

4.2.3 AtpD and AtpA as model proteins to study polyproline motifs

Only 20 of the 100 proteins identified in SILAC that contain a putative EF-P-dependent

polyproline motif actually showed a decrease in protein abundance in the efp mutant, suggesting

that additional factors influence whether or not a polyproline motif will require EF-P for efficient

translation. Literature examining other kinds of ribosome stalls (that stall despite the presence of

EF-P) suggest that many stall sites involve upstream amino acids interacting with the ribosome

exit tunnel to influence stalling61,70,124,125. Combined with my PoxB data where I found that up to

six amino acids rather than just three were required for EF-P-dependence, these data led to the

hypothesis that upstream amino acids influence polyproline motifs to either induce or protect

against ribosome stalling. To examine this possibility I employed two model proteins, AtpA and

AtpD. Both are subunits of the FOF1 ATP Synthase and are encoded on the same mRNA

transcript (Figure 13). In addition, both contain a PPG motif; however, my proteomic SILAC

analysis showed that only AtpD levels were affected by the deletion of efp (20.6 fold reduced

abundance in the efp mutant, contrasting with 1.05 for AtpA). A similar trend was observed in a

later SILAC experiment in E. coli wherein the authors found a 5.18 fold difference in the

abundance of AtpD in the efp mutant compared to 1.88 for AtpA130.

The identical polyproline motif, yet divergent degree of EF-P-dependence in vivo,

suggested AtpD and AtpA as prime candidates to assess the role of upstream residues on

polyproline-mediated ribosome stalling. I first verified the SILAC results using western blotting

that confirmed that the level of AtpD protein is lower in the Salmonella efp mutant (Figure 14A

and quantified in 11B). I then employed the previously used pXG10sf plasmid to generate C-

terminal translational fusions of the atpD and atpA genes to sfGFP93,99,110. Consistent with the

proteomic analysis, the fluorescence of the AtpD construct was dependent on EF-P whereas that

of AtpA was not (Figure 14C) and this was true throughout the growth curve (Figure 15).

Notably, when I mutated the PPG motif of AtpD to PLG (P214L), the requirement for EF-P was

abolished. Unsurprisingly, mutation of the AtpA PPG motif to PLG (P281L) did not have a

significant effect, as AtpA translation was already independent of EF-P. Conversely, when I

extended the PPG motif of AtpA to PPPG (R279P), I found that I could induce strong EF-P-

dependence in AtpA.

51

A)

B)

Protein

SILAC ratio

(WT/Δefp) SigB < 0.01

Polyproline

motif

AtpF 04.43 - -

AtpH 02.32 - -

AtpA 01.05 - PPG

AtpG 07.47 < 0.01 -

AtpD 20.56 < 0.01 PPG

AtpC 03.55 - -

Figure 13: ATP Synthase components show altered abundance in the efp mutant. A) Outline of the ATP Synthase operon encoding the eight subunits of the F1F0 ATP Synthase. The

black arrow indicates the transcription start site. Blue arrows indicate ORFs, yellow arrows

indicate ORFs containing a PPG motif. B) Six out of the eight components of FOF1 ATP

Synthase were identified in my SILAC anlysis of efp mutant Salmonella. Shown are their

WT/Δefp abundance ratios, whether or not their Significance B score was under 0.01 (indicating

significantly altered protein level in the efp mutant) and also if they contain a polyproline motif.

I B E HF atpA G CatpD

ATP Synthase operon

52

Figure 14: AtpD but not AtpA requires EF-P for its synthesis. A) Representative western blot

showing AtpD levels in the indicated strains of Salmonella. DnaK was included as a loading

control. B) Densitometry quantification of western blots showing mean AtpD/DnaK ratio relative

to wild-type (WT) across three biological replicates. Error bars indicate one standard deviation.

C) Fluorescence comparing synthesis of plasmid-encoded AtpD- and AtpA-sfGFP translational

fusions. Fluorescence values were taken at 10 hours post-inoculation, normalized to optical

density at 600nm (OD600) and are shown as a ratio comparing expression in the efp mutant

relative to wild-type Salmonella expressing the same construct. LacZ is included as a control.

Specific point mutations affecting the PPG motifs are indicated: P214L and P281L mutate the

PPG motifs of AtpD and AtpA respectively to PLG. R279P in AtpA generates a PPPG motif

conferring stronger ribosome stalling. Values are the average of at least 3 biological replicates

and error bars show one standard deviation. Figure was adapted from Elgamal S, Katz A, Hersch

et al. (2014), PLoS Genet 127.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

LacZ AtpD AtpDP214L

AtpA AtpAP281L

AtpAR279P

GFP

Flu

ore

sce

nce

(∆

efp

/WT)

Construct in pXG10sf

0

0.2

0.4

0.6

0.8

1

1.2

1.4

WT Δefp Δefp pEF-P

ΔatpD

Atp

D L

evel

s (r

elat

ive

to W

T)

A) B)

C)

53

Figure 15: AtpD but not AtpA shows decreased fluorescence throughout the growth curve

of efp mutant Salmonella. 16 hour growth curves of wild-type (solid lines) and efp mutant

(dashed lines) Salmonella containing pXG10sf plasmids with AtpD (A & B) or AtpA (C & D)

inserted as a translational fusion to sfGFP. GFP fluorescence (black lines in A & C) and optical

density at 600nm (grey lines in A & C) were measured every 15 minutes. The ratio of GFP

fluorescence / OD600 is also shown (B & D). At least three independent replicates were

conducted and one representative replicate is shown. AFU, arbitrary fluorescence units. Figure is

shown as it appears in Hersch et al. (2014), J Biol Chem 126.

54

4.2.4 Upstream residues influence EF-P-dependence in AtpD and AtpA

I examined the regions upstream of the AtpD and AtpA PPG motifs to delineate if they

contribute to stalls requiring rescue by EF-P. I swapped up to 40 codons upstream of the AtpA

PPG motif into the corresponding region of the pXG10sf-AtpD-sfGFP construct while leaving

the PPG motif and the remainder of the ORF unaltered. I also performed the reverse swaps

wherein AtpD regions were swapped into the pXG10fs-AtpA-sfGFP construct. I found that

swapping as few as two upstream codons from AtpA into the AtpD-sfGFP construct led to a

significant increase in expression in the efp mutant relative to wild-type Salmonella (Figure 16).

The effect was similar when I switched six or more residues but interestingly increased when

four upstream residues were swapped. I observed a similar reversal of EF-P-dependence when

swapping upstream regions of AtpD into the AtpA-sfGFP construct: a four amino acid swap led

to a very small increase in EF-P-dependence, which increased only marginally when the

swapped region was increased to six or more codons. My observation that swapping greater than

six residues upstream had marginal or no additional effect suggests that at least in this instance

the important interactions with the ribosome exit tunnel are occurring close to the peptidyl

transferase center and prior to the exit tunnel constriction that has been implicated in other

extended translational stall motifs61,70,71.

Interestingly, I observed no effect for either AtpA or AtpD when I swapped the amino

acid immediately upstream of the PPG motif (E212R for AtpD; R279E for AtpA). However,

swapping the residue two positions upstream of the PPG resulted in a drastic effect on EF-P-

dependence (N211R for AtpD; R278N for AtpA). The AtpD N211R construct partially

alleviated AtpD’s dependence on EF-P, though to a lesser degree than when both N211 and E212

were swapped to the corresponding arginines found in AtpA. In contrast, changing the arginine

positioned two amino acids upstream of the PPG motif of AtpA to the asparagine in that position

in AtpD (R279N) led to a dramatic increase in the dependence of this construct on EF-P,

surpassing the effect of all other AtpA constructs where more residues were altered. Thus it

appears that at least in the case of AtpA and AtpD the residue two positions upstream of the PPG

motif plays an important role in determining whether the ribosome will stall and require rescue

by EF-P. Furthermore, the data that I have presented here also demonstrates that other nearby

residues can dampen this effect.

55

Figure 16: Upstream residues influence EF-P-dependence in AtpD and AtpA. A) Sequences

of Salmonella AtpD and AtpA in proximity to their PPG motifs (bold). The relative position

when the glycine of PPG occupies the A site of the ribosome is shown above. The amino acid

position of the second proline of the PPG motif in each ORF is indicated below. B) Fluorescence

ratios comparing expression of plasmid borne AtpD-sfGFP translational fusions in efp mutant

relative to wild-type Salmonella. ‘Swap’ constructs indicate that the sequence from AtpA has

been swapped into AtpD-sfGFP for the specified number of amino acids upstream of the PPG

motif. LacZ, the unaltered AtpD-sfGFP construct (wt), P214L and R279P constructs are included

for comparison. Data shows the Δefp / WT GFP fluorescence ratio at 10 hours post-inoculation

normalized to optical density (600nm). Values are the average of at least 3 biological replicates

and error bars show one standard deviation. C) As in (B) but with the exception that sequences

from AtpD have been swapped into the AtpA-sfGFP construct. Figure was adapted from

Elgamal S, Katz A, Hersch et al. (2014), PLoS Genet 127.

V A Y R Q I S L L L R R P P G R E A

AtpD:

AtpA:

D K V S L V Y G Q M N E P P G N R L

- 1 2 - 1 1 - 1 0 - 9 - 8 - 7 - 6 - 5 - 4 - 3 - 2 - 1 E P A + 1 + 2 + 3

214 (AtpD)281 (AtpA)

A

B

0

0.2

0.4

0.6

0.8

1

1.2

1.4

wt P214L 40aaswap

24aaswap

12aaswap

6aaswap

4aaswap

2aaswap

E212R N211R M210L

LacZ AtpD

GFP

Flu

ore

scen

ce (

∆ef

p/W

T)

LacZ

AtpD (with regions of AtpA swapped in)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

wt R279P 40aaswap

24aaswap

12aaswap

6aaswap

4aaswap

2aaswap

R279E R278N L277M

LacZ AtpA

GFP

Flu

ore

scen

ce (

∆ef

p/W

T)C

LacZ

AtpA (with regions of AtpD swapped in)

56

4.2.5 The 5’ UTR plays a significant role in EF-P dependence

The reversal of EF-P dependence upon swapping upstream amino acids was incomplete

when compared to a negative control (LacZ), to a PPG::PLG mutation in AtpD, or to an

RPPG::PPPG mutation in AtpA (Figure 16). Complete reversal was not achieved even when

swapping up to 40 codons upstream of the PPG motif, a distance that accounts for more amino

acids than can be accommodated in the ribosomal exit tunnel69,131. This suggested that factors

outside of the ribosome were negatively affecting AtpD translation in the efp mutant.

To investigate additional regions impacting EF-P dependence, I again employed the

pXG10sf plasmid, involving a constitutively active promoter and C-terminally fused sfGFP as a

reporter93,99,110,132. I systematically generated atpD and atpA hybrids by serially swapping regions

of increasing length from the 5’ end of the mRNA transcript to the PPG motif, or in the reverse

direction (Figure 17). Results from this analysis demonstrated that the coding region beyond 12

codons upstream of the PPG motif had no additional influence on EF-P dependence even when

the entire N-terminus from the ATG start codon to the polyproline motif was swapped.

Contrastingly, there was a stark change in EF-P dependence when I included the 5’ UTR in the

swapped region or when only the 5’ UTR was swapped. Specifically, the atpD 5’ UTR confers

strong EF-P dependence onto the AtpA protein whereas the 5’ UTR of atpA appears to

significantly reduce the amount of EF-P dependence displayed by AtpD. Combined replacement

of the 5’ UTR and the 12 codons upstream of the PPG motif have a cumulative effect, suggesting

that the two regions affect EF-P dependence by different mechanisms and that complete

switching requires both elements. Indeed, the atpD construct with both the atpA 5’ UTR and

upstream PPG flanking region demonstrates higher fluorescence in the efp mutant than in wild-

type Salmonella; this resembles the lacZ negative control and may be due to decreased levels of

protease in the efp mutant as found in my SILAC analysis in Chapter 3.

57

Figure 17: The 5’ UTR plays a role in EF-P dependence in addition to residues immediately

upstream of the PPG motif. A) Outline of pXG10sf-AtpD and -AtpA constructs. Swap constructs

are shown with regions of the atpA gene swapped into the pXG10sf-AtpD construct. Naming

scheme is indicated at left. B) Regions of the atpA gene were swapped into the pXG10sf-AtpD

construct (as shown in panel A). Data shows the ratio of GFP fluorescence in ∆efp/WT

Salmonella normalized to OD600. Unaltered (wt), a PPG::PLG mutation (P214L), the ‘12aa’

construct, and pXG10sf-LacZ are shown for comparison. C) As in panel B but showing

constructs with regions of the atpD gene swapped into pXG10sf-AtpA (inverse of depiction in

panel A). Values are the mean of at least three biological replicates and error bars show one

standard deviation. Figure is shown as it appears in Hersch et al. (2014), J Biol Chem 126.

58

4.2.6 The 5’ UTR regions that include the SD sequence and a potential translation enhancer

play a significant role in EF-P dependence

Though native ATP synthase proteins are encoded on a single mRNA transcript, both

atpD and atpA possess their own Shine Dalgarno (SD) sequence that is crucial for ribosome

recruitment during translation initiation. Furthermore, their start codons are spaced 12 and 26

bases (respectively) after the preceding gene’s stop codon, suggesting that the translation of each

is independent and not coupled to that of the preceding gene (NCBI accession: NC_016856.1).

For the plasmid-based translational fusion assay that I employed, the 5’ UTRs of the atpD and

atpA constructs consisted of the 74 or 75 bases (respectively) upstream of the start codon.

To further dissect the region within the 5’ UTR involved in EF-P dependence, I

systematically swapped 15 base-pair stretches between atpD and atpA and measured expression

of the resulting construct by GFP fluorescence. I found that EF-P dependence was reversed most

significantly when regions -15 to -1 or -30 to -16 (relative to the start codon) were swapped

(Figure 18). Notably, these regions are both predicted to be heavily involved in translation

initiation: The -15 to -1 region includes the SD sequence, and the -30 to -16 region of atpD is

particularly AT-rich (74%) compared to atpA (47%), suggesting that it may act as a translation

enhancer47,49. Moreover, for all of the serial swap constructs generated, there appeared to be a

correlation between EF-P dependence and the degree of overall expression (Figure 18).

Specifically, constructs that were poorly expressed exhibited little difference in fluorescence

levels between wild-type and efp mutant strains, whereas constructs with high levels of

expression in the wild-type background were the most reduced in the efp mutant.

To ensure that this apparent loss of EF-P dependence was not due to an inability to

accurately measure differences at low fluorescence levels, I conducted western blotting of select

AtpD and AtpA constructs. The patterns of EF-P dependence observed by western blotting were

similar to those found by fluorescence measurements (Figure 19). For a few constructs where

fluorescence levels are less than two-fold greater than no-plasmid controls, protein levels were

very low or non-detectable by western blot in both the wild-type and efp mutant strains. These

constructs, including the atpD construct containing the whole 5’ UTR of atpA, were omitted

from all downstream analyses.

59

Figure 18: The 5’ UTR regions affecting EF-P dependence include the ribosome binding

site and correlate with expression levels. A) Sequence of the 5’ UTRs included in pXG10sf-

AtpD and -AtpA constructs. Putative Shine-Dalgarno sequences are underlined. Base position

relative to the translation start site (ATG) are indicated below. B) Regions of the atpA 5’ UTR

were serially swapped into the corresponding position of pXG10sf-AtpD. Example: ‘-15-1’

indicates the 15bp from position -15 to -1 (relative to the ATG start codon) were swapped. Data

shows GFP fluorescence in arbitrary fluorescence units (AFU) normalized to OD600 in WT (dark

grey) and ∆efp (light grey) Salmonella. Numbers above the columns indicate the ∆efp/WT ratio.

C) As in panel B but with regions of the atpD 5’ UTR swapped into the pXG10sf-AtpA

construct. Values are the mean of at least three biological replicates and error bars show one

standard deviation. Figure was adapted from Hersch et al. (2014), J Biol Chem 126.

60

Figure 19: GFP levels determined by immunoblotting follow similar trends as levels

determined by fluorescence. Western blot probing for GFP expressed from pXG10sf constructs

in wild-type (WT) and ∆efp Salmonella grown to mid log phase under conditions similar to those

used for fluorescence measurements. GFP – control is wild-type cells expressing pXG10sf-AtpD

but with a FLAG tag replacing GFP. Unless otherwise indicated, 10µg protein lysate was loaded

for each sample. DnaK was included as a loading control. The experiment was conducted in

triplicate and one representative replicate is shown. For the ‘AtpD-AtpA UTR’ and ‘AtpD

AGAGG::AGACG’ constructs where fluorescence levels were less than two-fold greater than

no-plasmid controls, protein levels were very low or non-detectable in both the WT and efp

mutant strains. The ‘AtpA – AtpD UTR from -60-46’, ‘AtpA – AtpD from ATG – PPG’, ‘AtpD

– AtpA UTR & 12aa’, and ‘AtpD – AtpA UTR & P214L’ constructs also had similarly low

fluorescence levels (Figures 14 and 15). These constructs were omitted from all downstream

analyses. Figure is shown as it appears in Hersch et al. (2014), J Biol Chem 126.

61

4.2.7 Mutations that affect translation initiation influence EF-P dependence

To further assess whether the 5’ UTR influences EF-P dependence through its role in

translation initiation, I first confirmed that the UTR instigates its effect via the polyproline stall

rather than by a separate unrelated mechanism. Indeed, mutation of the PPG motif to PLG

resulted in restoration of GFP fluorescence in the efp mutant even at high expression levels

obtained by 5’ UTR swapping (Figure 20). Of note, wild-type cells express the PPG and PLG

constructs to a similar degree, indicating that the motif does not influence protein synthesis aside

from instigating an EF-P-rescuable stall. Thus the stall motif only acts on elongation, yet its

impact on protein output is strongly influenced by the 5’ UTR, which is far upstream of the stall,

is not translated, and would be expected to influence initiation but not elongation.

To explicitly examine the role of translation initiation in this phenomenon, I mutated

individual nucleotides within the SD sequence and the start codon of atpD and atpA. I improved

the atpA SD sequence (AGGGGA) by mutating it to the consensus sequence (AGGAGG). For

atpD, I weakened ribosome binding by mutating the ATG start codon or by altering the wild-

type SD sequence (AGAGG) with a G to C mutation (AGAGC). Furthermore, I also

strengthened the atpD SD sequence by a G insertion yielding the consensus sequence

(AGGAGG). Consistent with my previous observations, the constructs displaying the highest

levels of expression in wild-type cells were the most affected by the loss of EF-P (Figure 21).

Specifically, strengthening the SD sequence of either atpD or atpA yielded increased dependence

on EF-P, while weakening the SD or the start codon of atpD reduced its dependence. Since these

point mutations target regions critical for ribosome binding, these data suggest that the 5’ UTR

influences EF-P dependence via its role in translation initiation. This finding suggests that the

transcript’s initiation rate (governed by the 5’ UTR) and the EF-P-rescuable stall in elongation

may compete to be the rate-limiting step of translation.

62

Figure 20: The effect of the 5’ UTR on EF-P dependence requires an intact stall motif. A) The -45-31 region of the atpA 5’ UTR was swapped into the pXG10sf-AtpD construct in

combination with mutating the PPG motif to PLG. Data shows GFP fluorescence in arbitrary

fluorescence units (AFU) normalized to OD600 comparing expression in WT (dark grey) to ∆efp

(light grey) Salmonella. Numbers above the columns indicate the ∆efp/WT ratio. B) As in panel

A but with the 5’ UTR of atpD (-74-1) swapped into the pXG10sf-AtpA construct. Values are

the mean of at least three biological replicates and error bars show one standard deviation. Figure

is shown as it appears in Hersch et al. (2014), J Biol Chem 126.

63

Figure 21: Single base mutations in the SD sequence or start codon alter expression and

EF-P dependence. A) Mutations in the SD sequence or in the ATG start codon of pXG10sf-

AtpD. The wild-type sequence (AGAGG) is underlined in Figure 18A. Data shows GFP

fluorescence in arbitrary fluorescence units (AFU) normalized to OD600 comparing expression in

WT (dark grey) to ∆efp (light grey) Salmonella. Numbers above the columns indicate the

∆efp/WT ratio. Unaltered (wt) construct is shown for comparison. B) As in panel A but with

mutation in the SD sequence of the pXG10sf-AtpA construct. The wild-type sequence

(AGGGGA) is underlined in Figure 18A. Values are the mean of at least three biological

replicates and error bars show one standard deviation. Figure is shown as it appears in Hersch et

al. (2014), J Biol Chem 126.

64

4.2.8 Initiation rate and stall strength correlate with EF-P dependence

To gain insight into the relation between initiation rate and EF-P dependence, I plotted

fluorescence in wild-type versus in efp mutant Salmonella for the full set of 5’ UTR mutant

constructs (Figure 22). I grouped constructs together such that all members of each group have

the identical ORF and only differ in their 5’ UTR. Plotting in this manner allowed for

visualization of changes in expression in the efp mutant (subjected to stalls in elongation) as

initiation rate (and thereby wild-type expression) increases.

Strikingly, for both atpD and atpA, I find that as expression increases in wild-type,

expression in the efp mutant follows a distinct curve that eventually reaches a maximum. Indeed,

the observed data could be fit well to a ‘1-exp’ curve of the form: 𝑦 = 𝐴(1 − 𝑒−𝐵𝑥) , where x

and y represent fluorescence in the wild-type and efp mutant strains respectively, and A and B are

constants particular to the ORF being analyzed. Specifically, A is the asymptote of the curve and

can be interpreted as the theoretical maximum expression in the efp mutant for that particular

ORF, at which point progression through the stall motif has overtaken initiation as the rate-

limiting step for all transcripts. This supports my hypothesis that the initiation rate and

elongation stall clearance compete to be the rate-limiting step of translation. Similarly, B is

inversely related to the slope of the curve and how quickly it reaches the asymptote.

Interestingly, the curve for the AtpD ORF was steeper than that for AtpA and reached a lower

asymptote. This emphasizes that the stall in AtpD is more EF-P dependent than that of AtpA,

resulting in a lower maximum expression in the efp mutant and a significant hindrance on

translation even at lower initiation rates.

To examine the additive effects of the upstream region and translation initiation rate, I

applied my plotting analysis to constructs where the 12 amino acids upstream of the atpD or

atpA PPG motifs were swapped. Interestingly, the data for the 12aa swap constructs resembles

the protein from which the upstream region originated, suggesting that the upstream residues

govern stall strength and thereby the arc and maximum of the expression curve (Figure 22 grey

icons). Of note, this relation between translation initiation and EF-P dependence is not restricted

to AtpD and AtpA in Salmonella, but is consistent with findings on other proteins and in a

different species (E. coli), conducted by the lab of my collaborator, Dr. Mike Ibba126.

65

Figure 22: Translation initiation and elongation stall strength influence protein level. GFP

fluorescence data plotted to compare expression in WT (x-axis) and ∆efp (y-axis) Salmonella.

The y-axis is expanded from 0 to 1 to clarify differences at low fluorescence levels. Each point

represents fluorescence data for one pXG10sf construct. The specific construct for each data

point is indicated by numerical label referring to Table 1. Icon groups signify constructs that all

have the same ORF (indicated in key) and only differ from one another in the 5’ UTR. For the

AtpD and AtpA ORF groups, the data points were fit to a ‘1-exp’ curve shown in the inset at top

left. P214L and P281L groups were connected linearly and the dashed line indicates equal

fluorescence in the WT and ∆efp mutant. Data is in arbitrary fluorescence units (AFU)

normalized to OD600 and is the mean of at least three biological replicates. Error bars showing

one standard deviation are included for both x- and y-axes. ‘AtpD – AtpA 12aa’ indicates 12

codons upstream of the atpA PPG motif were swapped into the AtpD construct, ‘AtpA – AtpD

12aa’ is the reciprocal. Figure is shown as it appears in Hersch et al. (2014), J Biol Chem 126.

66

Table 1: Constructs used in Figure 22

ORF Mutation or Swap in 5'

UTR Label

LacZ wt 1

AtpD wt 2

AGAGG::AGAGC 3

AtpA UTR from -75-61 4

AtpA UTR from -60-46 5

AtpA UTR from -45-31 6

AtpA UTR from -30-16 7

AtpA UTR from -15-1 8

AGAGG::AGGAGG 9

ATG::GTG 10

ATG::TTG 11

AtpA UTR from -45-31

& AGAGG::AGGAGG 12

AtpA wt 13

AtpD UTR 14

AGGGGA::AGGAGG 15

AtpD UTR from -74-61 16

AtpD UTR from -45-31 17

AtpD UTR from -30-16 18

AtpD UTR from -15-1 19

AtpD UTR &

AGAGG::AGGAGG 20

AtpD UTR from -30-1 21

AtpD UTR from -30-1

& AGAGG::AGGAGG 22

AtpD

P214L

wt 23

AtpA UTR from -45-31 24

AtpA

P281L

wt 25

AtpD UTR 26

AtpA

R279P wt 27

AtpD -

AtpA 12aa

wt 28

AGAGG::AGGAGG 29

AtpA UTR from -45-31 30

AtpA -

AtpD 12aa

wt 31

AtpD UTR 32

AGGGGA::AGGAGG 33

67

4.2.9 Modeling the interplay between translation initiation rate and elongation stalls

My observation of a maximum expression in the efp mutant as initiation rate increases

prompted me to expand upon the relation between EF-P dependent stalls and initiation rate. I

employed high-throughput datasets from published works measuring EF-P dependence such as

my Salmonella efp mutant SILAC data110, another group’s published E. coli efp mutant SILAC

data130, or ribosome profiling data127. I attempted to correlate these measures of EF-P

dependence with analogs for translation initiation rate including ribosome profiling reads127,

protein abundance110,130, or protein per mRNA133 in wild-type cells. None of these analyses

yielded a significant correlation (data not shown). This may be due to significant variation in the

strength of different stalling sequences, which can be influenced by upstream amino acids in an

unpredictable manner. Thus EF-P dependence may be observed for some weakly expressed

genes if they have a strong stall motif, and similarly some highly expressed genes encoding a

polyproline motif may not show EF-P dependence if the motif triggers weak stalling. This

heterogeneity may mask the correlation between translation initiation rate and EF-P dependence

in these high-throughput datasets.

To further explore the interplay between initiation rate and the stall strength of not just

EF-P dependent but also other elongation pauses, Dr. Assaf Katz generated a computational

model simulating translation of a hypothetical transcript such that he could modulate the

initiation rate and elongation stall strength. A thorough description of this method and the results

obtained by it can be found in Hersch et al. (2014)126. In brief, by comparing the number of

ribosomes able to complete translation on transcripts with varying strengths of initiation rate and

rate of progression through an elongation stall, the computational modeling results follow a

similar curve as the data I observed for atpD and atpA wherein the impact of the elongation stall

increases with initiation rate. This modelling work strongly supports my hypothesis that

initiation rate and EF-P dependent stalls in elongation contend to be the rate-limiting step of

translation.

68

4.2.10 Fate of stalled peptides in vivo

A caveat of my rate-limiting step hypothesis and Assaf’s computational modelling is that

they assume that ribosomes do not undergo premature release from the transcript during an

extended stall at an EF-P dependent motif. To assess whether this is a frequent occurrence in a

Salmonella efp mutant, I reanalyzed peptide data from my previous SILAC investigation110. In

this experiment proteins were fragmented into tryptic peptides that were quantified by mass

spectrometry. I examined proteins identified in SILAC that contain an APP, PPG or PPP motif

and I binned individual peptides observed in mass spectrometry as either ‘pre-’ or ‘post-motif’. I

predicted that premature disengagement of the ribosome would release truncated peptides, which

would manifest as a relative increase in peptides observed upstream of the polyproline motif

compared to peptides after the motif in the efp mutant strain. Such a decrease should not be

observed in the wild-type strain where the entire protein should be synthesized to completion.

I constrained my analysis to proteins with at least two peptides conclusively identified

both before and after the motif, allowing for statistical comparison by t-test of pre- and post-

motif peptides. Out of the 40 proteins for which sufficient peptide data was available, only one

demonstrated a significant difference in relative expression pre- and post-motif (Figure 23).

Furthermore, SILAC ratios pre- and post-motif were linearly correlated with an R2 value of

0.851. Though it remains possible that truncated peptides are being degraded more rapidly than

they can be detected, this data suggests that ribosomes do not prematurely disengage from the

transcript at most polyproline motifs in the absence of EF-P, but rather eventually resume

synthesis and complete the full-length protein.

69

Figure 23: Peptide abundance ratios do not change significantly before and after APP, PPG

or PPP motifs in Salmonella. A) My SILAC data was analyzed comparing the mean peptide

ratios (WT/∆efp) before and after the first APP, PPG or PPP motif of the protein. Only proteins

with an APP, PPG or PPP motif and at least two peptides conclusively identified both before and

after the motif were analyzed (40 proteins). A t-test was used to calculate statistical significance

comparing the average peptide abundance ratio pre- versus post-motif. Gene names and p-values

are shown for proteins with a difference between pre- and post-motif of greater than two. AtpD

and AtpA are also indicated. The only protein with p < 0.05 is highlighted as a black square

(Lon). The linear regression is shown with coefficient of determination (R2) indicated at right.

Dashed line indicates a 1:1 regression. B) All Lon peptides detected in the my SILAC assay are

shown plotting peptide ratio (WT/∆efp) against their location within the full-length Lon protein.

The dashed line indicates the location of the PPG motif. The average of all peptide ratios before

or after the PPG motif are shown as a solid line and error bars indicate one standard deviation.

Figure is shown as it appears in Hersch et al. (2014), J Biol Chem 126.

70

4.3 Discussion

In this chapter I examined PoxB, which was the first protein to demonstrate PYE

pathway-dependent synthesis in the literature yet does not contain a polyproline motif75,76. I

identified the novel motif GSCGPG as responsible for the dependence of PoxB on EF-P. This

finding expands the known repertoire of EF-P target motifs and demonstrates that, at least in

some instances, they may require sequences longer than three amino acids. This suggests that

certain weaker stall motifs such as the GPG of PoxB may instigate ribosomal stalls if sufficiently

strengthened by upstream interactions.

This concept was reinforced by my work on AtpD and AtpA demonstrating that residues

up to six amino acids upstream of the first proline can impact EF-P-rescuable stalls at PPG

motifs. Specifically, the amino acid two positions prior to the PPG motif (-2 position) had a

drastic impact in the case of AtpD and AtpA. The arginine in that position in AtpA appeared to

have a protective effect on stalling in AtpD, particularly in combination with the adjacent (-1

position) arginine. In contrast the asparagine in the -2 position of AtpD drastically induced EF-P-

dependence in AtpA. Interestingly, this effect was negated by swapping surrounding residues as

well, suggesting that neighbouring residues can dampen the stall-inducing effects.

The six residue motif in PoxB and my data for AtpD and AtpA indicate that upstream

amino acids in the exit tunnel of the ribosome can modulate the induction of EF-P-rescuable

stalling. This is similar to what has been observed for the macrolide-sensitive stalling of

ErmAL164,124, the translocon-relieved stall of SecM61,63,66,134, and the tryptophan-responsive

stalling of TnaC123,135. However, the ability of individual amino acids to promote or protect

against EF-P-rescuable stalling is not straightforward. We are unable to find any simple or

general rule that dictates whether a particular sequence will stall – both in the instance of AtpA

and AtpD as well as in ribosome profiling analyses conducted on efp mutants of E. coli127,136.

Thus, while the data indicates that upstream amino acids influence ribosome stalling, this effect

cannot conclusively be attributed to particular amino acids in particular positions.

Interestingly, even when I swapped up to 40 amino acids upstream of the polyproline

motif (the longest estimates for the length of the exit tunnel69,131), I found that it was only enough

to partially reverse the EF-P-dependence of AtpD and AtpA. Moreover, ribosome profiling

71

conducted by the lab of our collaborator Dr. Michael Ibba indicated that (similar to AtpD)

ribosomes accumulated at the PPG site of AtpA in an efp mutant of E. coli despite this apparent

stalling having no influence on protein synthesis. These observations suggested that there exists a

crucial further factor(s) influencing whether or not a PPG motif-containing protein will be

dependent on EF-P for its efficient translation.

Upon expanding my swap analysis of AtpD and AtpA, I found that the 5’ UTR plays a

crucial role in governing EF-P dependence, and all data suggests that this is through its effect on

translation initiation rate. Simply put, the data suggest that many proteins containing polyproline

motifs are unaffected by the loss of EF-P because poor initiation limits the rate of synthesis more

than the stall itself. It follows that the impact of polyproline motifs on protein expression can be

superseded not only by the rate of translation initiation, but also by the rate of elongation through

alternate stalls, including slow codons or starvation-sensitive regions53,56,58,137,138. That is to say,

an EF-P dependent stall will only have an observable effect on protein level if it impacts the rate

of synthesis more than initiation and any other step in translation, including elongation or

termination. This is a parsimonious explanation for the many instances observed when a

polyproline containing protein does not demonstrate EF-P dependence in vivo74,84,110,130.

Experimentally it is possible to observe an influence of both initiation and the stall

sequence on translation. This can occur in situations where initiation and stalling impose similar

constraints on the rate of protein synthesis and is due to heterogeneity in the population of

transcripts. In a given population of mRNAs some transcripts will successfully initiate several

rounds of translation while other transcripts will fail to initiate at all. Furthermore, the EF-P

dependent stalls on some transcripts will stochastically resolve faster than others. When

transcripts have high overall initiation rates, most transcripts in the population will have engaged

with more ribosomes than can be cleared through the polyproline induced stall in the absence of

EF-P, resulting in a buildup of blocked ribosomes upstream of the stall (Figure 24). For

messages where initiation rates are low, ribosome binding is sufficiently rare that most stalled

ribosomes will resume elongation before the next ribosome reaches the stall site. In the latter

case, the polyproline motif will have no observable effect on protein levels. The net output of

protein depends on how the population of transcripts is distributed among these states.

72

The model in Figure 24 highlights the rate-limiting interplay between translation

initiation and clearance of ribosome stalls, and it is supported by my experimental results. For 5’

UTR mutants of atpD and atpA, as GFP fluorescence increased in wild-type cells, expression in

the efp mutant appeared to approach an asymptote where stall clearance becomes the

predominant rate-limiting step of translation. This effect was dependent on an intact PPG motif

and implies a calculable maximum expression in the efp mutant that is related to the rate of stall

clearance. This stall strength is particular to individual ORFs and allows for a comparison of EF-

P dependence across all initiation rates. Interestingly, the slope of the atpD curve declines much

more rapidly than that of atpA and reaches a significantly lower maximum expression,

supporting the idea that the EF-P alleviated stall of AtpD is more difficult to bypass than that of

AtpA. This is supported by my previous work, where I found that the region upstream of the

PPG motif plays a significant role in the degree of EF-P dependence. Indeed, when I swapped

twelve codons upstream of the PPG motifs, expression at multiple different initiation rates

resembled the protein from which the twelve codons originated. Taken together, my data

demonstrate that the region upstream of the PPG motif influences the strength of the stall and

thereby the steepness and maximum of the expression curve as a function of initiation rate. The

in silico translational model generated by our collaborators supports this concept.

My results also suggest that stalls at polyproline motifs in the absence of EF-P are

eventually resolved and that protein synthesis proceeds to completion in most cases. If the

ribosome disengaged from polyproline induced stalls, then I would expect that proteins

translated with low rates of initiation would show a degree of EF-P dependence similar to that of

proteins derived from transcripts with much higher initiation rates. This supposition is supported

by my SILAC data, where I do not observe a general decrease in peptide counts after the stall

sequence compared to sequences upstream of the stall.

It is unlikely that the balance between translation initiation rate and impaired elongation

is limited to EF-P dependent stalls. Indeed, Assaf’s computational modelling is not restricted to

EF-P dependent pauses and could be applied to any stall in translation elongation regardless of

mechanism. The correlation between codon usage and gene expression is well described, where

highly expressed genes have a strong bias toward using codons that correspond to the most

abundant tRNAs50,52,54,139,140. Chu et al. observed that the impact of slowly translated codons on

protein synthesis could be modulated by altering the 5’ UTRs of reporter plasmids in yeast57.

73

Other studies have described a translation ‘ramp’ wherein slow, non-preferred codons are more

abundant at the 5’ end of a coding region, and this is supported by ribosome profiling data where

increased ribosome occupancy was observed at the 5’ end of ORFs141,142. The length of this slow

ramp was significantly larger for transcripts with high ribosome densities, suggesting

evolutionary pressure on highly expressed genes to prevent downstream ribosome jamming57,141-

143. For various transcripts, such a ramp could also allow time for protein folding, binding of

cofactors to the N-terminal peptide, or enabling stalls to affect translation rate via blocking new

ribosomes from binding the mRNA56,144-147.

Cumulatively, these works support the interplay that I have presented here between

translation initiation rate and elongation stalls. This concept, demonstrated herein for EF-P

dependent pauses, can be readily modeled in silico and can be further applied to other forms of

ribosome stalls. Moreover, it suggests that including a weak ribosome binding site and thereby a

low rate of translation initiation can be protective against downstream ribosome stalls impacting

protein synthesis. For instance, if a peptide requires a translational stall for proper regulation of a

downstream gene, a low initiation rate could protect the transcript against ribosome jamming.

This establishes a mechanistic rationale for evolutionary selection of weak, inefficient ribosome

binding sites that lead to poor translation initiation rates on certain mRNA transcripts.

74

Figure 24: Model of the interplay between translation initiation rate and elongation stalls.

Depiction of translation of an mRNA transcript with low initiation rate (left) or high initiation

rate (right) and the subsequent effect of no elongation stall (top), a weak stall (middle) or a

strong stall (bottom). Black line, mRNA transcript. Purple box, start codon. Brown box, stop

codon. Blue ovals, translating ribosomes. Yellow stop sign, weak stall motif. Red stop sign,

strong stall motif. ++++, high degree of protein synthesis per transcript. ++, medium protein

synthesis per transcript. +, low level of protein synthesis per transcript. Figure is shown as it

appears in Hersch et al. (2014), J Biol Chem 126.

75

5 Salmonella evolved to suppress its uptake of dicarboxylic

acids during stress

Acknowledgements

Bushra Ilyas contributed greatly to testing growth using fumarate and malate in addition

to succinate during her undergraduate thesis research under my supervision. Figure 25C presents

data that was produced by her and is shown here to support my work. Bushra also generated the

original dctA mutant strain that I used in multiple experiments.

Bojana Radan was involved in the intial screen of compounds leading to the discovery

that proline can stimulate growth in succinate. As well, Bojana Radan was involved in testing

select SGSC and ECOR strains as a pilot study preceding my extensive screen of these

collections.

In this chapter I mention an unpublished microarray conducted on rpoS mutant

Salmonella. This was conducted by my supervisor, Dr. William Navarre, during his post-doctoral

work and in collaboration with Dr. Michael McClelland and Dr. Jonathan Frye.

76

5.1 Overview

Previous work employing Biolog Phenotype MicroarraysTM revealed that PYE mutants

display increased susceptibility to a wide variety of cellular stressors including

pharmacologically unrelated antibiotics74,84. The phenotype microarray data also demonstrated

that PYE mutants appear to display a hyper-active metabolism relative to wild-type Salmonella

when under specific nutrient limited conditions74,84. This indicates that perhaps efp mutants are

metabolically overactive and this could contribute to the hypersusceptibility phenotype due to a

failure to shut down metabolism in response to stress. In this chapter I set out to test metabolic

phenotypes of efp mutant Salmonella, yet I discovered a fascinating and counter-intuitive

adaptation of wild-type Salmonella. Specifically, when exposed to minimal media conditions

where the sole carbon source is a dicarboxylic acid such as succinate, wild-type Salmonella shuts

down its growth for an extended, yet surprisingly consistent, length of time (over 30 hours). In

contrast, PYE mutants, and also mutants in the stress response sigma factor rpoS, do not show

this prolonged lag phase and grow readily under these conditions. Moreover, this phenotype

appears to be a Salmonella specific phenomenon, as many strains of the closely related species

E. coli grow readily under the same conditions. I describe my progress toward identifying the

mechanisms underlying this Salmonella adaptation and how it represses growth in response to

dicarboxylates.

5.2 Results

5.2.1 Wild-type but not efp or rpoS mutant Salmonella displays delayed growth using

dicarboxylic acids as a sole carbon source

PYE mutants appear to display a hyper-active metabolism relative to wild-type

Salmonella when under specific nutrient limited conditions74,84,88. As well, my SILAC analysis

presented in Chapter 3 revealed that efp mutants have reduced levels of a number of metabolic

proteins, including members of the ATP Synthase complex such as the catalytic subunit AtpD,

which showed a 20-fold lower abundance in the Salmonella efp mutant. In light of these

findings, I attempted to examine metabolic phenotypes of efp mutants.

77

I first aimed to assess the ability of efp mutants to construct a functional ATP Synthase

complex by testing their growth in minimal media with succinate as the sole carbon source. This

assay was first described in 1971 with the rationale that when utilizing succinate as a carbon

source E. coli can only produce ATP via oxidative phosphorylation, and so strains lacking a

functional ATP Synthase cannot grow148,149. Of note, this rationale was later disproved by

demonstrating that the no-growth phenotype can be bypassed by overexpressing dctA, the

primary aerobic transporter of dicarboxylic acids – suggesting that ATP could be synthesized via

the TCA cycle yet loss of ATP Synthase activity somehow leads to repression of dctA150. Upon

testing the growth of Salmonella efp mutants in minimal media containing succinate as the sole

carbon source, I found that they were able to grow readily. This was similar to the Salmonella

rpoS mutant employed as a positive control (since E. coli rpoS mutants were previously

demonstrated to show improved growth on succinate151). Fascinatingly, and in stark contrast to

the efp and rpoS mutants, wild-type Salmonella appeared to repress its growth and exhibited an

extended lag phase for over 30 hours before initiating logarithmic growth (Figure 25A and B).

The fact that rpoS and efp mutants in an isogenic background could grow readily suggested that

the strain of Salmonella was capable of growing on succinate yet makes a regulatory decision not

to. The data suggest that this regulation involves RpoS and some unknown protein(s) that

requires efp for its efficient translation. As well, the dicarboxylic acid transporter, DctA, was

required for growth, and mutants in the dctA gene showed no sign of growth by 48 hours.

The extended lag of wild-type Salmonella also occurred during growth using two other

dicarboxylic acids, fumarate and malate (Figure 25C). Consistent with succinate, the efp mutant

grew significantly earlier than wild-type Salmonella using fumarate or malate as a sole carbon

source. This suggests that the growth repression instigated by wild-type Salmonella is not

specific to succinate but also occurs during growth using other dicarboxylic acids.

78

A)

B) C)

Figure 25: Wild-type Salmonella displays an extended lag phase using dicarboxylic acids as

a sole carbon source but efp and rpoS mutants do not. A) Representative growth curve of

Salmonella Typhimurium 14028s strains grown in MOPS minimal media with 0.2% succinate as

the sole carbon source. Data shows optical density at 600nm (OD600) for 48 hours from the time

of inoculation. The data shown is representative of greater than three biological replicates for all

strains. An OD600 of 0.1 is emphasized by a dashed line. B) Graphs showing the average time in

hours that wild-type (WT) or mutant Salmonella takes to reach an OD of 0.1 as an analog of the

length of lag phase using succinate as the sole carbon source. The data shows the average of at

least three biological replicates and error bars show one standard deviation. >48h indicates the

strain did not grow by 48 hours. C) As in (B) but comparing the use of three different

dicarboxylic acids as the sole carbon source. Data for panel C was produced by Bushra Ilyas

during her undergraduate thesis work under my supervision.

0

0.1

0.2

0.3

0.4

0 5 10 15 20 25 30 35 40 45 50

OD

(6

00

nm

)

Time (hours)

WT

∆efp

∆rpoS

∆dctA

0

10

20

30

40

Tim

e (

ho

urs

) to

rea

ch

OD

≥ 0

.1

∆rpoS ∆dctA∆efpWT

>48h0

10

20

30

40Ti

me

(h

ou

rs)

to r

each

O

D ≥

0.1 Succinate

Fumarate

Malate

∆efpWT

79

5.2.2 Many Salmonella but few E. coli strains delay growth using succinate

My finding that wild-type Salmonella significantly delays its growth using dicarboxylic

acids as a sole carbon source prompted the question of whether this was a common phenomenon

or specific to Salmonella. To address this I compared Salmonella to the closely related bacterium

E. coli and found that E. coli grew readily in minimal media using succinate as a sole carbon

source (Figure 26A). To examine if this difference extended to additional strains, I conducted a

screen testing growth using succinate for all 105 non-typhoidal strains in the Salmonella Genetic

Stock Centre (SGSC) collection, as well as all 72 strains of the E. coli Reference (ECOR)

collection. Indeed, I found that (though there are exceptions) the majority of E. coli strains grow

more readily in succinate media compared to most Salmonella strains, which display extended

lag phases (Figure 26B-E). Once logarithmic growth was initiated, Salmonella also appeared to

trend towards a slightly longer doubling time than the majority of E. coli strains (Figure 26C and

F). To ensure that the observed effects were not due to variations in RpoS, I also tested all SGSC

and ECOR collection strains for catalase activity as an analog for functional RpoS. I found that

regardless of catalase activity the same trend held true that E. coli strains generally showed

shorter lag phases than Salmonella when using succinate as the sole carbon source (Figure 26D).

80

0

0.2

0.4

0.6

0.8

0 10 20 30 40 50

OD

(60

0n

m)

Time (h)

E. coli K12 WT Salmonella WT Salmonella ∆rpoSE. coli K12Salmonella 14028s

Salmonella 14028s ∆rpoS

All SGSC SalmonellaSubsp. entericaAll ECOR E. coli

0

1

2

3

4

5

0 10 20 30 40 50 60

Do

ub

ling

tim

e (h

ou

rs)

fro

m O

D 0

.1 t

o O

D 0

.2

Time (hours) to reach OD ≥ 0.1

Salmonella

Salmonella with a >72h rep

E. coli

E. coli with a >72h rep

Salmonella

Salmonella with a >72h rep

E. coli

E. coli with a >72h rep

0

10

20

30

0 20 40 60 80

0

0.5

1

0 10 20 30 40 50 60

Cat

alas

e

Time (hours) to reach OD ≥ 0.1

Salmonella

Salmonella with a >72h rep

E. coli

E. coli with a >72h rep

+

+/−

Salmonella

Salmonella with a >72h rep

E. coli

E. coli with a >72h rep

All SGSC Salmonella

All ECOR E. coli

All SGSC Salmonella

All ECOR E. coli

A) B)

C)

D)

E) F)

81

Figure 26: E. coli grows earlier than Salmonella using succinate as the sole carbon source.

Growth in MOPS minimal media with 0.2% succinate as the sole carbon source. Growth was

conducted in a TECAN Infinite M200 plate reader and reads were taken every 15 minutes. A)

Representative growth curve. An OD600 of 0.1 is emphasized by a dashed line. B) Percentage of

SGSC Salmonella (green line) or ECOR E. coli (blue line) strains that (on average) had

surpassed an OD600 of 0.1 by indicated times post inoculation. Red line is for Salmonella but

excludes the 14 tested strains of the SARC collection that do not belong to subspecies enterica.

C) Overview of 105 Salmonella strains (SGSC collection) and 72 E. coli strains (ECOR

collection). Each strain is plotted by average time it takes to reach OD 0.1 (x-axis) compared to

doubling time during growth from OD 0.1 to OD 0.2. All Salmonella are coloured black and all

E. coli strains are coloured red. Strains where at least one replicate that did not grow by 72 hours

are shown as triangles; these values are the average of the remaining replicates. Inset at top right

shows zoomed out view to include outliers. D) Similar to (C) but comparing time to reach OD

0.1 with catalase result. + or − indicate that the strain showed positive or negative (respectively)

catalase result in all replicates. +/− indicates either delayed positive or inconsistent catalase

results across replicates. Data is the average of at least three biological replicates with the

exception of some strains that had at least one replicate that did not reach OD 0.1 by 72h (shown

as triangles that are the average of the remaining replicates). E) Average time in hours for strains

to reach an OD of 0.1. Data compares all 105 Salmonella strains to all 72 E. coli strains tested.

Line indicates the median, boxes show the 25th to 75th percentiles, and whiskers show the 10th

to 90th percentiles. An unpaired t-test with Welch’s correction indicated a p-value < 0.0001. F)

As in (E) but comparing doubling time in hours. An unpaired t-test with Welch’s correction

indicated a p-value < 0.05 with all data points included or p-value < 0.0001 when excluding slow

growing outliers (doubling time > 5h).

82

5.2.3 The RpoS stabilizer IraP contributes to growth shutdown in succinate media

The repression of wild-type Salmonella’s growth when using dicarboxylic acids as the

sole carbon source appears to involve the general stress response sigma factor RpoS, since

mutation of the rpoS gene resulted in early growth on succinate (Figure 25). One of the major

mechanisms by which RpoS activity is regulated – particularly under nutrient stress – is via its

degradation, and three anti-adaptor proteins are known (IraP, RssC and IraD) that inhibit RssB to

stabilize RpoS in response to specific kinds of stress (Figure 1)17,152. I investigated whether one

of these known RssB anti-adaptors is involved in sensing succinate media as a stress and

instigating growth shutdown via RpoS stabilization. I generated deletions of each of the anti-

adaptor genes and tested the mutant strains for growth in minimal media with succinate as the

sole carbon source. I found that deletion of iraP led to drastically earlier growth of Salmonella

under these conditions (similar to deletion of rpoS), and this phenotype could be partially

complemented by expressing IraP from its native promoter on a plasmid (Figure 27). In contrast,

deletion of the other anti-adaptors, RssC and IraD, had only minor or negligible effects. Deletion

of the rssB gene itself yielded inconsistent results, ranging from growth similar to wild-type

Salmonella to early growth similar to an rpoS mutant. This is likely due to suppressor mutations

in rpoS arising in this strain to compensate for the lack of rssB-mediated RpoS degradation. In

summary, though the mechanism by which iraP is induced remains unknown, my findings

demonstrate that IraP plays a role in the RpoS-mediated repression of Salmonella’s growth using

succinate as the sole carbon source.

83

A)

B)

C) D)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50

OD

(6

00

nm

)

Time (h)

WT

ΔrpoS

ΔIraP

∆rssB

∆rssC

∆iraD

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50

OD

(6

00

nm

)

Time (hours)

WT

ΔrpoS

ΔIraP

ΔIraP pIraP

0

10

20

30

40

50

Tim

e (h

ou

rs)

to

reac

h O

D ≥

0.1

0

10

20

30

40

50

Tim

e (h

ou

rs)

to

reac

h O

D ≥

0.1

WT

ΔrpoS

ΔiraP

ΔiraP pIraP

WT

ΔrpoS

ΔiraP

ΔrssB

ΔrssC

ΔiraD

84

Figure 27: Deletion of the RpoS stabilizer IraP results in early growth on succinate. Growth

of Salmonella in MOPS minimal media with 0.2% succinate as the sole carbon source. A) The

three known RssB antiadaptors were deleted from the Salmonella chromosome and growth is

shown along with an rssB mutant. Data is representative of at least three biological replicates for

all but the ΔrssB strain, which varied between replicates as described in the text. B) Exogenous

expression of the Salmonella iraP gene from its native promoter on a plasmid partially

complements the growth delay phenotype. Complementation data is representative of two

biological replicates. C and D) Graphs showing the average time in hours that wild-type (WT) or

mutant Salmonella takes to reach an OD of 0.1 as an analog of the length of lag phase using

succinate as the sole carbon source. The data shows the average of at least three biological

replicates for all strains in (C) except ‘∆iraD’ for which only two replicates were conducted.

Data in (D) is the average of two biological replicates. Error bars show one standard deviation.

85

5.2.4 Supplementation with proline or citrate induces diauxic growth using succinate and

subsequent repression requires the stringent response

To examine how IraP may be induced and whether Salmonella requires additional

nutrients, I supplemented succinate media with various compounds. The addition of minute

amounts of either proline or citrate induces growth in succinate media in a manner resembling a

diauxy wherein growth is repressed again following depletion of the proline or citrate (Figure

28A and C). This suggests that the addition of these compounds can provide Salmonella with a

metabolite that is either limiting with succinate as a sole carbon source or can act as a regulatory

signal stimulating growth. Interestingly, this critical metabolite does not appear to be proline

itself as growth induction by proline (but not citrate) required the enzyme PutA, which degrades

proline to glutamate.

In parallel, I also examined the role of a number of Salmonella mutants involved in

regulating starvation, including a relA spoT double mutant that cannot produce the stringent

response secondary messenger ppGpp. This ppGpp0 strain shows slightly earlier growth in

succinate than wild-type Salmonella but moreover does not repress growth following proline or

citrate stimulation (Figure 28B and D). This suggests that following induction by proline or

citrate ppGpp plays a significant role in restoring Salmonella’s repressed state and this signal

may contribute to growth shutdown even in the absence of these inducers.

86

A)

B)

C) D)

WT

ΔrpoS

WT + Pro

WT + Cit

ΔputA + Pro

ΔputA + Cit

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50

OD

(6

00

nm

)

Time (hours)

WT

ΔrpoS

ppGpp0

ppGpp0 + Pro

ppGpp0 + Cit

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50

OD

(6

00

nm

)

Time (hours)

0

10

20

30

40

Tim

e (h

ou

rs)

to

reac

h O

D ≥

0.1

0

10

20

30

40

Tim

e (h

ou

rs)

to

reac

h O

D ≥

0.1

WT

Pro Cit

∆putA

Pro Cit

ppGppO

Pro Cit

87

Figure 28: Proline or citrate induce growth using succinate and subsequent repression

involves the stringent response. A) Representative growth curve of Salmonella growth in

MOPS minimal media with 0.2% succinate as the primary carbon source and supplemented with

0.005% proline (+ Pro) or 0.005% citrate (+ Cit) where indicated. B) Representative growth

curve of Salmonella growth in MOPS minimal media with 0.2% succinate as the primary carbon

source. A relA spoT double mutant that cannot make the stringent response messenger ppGpp

(ppGpp0) exhibits an intermediate growth phenotype and does not shut down growth following

proline or citrate induction. C and D) Graphs showing the average time in hours that wild-type

(WT) or mutant Salmonella takes to reach an OD of 0.1 as an analog of the length of lag phase

using succinate as the sole carbon source. The data shows the average of at least three biological

replicates for all except ‘∆putA + Cit’ for which only two replicates were conducted. Error bars

show one standard deviation.

88

5.2.5 Growth lag appears to be due to repression of succinate import

I examined unpublished microarray data of a Salmonella rpoS mutant conducted during

Dr. Navarre’s post-doctoral research. I noticed that the gene encoding the primary dicarboxylate

transporter, dctA, demonstrated up to a 4.4 fold increase in expression in the rpoS mutant. This

suggests that RpoS may repress the expression of this transporter and thereby restrict Salmonella

from taking up dicarboxylates such as succinate for growth. To test if wild-type Salmonella does

not grow on succinate due to repression of dctA, I constitutively expressed dctA from a plasmid

and tested growth in minimal media with succinate as the sole carbon source. I found that

constitutive expression of dctA but not lacZ resulted in earlier growth in succinate media,

indicating that the limiting factor in Salmonella’s growth using succinate is synthesis of the

dicarboxylate importer DctA (Figure 29A and C). It therefore appears that a role of RpoS in

Salmonella is to repress dctA expression and restrict the uptake of dicarboxylic acids.

5.2.6 The E. coli dctA promoter is sufficient to induce Salmonella growth using succinate

In light of my findings that Salmonella’s suppressed growth phenotype is largely

mediated by repression of the dctA gene, I examined the role of the dctA promoter (PdctA). Since

the phenotype appears to be divergent between Salmonella and the closely related species E. coli,

I swapped the E. coli dctA promoter into the Salmonella chromosome using an upstream

chloramphenicol resistance cassette to select for successful recombination. As a control for

artefacts of introducing the chloramphenicol resistance cassette, I conducted the same swap but

inserted Salmonella’s native dctA promoter and found no difference from wild-type Salmonella

when grown in minimal media with succinate as the sole carbon source. In contrast, I found that

replacing the Salmonella dctA promoter with that of E. coli was sufficient to abolish

Salmonella’s ability to repress its uptake of dicarboxylic acids, and this strain grew readily in

succinate media (Figure 29B and D).

89

A)

B)

C) D)

Figure 29: Expression of dctA induces growth using succinate. Growth of Salmonella in

MOPS minimal media with 0.2% succinate as the sole carbon source. A) Overexpression of dctA

from a plasmid induces Salmonella growth using succinate. WT pLacZ and pDctA indicate wild-

type Salmonella containing a pXG10sf plasmid encoding full length lacZ or dctA with

expression driven by the constitutively active PLtet0-1 promoter. The rpoS mutant is shown for

comparison. Data is representative of three biological replicates. B) The E. coli dctA promoter

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50

OD

(6

00

nm

)

Time (hours)

WT

∆rpoS

WT pXG10sf

WT pXG10sf-DctA

WT

ΔrpoS

WT pLacZ

WT pDctA

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 10 20 30 40 50

OD

(60

0n

m)

Time (hours)

WT

CmR-PdctAsalm in chrom

CmR-PdctAcoli in chrom

∆iraP

S. Typhimurium

PdctA (Salm.)

PdctA (E. coli)

ΔiraP

0

10

20

30

40

Tim

e (h

ou

rs)

to

reac

h O

D ≥

0.1

0

10

20

30

40

Tim

e (h

ou

rs)

to

reac

h O

D ≥

0.1

∆rpoSWT WT pLacZ

WT pDctA

PdctA

(Salm.)WT PdctA

(E. coli)∆iraP

90

(PdctA E. coli) with an upstream chloramphenicol resistance cassette for selection was inserted

into the Salmonella chromosome by lambda red recombination to replace the native dctA

promoter. Replacement with Salmonella’s native dctA promoter controls for effects due to the

chloramphenicol resistance cassette (PdctA Salm.). ΔiraP is shown for comparison. Data shown is

representative of greater than three biological replicates. C and D) Graphs showing the average

time in hours that wild-type (WT) or mutant Salmonella takes to reach an OD of 0.1 as an analog

of the length of lag phase using succinate as the sole carbon source. The data shows the average

of at least three biological replicates and error bars show one standard deviation.

91

5.3 Discussion

In this chapter I discovered not just a PYE mutant phenotype but also a broader and more

interesting characteristic of wild-type Salmonella itself and how it regulates its metabolism in

response to environmental conditions that it perceives as stressful. I chose to follow up this

avenue of research and, through my work, I found that wild-type Salmonella restricts its growth

when using dicarboxylates as a sole carbon source and exhibits an extended lag phase lasting

over thirty hours. This phenotype does not reflect a metabolic inability of Salmonella to utilize

dicarboxylates, as I have discovered multiple mutations in regulatory genes that allow the cells to

grow early in succinate media. It appears that Salmonella employs RpoS, IraP and to some

degree RelA or SpoT to sense this environment as a stress and shut down expression of dctA to

restrict its import of dicarboxylic acids. Interestingly, growth is also stimulated by the addition of

small amounts of proline or citrate, suggesting that these supplements can alleviate growth

repression. Subsequent growth repression following proline or citrate induction required ppGpp.

The stringent response and ppGpp have been demonstrated to impact the expression of both rpoS

and iraP, suggesting that RelA or SpoT may be involved in the initial sensing of succinate media

as a stress and activating the IraP- and RpoS-mediated shutdown of growth17.

Importantly, I demonstrate that this phenotype is divergent between Salmonella and the

closely related (but non-intracellular) bacteria, E. coli. I discuss this phenotype as a trait that

Salmonella has acquired since diverging from E. coli, however it should be noted that it remains

possible that the opposite is true and E. coli has lost the ability to repress the uptake of

dicarboxylates since diverging from Salmonella. The trend held true amongst most tested strains

from the SGSC and ECOR collections, which sample a wide range of the genetic diversity of

these species. Yet there are exceptions, including a number of Salmonella strains that grew early

despite having a catalase positive phenotype. In all instances where multiple strains from the

same Salmonella serovar were tested, at least one exhibited an extended lag phase

(Supplementary DataTable 2). Thus the earlier growth does not appear to be a trait of particular

serovars but rather individual strains seem to have lost the delayed growth phenotype. This could

occur by mutations in genes other than rpoS, such as efp or iraP, that grant early growth in

succinate while remaining catalase positive. Alternatively, some of these strains may never have

acquired this adaptation as opposed to lost it due to mutation. This particularly applies to the

more phylogenetically distinct Salmonella that do not belong to subspecies I (subsp. enterica),

92

where 11 of the 14 tested strains grew earlier than the Salmonella average of 29 hours

(Supplementary DataTable 2). This suggests that delayed growth using succinate may be an

adaptation that primarily benefits subsp. enterica Salmonella, which includes the majority of

serovars responsible for human infections153. However, this is confounded by the fact that 5 of

these strains tested catalase negative, suggesting that their result may simply reflect a mutation in

rpoS. Combined with the low number of non-subsp. enterica Salmonella strains tested, there is

insufficient data to support the subspecies I concept conclusively.

Other exceptions include some Salmonella strains that display an extended lag phase

despite having no catalase activity. It is possible that these strains contain a mutation in their

catalase gene itself as opposed to rpoS. Alternatively, in addition to mutations altering RpoS

activity, these strains may also contain suppressor mutations in genes such as the dicarboxylate-

sensing two-component system dcuSR, which could delay growth in this assay. There also

remains the possibility that these strains employ an alternate RpoS-independent mechanism of

regulating the delayed growth phenotype

Finally, there are multiple E. coli strains that are catalase positive yet show an extended

lag in their growth using succinate. While it is possible that these have mutations in genes

required for the uptake of succinate (such as dctA or dcuSR), these may be genuine variations in

how E. coli strains respond to succinate. Since a major difference between Salmonella and E.

coli is their virulence, and the ECOR collection contains a number of strains that have been

shown to demonstrate indicators of virulence, I compared whether these potentially virulent E.

coli strains correlated with growth in succinate. Seven of the 72 E. coli strains averaged longer

than 24h to reach an OD of 0.1 in succinate media and four of these averaged longer than 29h

(the average time for all tested Salmonella strains) (Supplementary DataTable 2). Of these seven,

only ECOR 52 and to a lesser degree ECOR 48 demonstrated hemolytic and cytotoxic activity in

the literature, suggesting that growth using succinate is not correlated with indicators of

virulence in these E. coli strains154.

I discovered that replacing the Salmonella dctA promoter with that of E. coli is sufficient

to abolish the ability of Salmonella to repress its uptake of succinate. This result strongly

suggests that, since diverging from E. coli, Salmonella has obtained a regulatory element in its

dctA promoter that allows it to be repressed under these conditions. My work and microarray

93

data suggest that RpoS is involved in this regulation; yet RpoS is a transcriptional activator. The

lack of growth on dicarboxylates suggests that dctA is tightly repressed, suggesting against solely

sigma factor competition for RNA polymerase, but rather that RpoS likely acts via an

intermediate and yet unknown transcription factor (potentially a protein factor or a small RNA)

(Figure 30). Based on my chromosomal promoter swap experiment, the binding site of this

repressor is likely in a region that differs between the Salmonella and E. coli dctA promoters. An

alignment of these promoters demonstrates a sequence identity score of 77.9%, indicating that

overall the promoter is fairly well conserved. However there are specific distinctions such as a 9

bp deletion (in the Salmonella promoter) adjacent to a region of dissimilarity approximately 100

bp upstream of the conserved -35 box of the promoter (Figure 31). This distinct sequence could

be sufficient to recruit a repressor to the Salmonella promoter and would be eliminated if

replaced by the E. coli promoter. However, my preliminary analyses using prediction software

(such as BPROM) and examination of transcription factor consensus sequence databases (such as

RegulonDB) did not yielded significant leads regarding a specific regulator that could bind to

this sequence.

The difference in the response of Salmonella and E. coli to dicarboxylic acids may offer

important clues to identifying the evolutionary advantage conveyed by this adaptation. Since

many of the traits that Salmonella has acquired since their divergence are related to its

pathogenic lifestyle, it follows that this phenotype may reflect a situation that Salmonella

encounters during infection of a host. The recent finding that succinate accumulates to high

levels in activated macrophage suggests that Salmonella’s intracellular survival in macrophage

may represent this crucial selective environment155. It is conceivable that Salmonella recognizes

the succinate produced by activated macrophage and restricts its uptake of this dicarboxylate in

response. In the next chapter I will expand upon this hypothesis.

94

Figure 30: Working model of Salmonella’s response to succinate as the sole carbon source.

IraP becomes induced and stabilizes rpoS. Since rpoS is an activator of transcription, it follows

that its activity increases expression of an unknown repressor that turns off expression of dctA.

Since replacing the Salmonella dctA promoter with the E. coli one induces growth on succinate,

it is likely that this repressor binds to a region that is divergent between the two promoters.

Without DctA, the cells do not import their only carbon source (succinate) and do not grow. The

stringent response second messenger ppGpp and supplemented proline or citrate influence

growth using succinate; however, their mechanism and position in the signalling cascade remains

uncertain.

RpoS(σS)

dctA geneSuccinate

import and growth

Succinate as sole carbon source

IraP

Repressor?

Divergent between Salmonella and E. coli

?ppGppProline, Citrate

95

Figure 31: Alignment of Salmonella and E. coli dctA promoters conducted using ClustalΩ

software156. The promoters span 500bp each, which was the length swapped into the Salmonella

chromosome in Figure 29B and D. Sequence from E. coli K12 is on top, sequence from

Salmonella 14028s is on bottom. Asterisks indicate identity and the two promoters share 77.9%

identity across the 500bp. Red rectangles indicate the 5’ UTR identified by RegulonDB157 and in

references 158,159. Blue rectangles indicate the predicted -10 and -35 boxes identified by

RegulonDB157 and reference 158.

ATG start codon

96

6 Salmonella versus macrophage-produced dicarboxylic

acids

Acknowledgements

I would especially like to thank Ryan Gaudet from the lab of Dr. Gray-Owen for his

generous donation of the THP-1 monocyte cell line as well as for his guidance and training for

working with them. I would also like to thank Dr. Teresa O’Meara from the lab of Dr. Leah

Cowen for the generous donation of the J774 macrophage cell line.

97

6.1 Overview

In this chapter I probe the question of why Salmonella may have acquired the trait of

blocking dicarboxylate utilization and what evolutionary advantage it may gain by it. Succinate

levels were recently shown to increase significantly in activated macrophage and Salmonella (but

not E. coli) has adapted to survive effectively in this environment; moreover, survival in

macrophage is required for Salmonella virulence30,31,155. I examined the possibility that activated

macrophage produce succinate not only as an inflammatory signal but also as a weapon to inhibit

phagocytosed bacteria, and Salmonella has evolved to reduce its import of dicarboxylic acids as

a defence mechanism. Using dctA overexpression strains and Salmonella containing the E. coli

dctA promoter to induce uptake of succinate, I demonstrate that Salmonella’s repression of

succinate uptake does not influence its survival in either acidified succinate or in macrophage.

Another dicarboxylic acid, itaconate, is also heavily induced in activated macrophage and

inhibits the bacterial glyoxylate shunt160-163. Moreover, itaconate degradation enzymes have been

characterized, and Salmonella encodes orthologs of these genes in an operon164. I examine

Salmonella’s response to this dicarboxylic acid, identifying the regulator of the degradation

operon and its specific response to itaconate. Using this regulator and its target promoter, I

construct an itaconate biosensor and examine the Salmonella itaconate response in macrophage.

However, despite its potent response to itaconate, deletion of the itaconate degradation operon or

its regulator does not influence Salmonella survival in macrophage under the conditions tested.

These data suggest that degradation of itaconate or repressing succinate uptake may offer

advantages other than enhanced survival in macrophage.

6.2 Results

6.2.1 Hypothesis and rationale that Salmonella may repress uptake of dicarboxylic acids to

improve survival in activated macrophage

Macrophage activated by LPS drastically increase their levels of two dicarboxylic acids,

succinate and itaconate, potentially creating a selective niche where repressing uptake of

dicarboxylic acids may provide Salmonella with a survival advantage155,160,162. Succinate in

98

macrophage was demonstrated to play a role in inflammatory signalling via stabilization of HIF-

1α, leading to increased IL-1β synthesis155. Meanwhile itaconate can inhibit the bacterial

glyoxylate shunt, which is required for growth using carbon sources with two or fewer carbons

or when using fatty acids as a carbon source161,163. Moreover, a cluster of genes have been

reported that degrade itaconate, and Salmonella Typhimurium contains orthologs of this operon

in a pathogenicity island (SPI-13)164.

The accumulation of succinate and itaconate in activated macrophage could have a

function beyond the characterized inflammation signalling and inhibition of the glyoxylate shunt.

Both compounds are dicarboxylic acids, indicating that they have two carboxyl groups with

characteristic acid dissociation constants (pKa). Indeed the Salmonella-containing vacuole within

macrophage is about pH 5.0, but the low estimates reach pH 4.4, which is similar to the pKa’s of

succinate and itaconate (Figure 32A)34,165. This suggests that in the acidified phagosome,

succinate and itaconate could become protonated either on both carboxyl groups (di-protonated)

or on one (hemi-protonated). In the di-protonated form, these molecules lose their negative

charge, allowing them to traverse the bacterial membrane freely (Figure 32B). Upon reaching the

more neutral pH of the cytoplasm, the equilibrium shifts to favor release of the proton, thereby

acidifying the bacterial cytoplasm. Thus, acidified organic acids such as succinate and itaconate

can act as proton shuttles to force bacteria to acidify in an acidic environment such as the

phagosome. Indeed I have demonstrated that addition of succinate or itaconate to acidifed LPM

media significantly reduces Salmonella’s survival in vitro (Figure 32C).

In the hemi-protonated form, which would account for the majority of molecules at the

estimated pH of the Salmonella containing vacuole, succinate and itaconate would still require a

transporter such as DctA to gain access to the cytoplasm. Yet they would shuttle a proton and

contribute to acidification. As well, DctA symports dicarboxylates with two protons, resulting in

further acidification (Figure 32B). This suggests that in the context of a macrophage phagosome,

Salmonella may benefit from repressing DctA to exclude succinate (and potentially itaconate) in

order to prevent acidification of its cytoplasm. The repression of growth in succinate media

described in the previous chapter may represent such an adaptation. Moreover the growth lag

phenotype was mediated by repression of dctA via the stress response sigma factor RpoS, which

is heavily involved in the organic acid stress response152,166.

99

A)

B)

C)

Figure 32: Dicarboxylic acids can act as proton shuttles in acidified conditions. A) Depiction

of the chemical structures of succinate, fumarate, malate and itaconate in their acidifed form.

Their pKa’s are indicated. B) Depiction of the rationale behind acidifed dicarboxylic acids acting

as proton shuttles using succinate as an example. At pH above the lower pKa the succinate is

hemi-protonated and remains charged and so cannot freely pass through the membrane without a

transporter. C) Survival of wild-type Salmonella is reduced when succinate or itaconate are

added to acidified LPM media. Data shows percent survival at 3h relative to at 0h and is the

average of at least three biological replicates. Error bars indicate one standard deviation.

pKa = 5.6

pKa = 4.2

pKa = 5.5

pKa = 3.8

Succinate ItaconateFumarate Malate

pKa = 4.4

pKa = 3.0

pKa = 5.1

pKa = 3.4

--

pKa = 5.6

pKa = 4.2

pH = 7

pH ~ 7

DctA

H+

H+

Succinate

-

H+

H+ H+H+ H+

H+

pH = 5

pH ~ 7

DctA

2 H+

Succinate3 H+

Succinate2 H+

Succinate2 H+

Cytoplasm

Periplasm

IM

H+

H+ H+H+

H+

H+

H+

H+

H+

H+

pH = 3

pH ~ 7

Diprotonated succinate

Succinate2 H+

0.001

0.01

0.1

1

10

100

LPM mediapH 4.4

+ 0.4%Succinate

+ 0.4%Itaconate

Perc

ent

surv

ival

af

ter

3 h

ou

rs

100

6.2.2 Constitutive expression of dctA from a plasmid reduces Salmonella survival in acidified

succinate and in macrophage

To assess whether repression of dctA granted Salmonella a survival advantage by

excluding succinate under acidic conditions, I overexpressed dctA from a plasmid, which I

previously demonstrated would induce Salmonella uptake and growth using succinate as a sole

carbon source (Figure 29). I subjected strains constitutively expressing dctA or lacZ to acidified

(pH 4.4) LPM media containing 0.4% succinate as well as tested their survival in the human

THP-1 macrophage cell line. I found that overexpression of dctA but not lacZ led to decreased

survival relative to wild-type Salmonella under both conditions (Figure 33A-B).

At first this result was encouraging, as it suggested that forcing Salmonella to express

dctA and import succinate was detrimental to survival under acidic conditions, including in a

macrophage phagosome. However, overexpression of dctA has been demonstrated to be toxic to

E. coli and so there remained the possibility that the decreased survival was not due to succinate

import at all but rather due to cellular stress introduced by constitutive dctA expression150,167. To

address this concern I generated point mutants in dctA, N301A and S380D, which are defective

for succinate transport168. Despite their lack of transport function, these mutants still conveyed

toxicity when overexpressed and yielded smaller colonies than wild-type Salmonella (Figure

33C). The strain expressing pDctAS380D appeared to be more toxic than wild-type DctA and

yielded a mix of very small colonies or colonies that grew as well as wild-type Salmonella and

likely acquired a mutation in the plasmid’s dctA gene within one overnight growth cycle. In

contrast, the strain expressing pDctAN301A yielded similar sized colonies as the strain expressing

wild-type dctA, suggesting the N301A mutant as a valid candidate for further testing. When

subjected to acidified succinate media, survival of the strain overexpressing the dctA N301A

mutant was as low as the strain overexpressing wild-type dctA, implicating that the reduced

survival was not due to succinate uptake but rather an artefact of overexpression of the dctA gene

to toxic levels.

101

Figure 33: Overexpression of DctA from a plasmid decreases survival in macrophage due

to toxicity. A) Infection of THP-1 macrophage examining survival of wild-type Salmonella with

no plasmid (WT) compared to isogenic strains constitutively expressing LacZ (pLacZ) or DctA

(pDctA) from the pXG10sf plasmid. Survival of E. coli and ∆phoP mutant Salmonella are

included as controls for comparison. Data shows CFU recovered at 24 hours post-infection

relative to wild-type Salmonella and is the average of three biological replicates. Error bars show

one standard deviation. B) Survival of wild-type or dctA mutant Salmonella treated with LPM

media containing 0.4% succinate and acidified to pH 4.4. CFU recovered at 3 hours were

normalized to input CFU at 0h and expressed as percent survival. Data shows the average across

three biological replicates and error bars indicate one standard deviation. Where indicated, genes

were expressed from the ampicillin resistant version of the pXG10sf plasmid under the control of

the constitutively active PLtet0-1 promoter. C) Representative image showing overnight growth

of wild-type Salmonella on an LB agar plate comparing colony size when constitutively

expressing DctA point mutants. Black arrow indicates a small colony of the strain expressing

DctAS380D; white arrow indicates a large colony likely resulting from a suppressor mutation.

0.001

0.01

0.1

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10

100

WT ∆dctA pLacZ ∆dctA pDctA ∆dctA pDctA N301A

Perc

ent

surv

ival

afte

r 3

ho

urs

pDctAS380D No plasmid

pDctAN301A pDctA

00.20.40.60.8

11.21.41.61.8

E coli K12 14028s WT ∆phoP WT pXG10sf-LacZ

WT pXG10sf-DctA

CFU

rec

ove

red

at

24

h

(rel

ativ

e to

WT)

E. coliK12

WT14028s

∆phoP WTpLacZ

WTpDctA

WT ∆dctApLacZ

∆dctApDctA

∆dctApDctAN301A

A)

B) C)

102

6.2.3 Chromosomal E. coli dctA promoter does not reduce Salmonella survival

Since I found that overexpression of dctA from a plasmid reduced Salmonella survival

due to an inherent toxicity artefact, the impact of succinate import in acidified media or in

macrophage remained uncertain. I therefore employed my PdctA swap strain wherein I replaced

the Salmonella chromosomal dctA promoter with that of E. coli. This strain does not repress dctA

and grows readily in succinate media (Figure 29), yet does not constitutively overexpress dctA

from a plasmid and so does not exhibit the associated toxic effects. Thus any reduced survival of

this strain would be due to increased succinate import rather than an artefact of toxicity. I

examined survival of this strain compared to a control with the native Salmonella dctA promoter

swapped into the chromosome by the same method. Underwhelmingly, the strain with the E. coli

dctA promoter showed no significant reduction in survival in acidified succinate or in either

human THP-1 or mouse J774 macrophage cell lines (Figure 34). Moreover, deletion of dctA or

iraP genes in Salmonella did not appear to significantly influence survival in THP-1 macrophage

(Figure 34B). Cumulatively, this data suggests that the Salmonella phenotype of repressing dctA

in minimal media with dicarboxylates as a sole carbon source does not convey a survival

advantage in acidified succinate or in macrophage.

103

Figure 34: Replacement of the Salmonella dctA promoter does not influence survival in

acidified succinate or macrophage despite growing precociously in succinate media as

demonstrated in Figure 29. A) Survival of Salmonella treated with LPM media containing 0.2%

succinate and acidified to pH 4.4. Wild-type (WT) is compared to Salmonella with its

chromosomal dctA promoter replaced with the E. coli dctA promoter (PdctA E. coli) or

Salmonella’s native dctA promoter as a control (PdctA Salm). An rpoS mutant is shown as a

positive control. CFU recovered at 3 hours were normalized to input CFU at 0h and expressed as

percent survival. Data shows the average across three biological replicates and error bars indicate

one standard deviation. B) Infection of THP-1 human macrophage comparing survival of wild-

type Salmonella (WT) with various mutant strains and the dctA promoter swap strain. A phoP

mutant is included as a positive control. Data shows a logarithm of CFU recovered at 24 hours

post infection and is the average of three biological replicates. Error bars show one standard

deviation. C) As in (B) but showing CFU recovered from mouse J774 macrophage.

4.5

5

5.5

6

6.5

Log

(CFU

/mL

reco

vere

d a

t 2

4h

)

Human THP-1 Macrophage

PdctA

(Salm)PdctA

(E. coli)WT ∆iraP ∆rpoS ∆dctA ∆rpoS

∆dctA∆phoP

0.001

0.01

0.1

1

10

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Perc

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surv

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afte

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ho

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LPM pH 4.4 + 0.2% Succinate

PdctA

(Salm)PdctA

(E. coli)WT ∆rpoS

5

6

7

8

Log

(CFU

/mL

reco

vere

d a

t 2

4h

)

Mouse J774 Macrophage

PdctA

(Salm)PdctA

(E. coli)WT ∆phoP

A)

B)

C)

104

6.2.4 Salmonella contains genes that can degrade itaconate

In addition to succinate, LPS-activated macrophage synthesize another dicarboxylate

called itaconate via the immune-responsive gene 1 (Irg1) protein160,162. Itaconate (methenylated

succinate) is similar in structure to succinate (Figure 32A) and has been demonstrated to inhibit

the bacterial glyoxylate shunt161,163. Recently, a cluster of Yersinia pestis genes were

demonstrated to encode proteins that can degrade itaconate in vitro164. This “itaconate

degradation operon” has orthologs in a number of bacteria, including Salmonella where it is

encoded by STM3120-STM3118 (LT2 nomenclature) (Figure 35). Moreover, these genes are

encoded in a Salmonella pathogenicity island (SPI-13), are amongst the most highly induced

genes of Salmonella during infection of macrophage, and were important for virulence of

Salmonella Gallinarum in an in vivo chick infection model169-171. In addition to STM3120-18,

there is another gene, STM3117, which appears to be encoded in the same operon. As well, the

adjacent gene, STM3121, is encoded in the reverse direction and is annotated as a predicted

lysR-type transcriptional regulator.

105

Figure 35: The itaconate degradation operon. Figure adapted from Pujol et al. (2005)172

comparing Yersinia and Salmonella operons. I have added in the operon from S. Typhimurium

for comparison. Sasikaran et al. (2014)164 demonstrated that these Yersinia proteins (STM3120-

3118 in Salmonella) and orthologs in Pseudomonas can break down itaconate in vitro.

Furthermore, the RNAseq analysis conducted by Srikumar et al. (2015)171 demonstrated a

massive upregulation (over 100 fold) of this operon in Salmonella infecting macrophage.

106

6.2.5 The itaconate degradation operon is induced by STM3121 in the presence of itaconate

To elucidate the role of the itaconate degradation operon in Salmonella and characterize

its induction, I examined the STM3120 promoter that drives expression of the operon. I

generated a plasmid-borne transcriptional fusion reporter of the 333bp upstream of the STM3120

start codon (P3120) driving expression of lacZ fused to sfGFP. Using this reporter I determined

that the STM3120 promoter exhibits very little basal expression but is strongly inducible by

itaconate (but not succinate) in various media (Figure 36A).

Since the adjacent gene, STM3121, is annotated as a putative lysR-type transcriptional

regulator, I examined its potential to regulate the expression of the itaconate degradation

operon’s STM3120 promoter. Using the same reporter system I found that itaconate induction of

P3120 was abolished in an STM3121 deletion mutant of Salmonella (Figure 36B). This

demonstrates that STM3121 is acting as an itaconate-sensing transcriptional activator that is

necessary for induction of the itaconate degradation operon. Furthermore, to test whether

STM3121 is sufficient for itaconate-induced expression, I generated an alternate version of the

transcriptional fusion plasmid that includes the STM3121 ORF encoded in the reverse direction

of the P3120-GFP fusion (pSTM3121-P3120). I found that inclusion of the STM3121 gene on the

plasmid was able to complement the Salmonella STM3121 mutant (Figure 36C). Furthermore,

the plasmid including STM3121 was able to exhibit itaconate-induced P3120-GFP expression in E.

coli K12, which lacks the itaconate degradation operon and regulator. Cumulatively, these data

demonstrate that the promoter of STM3120 is induced in the presence of itaconate, and the

transcriptional activator STM3121 is both necessary and sufficient for this induction.

107

Figure 36: The STM3120 promoter is inducible by itaconate and STM3121 is necessary

and sufficient for this induction. Plasmid-borne transcriptional fusion of P3120 driving

expression of sfGFP in wild-type (WT) or STM3121 knockout (∆STM3121) Salmonella. Figures

show GFP fluorescence normalized to optical density at 600nm (OD600) after 16h of growth in

the indicated media. Where indicated, glycerol, glucose, succinate or itaconate were added to

0.2% (w/v). Data are the average of at least three biological replicates. A) P3120 is inducible by

itaconate (but not succinate). B) STM3121 is necessary for itaconate induction of P3120. C)

STM3121 encoded on the same plasmid as the transcriptional fusion (pSTM3121-P3120) is

sufficient to complement the ΔSTM3121 strain and to allow itaconate induction in E. coli K12.

0

5000

10000

15000

20000

GFP

Flu

ore

scen

ce /

OD

60

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No supplement

+ Itaconate

+ Succinate

MOPS Glycerol

MOPS Glucose

LB LPM pH 5.8

0

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10000

15000

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∆STM3121

MOPS Glycerol

+ Itaconate

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WT ∆STM3121 E. coli WT ∆STM3121 E. coli

pP3120 pSTM3121-P3120

A)

B)

C)

108

6.2.6 Deletion of the itaconate degradation operon does not reduce Salmonella survival

My discovery that Salmonella induces expression of the STM3120-17 operon in the

presence of itaconate ties together the findings in the literature that activated macrophage

accumulate itaconate and that Salmonella induces this operon when infecting

macrophage160,162,170,171. Indeed the itaconate-specific induction makes a lot of sense, as the

operon encodes homologs of genes shown to degrade itaconate164. Collectively, these data

suggest that Salmonella encounters itaconate in the phagosome of macrophage and that its

degradation contributes to bacterial survival in this environment. Since itaconate, like succinate,

is a dicarboxylic acid with the potential to shuttle protons across the bacterial membrane, I

examined survival of Salmonella in acidifed media containing itaconate. Though itaconate

reduced Salmonella survival compared to media without the weak acid (Figure 32C), mutants

lacking the itaconate degradation operon or its regulator showed no further reduction in survival

(Figure 37A). As well, the mutants also did not show reduced survival at 24 hours post-infection

in either human THP-1 or mouse J774 macrophage cell lines (Figure 37B and C). This data

suggests that, although the itaconate degradation operon is induced in macrophage and responds

to itaconate, it does not convey a survival advantage in acidified itaconate or macrophage cell

lines at the tested time points.

109

Figure 37: Deletion of the Salmonella itaconate degradation operon or its regulator does

not influence survival in acidified itaconate or macrophage. A) Indicated bacterial strains

were incubated in acidified LPM media containing 0.4% itaconate. CFU recovered at 3 hours

were normalized to CFU at 0h and expressed as percent survival. Data shows the average across

three biological replicates and error bars indicate one standard deviation. B) and C) Infection of

THP-1 (B) or J774 (C) macrophage comparing survival of wild-type Salmonella (WT) with

mutant strains lacking the itaconate degradation operon (∆STM3120-17) or its regulator

(∆STM3121). A phoP mutant is included as a control. Data shows a logarithm of CFU recovered

at 24 hours post infection and is the average of three biological replicates. Error bars show one

standard deviation.

0.0001

0.001

0.01

0.1

1

Perc

ent

surv

ival

afte

r 3

ho

urs

LPM pH 4.4 + 0.4% Itaconate

WT ∆STM3120-17 ∆STM3121

4.5

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5.5

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Log

(CFU

/mL

reco

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d a

t 2

4h

)

Human THP-1 Macrophage

∆STM3120-17∆STM3121WT ∆phoP

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(CFU

/mL

reco

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Mouse J774 Macrophage

∆STM3120-17∆STM3121WT ∆phoP

A)

B)

C)

110

6.2.7 The itaconate degradation operon is induced in J774 but not THP-1 macrophage

I found that deletion of the itaconate degradation operon had no influence on CFU

recovered at 24h from either human THP-1 or mouse J774 macrophage cell lines. A possible

explanation for this lack of phenotype is that the cell lines do not produce itaconate under the

growth conditions employed and thus do not induce the STM3120 promoter. To assess this

possibility I modified the P3120 transcriptional fusion plasmid to include a constitutively active

mCherry gene in an independent region of the plasmid from the P3120-GFP reporter. When used

for fluorescence microscopy, this new plasmid (pICM for independent constitutive mCherry)

illuminates all bacteria red while fluorescing green in response to induction of P3120 (likely by

itaconate based on my earlier findings). I examined fluorescence in wild-type or STM3121

mutant Salmonella at 8 hours post-infection of human THP-1 and mouse J774 macrophage cell

lines. I found that P3120 is strongly induced in J774 macrophage, suggesting that the cells are

capable of producing itaconate as well as introducing it into the Salmonella-containing vacuole

where it can be recognized by the bacteria (Figure 38). In agreement with my previous data, this

induction in macrophage is dependent on STM3121. Cumulatively this suggests that the absence

of a survival defect upon deletion of the itaconate degradation operon cannot be explained by

lack of itaconate production in J774 macrophage, implying that STM3120-17 are not required for

Salmonella survival in these cells at 24h. In contrast, the P3120 reporter did not fluoresce in THP-

1 cells under the tested conditions (Figure 38). This may reflect an inability of these cells to

produce itaconate or import it into the phagosome, or may suggest that additional stimulus

beyond LPS and bacterial infection is required to stimulate itaconate production in these cells.

111

Figure 38: The STM3120 promoter is induced in mouse but not human macrophage and

induction requires STM3121. A) Representative fluorescence microscopy images of macrophage

infected for 8 hours with wild-type (left two columns) or ΔSTM3121 (right column) Salmonella

containing pICM-PSTM3120. This plasmid expresses mCherry constitutively and sfGFP expression

is driven by the STM3120 promoter. Blue is DAPI stain for DNA. The 5µm scale bar in the

bottom right is applicable to all images. Three biological replicates have been conducted with

similar results. B) Quantification using ImageJ software of fluorescence intensity of greater than

125 bacteria per condition across three biological replicates. Data shows the GFP/mCherry

fluorescence ratio as a boxplot wherein the center line indicates the median, boxes indicate the

25-75th percentile, whiskers show the 5-95th percentile, and dots indicate individual quantified

bacteria beyond that range. An unpaired t-test with Welch’s correction indicated a p-value <

0.0001 comparing WT Salmonella in J774 macrophage compared to either of the other

conditions.

DAPI

mCherry(constitutively expressed

from pICM)

sfGFP(Expression driven by STM3120 promoter)

Merge

J774 (Mouse)∆STM3121 Salm.THP-1 (Human) J774 (Mouse)

J774WT Salm

J774∆STM3121

THP-1WT Salm.

A)

B)

112

6.3 Discussion

In this chapter I attempted to elucidate the evolutionary advantage conveyed by

Salmonella’s repression of dicarboxylate import. Since the phenotype is significantly less

prominent in E. coli, and LPS-activated macrophage accumulate the dicarboxylates succinate

and itaconate to high levels, I hypothesized that the selective pressure may involve Salmonella’s

adaptation to intracellular growth. Indeed, I demonstrated that these organic acids can exacerbate

acid stress in vitro and may contribute to acidification of phagocytosed bacteria within

macrophage. However, Salmonella with the E. coli dctA promoter (which grows readily in

succinate media), or Salmonella lacking the itaconate degradation operon or its regulator,

displayed no decrease in survival in acidified dicarboxylate media or in either human THP-1 or

mouse J774 macrophage cell lines. This result narrows the range of potential selective pressures

that could have contributed to the development of this phenotype in Salmonella and suggests

alternative hypotheses beyond survival within macrophage:

For succinate, overexpression of dctA from a plasmid was toxic to the cells and led to

reduced survival. It is conceivable that Salmonella represses this gene in order to eliminate any

toxic effects when the cell encounters stress. Though replacement of the dctA promoter with that

of E. coli yielded enough expression to instigate growth using succinate, it is possible that the

small degree of toxicity was masked by alternate cellular stress coping mechanisms that allow

the cell to maintain full survival. It is therefore possible that the repression of dctA may still

relate to reducing toxic expression due to stress, but only to a small degree that I could not detect

by the methods employed in this chapter. However, dctA is also toxic when overexpressed in E.

coli, and so the fact that E. coli does not instigate this repression to the same degree argues

against this hypothesis.

An alternate hypothesis is that Salmonella’s repression of dicarboxylate import may not

relate to survival per se, but may reflect a growth advantage relating to Salmonella’s induction of

inflammation. The inflammation induced by S. Typhimurium leads to an oxidative burst and the

oxidation of thiosulfate to tetrathionate, a compound that Salmonella is distinctively equipped to

use as a terminal electron acceptor39,40. Inflammation therefore provides Salmonella with the

potential to respire in the anaerobic gut and employ this ability to outcompete other microbiota

by utilizing alternate carbon sources such as ethanolamine that require the electron transport

113

chain to provide energy41. This important breakthrough demonstrated an evolutionary advantage

for why non-typhoidal Salmonella induces inflammation. This relates to dicarboxylates, since

succinate accumulation in activated macrophage was demonstrated to play a role in induction of

inflammation155. Thus, if Salmonella “wants” to induce inflammation, it would benefit from

maximizing succinate levels in macrophage by repressing succinate uptake and degradation as a

carbon source. Though none of the data presented here tests this theory, it will be the subject of

further research in the Navarre lab.

Finally, it also remains a possibility that Salmonella’s repression of dicarboxylate uptake

may not relate to succinate accumulation in activated macrophage but may reflect an advantage

in another extracellular environment.

Regarding itaconate, my findings that itaconate is produced in J774 macrophage yet

deletion of the Salmonella itaconate degradation operon yielded no reduction in survival could

suggest a number of possibilities. One is that the primary advantage of the itaconate degradation

operon may relate to some extracellular environment, although the strong induction of this

operon in macrophage strongly argues for an important role in this context. Indeed, my

identification of the specific induction of the STM3120-17 operon by itaconate likely explains

the lack of expression of this operon in a variety of tested growth conditions and the high degree

of induction in mouse macrophage170,171,173. Another hypothesis is that Salmonella possesses

redundant mechanisms to protect itself against itaconate in macrophage, such as exclusion of

itaconate uptake. Such a mechanism could not be induced by STM3121, since deletion of this

itaconate-responding transcriptional activator also conveyed no survival defect. It is possible that

Salmonella employs nutrient sources in macrophage that do not require the glyoxylate shunt and

so are not inhibited by itaconate. It follows that induction of the itaconate degradation operon

could allow Salmonella to supplement its metabolism by degrading itaconate to a useable carbon

source rather than to prevent itaconate poisoning. Finally, it is also possible that itaconate plays a

critical role in controlling bacterial growth during long term intracellular growth. Since I only

tested survival at 24 hour post-infection, any effects on longer timescales would not be identified

by my method.

Interestingly, using my itaconate biosensor I found that P3120 was induced strongly in

mouse J774 but not in human THP-1 macrophage. Though it remains possible that this reflects

114

an artefact of the cell line, my data is supported by the work of Michelucci et al (2013), who

demonstrated that, following LPS-activation, itaconate levels were approximately 125-fold lower

in human PBMC-derived macrophage than in mouse RAW264.7 macrophage160. Moreover, I

employed the Prokaryotic Genome Analysis Tool (PGAT)112 to examine conservation of the

itaconate operon within Salmonella enterica and found that all strains in the database from

Salmonella serovars Typhi and Paratyphi A exclusively lack this operon (Figure 39).

Importantly, these serovars are typhoidal and specifically human-adapted24. In contrast, other

typhoidal but not human-specific serovars such as Paratyphi B and C still retained the itaconate

degradation operon. Interestingly, a non-typhoidal and not human-specific serovar, S. Agona,

also appears to lack the itaconate degradation operon. Though there is only one strain of S.

Agona in the PGAT database, I manually searched another S. Agona complete genome on

GenBank (CP006876.1) and found a similar lack of the operon174. How S. Agona (the only non-

human-specific serovar lacking the itaconate degradation operon in the PGAT database) is

affected by itaconate during infections remains an unanswered question.

Cumulatively, these data suggest that at least in response to Salmonella infection, human

macrophage may be less able to synthesize itaconate than other species. If verified, this

fundamental difference in the innate immune system of humans compared to model organisms

such as mice must be considered when investigating the innate immune response to pathogens. It

is also possible that human macrophage require additional inflammatory cues beyond LPS – such

as interferon gamma (IFN-γ) – to fully induce itaconate synthesis. Ultimately, if the human

immune system does not employ itaconate and human-adapted pathogens do not encode

defences against it, the inhibitory properties of itaconate could propose it as a potential novel

antimicrobial compound in humans.

115

15 17 18 19 20 21 22 23 24

S. enterica 4 5 12 i 08-1736

S. enterica Agona SL483

S. enterica arizonae 62:z4,z23:--

S. enterica Choleraesuis A50

S. enterica Choleraesuis SC-B67

S. enterica Dublin CT_02021853

S. enterica Dublin SD3246

S. enterica Enteritidis P125109

S. enterica Gallinarum 287/91

S. enterica Gallinarum SG9

S. enterica Gallinarum/pullorum RKS5078

S. enterica Heidelberg B182

S. enterica Heidelberg SL476

S. enterica Newport SL254

S. enterica Paratyphi A AKU_12601

S. enterica Paratyphi A ATCC 9150

S. enterica Paratyphi B SPB7

S. enterica Paratyphi C RKS4594

S. enterica Schwarzengrund CVM19633

S. enterica Typhi CT18

S. enterica Typhi P-stx-12

S. enterica Typhi Ty2

S. enterica Typhimurium 14028S

S. enterica Typhimurium 415DRC

S. enterica Typhimurium 798

S. enterica Typhimurium A130

S. enterica Typhimurium BC_2557

S. enterica Typhimurium BC_2558

S. enterica Typhimurium BC_2559

S. enterica Typhimurium BC_2560

S. enterica Typhimurium BC_2561

S. enterica Typhimurium BC_2562

S. enterica Typhimurium BC_2563

S. enterica Typhimurium BC_2564

S. enterica Typhimurium BC_2565

S. enterica Typhimurium BC_2566

S. enterica Typhimurium BC_2567

S. enterica Typhimurium BC_2568

S. enterica Typhimurium C13184

S. enterica Typhimurium D23580

S. enterica Typhimurium LT2

S. enterica Typhimurium LT7

S. enterica Typhimurium M1175849

S. enterica Typhimurium M1776464

S. enterica Typhimurium PB1

S. enterica Typhimurium PB2

S. enterica Typhimurium PB3

S. enterica Typhimurium SARA10

S. enterica Typhimurium SARA12

S. enterica Typhimurium SARA4

S. enterica Typhimurium SARA9

S. enterica Typhimurium SL1344

S. enterica Typhimurium SOHS02-20

S. enterica Typhimurium SOHS02-68

S. enterica Typhimurium SOHS03-1

S. enterica Typhimurium SOHS04-44

S. enterica Typhimurium ST13

S. enterica Typhimurium ST14

S. enterica Typhimurium ST15

S. enterica Typhimurium ST16

S. enterica Typhimurium ST17

S. enterica Typhimurium ST18

S. enterica Typhimurium ST19

S. enterica Typhimurium ST24

S. enterica Typhimurium ST25

S. enterica Typhimurium ST29

S. enterica Typhimurium ST32

S. enterica Typhimurium ST33

S. enterica Typhimurium ST34

S. enterica Typhimurium ST35

S. enterica Typhimurium ST36

S. enterica Typhimurium ST4/74

S. enterica Typhimurium ST40

S. enterica Typhimurium T000240

S. enterica Typhimurium UK-1

STM31__A)

116

B)

Figure 39: Salmonella serovars Typhi, Paratyphi A and Agona uniquely lack the itaconate

degradation operon. A) Comparison of the presence (green) or absence (red) of the itaconate

degradation operon (STM3120-STM3117), its regulator (STM3121), and the neighbouring genes

in the 69 Salmonella strains in the Prokaryotic Genome Analysis Tool (PGAT) database using

the Ortholog Search Tool112. Numbers at the top indicate gene loci (S. Typhimurium LT2

nomenclature) from STM3115 to STM3124. STM3116 is pheV encoding tRNA-Phe (GAA

codon) rather than a protein and so was not analyzed by PGAT. B) The itaconate degradation

operon region comparing S. Typhimurium to S. Typhi and S. Agona serovars using PGAT’s

Synteny Mapper tool. Asterisk indicates STM3116 encoding tRNA-Phe. P indicates a

pseudogene was identified by the software.

117

7 Discussion and Future Directions

7.1 Summary and Discussion

At the start of my graduate research little was known about the mechanism of EF-P

except that it had been shown to modestly stimulate first peptide bond synthesis in vitro and was

demonstrated to bind to the ribosome between the P and E sites77,79. As well, efp mutants exhibit

pleiotropic phenotypes ranging from overactive metabolism using specific nutrient sources to

hyper-susceptibility to a variety of different cellular stressors74,84. I contributed to this work in

the first months of my graduate research by demonstrating that efp mutants also display a

permeability defect, which could contribute to some of the phenotypes. Yet the mechanism of

how EF-P influenced translation remained unknown.

In this thesis I employed SILAC to demonstrate in vivo that EF-P is not a general

translation factor but rather impacts the synthesis of a specific subset of proteins in Salmonella.

This was in agreement with previously conducted 2D-DIGE analyses conducted by a former

graduate student in the lab, Dr. Betty Zou74,84. Shortly after the completion of my SILAC

experiment, two papers were published in Science that demonstrated that the primary role of EF-

P is to rescue ribosomes stalled at polyproline motifs114,115. Specifically, the motifs PPP and PPG

were heavily dependent on EF-P for their efficient translation in vitro. I compared the presence

of these motifs to my in vivo SILAC data and discovered that polyproline motifs are not always

necessary and are not solely sufficient to render a protein dependent on EF-P for its efficient

expression. I further went on to identify a novel EF-P-dependent motif, GSCGPG, in PoxB, and

employed the model proteins AtpA and AtpD to demonstrate that the residues upstream of

polyproline motifs play a significant role in influencing ribosome stalling. Moreover, I found that

the rate of translation initiation strongly effects whether or not a polyproline motif will reduce

protein synthesis in the absence of EF-P. I rationalized that this reflects the rate limiting step of

translation and that ribosome stalls will only impact protein synthesis if they become more rate

limiting than translation initiation. Thus, a transcript with a low rate of initiation is protected

against ribosome stalls influencing protein output, since initiation remains the rate limiting step.

This innovation provides a parsimonious explanation for the differences in protein abundance

observed for proteins with identical polyproline motifs.

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Collectively, my work on EF-P demonstrates that the impact of EF-P on protein synthesis

is determined by the stall strength (influenced not only by a polyproline motif but also by

upstream residues) and, moreover, by the rate of translation initiation. This overarching concept

is significant for understanding EF-P-dependent translation in vivo and is furthermore applicable

to other kinds of ribosome stalls as well, such as at rare codons. Indeed, bacteria may employ

weak ribosome binding sites to reduce translation initiation rate in order to protect against

downstream ribosome stalling. This would allow for functional stalls, such as for N-terminal

sequence recognition or for regulation of downstream genes, without costing the cell resources

due to ribosome jamming reducing translation efficiency.

In addition to my work elucidating the mechanism of how EF-P influences translation, I

also investigated how deletion of efp effects Salmonella. Many of the EF-P-dependent genes

identified by my SILAC analysis can provide an explanation for certain efp mutant phenotypes.

For instance, levels of GgT were strongly reduced in the efp mutant, and this could explain the

inability of PYE mutants to use γ-glutamyl-glycine as a nitrogen source and their resistance to

the nitrogen donating comound S-Nitrosoglutathione (GSNO)74,84,110. Altered expression of a

number of metabolic proteins, including the catalytic subunit of ATP Synthase, AtpD, may lead

to the increased metabolic activity of the PYE mutants and potentially contribute to their hyper-

susceptibility to stress74,84.

While investigating the hyperactive metabolism of efp mutants, I discovered that wild-

type Salmonella exhibits a drastically long lag phase in response to media with a dicarboxylic

acid as the sole carbon source. I determined that Salmonella’s growth shutdown was instigated

via repression of the primary dicarboxylate importer, DctA, and required the stress response

sigma factor, RpoS, and an RpoS-stabilizer, IraP. Furthermore, I found that the closely related

species E. coli does not exhibit this phenotype and that replacing the dctA promoter with that of

E. coli abolishes the ability of Salmonella to repress its growth. This demonstrates that under

these conditions Salmonella employs a transcriptional repressor to bind to a region of the dctA

promoter that is different between Salmonella and E. coli. This is significant, as it implies that

Salmonella has developed this adaptation since diverging from E. coli. Many such differences

pertain to Salmonella’s virulent lifestyle, including acquisition of the Salmonella pathogenicity

islands (SPI) and regulatory alterations such as implementing the PhoPQ two-component system

to recognize the intracellular environment encountered by Salmonella19,32,33. Thus, the difference

119

between Salmonella and E. coli may suggest that repression of dicarboxylate uptake may reflect

an adaptation for virulence.

The potential for the dicarboxylate exclusion phenotype to play a role in virulence is

supported by the finding that activated macrophage accumulate succinate as well as another

dicarboxylate, itaconate155,160,162. Acidification of the Salmonella containing vacuole and the

pKa’s of these dicarboxylic acids suggested the hypothesis that macrophage may employ these

weak organic acids to shuttle protons into phagocytosed bacteria – forcing them to acidify.

However, when I abolished Salmonella’s ability to repress dctA by swapping in the E. coli

promoter, I found that the bacteria showed no reduction in survival in acidified succinate media

or in macrophage. This suggests that the evolutionary advantage of the Salmonella dicarboxylate

phenotype is either relevant at other time points beyond 24 hour post macrophage infection,

grants only a modest survival advantage below the detectable limits of my methods, or does not

involve survival in the macrophage phagosome. It is also possible that dicarboxylate repression

is useful to Salmonella in extracellular environments for survival or metabolic prioritization, and

the accumulation of succinate in activated macrophage is an unrelated coincidence.

An alternate hypothesis is that Salmonella does not import and degrade succinate in

macrophage in order to maximize succinate levels leading to HIF-1α stabilization, IL-1β

synthesis and the induction of inflammation155. This would not grant Salmonella an advantage in

survival in macrophage per se, but would give the Salmonella remaining in the gut lumen a

growth advantage by maximizing the abundance of tetrathionate produced by the immune

system’s oxidative burst39-41. If verified, such a result would link the growth repression that I

identified for Salmonella using dicarboxylates as a carbon source to the virulent lifestyle specific

to Salmonella. Furthermore, it would represent a novel mechanism exemplifying a bacterium

modulating the host immune response by manipulating metabolite concentration rather than

employing injected effector proteins. Cumulatively, my work examining Salmonella’s repression

of growth using the dicarboxylic acid succinate makes significant progress towards

characterizing this extended lag phase phenotype. My findings lend insight into the mechanism

underlying this response and further narrow the scope of why Salmonella has developed this

adaptation and what evolutionary advantage it gains from it.

120

In addition to its response to other dicarboxylates such as succinate, Salmonella also

appears to be able to neutralize the inhibitory effects of itaconate by encoding an itaconate

degradation operon (STM3120-17) in SPI-13161,163,164. These genes are heavily expressed in

mouse macrophage but not in a variety of other tested conditions170,171,173. I demonstrated that

this operon is induced in response to the presence of itaconate in the media and, moreover, that

the neighbouring gene STM3121 is both necessary and sufficient for this induction. However,

upon deletion of the itaconate degradation operon or its regulator, I found no reduction in

Salmonella survival in acidified itaconate media or in macrophage. Since the operon is strongly

induced in mouse macrophage, it is unlikely that it conveys no advantage to Salmonella in this

environment. Therefore, my findings that the operon conveyed no survival advantage at 24 hours

suggests that it may be important for longer infections, may be only one of redundant itaconate

defence mechanisms, or may not be required for survival per se, but may allow Salmonella to use

itaconate in macrophage as a supplementary carbon source.

To further examine itaconate synthesis in macrophage, I employed the itaconate

degradation operon promoter (P3120) to generate a fluorescence-based itaconate biosensor.

Interestingly, I found that in response to Salmonella infection, itaconate was produced strongly

in mouse J774 macrophage but not in the human THP-1 macrophage cell line. This reflects the

work of Michelucci et al. (2013), who found that itaconate synthesis in human PBMC-derived

macrophage was approximately 125-fold lower than in mouse RAW264.7 macrophage160. By

genome comparison I also found that the itaconate degradation operon is present in most

Salmonella strains but is distinctively absent in the human-specific and typhoidal serovars S.

Typhi and S. Paratyphi A, as well as in serovar Agona. The absence of the itaconate degradation

operon in S. Agona is an interesting outlier, since it is non-typhoidal and also not human specific.

Even if itaconate does not greatly accumulate in human cells, this serovar may encounter it in

other species. It is possible that S. Agona includes alternate itaconate defence mechanisms that

may also be present in S. Typhimurium and could account for the lack of survival defect that I

observed upon deletion of STM3120-17. Cumulatively, the lower itaconate levels in human cells

in my data and in the literature, combined with the lack of the itaconate degradation operon in all

human-specific strains in my analysis, may suggest that the human immune system either

requires strong pro-inflammatory stimulus beyond just LPS (such as by cytokines like IFN-γ) or

does not synthesize itaconate to the same degree as other species including mice.

121

If verified, the difference in itaconate synthesis between humans and mice may highlight

important limitations of conducting mouse studies of bacterial pathogenesis. On the other hand,

the lack of defence mechanisms in human-adapted microbes may render them susceptible to

itaconate as an antimicrobial compound. Moreover, its generation by the immune system to some

degree may suggest that it is not toxic to human cells, and it is also readily and cheaply available,

as it has been used in industrial polymer synthesis for decades175. In summary, my findings

demonstrate that Salmonella Typhimurium employs an itaconate-sensing transcriptional activator

to induce the expression of an itaconate degradation operon in SPI-13. Though deletion of this

operon had no effect on survival in macrophage at 24 hours, it may convey a survival advantage

in mouse macrophage at later timepoints or indicate redundant defence mechanisms. Finally, my

finding that human THP-1 macrophage do not induce expression from P3120 under the conditions

tested, and my discovery that human-adapted strains of Salmonella do not encode the itaconate

degradation operon, may propose itaconate as a potential antimicrobial compound that could be

employed against such strains.

7.2 Future Directions

7.2.1 Regulation of EF-P and its potential regulatory activity

My data and work in the literature demonstrate that EF-P is involved in the efficient

translation of a specific subset of proteins, including some involved in central

metabolism110,127,130. As well, Salmonella and E. coli efp mutants are viable but display a variety

of pleiotropic phenotypes, including impaired growth, compared to wild-type cells74,83,84. These

data suggest that efp expression is concomitant with growth, similar to ribosomal genes and other

general translation factors. Interestingly, high throughput analyses in the literature suggest that

efp expression may be governed by the stringent response as well as by catabolite

repression176,177. To demonstrate regulation of efp by these pathways in a targeted manner,

stringent response (∆relA ∆spoT) and catabolite repression (∆crp) mutants could be employed to

assess protein (western blot) and mRNA (RT-qPCR) levels of EF-P, PoxA, YjeK and YfcM

under conditions of amino acid starvation or when growing on alternative carbon sources.

Results would demonstrate whether the expression of efp, like many genes involved in

translation, is linked to cellular metabolism and growth rate.

122

It also remains possible that EF-P itself plays a regulatory role under certain conditions

wherein its activity is reduced to alter the synthesis of specific proteins. Conceivably, storing

stalled ribosomes on particular transcripts could also allow for rapid translation of these proteins

following a change of environment and recovery of EF-P activity. To examine whether EF-P

activity is altered, my EF-P-dependent translational fusions could be employed. Specifically,

highly EF-P-dependent constructs (with a high translation initiation rate) and their mutated

polyproline motif counterparts (EF-P-independent) could be subjected to a variety of growth

conditions. EF-P activity could then be assessed in a high-throughput manner by comparing

expression of the independent versus EF-P-dependent constructs. If the independent construct

retained high expression while the dependent construct showed reduced fluorescence, it would

suggest that EF-P activity is reduced under the tested condition in a manner distinct from general

translation. As a follow-up to this high throughput screen, EF-P, PoxA, YjeK and YfcM protein

and mRNA levels could be assessed by western blot or RT-qPCR. Results from these

experiments would lend insight into how bacteria regulate the expression of efp and,

furthermore, whether EF-P itself plays a regulatory role in certain environments.

7.2.2 Characterize the repression of the Salmonella dctA promoter

In this thesis I demonstrated that RpoS and DctA are involved in Salmonella’s growth

repression in minimal media with succinate as the sole carbon source. Specifically, deletion of

rpoS or replacement of the dctA promoter with that of E. coli reduced the length of the

Salmonella lag phase. Since RpoS is primarily an activator of transcription, this suggests that it

induces an intermediate transcriptional repressor that binds to and shuts down the Salmonella

dctA promoter. To confirm that the effects of RpoS and the dctA promoter are linked, the rpoS

mutation could be introduced into the strain with E. coli’s dctA promoter and additive effects

could be assessed. If the reduction in Salmonella lag time is not additive, it would suggest that

RpoS and the repression of DctA act in the same pathway. Furthermore, the impact of RpoS on

dctA expression could be assessed by examining dctA mRNA levels under conditions of stress

that induce RpoS activity but do not relate to dicarboxylic acids. Examples could include

treatment with hydrogen peroxide or high osmolarity. Results could demonstrate whether or not

123

general induction of RpoS activity is sufficient to repress dctA expression or if there is an

additional dicarboxylate-specific input.

My finding that the E. coli dctA promoter is active in succinate media implies that there is

a transcriptional repressor that binds to the dctA promoter of Salmonella but not of E. coli. The

sequence differences between these promoters could be used to identify potential candidate

regulators by examining databases such as PRODORIC and TEC to find transcription factor

consensus binding sites present in the Salmonella but not the E. coli dctA promoter178,179. An

alternative approach could involve biotinylating the dctA promoter using chemically generated

PCR primers such that it could be pulled down using streptavidin beads. Thus the Salmonella

dctA promoter (or the E. coli one as a control) could be used as bait and combined with cell

lysates generated under growing or growth repressed conditions to pull down DNA-binding

proteins for subsequent mass spectrometry analysis and identification. Findings from these

analyses could be followed-up by generating deletion mutants of candidate regulators and

assessing growth in MOPS minimal medium with succinate as the sole carbon source. The

regulation of these transcription factors by RpoS could also be assessed using RT-qPCR under

RpoS-inducing stress conditions and with the rpoS deletion mutant.

An alternative screen could also be conducted to identify regulators of dctA by

conducting Tn-Seq or mutagenesis followed by genome sequencing. Specifically a mutant

library could be grown in minimal media using succinate as a sole carbon source to select for

mutants that grow readily. To deplete the library of rpoS mutants, the culture could be further

treated with hydrogen peroxide. Surviving cells could be plated for single colonies and

individual clones sequenced to identify the mutation that conveyed growth using succinate.

Results could be confirmed by targeted deletions introduced by lambda red recombination.

Cumulatively, findings from these experiments could elucidate the regulatory pathway that

Salmonella employs to repress dctA.

7.2.3 Elucidate the role of Salmonella dctA repression in macrophage,

Using a strain with the E. coli dctA promoter inserted into the Salmonella chromosome I

demonstrated that the repression of dctA in minimal media with a dicarboxylate as a sole carbon

124

source does not relate to Salmonella survival in macrophage at 24h. However, Salmonella may

still be responding to succinate in macrophage. Macrophage activated by LPS have been

demonstrated to accumulate succinate as an inflammatory signal acting by stabilization HIF-1α

and resulting in IL-1β production155. Since it has been shown that Salmonella serovars that

induce gastroenteritis instigate inflammation in order to acquire the terminal electron acceptor

tetrathionate, it is possible that Salmonella represses its uptake of succinate to maximize

succinate concentration in macrophage and promote inflammation40. To assess this, macrophage

could be infected with Salmonella containing either its native or the E. coli dctA promoter, and

induction of IL-1β could be quantified by RT-qPCR or by enzyme-linked immunosorbent assay

(ELISA). If IL-1β synthesis is reduced in macrophage infected with E. coli dctA promoter

Salmonella, it would suggest that Salmonella uptake and degradation of succinate is detrimental

to inducing inflammation. If verified, this could represent a rationale for the observed phenotype

in succinate media and an important novel aspect of Salmonella’s virulent lifestyle.

7.2.4 Investigate alternate itaconate defence mechanisms

My finding that deletion of the itaconate degradation operon had little effect on survival

of Salmonella in macrophage could suggest that the bacterium encodes alternate mechanisms to

defend against itaconate. Since itaconate would have to penetrate the bacterial cytoplasm in order

to inhibit the glyoxylate shunt, it is conceivable that Salmonella restricts its uptake of itaconate

similarly to my findings in minimal media with other dicarboxylates. Similarly, the use of

itaconate as a carbon source (via degradation by STM3120-17) could be assessed for various

mutants lacking transporters such as DctA. Uptake of itaconate could also be assessed by

examining induction of P3120 in these transporter mutants. If itaconate required dctA (for

instance) to bypass the bacterial membrane, deletion or repression of the transporter would

restrict itaconate uptake and reduce the induction of P3120 via STM3121. A potential caveat of

this induction assay is that it assumes STM3121 recognizes cytoplasmic itaconate to induce its

transcriptional activation effects. STM3121 is predicted to be a cytosolic lysR-type regulator but

it remains possible that it could acquire an activation signal from a transmembrane sensor. If this

were the case, such a sensor would have to be common between Salmonella and E. coli, as

STM3121 was sufficient to allow itaconate-induced expression of P3120 in E. coli.

125

My genomic analysis of Salmonella serovars revealed that the human adapted serovars

Typhi and Paratyphi A do not encode the itaconate degradation operon. A non-typhoidal serovar,

S. Agona, also lacks the itaconate degradation operon, and its SPI-13 resembles that of S. Typhi

and Paratyphi A. The degree of itaconate inhibition of these serovars could be assessed in

minimal media where the only carbon source requires the glyoxylate shunt (for example,

acetate). This assay could determine whether these strains contain alternate mechanisms for

preventing itaconate toxicity or if they are inhibited by itaconate, suggesting that they have lost

the operon because they do not encounter it in their particular growth niches. This may relate to

the reduced inflammation induced by typhoidal Salmonella, or to the reduced itaconate

production observed in human macrophage. If these strains lack any defence against itaconate, it

could propose the use of itaconate as an antimicrobial agent against them.

7.2.5 Scrutinize the induction of itaconate synthesis in human and mouse macrophage

My itaconate biosensor could be employed to further examine how and when

macrophage produce itaconate. The biosensor is fluorescence-based and lends itself readily to

fluorescence microscopy. The dynamics of itaconate synthesis in macrophage could be observed

using live microscopy to reveal the time when infecting Salmonella first encounter itaconate.

Moreover, by comparison to standard curves, the levels of itaconate encountered by Salmonella

in a macrophage phagosome could be semi-quantitatively determined. Itaconate concentration

could be verified using liquid chromatography coupled to mass spectrometry. The biosensor

could also rapidly probe for itaconate synthesis to examine whether it is specific to phagocytes or

can be produced in other cells types as well, such as epithelial cells. This could be extended to

multiple human cells lines and donor-derived macrophage to scrutinize whether the reduced

itaconate production in THP-1 macrophage represents a bona fide phenomenon of human cells. It

is also possible that human cells require additional inflammatory cues to trigger itaconate

production, and my biosensor could be employed to identify such inducers. Various cytokines

such as IFN-γ could be used to stimulate macrophage infected with Salmonella containing the

biosensor to test for itaconate synthesis. Finally, since Salmonella may contain alternative

mechanisms of excluding itaconate and thereby negating the requirement for induction of P3120 in

human cells, the experiment could also be conducted using E. coli with the pSTM3121-P3120

126

version of the itaconate biosensor. Results from these experiments would lend insight into the

mechanisms of itaconate utilization by the immune system and, moreover, could reveal an

important difference between human and mouse phagocytes.

7.3 Conclusions

My work determined that EF-P will only impact protein synthesis if ribosome stalls at

polyproline motifs become more rate limiting than translation initiation. This is influenced

primarily by the ribosome binding site and thereby the rate of initiation of translation, and by the

strength of the EF-P dependent stall itself, which is determined by the polyproline motif and

nearby upstream residues. This concept not only clarifies the factors underlying whether a

protein will or will not require EF-P for its efficient translation in vivo, but also can be applied to

other forms of ribosome stalls. Moreover, it proposes a rationale for the evolution of inefficient

ribosome binding sites and poor initiation rates, as they can play a protective role against

downstream ribosome stalling impacting protein synthesis.

In addition, I also discovered a novel Salmonella phenotype wherein it shuts down its

growth for an extended lag phase when using dicarboxylic acids as a sole carbon source. This

repression of dicarboxylate uptake requires a number of stress response regulators and may relate

to the accumulation of succinate and itaconate in activated macrophage. When I abolished

Salmonella’s ability to shut down dicarboxylate uptake or when I deleted the itaconate

degradation operon, my data showed no reduction in survival in acidified dicarboxylic acids or in

macrophage at 24 hours post infection. The advantage conveyed by Salmonella’s dicarboxylate

exclusion phenotype may relate to survival in other conditions, or to modulating the host

inflammatory response for the acquisition of supplementary nutrients such as tetrathionate.

Finally, my findings employing an itaconate biosensor in THP-1 macrophage, combined with

previous findings in the literature, suggest that human cells might not produce itaconate as

robustly as mouse cells. I discovered that human adapted serovars of Salmonella lack the

itaconate degradation operon and this may render them susceptible to the use of itaconate as an

antimicrobial compound. In conclusion, my findings identify a novel Salmonella phenotype and

narrow down the potential evolutionary advantages that led to its development.

127

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