Post-Transcriptional Regulation of Virulence Gene Expression in Enterohaemorrhagic E

Post-transcriptional regulation of virulence gene expression in enterohaemorrhagic E. coli

Brandon Mitchell Sy

A thesis in fulfillment of the requirements for the degree of Doctor of Philosophy

School of Biotechnology and Biomolecular Sciences

Faculty of Science

University of New South Wales

September, 2019

Surname/Family Name : Sy Given Name/s : Brandon Mitchell Abbreviation for degree as give in the : PhD University calendar Faculty : Science School : Biotechnology and Biomolecular Science Post-transcriptional regulation of virulence gene expression in Thesis Title : enterohaemorrhagic E. coli

Abstract Enterohaemorrhagic E. coli (EHEC) is a significant foodborne pathogen responsible for outbreaks of haemorrhagic colitis and haemolytic uremic syndrome (HUS). Renal tissue destruction caused by HUS is caused by the release of Shiga toxins (Stx) encoded on lambdoid bacteriophage (StxΦ). Toxin release is dependent on bacteriophage lytic induction and can be triggered by antibiotics. New treatments for EHEC infections are required and this thesis examines the contribution of posttranscriptional regulation of Stx and virulence genes mediated by regulatory small RNAs.

The EHEC haem receptor, chuA, is regulated in response to iron availability and temperature. Here, chuA was found to be subject to Rho-termination and directly activated by the Crp-cAMP regulated sRNA, CyaR, in a temperature independent manner. This reveals two additional layers of regulation employed by EHEC to ensure this haem receptor is only expressed within a host and suggests that chuA employs a transcriptional and post-transcriptional AND/OR-logic gate to integrate multiple signals that indicate host infection.

Transcriptome-wide Hfq-binding sites have shown that the StxΦ transcribed at least 11 regulatory small RNAs. Here, a novel small RNA termed StxS was found to be transcribed from the late phage and Shiga toxin promoter PR’ during lysogeny. This transcript is processed by the endoribonuclease RNase E into a functional sRNA that activates the stress sigma factor RpoS and represses Stx production. These demonstrate how the StxΦ modulates host stress responses and fitness through post-transcriptional regulation. The PR’-tR’ organization critical for transcription of StxS is conserved in lambdoid bacteriophage, suggesting that PR’ may be the origin of an assortment of sRNAs that regulate bacterial pathogenicity and fitness.

RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation. Total RNA associated protein purification (TRAPP) was used to recover the RNA-binding proteome of EHEC. TRAPP recovered 443 proteins, 35 being EHEC- specific, including the effector proteins EspY2 and EspN. Four of these were confirmed to be RNA-binding through 32P radiolabelling of RNA-protein complexes. This showed that pathogen specific RBPs may play a key role in EHEC pathogenesis.

Together, these results highlight the complexity of post-transcriptional regulation in EHEC, and how it affects EHEC pathogenicity and fitness.

Declaration relating to disposition of project thesis/dissertation

I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.

I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only).

………………………………………………… ……………………………………..……… ……….……………………...… ………… ……… ….… Signature Witness Signature Date The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research.

FOR OFFICE USE ONLY Date of completion of requirements for Award:

ORIGINALITY STATEMENT

‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’

Signed ……………………………………………......

Date ……………………………………………......

iii

INCLUSION OF PUBLICATIONS STATEMENT UNSW is supportive of candidates publishing their research results during their candidature as detailed in the UNSW Thesis Examination Procedure.

Publications can be used in their thesis in lieu of a Chapter if: • The student contributed greater than 50% of the content in the publication and is the “primary author”, ie. the student was responsible primarily for the planning, execution and preparation of the work for publication • The student has approval to include the publication in their thesis in lieu of a Chapter from their supervisor and Postgraduate Coordinator. • The publication is not subject to any obligations or contractual agreements with a third party that would constrain its inclusion in the thesis

Please indicate whether this thesis contains published material or not. This thesis contains no publications, either published or submitted for ☒ publication

Some of the work described in this thesis has been published and it has ☐ been documented in the relevant Chapters with acknowledgement

This thesis has publications (either published or submitted for publication) ☐ incorporated into it in lieu of a chapter and the details are presented below

CANDIDATE’S DECLARATION I declare that: • I have complied with the Thesis Examination Procedure • where I have used a publication in lieu of a Chapter, the listed publication(s) below meet(s) the requirements to be included in the thesis. Name Signature Date (dd/mm/yy)

‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.

I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.'

Signed ……………………………………………......

Date ……………………………………………......

AUTHENTICITY STATEMENT

‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’

Signed ……………………………………………......

Date ……………………………………………......

ACKNOWLEDGEMENTS

To Jai, thank you for taking a chance on me 4 years ago. You’ve always been approachable, and you’ve been a great mentor to me all around. Thank you for pushing me when I needed to be pushed. This thesis wouldn’t have been possible without you.

To Prof. David Tollervey, Prof. David Gally and Dr. Vadim Shchepachev, thank you for your help with learning and performing the RBPome experiments.

To the past members of the Tree Lab. Thank you, Shafa, for helping me adjust to my new life in this University four years ago. Thank you, Lawrence and Becky, for giving me countless laughs during your honours year. Thank you, Julia, for being the older sister I never asked for.

To the current members of the Tree Lab. Thank you, Sylvania, for giving me a different perspective on things. Thank you, Winton, for being my biggest lab bro (both literally and figuratively). Thank you, Dan, for the coffee runs, football chats, and for looking at my writing for me. You guys all made the process of writing this less painful.

To Suresh, Nat, Sam, Cal, Roly and Erika, you guys are basically my family here in Sydney. I’d say you guys kept me sane, but that’s disputable. No, I still cannot make you guys Adderall.

To Jaira, thank you for your patience with me while I’ve been writing this thesis. Your support has been amazing, and this year would have been hell without you.

Most importantly, to my family. Thank you for being there every step of the way. Thank you, mum, for encouraging me, visiting me and supporting me with whatever I do. You’ve sacrificed so much for me to become the person I am today. I love you, and I hope I make you proud.

To my grandfather, 陈昌炮. This one’s for you. I wish you were here.

PUBLICATIONS AND PRESENTATIONS

PUBLICATIONS:

Sy, B. et al. (2018) ‘High-Resolution, High-Throughput Analysis of Hfq-Binding Sites Using UV Crosslinking and Analysis of cDNA (CRAC)’, in Arluison, V. and Valverde, C. (eds) Bacterial Regulatory RNA: Methods and Protocols. New York, NY: Springer New York, pp. 251–272. doi: 10.1007/978-1-4939-7634-8_15.

McAteer, S.P., Sy, B.M., Wong, J.L., Tollervey, D., Gally, D., Tree, J.J. (2018) ‘Ribosome maturation by the endoribonuclease YbeY stabilizes a type 3 secretion system transcript required for virulence of enterohemorrhagic Escherichia coli’, Journal of Biological Chemistry, 293(23), pp. 9006–9016. doi: 10.1074/jbc.RA117.000300.

Iosub, I. A., Marchioretto, M., Sy, B.M., McKellar, S., Nieken, K.J., van Nues, R.W., Tree, J.J., Viero, G., Granneman, S. (2018) ‘Hfq CLASH uncovers sRNA-target interaction networks enhancing adaptation to nutrient availability’,. doi: http://dx.doi.org/10.1101/481986. -- submitted to eLife

Sy, B., Lan, R., Tree, J.J. (2019) ‘Early termination of the Shiga toxin transcript generates a regulatory small RNA’ (manuscript in preparation)

PRESENTATIONS

5th Annual JAMS Symposium, Sydney, NSW, Australia 2016 Poster Presentation

1st Meeting, Bugs by the Beach, Wollongong, NSW, Australia 2017 Poster Presentation

15th Molecular Biology of Bacterial Pathogens (BacPath 14), Hahndorf, SA, Australia 2017 Oral Presentation

Sydney Micro, University of Sydney, NSW, Australia 2018 Oral Presentation

5th Meeting of Regulating with RNA in Bacteria and Archaea, Seville, Spain 2018 Poster Presentation

16th Molecular Biology of Bacterial Pathogens, Swan Valley (BacPath 15), WA, Australia 2019 Oral Presentation

vii

Abstract

ABSTRACT

Enterohaemorrhagic E. coli (EHEC) is a significant foodborne pathogen that is responsible for outbreaks of haemorrhagic colitis and haemolytic uremic syndrome (HUS) worldwide. Severe disease outcomes brought about by EHEC infection are due to acquisition of pathogenicity islands by horizontal gene transfer, such as the Shiga toxin (Stx). Stx is the characteristic virulence factor of EHEC, and causes capillary damage in the renal and nervous systems. Stx is encoded on the lambdoid bacteriophage StxΦ. DNA damage induces the StxΦ lytic cycle and leads to toxin release. The use of antibiotics to treat EHEC infections is contraindicated, as these can induce expression of Stx and increase the likelihood of progression to HUS and death.

EHEC experiences different environments in the host during its transmission through the gastrointestinal tract. To survive in these changing environments, EHEC coordinates its gene expression using both transcriptional and post- transcriptional mechanisms. Small non-coding RNAs (sRNAs) and RNA-binding proteins play a major role in controlling EHEC gene expression and pathogenesis at a post-transcriptional level. Previously, 55 sRNAs encoded within EHEC- specific genomic loci were identified, including the Stx2Φ-encoded sRNA AsxR. However, the vast majority of these sRNAs have not been characterised.

This thesis aimed to better understand how horizontally transferred elements affect post-transcriptional regulation in EHEC. The EHEC haem receptor, chuA, is regulated in response to iron availability and temperature. Here, chuA was found to be subject to Rho-termination and directly activated by the Crp-cAMP regulated Hfq-dependent sRNA, CyaR, in a temperature independent manner. This interaction reveals two additional layers of regulation employed by EHEC to ensure this haem receptor is only expressed within a host and suggests that chuA employs a transcriptional and post-transcriptional AND/OR-logic gate to integrate multiple signals that collectively indicate host infection.

viii

Abstract

In Chapter 4, the late phage promoter PR’ of the StxΦ was found to constitutively express a 255 nt sRNA, here termed StxS, that is processed by RNase E into a functional 74 nucleotide sRNA. StxS was found to interact with the stress and stationary phase sigma factor RpoS. The use of GFP-translational fusions demonstrated that StxS activates RpoS through direct base-pairing, and this allowed for growth to higher stationary phase cell densities in minimal media. Significantly, StxS also repressed production of Shiga toxin in an indirect manner. This chapter demonstrated how a phage-encoded sRNA can regulate stress response genes in the core genome and presents a potentially novel class of sRNA that arises from anti-terminated transcripts.

RNA-binding proteins play key roles in gene regulation across all kingdoms of life. In bacteria, multiple sRNAs bind to unknown RNA chaperones, making it increasingly apparent there are many RBPs yet to be discovered. In Chapter 5, total RNA-associated protein purification (TRAPP), a silica-based organic extraction method for RBPs was used in EHEC. TRAPP recovered 443 proteins that were two-fold enriched following UV-crosslinking and 35 of these were EHEC specific. The RNA-binding potential of four of these proteins were verified by radiolabelling the RNA-protein complexes using polynucleotide kinase (PNK). Two of these proteins had no known RNA-binding domains, and another protein, EspY2, is a secreted effector protein. Significantly, no bacterial effectors have previously been shown to be RNA-binding. The use of TRAPP in EHEC has led to the discovery of novel pathogen-specific RNA-binding proteins that may lead to a better understanding of EHEC pathogenesis.

Post-transcriptional regulation of horizontally acquired genes and by phage- encoded sRNAs is important for EHEC pathogenicity and fitness. This thesis has demonstrated novel mechanisms of post-transcriptional regulation of EHEC virulence factors. An increased understanding of EHEC post-transcriptional regulation may lead to novel approaches to treat infection by a pathogen where antibiotics are contraindicated.

Table of Contents

TABLE OF CONTENTS

ORIGINALITY STATEMENT ...... iii INCLUSION OF PUBLICATIONS STATEMENT ...... iv COPYRIGHT STATEMENT ...... v AUTHENTICITY STATEMENT ...... v ACKNOWLEDGEMENTS ...... vi PUBLICATIONS AND PRESENTATIONS ...... vii ABSTRACT ...... viii TABLE OF CONTENTS ...... x LIST OF FIGURES ...... xiii LIST OF TABLES ...... xv List of abbreviations ...... xvi Chapter 1: Introduction and Literature Review ...... 1 1.1 Escherichia coli ...... 1 1.1.1 Classifications of diarrheagenic E. coli ...... 2 1.2 Enterohaemorrhagic E. coli (EHEC) ...... 3 1.2.1 Haemorrhagic colitis and haemolytic uremic syndrome ...... 3 1.2.2 Virulence factors of EHEC ...... 5 1.2.2.1 Locus of enterocyte effacement and the type III secretion system ...... 5 1.2.2.2 Shiga toxin ...... 8 1.3 Shiga toxin encoding bacteriophage ...... 13 1.3.1 Regulation of the lysogenic-lytic decision ...... 13 1.3.2 Post-transcriptional regulation of Stx phage ...... 18 1.4 Small regulatory RNAs ...... 18 1.4.1 Mechanisms of action of sRNAs ...... 19 1.4.1.1 Protein-binding sRNAs...... 19 1.4.1.2 Trans-encoded sRNAs...... 20 1.4.1.2.1 Regulation of translation by trans-encoded sRNAs ...... 20 1.4.1.2.2 Regulation of transcript stability by trans-encoded sRNAs ...... 22 1.4.1.2.3 Regulation of transcription termination by sRNAs ...... 23 1.4.1.2.4 Sponging interactions by sRNAs ...... 23 1.4.2 RNA chaperones for sRNA function ...... 27 1.4.2.1 Hfq ...... 27 1.4.2.1.1 Structure ...... 27 1.4.2.1.2 Hfq facilitates annealing of sRNA-mRNA seed sequences ...... 28 1.4.2.2 ProQ/FinO domain proteins ...... 30 1.4.3 Physiological roles of sRNAs ...... 30 1.4.3.1 Stress response ...... 31 1.4.3.2 Cellular metabolism ...... 33 1.4.3.3 Iron homeostasis ...... 35 1.4.3.4 Virulence ...... 36 1.5 System-wide approaches to studying sRNAs ...... 38 1.6 Research aims ...... 44

Table of Contents

Chapter 2: General Materials and Methods ...... 46 2.1 Bacterial strains and growth conditions ...... 46 2.2 DNA manipulation and strain construction ...... 46 2.2.1 Genomic and plasmid DNA extraction ...... 46 2.2.2 Primer design, polymerase chain reaction and gel purification ...... 46 2.2.3 Restriction digests and ligation ...... 47 2.2.4 Preparation of heat-shock competent E. coli ...... 47 2.2.5 Heat-shock transformation into E. coli ...... 48 2.2.6 Transformation into E. coli via electroporation ...... 48 2.3 RNA manipulation and analysis ...... 49 2.3.1 RNA extraction ...... 49 2.3.2 Reverse transcription via SuperScript Reverse Transcriptase ...... 49 2.3.3 Differential RNA-seq (dRNA-Seq) ...... 50 2.3.4 Analysis of RNA-seq data ...... Error! Bookmark not defined. Chapter 3: sRNA regulation of the outer membrane haem receptor chuA 52 3.1 Introduction ...... 52 3.2 Materials and Methods ...... 55 3.2.1 Bacterial strains and culture conditions...... 55 3.2.2 In silico prediction of interacting sRNAs ...... 59 3.2.3 Construction of GFP-translational fusions and sRNA expression vectors for testing sRNA-mRNA interactions ...... 59 3.2.4 Confirmation of sRNA-chuA interactions using the sfGFP 2-plasmid system ...... 60 3.2.5 Secondary structure prediction ...... 61 3.2.6 Prediction of Rho-utilization sites in EHEC ...... 61 3.3 Results ...... 62 3.3.1 The EHEC outer membrane haem receptor ChuA is subject to regulation by sRNAs ...... 62 3.3.2 CyaR, ChiX and AsxR interact with both UPEC and EHEC chuA ...... 63 3.3.3 CyaR directly interacts with the chuA 5’ UTR ...... 66 3.3.4 CyaR interaction with chuA is not dependent on temperature ...... 67 3.3.5 The 5’UTR of chuA contains a sequence that inhibits its expression ...... 69 3.3.6 The 5’UTR of chuA is subject to transcription termination by Rho ...... 72 3.4 Discussion ...... 75

Chapter 4: The Shiga toxin promoter PR’ transcribes a functional sRNA that activates the stress sigma factor RpoS ...... 82 4.1 Introduction ...... 82 4.2 Materials and Methods ...... 86 4.2.1 Strains, Plasmids and Primers ...... 86 4.2.2 Genetic modification of E. coli O157:H7 str. Sakai via allelic exchange ...... 91 4.2.3 Genetic modification of E. coli O157:H7 str. Sakai via CRISPR-Cas9 ...... 92 4.2.4 Genomic DNA sequencing of mutants ...... 93 4.2.5 TBE-Urea polyacrylamide gel electrophoresis ...... 94 4.2.6 Northern blot ...... 94 4.2.7 5’RLM-RACE to identify transcription start sites and processing sites ...... 94 4.2.8 Phylogenetic analysis of the Q antiterminator and StxS ...... 95 4.2.9 Transient inactivation of RNase E ...... 95 4.2.10 RNase-E CRAC ...... 96 4.2.11 MS2-affinity purification and sequencing (MAPS) ...... 99 4.2.12 Small RNA control of superfolder GFP translational fusions ...... 99 4.2.13 Small RNA control of enhanced GFP-transcriptional fusions ...... 101 4.2.14 Phage plaquing assays ...... 101 xi

Table of Contents

4.2.15 Quantitative RT-PCR ...... 102 4.2.16 Growth curve measurements ...... 102 4.2.17 Measurement of Stx production in EHEC ...... 103 4.3 Results ...... 104 4.3.1 The Shiga toxin promoter PR’ transcribes an Hfq-binding sRNA ...... 104 4.3.2 StxS is transcribed by both Stx1Φ and Stx2Φ ...... 108 4.3.3 StxS is processed by RNase E into a stable transcript ...... 108 4.3.4 The presence of StxS is correlated with the primary sequence of Q ...... 111 4.3.5 StxS does not affect the induction of the Shiga-toxin producing Sp5 prophage ..... 114 4.3.6 The stable StxS transcript activates rpoS translation similarly to core-genome sRNAs ...... 118 4.3.7 StxS is needed for EHEC to reach higher cell densities in late stationary phase ... 125 4.3.8 Deletion of StxS causes higher production of Shiga toxin ...... 127 4.4 Discussion ...... 131 Chapter 5: Identifying the pathogen-specific RNA-binding proteome of EHEC using TRAPP ...... 135 5.1 Introduction ...... 135 5.2 Materials and Methods ...... 140 5.2.1 Strains, plasmids and primers ...... 140 5.2.2 Purification of the EHEC RBPome using TRAPP ...... 141 5.2.3 In-gel digestion and mass spectrometry ...... 143 5.2.4 TRAPP data analysis ...... 144 5.2.5 Functional annotation of proteins using gene ontology ...... 145 5.2.6 Prediction of RNA-binding domains using APRICOT ...... 145 5.2.7 Dual-affinity tagging of candidate RBPs ...... 146 5.2.8 Western blots to confirm protein expression ...... 146 5.2.9 PNK assay to confirm RNA-binding properties of candidate RBPs ...... 147 5.3 Results ...... 150 5.3.1 Identification of RNA-binding proteins in enterohaemorrhagic E. coli ...... 150 5.3.2 TRAPP enriches for RNA-binding proteins ...... 152 5.3.3 Validation of EHEC TRAPP results ...... 155 5.4 Discussion ...... 161 Chapter 6: Discussion ...... 167 6.1 Overview of EHEC ...... 167 6.2 Overview of small RNA regulation of EHEC pathogenicity and virulence 168 6.2.1 Core genome sRNAs regulate PAI-encoded genes in EHEC ...... 168 6.2.2 The phage-encoded sRNA StxS modulate the host stress response ...... 171 6.3 Rho-termination is important in EHEC gene regulation ...... 174 6.4 Pathogen-specific RNA-binding proteins may be important for post- transcriptional regulation in EHEC ...... 175 6.5 Conclusion ...... 179 References ...... 181 Appendix: Supplementary Figures and Tables ...... 229

xii

LIST OF FIGURES

Figure 1-1: Overview of retrograde transport of Shiga toxins and disruption of protein translation...... 11 Figure 1-2: Regulation of StxΦ lysogeny...... 17 Figure 1-3: Mechanisms of sRNA-mediated post-transcriptional regulation...... 26 Figure 1-4: System-wide approaches for discovery of sRNA targets...... 43 Figure 3-1: The outer membrane haem receptor chuA is regulated by trans-encoded sRNAs...... 65 Figure 3-2: CyaR, but not AsxR or ChiX, regulates chuA translation through direct binding...... 68 Figure 3-3: CyaR does not activate chuA by preventing RNA-thermometer formation...... 69 Figure 3-4: The 5’UTR of chuA contains a sequence feature that inhibits its translation...... 71 Figure 3-5: The outer membrane receptor chuA is subject to transcription termination by Rho...... 74 Figure 3-6: ChuA utilises an AND-OR or AND-AND logic gate using environmental signals as input...... 78 Figure 4-1: Transcriptional landscape in Stx2Φ...... 105 Figure 4-2: Transcriptional landscape in Stx1Φ...... 106 Figure 4-3: Shiga-toxin encoding bacteriophages transcribe a small non-coding RNA from the late promoter PR’...... 107

Figure 4-4: StxS is expressed from the PR’ promoter as a 255 nt transcript that is processed by RNase E into a 74 nt sRNA...... 110 Figure 4-5: Alignment of StxS from different Shiga-toxigenic bacteria...... 112 Figure 4-6: The presence of StxS may be related to the primary sequence of the antiterminator Q...... 113 Figure 4-7: Expression of StxS has no effect on Stx2Φ plaquing...... 117 Figure 4-8: The StxΦ PR’ transcribed sRNA activates the general stationary phase and stress sigma factor rpoS...... 121 Figure 4-9: MS2-affinity purification to identify StxS targets...... 124 Figure 4-10: StxS promotes growth to higher cell densities in minimum media and mild resistance to osmotic stress but not acid resistance, in an RpoS dependent manner...... 128

xiii

Figure 4-11: Deletion of StxS promotes expression of Shiga toxin independent of phage induction...... 130 Figure 5-1: UV-crosslinking results in increased protein yield following TRAPP...... 151 Figure 5-2: TRAPP enriches for RNA-binding proteins and proteins containing RNA- binding domains...... 154 Figure 5-3: EHEC TRAPP identifies putative pathogen specific RBPs...... 157 Figure 5-4: PNK assays verify the RNA-binding potential of TRAPP enriched proteins...... 160 Supplementary Figure S1: Genomic DNA sequencing of StxS mutants to confirm deletion or repair in Stx1Φ...... 230 Supplementary Figure S2: Genomic DNA sequencing of StxS mutants to confirm deletion or repair in Stx2Φ...... 231 Supplementary Figure S3: GO term analysis on twofold enriched proteins recovered by TRAPP in non-crosslinked samples...... 254 Supplementary Figure S4: Western blot confirming expression of candidate RBPs from pBAD24...... 256

xiv

LIST OF TABLES

Table 3-1: Strains used in Chapter 3 ...... 56 Table 3-2: Plasmids used in Chapter 3 ...... 56 Table 3-3: Oligonucleotides used in Chapter 3 ...... 57 Table 4-1: Bacterial strains used in Chapter 4 ...... 86 Table 4-2: Plasmids used in Chapter 4 ...... 87 Table 4-3: Oligonucleotides used in Chapter 4 ...... 88 Table 4-4: StxS hybrids recovered from RNase E CLASH containing more than 1 hybrid read. Chiastic hybrids are defined as being detected in both the 5’-sRNA- mRNA-3’ and 5’-mRNA-sRNA-3’ directions ...... 119 Table 5-1: Bacterial strains used in Chapter 5 ...... 140 Table 5-2: Plasmids used in Chapter 5 ...... 140 Table 5-3: Oligonucleotides used in Chapter 5 ...... 141 Table 5-4: Pathogen-specific proteins enriched by TRAPP ...... 155 Supplementary Table 1: GBlocks used in this study ...... 232 Supplementary Table S2: Secondary SNPs detected in constructed Sakai mutants 234 Supplementary Table S3: Secondary indels detected in constructed Sakai mutants 235 Supplementary Table S4: Statistically significant twofold enriched proteins recovered with TRAPP ...... 236 Supplementary Table S5: List of RNA-binding domains used as search terms for APRICOT ...... 255

List of abbreviations Amp ampicillin AP alkaline phosphatase BCM bicylomycin bp base pairs cAMP cyclic adenosine monophosphate CFU colony forming units CLASH crosslinking, ligation and sequencing of hybrids Cm chloramphenicol CRAC crosslinking and analysis of cDNA CRISPR clustered regularly interspaced short palindromic repeats DMSO dimethylsulfoxide DNA deoxyribonucleic acid dRNA-seq differential RNA-sequencing DTT dithiothreitol EHEC enterohaemorrhagic E. coli F phi (phage) FC fold change FSC forward scatter g centrifugal force Gb3 globotriaosylceramide GFP green fluorescent protein GO gene ontology h hours HEPES 2-[4-(2-hydroxyethyl)piperazin-1-yl]ethanesulfonic acid HTF 6x His-TEV- 3X FLAG HUS haemolytic uremic syndrome Kan kanamycin ks Kolmogorov-Smirnov LB Luria broth LEE locus of enterocyte effacement LFQ label-free quantification M molarity (mol/L) MAPS MS2-affinity purification and sequencing MEM minimum essential medium mL milliliters MOPS 3-Morpholinopropane-1-sulfonic acid MS mass spectrometry nt nucleotides OD optical density OOPS orthogonal organic phase separation

xvi

PAGE polyacrylamide gel electrophoresis PAI pathogenicity island PCR polymerase chain reaction PIPES piperazine-N,N′-bis(2-ethanesulfonic acid) PNK polynucleotide kinase PR’ late phage promoter Q phage antiterminator qPCR quantitiative real-time PCR RBD RNA-binding domain RBP RNA-binding protein RLM-RACE RNA-ligase mediated rapid amplification of cDNA ends RNA ribonucleic acid RNAT RNA thermometer rne RNase E rpm revolutions per minute Sp spectinomycin sRNA trans-encoded small non-coding RNA SSC side scatter Stx Shiga toxin T3SS type III secretion system TAP tobacco acid pyrophosphatase TBE tris-borate-EDTA Tet tetracyline TEX terminator exonuclease tR' late phage terminator TraDIS transposon directed insertion site sequencing TRAPP total RNA-associated proteome purification UPEC uropathogenic E. coli UTR untranslated region UV ultraviolet light V volts WT wild-type λ phage lambda

xvii

Chapter 1

Chapter 1: Introduction and Literature Review

1.1 Escherichia coli Escherichia coli is found in the environment and in the gastrointestinal tract (GIT) of warm-blooded organisms. Typically, it is a commensal, becoming a notable member of the human gastrointestinal flora and persisting within its host for the entirety of the host’s lifespan. It colonizes the mucus layer of the caecum and colon within the first few hours after the host’s birth. This colonization then continues throughout the life of the host with E. coli becoming the most prominent facultative anaerobe of more than approximately 500 other bacterial species in the gastrointestinal tract (Tenaillon et al., 2010). Although the relationship between E. coli and its host is classified as commensal, the host derives some benefit. Commensal E. coli forms part of the normal flora of the gastrointestinal tract and competes with invading bacterial pathogens for access to nutrients, conferring a level of colonization resistance (Hudault, Guignot and Servin, 2001).

E. coli is a highly genetically diverse species, with only approximately 20% of its genome shared among all strains (Lukjancenko, Wassenaar and Ussery, 2010). Pathotypes of E. coli have acquired repertoires of virulence genes that allow them to cause disease within the host. Pathotypes of E. coli can cause gastrointestinal disease, urinal tract infections, and meningitis using a diverse range of accessory virulence factors. Examples of these pathotypes include enteropathogenic E. coli (EPEC), enterohemorrhagic E. coli (EHEC), enterotoxigenic E. coli (ETEC), enteroaggregative E. coli (EAEC), and diffusely adherent E. coli (DAEC) that are all capable of causing gastrointestinal infections leading to diarrheal disease, enteroinvasive E. coli (EIEC) that are able to cause dysentery, uropathogenic E. coli (UPEC) that is a common cause of urinary tract infections, and meningitis- associated E. coli (MNEC) (Kaper, Nataro and Mobley, 2004). Pathotypes of E. coli that infect the gastrointestinal tract are discussed in the following sections.

Chapter 1

1.1.1 Classifications of diarrheagenic E. coli

Enteropathogenic E. coli (EPEC) cause watery and sometimes bloody diarrhea in infected individuals and can be distinguished from other pathotypes by its attaching and effacing histopathology (Hu and Torres, 2015). The bacterium attaches to intestinal epithelia cells and causes loss of microvilli and restructuring of actin to form a pedestal underneath the bacterial attachment site. This causes tight junction disruption and results in increased membrane permeability, mitochondrial dysfunction and inhibition of cell transporters. The genes required for intimate adhesion are encoded on the locus of enterocyte effacement (LEE). The LEE encodes a type 3 secretion system (T3SS), which acts as a molecular syringe that injects effector proteins into the host cell. Effectors secreted by EPEC can cause membrane remodeling, ion secretion and cytoskeletal changes (Kaper, Nataro and Mobley, 2004; Dean and Kenny, 2009). These perturbations disrupt cellular homeostasis and trigger the host immune response (Wong et al., 2011).

Enterotoxigenic E. coli (ETEC) is a noninvasive pathotype that causes mild to severe watery diarrhea, vomiting, stomach cramps and occasionally causes mild fevers. Transmission of this pathotype occurs primarily via contaminated water and is a major cause of traveler’s diarrhea (Kaper, Nataro and Mobley, 2004). Two major enterotoxins secreted by ETEC, termed heat-labile and heat-stable enterotoxins, are the primary virulence factors that cause disease in infected individuals. Heat-labile enterotoxins (LT) have ADP-ribosyl transferase activity which activates G proteins, which in turn causes permanent activation of adenylate cyclase due to the lack of GTPase activity (Fleckenstein et al., 2010; Mirhoseini, Amani and Nazarian, 2018). The activation of this enzyme leads to a cascade that results in the activation of the cystic fibrosis transmembrane conductance regulator (CFTR), an ion channel that regulates water and electrolyte flow in enterocytes. Hyperactivation of CFTR results in secretion of ions and an excess of water in the intestinal lumen, resulting in diarrhea. Heat- stable enterotoxins (ST) work in a similar manner, directly activating the extracellular portion of guanylate cyclase, which leads to increased levels of 2

Chapter 1

cGMP, also increasing CFTR activation and resulting in increased secretion of ions (Sears and Kaper, 1996; Mirhoseini, Amani and Nazarian, 2018)

Enteroaggregative E. coli (EAEC) can be distinguished from other pathotypes by its adherence to HEp-2 cells in an auto-aggregative manner, adhering to each other in a characteristic stacked brick configuration. Like other enteric pathotypes of E. coli, EAEC causes enteritis and diarrhea in infected individuals. EAEC can adhere to both human ileal and colonic tissue, allowing colonization of the intestinal mucosa, followed by release of enterotoxins and cytotoxins. EAEC adherence in mediated by aggregative adherence fimbriae (AAF) (Knutton et al., 1992). The enterotoxins and cytotoxins secreted by EAEC upon adhesion are mostly serine protease autotransporter toxins (SPATEs). SPATEs can be broadly classified into Class I SPATEs such as pic, sigA and sat, which are cytotoxic, and Class II SPATEs, such as pet and sepA, which have a broader range of functions, such as cleavage of mucin (Jensen et al., 2014). Eighty-six percent of EAEC strains contain a Class I SPATE (Harrington, Dudley and Nataro, 2006; Boisen et al., 2009). Individual toxins secreted by EAEC are not essential for its virulence, but together disrupt the intestinal epithelium. EAEC strains are heterogeneous, making identifying specific virulence factors that would directly impact pathogenesis difficult. Further study is needed to better understand this pathotype.

1.2 Enterohaemorrhagic E. coli (EHEC) Enterohemorrhagic E. coli (EHEC) is a pathotype of E. coli that is characterized by the presence of the type 3 secretion system, encoded by the Locus of Enterocyte Effacement (LEE), and the release of Shiga toxins. Both virulence factors play important roles in EHEC disease progression that includes diarrheal disease, haemorrhagic colitis (bloody diarrhea), and in a subset of the cases hemolytic uremic syndrome (HUS), a common cause of pediatric renal failure (Lynn et al., 2005).

1.2.1 Haemorrhagic colitis and haemolytic uremic syndrome

Chapter 1

Haemorrhagic colitis occurs in approximately 90% of culture confirmed cases, while HUS develops in approximately 15% of pediatric patients (Karch, Tarr and Bielaszewska, 2005). The first recorded outbreak of this pathotype was by serotype O157:H7 strain EDL933 and was recorded in 1982 in Oregon and Michigan, USA following isolation of this strain from patients presenting with bloody diarrhea and abdominal cramps after consumption of under-cooked hamburgers. Sporadic outbreaks of this pathotype persist, with hundreds of cases occurring worldwide including notable outbreaks in Sakai, Japan in 1996, South Wales, UK in 2005 , and a 2014 outbreak in 4 states of the USA (Michino et al., 1999; Pennington, 2014). Annually, approximately 230,000 cases of illnesses are caused by EHEC in the United States alone (Hale et al., 2012). Serotype O157:H7 is the most prominent and well known of these strains, accounting for 40.3% of EHEC cases in the United States. The remaining 59.7% are predominately caused by the ‘big six’ non-O157 serotypes O26:H11, O45:H2, O103:H2, O111:H8, O121:H19 and O145:H28. There are an estimated 2.8 million acute illnesses caused by STEC globally per year (341 notified cases in Australia in 2016) , with 3890 proceeding to HUS causing 230 deaths (Majowicz et al., 2014; Ingle et al., 2019). The OzFoodNet Working Group reported in their 2011 annual review that in 58 reported cases of EHEC infection with serotype information in Australia, 38% were identified as O157:H7, 17% O111:H8, 12% O26:H11 and 7% O128:H2. In 2010, O157:H7 represented 59% of serotype confirmed reported cases (OzFoodNet, 2015).

EHEC infections occur upon consumption of contaminated food, usually the meat of ruminants such as cows. Symptoms including abdominal pain, fever, vomiting and diarrhea occur on average 3 days after ingestion (ranging from 2-12 days) (Karmali, 2004). Diarrhea is non-bloody for the first 1-3 days of a typical O157:H7 infection, after which 90% of cases transition to bloody diarrhea. While fevers do occur early in O157:H7 infections, patients are normally afebrile by the time hemorrhagic colitis manifests. The lack of fever distinguishes EHEC infections from other diarrheagenic pathotypes of E. coli. Other distinguishing characteristics include an increased intensity of abdominal pain, tenderness in the abdominal area, and painful defecation (Tarr, Gordon and Chandler, 2005). 4

Chapter 1

If left untreated, 15-20% of infections in children transition to HUS, characterized by thrombocytopenia and microangiopathic hemolytic anemia with acute renal failure (Obrig, 2010). Endothelial cells swell, and detach from the basement membrane, collecting in the sub-endothelial space as debris. Platelets are attracted to the stressed area due to the expression of Von Willebrand factor, and the formation of microthrombi causes blood vessel obstruction, which results in the damage or destruction of red blood cells (Ruggenenti, Noris and Remuzzi, 2001).

HUS commonly has effects on the other major organ systems as well. Increased morbidity rates are associated with central nervous system complications. Patients may experience seizures, comas, stroke, hemiparesis, facial palsy, cortical blindness, dysphasia and diplopia, among others. These complications may be brought about by endothelial injury and hypoxia and exacerbated by hyponatremia, hypertension and uremia. High-blood pressure complications have also been observed in patients MRIs, including reversible posterior leukoencephalopathy syndrome and cerebral hemorrhage (Eriksson, Boyd and Tasker, 2001). In more serious cases of EHEC infection, the digestive tract can also manifest symptoms such as bowel necrosis and perforation, rectal prolapse, peritonitis and intusseception (de Buys Roessingh et al., 2007).

1.2.2 Virulence factors of EHEC

1.2.2.1 Locus of enterocyte effacement and the type III secretion system

EHECs are partially defined by their ability to induce attaching and effacing lesions (A/E) on the intestinal epithelia, specifically on the apical surface of enterocytes. This attachment causes changes in the cytoskeleton, forming actin- rich pedestals, and destruction of microvilli (Knutton et al., 1989). The ability to form attaching and effacing legions is conferred by genes located on the Locus of Enterocyte Effacement (LEE), a 35-kb pathogenicity island conserved among the A/E pathogens that include enteropathogenic E. coli, EHEC, and Citrobacter rodentium. The EHEC O157:H7 LEE contains 41 open reading frames, and is

Chapter 1

organized into 5 major polycistronic operons. Genes found in the LEE include eae, encoding the autotransporter intimin, which mediates adherence of A/E bacteria to their epithelial targets through the translocated Intimin receptor (Tir), and genes required for the expression of the type 3 secretion system (T3SS) and secreted effector proteins (Stevens and Frankel, 2014).

The LEE is tightly regulated by the LEE-encoded regulator (Ler). The Ler protein is encoded within the LEE1 operon, and is controlled by environmental factors, such as levels of glucose, fucose, iron, pH, calcium, and genetic factors such as Fis and PchA (Goldberg et al., 2001; Snider et al., 2009; Njoroge et al., 2012). The LEE is transcriptionally repressed by H-NS, a DNA binding protein that binds AT-rich DNA and is associated with repression of horizontally acquired DNA. PchA is thought to relieve H-NS repression of the LEE1, allowing for expression of Ler, and transcriptional activation of all 5 operons of the LEE (Bustamante et al., 2001).

Additional regulators that contribute to the regulation of the LEE include GrlA, a transcriptional activator that binds to the 18-bp sequence between the -35 and - 10 elements of the P1 promoter of LEE1 and drives expression of ler (Puente et al., 2005; Jiménez et al., 2010). The activity of this transcription factor is regulated by GrlR, which can sequester GrlA from its binding site and prevent LEE activation (Deng et al., 2004; Iyoda et al., 2006; Padavannil et al., 2013). QseA is a LysR-type transcriptional regulator that acts as an activator of LEE transcription via ler, while QseD acts as a repressor (Sperandio, Li and Kaper, 2002; Sharp and Sperandio, 2007; Habdas et al., 2010; Kendall, Rasko and Sperandio, 2010) .

The primary role of the type 3 secretion system (T3SS) in gram-negative bacteria is to provide a direct conduit for effectors to enter the host cytoplasm (Gauthier, Thomas and Finlay, 2003). Otherwise known as an injectisome, the T3SS is a supramolecular structure resembling a needle and syringe, which passes through the three membranes (inner and outer bacterial membrane, and the eukaryotic membrane), the peptidoglycan layer, and the extracellular space. The basal 6

Chapter 1

apparatus of the T3SS is a 3.5 MDa multiring syringe-like structure embedded within the inner and outer membranes of the bacterium with a filament and needle protruding from the outer membrane (Kubori et al., 1998). Initially discovered in Salmonella typhimurium, it has since been visualized using cryo-electron microscopy and found to be structurally conserved in other prokaryotes as well, such as Shigella, Yersinia, Vibrio, Pseudomonas, Chlamydia and pathogenic E. coli (Notti and Erec Stebbins, 2016; Hu et al., 2018).

The proteins that compose the T3SS can be categorized into three broad groups: structural proteins, effectors and chaperones. In EHEC the T3SS structural components consist of a basal apparatus and the translocation apparatus. The base is a multiring structure, with two inner rings connected to two outer rings through a neck. Proteins that form the base are EscC (which forms the outer ring), EscJ, EscI, EscF, EscV, EscR, EscS, EscT, EscU form the inner membrane machinery, and the translocon and needle tip are formed by EspD, EspB and EspA (Galán and Wolf-Watz, 2006; Ogino et al., 2006). The needle extends from the extracellular portion of the base and provides a direct conduit for effector protein transfer from the bacteria to the host cell. EPEC and EHEC T3S systems have needles approximately 260 nm longer than other T3SS expressing bacteria, due to the polymerization of EspA subunits at the end of the conventional needle filament (Knutton et al., 1998)

In EHEC, the T3SS is used to transport effector proteins that mediate attachment and effacement, evade host immune responses such as phagocytosis, and to manipulate inflammatory and cell survival pathways. Seven conserved T3SS- dependent effectors are encoded in the LEE, and a further 32 effectors are encoded within cryptic prophages and along the O-islands of EHEC (Tobe et al., 2006). Tir is a LEE-encoded receptor encoded by the espE gene and is a receptor for the transmembrane protein Intimin. Intimin is encoded by the LEE gene eae and is presented on the outer membrane of the cell. Once Tir has been translocated through the T3SS, it inserts into the host cell membrane and binds intimin on the surface of the bacterial cell, allowing for attachment between EHEC and the host. Unlike in EPEC, EHEC Tir is not tyrosine phosphorylated, and 7

Chapter 1

mediates actin polymerization by indirectly binding to a T3SS translocated effector protein known as TccP (EspFU), which initiates the polymerization pathway (Campellone, Robbins and Leong, 2004; Garmendia et al., 2004). This demonstrates the precise coordination of LEE gene expression required for proper EHEC adherence and colonization.

1.2.2.2 Shiga toxin

Haemolytic uremic syndrome is the most severe disease outcome for EHEC infection and is caused by the production of Shiga toxins. Shiga toxins are AB5 toxins that have a single 32 kDa StxA component and 5 subunits of an 8 kDa StxB component (Melton-Celsa, 2014). The StxB subunit allows for receptor binding, while the StxA component has RNA N-glycosidase activity. The B subunit contains 3 binding sites for the Shiga toxin receptor, the cell membrane component globotriaosylceramide (Gb3), which is highly expressed in Paneth and renal glomerular endothelial cells. Microvascular and neural tissue of the central nervous system have also been found to highly express Gb3, and is the likely cause of neurological disorders in approximately 30% of HUS cases (Obata et al., 2008). Following attachment of EHEC from the intestinal lumen, large amounts of Shiga toxin can be released into the intestinal epithelium. How the toxins reach the kidney, however, is not readily apparent. Stx cannot be detected in the blood plasma of HUS patients (Karmali et al., 1985; Caprioli et al., 1992). Evidence has been found for Stx binding to polymorphonuclear leukocytes (PMNs), which may explain how the toxin enters the circulatory system. Binding of Stx to PMNs is a topic currently under debate, as some groups have failed to reproduce the binding of Stx to PMNs (Flagler et al., 2007; Geelen et al., 2007), while this interaction has been confirmed by others (Brigotti et al., 2006, 2008; Griener et al., 2007; Ståhl et al., 2009). This has been attributed to partial unfolding of the toxin, in a manner where it maintains its Gb3-binding and enzymatic functions, but loses its ability to bind to neutrophils (Brigotti et al., 2011). The same group reported TLR4 as the neutrophil receptor that recognizes Shiga toxins and mediates the Stx/PMN interaction (Brigotti et al., 2013). Shiga

Chapter 1

toxins are then released via blood-cell derived microvesicles and endocytosed by renal endothelial cells, leading to HUS (Ståhl et al., 2015). Alternatively, Stx has been proposed to enter systemic circulation by crossing the intestinal epithelium through M cells, then being captured by macrophages (Etienne- Mesmin et al., 2011). Additional studies will be required to understand how Stx is able to enter the bloodstream and spread through the host.

Once the StxB subunit binds to Gb3 on the host cell surface, the toxin is endocytosed and transported to the endoplasmic reticulum (ER) and the Golgi apparatus via retrotranslocation (Ivarsson, Leroux and Castagner, 2012). The StxA component can be further subdivided into the N-terminal StxA1 subunit, which contains the RNA N-glycosidase domain and the C-terminal StxA2 subunit, which tethers the A subunit to the B5 subunit. In the endoplasmic reticulum, furin (a proprotein convertase member of the trypsin family) cleaves the StxA subunit into its two components, allowing StxA1 to enter the cytosol by chaperone- mediated transfer (Garred, Van Deurs and Sandvig, 1995). StxA1 inhibits protein synthesis by depurination of adenine at position 4324 on the 28S RNA of the 60S ribosomal subunit, leading to cleavage of 28S rRNA (Figure 1.1). This leads to ribotoxic stress and endothelial cell apoptosis due to p38 and JNK pathway activation (Endo et al., 1988; Smith et al., 2003). The damage to endothelial cells causes them to detach from the basement membrane, causing capillary narrowing and reduced blood supply to the kidney, resulting in organ failure (Gyles, 2007). Shiga toxins have also been found to act on the innate immune system, specifically via inhibition of protective factor H, a regulator of the complement alternative pathway that prevents self-attack. This uncontrolled activation of complement results in injury to self, particularly in the kidneys, leading to the endothelial damage characteristics of HUS. Recently, it has been observed that Shiga toxins, in particular Stx2a, can directly target erythrocytes, particularly during the intermediate stages of erythropoiesis, which may contribute to the hemolytic anemia that accompanies more severe cases of EHEC infection (Betz et al., 2016)

Chapter 1

Shiga toxins are classified into two major types, Shiga toxin 1 (Stx1) and Shiga toxin 2 (Stx2). Shiga toxin 1 has almost complete identity with Shiga toxin produced by Shigella dysenteriae save for one amino acid (Calderwood et al., 1987). The two share approximately 60% amino acid identity, having 55% and 57% amino acid identity for the A and B subunits, respectively (Tesh and O’Brien, 1991). Epidemiological studies show that severe diseases are more frequently associated with Stx2 than Stx1, and this is supported by studies in mice and primates, which have found that the LD50 of Stx1 is >1000 ng/kg, while the Stx2 has an LD50 of 6.5 ng/kg (Kawano et al., 2008; Stearns-Kurosawa et al., 2010;

Fuller et al., 2011). It has been suggested that the lower LD50 associated with Stx2 may be due to the increased catalytic activity and ribosomal affinity of Stx2A1 compared to Stx1A1 (Basu et al., 2015). Other epidemiological studies have observed that HUS is more likely to develop in an infected individual if the O157:H7 strain expresses only Stx2 variants instead of both Stx1 and Stx2 types (Orth et al., 2007).

Chapter 1

Figure 1-1: Overview of retrograde transport of Shiga toxins and disruption of protein translation. Shiga toxins bind to Gb3 receptors on the surface of intestinal epithelial cells leading to their endocytosis. It moves by retrograde transport to the endoplasmic reticulum, where it cleaves a specific adenine residue to block host cell translation.

Chapter 1

Using sequencing and PCR based subtyping methods (Scheutz et al., 2012) further subdivided Stx1 into Stx1a, Stx1c and Stx1d, while Stx2 was subdivided into Stx2a-Stx2g. The same study devised a Stx nomenclature system that assigns Stx (without number) to Shiga toxins produced by Shigella, while those with numbers are assigned to those produced by E. coli. This system of nomenclature and subtyping of Shiga toxins is valuable for both the research and the medical fields, as different subtypes have been observed to demonstrate varying degrees of pathogenicity and virulence (Scheutz et al., 2012). Since this nomenclature was adopted, another Stx2 subtype, Stx2h, was reported (Bai et al., 2018). Despite only having a few differences in amino acid sequence, these changes are enough to cause differences in the severity of the disease progression. Epidemiological studies indicate that Stx2a, Stx2c and Stx2d are most frequently associated with development of haemorrhagic colitis and HUS in humans, while Stx2b, Stx2e, Stx2f and Stx2g are considered to be less potent variants of the toxin and less associated with human disease (Persson et al., 2007; Kawano et al., 2008; Scheutz et al., 2012). The difference in potency between subtypes has been hypothesized to be due to differences in receptor preference, post-translational modifications, and protein stability. Stx2e contains nine amino acid changes in its B subunit (when compared to Stx2a) and binds globotetraosylceramide (Gb4) instead of Gb3 that is bound by Stx2a (Tyrrell et al., 1992). To further support this hypothesis, altering two amino acids in Stx2e was sufficient to switch preference back to Gb3 (Ling et al., 1998). The toxicity of Shiga toxin variants may also be altered by post-translational processing. Two C-terminal amino acids are cleaved from Stx2d by elastase and increases the potency of the toxin in mouse models (Melton-Celsa, Kokai-Kun and O’Brien, 2002). Stx2c and Stx2d differ by only two amino acids in the A subunit, yet Stx2c is 10-fold less toxic in mice. This difference in toxicity was associated with decreased stability of Stx2c, potentially due to its less organized secondary structure (Bunger et al., 2015).

Chapter 1

1.3 Shiga toxin encoding bacteriophage

The Shiga toxin encoding genes, stxA and stxB, are encoded within lambdoid prophages integrated into the bacterial genome and are referred to as Stx phages. The expression of Stx is controlled by lytic induction of these bacteriophages (Wagner et al., 2001). Lambdoid bacteriophages have a similar genomic organization to the well-studied bacteriophage, λ (Allison, 2007; Krüger and Lucchesi, 2015). Despite similarities in gene organization, stx phage genomes (save for ΦP27, encoding Stx2e) are larger than λ phages, with some being up to 50% larger (Recktenwald and Schmidt, 2002; Allison et al., 2003). Stx phages are genetically diverse, displaying a high degree of mosaicism, and can integrate themselves into different chromosomal regions, such as wrbA, sbcB, argW and ssrA (Unkmeir and Schmidt, 2000; Ogura et al., 2007). Functionally, gene clusters have been found in stx phages that are similar to that of λ phages, such as those coding for recombination, early regulation, replication, late regulation, lysis, and the formation of the head and tail structure of the virus. These however, do not account for the differences in size between Stx and λ phages, and approximately 40-60% of the genes coded by sequenced stx phages have not yet been functionally characterised, including many that are highly conserved across Stx phage types (Smith et al., 2012).

Stx phages are temperate, which means that they may integrate themselves into the chromosome of their host following infection and replicate with the host genome until they re-enter a lytic cycle.

1.3.1 Regulation of the lysogenic-lytic decision

Our current understanding of Stx phage regulation is informed by detailed studies on phage λ regulation. During lysogeny, the Stx phage genome is in a state of quiescence and is replicated with the chromosome of the host. In the case of EHEC, when the stx phage is integrated into the chromosome, the expression of most StxΦ genes, including that of the toxin itself, is inhibited (Wagner et al., 2001).

Chapter 1

Stx phage lysogeny is maintained by the repressor proteins cI and Cro, which are transcribed from the promoters PRM/PRE and PR, respectively. Genes in lambdoid phages can generally be categorized into early, middle and late genes, which reflect the sequential cascade of gene expression during lytic induction of the phage. cI is a repressor protein that is essential to the maintenance of prophage lysogeny. cI binds to the early operators OL and OR, which regulate transcription of the early left and right promoters PL and PR, respectively. Typically λ phages have three tandem sequences on the left and right operators, dubbed OL1, OL2,

OL3 and OR1, OR2 and OR3, but Stx phages may have slightly different configurations; for example, the Stx phage 933W has only two tandem repeats for OL, while H-19B has four repeats for OR (Shi and Friedman, 2001; Tyler, Mills and Friedman, 2004). cI is a homodimer that has the highest affinity for OR1, followed by OR2, and then OR3. Once cI binds to OR1, the free carboxyl domain of the bound dimer helps facilitate the binding of a second repressor molecule to

OR2, via repressor-repressor interactions. A third cI repressor dimer binds to OR3 independent of cI bound at OR1 and OR2, and does not occur unless cI is expressed at high concentrations. Repressor binding to OL3 and OR3 in λ requires cI octamerization, which forms a large DNA loop between the two (Dodd et al., 2001). During lysogeny, cI tetramers bind to OL1 and OL2 to repress PL, and OR1 and OR2 to repress PR (Ptashne, 2004). The two sets of tetramers interact to form an octamer to further repress the early promoters (Révet et al.,

1999). cI octamer binding to the OR2 site activates transcription from the PRM promoter, increasing expression of cI. Excess cI forms a dimer and binds to the

OR3 and OL3 sites, which represses PRM, reducing the level of cI and maintaining the narrow range required for maintenance of lysogeny (Figure 1.2A) (Dodd et al., 2004; Svenningsen et al., 2005). In this configuration, lysogeny is maintained, and the prophage will remain in the bacterial chromosome until an inducing stimulus is introduced. During lysogeny, expression of the Shiga toxin genes and those required for lysis from the constitutively active late phage promoter PR’ is terminated by the late terminator tR’ (Luk and Szybalski, 1983; Wagner et al., 2001)

Chapter 1

The phage lytic cycle is activated by DNA damage within the cell, and can be induced by treatment with ultraviolet light or the chemotherapy drug mitomycin C. Upon exposure to these agents, the bacterial SOS response is triggered, and central to this response is the protein RecA. RecA functions in the repair and maintenance of DNA, and plays a key role in homologous recombination(Chen, Yang and Pavletich, 2008). RecA is activated by binding to single stranded DNA (ssDNA), forming a nucleoprotein that acts as a co-protease to facilitate the self- cleavage of cI at a “hinge” region located in the middle of the carboxy and amino domains. This self-cleavage prevents cI dimerization and abolishes its DNA- binding ability (Galkin et al., 2009). It has been demonstrated that the C-terminal domain of RecA plays a role in this proteolysis by positioning cI to orient the hinge region in a manner that is optimal for cleavage (Galkin et al., 2009). The derepression of PR due to the cleavage of the cI repressor by RecA allows for the expression of Cro, which can also bind to the left and rightward operators of the prophage. Unlike cI, Cro binds first to OR3, causing a reduction in the level of cI due to occlusion of PRM. Derepression of PR also allows for transcription of genes to the right of cro, which are the early lysis genes. The early lysis genes include O and P, which are required for phage DNA replication, and the anti-terminator protein Q. The late lysis genes are transcribed from the late phage PR’ promoter and include the stxA and stxB genes (Figure 1.2B) (Luk and Szybalski, 1983; Wagner et al., 2001).

Anti-terminator Q is expressed during the early lytic cycle and interacts with both

RNA polymerase holoenzyme and the nascent RNA transcribed from PR’. Q binds the nascent RNA at the Q utilization site (qut), a pause-inducing element that resembles a -10 element (Somasekhar and Szybalski, 1983, 1987). The housekeeping sigma factor, σ70, also interacts with Q and modifies the elongating

RNAP allowing it to bypass intrinsic terminators (such as tR’) (Yang et al., 1987; Santangelo and Artsimovitch, 2011). Q is thought to mediate antitermination by inhibiting pausing, protecting newly transcribed RNA from the activity of Rho and by termination inducing oligonucleotides (Shankar, Hatoum and Roberts, 2007).

The late promoter PR’ is constitutively active, but also constitutively terminated at tR’ during lysogeny. Expression of anti-terminator Q during lytic induction modifies 15

Chapter 1

RNAP, allowing it to transcribe through tR’ and intrinsic terminators within the Stx phage late transcript, including the Shiga toxin genes, stxA and stxB (Wagner et al., 2001).

While stx2AB in Stx2 phages rely solely on PR’ for their transcription, stx1AB in Stx1-encoding bacteriophages are also regulated by an independent promoter, termed Pstx1. This promoter is positioned upstream of the stxA and stxB genes, and downstream of the tR’ terminator, so does not require Q anti-termination. Pstx1 is regulated by the ferric uptake regulator (Fur), and so production of Stx1AB can be induced not only by the SOS-response through PR’, but also by growth of the lysogen in low iron environments (Calderwood et al., 1987). Single deletions of the late phage promoter PR’ and PStx1 did not reduce the levels of Stx1 accumulated after induction with mitomycin C, but a double deletion of both promoters resulted in reduced toxin level. This suggested that expression of Stx1 was dependent on environmental conditions (Wagner et al., 2002).

Shiga toxin producing lambdoid phages are more prone to spontaneous induction than their non-toxin encoding counterparts, which has been suggested to increase the virulence of the bacteria harboring the phage and aiding in its dispersion. (Livny and Friedman, 2004). While the exact reason for this is not known, the increased incidence of spontaneous induction has been attributed to lower levels of active RecA required for lytic induction (Livny and Friedman, 2004; Bonanno et al., 2016; Colon et al., 2016). In addition, in the presence of inducing agents such as UV light or mitomycin C, Stx+ phages 933W and BAA2326 appear to induce more readily than toxin-negative lambdoid phages (Colon et al., 2016).

Chapter 1

Figure 1-0-2: Regulation of StxΦ lysogeny. A. CI-mediated repression of Q, adapted from (Ptashne, 2004). The repressor cI forms tetramers that bind to OL and OR operators to repress transcription from early promoters PL and PR, respectively. This prevents transcription of the anti-terminator Q, which results in the maintenance of lysogeny. B. Expression of stxAB due to lytic induction. Shiga toxin and lysis genes are transcribed from the constitutively active late promoter

PR’, but transcription is prematurely terminated by tR’. Upon exposure to extensive DNA damage, RecA promotes cleavage of cI repressor. This allows for expression of the anti-terminator Q, which modifies RNA polymerase to allow it to transcribe past tR’ and into the toxin and lysis genes.

Chapter 1

1.3.2 Post-transcriptional regulation of Stx phage

Due to its lambdoid nature, the post-transcriptional mechanisms that regulate λ phage are likely to be applicable to StxΦ. OOP is an RNA transcribed by the PO promoter antisense to the 3’ end of the cII gene and is induced when λ lysogens are exposed to ultraviolet light. OOP inhibits cII gene expression through induction of RNase III cleavage, promoting the switch to the lytic pathway (Wulff and Krinke, 1987; Krinke, Mahoney and Wulff, 1991). Expression of the cII gene results in the transcription from the PAQ promoter antisense to the Q coding region, which contributes to maintenance of lysogeny by repressing Q (Ho and Rosenberg, 1985; Hoopes and McClure, 1985). Polyadenylation also appears to be important to lysogenization of the Stx phage. Mutation of the E. coli major poly(A) polymerase gene pcnB appears to cause reduced lysogenization efficiency and impaired StxΦ induction (Nowicki et al., 2015).

1.4 Small regulatory RNAs

RNA-mediated regulation is found across all kingdoms of life. RNA regulation is prevalent in bacteria and includes riboswitches, which are highly structured elements in the 5’UTR of mRNAs, RNA thermometers, whose conformations depend on the temperature of the environment, and the CRISPR-Cas system, which is a bacterial immune system against invading plasmids and phages (Sorek, Kunin and Hugenholtz, 2008; Waters and Storz, 2009; Wagner and Romby, 2015; Bossi and Figueroa-Bossi, 2016)

One major form of post-transcriptional regulation by RNAs in bacteria is performed by small non-coding RNAs (sRNAs). Bacterial sRNAs are more diverse in size, ranging from 50 to 500 nucleotides in length. Genome-wide transcriptomics and searches for orphan promoters and terminators, coupled with analysis of conserved regions led to the discovery of small RNAs in the intergenic regions of E. coli (Argaman et al., 2001). Since then sRNAs have also been found to arise from the 3’UTR of mRNA transcripts, and may be processed from within mRNA coding regions (Chao et al., 2012; Dar and Sorek, 2018). Small RNAs can modulate expression in bacteria by regulating translation initiation, transcript 18

Chapter 1

degradation, transcription termination and through sponging interactions (Waters and Storz, 2009; Chen, Morita and Gottesman, 2019). They have been shown to affect a wide variety of cellular processes, such as carbon metabolism, general stress tolerance, iron acquisition, outer membrane maintenance and bacterial virulence.

1.4.1 Mechanisms of action of sRNAs

1.4.1.1 Protein-binding sRNAs

A limited number of sRNAs have been shown to act as regulators by sequestering regulatory proteins away from their targets. An example of this are members of the CsrB/RsmZ family of sRNAs, which acts as a decoy for CsrA. This protein acts as a global regulator by binding to GGA motifs positioned within stem loops (Dubey et al., 2005; Baker et al., 2007). CsrA can act as both repressor or activator. As a repressor, CsrA is commonly recruited to a high affinity binding site upstream of the Shine-Dalgarno (SD) sequence that allows binding of lower affinity GGA motifs within the SD. CsrA can also activate translation by unfolding repressive secondary structure that occludes ribosome access to the SD. This mechanism was recently demonstrated to play an important role in expression of the T3SS in EHEC (Wang et al., 2018) The sRNAs CsrB and CsrC contain repeats of the CsrA binding motif and act as a decoy for the protein, indirectly regulating CsrA targets (Liu et al., 1997).

In E. coli, GlmY and GlmZ are paralogous sRNAs that have distinct functions. Both sRNAs activate the expression of the glucosamine-6-phosphate (GlcN6P) synthase GlmS (Urban et al., 2007; Reichenbach et al., 2008; Urban and Vogel, 2008). GlmZ, directly activates GlmS via an Hfq-dependent interaction. GlmZ is regulated by RapZ, an RNase E adapter that promotes degradation of this sRNA. GlmY however, contains a secondary structure similar to GlmZ and acts as a decoy for RapZ allowing for accumulation of GlmZ. This results in the indirect activation of GlmS by GlmY when GlcN6P levels are low (Göpel et al., 2013; Gruber and Sperandio, 2015; Gonzalez et al., 2017).

Chapter 1

A further example of protein regulation by sRNAs is found in the 6S RNA, which is conserved across more than a hundred bacterial species. This sRNA is characterized by a highly conserved secondary structure with a central bulge that makes it resemble an open promoter. In E. coli, the accumulation of this sRNA as the cell enters stationary phase sequesters σ70-RNA polymerase, allowing transcription of stationary sigma factor (σS)-dependent promoters and expression of genes required for adaptation to stationary phase (Wassarman and Saecker, 2006). 6S RNA transcription is regulated in response to range of environmental signals. In Burkholderia cenocepacia, 6S RNA accumulation occurs upon exposure to oxidative stress. In Bradyrhizobium japonicum, this is triggered when the bacteria are in the root-nodules of their soybean symbionts. Virulence, sporulation, and growth have also been found to be controlled by this 6S RNA (Steuten, Schneider and Wagner, 2014).

1.4.1.2 Trans-encoded sRNAs

Trans-encoded sRNAs account for the majority of characterised regulatory sRNAs and act on their mRNA targets by direct base-pairing. Small RNA regulation can be positive or negative and can regulate at the levels of transcription, translation, or through sRNA-sponging interactions.

1.4.1.2.1 Regulation of translation by trans-encoded sRNAs

Negative regulation of translation initiation by sRNAs can occur due to sRNA- binding to the 5’UTR of their targets, specifically on or near the ribosomal binding site (RBS). This sterically hinders initiating 30S ribosomes, preventing translation (Figure 1.3A) (Bouvier et al., 2008). Small RNAs that bind the RBS sequence are the most well studied, such as the interactions between ompF-MicF, ptsG-SgrS and ompA-CyaR (Andersen and Delihas, 1990; Vanderpool and Gottesman, 2004; De Lay and Gottesman, 2009). However, small RNA binding to regions of the mRNA outside the RBS can also block translation initiation. The sRNAs OmrA and OmrB base pair with the csgD 5’UTR 62 nucleotides upstream of its ribosomal binding site. The 5’UTR of csgD forms two stem loops, SL1 and SL2. 20

Chapter 1

OmrA and OmrB directly binds to the 5’ end of the SL1 secondary structure. OmrA/B binding to this structure may partially open it, blocking a ribosomal loading site and inhibiting translation (Holmqvist et al., 2010).

Small RNAs can inhibit translation of their targets without affecting RBS accessibility by preventing proper ribosomal loading. Stem-loop regions along the first 20-50 nucleotides of a protein coding region can act as standby sites to facilitate ribosomal loading. For example, a stem-loop region is formed within the first 21 nucleotides of the iron-siderophore receptor fepA coding region. This forms an inhibitory structure that constrains lateral movement of the ribosome to position it at the RBS. The sRNAs OmrA and OmrB can directly bind to fepA and prevent formation of this structure. This causes the ribosome to “slide” off the transcript, thus inhibiting fepA translation (Figure 1.3B) (Jagodnik, Chiaruttini and Guillier, 2017). In a related mechanism, the tisB mRNA folds into a structure that blocks its RBS. The +42 nt region of this mRNA is single stranded however, and acts as a ribosomal standby site, allowing the 30S ribosome to relocate to the RBS when the transcript transiently unfolds (“breathes”). This +42 nt standby region acts as a target for the IstR1 sRNA, which binds to it in an Hfq-independent manner. Binding of IstR1 to tisB forms a duplex that recruits RNase III, cleaving off the standby site, leaving a truncated, translationally inert tisB transcript (Romilly, Deindl and Wagner, 2019).

Small non-coding RNAs can also activate translation. The 5’UTRs of mRNAs can form intrinsic secondary structures that block ribosomal binding sites. Trans- encoded sRNAs can activate translation by preventing the formation of these secondary structures through an anti-antisense mechanism, where base-pairing exposes the RBS sequence allowing for ribosomal access (Figure 1.3C). For example, the 576 nucleotide 5’UTR of rpoS forms a stem-loop that occludes the ribosomal binding site (Brown and Elliott, 1997; Soper and Woodson, 2008). The sRNAs DsrA, RprA and ArcZ base pair with the 5’UTR and prevent the formation of this intrinsic structure, which exposes the ribosome binding site and activates translation (Majdalani et al., 1998, 2001; Mandin and Gottesman, 2010; McCullen et al., 2010). 21

Chapter 1

1.4.1.2.2 Regulation of transcript stability by trans-encoded sRNAs

Small RNA repression of translation has both direct and indirect effects on mRNA stability. Because initiating and elongating ribosomes serve as protection against endonucleases (Deana and Belasco, 2005), sRNA-mediated inhibition of translation initiation can make transcripts more vulnerable to degradation by ribonucleases. Interactions between an Hfq-sRNA complex and its target mRNA can also recruit RNase E and process the mRNA 6-10nt downstream of the duplexed sRNA-mRNA nucleotides (Figure 1.3A) (Bandyra et al., 2012; Rice, Balasubramanian and Vanderpool, 2012; Waters et al., 2017). This is due to the ability of Hfq to bind to the C-terminal region of the major E. coli endonuclease RNase E (Morita et al., 2004; Ikeda et al., 2011).

Transcript degradation is often an event that follows translational inhibition, but is not required for efficient target repression (Vanderpool and Gottesman, 2004; Morita, Mochizuki and Aiba, 2006; Maki et al., 2008). There are instances however, where transcript degradation is the primary mechanism of repression. In Salmonella typhimurium, MicC binds to the coding region of its target mRNA ompD. The “seed” region on MicC that base pairs with the target mRNA guides RNase E and Hfq to the transcript. The 5’-monophosphate on the sRNA acts as a signal for mRNA cleavage via RNase E (Figure 1.3D) (Bandyra et al., 2012). sRNAs can also enhance target transcript stability by reducing their susceptibility to degradation by endonucleases (Figure 1.3E). For example, the sRNA RydC, which is conserved in γ-proteobacteria, can activate cfa mRNA in a translation- independent manner by base-pairing with an RNase E cleavage site found in its 5’UTR. (Fröhlich et al., 2013). Small RNA base pairing can also stabilise RNase E processing intermediates. The pldB-yigL transcript is processed by RNase E into a truncated transcript containing that starts from the 3’ end of the pldB coding region (‘pldB-yigL). Upon exposure to phosphosugar stress, SgrS interacts with the truncated ‘pldB-yigL transcript at the 3’ region of pldB. This activates

Chapter 1

translation of yigL by preventing its further degradation by RNase E (Papenfort et al., 2013).

1.4.1.2.3 Regulation of transcription termination by sRNAs

Transcription termination is a cellular process that is crucial for maintaining genomic stability and accurate gene expression, and can be generally categorised into intrinsic or Rho-dependent termination events (Ray-Soni, Bellecourt and Landick, 2016). Exposing E. coli to the antibiotic bicyclomycin (an antibiotic that inhibits Rho) reveals that Rho is responsible for approximately 20- 30% of all termination events in E. coli (Peters et al., 2009, 2012). Direct regulation of transcription has been observed through sRNA-mediated anti- termination in E. coli, where the binding of the sRNAs DsrA, RprA or ArcZ with the 5’UTR of rpoS results in the inhibition of Rho terminator loading and/or translocation (Figure 1.3F) (Sedlyarova et al., 2016). Deep-sequencing of bicyclomycin-treated cells found that out of 1,200 5’UTRs longer than 80 nucleotides (the minimal sequence length required for Rho binding), at least half of these were subject to Rho termination, and that sRNA-mediated anti- termination of transcripts was a widespread occurrence (Sedlyarova et al., 2016). Remarkably, the rho transcript is a target for the SraL sRNA, which stabilises the mRNA by preventing premature termination by its own protein (Chen, Morita and Gottesman, 2019)

1.4.1.2.4 Sponging interactions by sRNAs

Small RNAs have also be shown to participate in sRNA sponging interactions where sRNAs inhibit the function of other sRNAs through direct base-pairing. In EHEC, two sRNAs, termed AgvB and AsxR, act as sponges for the sRNAs GcvB and FnrS, respectively. By targeting the seed regions of these sRNAs, they indirectly upregulate their targets (Figure 1.3G) (Tree et al., 2014). In commensal E. coli, SroC is a sRNA generated by the RNA decay of the gltIJKL operon. SroC can indirectly activate its parent transcript in a feed forward loop by base pairing with GcvB, which is a gltIJKL inhibitor. (Miyakoshi, Chao and Vogel, 2015).

Chapter 1

Similar to how sRNAs are sponged by other sRNAs, tRNA fragments have also been found to act as sRNA sponges. The use of MS2-affinity purification and RNA-sequencing (MAPS) revealed that the 3’ externally transcribed spacer (ETS) regions of tRNAs, which are canonically excised and degraded during tRNA maturation, can also act as sRNA sponges. In particular, the 3’ETS of LeuZ can sponge the RyhB and RybB sRNAs to modulate the levels of these sRNAs, resulting in increased E. coli fitness (Lalaouna et al., 2015). This mechanism, and those described in the previous sections, emphasise the versatility of sRNAs and the different ways they can influence gene expression.

Chapter 1

Figure 1-3: Mechanisms of sRNA-mediated post-transcriptional regulation. A. Small RNA binding to ribosomal binding sites prevent initiation of translation. B. Small RNAs can prevent the formation of structured ribosome standby sites, which causes improper ribosome loading. C. Small RNAs can prevent the formation of secondary structures in mRNA 5’UTRs that can occlude ribosomal binding sites, which activates target translation. D. Small RNAs binding within their targets coding region can promote RNase E degradation of both the sRNA and the transcript. E. Small RNAs can bind to RNase E cleavage sites and stabilize their target. F. Small RNAs can prevent premature transcription termination by interfering with Rho activity. G. Small RNAs can act as a sponge for other small RNAs.

Chapter 1

1.4.2 RNA chaperones for sRNA function

RNA chaperones play a major role in non-coding RNA function (Woodson, Panja and Santiago-Frangos, 2018). In gram-negative bacteria, the annealing of sRNAs to their mRNA targets is facilitated by the RNA-chaperones Hfq and ProQ.

1.4.2.1 Hfq Due to the imperfect base pairing between sRNAs and their targets, RNA chaperones are often required to increase the kinetics of annealing to biologically useful rates. The most well studied small RNA chaperone is Hfq, which is required for the function of at least 90 trans-encoded sRNAs in E. coli, and is largely conserved in Enterobacteriaceae. (Urban and Vogel, 2007). Deletion of hfq leads to pleiotropic effects in a broad range of bacterial pathogens including changes in cell viability, and decreased virulence (Tsui, Leung and Winkler, 1994; Sittka et al., 2007; Chao and Vogel, 2010; Kakoschke et al., 2014).

1.4.2.1.1 Structure Hfq is a hexameric toroid with proximal, distal, and lateral RNA-binding sites (Updegrove, Zhang and Storz, 2016). The proximal surface of Hfq binds to single- stranded, U-rich regions of RNA. Polyuridine sequences bind the central pore of the proximal surface with each subunit of Hfq coming into contact with one uridine nucleotide (Schumacher et al., 2002). The polyuridine tracts are commonly found on Rho-independent terminators that have been identified in many Hfq- dependent sRNAs. The length of these polyU tracts have been found to be important for sRNA biogenesis and efficient Hfq binding (Otaka et al., 2011; Morita, Nishino and Aiba, 2017). The distal surface of the Hfq hexamer binds to triplet ARN repeats (Link, Valentin-Hansen and Brennan, 2009; Robinson et al., 2014; Tree et al., 2014). Each monomer of Hfq can bind one triplet motif, allowing for potentially six repeats on a single hexamer, though Hfq bound to 2-4 repeats have been more readily observed (Link, Valentin-Hansen and Brennan, 2009; Holmqvist et al., 2016). These motifs typically facilitate the binding of Hfq to mRNA and are commonly positioned upstream of the sRNA-mRNA base pairing regions (Beisel et al., 2012; Holmqvist et al., 2016). 27

Chapter 1

The lateral surface of Hfq, otherwise known as the rim, has also been found to bind to sRNAs and contributes to its stabilization. The rim consists of one RNA binding patch per monomer; in the case of E. coli, this binding patch is composed largely of arginine. The positive charge of this arginine-rich site is thought to reduce electrostatic repulsion of the RNA phosphate backbones and facilitate sRNA-mRNA base-pairing (Panja, Schu and Woodson, 2013; Zheng, Panja and Woodson, 2016). In this model, the distal site interacts with an ARNx motif, commonly found near the RBS of mRNAs, and the proximal site interacts with the polyU tails of sRNAs to position the mRNA and sRNA seed regions at the lateral edge of the Hfq hexamer where an arginine-rich patch facilitates duplex formation between the seeds (Sauer, Schmidt and Weichenrieder, 2012; Dimastrogiovanni et al., 2014).

Hfq-binding sRNAs interact with the chaperone in at least two distinct modes termed Class I and Class II. Class I sRNAs are those that bind the proximal and lateral surfaces of Hfq to maintain their stability. Class II sRNAs are those that bind to Hfq using both the proximal and distal surface (Schu et al., 2015). Members of this class have AAN motifs along their body near the 5’ end and have lower turnover rates than Class I sRNAs. The acidic tip of the unstructured C- terminal domain (CTD) “sweeps” the distal face of Hfq, competing with bound RNAs for binding and increasing the rate of RNA dissociation (Santiago-Frangos et al., 2016, 2017)

1.4.2.1.2 Hfq facilitates annealing of sRNA-mRNA seed sequences

To facilitate the formation of the sRNA-mRNA complex in vivo, Hfq overcomes biochemical limitations that prevent nucleation of RNA helices and stabilizes unstructured RNA conformations that promote base-pairing at seed regions (Santiago-Frangos and Woodson, 2018). At the same time, Hfq needs to be able to rapidly cycle and exchange non-cognate RNAs on its surfaces while being able to form stable complexes with its RNA substrates.

Chapter 1

RNA duplex formation between sRNAs and mRNAs occur at “seed” regions – typically single stranded regions that range from 7 to > 20 nucleotides. These regions bind with imperfect complementarity and allow one sRNA to target multiple mRNA targets, and also a single mRNA to be regulated by many sRNAs (Fender et al., 2010). Multiple studies also show that Hfq can change the structure of both sRNAs and mRNAs upon binding (Soper, Doxzen and Woodson, 2011; Henderson et al., 2013; Santiago-Frangos and Woodson, 2018). RNAs can form secondary or tertiary structures that prevent base pairing with their respective targets. Binding to Hfq results in restructuring or reorientation of the RNA to make the seed sequence accessible for base-pairing (A. Zhang et al., 2002; Gorski, Vogel and Doudna, 2017). Once a stable ternary complex has been formed and the base pairing regions of the RNA pairs have been presented, Hfq can stabilize a helix initiation complex that is followed by rapid zippering of the RNA pairs. Following sRNA-mRNA base-pairing, Hfq is thought to cycle off of the duplex by recruiting new mRNAs or sRNAs, or passing the duplex along to RNase E to complete sRNA regulation (Fender et al., 2010; Bandyra et al., 2012; Santiago-Frangos et al., 2017; Waters et al., 2017).

Non-canonical Hfq-dependent mechanisms of sRNA regulation have also begun to emerge. Repression of the mannose transporter manX in E. coli by DicF and SgrS also occurs in an Hfq-dependent manner. However, instead of Hfq facilitating sRNA-mRNA duplex formation, a role reversal is observed, where sRNA annealing to the mRNA recruits Hfq to inhibit target translation (Azam and Vanderpool, 2018). The sRNAs OmrA and OmrB are also able to inhibit the translation of the diguanylate cyclase DgcM in an Hfq-dependent manner. In the absence of Hfq, the 5’UTR of dgcM forms a “closed” secondary structure that makes the sRNA-binding site inaccessible. Hfq restructures the 5’UTR and changes it to its “open” conformation, allowing for regulation by OmrA and OmrB (Hoekzema et al., 2019)

Chapter 1

1.4.2.2 ProQ/FinO domain proteins

Not all bacterial genera encode a Hfq homologue, but all bacterial transcriptomes sequenced appear to encode regulatory small RNAs. Bacteria such as Helicobacter or Mycobacterium, have no Hfq homologue and this has led to a search for other RNA-binding proteins that may have similar chaperone roles (Chao and Vogel, 2010; Wagner and Romby, 2015). FinO is an RNA chaperone that has previously been shown to facilitate the interaction between the plasmid encoded antisense RNA (asRNA) FinJ with the mRNA traJ (Biesen and Frost, 1994). In recent years, other FinO-domain proteins have been identified and appear to bind large repertoires of regulatory RNAs. In Legionella, a FinO-domain protein named RocC was found to repress natural transformation through a trans-activating sRNA RocR (Attaiech et al., 2016). Another FinO-domain containing protein, ProQ, was first discovered in E. coli and was found to control the translation of a proline transporter ProP (G. Chaulk et al., 2011).

The use of Grad-seq (gradient profiling by sequencing), led to the discovery of ProQ as a major small RNA-binding protein in Salmonella enterica that has global effects on expression and post-transcriptional regulation (Smirnov et al., 2016). The first sRNA confirmed to bind to this chaperone is RaiZ, and ProQ is essential for RaiZ-mediated repression of hupA (Smirnov et al., 2017). Using CLIP-seq, ProQ was shown to bind to over 50 sRNAs and to the 3’UTRs of mRNAs. There is little crossover between ProQ and Hfq binding RNAs, with ProQ lacking a clear consensus sequence binding motif, but appears to recognize structured RNA motifs. ProQ can protect bound RNAs from RNase II 3’5’ exoribonucleolytic activity through steric hindrance (Holmqvist et al., 2018). The discovery of ProQ as a global RNA binding protein suggests that more RNA chaperones may be required for regulatory RNA interactions in well characterised enterics and bacteria that lack RNA chaperones known RNA chaperones.

1.4.3 Physiological roles of sRNAs

Chapter 1

Due to the limited complementarity required for sRNA base-pairing, a single sRNA may have multiple targets. This allows sRNAs to play a key role in multiple facets of bacterial physiology. While not a comprehensive list of all the ways sRNAs impact bacterial physiology, several of these roles are detailed below.

1.4.3.1 Stress response Small RNAs frequently control bacterial stress responses, such as oxidative or outer membrane stress, or changes in osmolarity and pH (Holmqvist and Wagner, 2017). Sigma factor 38, otherwise known as σS or RpoS, is a key regulator in stationary phase transcription in E. coli, and the master transcriptional regulator for the general stress response. RpoS controls approximately 10% of all protein coding genes in E. coli (Weber et al., 2005; Gottesman, 2019). Multiple small RNAs regulate the translation of rpoS in response to external and internal stress. At low temperatures, the sRNA DsrA downregulates the translation of the global transcriptional silencer H-NS, causing de-repression of rpoS transcription. DsrA also promotes efficient translation of rpoS by relieving secondary structure near the ribosomal binding site (RBS) that allows the ribosome access to the mRNA and by preventing premature termination by Rho (Lease, Cusick and Belfort, 1998; Sedlyarova et al., 2016). Under aerobic conditions, the sRNA ArcZ is transcribed and processed into an active form that de-represses rpoS translation by a similar mechanism - preventing occlusion of the RBS by native secondary structure. ArcZ is a processed sRNA that is a member of the ArcA/ArcB regulatory network, and links this oxygen response network with the stress response induced by RpoS (Mandin and Gottesman, 2010). RprA is a non-essential sRNA that positively regulates rpoS translation in response to cell envelope stress, possibly brought about by changes in osmotic pressure (Majdalani et al., 2001). OxyS is a sRNA that unlike the previous three, is an inhibitor of rpoS. OxyS is regulated by OxyR, which is expressed when cell is under oxidative stress. The repression of RpoS via OxyS is thought to help the cell utilize the stress-response that is more specific to oxidative stress, instead of relying on the RpoS regulated general stress response (Altuvia et al., 1997).

Chapter 1

The envelope stress response (ESR) is triggered in response to inner or outer membrane stress and is controlled by the Cpx two-component and the outer membrane stress sigma factor RpoE, respectively (Grabowicz and Silhavy, 2017). sRNAs have a major role in both regulatory circuits. RpoE can activate over 100 genes in E. coli relating to membrane homeostasis, but has no repressive action (Rhodius et al., 2006). Instead, RpoE activates the transcription of three repressive sRNAs, RybB, MicA and MicL. MicA and RybB repress genes that encode for outer membrane proteins, preventing their de novo synthesis (Rasmussen et al., 2005; Johansen et al., 2006; Papenfort et al., 2006; Bossi and Figueroa-Bossi, 2007; Udekwu and Wagner, 2007). MicL is a sRNA that solely represses the translation of the outer membrane lipoprotein Lpp, which connects the outer membrane to the cell wall (Guo et al., 2014). Post-transcriptional control of the outer membrane proteins allow the cell to quickly respond to the perceived stress and prevent accumulation of misfolded proteins, as the mRNAs targeted by the σE -dependent sRNAs are quickly degraded after base pairing (Papenfort et al., 2006).

Inner membrane stress is sensed by the CpxAR two-component system in response to the accumulation of misfolded membrane proteins. (Nevesinjac and Raivio, 2005; Hunke, Keller and Müller, 2012). An accessory of this two- component system is CpxP, which directs misfolded membrane proteins in the periplasm for degradation and represses the Cpx system by interacting with CpxA (Raivio, Popkin and Silhavy, 1999; Isaac et al., 2005). The 3’ UTR of CpxP encodes for an Hfq-dependent sRNA, CpxQ, which targets mRNAs that encode for periplasmic and inner membrane proteins (Chao and Vogel, 2016; Grabowicz, Koren and Silhavy, 2016).

Under acidic stress, E. coli expresses two glutamate decarboxylase enzymes, GadA and GadB. These enzymes are regulated by GadX, which in turn is regulated by the RpoS-controlled sRNA GadY. The sequence of GadY partially overlaps with GadX, and stabilizes the transcript (Opdyke, Kang and Storz, 2004; Opdyke et al., 2011). Cellular exposure to stress requires a rapid response that 32

Chapter 1

is faster than the established rates of mRNA degradation and transcript elongation (Chen et al., 2015; Holmqvist and Wagner, 2017). Hence, the integration of sRNAs into stress response pathways are thought to provide the rapid response kinetics required to quickly alleviate the cell from stress.

1.4.3.2 Cellular metabolism

Carbon metabolism is modulated by several sRNAs, some examples being Spot42, SgrS and GlmYZ. Spot42 is an sRNA that regulates galK, a member of the galETKM operon, which encode for genes used for the metabolism of sugars and the biosynthesis of lipopolysaccharides. GalK is a galactokinase whose expression is dependent on the bacterial carbon source. When glucose is adequate, Spot42 downregulates GalK by occlusion of the ribosomal binding site. In the absence of glucose, Spot42 is repressed by cyclic-AMP receptor protein (CRP)-cAMP, a key regulator in catabolite repression as cAMP is produced in the absence of glucose (Polayes et al., 1988; Møller et al., 2002; Beisel et al., 2012).

Glucose homeostasis is also disrupted when phosphosugars (such as glucose- 6-phosphate) accumulate in the cell. When this occurs, SgrR activates expression of the sRNA SgrS, which downregulates ptsG by promoting RNase E degradation of the transcript (Vanderpool and Gottesman, 2004). PtsG is a glucose transporter, and so its downregulation prevents further accumulation of toxic phosphosugars. SgrS also serves as an example of a bifunctional RNA, as it encodes a short protein, termed SgrT. While translation of SgrT is also a response to glucose-phosphate stress, the mechanism by which it alleviates that stress is unrelated to SgrS-ptsG interaction. SgrT is thought to reduce the stress brought about by phosphosugar accumulation by blocking pre-existing glucose transporters in the cell, while SgrS fulfills its role in the prevention of further transporter synthesis (Wadler and Vanderpool, 2007). Catabolism of glucosamine-6-phosphate is regulated by the sRNAs GlmY and GlmZ, as discussed in Section 1.4.1.1.

Chapter 1

Amino acid biosynthesis and uptake is tightly controlled to prevent de novo synthesis in the presence of exogenous amino acids. Small RNAs play key roles in this regulation and are exemplified by GcvB and RybB. GcvB is a conserved sRNA found in E. coli, Salmonella typhimurium, Yersinia and Pasteurella species that regulates a wide range of targets involved in amino acid catabolism and transport. When glycine is abundant, GcvB is induced by the transcriptional regulator GcvA, which also activates the gcvTHP glycine degradation operon. The first role found for GcvB was the repression of oligopeptide and dipeptide transporters OppA and DppA (Urbanowski, Stauffer and Stauffer, 2000). Since then, the GcvB regulon has been greatly expanded, with this sRNA being found to regulate other amino acid transporters such as ArgT, CycA and SstT, and amino acid biosynthesis proteins such as GdhA and SerA. The Salmonella GcvB regulon consists of at least 45 different mRNAs (Pulvermacher, Stauffer and Stauffer, 2009c, 2009a, 2009b; Sharma et al., 2011). Quantitative liquid proteomics of a GcvB mutant in Pasteruella showed the differential expression of 46 proteins (Gulliver et al., 2018). The sRNA RybA, which is conserved in E. coli, Salmonella and Klebsiella, can regulate members of the aromatic amino acid biosynthesis pathway aroF and aroL under oxidative stress. Regulation of these genes does not seem to be through direct base pairing, but through regulation of the tyrosine biosynthesis enzyme TyrA. The mechanism of this regulation is still being investigated (Gerstle et al., 2012). In the model Gram positive bacteria, Bacillus subtilis, the sRNA Sr1 regulates arginine catabolism through repression of the transcriptional activator AhrC that controls the arginine catabolic operons rocABC and rocDEF. In Staphylococcus aureus, the conserved sRNA RsaE down-regulates arginine catabolism by directly targeting rocF mRNA. (Heidrich et al., 2006; Heidrich, Moll and Brantl, 2007; Rochat et al., 2018).

Chapter 1

1.4.3.3 Iron homeostasis

While iron is the fourth most abundant element on Earth, the majority is not readily bioavailable. Under aerobic conditions and neutral pH, iron is precipitated as oxyhydroxide polymers limiting free iron to about 10-18 M (Braun and Killman 1999; Guerinot 1994). Additionally, iron is sequestered by host proteins such as haemoglobin, transferrin, and lactoferrin, starving bacterial cells of iron as a form of innate immunity. In response, bacteria have developed different ways to maintain their iron homeostasis. Many genes that contribute to iron homeostasis are under the control of the transcription factor Fur. When the cellular concentration of iron is sufficient, Fur represses around 100 genes needed for iron uptake. Fur can also activate genes indirectly through the sRNA RyhB, which is repressed by Fur. RyhB has since been found to be important for maintaining bacterial iron homeostasis through an intracellular iron scavenging response (Massé, Vanderpool and Gottesman, 2005). Since its discovery in E. coli, homologs for this sRNA have been found in a variety of Gammaproteobacteria such as Vibrio, Shigella, Yersinia and Klebsiella (Davis et al., 2005; Murphy and Payne, 2007; Deng et al., 2012; Huang et al., 2012). Functionally analogous sRNAs have been found as well, such as PrrF and PrrH in Pseudomonas, ArrF in Azotobacter, and NrrF in Neisseria (Wilderman et al., 2004; Mellin et al., 2007; Jung and Kwon, 2008; Ducey et al., 2009). RyhB controls its targets in different ways. For instance, the RyhB targets sodB and sdhC are regulated through the more canonical sRNA mechanism of translation initiation inhibition (Massé and Gottesman, 2002; Massé, Escorcia and Gottesman, 2003). Of the 56 confirmed targets for RyhB, approximately two- thirds encode for proteins that require iron (Chareyre and Mandin, 2018). Blocking translation allows RyhB to limit the levels of iron-utilizing proteins in order to save the acquired iron for more essential processes. RyhB can also cause the discoordinate expression of polycistronic operons. By base-pairing to the intergenic region of iscRS of the polycistronic iscRSUA operon, RyhB activates the transcriptional regulator IscR by stabilizing the transcript, while signaling for the degradation of the downstream iscSUA transcripts (Desnoyers et al., 2009). RyhB has also been found to activate the expression of the 35

Chapter 1

shikimate transporter shiA and the Fe3+-bound siderophore transporter cirA (Prévost et al., 2007; Salvail et al., 2013). The activation of these promote the intake of iron using siderophores.

1.4.3.4 Virulence

Virulence gene expression is tightly controlled at the transcriptional and post- transcriptional level to allow expression of potentially antigenic structures only during host infection, and in response to discrete environmental signals. One- and two-component sensors play critical roles in sensing the local environment and regulatory small RNAs are emerging as important post-transcriptional regulators that extend these regulons.

Deletion of the sRNA chaperones, Hfq and ProQ, leads to attenuation of virulence in a broad range of human and animal pathogens (Chao and Vogel, 2010; Westermann et al., 2019). In Vibrio, the deletion of hfq results in a significant loss in virulence, with the mutants being unable to colonize the mouse intestine, and an overall decrease in bacterial fitness and adaptability (Ding, Davis and Waldor, 2004). In enterohaemorrhagic E. coli, the prophage-encoded sRNA DicF is expressed under oxygen-limited conditions and promotes the expression of the T3SS by activating PchA (Melson and Kendall, 2019). Other pathogenic bacteria also use sRNAs to regulate virulence. In Salmonella typhimurium STnc540 is a ProQ-dependent 3’UTR derived sRNA that regulates an infection-induced magnesium transporter (Westermann et al., 2019). In Staphylococcus aureus, the 514-nt long RNAIII has multiple functional roles, both as an activator and a repressor. It also contains a short CDS that encodes hld, a haemolysin, and represses Rot (a global toxin repressor) and other adhesion and surface proteins through RNA-RNA interactions, allowing the pathogen to transition from a defensive colonizing state to an offensive one (Boisset et al., 2007).

In EHEC and EPEC, Hfq has been found to regulate genes that are expressed from the LEE. Hfq has been found to have strain-dependent, and growth stage-

Chapter 1

dependent effects on LEE expression. In EHEC O157:H7 str. EDL933, deletion of hfq represses expression of the LEE. Hfq represses the global regulator of LEE, grlA mRNA during the exponential phase, resulting in the repression of ler and silencing of the LEE-encoded operons (Shakhnovich, Davis and Waldor, 2009). Once stationary phase is reached however, ler repression via Hfq is independent of grlRA. In EHEC O157:H7 str. 86-24 however, Hfq is an activator of the LEE in a ler dependent manner. This is possibly due to the presence of strain specific sRNAs in each EHEC strain (Kendall et al., 2011). EHEC-specific sRNAs have been found in multiple specific biotypes. In EHEC 86-24, RNA sequencing led to the discovery of 6 EHEC-specific Hfq-dependent sRNAs. Three of these, sRNA350, sRNA103 and sRNA56, activate the LEE, though the direct targets of these sRNAs have yet to be identified (Gruber and Sperandio, 2015). Another EHEC-specific sRNA Esr41 was found in the Sakai strain of EHEC, specifically on the Sakai prophage-like element 1 (SpLE1) pathogenicity island and was initially found to enhance cell motility by increasing the expression of fliC (Sudo et al., 2014). Esr41 also regulates the expression of iron transport and storage genes cirA, chuA and bfr similar to RyhB and represses LEE expression via repression of ler and pch (Waters et al., 2017; Sudo et al., 2018). More sRNAs have been found on other pathogenicity islands of EHEC, but the effects these may have on virulence and pathogenicity is still being investigated.

Small RNAs that are conserved between pathogenic and non-pathogenic strains of E. coli can also regulate virulence. In EHEC, the sRNAs GlmY and GlmZ control genes expressed from the LEE4 and LEE5 operons. GlmZ directly binds to LEE4 transcript and destabilizes the genes on the 3’ end, and the downstream LEE5 operon. GlmY on the other hand indirectly regulates LEE4 by binding to RapZ, which normally binds to GlmZ and facilitates its degradation by RNase E. By binding to RapZ, GlmY increases the level of free GlmZ, allowing it to repress the LEE. These sRNAs also increase translation of espFU, a type III effector protein that is needed for pedestal formation and adhesion (Gruber and Sperandio, 2014, 2015).

Chapter 1

1.5 System-wide approaches to studying sRNAs

The development of tiled DNA microarrays, advances in RNA-sequencing technologies and the discovery of Hfq and ProQ as sRNA chaperones has led to a rapid increase in the amount of known sRNAs, particularly in model species such as E. coli and S. enterica (Tree et al., 2014; Barquist and Vogel, 2015). To better understand bacterial post-transcriptional networks however, it is important to be able to identify their function and understand the interplay between the different RNA species. A short ‘pulse’ of sRNA transcription (commonly 15 minutes) followed by RNA-seq analysis is still a widely used technique to identify sRNA targets that have altered mRNA stability. The short pulse is suggested to limit indirect effects of sRNA regulation. This is commonly followed by verification of sRNA-mRNA interactions using GFP or lacZ reporter translational fusions (Urban and Vogel, 2007; Corcoran et al., 2012). To prove direct interactions between sRNAs and candidate targets, most studies make compensatory points mutations in the sRNA and mRNA seed that destabilize the interactions when used with cognate wild type alleles but re-establish seed sequence interactions when used in combination. However direct interactions between interacting RNAs may also be verified using psoralen crosslinking, or evolutionary conservation of the base-pairing (ie: naturally occurring compensatory changes in sRNA and mRNA seeds).

Computational methods have also been developed that attempt to predict the mRNA targets of specific sRNAs (Backofen and Hess, 2010). These predictions rely on the thermodynamics of RNA hybridization but may also factor RNA structure and accessibility of seed regions. Commonly employed examples include IntaRNA and TargetRNA2 (Busch, Richter and Backofen, 2008; Kery et al., 2014; Mann, Wright and Backofen, 2017). CopraRNA is an extension of the IntaRNA program that accounts for the evolutionary conservation of the interacting seed sequences and assumes that bona fide interactions should be conserved across evolutionary related species (Wright et al., 2014). These methods yield a high number of false positives, primarily due to the short and imperfect complementarity that characterize sRNA-mRNA base-pairing and lack 38

Chapter 1

of conservation in sequences and structures associated with sRNA binding. In addition, these programs rely on already established mechanisms of sRNA binding and would not be able to account for undiscovered types of interactions. While inexpensive and rapid, computational predictions are prone to a high number of false positives and so experimental methods to find sRNA targets are required to confirm predictions.

MS2-affinity purification and sequencing (MAPS), is a method to purify sRNA- mRNA interactions by using the sRNA as a ‘bait’ (Yoon, Srikantan and Gorospe, 2012; Lalaouna and Massé, 2015). Small RNAs are tagged on their 5’ ends with the MS2-aptamer and expressed in a bacterial strain where the sRNA has been deleted. These cells are lysed and passed through a column loaded with MS2 viral coat protein fused to a maltose binding protein. As the lysate is passed through, the MS2 aptamer on the tagged sRNA and base-paired mRNA targets bind the MS2 coat protein with high affinity. These RNA duplexes, and RNA binding proteins can be eluted from the column using maltose and identified using RNA-seq, northern blotting or RT-qPCR. The use of this method led to discovery of a 3’ external transcribed spacer of tRNA acting as a sRNA sponge and in general can be used to identify the targets of a single sRNA (Lalaouna et al., 2015, 2017)

While MAPS serves as an alternative experimental method to pulse expression and sequencing for finding targets for an individual sRNA, it does not allow for global analysis of the sRNA interactome. Recently several techniques have been developed for capturing and sequencing the in vivo sRNA interactome using RNA proximity-dependent ligation. Protocols that rely on crosslinking and proximity ligation are allowing a systems-level view of sRNA function.

In Psuedomonas aeruginosa, global small non-coding RNA target identification by ligation and sequencing (GRIL-seq) was used to identify the targets of the iron regulated sRNA PrrF1. In GRIL-seq, a two-plasmid system is used, where the expression of an sRNA of interest is placed under the control of an arabinose inducible promoter. On a separate plasmid, T4 RNA-ligase is placed under the 39

Chapter 1

control of the IPTG-inducible tac promoter. Expression of ligase is first induced, followed by expression of the small RNA of interest. T4 RNA ligase ligates 3’ and 5’ ends of RNA together and facilitates the proximity-dependent ligation of the sRNA with its mRNA targets. The sRNA-mRNA chimeras are enriched from denatured total RNA by annealing to polyadenylated oligonucleotides specific to the sRNA of interest, then sequenced (Han, Tjaden and Lory, 2016). An expansion of this method, Hi-GRIL-seq, was also developed to capture the full RNA interactome in Pseudomonas aeruginosa. In this method, there is no enrichment of a single sRNA. Instead, proximity ligation by T4 RNA ligase is done on endogenously expressed sRNAs, followed by sequencing. From this, 0.29% of the reads returned were found to be chimeric hybrids. Using this method identified the interaction of the Sr0161 and ErsA sRNAs with the major porin OprD, which is responsible for the uptake of carbapenem antibiotics (Zhang et al., 2017).

RNA-interaction by ligation and sequencing (RIL-seq) (Melamed et al., 2016) and UV cross-linking and sequencing of hybrids (CLASH) are methods that use UV to crosslink RNA to an affinity tagged RNA-binding protein of interest (Helwak et al., 2013; Waters et al., 2017). Briefly, a tagged RNA-binding protein of interest is UV-crosslinked to interacting RNAs, and these complexes are purified using protein affinity purification tags introduced into the ‘bait’ RNA-binding protein. UV- crosslinked RNAs are trimmed and have linkers ligated onto either side using T4 RNA ligase. During these steps, base-paired RNA duplexes on the protein have their 5’ and 3’ ends in close proximity, and may be ligated forming one single RNA termed a chimera or hybrid. The protein of interest is digested, leaving the RNAs behind to be sequenced. Once sequenced, RNAs can be mapped to the transcriptome, and chimeras formed from proximity ligation map to two different regions of the transcriptome. These hybrid reads can be collated to get a snapshot of the global RNA interactome in one experiment. RIL-seq has been used on the RNA chaperone Hfq in E. coli and recovered 2817 statistically significant RNA-RNA interaction on Hfq, with a high recovery of known sRNA- mRNA interactions. One novel interaction that was discovered using this method and verified was a sponging interaction between PspH and Spot42 (Melamed et 40

Chapter 1

al., 2016). Scaffold proteins used for CLASH are tagged with a dual-affinity tag, allowing for more stringent purification of RNA-protein complexes. CLASH has been used on RNase E in EHEC, identifying 1733 statistically significant sRNA- mRNA interactions. Using this method, a novel interaction between the sRNA Esr41 and cirA was uncovered. Esr41 represses cirA, providing colicin resistance for E. coli (Waters et al., 2017). All the techniques described provide a snapshot of the RNA interactome under specific conditions and are powerful tools to better understand sRNA-mediated regulation of gene expression.

Chapter 1

MAPS GRIL-seq RIL-seq CLASH

pBAD pBAD tac mRNA mRNA MS2-s RN A s RN A ligas e sRNA sRNA FLAG

His 6-TEV-FLAG sc aold RBP sc aold RBP

arabinose induction in vivo proximity in vivo UV-crosslinking in vivo UV-crosslinking cell lysis ligation FLAG puri cation FLAG puri cation T4 RNA ligase sRNA

target resin FLAG MS2 sRNA Bead

MS2-sRNA capture on annealing to poly(A) oligo RNase A/T1 trimming TEV elution MS2-MBP resin magnetic oligo d(T) capture proximity and adapter ligation denaturing Ni-NTA puri cation proximity and adapter ligation MBP - T4 RNA ligase T4 RNA ligase 2 MS N TA - Bead Ni amylose resin magnetic oligo d(T) bead

maltose elution Proteinase K digestion Proteinase K digestion RNA extraction RNA sequencing RNA extraction RNA extraction RNA sequencing RNA sequencing RNA sequencing Reads Reads Reads Reads

Coordinates Coordinates Coordinates Coordinates 42

Chapter 1

Figure 1-4: System-wide approaches for discovery of sRNA targets. Schematics for MAPS, GRIL-seq, RIL-seq and CLASH are shown. Tagging a sRNA of interest with the MS2-aptamer allows for target identification using MS2 affinity purification and sequencing. In GRIL-seq T4 RNA-ligase and a sRNA of interest are co-expressed on plasmids. Proximity ligation, followed by purification of hybrids allows for target identification of a single sRNA. RIL-seq and CLASH are both used to identify RNA- interactomes. In RIL-seq, a chromosomally tagged RNA-binding protein is used as a scaffold for in vivo UV-crosslinking. Proximity ligation is used to ligate sRNAs to their targets. Protein is eluted and the remaining RNA is sequenced. In CLASH, a scaffold protein is tagged with a dual affinity tag and is UV-crosslinked to binding sRNAs. The protein is purified using two denaturing purifications to greatly reduce background. sRNA-mRNA pairs are ligated using T4 RNA ligase. Protein is eluted, and the remaining RNA is sequenced.

Chapter 1

1.6 Research aims

Enterohaemorrhagic E. coli is a significant human pathogen with multiple horizontally transferred genetic elements that add to its pathogenicity. The discovery of pathogen specific sRNAs and techniques to profile sRNA interactions transcriptome-wide have made it timely to understand their function and how they might regulate EHEC virulence.

Analysis of Hfq-binding sites on the transcriptome of EHEC str. Sakai have revealed that 11 putative sRNAs are encoded across two Shiga-toxin encoding bacteriophages (Tree et al., 2014). One of these phage-encoded sRNAs, AsxR, indirectly activates expression of the haem oxygenase ChuS by acting as a sponge for the FnrS sRNA. The 5’UTR of the outer membrane haem receptor ChuA, the gene immediately upstream of chuS, also has been shown to contain Hfq-binding sites. The binding of Hfq to the 5’UTR of chuA suggests that expression of this receptor is subject to regulation by sRNAs. This leads to the question of which sRNAs regulate chuA expression, and what the significance of this regulation is to EHEC haem uptake.

The acquisition of prophages affects the fitness of host bacteria, and is a reservoir for post-transcriptional regulators of genes on the core genome (Wang et al., 2010; Altuvia, Storz and Papenfort, 2018). The discovery of sRNAs within the StxΦ, which encode for the Shiga toxins in EHEC, makes it important to understand their role in the EHEC post-transcriptional network. The next aim of this thesis is to characterize and to find the targets for putative sRNAs found on the StxΦ.

The use of RNA-binding proteins as scaffolds for UV-crosslinking has been an important development for better understanding bacterial post-transcriptional networks. However, sRNAs that bind to neither Hfq nor ProQ sRNAs have been identified, suggesting that there are other global RBPs that have not yet been characterized (Smirnov et al., 2016). In addition, the discovery of sRNAs encoded 44

Chapter 1

only on the pathogenicity islands of EHEC suggests that other post- transcriptional regulators, such as RNA-binding proteins, may be pathogen- specific. The final aim of this thesis is to discover pathogen-specific RBPs that may contribute to EHEC post-transcriptional regulation.

Chapter 2

Chapter 2: General Materials and Methods

2.1 Bacterial strains and growth conditions Bacterial strains used in this thesis are discussed in the chapters where they were used. Unless otherwise stated, E. coli strains were grown at 37oC in LB broth (10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl. Where stated, cells were grown in minimum M9 media (12.8 g/L Na2HPO4, 3 g/L KH2PO4, 0.5 g/L NaCl, 1 g/L NH4Cl, 2 mM MgSO4, 0.1 mM CaCl2 and 0.4% glucose) or MEM-HEPES supplemented with 0.1% glucose and 250 nM Fe(NO3)3. Where necessary, ampicillin (100 µg/mL), kanamycin (50 µg/mL), tetracycline (10 µg/mL), chloramphenicol (34 µg/mL) or spectinomycin (50 µg/mL) were added.

2.2 DNA manipulation and strain construction

2.2.1 Genomic and plasmid DNA extraction

Routine genomic DNA extractions were performed using the Promega Wizard ® Genomic DNA Purification Kit (cat. no. A1120). Routine plasmid DNA extractions were performed using the Promega Wizard ® Plus SV Minipreps DNA Purification System (cat. no. A1340). The manufacturer’s protocol was followed for both kits. Purified DNA was quantified using a ThermoFisher NanoDrop 1000 UV-VIS Spectrophotometer.

2.2.2 Primer design, polymerase chain reaction and gel purification

Primers and oligonucleotides used in this study were designed using Netprimer (http://www.premierbiosoft.com/netprimer/), Snapgene (GSL Biotech LLC) or the Integrated Genome Browser (Nicol et al., 2009; Freese et al., 2016) and manufactured by Integrated DNA Technologies (IDT). Colony and routine PCRs were performed using Promega GoTaq® G2 Green Master Mix (cat no. M7822). GoTaq® PCR reactions were prepared by mixing 5 µL of GoTaq® G2 Green Master Mix with 1 µL each of 10 µM forward and reverse primer and 3 µL of MilliQ water. Colonies were picked and added directly into the reaction. For general GoTaq® PCRs, the following cycling conditions were used: initial denaturation

Chapter 2

step at 98oC for 3 minutes, followed by 25 cycles of 95oC denaturation for 10 seconds, 55oC annealing for 15 seconds, and 72oC extension for 30 seconds per kb. A final extension at 72oC for 5 minutes is done followed by an infinite hold at 12oC. High-fidelity amplification was performed using Phusion® High-Fidelity DNA Polymerase (New England Biolabs Inc., cat no. M0530S). Initial denaturation is done at 98oC for 30 seconds, followed by 30 cycles of a 95oC denaturation for 10 seconds, 55oC annealing temperature for 15 seconds and 72oC extension for 30 seconds per kb. Final extension is done at 72oC for 8 minutes before an infinite hold at 4oC. PCR products are visualized on 1-2% agarose gels stained with SYBR Safe (ThermoFisher cat no. S33102). Routine PCR product clean up and gel extractions are performed using the Wizard® SV Gel and PCR Clean-up System (cat no. A9282)

2.2.3 Restriction digests and ligation

Restriction enzymes digest reactions were prepared according to the manufacturer’s recommendations (New England Biolabs, ThermoFisher Scientific). Restriction digest reactions were incubated at 37oC for one hour. One unit of FastAP alkaline phosphatase (ThermoFisher Scientific) was added to plasmid DNA 45 minutes into the restriction digest. Enzymes are deactivated by incubating at 65oC for 20 minutes. Sticky-end and blunt-end ligations were done using ThermoFisher T4 DNA ligase in T4 DNA ligase buffer and incubated for at least 1 hour before transformation into E. coli DH5a.

2.2.4 Preparation of heat-shock competent E. coli

Heat-shock competent E. coli were prepared according to the Inoue method as detailed in (Sambrook and Russell, 2000). Briefly, DH5α or Top10F’ cells were grown overnight in LB broth at 37oC with shaking at 200 rpm. After 16 hours, cells were subcultured 1/100 in LB and grown for 8 hours under the same conditions. These were subcultured 1/50, 1/100 and 1/200 in 50 mL of SOB media and grown o overnight at 20 C. Once an OD600 of 0.55-0.65 is reached, cells were incubated on ice for 10 minutes before being pelleted by centrifuging at 2500 xg for 10 minutes at 4oC. Next, the supernatant was decanted and pellet was resuspended 47

Chapter 2

in 16 mL of chilled Inoue transformation buffer (55 mM MnCl2, 15 mM CaCl2, 250 mM KCl, 10 mM PIPES), then harvested by centrifuging again under the same conditions. Supernatant was removed, and cells were resuspended in 4 mL of Inoue transformation buffer, followed by the addition of 300 µL of DMSO. Bacteria was left on ice for 10 minutes, then 100 µL aliquots were made. These aliquots were snap-frozen in liquid nitrogen before being stored at -80oC for future use.

2.2.5 Heat-shock transformation into E. coli

Heat-shock competent E. coli were removed from -80oC and thawed on ice. Up to 2 µg of plasmid DNA is added per 50 µL of cells (volume of added to cells should not exceed 1/10 of the cell volume) and incubated on ice for 20 minutes. Cells are heat shocked at 42oC for one minute. Cells are cooled on ice for one minute before adding 800 µL of SOC media. Transformed cells are left to recover at 37oC or 30oC for 1.5 hours and then plated onto LB agar supplemented with the appropriate antibiotics.

2.2.6 Transformation into E. coli via electroporation

An overnight culture of E. coli was subcultured 1/100 in 20 mL of LB medium supplemented with appropriate antibiotics per transformation. Cells were then grown to an OD600 of 0.6, and pelleted by centrifuging at 4000 xg for 15 minutes at 4oC. Supernatant wa then removed and cells were resuspended in 1 mL of ice- cold 10% glycerol. Cells were spun down at 16000 xg for 1 minute at 4oC, followed by disposal of the supernatant. This wash was repeated twice more. Following the last wash, cells were resuspended in 100 µL of ice-cold 10% glycerol and up to 5 µg of plasmid DNA was added. These were incubated on ice for 10 minutes before being transferred into a 0.1 cm electroporation cuvette and pulsed using a BioRad Micropulser ® Electroporation apparatus at 1.8 kV. The cuvette was immediately removed and 800 µL of SOC media was immediately added to the cells. Transformed cells are incubated at 37oC or 30oC before plating onto LB agar supplemented with the appropriate antibiotics.

Chapter 2

2.3 RNA manipulation and analysis

2.3.1 RNA extraction

Routine RNA extractions from bacteria was performed using the guanidinium thiocyanate (GTC)-phenol extraction protocol described by Tollervey and Mattaj (1987) with a few modifications. Cells were grown to the desired growth phase in either LB broth, minimal M9 media or MEM-HEPES supplemented with 0.1% glucose and 250 nM Fe(NO3)3. Upon reaching the desire OD600, cells were pelleted by centrifuging at 4000 xg for 10 minutes at 4oC. Supernatant was then removed, and 1 mL of 1:1 GTC buffer-phenol (GTC buffer: 4M guanidinium thiocyanate, 2% Sarkosyl, 50 mM Tris pH 8.0, 10 mM EDTA, 1% β- mercaptoethanol) and 250 µL of 0.1 mm zirconia-silica beads were added to the pellet. Cells were lysed by vortexing, and placed in a 65oC water bath for 5 minutes. Next, cell lysate is transferred to a clean tube containing 350 µL chloroform and 120 µL of 3M NaOAc (pH 5.2). Samples were mixed by vortexing for 20 seconds and phases were separated by spinning for 5 minutes at maximum speed at room temperature. The aqueous layer was transferred into a new tube containing 550 µL of 25:24:1 phenol:chloroform:isoamyl alcohol, vortexed and spun as in the previous step. The aqueous layer was transferred into 450 µL of chloroform and is processed as in the previous steps. The aqueous layer was transferred into 1 mL of ice-cold absolute ethanol and incubated at - 80oC for 30 minutes. Samples were then spun for 30 minutes at 16000 xg at 4oC. Supernatant is removed, and the pellet was washed twice by adding 700 µL of 70% ethanol and spinning at 16000 xg for 20 minutes at 4oC. Pellets were left to air dry before resuspending in 40 µL of nuclease free H2O.

2.3.2 Reverse transcription via SuperScript Reverse Transcriptase

Reverse transcription of RNA was performed using SuperScript III or IV Reverse Transcriptase (Invitrogen, cat nos. 1808044 and 1809050) according to the manufacturer’s instructions. Briefly, 1 µL of 50 µM random hexamers or 2 µM gene-specific primer and 1 µL of 10 mM dNTP mix was added to a maximum of 5 µg of total RNA in a 13 µL reaction. This was incubated at 65oC for 5 minutes 49

Chapter 2

then on ice for 1 minute. To these, 4 µL of 5X First-Strand buffer, 1 µL of 0.1M DTT, and 1 µL of rRNasin® (Promega, cat no. N2511) and 1 µL of SuperScript RTIII or IV was added. Reactions were incubated at 50oC for 60 minutes for RT- III or 10 minutes for RT-IV. The reverse transcriptase was inactivated by incubating at 70-80oC for 10 minutes. RNA was removed by adding 5 units of RNase H (NEB, cat no. M0297S) and incubating at 37oC for 30 minutes.

2.3.3 Differential RNA-seq (dRNA-Seq)

Differential RNA-seq is used to identify transcription start sites transcriptome wide by differentiating primary and processed transcripts through the use of terminator exonuclease (TEX), which degrades RNA with 5’-monophosphates but not transcripts with 5’-triphosphates (Sharma et al., 2010). E. coli O157:H7 str. Sakai stx- was grown to an OD600 of 0.8 in MEM-HEPES supplemented with

0.1% glucose and 250 nM of Fe(NO3)3. In a 15 mL centrifuge tube, 6 mL of RNAProtect Bacteria Reagent (Qiagen, cat. No. 76506) was added to 3 mL of bacterial culture. RNA was extracted using the RNeasy® Mini Kit (Qiagen, cat. no. 74104) following Protocol 4 of the RNAProtect Bacteria Reagent handbook, and Protocol 7 of the RNeasy® Mini Kit handbook provided by the manufacturer. RNA was quality checked using an RNA Nano 6000 chip (Agilent) in an Agilent 2100 Bioanalyzer.

Extracted RNA was sent to Vertis Biotechnologie (Freising, Germany) for dRNA- seq. Upon arrival, total RNA was depleted of ribosomal RNA using a Ribo-Zero rRNA Removal Kit (Illumina). The sample was split into two, with one being treated with Terminator RNA exonuclease (TEX; Epicentre), the other being an untreated control. Both samples were then treated with TAP to remove pyrophosphates from 5-triphosphorylated ends, converting then into monophosphates. Oligonucleotide adapters were ligated to the 5’ and 3’ ends of the RNA samples, and cDNA synthesis was done using M-MLV reverse transcriptase. The cDNA libraries were amplified via PCR with a high-fidelity polymerase, then paired-end sequenced on an Illumina NextSeq500 platform.

Chapter 2

The quality of raw sequence data was checked using FastQC (Andrews et al., 2010). Adapters were trimmed using Flexbar v3.5.0 (Dodt et al., 2012). Sequences were aligned to the E. coli O157:H7 str. Sakai genome (accession number NC_002695.1) using the novoalign command from Novocraft v3.04.06. Read counts were calculated using the pyReadCounters.py tool of the pyCRAC suite of scripts (Webb et al., 2014). Transcription start sites were called using TSSPredator v.1.06 (Dugar et al., 2013).

Chapter 3

Chapter 3: sRNA regulation of the outer membrane haem receptor chuA

3.1 Introduction Nutritional immunity is one of the first lines of defence against bacterial infection (Weinberg, 1975; Hood and Skaar, 2012). Iron is a nutrient that is essential for pathogenic bacteria to infect and thrive in a mammalian host due to its role as a cofactor in many enzymes, such as those for DNA replication, transcription, and central metabolic processes (Evstatiev and Gasche, 2012). As such, the host sequesters iron intracellularly through molecules such as haem and ferritin, and have extracellular high affinity iron binding molecules such as transferrin in serum or lactoferrin at mucosal surfaces. These ensure that the concentration of extracellular free iron in the host is too low to support bacterial growth and infection. In order to circumvent this nutritional deficit, bacteria are able to scavenge iron from these iron-binding molecules. Siderophores for example, can remove iron from transferrin or ferritin and then be recognized by membrane- bound siderophore receptors (Ellermann and Arthur, 2017). Transferrin or lactoferrin receptors can also mediate bacterial uptake of iron (Morgenthau et al., 2013).

The ability to uptake haem is essential for the virulence of several bacterial pathogens. Camplylobacter jejuni, a common cause of food poisoning, is unable to synthesise its own siderophores, and requires the TonB-dependent exogenous enterobactin siderophore receptor CfrA (Naikare et al., 2013). C. jejuni also contains an outer membrane haem receptor, chuA, which is essential for its growth in environments where hemin is the main iron source (Ridley et al., 2006). Haemophilus influenzae is an example of a haem auxotroph that requires haem for colonisation. H. influenzae acquires haem from haem-haemopexin complexes through an outer membrane receptor HxuC, and a two-component secretion system HxuB/HxuA. HxuA captures the haem-haemopexin complex and haem is separated from the complex by HxuC (Fournier, Smith and Delepelaire, 2011; Zambolin et al., 2016). Uropathogenic E. coli (UPEC) requires outer membrane haem receptors such as chuA and hma for colonization of the urinary tract (Torres et al., 2001; Hagan and Mobley, 2009). One UPEC strain, CFT073, encodes for 52

Chapter 3

at least 14 different outer membrane iron receptors, stressing the importance of iron acquisition for this pathotype (Garcia, Brumbaugh and Mobley, 2011). The use of an outer membrane haem receptor to scavenge haem from the host is common among gram-negative bacteria, and are found in other important human pathogens such as Yersinia pestis, Psudomonas aeruginosa, Bordetella pertussis and Neisseria gonorrhoeae (Turner et al., 1998; Johnson, Ochsner and Vasil, 2000; Rossi et al., 2001; Vanderpool and Armstrong, 2001)

Enterohaemorrhagic Escherichia coli (EHEC) is a gram-negative enteric pathogen and is the causative agent of haemorrhagic colitis which can progress into haemolytic uremic syndrome. The primary reservoir of this pathogen are ruminants such as cattle and as such transmission mainly occurs via the consumption of infected material. Similar to other pathogens, EHEC requires iron for survival and optimal growth. One of the primary strategies for EHEC haem uptake within the host is through the expression of a haem uptake operon that is upregulated during iron limitation (Torres and Payne, 1997). The Chu locus is homologous to the Shu locus found in Shigella, and contains genes that allow for haem uptake, transport, utilization and degradation. The locus consists of a bicistronic operon (chuAS) as well as two polycistronic operons (chuTWXY and chuUV). ChuA is a TonB-dependent outer membrane haem receptor that imports haem into the periplasm. ChuA couples with the inner membrane TonB-ExbB- ExbD complex and uses the protein motive force generated by the Ton complex to allow transport of haem across the outer membrane (Celia et al., 2016). The periplasmic binding protein ChuT binds to haem, and via the ABC transporter ChuUV, is shuttled into the cytoplasm. Haem is processed by the haem oxygenase ChuS, which converts haem into biliverdin, carbon monoxide and free iron. High concentrations of haem is toxic to the cell (Anzaldi and Skaar, 2010), and so processing by ChuS prevents its accumulation. In an anaerobic environment, this role is taken up by the SAM-methyltransferase ChuW and the anaerobilin reductase ChuY (LaMattina, Nix and Lanzilotta, 2016; LaMattina et al., 2017).

Chapter 3

The haem uptake operon is part of a larger regulatory network that allows EHEC to achieve iron homeostasis. Transcription of chuA is controlled by the iron- dependent transcriptional repressor Fur. In iron-rich conditions, Fur binds to iron which increases the affinity of Fur for its DNA-binding site ~1000-fold (Bags and Neilands, 1987). Fur typically binds to sites in between the -35 and -10 sites of a promoter and causes transcriptional repression of its targets. A consensus binding sequence has not been established. Instead, a Fur box is characterised as being three repeats of a 6-bp motif or two overlapping heptamer inverted repeats (Escolar, Pérez-Martín and De Lorenzo, 1998; Baichoo and Helmann, 2002). In iron-poor conditions, Fur is no longer bound to iron, allowing for the transcription of genes previously bound by Fur. In H. pylori, iron-free Fur (Apo- Fur) has also been shown to repress its own set of genes and has its own regulon (Ernst, Bereswill, et al., 2005; Ernst, Homuth, et al., 2005). Fur also regulates a small non-coding RNA (sRNA) RyhB that is central to maintaining iron homeostasis. Small RNAs are non-coding RNAs approximately 50-300 nucleotides in length that are essential components of the bacterial posttranscriptional network. RyhB prevents the expression of non-essential proteins that require Fe as a cofactor such as the TCA cycle and respiration genes sdhC and fumAC, as well as iron storage genes such as bacterioferritin. Genes that contribute to iron uptake, such as the shikimate permease shiA and the colicin I receptor cirA, are positively regulated by this sRNA.

EHEC only needs to produce and utilize its haem uptake operon when it is in an iron-poor, haem-rich host. For that reason, as well as the toxicity that accompanies haem and iron over accumulation, it is necessary for this pathogen to precisely control expression of this operon. FourU RNA-thermometers are used in different bacterial pathogens, such as Shigella dysenteriae, Yersinia pseudotuberculosis, Vibrio cholerae, and Salmonella typhimurium, to regulate expression of their virulence factors in a temperature-dependent manner (Waldminghaus et al., 2007; Kouse et al., 2013; Weber et al., 2014; Righetti et al., 2016). A FourU RNA-thermometer was found in chuA that blocks the Shine- Dalgarno sequence and is formed at temperatures below 37oC, indicating that the pathogen is outside of the host (Kouse et al., 2013). This precision in control 54

Chapter 3

over chuA prevents its needless expression during transmission or in non-host environments.

Hfq-CRAC (crosslinking and analysis of cDNA) is a method that allows for identification of Hfq-binding sites transcriptome wide. In this method, a chromosomally tagged Hfq is UV-crosslinked to RNA in vivo. Hfq-RNA complexes are co-purified under denaturing conditions, and the recovered RNA is sequenced (Tree et al., 2014). The use of Hfq-CRAC in EHEC showed that the phage-encoded sRNA AsxR positively regulates the haem oxygenase chuS indirectly by sponging interactions with the negatively regulating sRNA, FnrS. The same experiment showed that the 5’UTR of chuA binds strongly with the RNA chaperone Hfq, making it a candidate for regulation via sRNAs. This chapter aims to find sRNAs that regulate chuA, as well as to investigate other modes of post-transcriptional regulation that might occur on this gene.

3.2 Materials and Methods

3.2.1 Bacterial strains and culture conditions

Bacterial strains, oligonucleotides and plasmids used for this study are listed in Tables 1-3. E. coli was routinely grown at 37oC in liquid Luria-Bertani (LB) broth or on solid LB-agar plates. Bacterial media was supplemented with ampicillin (100 µg/mL) or chloramphenicol (34 µg/mL) where appropriate. For inhibition of Rho termination, bicyclomycin benzoate (50 µg/mL) was added to exponential phase (OD600 = 0.6) cultures for 30 minutes before being assayed.

Chapter 3

Table 3-1: Strains used in Chapter 3

Serotype Strain Genotype Reference E. coli O157:H7 Sakai Δstx1 stx2A::kan, KanR (Dahan et al., 2004) fhuA2 lac(del)U169 phoA glnV44 80' lacZ(del)M15 gyrA96 recA1 relA1 endA1 thi-1 E. coli K12 DH5α hsdR17 (Taylor, Walker and Mclnnes, 1993)

E. coli O6:K2:H1 CFT073 (Welch et al., 2002)

Table 3-2: Plasmids used in Chapter 3

Plasmid Description Reference (Corcoran et al., pXG10SF PLtetO-1 sfGFP fusion vector, CmR 2012) Expressys, pZE12luc PLlacO-1 luciferase expression vector, ApR Germany scrambled sRNA control for pZE12 expressed pJV300 sRNAs (Sittka et al., 2007) pXG10SF::chuA chuA -328 -> +15 This study pZE12::CyaR pZE12 with CyaR cloned at the +1 site This study pZE12::SgrS pZE12 with SgrS cloned at the +1 site This study pZE12::RyeB pZE12 with RyeB cloned at the +1 site This study pZE12::ChiX pZE12 with ChiX cloned at the +1 site This study pZE12::RyhB pZE12 with RyhB cloned at the +1 site This study pZE12::DicF pZE12 with DicF cloned at the +1 site This study pZE12::sroC pZE12 with sroC cloned at the +1 site This study pZE12::MicC pZE12 with MicC cloned at the +1 site This study pZE12::FnrS pZE12 with FnrS cloned at the +1 site This study pZE12::AsxR pZE12 with AsxR cloned at the +1 site (Tree et al., 2014) pXG10SF::chuA.G298 C chuA -30 G -> C This study pXG10SF::chuA.T310 chuA -18 T -> A; destabilizes RNA A thermometer This study pXG10SF::chuA.81- 328 chuA -247 -> +15 This study pXG10SF::chuA.152- 328 chuA -176-> +15 This study pXG10SF::chuA.214- 328 chuA -114 -> +15 This study pXG10SF::chuA.253- 328 chuA -75 -> +15 This study pXG10SF::chuA- ChiX.M1 chuA -177 -> -175 AGA -> CCT This study pXG10SF::chuA- ChiX.M2 chuA -144 -> -142 CAG -> CCT This study

Chapter 3

pXG10SF::chuA- ChiX.M3 chuA -137 -> -135 TTT -> GGG This study pXG10SF::chuA- AsxR.M1 chuA -133 -> -131 ATG -> CGA This study pZE12::CyaR.C27G CyaR +27 C -> G This study pZE12::ChiX.M1 ChiX +47-49 TCT-->AGG This study pZE12::ChiX.M2 ChiX +41-43 AAA-->CCC This study pZE12::AsxR.M1 AsxR +19-21 CAT-->TCG This study

Table 3-3: Oligonucleotides used in Chapter 3

Primer Sequence Notes 5’- BS_ChuA.5UTRcds. GCGAAGATGCATTTGATCATAAAAA NsiI.F ATTGATCGA-3’ cloning chuA into pXG10SF 5’- BS_ChuA.5utrCDS. GAAAAAGCTAGCGGCCAATAAACT cloning EHEC chuA into NheI.R CAAACGCAAC-3’ pXG10SF 5' - BS_UPEC_ChuA_5 GAAAAAGCTAGCCAACAAACTCAAA cloning UPEC chuA into utrCDS.NheI.R CGCAACGAGG - 3' pXG10SF 5’- GCTGAAAAACATAACCCATAAAATG cloning CyaR into pZE12luc; BS_CyaR.5P.F C-3’ 5' phosphorylated 5’- GAAAAATCTAGACGGTTATCAGGGA BS_CyaR.XbaI.R TAGGGC-3’ cloning CyaR into pZE12luc 5’- cloning DicF into pZE12luc; BS_DicF.5P.F TTCTGGTGACGTTTGGCGGCATC-3’ 5' phosphorylated 5’- GAAAAATCTAGACACTGCATCACAA BS_DicF.XbaI.R AATTCAC-3’ cloning DicF into pZE12luc cloning MicC into pZE12luc; BS_MicC.5P.F 5’-GTTATATGCCTTTATTGTCAC-3’ 5' phosphorylated 5’- GTTTTTTCTAGACACCGATTAAATG BS_MicC.XbaI.R CTCTGGA-3’ cloning MicC into pZE12luc 5’- GATGAAGCAAGGAGGTGCCCCATT cloning SgrS into pZE12luc; BS_SgrS.5P.F C-3’ 5' phosphorylated 5’- GTTTTTTCTAGAGTACACCGTCCGA BS_SgrS.XbaI.R TACTCAATC-3’ cloning SgrS into pZE12luc pLlacO-B 5’- CGCACTGACCGAATTCATTAA-3’ linearising pZE12luc pLlacO-D 5’-GTGCTCAGTATCTTGTTATCCG-3’ linearising pZE12luc 5'- BS_81- GCGAAGATGCATAAGGGATAACGC cloning chuA-T1 into 328_ChuA_5UTR_F ACAGCTCTCTTC-3' pXG10SF 5'- BS_152- GCGAAGATGCATAGGGCGTGCTTT cloning chuA-T2 into 328_ChuA_5UTR_F TGCGTAAT-3' pXG10SF 5'- BS_214_328_ChuA GCGAAGATGCATAACTCACCCGGC cloning chuA-T3 into _5UTR_F GAATATTATC-3' pXG10SF 57

Chapter 3

5'- BS_253_328_ChuA GCGAAGATGCATAATTTATTTTTCG cloning chuA-T4 into _5UTR_F CCAGCCACC-3' pXG10SF for sequencing of pXG ZeCat 5'-TGGGATATATCAACGGTGGT -3' plasmids 5'- for generating chuA point BS_ChuA_G298C_ CATGAGGAAAATAAAAAATGGTGGC mutation at the CyaR F TGCCTGGGTTAATAC-3' binding site 5'- for generating chuA point BS_ChuA_G298C_ GTATTAACCCAGGCAGCCACCATTT mutation at the CyaR R TTTATTTTCCTCATG-3’ binding site 5'- AAACATAACCCATAAAATGGTAGCT for generating CyaR point BS_CyaR_C27G_F GTACCAGGAACCAC-3' mutation 5'- GTGGTTCCTGGTACAGCTACCATTT for generating CyaR point BS_CyaR_C27G_R TATGGGTTATGTTT-3' muation 5'- for generating chuA point TCTCCATGAGGATAATAAAAAATGC mutation that disrupts the BS_ChuA_T310A_F TGGCTGCCTGG-3' RNA thermometer 5'- for generating chuA point CCAGGCAGCCAGCATTTTTTATTAT mutation that disrupts the BS_ChuA_T310A_R CCTCATGGAGA-3' RNA thermometer 5'- TTTATATCCCATGAAATTGCAGGAT BS_ChuA_CAG184 AACTCGGCAATTACGCAAAAGCAC- for generating chuA point CCT_F 3' mutation at ChiX-M1 site 5'- BS_ChuA_CAG184 GTGCTTTTGCGTAATTGCCGAGTTA for generating chuA point CCT_R TCCTGCAATTTCATGGGATATAAA-3' mutation at ChiX-M1 site 5'- AATATCGCTATTGGCCCGTCAACCT BS_ChiX_TCT47AG GGAATTTCATTTTTTTATTATTATGC for generating ChiX-M1 G_F CGTCA-3' point mutation 5'- TGACGGCATAATAATAAAAAAATGA BS_ChiX_TCT47AG AATTCCAGGTTGACGGGCCAATAG for generating ChiX-M1 G_R CGATATT-3' point mutation 5'- GTTTACAAGCGTTTATATCCCATGC BS_ChuA_TTT191G CCTTGCCTGATAACTCGGCAATTAC for generating chuA point GG_F G-3' mutation at ChiX-M2 site 5'- CGTAATTGCCGAGTTATCAGGCAA BS_ChuA_TTT191G GGGCATGGGATATAAACGCTTGTA for generating chuA point GG_R AAC-3' mutation at ChiX-M2 site 5'- CGCTATTGGCCCGTCAAAGAGGAA BS_ChiX_AAA41CC GGGCATTTTTTTATTATTATGCCGT for generating ChiX-M2 C_F CACTTTAA-3' point mutation 5'- TTAAAGTGACGGCATAATAATAAAA BS_ChiX_AAA41CC AAATGCCCTTCCTCTTTGACGGGCC for generating ChiX-M2 C_R AATAGCG-3' point mutation 5'- GGTGAGTTTACAAGCGTTTATATCC BS_ChuA_ATG195 TCGGAAATTGCCTGATAACTCGGCA for generating chuA point CGA_F ATTAC-3' mutation at AsxR-M1 site 58

Chapter 3

5'- GTAATTGCCGAGTTATCAGGCAATT BS_ChuA_ATG195 TCCGAGGATATAAACGCTTGTAAAC for generating chuA point CGA_R TCACC-3' mutation at AsxR-M1 site 5'- BS_AsxR_CAT19T GGTTCGGGCAATTGCCGAAGATAC for generating AsxR point CG_F TCGTTTTAATAATCGGTGCTCAG-3' mutation 5'- BS_AsxR_CAT19T CTGAGCACCGATTATTAAAACGAGT for generating AsxR point CG_R ATCTTCGGCAATTGCCCGAACC-3’ mutation

3.2.2 In silico prediction of interacting sRNAs

A list of published sRNAs present in enterohaemorrhagic E. coli was taken from the Bacterial Small Regulatory RNA Database (Li et al., 2013). sRNAs listed as not being Hfq-binding were filtered out, and the sequences of the remaining sRNAs were input into IntaRNA (Busch, Richter and Backofen, 2008; Mann, Wright and Backofen, 2017) to search for interactions with the 5’UTR of chuA. sRNAs that were predicted to bind to regions of the chuA 5’UTR that were not Hfq-binding were removed from consideration.

3.2.3 Construction of GFP-translational fusions and sRNA expression vectors for testing sRNA-mRNA interactions

pXG10SF, which encodes for superfolder GFP under the control of the PLtetO-1 promoter was extracted from DH5a using the Wizard® Plus SV Miniprep DNA purification system (Promega) according to the manufacturer’s protocol. Full- length and truncated versions of the chuA 5’UTR plus the first 45 base pairs of the coding sequence were amplified from genomic DNA extracted from E. coli O157:H7 Sakai stx- using primers BS_ChuA.5UTRcds.NsiI.F and BS_ChuA.5utrCDS.NheI.R that incorporate NsiI and NheI restriction sites to the 5’ and 3’ ends, respectively. Both purified plasmid and extracted PCR amplicons were digested with NsiI and NheI (FastDigest, ThermoFisher) and ligated with T4 DNA ligase (ThermoFisher) (Section 2.2.3) .

Candidate Hfq-binding sRNAs were chosen using in silico predictions with IntaRNA. sRNAs predicted to interact with Hfq-binding regions of the chuA 5’UTR

Chapter 3

(Section 3.2.2) were cloned into pZE12 according to the method described in (Urban and Vogel, 2007). Briefly, a fragment of the pZE12-luc plasmid was amplified using primers pLlacO-B and pLlacO-D Phusion® High-Fidelity DNA polymerase and digested with XbaI (Fast Digest, ThermoFisher) (Section 2.2.2 and 2.2.3). Digestion of the fragment yields two products, the larger of which is used as a backbone for cloning. Candidate sRNAs MicC, DicF, SgrS and CyaR were PCR amplified from genomic DNA using primer pairs BS_MicC.5P. and BS_MicC.XbaI.R, BS_DicF.5P.F and BS_DicF.XbaI.R, BS_SgrS.5P.F and BS_SgrS.XbaI.R and BS_CyaR.5P.F and BS_CyaR.XbaI.R, respectively, designed to start from their +1 sites. Forward primers incorporate a 5’ phosphate for cloning, while reverse primers incorporate an XbaI site for digestion. The PCR amplicon is then digested and ligated into the digested pZE12 amplicon to yield the sRNA plasmids under the control of the PLlacO-1 promoter.

Mutations were made in the chuA or sRNA sequences by using the Quikchange® XL mutagenesis kit (Agilent) according to the method specified by the manufacturer. Primers for mutagenesis were designed using the Quikchange® primer design program.

All plasmids generated were confirmed to contain the insert using colony PCR and Sanger sequencing.

3.2.4 Confirmation of sRNA-chuA interactions using the sfGFP 2-plasmid system

The expression of sfGFP was monitored and quantified with and without candidate sRNAs using a BD FACSCanto II or a BD LSRFortessa™ Special Order Research Product cell analyser. Fluorescence was measured using a 530/30 nm bandpass filter. FSC and SSC were also measured to gate the bacterial population. For each sample, at least 100,000 events were recorded. Data was analysed using FlowJo software (BD) and statistics were calculated using Prism 8 (GraphPad) to obtain each sample’s mean median fluorescence

Chapter 3

intensity (mean MFI). p-values were calculated using a standard two-tailed student’s t-test.

Plasmids expressing the wild type or mutant chuA-GFP translational fusion and those expressing candidate sRNAs were co-transformed into E. coli DH5α as described in section 2.2.5. Three colonies from each transformation were purified and grown overnight in LB broth. Overnight cultures are subcultured 1/100 in 0.22

µm filtered LB broth and grown to exponential phase (OD600 = 0.6). These are diluted five-fold in 0.22 µm-filtered PBS, then read on the flow cytometer as described above. Plasmid pJV300 expressing a scrambled sRNA, and pXG1 and pXG0 plasmids expressing GFP and Lux, respectively, were used as controls.

3.2.5 Secondary structure prediction

The secondary structure for the 5’UTR of chuA was predicted using the mfold (unafold.rna.albany.edu/?q=mfold) and RNAfold (rna.tbi.univie.ac.at/cgi- bin/RNAWebSuite/RNAfold.cgi) webservers (Zuker, 2003; Lorenz et al., 2011) . Figures were drawn on RNAStructure version 6.0.1 (Reuter and Mathews, 2010).

3.2.6 Prediction of Rho-utilization sites in EHEC

To predict genome-wide Rho-utilization sites, the genome sequence of E. coli O157:H7 str. Sakai was used as input for RhoTermPredict (Di Salvo et al., 2019). To predict rut sites, the software analyses sliding 78 nt windows genome wide for sites where C to G ratios were greater than 1 and contained regularly spaced (11-13 nt) C residues, a proposed motif for rho binding (Allfano et al., 1991; Mitra et al., 2017). The software then analyses a 150 nt window downstream of the putative rut site for potential RNA polymerase pause sites. Pause sites were defined as hairpin structures with a GC-rich stem and an 4-8 nucleotide loop, or pause-inducing sequence element G-11G-10(C/T)-1G+1, where the -1 refers to the position of the RNA 3’ end (Fredrickson et al., 2014; Vvedenskaya et al., 2014). A higher score was assigned to sequences where a hairpin was in closer proximity to the consensus pause site.

Chapter 3

3.3 Results

3.3.1 The EHEC outer membrane haem receptor ChuA is subject to regulation by sRNAs

Hfq-CRAC uses UV-crosslinking, denaturing purification and sequencing to reveal Hfq-binding sites transcriptome wide (Tree et al., 2014). Point deletions found in the sequencing data correspond to sites of direct Hfq-binding. Analysis of a previous Hfq-CRAC dataset reveals that the outer membrane haem receptor chuA strongly binds to Hfq (Figure 3.1A). ChuA is encoded on a bicistronic operon that also encodes for the haem oxygenase chuS, which is directly regulated by the sRNAs RyhB and FnrS, and indirectly by the phage-encoded anti-sRNA AsxR in EHEC (Tree et al., 2014). It has previously been found that the 5’UTRs of UPEC and EHEC chuA are 85% similar, with a majority of sequence divergence being found between the +100 and +247 region. Specifically, a 74-bp motif in UPEC chuA beginning at +169 was replaced with a different 73-bp sequence, likely due to a recombination event involving the inverted 6-bp regions that flank this site (Nagy et al., 2001). Upon observation, it was found that one of the major Hfq-binding sites located at +189-+217 of the EHEC chuA 5’UTR lies along this region of sequence divergence (Figure 3.1B) (Nagy et al., 2001; Kouse et al., 2013). Predictions using IntaRNA revealed that AsxR may bind to the +189-+203 region of EHEC chuA, which overlaps the divergent 73-bp motif (Figure 3.2D). It is possible that the divergent motif was maintained by EHEC in order to allow for regulation by pathotype-specific sRNAs such as AsxR. Furthermore, this suggests that the outer membrane haem receptors of EHEC and UPEC may be subject to post-transcriptional regulation by different repertoires of sRNAs.

To identify sRNAs that may regulate EHEC chuA, a list of 160 sRNAs present in EHEC was taken from the Bacterial Small Regulatory RNA Database (BSRD), a repository for published sRNA sequences across 957 genomes (Li et al., 2013). Of the 160 sRNAs retrieved from the database, 44 were Hfq-binding, and were investigated further. IntaRNA was used to predict the interaction between these

Chapter 3

44 sRNAs and the EHEC chuA 5’UTR. To further narrow down this list, the chuA 5’UTR regions where the sRNAs were predicted to bind were searched for Hfq- binding sites. Small RNAs that were predicted to bind to regions that did not correspond to the direct Hfq-binding sites of chuA were discarded. This analysis yielded 10 sRNAs that may interact and have a regulatory effect on chuA.

The 5’UTR of EHEC chuA, starting from the +1 site up until the 15th codon of the coding sequence was cloned into the plasmid pXG10SF. The chuA-GFP translational fusion was co-transformed into E. coli DH5α with pZE12 plasmids expressing the candidate regulatory sRNAs and fluorescence was measured using a flow cytometer (Section 3.2.4). In the DH5α background (tetR- lacI-), both

PLtetO-1 and PLlacO-1 promoters are de-repressed leading to high levels of constitutive transcription. Of the 10 candidate sRNAs, 3 (CyaR, ChiX, AsxR) showed statistically significant differences in reporter fluorescence, indicating a regulatory effect on chuA-GFP. CyaR upregulated chuA-GFP translation by 30%, while ChiX and AsxR reduced fluorescence by 42% and 37% (Figure 3.1C). These sRNAs were studied in more detail

3.3.2 CyaR, ChiX and AsxR interact with both UPEC and EHEC chuA

The +100-+247 region of sequence divergence between EHEC and UPEC chuA described in Section 3.3.1 includes the predicted binding sites for ChiX (+131- +156) and AsxR (+184-+197) while CyaR binds to the conserved region between +290 and +305. It was therefore possible to do a preliminary test for whether the sequence divergence between UPEC and EHEC chuA 5’UTRs allowed for regulation by different sets of sRNAs (Figure 3.1B).

The effect of CyaR, ChiX and AsxR on the 5’UTR from UPEC chuA was tested by amplifying the 328 nucleotides of the 5’UTR plus the first 15 nucleotides of the chuA coding region from UPEC strain CFT073 and cloning into the pXG10SF GFP-translational fusion vector (Section 2.2.2-3). Fluorescence was assayed in the same manner as above. The UPEC chuA GFP-translational fusion gave 1.2- fold higher level of basal fluorescence compared to EHEC chuA (Figure 3.1D)

Chapter 3

indicating differences in chuA expression between the pathotypes. However, there were no significant changes in the fold-change regulation of UPEC and EHEC chuA translational fusions by CyaR, ChiX and AsxR. These results indicate sequence variation in the chuA 5’ UTR may lead to higher expression of

ChuAUPEC compared to ChuAEHEC due to differences in post-transcriptional regulation. However, despite sequence variation around one of the Hfq-binding sites, both mRNAs are subject to similar Hfq-dependent sRNA regulation by CyaR, ChiX and AsxR.

Chapter 3

Figure 3-1: The outer membrane haem receptor chuA is regulated by trans- encoded sRNAs. A. Analysis of Hfq-CRAC data generated in (Tree et al., 2014) reveals that the 5’UTR of chuA binds to Hfq. RNA-sequencing data reads (red) and point deletions (green) are indicated. B. Aligning the chuA 5’UTR sequences of UPEC and EHEC shows sequence variation at an Hfq-binding site. Fur box, putative binding sites for CyaR, ChiX and AsxR, as well as the RNA-thermometer and start codon are indicated. C. Fluorescence of a chuAEHEC-sfGFP fusion was measured in the presence of putative binding sRNAs. JV300 is pZE12 containing a scrambled sRNA and is used as a control. D. Fluorescence of chuAEHEC and chuAUPEC sfGFP translational fusions were measured in the presence of CyaR, ChiX and AsxR. Fluorescence represents the mean median fluorescence intensity of three biological replicates (*p < 0.05, ** p < 0.01, *** p < 0.001).

Chapter 3

3.3.3 CyaR directly interacts with the chuA 5’ UTR

To test for direct interactions between chuA and the candidate regulatory sRNAs, point mutations were made in either the mRNA or the sRNA that were predicted to abolish the sRNA-mRNA interaction (Figure 3.2, left panels). Point mutations in the small RNA are intended to disrupt sRNA seed pairing, but may also have also disrupt overlapping functional domains. To minimise the chance of disrupting other functonal domains, point mutations were introduced into an unstructured region of the sRNA. The mutations were made to be compensatory, such that base-pairing between the sRNAs and chuA should be restored when the mutants are tested together. Compensatory point mutations were made in AsxR and its predicted binding site in chuA (Figure 3.2D). However, upon measuring fluorescence, the observed effect of the wild-type chuA-AsxR interaction was inconsistent with the initial screen and further research into this interaction was not pursued.

A predicted trinucleotide compensatory mutation was made for the top scoring ChiX-chuA interaction as predicted by IntaRNA (Figure 3.2B and C). The M1 mutation made in ChiX resulted in the de-repression of chuA-GFP expression. However, this effect was not observed when the compensatory chuA M2 mutation was introduced (Figure 3.2B). This suggested that while the nucleotides mutated in ChiX contributed to chuA regulation, they did not interact with the predicted chuA interaction site. A second set of compensatory point mutants were made for the next highest scoring interaction (ΔG=-3.87 kcal/mol) that used the same ChiX seed region that was previously tested (Figure 3.2C). Mutations in either chuA or ChiX alone resulted in the disruption of the chuA-ChiX interaction, but testing the mutants together did not restore the repression of chuA. These results indicated that while ChiX effects translation of chuA, it is not due to a direct interaction with the chuA M1 or M2 site and may occur indirectly.

CyaR is predicted to bind approximately 15 nucleotides upstream of the chuA Shine-Dalgarno sequence (Figure 3.2A). Unlike the previous two sRNAs, CyaR activated translation of chuA 1.3-fold (Figure 3.1C). Mutating CyaR at the M1 site

Chapter 3

reduced the activation caused by the CyaR-chuA interaction by 10%, while the point mutation made on chuA completely abolished the interaction. Compensatory point mutations partially restore the translational activation, verifying the observed interaction from the initial screen and demonstrating that CyaR directly binds to the 5’UTR of chuA to de-repress its translation (Figure 3.2A).

3.3.4 CyaR interaction with chuA is not dependent on temperature

Regulation of chuA occurs on both the transcriptional and the translational level. Transcription of chuA is inhibited by Fur when cells are grown in iron-rich conditions (Torres and Payne, 1997), while translation of the chuA transcript varies depending on the environmental temperature. At 25oC, translation is inhibited by the formation of a FourU RNA thermometer that occludes the ribosomal binding site (Kouse et al., 2013). This inhibitory structure lies ~15 nucleotides downstream of the CyaR binding site. To determine whether CyaR- mediated activation of chuA translation occurs by inhibiting the formation of this RNA-thermometer, a point mutation known to disrupt the formation of the FourU hairpin loop was made on the 5’UTR of the chuA-GFP translational fusion (Kouse et al., 2013). Expression of wild type and mutant chuA-sfGFP translational fusion was monitored in the presence and absence of CyaR. Disruption of the RNA thermometer via the U310A point mutation resulted in a 2.6-fold increase in fluorescence at 25oC compared to the wild-type confirming the FourU temperature-dependent regulation of chuA translation. Disruption of this secondary structure however did not affect CyaR-mediated activation of chuA, as a 1.6-fold increase in fluorescence was observed when CyaR was present in the U310A mutant of chuA (Figure 3.3B-C). This demonstrated that CyaR- mediated activation of chuA translation is not epistatic with the FourU structure and likely does not activate chuA by preventing the formation of the RNA- thermometer.

Chapter 3

Figure 3-2: CyaR, but not AsxR or ChiX, regulates chuA translation through direct binding. A-D. Left panels shows base pairing of chuA-sRNA pairs predicted using IntaRNA (Mann, Wright and Backofen, 2017). Compensatory point mutations on either the sRNA or the mRNA were made that are predicted to disrupt the predicted sRNA-mRNA interaction (M1-M4) for A. CyaR, B-C. ChiX and D. AsxR. On the right, chuA-sfGFP fluorescence was measured in the presence of either the mRNA or sRNA point mutants. An interaction is direct if pairing compensatory point mutants restores the wild-type interaction. Fluorescence represents the mean median fluorescence intensity of three biological replicates. (*p < 0.05). 68

Chapter 3

Figure 3-3: CyaR does not activate chuA by preventing RNA-thermometer formation. A. A point mutant known to disrupt RNA-thermometer formation (Kouse et al., 2013) was introduced into the chuA-sfGFP translational fusion. B- C. Fluorescence for the chuA-sfGFP translational fusions with and without the RNA thermometer was measured with and without CyaR at 25oC. Fluorescence represents the mean median fluorescence intensity of three biological replicates (* p < 0.05 ** p < 0.01).

3.3.5 The 5’UTR of chuA contains a sequence that inhibits its expression

While the activation of chuA translation by CyaR is not due to disruption of the RNA thermometer, in silico folding predictions on the 5’UTR of chuA using mfold and RNAfold showed various secondary structures forming throughout the 5’UTR, including some that occluded sequences downstream of the ribosomal binding site. To understand whether the CyaR-chuA interaction affects secondary structure formation, 4 truncations were made in the chuA 5’UTR, cloned into the GFP-translational fusion vector and fluorescence was tested in the presence and absence of CyaR.

Chapter 3

Predicted secondary structure and the presence of (ARN)x motifs were used as guides in making truncates of the chuA 5’UTR. Binding of RNA to the distal face of Hfq is stabilized by the presence of the (ARN)x motifs, where A is an adenine, R is a purine, and N is any nucleotide (Soper and Woodson, 2008; Zhang et al.,

2013; Schu et al., 2015). A search through the 5’UTR of chuA showed 10 (ARN)4 sites with one mismatch (ARN4M1), and this was used as one of the bases for making the truncates. The location of the motifs do not correspond to the regions of direct Hfq-binding as indicated by Hfq-CRAC, though this is can be explained by chuA binding occurring on the rim region of Hfq (Schu et al., 2015) The first truncate (T1) begins at +81 and removes the first 80 nucleotides of the 5’UTR.

This removes a section that forms two hairpins as well as 9 out of the 10 ARN4M1. The second (T2) and third (T3) truncates begin at +152 and +214, respectively, and each one of these removes another major hairpin from the overall predicted secondary structure while maintaining the RNA-thermometer. The second truncate T2 also removes the last ARN4M1 motif. The fourth truncate (T4) begins at 253 and leaves only the CyaR binding site, the RNA-thermometer, RBS, and the start codon (Figure 3.4A-B). In the absence of CyaR, the chuA 5’UTR T1-T4 truncates caused 2.1-, 1.9-, 2.9- and 9.2-fold increases in chuA translation, respectively (Figure 3.4C). The activation due to CyaR is still observed in all four truncates. This suggests that CyaR-mediated activation of chuA acts neither through alleviation of an inhibitory secondary structure nor through base-pairing with a regulatory sequence in the upstream +1-+253 region of the 5’UTR. However, a dramatic increase in chuA-GFP expression was observed between the T3 and T4 truncates (2.7-fold) and may indicate the presence of an unidentified inhibitory region that represses expression of chuA.

Chapter 3

Figure 3-4: The 5’UTR of chuA contains a sequence feature that inhibits its translation. A. The secondary structure for the chuA 5’UTR was predicted using the RNAFold webserver. The +6 site is where the predicted structure starts, and the start codon is indicated. Also labelled are the FourU RNA-thermometer, the CyaR binding site, and the locations where the truncates were made. B. Truncates of the chuA 5’UTR were made based on the predicted secondary structure. C. Fluorescence of the full and truncated chuA-5’UTR were measured in the presence and absence of CyaR. Fluorescence represents the mean median fluorescence intensity of three biological replicates. (*p < 0.05, ** p < 0.01, *** p < 0.001)

Chapter 3

3.3.6 The 5’UTR of chuA is subject to transcription termination by Rho

In E. coli, the average 5’-UTR is approximately 25-35 nucleotides in length (Kim et al., 2012; Evfratov et al., 2017). The predicted 5’UTR of chuA is 328 nucleotides, making it an unusually long prokaryotic untranslated region (Wyckoff et al., 1998; Kouse et al., 2013). However, differential RNA-seq, which is a variant of RNA-seq that is used to determine transcription start sites transcriptome wide, revealed that the 5’UTR is 290 nucleotides (Section 2.3.3). Recent analysis of a commensal strain of E. coli (K12 str. MG1655) in presence and absence of the Rho-inhibitor bicyclomycin demonstrated that 635 out of 1,203 5’UTRs longer than 80 nucleotides (an estimated length required for Rho binding) were subject to Rho activity (Zwiefka, Kohn and Widger, 1993; Sedlyarova et al., 2016). To assess whether chuA is subject to Rho termination, Rho-utilization (rut) sites on the EHEC str Sakai genome were predicted using RhoTermPredict. This algorithm predicts Rho-utilization sites by searching for a consensus Rho-binding motif characterized by regularly spaced cytosines, a C:G ratio greater than 1.2 and then searching for the presence of possible RNA polymerase pause sites 150 nt downstream (Di Salvo et al., 2019). Using RhoTermPredict, 27,862 Rho- utilization sites were predicted genome wide. Of these, only one was found within the 5’UTR of chuA, with the associated RNA polymerase pause site located in the region between truncates T3 and T4 (Figure 3.5A). To understand if this was a bona fide Rho-utilisation site, data from a previous EHEC RNA-seq experiment (Waters et al., 2017) was analysed. To determine whether a transcript could be terminated by Rho, the ratio of reads between an 80 nt window immediately downstream of the +1 site determined by dRNA-seq (proximal) and an 80-nt window 200 nt downstream of the +1 site (distal) was measured. A putative Rho- terminated transcript has previously been characterised as having a proximal to distal coverage ratio greater than 1.5 (Sedlyarova et al., 2016). The calculated ratio for chuA was 2.24, suggesting that the 5’UTR may be subject to transcription termination by Rho (Figure 3.5A).

An Affymetrix E. coli Genome 2.0 microarray was previously used to analyse gene expression in E. coli O157:H7 str. EDL933 upon exposure to 100 µg/mL of 72

Chapter 3

bicyclomycin for 20 minutes at the exponential phase (Figure 3.5B).The expression of chuA was increased 78-fold upon exposure to bicyclomycin, further suggesting that this transcript may be subject to Rho termination (Cardinale et al., 2008).

RpoS, a known Rho-terminated transcript, is subject to anti-termination via small RNAs. The sRNAs DsrA, RprA and ArcZ can activate rpoS in part by preventing premature termination by Rho (Sedlyarova et al., 2016). This could indicate that CyaR-mediated activation of chuA may utilise the same mechanism. Strains containing the chuA-GFP translational fusions with and without CyaR were grown to exponential phase, then half of the culture was exposed to 50 µg/mL of bicyclomycin benzoate for 30 minutes. A culture containing an rpoS-GFP translational fusion was also grown and treated the same way to act as a positive control for inhibition of Rho. In both the presence and absence of CyaR, chuA showed a ~1.2-fold increase in fluorescence when treated with bicyclomycin benzoate, indicating that this 5’UTR is indeed subject to transcription termination by Rho (Figure 3.5C). In both the treated and untreated cultures however, upregulation of chuA due to CyaR is still observed. While this experiment confirms Rho termination along the 5’UTR of chuA, it is unlikely that CyaR acts as a Rho anti-terminator to increase expression of chuA.

Chapter 3

Figure 3-5: The outer membrane receptor chuA is subject to transcription termination by Rho. A. RhoTermPredict (Di Salvo et al., 2019) was used to predict rut sites (green) along chuA. Total RNA-seq data is indicated in blue, and Hfq-CRAC data is indicated in red. Data presented is raw number of reads. B. Gene expression data of chuA after exposing EHEC str. EDL933 to bicyclomycin (Cardinale et al., 2008) was obtained from the NCBI GEO database (accession number GSE10345) and analysed using GEO2R. C. Fluorescence of bicyclomycin benzoate induced or uninduced cells carrying the chuA-sfGFP translational fusion were measured in the presence or absence of CyaR. An rpoS-sfGFP translational fusion was included as a control for successful Rho inhibition. Fluorescence represents the mean median fluorescence intensity of three biological replicates. (*p < 0.05, **p < 0.01)

Chapter 3

3.4 Discussion

To successfully grow and colonise a host, pathogens utilise systems that allow them to retrieve trace minerals required for growth which are normally sequestered. Various pathotypes of E. coli, such as EHEC and UPEC, have an outer membrane haem receptor which allows them to take up iron from the host in the form of haem. The transcription and translation of this receptor is regulated by environmental factors. Transcription of chuA is repressed in the presence of iron by Fur (Torres and Payne, 1997), while translation is regulated by temperature through a FourU RNA-thermometer that occludes the ribosomal binding site (Kouse et al., 2013). The combination of these two modes of regulation allows the pathogen to sense signals associated with the host environment and activate expression of this haem receptor inside the host. This is an example of an AND-logic gate, where low iron levels AND high temperature are required for expression of chuA in the host.

Iron-utilization and uptake genes have both been found to be regulated by Hfq- binding sRNAs, the most well studied being RyhB (Massé, Escorcia and Gottesman, 2003; Massé et al., 2007; Chareyre and Mandin, 2018). UV- crosslinking of RNAs bound to Hfq in vivo followed by sequencing has also led to the discovery of Hfq binding sites along the 5’UTR of chuA. One of these sites was along a region of sequence divergence between the 5’UTRs of UPEC and EHEC chuA (Nagy et al., 2001)(Figure 3.1B). This difference in sequence could affect not only how Hfq binds to this part of the 5’UTR, but also the repertoire of sRNAs that would post-transcriptionally regulate this gene in response to the differing environments in the gastrointestinal and urinary tracts.

The use of in silico predictions and translational fusions have shown that expression of chuA is activated by the sRNA CyaR through a direct interaction, and regulated indirectly by ChiX. ChiX is a sRNA that silences the expression of the enterobacterial chitoporin chiP in the absence of chitin-derived oligosaccharides (Figueroa-Bossi et al., 2009). CyaR is a sRNA activated by the

Chapter 3

global regulator Crp when cyclic-AMP levels are high, such as in the absence of glucose (De Lay and Gottesman, 2009).

The infectious dose of EHEC is very low, with less than 100 cells being required for infection (Kaper, Nataro and Mobley, 2004). The gastrointestinal tracts of mammals are complex, and form multiple microenvironments and niches along its longitudinal axis (Donaldson, Lee and Mazmanian, 2015). As a result, to efficiently colonise and infect the host, EHEC must precisely coordinate its virulence factors in response to the environmental signals it receives from the gastrointestinal tract (Hughes et al., 2009). An example of one of these cues is the array of sugars that is available in the environment. The primary niche for EHEC is the colon, where the primary source of carbon is mucus, which is an anaerobic environment composed of polysaccharides. Bacteroides thetaiotaomicron (Bt), which is present in the same environment, is able to cleave fucose from these polysaccharides, providing a viable carbon source for EHEC (Xu et al., 2003; Fischbach and Sonnenburg, 2011). Other virulence factors are also regulated by carbon. For example, EHEC senses fucose via the FusKR system, which represses the T3SS via repression of Ler (Pacheco et al., 2012). The repression of T3SS during this stage allows EHEC to outcompete other bacteria in the environment. The presence of Bt has also been found to correlate with increased expression of mucinase in EHEC, which allows it to make its way to the intestinal epithelium. Closer to the intestinal epithelium, the microaerobic environment, lower levels of fucose, and higher levels of succinate reduces the level of FusKR expression and produces a gluconeogenic environment and induces expression of Cra. Activation of Cra can induce expression of genes required for T3SS and adhesion (Njoroge et al., 2012; Curtis et al., 2014; Carlson- Banning and Sperandio, 2016).

In gluconeogenic environments, Cra is able to enhance binding of the cAMP receptor protein Crp to its targets (Ryu et al., 1995). Expression of Crp can also be activated in the presence of Cra (Zhang, Aboulwafa and Saier, 2014). In these environments, the formation of the Crp-cAMP complex can repress the expression of the sRNA Spot42 (Beisel and Storz, 2011). Spot42 sRNA has been 76

Chapter 3

found to be able to repress the sepL gene in LEE4, further showing the links between the carbon composition in the environment and type 3 secretion (Wang et al., 2018). The Crp-cAMP complex can also activate the expression of CyaR (De Lay and Gottesman, 2009). The activation of chuA by CyaR may expand the regulation of this haem receptor to that of a two-level AND-OR logic gate that uses iron levels, temperature and carbon source as input signals. The expression of chuA uses an AND-OR logic gate that would require low levels of iron AND either a poor carbon source to activate translation via CyaR OR high temperature to melt the FourU RNA-thermometer (Figure 3.6). In summary, this chapter has demonstrated the activation of ChuA by CyaR, using an AND-OR logic gate that requires the coordination between iron and carbon metabolism pathways to sense the host-environment and to express the relevant genes for survival and virulence.

Chapter 3

Figure 3-6: ChuA utilises an AND-OR or AND-AND logic gate using environmental signals as input. EHEC utilizes an AND-OR logic gate using environmental signals in the host to sense when it needs to express chuA. In the AND-OR logic gate, transcription of chuA is possible due to repression of Fur by an iron-poor environment. Translation of the chuA transcript can proceed at 37oC due to RNA-thermometer melting OR through CyaR activation.

Chapter 3

In addition to the activation by CyaR, this chapter has shown that chuA is subject to additional layers of post-transcriptional regulation. The 5’UTR of chuA was predicted to be 328 nucleotides long, however differential RNA-seq shows that it is 290 nucleotides. The length of the chuA 5’UTR is far greater than the average 5’UTR length in E. coli (Kim et al., 2012; Evfratov et al., 2017). Previous work has shown that there is a conserved RNA-thermometer in the chuA/shuA 5’UTRs of EHEC, EPEC, UPEC and Shigella that occludes the ribosomal binding site at lower temperatures (Kouse et al., 2013). Mutations made in the RNA- thermometer to prevent its formation even at 25oC did not alleviate CyaR- mediated activation of chuA (Figure 3.3B). One mechanism for target activation via sRNAs is through the disruption of secondary structures that can occlude the ribosomal binding site. An example of this is sRNA activation of rpoS by DsrA, ArcZ and RprA (Majdalani et al., 2001; Mandin and Gottesman, 2010; McCullen et al., 2010). Truncations of the chuA 5’UTR displayed progressively higher amounts of translation, however all truncates were still subject to activation by

CyaR. The presence of ARNx motifs were used as one of the bases for generating the truncates, though Hfq-binding was not observed to occur on regions of the chuA 5’UTR that contained these motifs. This is because CyaR is an example of a Class II sRNA, which binds to both the proximal and distal faces of Hfq, while the UA-rich regions of the chuA 5’UTR binds to the rim (Schu et al., 2015). This is supported by the maximal point deletions generated by Hfq-CRAC indicative of direct contact. Upon testing the effects of the chuA 5’UTR truncation, 9.2- and 2.8-fold increase in translation was observed in T4 relative to the full-length UTR and T3, respectively (Figure 3.4C). This showed that while CyaR-mediated activation of chuA was not due to disruption of secondary structures, there is an unknown inhibitory sequence or structure in the +215 to +253 region of chuA.

It has been previously demonstrated that the 5’UTR of chuA is regulated by the RNA-binding protein Hfq (Tree et al., 2014). This suggests that the 5’UTR of chuA may be subject to regulation by additional RNA-binding proteins. In commensal E. coli strains, Rho can prematurely terminate over 50% of genes with long 5’UTRs (> 80 nts) (Sedlyarova et al., 2016). Using RhoTermPredict, a rho utilization site was predicted to be along the genomic region proximal to the chuA 79

Chapter 3

+1 site verified by dRNA-seq (Figure 3.5A). A pause site for RNA polymerase was found to lie in the region between T3 and T4 of chuA, which could explain the sharp increase in fluorescence in between the two truncates. Further evidence of Rho termination on chuA was found by analysis of EHEC gene expression data upon exposure to the Rho inhibitor bicyclomycin (Figure 3.5B) (Cardinale et al., 2008; Waters et al., 2017). Small RNAs have recently been shown to modulate Rho termination. The stress sigma factor RpoS, another gene that contains a long 5’UTR, is upregulated by three sRNAs depending on the environment – ArcZ, RprA or DsrA. These sRNAs act in part by preventing premature termination of rpoS by Rho. Here, the effect of Rho termination on the expression of chuA was demonstrated using the semisynthetic derivative of bicylomycin, bicyclomycin benzoate. The addition of a benzoate group to bicyclomycin heavily reduces its antibacterial potency (Muller et al., 1979). However, experiments were carried out using this derivative due to unavailability of bicyclomycin. Nevertheless, upon exposure to this antibiotic, CyaR-mediated activation was still observed, though not proportional to the levels observed from bicyclomycin treatment in EDL933. The reduced potency of bicylomycin benzoate would cause only partial inhibition of Rho, resulting in the modest increase in chuA translation upon exposure to this derivative. Rho is an essential gene in E. coli, and for this reason, the use of a Rho deletion strain was not feasible (Yamamoto et al., 2009). The low amounts of Rho inhibition could also explain de-repression of chuA in the presence of CyaR after bicyclomycin benzoate treatment. Despite this, the results from this chapter show that the 5’UTR of the outer membrane haem receptor chuA is subject to four layers of regulation. Initiation of transcription is regulated by Fur, premature transcription termination occurs due to Rho, and chuA is also post-transcriptionally regulated by a FourU RNA-thermometer and by the sRNA CyaR.

One of the major physiological roles sRNAs play in bacteria is the maintenance of iron homeostasis and carbon utilisation. This chapter has demonstrated activation of the outer membrane haem receptor chuA by the CRP-cAMP activated sRNA CyaR through direct binding. This may potentially link sugar and iron sensing pathways of EHEC in an AND-OR logic gate to sense its location in 80

Chapter 3

the host and coordinate the expression of genes required for an appropriate response.

Chapter 4

Chapter 4: The Shiga toxin promoter PR’ transcribes a functional sRNA that activates the stress sigma factor RpoS

4.1 Introduction

The low infectious dose of enterohaemorrhagic E. coli O157:H7 is largely due to horizontally acquired DNA that it accumulated during its evolution. These include key virulence factors, such as the locus of enterocyte effacement (Section 1.2.2.1) (Jores, Rumer and Wieler, 2004; Konczy et al., 2008). The genome of EHEC is mosaic, with ~16% of its genome originating from over 18 prophages (Brüssow et al., 2004; Asadulghani et al., 2009). These include the Sp5 and Sp15 prophages, which encode Shiga toxins Stx2 and Stx1, respectively. In addition to these, 3.6% of the core genome of EHEC, which it shares the commensal E. coli K-12, contains 9 cryptic prophage elements. These cryptic prophages provide benefits such as improved antibiotic resistance, increased tolerance to environmental stress and increased biofilm formation (Wang et al., 2010). This is due to the addition of genes such as virulence factors, secretion systems and post-transcriptional regulators (Altuvia, Storz and Papenfort, 2018). The genes encoded by lysogenised prophages can integrate themselves into bacterial regulatory circuits, and if beneficial, can be retained within the host chromosome.

Small RNAs play a significant role in the virulence of various bacterial pathogens (Chao and Vogel, 2010). Some examples include UPEC and S. enterica, where disruption of hfq has been shown to have an adverse effect on pathogenicity through disruption of functions related to virulence such as motility and the ability to colonise or infect their hosts (Kulesus et al., 2008; Hébrard et al., 2012). In Vibrio cholerae, the sRNA VrrA positively regulates the formation of outer membrane vesicles, the release of which has been associated with toxin and virulence factor transport as well as immune system evasion (Song and Wai, 2009; Song, Sabharwal and Wai, 2010; Perez-Reytor et al., 2017)

High-throughput techniques such as crosslinking and immunoprecipitation of sRNA-binding proteins (Sittka et al., 2008; Tree et al., 2014; Holmqvist et al.,

Chapter 4

2016), analysis of mRNA decay patterns (Chen et al., 2015; Mohanty and Kushner, 2016; Chao et al., 2017) and other next-generation RNA-sequencing methods have emerged. This has led to the discovery of novel classes of sRNAs, including 3’UTRs as a reservoir for sRNA biogenesis (Chao et al., 2012; Chao and Vogel, 2016; Ren, Guo and Sun, 2017), and a class of sRNAs called decay- generated noncoding RNAs (decRNAs), which originate from the middle of a protein-coding gene (Dar and Sorek, 2018).

An added benefit of these high-throughput techniques is that it allows for the discovery of sRNAs within genomic islands acquired through horizontal gene transfer. These sRNAs are only present in a subset of strains and are difficult to identify using in silico methods, which mainly rely on conserved RNA structures or non-synonymous elements indicative of non-coding elements. For example, the use of Dual RNA-seq to profile gene expression in both the pathogen and the host after infection led to the discovery of PinT in S. enterica. PinT is a sRNA encoded on a horizontally transferred genomic island and regulates the prophage-encoded SopE and SopE2 effectors (Westermann et al., 2016). In the same organism, a systemic search using a predictive algorithm and sequencing following bacterial uptake by macrophages led to the discovery of 19 sRNAs along the pathogenicity islands of Salmonella, though a majority of these were antisense RNAs (Padalon-Brauch et al., 2008). The role of horizontal gene transfer in the evolution of sRNAs is highlighted by the sRNA EcsR2 in E. coli, which is encoded in the yagU-ykgJ intergenic region and has been found to interact with ansB, which encodes for a periplasmic L-asparaginase. Using phylogenetic analysis and by tracing back the evolutionary history of the yagU- ykgJ arrangement, it was found that EcsR2 evolved from a vestigial bacteriophage gene, and explains why this sRNA is only present in E. coli (Kacharia, Millar and Raghavan, 2017).

Prophages provide a reservoir for regulatory sRNAs. One of the first studied non- coding regulatory RNAs was the OOP RNA, encoded on phage λ. OOP base pairs with the cII-O mRNA, recruiting RNase III, and resulting in its degradation (Wulff and Krinke, 1987). Regulatory non-coding RNAs that originate in phages 83

Chapter 4

other than λ include DicF, one of the first studied trans-encoded sRNAs. This sRNA is processed by RNase E from the dicB operon that originates from the defective lambdoid prophage Qin/Kim (Bouché and Bouché, 1989) and has been implicated in regulating metabolic functions as well as cell division. DicF base pairs with ftsZ to repress cell division, and can base pair with xylR and pykA to regulate carbon metabolism (Balasubramanian et al., 2016). Phage-encoded sRNAs are not limited to E. coli. In Salmonella, the IsrK is transcribed from the Gifsy-1 prophage as a long, translationally inactive mRNA isoform and as a short sRNA. IsrK sRNA can bind to the inactive mRNA isoform and activate its translation. The product of this longer isoform can then activate the anti- terminator AntQ, the overexpression of which can have adverse effects on genome integrity (Hershko-Shalev et al., 2016).

Purification of a dual affinity-tagged Hfq UV-crosslinked to interacting RNAs followed by sequencing (Hfq-CRAC), led to the discovery of 55 small RNAs within the pathogenicity islands of E. coli O157:H7 str. Sakai. The same study characterised two of these sRNAs, AgvB and AsxR that act as “anti-sRNAs” or sRNA sponges against the core genome encoded sRNAs GcvB and FnrS, respectively (Tree et al., 2014). Like the conserved phage-encoded sRNA DicF, these pathogenicity island encoded sRNAs have been integrated into the regulatory circuits of EHEC and help maintain its fitness. For example, the sRNA Esr41 plays a role in iron homeostasis by base-pairing with the iron-siderophore complex uptake receptor cirA, bacterioferritin (bfr), and the outer membrane haem receptor chuA (Waters et al., 2017).

EHEC isolates may contain multiple Shiga-toxigenic bacteriophages that encode Stx, the characteristic virulence factor of this pathotype. The severe disease and morbidity associated with EHEC is caused by the release of these toxins. The toxins are integrated within the late region of these lambdoid Stx phages and the release of Shiga toxins is linked to its lysogenic-lytic decision (Section 1.3) (Schmidt, 2001; Kruger and Lucchesi, 2014). During lysogeny, transcription of the toxin and lytic genes from the constitutively active late promoter PR’ is prematurely terminated due to the presence of a Rho-independent terminator tR’ 84

Chapter 4

(Figure 1.2B) (Wagner et al., 2001). When exposed to high amounts of stress and DNA damage, RecA promotes cleavage of the cI repressor (Sauer, Ross and Ptashne, 1982), allowing for the expression of an anti-terminator protein Q, which modifies RNA polymerase to be able to transcribe past tR’ and into the toxin and lysis genes (Yarnell and Roberts, 1992; Oppenheim et al., 2005).

Of the 55 sRNAs in the EHEC pathogenicity islands identified through Hfq-CRAC, 11 are located on the Stx phages. The previously described AsxR is encoded 3’ of stx2AB. Overexpression of the Stx2Φ-encoded sRNA103, also known as EcOnc27 in the Hfq-CRAC dataset, has been found to upregulate fimZ, a transcription factor that regulates fimbriae, and the effector espA (Gruber and Sperandio, 2015), although the mechanism of activation due to this sRNA is still not known. This suggests that characterising the remaining StxΦ-encoded sRNAs is important to better understand the post-transcriptional network of EHEC.

Acquisition of the Stx phages in EHEC strains is known to confer severe disease phenotypes but only recently is it beginning to be appreciated that sRNAs within the phage also act to remodel bacterial host gene regulation. For example, expression of Cro and cII from Shiga-toxigenic bacteriophages can modulate expression of the T3SS and affect EHEC motility (Xu et al., 2012; Hernandez- Doria and Sperandio, 2018). While the functions of the different Stx phage sRNAs vary widely, all of them may affect the virulence of EHEC to a certain degree. As such, identifying the functions of the other sRNAs could prove to be a useful tool in understanding how the Stx phage affects the virulence of the host. This chapter aims to identify the function of an uncharacterised sRNA transcribed from the Stx phage and identify its targets as well as the potential roles it may have on the fitness and virulence of E. coli O157:H7 str. Sakai.

Chapter 4

4.2 Materials and Methods

4.2.1 Strains, Plasmids and Primers

Bacterial strains and plasmids used for this study are listed in Tables 1-3. E. coli was routinely grown at either 30oC or 37oC in liquid LB, minimal M9 or MEM-

HEPES supplemented with 0.1% glucose and 250 nM Fe(NO3)3, or solid LB agar plates. Bacterial media was supplemented with ampicillin (100 µg/mL), chloramphenicol (34 µg/mL), kanamycin (50 µg/mL), spectinomycin (50 µg/mL, or tetracycline (10 µg/mL) where appropriate.

Table 4-1: Bacterial strains used in Chapter 4

Genotype; Method of Strains construction Reference O157:H7 str. Sakai stx- Δstx1 stx2A::kan, KanR (Dahan et al., 2004) JJT378 ΔstxS1; Allelic exchange This study JJT379 ΔstxS2; Allelic exchange This study ΔstxS1ΔstxS2; Allelic JJT380 exchange This study JJT454 Δrpos; Allelic exchange This study ΔstxS1ΔstxS2Δrpos; Allelic JJT437 exchange This study O157:H7 str. Sakai stx+ wild-type (Hayashi et al., 2001) JJT384 ΔstxS1;Allelic exchange This study JJT385 ΔstxS2;Allelic exchange This study ΔstxS1ΔstxS2; Allelic JJT438 exchange This study ΔstxS1::stxS1; Allelic exchange deletion; CRISPR JJT701 repair This study ΔstxS2::stxS2; Allelic exchange deletion; CRISPR JJT702 repair This study JJT953 ΔstxS2; CRISPR This study JJT957 ΔstxSL2; CRISPR This study ΔstxS2::stxS2; CRISPR JJT1027 deletion, CRISPR repair This study ΔstxSL2::stxSL2; CRISPR JJT993 deletion, CRISPR repair This study K-12 MG1655 (Blattner et al., 1997) fhuA2 lac(del)U169 phoA glnV44 80' (Taylor, Walker and Mclnnes, DH5a lacZ(del)M15 gyrA96 recA1 1993) 86

Chapter 4

relA1 endA1 thi-1 hsdR17

Hfr(PO1), lacZ43(Fs), λ-, rne- N3431 3071(ts), relA1, spoT1, thiE1 (Babitzke and Kushner, 1991)

Table 4-2: Plasmids used in Chapter 4

Plasmid Description Reference pTOF25 allelic exchange plasmid; temperature-sensitive, CmR (Merlin, Mcateer and Masters, 2002) pCP20 temperature-sensitive plasmid for expression of FLP (Cherepanov and recombinase, ApR CmR Wackernagel, 1995) pTOF1 template for amplification of FRT-TetRA-FRT (Tree et al., 2014) pTOF25::EcO for deletion of stxS1 in Sakai stx-, temperature sensitive, This study nc15::stx-::Tet CmR TetR RA pTOF25::EcO for deletion of stxS1 in Sakai, temperature sensitive, This study nc15::TetRA CmR TetR pTOF25::EcO for deletion of stxS2 in Sakai, temperature sensitive, This study nc65::TetRA CmR TetR pTOF25::Rpo for deletion of rpoS in Sakai, temperature sensitive, This study S::TetRA CmR TetR pXG10SF PLtetO-1 sfGFP fusion vector, CmR (Corcoran et al., 2012) pZE12luc PLlacO-1 luciferase expression vector, ApR Expressys, Germany pXG0 PLtetO-1 luciferase expression vector, CmR (Urban and Vogel, 2007) pXG1 PLtetO-1 sfGFP expression vector, CmR (Urban and Vogel, 2007) pJV300 scrambled sRNA control for pZE12 expressed sRNAs (Sittka et al., 2007) pXG10::rpoS rpoS-sfGFP translational fusion This study pZE12::stxS1 full length stxS1 expression vector This study pZE12::stxS2 full length stxS2 expression vector This study pXG10::rpoS- T->C point mutation at the 466th nucleotide of the rpoS This study T466C 5'UTR pZE12::stxS2- A->C point mutation at the 190th nucleotide of full length This study A190C stxS2 pBR322 standard cloning vector, ApR (Bolivar et al., 1977) pBR322::rpoS full length rpoS under control of its native promoter This study pBR322::stxS full length stxS2 under control of its native promoter This study pAJR70 for generating eGFP transcriptional fusions, contains (Roe et al., 2004) optimised SD sequence, CmR, pAJR70::rpoS rpoS-eGFP transcriptional fusions This study pTargetF gRNA plasmid for CRISPR-Cas9, SpR (Jiang et al., 2015) pTargetF::FR gRNA plasmid for CRISPR-Cas9 containing sgRNA for This study T targeting FRT, SpR 87

Chapter 4

pTargetT::FR gRNA plasmid for CRISPR-Cas9 containing sgRNA for This study T::stxS1 targeting FRT, contains repair template for stxS1 pTargetT::FR gRNA plasmid for CRISPR-Cas9 containing sgRNA for This study T::stxS2 targeting FRT, contains repair template for stxS2 pTargetT::Stx gRNA plasmid for CRISPR-Cas9 containing sgRNA for This study SL::stxS2 targeting the StxSL deletion, contains repair template for stxS2 pTargetT::Stx gRNA plasmid for CRISPR-Cas9 containing sgRNA for This study S2::stxS2 targeting the StxS2 deletion, contains repair template for stxS2 pCas repA101(Ts) kan Pcas-cas9 ParaB-Red lacIq Ptrc- (Jiang et al., sgRNA-pMB1 2015)

Table 4-3: Oligonucleotides used in Chapter 4

Oligonucleotide Sequence Purpose ATGCTGTTACGCAAACTTCGT BS_StxS_NB TACAGGGTTA detection of stxS TTGTTATTAATTACGGTCGCA BS_StxSL_NB CCTTCCTTTCTGTG detection of stxSL BS_StxS_RACE_R GCTGGCAAACTCGTAGAGCA reverse RACE primer for StxS CGATTTTTAAGATTTTGTTATT BS_StxSL_RACE_R AATTACG reverse RACE primer for StxSL BS_EcOnc65.DelA.Sm GTTTTTCCCGGGGAAACAGC generating 5' SOE PCR product aI.F GGTCAATACAAATCAC for stxS2 deletion CCGTTCCAAGCGGCCGCAAG BS_EcOnc65.DelA.NotI AGCGCAATGTAACCACTCTTA generating 5' SOE PCR product .R TCATGA for stxS2 deletion CGCTCTTGCGGCCGCTTGGA ACGGGCCAGCCTCCCCCAGT BS_EcOnc65.DelB.NotI GGCTGGCTTTTTTATGTCCGT generating 3' SOE PCR product .F AGCGTCAA for stxS2 deletion BS_EcOnc65.DelB.SalI GAAAAAGTCGACCCGTTGTC generating 3' SOE PCR product .R ATGGAAACCGTTGTC for stxS2 deletion BS_EcOnc15.DelA.Sm GTTTTTCCCGGGCGAACAGA generating 5' SOE PCR product aI.F GTCTTGTCCATGATAATC for stxS1 deletion BS_EcOnc15.DelA.Sm GTTTTTCCCGGGCCGCCTGC generating 5' SOE PCR product aI.F2 TATTTTCACTGAGCTATTC for stxS1 deletion in stx- Sakai CCGTTCCAAGCGGCCGCAAG AGCGGCCAGCCTCCCCCAGT BS_EcOnc15.DelA.NotI GGCTGGCTTTTTTATGTCCGT generating 5' SOE PCR product .R AACATCC for stxS1 deletion CGCTCTTGCGGCCGCTTGGA BS_EcOnc15.DelB.NotI ACGGGGCAATGTAACCACTC generating 3' SOE PCR product .F TTATCATG for stxS1 deletion BS_EcOnc15.DelB.SalI GAAAAAGTCGACCTTAGCCC generating 3' SOE PCR product .R ATCCCAATCCCTTCAATACC for stxS1 deletion GTCGTTAAATAGCCGCTTATG BS_pTOF25_F TC sequencing pTOF25 plasmid CACACCCGCCGCGCTTAATG BS_pTOF25_R C sequencing pTOF25 plasmid GAAAAAGAATTCCGATGATGC BS_pBR322_StxS_F GATGGTGATATGC cloning StxS into pBR322 GAAAAAGAATTCCTGACTGAC BS_pBR322_StxS_R TACATTCTGAACTTCCC cloning StxS into pBR322

Chapter 4

5' phosphorylated forward BS_EcOnc15_ZE12_5 primer for cloning StxS1 into p_F GGTTACATTGCCACGCTGCT pZE12 GAAAAATCTAGATTGATACAC reverse primer for cloning BS_EcOnc15_ZE12_R AGGATGTTACG StxS1 into pZE12 5' phosphorylated forward BS_EcOnc65_ZE12_5 primer for cloning StxS2 into P_F GGTTACATTGCCACGCAGTC pZE12 GAAAAATCTAGACCATTGCTG reverse primer for cloning BS_EcOnc65_ZE12_R CTTTGACGCTA StxS2 into pZE12 GCGAAGATGCATTTCTGAGTC forward primer for cloning rpoS BS_RpoS_5UTR_F TTCGGGTGAAC into pXG1SF GAAAAAGCTAGCATCATGAAC reverse primer for cloning rpoS BS_RpoS_5UTR_R TTTCAGCGTATTC into pXG1SF CAGACCGGCAACAACTGACT primer to check for deletion of BS_EcOnc15_DelF G stxS1 TTAAGACGAAAGCTCGGTGA primer to check for deletion of BS_EcOnc15_DelR AGC stxS1 GTGCCAATGCAGGAATATATG primer to check for allelic BS_EcOnc65_DelF GATC exchange deletion of stxS2 CGCTGCGACACGTTGCAGAG primer to check for allelic BS_EcOnc65_DelR T exchange deletion of stxS2 GTTTTTGAATTCACCCTGTAA BS_MAPS_EcOnc65_F CGAAGTTTGCG cloning StxS2 into pBAD-MS2 BS_MAPS_EcOnc65_ GTTTTTGCATGCCGCCATTGC R TGCTTTGACGC cloning StxS2 into pBAD-MS2 TTTACGGATTTCCCCTTGTAC for generating rpoS 5'UTR point BS_RpoS_T466C_F CGAATTTCAAAATGCAAGCG mutation CGCTTGCATTTTGAAATTCGG for generating rpoS 5'UTR point BS_RpoS_T466C_R TACAAGGGGAAATCCGTAAA mutation BS_EcOnc65_A190C_ TATTGCAGGATAACCCTGTAC for generating StxS point F CGAAGTTTGCGTAACAG mutation BS_EcOnc65_A190C_ CTGTTACGCAAACTTCGGTAC for generating StxS point R AGGGTTATCCTGCAATA mutation GTTTTTCCCGGGGAGCATATT generating 5' SOE PCR product BS_RpoS.DelA.SmaI.F TTCGAGATGGATGGTGC for rpoS deletion CCGTTCCAAGCGGCCGCAAG AGCGTAAGCATCTGTCAGAAA generating 5' SOE PCR product BS_RpoS.DelA.NotI.R GGCCAGTC for rpoS deletion CGCTCTTGCGGCCGCTTGGA ACGGAAGGTGGCTCCTACCC generating 3' SOE PCR product BS_RpoS.DelB.NotI.F GTGATC for rpoS deletion GAAAAAGTCGACCGACGTCT generating 3' SOE PCR product BS_RpoS.DelB.SalI.R ACAGCGCAGCAACC for rpoS deletion TTCGGGATGGTTATAAAAGGC BS_RpoS.Del.F AGGC checking rpoS deletion CCAATTCTGGTATGTTGATTA BS_RpoS.Del.R CGCC checking rpoS deletion AGCGAGGAAGCGGAAGAGC BS_pTARGET_F_Seq G sequencing pTARGET plasmid 5' phosphorylated generic ACTAGTATTATACCTAGGACT reverse primer for cloning BS_pTARGETF_R_5P GAG sgRNAs into pTARGET GTTTTTAAGCTTCTCCTTGAG generating repair template for BS_pTT_Ec15_F CACCATACGATAAC stxS1 GTTTTTCTCGAGGGTGTATAG generating repair template for BS_pTT_Ec15_R CCCGCCTTTAC stxS1 89

Chapter 4

GTTTTTAAGCTTCAGTGCTGT generating repair template for BS_pTT_Ec65_F GACGATGATGC stxS2 GTTTTTCTCGAGCTAATGGCG generating repair template for BS_pTT_Ec65_R GTATGTGATATGGT stxS2 GAGAATAGGAACTTCGGAAT BS_FRT_sgRNA_F GTTTTAGAGCTAGAAATA sgRNA for FRT scar GTTTTTAAGCTTGCCTTTACC amplifying StxS gBlock for BS_stxS_GB_F GGAACAATCGC pTarget cloning GTTTTTCTCGAGCTGGATTTG amplifying StxS gBlock for BS_stxS_GB_R AACCAGCGACC pTarget cloning TCGGCGGGTTCGACTGCGGT BS_StxS2_tgRNA4_F TTTAGAGCTAGAAATA sgRNA for stxS2 GATGATGCGATGGTGATATG primer to check for CRISPR- BS_stxS_delcheck_F C Cas9 deletion of stxS2 CTGTCCCGATGAGCTAATGG primer to check for CRISPR- BS_stxS_delcheck_R C Cas9 deletion/repair of stxS2 GGGAGGCTGGCAAACTCGTA TGCGTGGCAATGTAACCACT generating 5' SOE PCR product BS_StxS_UP_R2 C for stxS2 deletion GAGTGGTTACATTGCCACGC ATACGAGTTTGCCAGCCTCC generating 3' SOE PCR product BS_StxS_DOWN_F2 C for stxS2 deletion GTTTTTGGATCCCAAGGAGTT cloning rpoS-GFP BS_pAJR_RpoS_F GTGATCAAGCC transcriptional fusion GTTTTTGGATCCAATCATGAA cloning rpoS-GFP BS_pAJR_RpoS_R2 CTTTCAGCGTATTC transcriptional fusion BS_pAJR70_Seq2 AGATGAACTTCAGGGTCAGC sequencing pAJR70 TGCCGTATTTTAAGTATTGCG sgRNA for repair for StxSL BS_delStxSL_gRNA_F TTTTAGAGCTAGAAATA deletion BS_delstxS2rep_gRNA GGCTGGCAAACTCGTATGCG sgRNA for repair of StxS2 _F GTTTTAGAGCTAGAAATA deletion CCCGAAAAAGTAAAATCACGT primer to check for CRISPR- BS_StxS_repcheck_F C Cas9 repair of stxS2 pLlacO-B CGCACTGACCGAATTCATTAA linearising pZE12-luc GTGCTCAGTATCTTGTTATCC pLlacO-D G linearising pZE12-luc qPCR primer for uidA CAGTCTGGATCGCGAAAACT (Jinneman, Yoshitomi and G uidAF241 Weagant, 2003) qPCR primer for uidA ACCAGACGTTGCCCACATAAT (Jinneman, Yoshitomi and T uidAR383 Weagant, 2003) qPCR primer for stx2 GATGTTTATGGCGGTTTTATT (Jinneman, Yoshitomi and TGC Stx2F1218 Weagant, 2003) qPCR primer for stx2 TGGAAAACTCAATTTTACCTT (Jinneman, Yoshitomi and TAGCA Stx2R1300 Weagant, 2003) 5'-invddT- ACACrGrArCrGrCrUrCrUrUrCrC rGrArUrCrUrNrNrNrUrArArGrCr RNA linker used for 5’ RLM- L5Aa N-OH-3' RACE AATGATACGGCGACCACCGA GATCTACACTCTTTCCCTACA Amplification of RLM-RACE P5 CGACGCTCTTCCGATCT library

Chapter 4

4.2.2 Genetic modification of E. coli O157:H7 str. Sakai via allelic exchange

Genomic modifications of E. coli O157:H7 str. Sakai (both stx- and stx+) using allelic exchange were performed using the pTOF25 system as described in (Merlin, Mcateer and Masters, 2002). For deletion of stxS1 in E. coli O157:H7 Sakai stx- and stx+, flanking regions were amplified from genomic DNA using BS_EcOnc15.DelA.SmaI.F2 (for stx-) or BS_EcOnc15.DelA.SmaI.F (for stx+) and BS_EcOnc15.DelA.NotI.R, and BS_EcOnc15.DelB.NotI.F and BS_EcOnc15.DelB.SalI.R (Section 2.2.1-2). For deletion of stxS2 in E. coli O157:H7 Sakai stx- and stx+, flanking regions were amplified from their respective genomic DNA using BS_EcOnc65.DelA.SmaI.F and BS_EcOnc65.DelA.NotI.R, and BS_EcOnc65.DelB.NotI.F and BS_EcOnc65.DelB.SalI.R.. For deletion of rpoS in E. coli O157:H7 Sakai stx- and stx+, flanking regions were amplified from their respective genomic DNA using BS_rpoS.DelA.SmaI.F and BS_rpoS.DelA.NotI.R, and BS_rpoS.DelB.NotI.F and BS_rpoS.DelB.SalI.R. All amplified flanking regions were joined together using splicing by overlap extension (SOE) PCR. SOE PCR was done by adding 0.75 µL each of gel purified product into a PCR reaction containing the leftmost and rightmost primers of each allelic exchange template. SOE PCR products were gel purified and cloned into pTOF25 using SmaI and SalI (FastDigest, ThermoFisher). Plasmids are verified by Sanger sequencing. An FRT-tetRA-FRT cassette was cut from pTOF1 using NotI and cloned into pTOF25 plasmids containing the allelic exchange templates (Section 2.2.3).

Verified pTOF25 plasmids containing the allelic exchange templates as well as the tetracycline resistance cassette were electroporated into their respective target strains as outlined in Section 2.2.6. Transformants were selected for both chloramphenicol and tetracycline resistance. Three colonies were taken and o grown in the same antibiotics at 30 C overnight. These were plated on LB-Cm34- 4 6 o o Tc10 plates at 1 and 1 dilutions and grown at 30 C and 42 C. If a viability drop o o was observed at 42 C, 6 colonies were inoculated into 10 mL of LB-Tet10 at 30 C overnight. This was repeated three times. These were plated on LB-Tet10 (for o stx+) or LB-Tc10Km50 (for stx-) made without NaCl and 6% sucrose at 42 C.

Chapter 4

Colonies from this step were tested for tetracycline resistance and chloramphenicol susceptibility by pick and patching onto LB-Tc10 and LB-Cm34 plates. Deletion of the target gene and insertion of the tetracycline resistance cassette was verified using colony PCR. The tetracycline cassette was removed by transforming the pCP20 plasmid, which expresses FLP recombinase, into the strains through electroporation. The strains were once again verified using PCR.

4.2.3 Genetic modification of E. coli O157:H7 str. Sakai via CRISPR-Cas9

Genetic modifications in E. coli O157:H7 str. Sakai stx+ using CRISPR-Cas9 were done using the two-plasmid system described in (Jiang et al., 2015). The sgRNA to create full and partial deletions of stxS2 in E. coli O157:H7 str. Sakai stx+ was cloned into pTarget-F by inverse PCR with Phusion using BS_StxS2_tgRNA4_F and BS_pTargetF_R_5P (5’ phosphorylated) (Section 2.2.1-2). Following amplification, 1 µL of DpnI (NEB) was added to the reaction and incubated for 1 hour at 37oC. The linearized vector was purified using the Wizard ® SV Gel and PCR Clean-Up System (Promega, cat no. A9282), and 6 µL of the purified amplicon was used in a T4 DNA ligase (ThermoFisher) reaction to circularise the vector (Section 2.2.3. The reaction was incubated at room temperature for 1 hour before heat-shock transformation into ultra-competent DH5α cells (Section 2.2.5). Plasmids were extracted using the Wizard® Plus SV Minipreps DNA Purification System (Promega) according to the manufacter’s protocol, and replacement of the sgRNA was confirmed via Sanger sequencing using BS_pTARGET_F_Seq. The flanking regions for homologous recombination for partial deletion of stxS2 were amplified from a gBlock (IDT) (Supplementary Table S1) using BS_stxS_GB_F and BS_stxS_GB_R. The flanking regions for a full deletion were amplified from genomic DNA using BS_StxS_GB_F and BS_StxS_UP_R2 and BS_StxS_DOWN_F2 and BS_StxS_GB_R. The flanking regions for the full deletion were sewn together via SOE PCR as previously described (Section 4.2.2). These were cloned into pTargetF containing the sgRNA using HindIII and XhoI (NEB). The plasmids containing both the sgRNA and the repair template for homologous recombination are termed pTargetT. For repairing strains generated via allelic

Chapter 4

exchange, the sgRNA was cloned into pTargetF using BS_FRT_sgRNA_F, and the repair template was amplified from genomic DNA using BS_pTT_Ec15_F and BS_pTT_Ec15_R for the stxS1 deletion, and BS_pTT_Ec65_F and BS_pTT_Ec65_R for the stxS2 deletion. For repairing strains generated by CRISPR-Cas9, sgRNAs targeting the partial and full deletions of stxS2 were cloned into pTargetF using BS_delstxSLrep_gRNA_F and BS_delstxS2rep_gRNA_F2, respectively. The repair template was amplified from genomic DNA as was done for the repair of the allelic exchange deleted stxS2.

All incubations were done at 30oC unless specified otherwise. The pCas plasmid was electroporated into E. coli O157:H7 str. Sakai stx+ as outlined in Section

2.2.6. Transformants were streaked out for single colonies on LB-Km50 plates.

Single colonies were incubated overnight in LB-Km50 broth, then subcultured

1/100 the next day. Once an OD600 of 0.45-0.6 was reached, λ-Red was induced by adding 50 mM of L-arabinose for 30 minutes. Cells were harvested and the desired pTargetT plasmid was electroporated as above. Transformants were plated onto LB-Km50Sm50 plates. Colony PCR was done to verify that the desired modification was present, and plasmids were cured by streaking out positive o colonies onto LB-Km50 containing 0.5 mM IPTG and incubated at 42 C overnight.

4.2.4 Genomic DNA sequencing of mutants

To check for successful chromosomal deletions and repairs, genomic DNA was extracted using the Wizard® Genomic DNA extraction kit (Promega, cat. no. A1120) according to the manufacturer’s instructions. Libraries were prepared using the Nextera XT DNA Library Prepkit (Illumina) and sequenced on a MiSeq platform. The quality of the raw reads were checked using FastQC (Andrews et al., 2010), and mapped to the Sakai genome using the Burrows-Wheeler Aligner (BWA)(Li and Durbin, 2010). The output sam file was converted to the bam file format using samtools (Li et al., 2009). SNPs were called using bcftools, and low- quality SNPs were filtered out using in-house Python scripts. Indels were called by manual inspection by visualising bam files on the Integrative Genomics Viewer (Robinson et al., 2011; Thorvaldsdóttir, Robinson and Mesirov, 2013).

Chapter 4

4.2.5 TBE-Urea polyacrylamide gel electrophoresis

RNA was extracted as per Section 2.3.1. Electrophoresis of RNA was done on 8% polyacrylamide gels in TBE-urea (1x TBE, 8M urea, 8% acrylamide). TEMED and 1% APS was added before casting the gel. The gel was prewarmed by running the gel for 30 minutes at 200V. RNA samples were prepared by mixing with 2x formamide loading buffer (80% formamide, 10 mM EDTA, 0.025% bromophenol blue, 0.025% xylene cyanol) and incubated at 65oC for 10 minutes. Samples were loaded and run for at least 3 hours at 200V in a 16.5 x 14.5 x 0.4 mm gel.

4.2.6 Northern blot

RNA run on polyacrylamide gels were transferred onto a nylon membrane at 30V in 0.5X TBE buffer for at least 4 hours. RNA was immobilized on the membrane by UV-crosslinking in a Stratagene Auto-Crosslinker with 1200 mJ of UV. Pre- hybridization of the membrane was done by wetting the membrane with Ambion™ ULTRAhyb™ Ultrasensitive hybridization buffer (cat no. AM8670) at 42oC for 30 minutes. Oligonucleotides for probing membranes are 30-35 nucleotides long and were designed on Netprimer and Integrated Genome Browser. Probes were labelled with 32P-ATP using T4 polynucleotide kinase (1.5 µL 10 mM oligonucleotide probe, 2.5 µL 32P-ATP, 1.5 µL 10X PNK buffer, 1 µL T4 PNK, 9 µL MilliQ) for 1 hour at 37oC. Radiolabelled probes were purified and separated from excess nucleotides using a GE Healthcare Illustra Microspin™ G-50 column (cat no. 27-53301) according to the manufacturer’s instructions. Radiolabelled probes were added to the prehybridized membrane and incubated overnight at 42oC. The next day, the membrane was washed three times by incubating with 2x SSPE buffer (0.3M NaCl, 20mM NaH2PO4, 2mM EDTA) with 0.1% SDS for 10 minutes at 42oC. Membranes were exposed on a BAS Storage Phosphor Screen and imaged on a Typhoon™ FLA9500.

4.2.7 5’RLM-RACE to identify transcription start sites and processing sites

Chapter 4

5’ RLM-RACE was performed as previously described (Fromont-racine et al., 1993; Liu and Gorovsky, 1994) with some modifications. Conversion of triphosphate ends of total RNA into monophosphates was done by mixing 6 µg of total RNA, 1 µL of 10X reaction buffer, and 1 unit of tobacco acid pyrophosphatase (TAP, Epicentre, discontinued) or 1 µL of RNA 5’ pyrophosphohydrolase (RppH, NEB, cat no. M0356S) in a 10 µL reaction and incubating at 37oC for 1 hour. A 5’ RNA linker was ligated by taking 2 µL of the TAP/RppH treated RNA and adding 200 pmol of the linker, 1 µL of 10X T4 RNA ligase buffer, and 2 µL of T4 RNA ligase (NEB, cat no. M0204S) in a 10 µL reaction and incubating at 16oC overnight. RNA is ethanol precipitated, and reverse transcribed to cDNA using Superscript IV Reverse Transcriptase (ThermoFisher, cat no. 18090010) as in section 2.3.2. StxS or StxSL was amplified from cDNA using P5 as the forward primer and either BS_StxS_RACE_R or BS_StxSL_RACE_R as the reverse primer, respectively. Amplicons were cloned into pGEM®-T Easy (Promega, cat no. A1360) by A- tailing the PCR products using Taq polymerase (NEB, cat no. M0267S) followed by TA-cloning according to the manufacturer’s instructions.

4.2.8 Phylogenetic analysis of the Q antiterminator and StxS

Whole genome sequences for different STEC isolates were downloaded from Genbank. BLASTn searches were done to detect Q antiterminators in the genome associated with either the Shiga toxins or the StxS sRNA. These sequences were extracted and aligned using MUSCLE (Edgar, 2004). A maximum-likelihood phylogenetic tree was inferred using FastTree 2.1 (Price, Dehal and Arkin, 2010).

4.2.9 Transient inactivation of RNase E

E. coli str. N3431 (rne-3071) containing pBR322 or pBR322::stxS was grown at o 28 C in LB-Amp100 medium to an OD600 of 2.0. RNase E was inactivated by shifting the incubation temperature to 44oC for 30 minutes before harvesting total RNA (Babitzke and Kushner, 1991; Chao et al., 2017).

Chapter 4

4.2.10 RNase-E CRAC

Identification of RNase E binding sites transcriptome wide was done using CRAC as described previously (Tree et al., 2014). Briefly, overnight cultures of E. coli O157:H7 str. Sakai rne-HTF (chromosomal copy of RNase E tagged with the HTF dual-affinity-tag) were sub-cultured into 800 mL of minimum M9 media and grown to an OD600 of 2.0. Cells were crosslinked with 1800 mJ of UV-C in a W5 small diameter UV-crosslinker system (UVO3) as described in (Granneman, Petfalski and Tollervey, 2011). Cells were pelleted at 4,000 xg for 10 minutes, resuspended in 40 mL of ice-cold PBS and re-pelleted at 4,000 xg for 15 minutes. Pellets were snap-frozen in liquid nitrogen. To the pellet, 1.5 mL of lysis buffer

[50 mM Tris-HCl (pH7.8), 1.5 mM MgCl2, 150 mM NaCl, 0.1% Nonidet P-40, 5 mM β-mercaptoethanol and 1 tablet/50 mL of cOmplete™ Protease Inhibitor cocktail (Roche)] and 3 mL of 0.1 mm zirconia beads were added. Cell lysis was done by vortexing for 8 minutes at 1-minute intervals with a 1-minute rest on ice between each interval. Lysate was cleared by adding 2 mL of lysis buffer and centrifuging at 4000 xg for 20 minutes. The clarified lysate was transferred into 1.5 mL tubes and further clarified by centrifuging at 16,000 xg for 20 minutes. Clarified lysates were loaded onto 75 µL of M2 Anti-FLAG resin (Sigma-Aldrich, cat no. A2220) that was washed three times with TNM150 (50 mM Tris-HCl pH 7.8, 150 mM NaCl, 0.1% Nonidet P-40, and 5 mM β-mercaptoethanol) and incubated for 2 hours at 4oC. Resin was washed three times each with TNM1000 (50 mM Tris-HCl pH 7.8, 1 M NaCl, 0.1% Nonidet P-40, and 5 mM β- mercaptoethanol) and TNM150. Following the washes, resin was resuspended in 500 µL of TNM150 and incubated with 230U of TEV protease for 2 hours at 18oC. The resin was gravity filtered through a Bio-Rad Micro Bio-Spin® Chromatography column (cat. no. 7326204) to collect the eluate. Approximately 0.15 units of RNace-It™ Ribonuclease Cocktail (Agilent, cat. no. 400720) was added to 500 µL of the TEV eluate and incubated for 7 minutes at 20oC. Samples were immediately placed on ice for 1 minute and transferred to 1.7 mL microcentrifuge tubes containing 0.4 g of guanidinium hydrochloride, 300 mM NaCl and 10 mM imidazole (pH 8.0) to terminate the reaction. RNace-IT™ treated

Chapter 4

eluates were loaded onto 100 µL of Ni-NTA slurries that were washed three times with 700 µL of wash buffer I (6 M guanidine-HCl, 50 mM Tris-HCl pH 7.8, 300 mM NaCl, 0.1% NP-40 and 5 mM β-mercaptoethanol). This was incubated overnight on a rotating platform at 4oC. Resin was washed three times each with ice-cold wash buffer I and 1X PNK buffer (50 mM Tris-HCl pH 7.8, 10 mM MgCl2, 0.5% Nonidet P-40, and 5 mM β-mercaptoethanol) and transferred onto a Pierce™ Spin Column (ThermoFisher, cat. no. 69705). All subsequent reactions are done on-column in 80 µL volumes. The 3’ ends of the crosslinked RNAs were dephosphorylated by incubating the resin with 4 units of thermosensitive alkaline phosphatase (TSAP, Promega, cat no. M9910) and 2 units of recombinant RNase inhibitor (rRNAsin, Promega, cat no. N2511) in PNK reaction buffer (250 mM Tris-HCl pH 7.8, 50 mM MgCl2, 50 mM β-mercaptoethanol) for 1 hour at 20oC. Resin was washed once wash buffer I and three times in 1X PNK buffer. The 5’ ends of crosslinked RNA were radiolabelled by incubating the resin with 4 µL of T4 polynucleotide kinase (NEB, cat no. M0201S) and 3 µL of 32P-γATP (PerkinElmer, cat no. BLU502A250UC) in PNK reaction buffer for 100 minutes at 20oC, followed by spiking the reaction with 12.5 µM ATP, then incubating further for 50 minutes. The resin was washed three times with wash buffer I and three times with 1X PNK buffer. The 3’ miRCat-33 linker was ligated onto the crosslinked RNA by incubating with 4 µL of T4 RNA ligase I, 8 µl of 10 mM miRCat-33 and 2 µL of rRNAsin in PNK reaction buffer for 16 hours at 16oC. The resin was washed once with wash buffer I and three times with 1X PNK buffer. The 5’ linkers were ligated onto the bound RNA by incubating for 6 hours at 16oC with 4 µL of T4 RNA ligase I (NEB, cat no. M0204S), 1 µL of 100 mM of 5’ linker, 2 µL of rRNAsin, and 1 µL of 10 mM ATP in PNK reaction buffer. The resin was washed once with wash buffer I and three times with wash buffer II (50 mM Tris- HCl pH 7.8, 50 mM NaCl, 10 mM imidazole, 0.1% NP-40 and 5 mM β- mercaptoethanol). To elute the RNase E-RNA complexes, columns were capped and incubated on ice for 10 minutes in 200 µL of elution buffer (50 mM Tris-HCl pH 7.8, 50 mM NaCl, 150 mM imidazole, 0.1% NP-40 and 5 mM β- mercaptoethanol) was added. Complexes were eluted into a clean microcentrifuge tube and the elution was repeated. To 400 µL of elution, 100 µL

Chapter 4

of trichloroacetic acid and 2 µL of GlycoBlue co-precipitant was added and incubated on ice for 1 hour. Samples were centrifuged for 30 minutes at 14000 xg at 4oC to precipitate the complexes. Supernatant was removed and pellet was washed with 800 µL of ice cold acetone. Samples were spun again at 16000 xg for 20 minutes at 4oC. Supernatant was removed and pellet was air dried on ice before resuspending in 20 µL of 1X NuPAGE® LDS Sample buffer (Invitrogen, cat no. NP0008). RNase E-RNA complexes were run for 80 minutes in NuPAGE® MOPS SDS running buffer (Invitrogen, cat no. NP0001) at 120V. The radiolabelled complexes were visualised by exposing the gel onto a Kodak BioMax MS film. The band corresponding to RNase E, as well as a portion of the gel above it was excised. The gel slice was fragmented by centrifugation through small diameter hole in a 1.5 ml centrifuge tube and 400 µL of wash buffer II supplemented with 1% SDS, 5 mM EDTA and 100 µg of proteinase K was added and incubated at 55oC for 2 hours. To the supernatant, 50 µL of 3M NaOAc (pH 5.2) and 500 µL of 25:24:1 phenol:chloroform:isoamyl alcohol was added. Samples were vortexed and centrifuged for 5 minutes at 16000 xg. The aqueous layer was transferred into a clean microcentrifuge tube containing 1 mL of ice- cold absolute ethanol. Samples were incubated at -80oC for at least 30 minutes then centrifuged for 30 minutes at 4oC. Supernatant was removed and pellet was washed twice with 700 µL of 70% ethanol, then air-dried. The pellet was resuspended in 9 µL of MQ, 2 µL of 100 mM reverse transcription oligonucleotide and 2 µL of 5 mM dNTPs. Extracted RNA was reverse transcribed using SuperScript III reverse transcriptase according to the manufacturer’s protocol. Libraries were amplified from 2 µL of cDNA using Takara LA Taq with P5 and PE_miRCat PCR primers for 224 cycles. 8 reactions were pooled together and DNA was ethanol precipitated. PCR products were run on a 3% MetaPhor™ agarose (Lonza BioSciences, cat no. 50181) gel. Amplicons, as well as the smears above them up to 100 bp were gel extracted using a MinElute gel extraction kit (Qiagen, cat no. 28004). The libraries were pooled and sequenced by the Ramaciotti Centre for Genomics using paired-end 75 bp NextSeq500. RNase E binding sites were identified using the PyCRAC software package as described in (Tree et al., 2014; Webb et al., 2014).

Chapter 4

4.2.11 MS2-affinity purification and sequencing (MAPS)

MAPS was performed as described in (Lalaouna et al., 2015, 2017). Briefly, the 5’ end of StxS was tagged with the MS2 affinity tag by amplifying the sRNA from genomic DNA using BS_MAPS_EcOnc65_F and BS_MAPS_EcOnc65_R and cloning into pBAD-MS2 using EcoRI and SphI (NEB). pBAD-MS2 plasmids were transformed into E. coli O157:H7 str. Sakai stx- ΔstxS1/stxS2 by electroporation

(Section 2.2.6) and plated onto LB-Amp100. An overnight culture was prepared and subcultured 1/100 into 100 mL of LB-Amp100. At an OD600 of ~1.0 (stationary phase), expression from the pBAD-MS2 plasmid was induced with 0.1% L- arabinose and incubated for 10 minutes. Following induction, 50 mL of culture was put on ice for 10 minutes, and cells were harvested by centrifuging at 4000 xg for 15 minutes at 4oC. The supernatant was removed and the pellet was resuspended in 2 mL of Buffer A (20 mM Tris-HCl pH 8.0, 150 mM KCl, 1 mM

MgCl2 and 1 mM DTT). Approximately 1 mL of 0.1 mm zirconia/silica beads (Daintree Scientific, cat no. 1107101z) were added, and cells were lysed on a vortex five times for 1 minute each with 1-minute rests on ice in between. Another 1.5 mL of Buffer A was added, and the lysate was cleared by spinning at 4000 xg for 20 minutes at 4oC. The soluble fraction was transferred into 1.5 mL microcentrifuge tubes and spun at 16000 xg for 20 minutes at 4oC to remove any remaining cell debris. To prepare the affinity purification column, amylose resin was washed with 2 mL of Buffer A, loaded with 200 pmol of MS2-MBP-His6 and washed again with 2 mL of Buffer A. All washes were done by gravity-filtration on ice. The lysate was loaded onto the column and allowed to pass through. The column was washed with 8 mL of Buffer A. MS2-RNA complexes were eluted twice with 200 µL of elution buffer (Buffer A with 15 mM maltose). RNA was extracted from the eluate using a phenol-chloroform extraction, then treated for one hour at 37oC with RQ1 RNase-free DNase (Promega, cat no. M6101). Following the reaction, the RNA was re-extracted with phenol-chloroform, and submitted to the Ramaciotti Centre for Genomics for RNA-seq on the NextSeq platform. Sequencing data was analysed as in Section 2.3.4.

4.2.12 Small RNA control of superfolder GFP translational fusions

Chapter 4

Interactions between rpoS and StxS were investigated through the use of the two-plasmid system described in (Urban and Vogel, 2007). An rpoS-GFP translational fusion was made by amplifying the 5’UTR of rpoS plus the first 30 base pairs of the coding sequence from genomic DNA extracted from E. coli O157:H7 str. Sakai using primers BS_RpoS_5UTR_F and BS_RpoS_5UTR_R and cloning into pXG10SF using NheI and NsiI (FastDigest, ThermoFisher). stxS1 and stxS2 were cloned into pZE12-luc as described in Section 3.2.2 using primers BS_EcOnc15_ZE12_5p_F and BS_EcOnc15_ZE12_R and BS_EcOnc65_ZE12_5P_F and BS_EcOnc65_ZE12_R, respectively. Point mutations were introduced to rpoS and stxS2 were using the Quikchange® XL mutagenesis kit (Agilent) according to the manufacturer’s instructions with primers BS_RpoS_T466C_F and BS_RpoS_T466C_R, and BS_EcOnc65_A190C_F and BS_EcOnc65_A190C_R, respectively.

Fluorescence of the rpoS-sfGFP translational fusion was quantified using a BD FACSCanto II as described in Section 3.2.4 with slight modifications. Overnight cultures are grown in 0.22 µm-filtered LB broth in 15 mL Falcon tubes and diluted 1/5 in 0.22 µm-filtered PBS the next day before being read on the flow cytometer.

100

Chapter 4

4.2.13 Small RNA control of enhanced GFP-transcriptional fusions

An rpoS-gfp transcriptional fusion was made by first amplifying the promoter, 5’UTR and first 30 nucleotides of rpoS from genomic DNA using BS_pAJR_RpoS_F and BS_pAJR_RpoS_R2. The product was cloned into pAJR70 using BamHI-HF (NEB) as in (Tree et al., 2011). The plasmid was confirmed via colony PCR and Sanger sequencing, and was electroporated into E. coli O157:H7 str. Sakai stx- and E. coli O157:H7 str. Sakai stx- ΔstxS1/S2. An overnight culture was subcultured 1/100 into either 0.22 µm-filtered LB broth or minimum M9 medium, and the OD600 and fluorescence was measured every hour for 9 hours using a FLUOStar Omega Microplate Reader (BMG Labtech).

4.2.14 Phage plaquing assays

The double-agar overlay method was used to measure plaque formation and phage titration (Islam et al., 2012; Bonanno et al., 2016). Plastic 10mm x 10 mm square petri plates were used containing approximately 35 mL of bottom LB agar. Top LB agar is prepared by mixing 10g/L of tryptone, 5 g/L of yeast extract, 10 g/L of NaCl with 2 g/L of agar. To 10 mL of molten top LB agar, 10 mM of CaCl2,

10 mM of MgSO4 and 1.5 µg/mL of mitomycin C (Sigma-Aldrich, cat no. M0503) and 500 µL of indicator bacteria (an overnight culture of E. coli K-12 str. MG1655) was added (Islam et al., 2012; Bonanno et al., 2016). The top agar is vortexed and poured onto the bottom agar layer and allowed to dry.

Phage were prepared by growing cells overnight, then subculturing 1/100 into 10 mL of LB broth containing 5 mM CaCl2. At an OD600 of ~0.3, 0.5 µg/mL of mitomycin C was added, and cultures were incubated at 37oC with shaking overnight (Islam et al., 2012; Bonanno et al., 2016). The next day, cells were spun down at 4000 xg for 15 minutes, and the supernatant was filtered through 0.22-µm low-protein binding PES filters (Millex GP). Filtrates were serially diluted in LB broth containing 5 mM CaCl2, and were spot plated onto the top agar (5 µL per spot). This was incubated overnight at 37oC, and plaques were enumerated the next day.

101

Chapter 4

4.2.15 Quantitative RT-PCR

The ratio of phage genomes to bacterial genomes was assessed using qPCR. E. coli O157:H7 str. Sakai stx- and mutants of interest were grown in LB-broth containing 0.25 µg/mL of mitomycin C. Genomic DNA was extracted at 0,5,8,10 and 12 hour time points using the ISOLATE II Genomic DNA Kit (Bioline) following the manufacturer’s protocol for blood DNA extraction to retain DNA packaged in phage particles. Genomic DNA was quantified using a Qubit dsDNA HS Assay Kit (ThermoFisher, cat. no. Q32851). The expression of stx2a and uidA were measured using primer pairs Stx2F1218 and Stx2R1300 and uidAF241 and uidAR383, respectively with the SensiFast™ SYBR® Rox+ Kit (Bioline). qPCR reactions were carried out in a Rotor-Gene Q (Qiagen). Conditions were set at an initial incubation of 95oC for 3 minutes, followed by 40 cycles at 95oC for 5 seconds, 65oC for 10 seconds and 72oC for 15 seconds.

4.2.16 Growth curve measurements

The growth curves of E. coli O157:H7 str. Sakai stx- in various nutrient conditions were measured using the Bioscreen C MBR (Growth Curves USA, Piscataway,

NJ). Overnight cultures in LB broth were first diluted 1/10 and their OD600 were measured using a spectrophotometer. Wild-type E. coli O157:H7 str. Sakai stx- was diluted 1/100 into three wells containing 300 µL of the desired growth media in a Bioscreen Honeycomb 10well plate (ThermoFisher, cat no. 9502550). Two wells containing only LB, minimum M9 or MEM-HEPES medium were used as blanks. The OD600 measurement of the wild type was used to standardise the amount of starting inoculum required for the other strains. The plate was o incubated in the Bioscreen C at 37 C with continuous low shaking. OD600 readings were taken every 20 minutes for 24 hours. Growth experiments were performed at least twice, and representative data are presented.

102

Chapter 4

4.2.17 Measurement of Stx production in EHEC

Overnight cultures of wild-type and ΔstxS1ΔstxS2 strains of EHEC str. Sakai stx+ in LB broth were washed and subcultured 1/100 into 1X minimum M9 medium. Samples were collected after 4, 6, 8 and 10 hours and centrifuged at 16,000 xg for 1 minute. Culture supernatants containing the toxin were diluted 1/50 in 1X minimum M9 medium and used in a RIDASCREEN® Verotoxin (R-Biopharm) ELISA according to the manufacturer’s instructions.

103

Chapter 4

4.3 Results

4.3.1 The Shiga toxin promoter PR’ transcribes an Hfq-binding sRNA

Transcriptome-wide Hfq-binding sites in E. coli O157:H7 str. Sakai have previously been identified using UV-crosslinking of Hfq-RNA complexes (Tree et al., 2014). This study identified 55 putative novel sRNAs along the pathogenicity islands of EHEC, with 7 located on the Sp5 Stx2 bacteriophage, and 4 on the Sp15 Stx1 bacteriophage (Figure 4.1 and 4.2). A subset of these sRNAs bookend the genes encoding the Shiga toxins, and one of these sRNAs, AsxR, has been shown to be a sRNA sponge for FnrS (Tree et al., 2014). In the Stx1Φ, the region between the constitutively active late promoter PR’ and the late terminator tR’ was found to be strongly associated with Hfq and was designated EcOnc15 in the Hfq

CRAC data set. Another Hfq-binding site was found in the PR’ to tR’ region of Stx2Φ, but was not annotated as a probable sRNA due to its proximity to a putative ORF. Given the sequence length of the region, the nature of tR’ as a Rho-independent terminator, and the strong association with Hfq, it was hypothesised that EcOnc15 was highly likely to be a functional trans-acting sRNA.

Northern analysis was initially used to probe for EcOnc15 in RNA extracted from E. coli O157:H7 str. Sakai stx(-) grown under a range of conditions and growth stages. Given that this putative sRNA is expressed from the Shiga toxin promoter

PR’, EcOnc15 was renamed Stx small RNA (StxS). Conditions tested included exponential and stationary phase cultures grown in LB broth, minimal M9 medium, and MEM-HEPES supplemented with 0.1% glucose and 250 nM of

Fe(NO3)3. The region between PR’ and tR’ is 255 nucleotides, however, a ~74- nucleotide transcript corresponding to the 3’ end of StxS was detected across all conditions (Figure 4.3A). The transcript was most highly abundant in stationary phase cultures, as well as cultures grown in minimal M9 media or MEM-HEPES (Figure 4.3A). The detection of this shorter fragment indicated either a processing event by a ribonuclease, or the presence of an internal promoter.

104

Chapter 4

Figure 4-1: Transcriptional landscape in Stx2Φ. RNA-sequencing datasets are shown for the Stx2Φ. Total RNA-sequencing reads are indicated on the left. Positive strand is on top, negative strand on the bottom. Differential RNA-seq data is shown in orange (TEX+) and green (TEX-). Hfq-binding data is shown in cyan and RNase E-binding data is shown in dark blue. Transcription start sites are shown in purple. ORFs are in gray, stx2AB is in red, Q is in green. Major phage genes and promoters, and Stx2Φ -encoded sRNAs are indicated. Data is presented in raw number of reads for dRNA-seq, and RPKM for RNase E and Hfq-CRAC. 105

Chapter 4

Figure 4-2: Transcriptional landscape in Stx1Φ. RNA-sequencing datasets are shown for the Stx1Φ. Total RNA-sequencing reads are indicated on the left. Positive strand is on top, negative strand on the bottom. Differential RNA-seq data is shown in orange (TEX+) and green (TEX-). Hfq-binding data is shown in cyan and RNase E-binding data is shown in dark blue. Transcription start sites are shown in purple. ORFs are in gray, stx1AB is in red, Q is in green. Major phage genes and promoters, and Stx2Φ -encoded sRNAs are indicated. Data is presented in raw number of reads for dRNA-seq, and RPKM for RNase E and Hfq-CRAC.

106

Chapter 4

Figure 4-3: Shiga-toxin encoding bacteriophages transcribe a small non-

coding RNA from the late promoter PR’. A. Northern blot of StxS expression in LB, minimum M9 and MEM-HEPES medium. RNA was collected at mid-

exponential phase (OD600 = 0.6), late exponential phase (OD600 = 1.0), and early

stationary phase (OD600 = 2.0). B. Northern blot for detection of StxS in WT and strains containing StxS deletions in either Stx1 (ΔstxS1), Stx2 (ΔstxS2), or both (ΔstxS1ΔstxS2). C. Schematic showing the wild-type Q-stx2AB region in Stx2Φ. Q (green), StxS (blue), and Stx2AB (red) are indicated. Approximate positions of

the late phage promoter PR’ and late terminator tR’ are indicated. Bottom shows the region deleted from the StxS mutants.

107

Chapter 4

4.3.2 StxS is transcribed by both Stx1Φ and Stx2Φ

To determine whether one or both phages were responsible for the transcription of StxS, single and double deletions of the PR’-tR’ region in Stx1Φ (StxS1) and Stx2Φ (StxS2) were constructed in E. coli O157:H7 str. Sakai stx(-) using allelic exchange (Merlin, Mcateer and Masters, 2002) (Figure 4.2C). To generate these mutants, the region downstream of the putative PR’ promoter identified in (Wagner et al., 2001) and upstream of the Rho-independent terminator predicted using ARNold was deleted (Naville et al., 2011). The deletions were confirmed using PCR. The StxS transcript was detected in both single deletions but not the double deletion strain, indicating that the StxS sRNA is transcribed from both Stx phages (Figure 4.3B).

4.3.3 StxS is processed by RNase E into a stable transcript

The Shiga toxins are transcribed from the lambdoid late promoter PR’ (Schmidt, 2001; Wagner et al., 2001). Transcription start sites across the EHEC transcriptome were mapped using dRNA-seq, which allowed for identification of triphosphorylated and monophosphorylated 5’ ends throughout the transcriptome. This method identified the location of well-characterised lambdoid phage promoters such as PL and PRM which drives the transcription of the replication protein O and the phage repressor cI, respectively. Through this, the late phage and stxAB promoter PR’ was detected in both Stx1 and Stx2 phages positioned at 2926201 and 1266203, respectively. Two additional transcription start sites were located between PR’ and stx2AB. One of these sites was downstream of tR’ and upstream of tRNA genes and is likely the promoter. The other site corresponded to the 5’ end of the previously detected StxS, suggesting that the sRNA is a primary transcript that is expressed from this internal promoter

(Figure 4.4A). To determine if StxS is transcribed from this promoter, or from PR’, the 5’ end of the detected StxS transcript was mapped using RLM-RACE with or without tobacco acid pyrophosphatase (TAP). Primary transcripts are triphosphorylated, preventing adapter ligation. TAP removes triphosphates from the 5’ ends of RNA and this was used to identify whether StxS was a primary

108

Chapter 4

transcript or a product of a processing event. PCR amplification and Sanger sequencing of the RLM-RACE products indicated that the 74 nt transcript detected by the Northern blot had a monophosphorylated 5’ end and is generated by ribonuclease cleavage (Figure 4.4B).

RNase E is the major endoribonuclease in E. coli, with the steady state levels of 60% of annotated coding sequences in E. coli K-12 being affected by a deletion of RNase E (Stead et al., 2011). RNase E binding sites in the E. coli O157:H7 str. Sakai transcriptome grown in minimal M9 media were identified using RNase E CRAC (Section 4.2.10). Analysis of the sequenced libraries showed significant

RNase E binding at the 5’ end of the StxSS fragment (Figure 4.4). Cleavage by RNase E occurs at RN↓WUU motifs (Chao et al., 2017). This motif was detected

8 nucleotides upstream of the 74 nt transcript (StxSS) and mapped to the 5’ monophosphorylated RNA found using dRNA-seq (Figure 4.4A), supporting

RNase E processing of StxS to generate the shorter 74nt StxSs sRNA.

To verify StxS processing by RNase E, the full sequence from PR’ to tR’ was cloned into pBR322 and transformed into E. coli N3431, a strain with a temperature sensitive RNase E allele (rne-3071). Accumulation of the full length

255 nt StxS transcript (StxSL) was observed upon shifting the culture to the non- permissive temperature (44oC) for 30 minutes (Figure 4.4C). Taken together, these results show that the late phage promoter PR’ encodes a 255 nt sRNA that is processed by RNase E into a stable 74 nt regulatory sRNA.

109

Chapter 4

StxS2 StxS1 StxS StxS 60000 StxS L StxS S 20000 S L

q 40000 q f f 10000 H 20000 H 0 0 50000 25000 (+) (+) 25000 12500 TEX TEX 0 0 2000 1500

(-) (-) 1000 1000 TEX TEX 500 0 0

E 50 E 30 20 25 10 RNase RNase RNase 0 0 1266100 1266200 1266300 1266400 1266500 2925900 2926000 2926100 2926200 2926300 GT ATT GT ATT

300 200

100

Figure 4-4: StxS is expressed from the PR’ promoter as a 255 nt transcript that is processed by RNase E into a 74 nt sRNA. A. Total RNA-sequencing data for StxS2 (left) and StxS1 (right). Raw number of reads are indicated on the left. Hfq-binding data is shown in cyan, dRNA-seq data is shown in orange (TEX+) and green (TEX-) and RNase E binding data is shown in blue. The coordinates and sequence of the RNase E cleavage site are indicated. B. 5’

RLM-RACE analysis of StxSS. RNA was collected from Sakai grown in minimal (M9) or virulence-inducing (MEM-HEPES). RLM-RACE was performed in the presence of absence of tobacco acid pyrophosphatase (TAP). C. Northern blot to detect full-length StxS. The sRNA and its native promoter were cloned into pBR322 and expressed in a temperature-sensitive RNase E mutant of E. coli. At stationary phase, cells were grown for an additional 30 minutes in the permissive (28oC) or non-permissive temperature (42oC).

110

Chapter 4

4.3.4 The presence of StxS is correlated with the primary sequence of Q

To understand whether the presence of StxS is correlated with more pathogenic serotypes or Shiga toxin subtypes of EHEC, the conservation of this sRNA was investigated. Majority of the serotypes that expressed StxS produced either the Stx1a or 2a toxin subtypes (Figure 4.5). The sRNA sequence was not identified in serotypes producing Stx2b-2f, but were found in those producing the 1c and 1d subtypes. In the serotypes where StxS was found, the 74 nucleotide 3’ end was 100% conserved, with only minor variations in the 5’ region. The conservation of the 3’ end of StxS suggests it plays a major role in StxS function.

A previous characterisation of Stx phages in STEC producing different Shiga toxin subtypes showed a correlation between the type of Q protein present and the level of phage induction (Steyert et al., 2012). Among the Q sequences covered in the study, the Q proteins that correlated with intermediate levels of Stx phage induction were found to have the StxS sRNA. To investigate this further, the Q amino acid sequences from the serotypes covered in the previous study, major disease causing non-O157 serotypes, O26:H11, O45:H2, O103:H2, O111:H8, O121:H19 and O145:H28 and other serotypes that produce different Shiga toxin subtypes were aligned and a phylogenetic tree was inferred. Q sequences from phage lambda and ΦKO20 that do not encode StxS were included in the analysis. Q proteins found in Shiga-toxigenic bacteriophages were found to be either 144 or 207 amino acids long, and this was reflected in the two major clades observed after inferring a phylogenetic tree (Figure 4.6). The presence of StxS was correlated with the presence of the shorter Q sequence, which is generally found upstream of the Stx1a and 2a toxin subtypes. Surprisingly, StxS could also be found in bacteriophages that did not produce Shiga toxin, but utilised the shorter 144 amino acid Q sequence. This included EPEC O55:H7, which is considered to be a progenitor of the EHEC1 lineage (Zhou et al., 2010). These proposed results suggest that StxS is generally expressed in Stx-encoding bacteriophages that produce the Stx 1 and 2a subtypes which have been associated with severe human disease.

111

Chapter 4

Figure 4-5: Alignment of StxS from different Shiga-toxigenic bacteria. Indicated are the q-utilisation site and the RNase E cleavage site. E. coli strains and serotypes are indicated, and genus species for non-E. coli strains. The subtype of Shiga toxin associated with each StxS are indicated, with genomic coordinates noted if there were none.

112

Chapter 4

StxS Associated stx O174:H8 str. DG131 1c 1c 82 O55:H7 str. CB9165 EPEC (-) O26:H11 str. 11368 1a 1a O103:H2 str. 12009 1a 1a O45:H2 str. FWSEC0003 1a 1a 98 O103:H2 str. 12009 2a 2a O145:H28 str. RM13514 2a 2a O121:H19 str. 2014C-4423 1a 1a 14 O121:H19 str. FWSEC0006 1a 1a O157:H7 str. EC4115 2964795 (-) O139:H1 str. S1191 2e 2e 12 74 100 O8:H19 str. MHI813 1d 1d O36:H42 str. 2014C 3075 1d 1d O111:H8 str. 2013C-4081 2a 2a 97 F765 2a 2aδ 90 O157:H7 str. EDL933 1a 1a 85 O157:H7 str. Sakai 1a 1a O111:H8 str. 2013C-4081 1a 1a F422 2a 2aγ O157:H7 str. EDL933 2aQ 2a 84 O157:H7 str. Sakai 2a 2aα 54 O157:H7 str. EC4115 2a 2a O45:H2 str. FWSEC0003 2132370 (-) O118:H12 str. EH250 contig11 (-) F403 2a 2aβ WGPS8 2c 2c O36:H14 str. 06-00048 2149995 (-) 99 O48:H21 str. 94c 1a 1a O48:H21 str. 94c 2a 2a 99 O91:H21 str. B2F1 2d 2d 82 O113:H21 str. RM10466 2a1 2a O91:H21 str. FWSEC0008 2d2 2d 93 98 O79:H7 str. 2011c-3198 2c 2c WGPS2 2c 2c 99 O36:H14 str. 06-00048 2g 2g 67 O26:H(-) str. S17-13 2e 2e 66 ΦKO3 (-) 91 O73:H18 str. C165-02 2d 2d 99 O2:H25 str. 7V 2g 2g 19 O174:H8 str. DG131 2b 2b O91:H21 str. FWSEC0008 2d1 2d 73 48 O157:H7 str. EC4115 2c 2c 90 WGPS4 2c 2c O118:H12 str. EH250 2b 2b O26:H11 str. 11368 3642537 (-) 46 96 O145:H28 str. RM13514 5088107 (-) 65 O26:H11 str. 11368 2202823 (-) O111:H8 str. 2013C-4081 2796603 (-) 100 O113:H21 str. RM10466 2a2 2a 100 O113:H21 str. RM10466 2d 2d O91:H21 str. FWSEC0008 2a 2a 88 λ (-) 83 O63:H6 str. 377323 2f 2f 100 O145:H34 str. 2015C-4136CT1 2f 2f

Figure 4-6: The presence of StxS may be related to the primary sequence of the anti-terminator Q. Maximum likelihood phylogenetic tree showing the relationship of Q proteins between different strains and serotypes of EHEC. Green dots indicate the presence of StxS downstream of Q, and the subtype of Shiga toxin associated with each anti-terminator is given.

113

Chapter 4

4.3.5 StxS does not affect the induction of the Shiga-toxin producing Sp5 prophage

Lambdoid bacteriophages express non-coding RNAs that regulate the lytic- lysogenic decision, such as OOP and an anti-sense Q mRNA. To determine if StxS played a role in maintaining Stx phage lysogeny, plaquing assays were performed on the single and double deletions of StxS made in E. coli O157:H7 str. Sakai stx(-) using allelic exchange. Single deletions of StxS in this background (JJT378 and JJT379) had no effect on phage induction, though a 10- fold decrease was observed in the double deletion strain (JJT380) (Figure 4.7A). Wild-type levels of induction could not be restored using a plasmid encoded copy of StxS and notably, transcription of StxS from this complementation construct was confirmed in an earlier analysis (Figure 4.3B). The complementation plasmid does not transfer into the recipient strain during transduction, preventing expression of the sRNA in the host. Notably, while the mitomycin C induction of the wild-type strain led to clearing of the culture (through Stx2Φ cell lysis), the ∆stxS1 ∆stxS2 double deletion culture did not clear after mitomycin C induction suggesting the Stx phages were inactive (Figure 4.7B). To track the accumulation of phage genomes following induction, total genomic DNA was isolated at four different time points from cultures grown in the presence of mitomycin C. The amount of phage lysis was assessed by measuring the ratio of StxΦ to bacterial genomes using qPCR and measuring the quantity of the stx2 gene relative to the control gene uidA. The number of phage genomes was ~10-fold higher in the wild-type strain compared to the ΔstxS1ΔstxS2 mutant, showing decreased phage induction upon deletion of the sRNA (Figure 4.7C). This phenotype could not be complemented using the plasmid-encoded StxS, suggesting that the decrease in phage induction may be due to secondary mutations.

To determine if secondary mutations had occurred during construction of the stxS deletion strains, the genomes of each mutant was sequencing using Illumina short read sequencing. Genome sequencing confirmed the StxS1 and StxS2 deletions (Supplementary Figures S1-S2) and showed that the non-lambdoid phage Sp7 was excised in the process of making the double deletion mutant. The 114

Chapter 4

mutants also contained large deletions in the Sp11 and Sp12 cryptic prophages (Supplementary Table S2). These prophages are cryptic but are known to be prone to genomic instability (Asadulghani et al., 2009; Chen et al., 2013).

These secondary mutations may have been introduced during the extensive subculturing used to generate mutants using allelic exchange. To reduce the number of generations between the parental and stxS mutants strains, CRISPR-Cas9 (Jiang et al., 2015) was used to generate a single stxS2 deletion in EHEC str. Sakai stx+. Additionally, to assess whether the processed 5’ region of StxS was required for StxS function, a partial deletion of stxS2 that started after PR’, maintaining the qut site, and ended at the RNase E cleavage site was constructed (Δpre-stxS2). Phage plaquing assays showed a 10-fold reduction in phage induction in the StxS2 deleted strains following exposure to mitomycin C. Both the full and partial deletions were repaired using CRISPR-Cas9, however this did not restore the phage-induction to wild-type levels (Figure 4.7D). During the process of making this set of deletions, it was observed that the Stx2 phage was being excised during the plasmid curing process (data not shown). It is likely that constitutive expression of Cas9 from the pCas plasmid caused persistent DNA damage, resulting in the induction of the SOS response and excision of the entire bacteriophage. It is possible that selecting for the mutant with the stxS2 deletions but still harboured the bacteriophage was also selecting for phage that had reduced lysis potential. It was also not feasible to construct a stxS double mutant due to the high sequence similarity of the sRNA, but not of the regions that would be used for homologous recombination.

In summary, single and double deletions of StxS were made in EHEC str. Sakai stx- (JJT378-380), and full and partial deletions of stxS2 were made in EHEC str. Sakai stx+ (JJT953, JJT957, JJT1027 and JJT993). Phage plaquing assays performed showed reduced phage lysis upon induction with mitomycin C in both sets of mutants. Secondary mutations in Sp7, Sp11 and Sp12 prophages may influence the lysis potential of this set of mutants, while limitations in CRISPR- Cas9 editing of StxS in Sakai stx+ may result in selecting for mutants with reduced lysis potential. Due to the limitations of using CRISPR-Cas9, single and 115

Chapter 4

double deletions of StxS were made in E. coli O157:H7 str. Sakai stx(+) using allelic exchange (JJT384, JJT385 and JJT438). In addition, the single deletions were repaired using CRISPR-Cas9 (Jiang et al., 2015), using a gRNA designed to target the FRT sequence that is introduced using allelic exchange (JJT701 and JJT702). The FRT sequence allowed for specific targeting of Stx1Φ or Stx2Φ. These mutants were confirmed using PCR and whole genome sequencing, and no significant secondary mutations, insertions or deletions were detected (Supplementary Figure S1 and S2, Supplementary Table S2). There were no detectable changes in plaquing in single, double or repaired mutants of stxS (Figure 4.7E). Taken together, this shows that StxS does not contribute to Stx2Φ induction or propagation, and suggests that this Hfq-binding sRNA may regulate gene expression outside of the phage lytic-lysogenic decision.

116

Chapter 4

Figure 4-7: Expression of StxS has no effect on Stx2Φ plaquing. A. Spot dilutions of StxΦ titers prepared from wild type EHEC str. Sakai stx- and isogenic mutants. Wild-type backgrounds and method of mutant construction are indicated. Strain names and their genotypes are indicated B. Growth of wild type and ΔstxS1ΔstxS2 EHEC str. Sakai stx- in LB medium supplemented with 250 ng/mL of mitomycin C. Quantitative PCR was used to calculate the ratio of phage (stx2) to bacterial (uidA) genomes. Error bars represent standard deviations of triplicate experiments. D. Spot dilutions of StxΦ titers prepared from wild type EHEC str. Sakai stx+ and isogenic mutants generated by CRISPR-Cas9. E. Spot dilutions of StxΦ titers prepared from wild type EHEC str. Sakai stx+ and isogenic mutants generated by allelic exchange.

117

Chapter 4

4.3.6 The stable StxS transcript activates rpoS translation similarly to core- genome sRNAs

Phage propagation experiments indicated that StxS does not act in cis, and instead may be a trans-acting Hfq-binding sRNA. To identify the mRNA targets of StxS, data generated previously from EHEC RNase E-CLASH, which profiles sRNA-RNA interactions by UV-crosslinking of sRNA-RNA pairs to a chromosomally dual-affinity tagged RNase E, was analysed. The sRNA interactome dataset was analysed by extracting all RNA-RNA hybrids that mapped to the StxS region in both Stx1Φ and Stx2Φ (Figure 4.8A) (Waters et al., 2017). Statistically significant interactions of StxS were found with the stationary phase stress sigma factor rpoS, the aldehyde-alcohol dehydrogenase adhE, molybdenum cofactor guanylyltransferase mobA, ribosome modulation factor rmf, hypothetical ORF ECs5419 and the yciF gene (Table 4.4). All the StxS hybrids observed mapped to the StxSS region, which is consistent with a functional role for the processed 74 nt fragment (Figure 4.8B). In silico predictions of the rpoS-StxS interaction using IntaRNA supports that StxS uses the same seed region with the rpoS 5’ UTR as the other well-characterised rpoS-targeting sRNAs DsrA, RprA and ArcZ (Figure 4.8C) (Majdalani et al., 2001; Mandin and Gottesman, 2010; McCullen et al., 2010; Mann, Wright and Backofen, 2017). The region of rpoS and StxS recovered in the RNase E-CLASH hybrids also contained these predicted interaction sites, supporting the IntaRNA prediction.

118

Chapter 4

Table 4-4: StxS hybrids recovered from RNase E CLASH containing more than 1 hybrid read. Chiastic hybrids are defined as being detected in both the 5’-sRNA-mRNA-3’ and 5’-mRNA-sRNA-3’ directions

Target Free energy of StxS Start End Target Start End Chiastic Adjusted p- common Total unique hybrids interaction (∆G allele sRNA sRNA RNA mRNA mRNA hybrids* value name kcal/mol) rpoS 5' StxS1 2926027 2925960 ECs3596 3588240 3588153 59 True -31.5 0 UTR rpoS 5' StxS2 1266378 1266423 ECs3596 3588284 3588123 54 True -25.1 0 UTR StxS1 2926006 2925943 ECs1741 adhE 1742187 1742098 10 True -34.5 0 StxS1 2926025 2925991 ECs4780 mobA 4838016 4837964 8 True -24.1 4.94E-07 StxS1 2925984 2925957 ECs1037 rmf 1145291 1145366 7 True -28.5 0.00018145 StxS2 1266378 1266413 ECs0850 nleD 928691 928743 7 True -23.8 0.00039463 StxS2 1266384 1266410 ECs0639 ybdL 715022 715055 5 True -18.5 0.00053373 StxS1 2926025 2925993 ECs0850 nleD 928692 928731 4 0 -20.9 0.00215123 hyp. StxS1 2926025 2925988 ECs2695 2652829 2652791 3 True -24.5 0.00480161 protein StxS1 2925990 2925963 ECs0423 hemB 450300 450268 3 0 -21.8 0.01285124 StxS1 2926029 2925996 ECs3550 gshA 3540046 3540009 2 True -23.3 0.02403665 StxS1 2926023 2925995 ECs0801 xis 892220 892170 2 0 -11.6 0.00656416 StxS1 2925988 2925962 ECs1041 ompA 1148833 1148793 2 0 -18.4 1.58E-09

119

Chapter 4

120

Chapter 4

Figure 4-8: The StxΦ PR’ transcribed sRNA activates the general stationary phase and stress sigma factor rpoS. A. Schematic describing the general workflow of RNase E CLASH (Waters et al., 2017). RNA is UV-crosslinked to RNase E chromosomally tagged with a His-TEV-FLAG dual-affinity tag in vivo. RNase E-RNA complexes are first purified using FLAG, then during His purification of the RNA-protein complexes, linkers are ligated to either side of crosslinked RNAs. This may also result in ligation of sRNA-mRNA pairs (hybrids) due to their proximity. These hybrids can be detected after sequencing of libraries. B. Statistically significant StxS-mRNA hybrids recovered by RNase E CLASH (Waters et al., 2017). Interacting mRNAs are indicated on the left. Coloured regions indicate the number of hybrids recovered and region of StxS ligated to the target mRNA. C. Alignment of StxSS with rpoS-interacting sRNAs ArcZ, DsrA and RprA. Nucleotides in bold and with asterisks on the bottom represent the conserved seed region. D. Predicted interaction between StxS and rpoS. Indicated are the base changes used for compensatory point mutations. E. Fluorescence of the rpoS-sfGFP translational fusion was measured in the presence and absence of StxS. Fluorescence of cells carrying compensatory point mutations were also measured. Fluorescence is presented as the mean median fluorescence intensity of three biological replicates F. Fluorescence of an rpoS-eGFP transcriptional fusion was measured in wild type and ΔstxS1 ΔstxS2 Sakai stx- grown in minimal M9.

121

Chapter 4

To verify the StxS interactions recovered by RNase E-CLASH and identify further RNA interaction partners, MS2 affinity purification and sequencing (MAPS) was employed (Lalaouna et al., 2015, 2017). Tagging a sRNA of interest with the MS2 aptamer allows for the co-purification and identification of its targets by sequencing. The 5’ end of StxSS was tagged with the MS2-aptamer by cloning into the pBAD-MS2 vector. To maximise mRNA target enrichment, the MS2- tagged StxSS was transformed into the JJT380 ΔstxS1ΔstxS2 mutant of O157:H7 str. Sakai stx(-) to prevent competition with endogenously expressed StxS (Section 2.2.6.). Statistical analysis of sequencing reads enriched by StxS-MAPS indicated a significant interaction between StxS and the EHEC-specific sRNA EcOnc07, as well as enrichment of rpoS and adhE supporting the published RNase E-CLASH dataset (Figure 4.9).

To confirm the interaction of StxS with rpoS, an rpoS-sfGFP translational fusion under the control of the constitutive P LtetO-1 promoter was constructed by cloning the 5’UTR and the first 30 nucleotides of the rpoS coding region into the pXG10SF vector. Both the full-length StxS1 and StxS2 sRNAs were cloned into the pZE12 vector, and transcription was driven by the constitutive PLlacO-1 promoter. The fluorescence of the rpoS-sfGFP translational fusion was monitored by flow cytometry in the presence and absence of StxS in DH5α. RpoS-sfGFP fluorescence was five-fold higher in the presence of StxS compared to a scrambled sRNA control vector indicating that StxS activates RpoS expression (Figure 4.8E). Point mutations that disrupt the predicted StxS-rpoS interaction were made in either the sRNA or the mRNA seed region, and these resulted in the loss of activation of rpoS-sfGFP translation. The point mutations in both the sRNA and the mRNA seed regions were designed so that pairing the mutants would restore sRNA-mRNA complementarity (Figure 4.8D). Indeed, when both mutants were transformed into DH5α, the activation of rpoS-GFP was restored (Figure 4.8E). Taken together, these results show that the StxS activates translation of rpoS through direct base-pairing using the same seed region as known rpoS-activating sRNAs DsrA, RprA and ArcZ.

122

Chapter 4

To confirm whether the activation of rpoS occurred in EHEC, a transcriptional fusion of rpoS with eGFP was constructed by cloning the native promoter, 5’UTR and the first 31 nucleotides of rpoS into the medium-copy vector pAJR70. This was transformed into the wild type and ΔstxS1ΔstxS2 mutant of O157:H7 str. Sakai stx(-) and fluorescence was measured during growth. A significant increase in translation of the eGFP fusion was detected in the wild-type strain compared to the ΔstxS1ΔstxS2 mutant, consistent with the results in DH5α. The increased fluorescence was observed during the early to late stationary phases of growth, consistent with earlier northern blot analyses demonstrating StxS accumulation at these growth stages (Figure 4.8F). These experiments confirm the function of StxS as a trans-acting sRNA that directly activates the translation of the stationary phase stress response sigma factor, RpoS.

123

Chapter 4

Figure 4-9: MS2-affinity purification to identify StxS targets. A. Schematic detailing the general workflow of MAPS. StxS is tagged with the MS2 aptamer by cloning into pBAD-MS2. MS2-tagged StxS is expressed in ΔstxS1ΔstxS2 Sakai. Cells are lysed, and lysates are loaded onto amylose resin containing MS2 protein. After washing, MS2-tagged RNA-RNA complex are eluted and sequenced. B. Sequencing reads for MS2-tagged StxS (red) and the control (green) are shown and verified rpoS, adhE and EcOnc07 as StxS targets.

124

Chapter 4

4.3.7 StxS is needed for EHEC to reach higher cell densities in late stationary phase

RpoS is a sigma factor that is required for adaptation to stationary phase, as well as regulation of genes needed for the general stress response (Gottesman, 2019). RpoS has been found to regulate approximately 23% of the annotated genes in E. coli str. K-12, and affects the expression of over 1,000 genes in O157:H7 str. EDL933 (Dong and Schellhorn, 2009; Wong et al., 2017). In EHEC, RpoS has been linked to pathogenicity and colonisation efficiency in the gastrointestinal tract. For example, the various acid resistance systems in EHEC are RpoS-dependent, and ΔrpoS mutants show complete abrogation of the glucose-repressed AR1 system, and reduced levels of the glutamate-dependent AR2 system and arginine-dependent AR3 system (Price et al., 2000).

The activation of RpoS via the StxS sRNA suggests that this sRNA may play a role in allowing EHEC to resist environmental stress or achieve maximal growth. Deletion of StxS did not affect acid or osmotic stress (Figure 4.10C and D). In order to test whether StxS could affect the growth of EHEC, growth curves for wild-type O157:H7 Sakai stx-, single and double mutations of StxS and an ΔrpoS mutant were measured in rich and minimal medium for 24 hours. All strains grew similarly in rich LB broth and DMEM media, however in minimal M9 medium, the double deletion mutant grew to a lower cell density in stationary phase compared to the wild-type strain (Figure 4.10A-B). Complementation of the double deletion mutant with a plasmid-encoded, constitutively transcribed StxS restored the stationary phase growth cell density of this strain to wild-type levels, though the lag phase was somewhat extended. The reduced stationary phase cell density was observed in M9 medium even when the in vivo-relevant alternate carbon sources galactose and mannose were utilised (Figure 4.10E) (Bertin et al., 2013). To understand if StxS activation of RpoS is responsible for the observed growth defect, StxS and RpoS epistasis was analysed using a ΔstxS1 ΔstxS2 ΔrpoS triple knockout strain. This strain showed further reduced stationary phase cell density compared to the wild type and double deletion strains, and this could be complemented using a plasmid-borne rpoS but not stxS. This shows that StxS 125

Chapter 4

acts epistatically with RpoS to increase cell density in stationary phase (Figure 4.10F). The differences in stationary phase cell density was further quantified by performing a viable cell count on the wild-type, ΔstxS1ΔstxS2, and ΔrpoS strains after 16 hours of growth in minimal medium. There was an 18% and 21% decrease in cell count in the double deletion and RpoS mutants, respectively, compared to wild type EHEC (Figure 4.10B). These results demonstrate that the StxS growth defect is dependent on RpoS and that StxS and RpoS act epistatically to promote high cell density in stationary phase during nutrient limited growth.

126

Chapter 4

127

Chapter 4

Figure 4-10: StxS promotes growth to higher cell densities in minimum media in an RpoS dependent manner. A. Growth of wild-type (filled black circles), ΔstxS1ΔstxS2 (grey inverted triangles) and ΔrpoS (black squares) EHEC str. Sakai in LB (i), M9 (ii) and MEM-HEPES (iii). Complementation is shown in M9 using a constitutively transcribed stxS (iv). B. CFU counts of wild- type, ΔstxS1ΔstxS2 and ΔrpoS EHEC str. Sakai after 16 hours of growth in M9. C. Wild-type, ΔstxS1ΔstxS2 and ΔrpoS EHEC str. Sakai grown in LB supplemented with (i) 400 mM NaCl (osmotic stress) or (ii) M9 pH 5.5 (mild acid stress). D. Acid shock assay to measure survival after exposure to pH 2.5 (top) or 7.0 (bottom). Strain names are indicated on the left, with their respective genotypes below. E. Growth of wild-type, ΔstxS1ΔstxS2, and ΔstxS1ΔstxS2 containing empty or complementation plasmid in M9 supplemented with galactose (left) or mannose (right). F. Growth of wild-type (black circle) ΔstxS1ΔstxS2ΔrpoS (grey triangle) in minimal M9 media supplemented with glucose. StxS (black diamond) or RpoS (inverted grey triangle) was complemented using pBR322 under their respective native promoters. (*p < 0.05, **p < 0.01, ***p < 0.001)

128

Chapter 4

4.3.8 Deletion of StxS causes higher production of Shiga toxin

Deletion of rpoS has previously been shown to result in increased levels of Shiga toxin production (Leenanon, Elhanafi and Drake, 2003). Furthermore, it has been shown that overexpression of rpoS in Stx phage lysogens of E. coli K-12 resulted in reduced Stx phage production (Imamovic et al., 2016). To determine whether StxS activation of RpoS affects production of Shiga toxin, levels of Stx produced by wild-type and ΔstxS1ΔstxS2 strains of EHEC str. Sakai stx+ grown in minimum M9 medium were measured using the ELISA-based RIDASCREEN® Verotoxin assay. Consistent with earlier findings, ΔstxS1ΔstxS2 EHEC str. Sakai stx+ did not reach high cell density at stationary phase when compared to the wild-type strain. However, at early (OD600=2.0) and late (OD600 ≥ 3.0) stationary phase, a three-fold increase in production of Shiga toxin was observed in the ΔstxS1ΔstxS2 strain (Figure 4.11A). To assess whether StxS-mediated activation of toxin production was due to an increase in Stx2Φ induction brought about by decreased levels of rpoS, phage particles were collected from mitomycin C induced and uninduced cultures of wild-type and ΔstxS1ΔstxS2 strains of EHEC str. Sakai grown in minimum M9 medium. No significant difference in phage induction was found between mitomycin C induced wild-type and deletion strains, and no phage production was observed in uninduced cultures (Figure 4.11B). This suggests that increased levels of Shiga toxin production may be attributed to an increase in Stx1, the expression of which is controlled by both the late phage promoter PR’ and the Fur-regulated PStx1. Taken together, these results show that the late phage promoter PR’ controls the expression of a regulatory sRNA StxS that indirectly represses Shiga toxin production, possibly in an RpoS-dependent manner.

129

Chapter 4

Figure 4-11: Deletion of StxS promotes expression of Shiga toxin independent of phage induction. A. Shiga toxin ELISA of wild-type (black triangles) and ΔstxS1ΔstxS2 (black diamond) strains of EHEC str. Sakai stx+ measuring toxin production of Stx at various stages of growth. B. Phage plaquing assay for wild-type and ΔstxS1ΔstxS2 strains of EHEC grown in minimum M9 medium in the presence or absence of mitomycin C (MMC).

130

Chapter 4

4.4 Discussion

Bacteriophages have played a major role in bacterial evolution and pathogenesis due to their ability to facilitate horizontal gene transfer. These phages can modulate the bacterial stress response to allow the host to adapt to different environmental niches through phage-encoded toxin-antitoxin systems and cell division inhibiting proteins. Cryptic phages also transfer virulence related genes (Wang et al., 2010). E. coli O157:H7 str. Sakai, is an archetypical EHEC that contains 18 prophages as well as 6 prophage-like elements, which comprise 16% of its genome (Hayashi et al., 2001). These horizontally transferred elements give this strain the genes that define its virulence, such as the stx1 and stx2 toxins, as well as type III secretion system effector proteins. Bacteriophages can also contribute to post-transcription regulation by expressing regulatory sRNAs. DicF, one of the first discovered sRNAs, is encoded on the cryptic Qin prophage and is an inhibitor of cell division (Bouché and Bouché, 1989; Balasubramanian et al., 2016). In EHEC, prophage-encoded sRNAs act as sponges for core-genome sRNAs GcvB and FnrS (Tree et al., 2014). This chapter introduces a novel sRNA

StxS that is transcribed from the constitutively active late promoter PR’ in Shiga toxin encoding bacteriophages during lysogeny. The results show that StxS is a positive regulator of RpoS and contributes to the overall fitness of EHEC.

It has been suggested that the primary sequence of Q may be associated with the regulation of the Shiga toxin (Steyert et al., 2012). Analysis of Q antiterminators in different Shiga-toxin encoding bacteriophages across the different subtypes of Stx has indicated that the presence of StxS may be tied to the primary sequence of Q. The presence of StxS is more frequently observed in strains that produce the Stx1a and 2a toxins, which cause more serious disease (Melton-Celsa, 2014) (Figure 4.5).

RpoS is the sigma factor responsible for adaptation to the transition to stationary phase as well as various environmental stresses such as heat, acid, starvation and oxidative stress (Gottesman, 2019). RpoS is also a key contributor to the virulence of many bacterial pathogens such as Salmonella and Vibrio. RpoS can 131

Chapter 4

directly regulate virulence genes and metabolic genes required for adaptation to host environments during infection (Dong and Schellhorn, 2010). In EHEC, RpoS affects the expression of 1135 genes, including genes related to acid adaptation and the LEE encoded type 3 secretion system, both of which are necessary for efficient colonisation of the gastrointestinal tract (Dong and Schellhorn, 2009). In this chapter, RpoS was found to be activated by the StxΦ-encoded sRNA StxS. Deletion of the sRNA did not affect adaptation to acidic stress and only a mild effect on osmotic stress (Figure 4.10C-D) potentially due to the redundancies in RpoS activating signals, such as the RprA and DsrA sRNAs (Majdalani et al., 2001; Bak et al., 2014). The ΔstxS1ΔstxS2 mutant grows to a significantly lower cell density in stationary phase in minimal media as compared to the wild type. This phenotype is not observed in rich media. Using a ΔstxS1ΔstxS2ΔrpoS triple knockout, it was shown that this phenotype is due to StxS activation of RpoS. As this is observed in minimal but not rich media, it is possible that StxS allows EHEC to better adapt to starvation stress. StxS may be an RpoS-activating signal that would allow EHEC to gain a growth advantage over other bacteria during colonisation.

RpoS has also been previously linked to regulation of Shiga toxin expression (Leenanon, Elhanafi and Drake, 2003; Imamovic et al., 2016). Therefore, StxS activation of RpoS was likely to affect expression of Stx. Using the ELISA-based RIDASCREEN® Verotoxin assay on wild-type and ΔstxS1ΔstxS2 strains of EHEC str. Sakai stx+, it was demonstrated that the mutant strain produces a higher basal level of toxin when grown in nutrient-poor conditions (Figure 4.11A). This increased level of toxin expression however was not due to activation of Stx2Φ induction (Figure 4.11B), suggesting that the increase in Shiga toxin production may be due to higher production of Stx1, which is controlled by both

PR’ and the Fur-regulated PStx1 (Wagner et al., 2002). Some overlap has been observed in the RpoS and Fur regulons (Lee et al., 2003; Lacour and Landini, 2004), which may indicate that StxS can indirectly repress transcription of stx1AB. In commensal strains of E. coli, RpoS mutants have previously been found to have twofold lower intracellular iron levels when compared to wild-type strains due to decreased expression of RpoS-dependent iron acquisition genes 132

Chapter 4

such as fepBD and entCBA (Dong, Kirchhof and Schellhorn, 2008). StxS may indirectly regulate toxin levels by lowering levels of intracellular iron in a RpoS- dependent manner. The resultant decrease in iron levels would in turn prevent

Fur repression of PStx1, allowing for increased Shiga toxin production. The increase in toxin production following deletion of StxS is also consistent with hfq mutants of EHEC producing higher amounts of Stx (Kendall et al., 2011). Indirect post-transcriptional regulation of Shiga toxin production by StxS may provide a threshold for stxAB transcription and prevent needless toxin expression, especially in nutrient-poor conditions. To confirm whether StxS regulates Shiga toxin production in an RpoS-dependent manner, a ∆stxS ∆rpoS mutant can be made in EHEC str. Sakai stx+. Following confirmation of the mutant, toxin levels can be measured using an ELISA.

The PR’-tR’ region of the Stx phage is approximately 255 nucleotides long, however the detected stable sRNA was only 74 nucleotides. The primary StxSL transcript is processed by RNase E into a functional transcript with a monophosphorylated 5’ end. Maturation of another processed sRNA ArcZ by RNase E cleavage activates its seed region (Chao et al., 2017). Given that StxS utilises the same seed region as ArcZ, processing by RNase E is likely to activate its seed region in the same manner. Using transcriptional and translational fusions of rpoS with GFP demonstrated that StxS directly activates RpoS. ArcZ, DsrA and RprA activate RpoS by preventing secondary structure formation at the 5’UTR and by blocking Rho transcription termination (Soper et al., 2010; Sedlyarova et al., 2016). StxS utilises the same seed region used by other RpoS activating sRNAs ArcZ, DsrA and RprA (Figure 4.6E). This suggests that StxS may activate RpoS in the same way as ArcZ, RprA and ArcZ. These results show that premature termination of the Shiga toxin transcript during lysogeny results in the generation of a functional sRNA that can activate translation of RpoS either through anti-antisense binding or through anti-termination of Rho.

Regulation of phage genes by anti-termination during lysogeny is common among phages, and the PR’-tR’ architecture is widespread in many lambdoid bacteriophages. It is possible that this region could provide sequence space for 133

Chapter 4

the evolution of sRNA-like features such as base-pairing seed regions and chaperone binding sites and as such would be an unexplored reservoir for functional sRNAs. The ubiquity of prophages within bacterial genomes would suggest that sRNAs that arise from anti-termination regulated promoters may be prevalent. The expression of StxS from the PR’ promoter, along with the prevalence of the lambdoid phage architecture suggests that PR’ in other phages may also encode functional sRNAs. An example of this class of sRNA can be found in Salmonella, where the IsrK sRNA is transcribed by the cryptic Gifsy-1 prophage and activates the translation of the downstream CDS. Translation of this longer transcript via the shorter IsrK isoform activates the toxic AntQ protein (Hershko-Shalev et al., 2016).. In the archetype bacteriophage λ, the late promoter PR’ has been found to produce a short 200 nt transcript known as λ 6S RNA (not to be confused with the bacterial 6S RNA also known as SsrA) (Jeffrey Sklar, Yot and Weissman, 1975; Roberts, 1975). There is no currently known function for this non-coding RNA. The results of this chapter suggest that it is plausible that λ 6S RNA may act as a trans-acting small regulatory RNA, though more work will need to be done to identify its function.

In summary, this chapter has demonstrated the expression of a functional trans- encoded sRNA StxS from the late StxΦ phage promoter PR’. StxS is processed by RNase E into its 74-nucleotide functional form and interacts with the stationary phase and general stress sigma factor RpoS. StxS activation of rpoS allows EHEC to grow to higher cell densities in minimal media. This sRNA also represses expression of the Shiga toxin in a phage-independent manner, possibly through indirectly blocking Stx1 transcription from the Stx1 promoter by increasing intracellular iron levels in an RpoS-dependent manner. This sRNA may represent a novel class of sRNAs that arise from anti-terminated promoters and demonstrates the role phage-encoded sRNAs have in maintaining host fitness and potentially their pathogenicity.

134

Chapter 5

Chapter 5: Identifying the pathogen-specific RNA-binding proteome of EHEC using TRAPP

5.1 Introduction RNA-binding proteins (RBPs) are essential molecules that are present in all forms of life. The most prevalent example are ribosomal proteins that are required for biogenesis of the ribosome and translation of mRNAs. In addition to their role in translation, RBPs play a key role in a myriad of cellular processes such as immunity, transcription termination, tRNA biogenesis and RNA decapping (Santangelo and Artsimovitch, 2011; Van Der Oost et al., 2014; Shepherd and Ibba, 2015; Höfer et al., 2016; Marbaniang and Vogel, 2016). RBPs are also essential components of post-transcriptional regulatory networks and regulate gene expression (Holmqvist and Vogel, 2018). In bacteria, RBPs typically exert their influence on post-transcriptional networks by regulating RNA stability and decay, translation initiation and transcript termination (Hör, Gorski and Vogel, 2018). The most common examples of RNA-binding proteins involved in post- transcriptional regulation are ribonucleases required for RNA metabolism, the RNA chaperones Hfq and ProQ, and the carbon storage regulator CsrA. Hfq, ProQ and CsrA are involved in networks that control various physiological processes, such as the responses to stress and to changing environments.

RNA-binding proteins are also key factors in the virulence of bacterial pathogens. In gram-negative bacteria, Hfq and ProQ are RNA chaperones that are required to facilitate base-pairing between sRNAs and their targets. While this is the most well-studied role for post-transcriptional regulation by Hfq, this RBP is also able to directly control translation by binding to 5’UTR of its targets. In P. aeruginosa, Hfq can bind to nascent transcripts and recruit the catabolite-repression control protein Crc. Recruitment of Crc stabilises the interaction of Hfq with its target mRNA (Kambara, Ramsey and Dove, 2018; Sonnleitner et al., 2018). Hfq has already been found to be essential for the virulence of V. cholerae, Y. pseudotuberculosis, and Y. enterocolitica (Ding, Davis and Waldor, 2004; Schiano, Bellows and Lathem, 2010; Kendall et al., 2011). Both Hfq and ProQ are required for full virulence of S. typhimurium (Sittka et al., 2007; Westermann

135

Chapter 5

et al., 2019). In EHEC, Hfq can affect the expression of genes expressed on the LEE pathogenicity island such as those required for type III secretion (T3S) and adhesion, as well as genes required for adaptation to the gastrointestinal tract (Hansen and Kaper, 2009; Shakhnovich, Davis and Waldor, 2009; McAteer et al., 2018; Melson and Kendall, 2019). The conserved endonuclease YbeY is also required to maintain sufficient levels of mature ribosomes, which in turn stabilises T3S in EHEC and virulence gene expression in V. cholerae and Y. enterocolitica (Vercruysse et al., 2014; Leskinen, Varjosalo and Skurnik, 2015; McAteer et al., 2018).

UV-crosslinking of RNAs to RNA-binding proteins coupled with RNA-sequencing allows for rapid advances in research on the RNA-interactome and post- transcriptional networks. Crosslinks induced by UV, as opposed to other crosslinking techniques such as formaldehyde, are covalent and irreversible. This allows for more stringent processing steps and trimming of RNA to identify precise RNA-binding sites on proteins (Brimacombe et al., 1988). In bacteria, the RBPs Hfq, ProQ, CsrA, YbeY, RelA, CspC/E and RNase E have all been used as scaffolds for crosslinking to better understand post-transcriptional regulons (Tree et al., 2014; Holmqvist et al., 2016, 2018; Melamed et al., 2016; Michaux et al., 2017; Waters et al., 2017; McAteer et al., 2018; Winther, Roghanian and Gerdes, 2018). Principal component analysis of Grad-seq profiles in Salmonella typhimurium has shown that there was an abundance of sRNAs that do not bind to either Hfq or ProQ, suggesting that there are undiscovered global RBPs (Smirnov et al., 2016). The central role RBPs have on post-transcriptional regulation make the discovery of novel RBPs a significant undertaking.

Purification of polyadenylated RNAs UV-crosslinked to RNA-binding proteins followed by mass spectrometry has been used to identify novel RBPs in eukaryotes (Baltz et al., 2012; Castello et al., 2012). The primary limitation of this + method however, is that it relies on the capture of RNAs that have poly(A) tails, which is only typical of mature eukaryotic mRNAs. As such, the RNA-binding proteins of prokaryotes, which do not have polyadenylated mRNAs, cannot be identified. Recently, novel total RNA-binding proteome (RBPomes) capture 136

Chapter 5

techniques been developed that overcome these limitations. In eukaryotes, labelling of newly transcribed RNAs with 5-ethynyluridine (5-EU), followed by UV- crosslinking and biotinylation using click chemistry has been used to identify the total RBPome of various cell lines (Bao et al., 2018; Huang et al., 2018). While this method allows for the capture of total RBPomes, they have the similar disadvantage of being exclusive to eukaryotic systems, as 5-EU labelling is currently only suitable for certain cell culture systems.

Recently, methods based on phase separation have been developed to extract the total RBPome. In orthogonal organic phase separation (OOPS), and protein- crosslinked RNA extraction (XRNAX), UV-crosslinked RNA-protein complexes are extracted using acidic guanidinium thiocyanate (GTC)-phenol chloroform. Non-crosslinked RNAs or proteins migrate to the aqueous or organic phases, respectively, while UV-crosslinking of RNA to an RBP combines their biochemical properties and results in migration to the interphase. Both methods were able to extract the total RBPome in a poly(A)+-independent manner, and OOPS was used successfully to identify RBPs in both human cells and commensal E. coli K- 12 (Queiroz et al., 2019; Trendel et al., 2019). PTex is a variation of these methods, and includes the addition of a phenol-toluol phase separation step before the acidic GTC-phenol extraction, allowing removal of DNA and lipid contaminants to enrich the crosslinked ribonucleoproteins (RNPs) before separating them from the non-crosslinked RNAs and proteins. This method was used to identify RBPs in human HEK293 cells as well as in Salmonella typhimurium (Urdaneta et al., 2019). These methods represented a significant leap forward in discovering RNA-binding proteins, as these methods could be generically applied to any cell type without the need for complex chemistry or specific sequence requirements.

Total RNA-associated protein purification (TRAPP) is also an organic extraction- based method for purifying the RBPome. Instead of a phase separation approach however, TRAPP uses silica beads to purify denatured RNA-protein complexes that have been UV-crosslinked in vivo. This is followed by nuclease treatment to digest the extracted RNAs. The protein that remains is subject to in-gel digestion 137

Chapter 5

with trypsin followed by mass spectrometry (Figure 5-1A). This method has been used to identify the RBPome in both S. cerevisiae as well as in commensal E. coli K-12. In yeast, PAR-TRAPP is an extension of the TRAPP method where cells are labelled with 4-thiouridine prior to UV-irradiation to improve crosslinking efficiency (Shchepachev et al., 2019).

Both OOPS and TRAPP performed in commensal E. coli recovered proteins not expected to be RNA-binding. Examples of these include enzymes that are related to glycolysis and NAD-binding (Queiroz et al., 2019; Shchepachev et al., 2019). Enterohaemorrhagic E. coli (EHEC) is a significant foodborne pathogen responsible for outbreaks of haemorrhagic colitis and haemolytic uremic syndrome. Many of the virulence factors in EHEC are encoded by pathogenicity islands that have been obtained by this pathotype by horizontal gene transfer. The representative EHEC strain Sakai has a 5.5 Mb genome, of which it shares 4.1 Mb of DNA with the commensal E. coli str. MG1655 (Hayashi et al., 2001). The remaining 1.4 Mb that arises from horizontal transfer presents a potential reservoir for the discovery of novel pathogen-specific RNA-binding proteins, particularly those that can play a role in bacterial virulence. Horizontally transferred elements have already been shown to be a reservoir for post- transcriptional regulators in the form of small non-coding RNAs (Tree et al., 2014; Waters et al., 2017). YopD, an effector protein that is a part of the T3S of Yersinia, is an example of a pathogen-specific RNA-binding protein. YopD can regulate effector synthesis and secretion by forming a complex with its chaperone LcrH. The YopD-LcrH complex can bind to target mRNAs at short-AU rich regions (Chen and Anderson, 2011; Kopaskie, Ligtenberg and Schneewind, 2013).

In this chapter, TRAPP is applied to enterohaemorrhagic E. coli O157:H7 str. Sakai to capture the total RBPome in conditions that are conducive to the expression of T3SS genes. The RNA-binding properties of several candidates were then confirmed using 32P-labelling of RBP complexes using polynucleotide kinase. Performing TRAPP in EHEC identified novel, putative pathogen-specific RNA-binding proteins, including the effector proteins EspY2 and EspN. These

138

Chapter 5

may provide novel avenues for understanding EHEC pathogenesis and RNA metabolism.

139

Chapter 5

5.2 Materials and Methods

5.2.1 Strains, plasmids and primers

Bacterial strains, plasmids and primers used for this chapter are listed in Tables 1-3. E. coli was routinely grown in either LB broth or MEM-HEPES supplemented with 0.1% glucose and 250 nM Fe(NO3)3. When necessary, ampicillin (100 µg/mL) or kanamycin (50 µg/mL) were added to the growth media.

Table 5-1: Bacterial strains used in Chapter 5

Strains Description Reference O157:H7 str. Sakai stx- Δstx1 stx2A::kan, KanR (Dahan et al., 2004)

O157:H7 str. Sakai stx- hfq-HTF hfq-HTF (Tree et al., 2014) O157:H7 str. ZAP193 YbeY-HTF ybeY-HTF (McAteer et al., 2018)

Table 5-2: Plasmids used in Chapter 5

Plasmid Description Reference arabinose inducible expression of sfGFP, used as (Malagon, pBAD24-sfGFP-x1 plasmid backbone 2013) pBAD24-ECs0073 ECs0073 with no stop codon This study pBAD24-ECs0604 ECs0604 with no stop codon This study pBAD24-ECs1163 ECs1163 with no stop codon This study pBAD24-ECs1184 ECs1184 with no stop codon This study pBAD24-ECs1762 ECs1762 with no stop codon This study pBAD24- ECs0073-HTF arabinose inducible expression of ECs0073-HTF This study pBAD24- ECs0604-HTF arabinose inducible expression of ECs0604-HTF This study pBAD24- ECs1163-HTF arabinose inducible expression of ECs1163-HTF This study pBAD24- ECs1184-HTF arabinose inducible expression of ECs1184-HTF This study pBAD24- ECs1762-HTF arabinose inducible expression of ECs1762-HTF This study pBAD24-sfGFP- HTF arabinose inducible expression of sfGFP-HTF This study (Tree et al., pTOF25-hfq-HTF amplify HTF for RBP-tagging 2014)

140

Chapter 5

Table 5-3: Oligonucleotides used in Chapter 5

Oligonucleotide Sequence Purpose cloning RBP gBlocks into BS_RBP_GB_F GTTTTTTTGGGCTAGCAGGA pBAD24 cloning RBP gBlocks into BS_RBP_GB_R GTTTTTGCCAAAACAGCCAAGC pBAD24 BS_pBAD24_Se q_F ATCGCAACTCTCTACTGTTTC sequencing pBAD24 GTTTTTTCTAGACGCTCTTCCTGCA cloning HTF tag into pBAD24- BS_HTF_XbaI_F GGATGGAG RBP plasmids GTTTTTGCATGCTCACGCGGCCGC cloning HTF tag into pBAD24- BS_HTF_SphI_R AGAATTCTCA RBP plasmids BS_pBAD24_sfG ATTAGCGGATCCTACCTGACGCTT FP_F TTTATC cloning sfGFP into pBAD24 BS_pBAD24_sfG GTTTTTTCTAGATTTGTACAGTTCA FP_R TCCATACCAT cloning sfGFP into pBAD24 BS_TEV_R ACCCTGAAAATACAAATTCTCGC screen for HTF-tagged RBPs

5.2.2 Purification of the EHEC RBPome using TRAPP

The TRAPP protocol for purification of the total RBPome of EHEC was done according to (Shchepachev et al., 2019) with some modifications. Overnight cultures of E. coli O157:H7 str. Sakai stx(-) were subcultured 1/100 into 800 mL of MEM-HEPES supplemented with 0.1% glucose and 250 nM Fe(NO3)3 and grown up to an OD600 of 0.8. Cells were UV-crosslinked with 1800 mJ of UV-C in a W5 small diameter UV-crosslinker (UVO3) as described in (Granneman, Petfalski and Tollervey, 2011). Cell pellets were prepared for UV-treated and untreated samples in triplicate. Cells were pelleted at 4000 xg for 10 minutes, resuspended in 40 mL of ice-cold PBS and re-pelleted at 4000 xg for 15 minutes at 4oC. To each pellet, 3 mL of a 1:1 mix between phenol (pH 8.0) and GTC- buffer (4M guanidinium thiocyanate, 50 mM Tris-HCl pH 8.0, 10 mM EDTA, 1% β-mercaptoethanol) was added, and transferred into three 2 mL microcentrifuge tubes. To each, 1 volume of 0.5 mm zirconia beads (Daintree Scientific, cat no. 11079-105z) was added, and cells were lysed in a FastPrep-24 5G twice for 40 seconds at 6 m/s with a one-minute rest on ice in between cycles.

Silica beads were washed before the experiment by resuspending 10g of silicon dioxide beads (HoneyWell-Fluka, cat no. S5631-500g) in 50 mL of 1M HCl and left for 24 hours at room temperature. Beads were washed three times with MilliQ water by spinning the beads in 50 mL centrifuge tubes at 3000 xg for 2 minutes 141

Chapter 5

and removing the supernatant. After the final wash, silica beads were resuspended in MilliQ to a 50% slurry.

Lysates were transferred into a 50 mL centrifuge tube, and 2 mL of 1:1 phenol:GTC-buffer was added. Samples were spun down at 4000 xg for 10 minutes at 4oC to clear the lysate. Supernatants were transferred into 1.5 mL microcentrifuge tubes and centrifuged at 16000 xg for 20 minutes at 4oC. The supernatants were pooled into a 50 mL centrifuge tube and the volume topped up to 10 mL with GTC-phenol. To each sample, 0.1 volume of sodium acetate (pH 4.0) and 1 volume of absolute ethanol was added and mixed using a vortex. This was followed by the addition of 1 mL silica beads (50% slurry in MilliQ) and 500 µL of absolute ethanol. Samples are incubated for 1 hour at room temperature on a rotating platform to facilitate the binding of nucleic acids to silica. Beads were spun down at 2500 xg at 4oC for 2 minutes and resuspended by vortexing in 15 mL of wash buffer I (4M guanidinium thiocyanate, 1M sodium acetate pH 4.0, 30% ethanol). Samples were spun at 2500 xg at 4oC for 2 minutes, wash buffer was removed, and pellet was resuspended in 15 mL of wash buffer I. This wash was repeated twice more. Following the third wash with wash buffer I, samples were washed three times with 10 mL of wash buffer II (100 mM NaCl, 50 mM Tris-HCl pH 6.4, 80% ethanol). Beads were resuspended in 1.5 mL of wash buffer II and transferred into 2 mL microcentrifuge tubes. Samples were pelleted at 2000 xg for 1 minutes at room temperature, and the supernatant was discarded. Residual wash buffer II was dried in a SpeedVac at 45oC for 10 minutes.

Nucleic acids and crosslinked RNPs were eluted by resuspending the dried silica beads in 500 µL of 10 mM Tris pH 8.0 then incubating at 37oC with shaking at 1100 RPM for 5 minutes. Beads are spun down at 2000 xg for 1 minute, and eluate is transferred into a clean 2 mL microcentrifuge tube. This is repeated twice more for a total of 1.5 mL of eluate per sample. Eluates were spun at 16000 xg for 1 minute at room temperature to pellet residual silica, and the supernatant was transferred into a new tube. The spin was repeated, and eluates were divided into two 2 mL Protein LoBind tubes (Eppendorf, cat no. 0030108302). DNA and 142

Chapter 5

RNA was digested using 0.5 µL of Benzonase per tube (Sigma-Aldrich, cat no. E1014) for 2 hours at 37oC. To specifically digest RNA, 0.5 µL of RNase A/T1 was added and samples were incubated in a SpeedVac at 45oC overnight to dry the samples.

Dried samples were resuspended in 75 µL of Laemmli buffer and incubated at 100oC for 10 minutes. Samples that were previously divided were pooled together, and 120 µL of each were loaded onto a gradient 4-20% Mini-PROTEAN TGX gel (Bio-Rad, cat no. 4561095) at 80V for 1.5 hours. The polyacrylamide gel was stained with Imperial protein stain (ThermoFisher, cat no, 24615) according to the manufacturer’s protocol.

5.2.3 In-gel digestion and mass spectrometry

Each lane of the gel, corresponding to one sample, was cut into three fractions and digested with trypsin as described in (Shevchenko et al., 2007). Each fraction was cut into 1 mm cubes using a clean scalpel and transferred into a microcentrifuge tube. To wash the gel fractions, equal volumes of 50 mM of ammonium bicarbonate and 100% acetonitrile were added to each tube and incubated at 37oC for 1 hour with shaking. Solution was discarded, and 50 mM ammonium bicarbonate was added to each tube. Samples were incubated at room temperature for 5 minutes; solution was discarded, and 100% acetonitrile was added. Samples were incubated at room temperature until the gel pieces shrunk and turned white in colour. Reduction of the samples was done by adding 10 mM dithiothreitol (DTT) in 50 mM ammonium bicarbonate and incubating for 30 minutes at 37oC. DTT was removed and acetonitrile was added. Samples were incubated for 5 minutes at room temperature, and acetonitrile was removed. Alkylation of the samples was done by adding 55 mM iodoacetamide in 50 mM ammonium bicarbonate and incubated for 20 minutes in the dark at room temperature. Gel pieces were washed using ammonium bicarbonate and acetonitrile as previously described, followed by an additional wash with acetonitrile to ensure dehydration of the gel pieces. Trypsin buffer (13 ng/µL trypsin in 10 mM ammonium bicarbonate and 10% acetonitrile) was added to

143

Chapter 5

cover the gel pieces and incubated on ice for 15 minutes. Samples were then incubated overnight at 37oC.

Following digestion, an equal volume of 0.1% trifluoroacetic acid was added to each sample and incubated for 15 minutes to deactivate trypsin and to adjust the pH (optimal range 1-4). StageTips were prepared and samples were loaded as described in (Rappsilber, Ishihama and Mann, 2003; Rappsilber, Mann and Ishihama, 2007). Three C18 Empore Disks were cut using a blunt syringe needle and loaded into 2200 µL pipette tips. StageTips were washed with 20 µL of methanol, then 40 µL of 0.15% TFA to wet and equilibrate the disks. Digested samples were loaded onto the StageTips. To ensure no digested peptides were left in the remaining gel pieces, 100% acetonitrile was added and incubated at room temperature for 5-10 minutes. Acetonitrile containing the peptides were then transferred into a clean LoBind microcentrifuge tube and dried in a SpeedVac at 60oC. The dried sample was resuspended in 60 µL of 0.1% TFA and passed through the StageTips. These were stored at -20oC until they were eluted in 20 µL of 80% acetonitrile in 0.1% TFA. LC-MS analysis was performed on an Orbitrap Fusion Lumos Tribrid Mass Spectrometer coupled to an Ultimate 3000 RSLCnano Systems. Peptide separation was done on a 50 cm EASY-Spray column at 50oC. Peptides were loaded onto the column at a flow rate of 0.3 µL/min, and eluted at a rate of 0.2 µL/min using the following gradient: 2-40% mobile phase B (80% acetonitrile and 0.1% formic acid) for 136 minutes, then to 95% for 11 minutes. After an additional 5 minutes at 95% mobile phase B, flow was shifted to 2% until the end of the run. LC-MS was performed by Christos Spanos at the Wellcome Trust Centre for Cell Biology in Edinburgh, Scotland.

5.2.4 TRAPP data analysis

144

Chapter 5

Processing of raw MS output was done on the MaxQuant software platform (Cox and Mann, 2008; Tyanova et al., 2016). The search was conducted against the E. coli O157:H7 reference proteome set using the Andromeda search engine. MaxQuant analysis of MS data generated a file “Peptides.txt”, which was used for further analysis in Perseus (Tyanova et al., 2016). Contaminants and spike- in proteins were removed. Label-free quantification (LFQ) intensities were log2 transformed. Only peptides with at least 3 valid values were considered, the remaining were discarded. Missing values were imputed using a width value of 0.3 and a downshift of 1.8. The average ratio of UV+ to UV- LFQ intensity for each peptide was calculated. A student’s t-test was used to calculate statistical significance, and false-discovery rates were calculated using the Benjamini- Hochberg method.

5.2.5 Functional annotation of proteins using gene ontology

Gene ontology term enrichment analysis was done on statistically significant enriched and depleted proteins detected by TRAPP using the Panther webservice as well as the BinGO Cytoscape plugin (Maere, Heymans and Kuiper, 2005; Mi et al., 2019). Gene lists were taken from the MaxQuant output and used as the input list for both software. The gene ontology list used was the most recent update as of July, 2019. For BinGO, the annotation for E. coli was taken in July, 2019 from the Gene Ontology Consortium website. The resulting tables detailing GO annotations assigned to each gene, as well as their enrichments and adjusted p-values, were used as inputs for REVIGO, which condenses the GO term outputs from Panther and BinGO into lists more suitable for visualisation (Supek et al., 2011).

5.2.6 Prediction of RNA-binding domains using APRICOT

APRICOT is an automated pipeline that was used to identify RNA-binding domains in TRAPP-enriched proteins based on the Conserved Domain Database (CDD) and InterPro. These databases had 50,648 and 28,926 entries as of February 2016, respectively. The program uses RPS-BLAST and InterProScan tools to search for conserved domains within the sequences based on the CDD

145

Chapter 5

and InterPro databases, respectively (Sharan et al., 2017). A list of RBD keywords was used as input and are noted in Table S2. The empirical distribution function (ECDF) of proteins containing RNA-binding domains was compared to that of all significantly enriched proteins or proteins with no predicted RBD using the Kolmogorov-Smirnov test function in R.

5.2.7 Dual-affinity tagging of candidate RBPs

Candidate PAI-specific RBPs were cloned into the pBAD24 plasmid under the control of an arabinose-inducible promoter. Candidates were amplified from gBlocks (Integrated DNA Technologies) using BS_RBP_GB_F and BS_RBP_GB_R. gBlocks were designed from the start codon until the last codon preceding the stop codon (Supplementary Table S1). These were cloned into the arabinose-inducible pBAD24 vector using XbaI and EcoRI to form pBAD24-RBP. The HTF-tag was amplified from pTOF25-Hfq-HTF using BS_HTF_XbaI_F and BS_HTF_SphI_R and cloned into pBAD24-RBP using XbaI and SphI. As a negative control, sfGFP was cloned into pBAD24. sfGFP was amplified from pBAD24-sfGFPx1 using primers BS_pBAD24_sfGFP_F and BS_pBAD24_sfGFP_R. These primers remove the native stop codon from sfGFP and incorporate BamHI and XbaI sites to the 5’ and 3’ ends respectively. The incorporated restriction enzymes were used to clone sfGFP into pBAD24- RBP-HTF.

5.2.8 Western blots to confirm protein expression

Proteins were run at 200V for 30 minutes or 120V for 1.5 hours on a Bolt™ 4- 12% Bis-Tris protein gel (Invitrogen, cat no. NP04120BOX) in NuPAGE™ MOPS SDS running buffer (Invitrogen, cat no. NP0001). Proteins were transferred onto 0.2 µm nitrocellulose paper (ThermoFisher, cat. no. LC2000) in NuPAGE™ Transfer Buffer at 30V for 1.5 hours (Invitrogen, cat no, NP00061). The membrane was rinsed in water, then washed three times for 5 minutes in 1X phosphate buffered saline (PBS). Membrane was blocked for 1 hour in blocking solution (5% skim milk powder in 1X PBS), then washed with 1X PBS as previously described. The blocked membrane was incubated in a 1/2000 dilution

146

Chapter 5

of 6x-His Tag Monoclonal Antibody (HIS.H8)-HRP (Invitrogen, cat. no MA1- 21315-HRP) in blocking solution overnight. Washes in 1X PBS were repeated, and antibodies were detected with Pierce ECL Western Blotting substrate (ThermoFisher, cat. no. 32106).

5.2.9 PNK assay to confirm RNA-binding properties of candidate RBPs

Overnight cultures of Sakai stx- harbouring the pBAD24 plasmids were subcultured 1/100 into 500 mL of LB broth or MEM-HEPES media supplemented with 0.1% glucose and 250 nM Fe(NO3)3 containing kanamycin and ampicillin. Cultures were grown at 37oC for 30 minutes at 200 rpm before adding 0.2% arabinose to induce protein expression. Incubation was continued until an OD600 of 0.8 was reached. Cultures were crosslinked with 500 mJ of UV-C using a Vari- X-Linker (UVO3). Following crosslinking, cells were immediately pelleted in a centrifuge at 4000 xg for 10 minutes. Pellets were resuspended in 40 mL of ice- cold PBS, transferred into clean 50 mL centrifuge tubes, and re-pelleted at 4000 xg for 15 minutes at 4oC. Pellets were stored at -80oC until the assay was performed.

To each pellet, 1.5 mL of lysis buffer (50 mM Tris-HCl pH 7.8, 1.5 mM MgCl2, 150 mM NaCl, 0.1% Nonidet P-40, 5 mM β-mercaptoethanol and 1 tablet/50 mL of cOmplete™ Protease Inhibitor cocktail (Roche)) and 3 mL of 0.1 mm zirconia/silica beads were added. Samples were lysed in a FastPrep-24 5G in a CoolBigPrep adapter at 6 m/s for 40 seconds for 2 cycles with a 1-minute rest in between. Following lysis, 2 mL of lysis buffer was added to each sample and the lysate was clarified by spinning at 4000 xg for 20 minutes at 4oC. The supernatant was transferred into clean 1.5 mL microcentrifuge tubes and clarified further by centrifuging at 16000 xg for 20 minutes at 4oC. 75 µL of M2 Anti-FLAG resin (Sigma-Aldrich, cat no. A2220) was equilibrated by washing three times with TNM150 (50 mM Tris-HCl pH 7.8, 150 mM NaCl, 0.1% Nonidet P-40, and 5 mM β-mercaptoethanol). Samples were incubated on M2 anti-FLAG resin for 2 hours at 4oC. Following incubation, resin was washed three times with TNM1000 (50 mM Tris-HCl pH 7.8, 1 M NaCl, 0.1% Nonidet P-40, and 5 mM β- mercaptoethanol) and TNM150. Resin was resuspended in 500 µL of TNM150 147

Chapter 5

and transferred into a clean 1.5 mL microcentrifuge tube. To elute the proteins from the resin, 230 units of TEV protease were added to each sample and incubated with rotation for 2 hours at 18oC. The resin was gravity filtered through a Bio-Rad Micro Bio-Spin® Chromatography column (cat. no. 7326204) to collect the eluate. To each sample, 0.15 units of RNace-It™ Ribonuclease Cocktail was added and incubated for exactly 7 minutes at 20oC. Samples were immediately transferred onto ice for 1 minute and transferred to microcentrifuge tubes containing 0.4 g of guanidinium hydrochloride, 300 mM NaCl and 10 mM imidazole pH 8.0 to terminate the reaction. Ribonuclease treated samples were loaded onto 150 µL of Pierce Ni-NTA Magnetic Agarose Beads (ThermoFisher, cat no. 78605). Ni-NTA beads were pre-washed three times with 700 µL of wash buffer I (6 M guanidine-HCl, 50 mM Tris-HCl pH 7.8, 300 mM NaCl, 0.1% NP-40 and 5 mM β-mercaptoethanol) before sample loading. Samples were incubated on the resin overnight at 4oC with rotation.

Resins were washed three times with wash buffer I and four times with 1x PNK buffer (50 mM Tris-HCl pH 7.8, 10 mM MgCl2, 0.5% Nonidet P-40, and 5 mM β- mercaptoethanol). The 5’ ends of crosslinked RNA were radiolabelled and 5’ phosphorylated by incubating the resin in an 80 µL reaction containing 4 µL (40 units) of T4 polynucleotide kinase (NEB, cat no. M0201S) and 3 µL of 32P-γATP (PerkinElmer, cat no. BLU502A250UC) in 5X PNK reaction buffer (250 mM Tris- o HCl pH 7.8, 50 mM MgCl2, 50 mM β-mercaptoethanol) for 30 minutes at 37 C. The resin was washed three times with wash buffer I and four times with wash buffer II (50 mM Tris-HCl pH 7.8, 50 mM NaCl, 10 mM imidazole, 0.1% NP-40 and 5 mM β-mercaptoethanol). Proteins and their associated RNAs were eluted by adding 200 µL of elution buffer (50 mM Tris-HCl pH 7.8, 50 mM NaCl, 300 mM imidazole, 0.1% NP-40 and 5 mM β-mercaptoethanol) and incubating for 10 minutes at 4oC on a rotating platform. This was done twice for a final elution volume of 400 µL.

To precipitate the protein, 2 µL of GlycoBlue co-precipitant and 120 µL of trichloroacetic acid was added to each sample, mixed by vortexing and incubated on ice for 1 hour. Samples were centrifuged for at 16000 xg for 30 minutes at 4oC. The supernatant was aspirated and the pellet was washed with 800 µL of 148

Chapter 5

ice-cold acetone. Samples were spun again at 16000 xg for 30 minutes at 4oC. The supernatant was removed, and the pellets were dried in a SpeedVac at 37oC for 1 minute. Pellets were resuspended in 20 µL of 1X NuPAGE® LDS Sample buffer (Invitrogen, cat no. NP0008). Proteins were run at 120V for 1.5 hours on a Bolt™ 4-12% Bis-Tris protein gel (Invitrogen, cat no. NP04120BOX) in NuPAGE™ MOPS SDS running buffer (Invitrogen, cat no. NP0001). Following the run, gel was exposed on a BAS-IP SR Phosphorimaging plate (GE Healthcare, cat. no. 28-9564-78).

149

Chapter 5

5.3 Results

5.3.1 Identification of RNA-binding proteins in enterohaemorrhagic E. coli

The genome of E. coli O157:H7 str. Sakai is approximately 16% larger than that of the commensal strain E. coli K12 str. MG1655 due to acquisition of horizontal DNA (Hayashi et al., 2001). Various DNA-binding proteins, including phage and virulence-associated transcriptional regulators, are encoded on pathogenicity islands on EHEC. It is likely that these horizontally acquired elements may also encode RNA-binding proteins required for regulation of virulence genes.

Total RNA-associated protein purification is an organic extraction-based method of capturing the total RBPome of a given organism (Figure 5.1A) (Shchepachev et al., 2019). TRAPP was performed on EHEC O157:H7 str. Sakai grown in an infection relevant media as described in (Shchepachev et al., 2019) with some modifications (Figure 5.1A). The protocol was performed in triplicate for both UV- crosslinked and non-UV-crosslinked samples. Aliquots (5 µL) were taken before nuclease treatment and separated on an agarose gel to confirm the presence of RNA in all replicates and RNPs in the UV-crosslinked samples. Smearing above the bands for ribosomal RNA was observed for UV-crosslinked samples, but not in the control (Figure 5.1B), indicating the presence of RNA-binding proteins crosslinked to RNAs of differing lengths. Following nuclease treatment, extracted proteins were separated on a 4-20% gradient polyacrylamide gel before MS analysis. Imperial staining of the polyacrylamide gel showed an enrichment of recovered proteins in UV-crosslinked samples (Figure 5.1C). Taken together, the results show that TRAPP was able to recover RNA-protein complexes following UV-crosslinking using silica binding and denaturing conditions.

150

Chapter 5

Figure 5-1: UV-crosslinking results in increased protein yield following TRAPP. A. Schematic detailing the general workflow of TRAPP. After growth to late exponential phase, RNA-protein complexes are UV-crosslinked in vivo. Using silica beads and acidic GTC-phenol, RNA and crosslinked RNPs are extracted. RNA is subject to nuclease digestion, and remaining protein is subject to in-gel digestion using trypsin. Digested peptides are analysed using mass spectrometry. B. A sample of RNA was collected before overnight nuclease treatment and drying, and run on an agarose gel. Indicated are the major RNA species and crosslinked RNPs. C. Imperial stained PAGE gel showing proteins recovered from UV-crosslinked and non-crosslinked samples.

151

Chapter 5

5.3.2 TRAPP enriches for RNA-binding proteins

Following separation on a PAGE gel, each lane was cut into three fractions, which were all subject to in-gel digestion using trypsin. Following digestion, peptides were loaded on StageTips before being analysed using LC/MS-MS (Section 5.2.3). Raw mass spectrometry data was quantified using MaxQuant which detected 2071 peptides. Filtering of the data was done using Perseus, which removed peptides without LFQ-intensity values in at least three of six samples. Of the 1390 remaining peptides, 19.14% were only identified in the UV- crosslinked samples, while 2.95% were only identified in the non-crosslinked samples. Missing LFQ-intensity values were imputed using the Perseus software package, and the average LFQ-intensity ratio between UV-crosslinking and non- UV-crosslinked samples was calculated. Imputation of missing values by Perseus maintained the same normal distribution observed before the missing values were replaced. Statistical significance (p < 0.05) was calculated using a student’s t-test and p-values were adjusted using the Benjamini-Hochberg method. 443 peptides were at least 2-fold enriched out of 795 with statistically significant peptides (Supplementary Table S3). Enriched proteins included several well studied RNA-binding proteins such as the chaperones Hfq and ProQ, the carbon storage regulator CsrA, and the cold shock proteins CspA, CspC, CspD and CspE.

Gene ontology (GO) was used to group gene descriptors and attributes to identify common functions within the TRAPP enriched RBPs (Ashburner et al., 2000; Carbon et al., 2019). GO analysis of the enriched proteins showed an enrichment for GO terms associated with RNA processes such as “RNA processing”, “RNA modification” and “Translation”, with “ribonucleoprotein complex subunit organization” showing the highest enrichment value (p < 0.01), consistent with TRAPP enrichment of RNA binding proteins (Figure 5.2A-B). Previous interactome studies have also shown that peptides involved in intermediate metabolic processes can also interact with RNA (Beckmann et al., 2015; Queiroz et al., 2019; Shchepachev et al., 2019). GO terms relating to metabolic processes, such as “organonitrogen compound biosynthetic process” (1.5-fold 152

Chapter 5

enrichment, p < 0.001), were observed. The same analysis was performed on the 238 proteins that were 2-fold enriched in the non-UV crosslinked replicates. GO terms that were enriched in this set were primarily associated with proteolysis and lipid transport (Supplementary Figure S3).

APRICOT is a computational pipeline that was developed for sequence-based identification and characterisation of RNA-binding domains (RBDs) and RNA- binding proteins (Sharan et al., 2017). All statistically significant proteins were used as input to search for RNA-binding domains within the dataset. All domains for the input proteins were predicted based on the CDD and InterPro databases, then proteins containing domains from a list of 112 RBDs were filtered out. The list of RBDs used for filtering was based on those used in previous studies are listed in Supplementary Table S4 (Tawk et al., 2017). Of the 795 proteins that showed statistically significant changes between UV-crosslinked and non- crosslinked samples, 274 and 259 proteins contained a putative RBD based on CDD and InterProScan predictions, respectively. An empirical cumulative distribution function (ecdf) was used to compare the distribution of enrichment scores for proteins containing a predicted RBD, and proteins with no predicted RBD. Comparing the cumulative distributions showed that proteins that were more highly enriched in the TRAPP dataset were more likely to contain an RNA- binding domain (Figure 5.2C). This result indicates that proteins with predicted RNA-binding domains were significantly enriched in the EHEC TRAPP dataset.

The dataset generated by EHEC TRAPP was compared with published commensal E. coli K-12 RBPome datasets generated by OOPS and TRAPP (Queiroz et al., 2019; Shchepachev et al., 2019). Of the enriched proteins, 77% were also detected in the K-12 TRAPP dataset. There was also a 43% overlap between the list of proteins enriched by TRAPP in EHEC and the list of proteins recovered from at least 3 replicates of OOPS (Figure 5.2E). The published K-12 TRAPP dataset was significantly larger than the dataset generated in this chapter, which can be attributed to differences in crosslinking due to the different media used to grow the cells and the omission of SILAC labelling in EHEC. Despite the reduced dataset size, the results indicate a strong overlap between

153

Chapter 5

the EHEC TRAPP dataset and published RBPome datasets for commensal E. coli.

Figure 5-2: TRAPP enriches for RNA-binding proteins and proteins containing RNA-binding domains. A-B. GO term analysis using BINGO (A) or Panther (B) shows enrichment for RNA-binding processes such as “ribonucleoprotein complex subunit formation”, and “RNA processing”. GO term enrichment value was plotted against negative log transformed p-values. C. eCDF of TRAPP enriched proteins with or without predicted RBDs. D. Venn diagram comparing enriched EHEC TRAPP proteins with those observed in K- 12 TRAPP and OOPS (Queiroz et al., 2019; Shchepachev et al., 2019).

154

Chapter 5

5.3.3 Validation of EHEC TRAPP results

Of the 443 two-fold enriched proteins identified by TRAPP, 35 are located within the pathogenicity islands of EHEC str. Sakai, representing 7.9% of the enriched proteins (Table 5.4 and Figure 5.3A). This included annotated RNA-binding proteins such as those of the LsoAB toxin-antitoxin system encoded on the pOSAK1 plasmid in EHEC str. Sakai. LsoA is an mRNA endoribonuclease that has anti-bacteriophage activity, and LsoB is its cognate antitoxin (Otsuka et al., 2007; Otsuka and Yonesaki, 2012). Of the 35 statistically significant proteins, 16 were associated with DNA-binding functions or DNA-binding domains, such as the phage cI repressor and DNA methylases. Silica beads used in the purification does not discriminate between DNA and RNA, so some co-purification of DNA- binding proteins can be expected. However, DNA-binding proteins are expected to represent a smaller proportion of the TRAPP-enriched proteins due to the inefficiency of DNA-protein UV-crosslinking (Angelov et al., 1988). Known DNA- binding proteins such as Crp and Fur were also enriched in RBPome purifications of K-12 (Supplementary Table S3) (Queiroz et al., 2019; Shchepachev et al., 2019). Of the remaining 19 EHEC-specific proteins, 3 were completely uncharacterised, and 2 were found to be non-LEE encoded effector proteins. Several proteins of the Chu iron acquisition operon, ChuX, ChuW and ChuS, were also recovered.

Table 5-4: Pathogen-specific proteins enriched by TRAPP. DNA-binding proteins are shaded in gray

Gene Log2FC Sakai S- Name UV+/UV- p-adj Description loop lsoB 5.28433 0.001 Antitoxin LsoB pOSAK1 lsoA 5.19087 0.0056 mRNA endoribonuclease LsoA pOSAK1 ECs1574 5.13897 0.0038 Integrase 78 ECs0274 5.1277 0.0181 cI repressor 16 (Sp1) ECs1072 4.89235 0.0053 uncharacterized protein 67 (Sp4) ECs1196 4.64848 0.0043 DNA methylase 69 (Sp5) ECs1664 4.60604 0.0349 putative transcriptional regulator 79 (Sp8) uncharacterized protein; giy-yig endonuclease ECs0285 4.49538 0.0086 domain containing 16 (Sp1) ECs4465 4.4323 0.007 uncharacterized protein; Fic-like; DNA-binding 253

155

Chapter 5

Gene Log2FC Sakai S- Name UV+/UV- p-adj Description loop 153 ECs3001 4.26933 0.0012 phage recombination protein bet (Sp15) ECs0604 4.171 0.0052 uncharacterized protein 44 ECs1654 4.14646 0.0365 uncharacterized protein; ferritin-like domain 79 (Sp8) 72 terB_2 3.70246 0.0022 tellurium resistance protein (SpLE1) eefR 3.60595 0.0049 transcription regulatory protein 87 ECs5307 3.57913 0.002 DNA methyltransferase M 293 ECs1089 3.49717 0.0095 IS629 protein 67 (Sp4) 72 rpmE2-2 3.47938 0.0023 50S ribosomal protein L31 type B 2 (SpLE1) 125 ECs2766 3.13472 0.0407 phage repressor protein cI (Sp14) 186 ECs3483 3.10836 0.0053 DinI-like protein prophage CP-933V (Sp17) ECs1762 3.05513 0.0161 uncharacterized protein 85 (Sp9) Class I SAM-dependent DNA 287 ECs5262 2.94515 0.0053 methyltransferase (SpLE6) espN 2.80297 0.0052 T3SS secreted effector EspN 77 (Sp6) ECs1662 2.78723 0.0067 hydrolase 79 (Sp8) chuX 2.7772 0.0029 haem utilization carrier protein 231 sopB 2.66078 0.0024 Protein SopB espY2 2.51436 0.0021 T3SS effector-like protein EspY2 5 chuS 2.50892 0.0029 hemin transporter 231 ECs1587 2.49981 0.0217 Single-stranded DNA-binding protein 78 (Sp7) ECs1653 2.44594 0.0181 uncharacterized protein; ferritin-like domain 79 (Sp8) ECs1163 2.22631 0.0308 RNA-binding protein 69 (Sp5) 274 ECs4998 2.22145 0.0026 DNA modification protein (Sp18) mobA 1.74127 0.0455 plasmid mobilization protein pOSAK1 chuW 1.4941 0.0166 anaerobilin synthase 231 ECs1955 1.36003 0.0139 ATPase 93 (Sp10) 287 ECs5259 1.17573 0.0122 uncharacterized protein (SpLE6)

To determine whether any of the identified RNA-binding proteins were required for virulence the EHEC TRAPP dataset was cross-referenced with an existing Transposon-directed insertion-site sequencing (TraDIS) study (Eckert et al., 2011). TraDIS is a high-throughput method that combines transposon mutagenesis with Illumina sequencing to assess the fitness benefit of genes. TraDIS has previously been applied in EHEC to screen for genes required for colonization and survival in cattle. A fitness score was assigned to each mutant by calculating the log2 fold-change difference between the output pool and the input pool (Eckert et al., 2011). A comparison was made between the list of

156

Chapter 5

enriched proteins from TRAPP and the list of mutants identified with TraDIS. Out of the 443 proteins that were at least twofold enriched, 37 were mutated in the TraDIS experiment, and 24 of these were attenuating mutants (log2FC < -1). ECs1163 was also mutated in the TraDIS study, but was assigned a fitness score of 0.71, which leaves the role of this protein in colonisation and fitness in cattle inconclusive. Of the 24 attenuated mutants enriched in TRAPP, 9 were pathogen-specific. The effector proteins EspY2 and EspN were enriched by TRAPP and found to be required for EHEC colonisation of calves (Figure 5.3B).

Figure 5-3: EHEC TRAPP identifies putative pathogen specific RBPs. A. Diagram indicating the location of prophages and S-loops in EHEC str. Sakai. Pathogen-specific proteins enriched by TRAPP are labelled. B. TRAPP enrichment values were plotted against fitness scores assigned by TraDIS.

157

Chapter 5

To verify that EHEC specific proteins enriched by TRAPP were bona fide RNA- binding proteins, a polynucleotide kinase labelling assay (PNK assay) was employed. PNK labelling allows detection of crosslinked RNAs by radiolabelling their 5’ ends with γ32P-ATP. Four proteins were selected from the list of statistically significant enriched proteins to be used in this assay. Two proteins, ECs0604 and ECs1762, are uncharacterized and have no conserved domains. The non-LEE encoded effector protein EspY2, and ECs1163, an RNA-binding protein of unknown function encoded on the Stx2 phage were also included in the analysis. An additional protein from the Stx2 phage, ECs1184, while not reaching statistical significance (FDR = 0.065) was included in the assay. Deletion of ECs1184 was found to be lethal to the host organism and has been hypothesized to be required for stable maintenance of the Sp5 lysogen (Mondal et al., 2016).

Genes encoding the candidate proteins were cloned into the arabinose-inducible plasmid pBAD24 and tagged with a His6-TEV-FLAG dual-affinity tag. These were transformed into EHEC str. Sakai and used for the PNK assay. Protein expression was first confirmed with a mock purification, which follows the PNK assay procedure outlined in Section 5.2.9 but omits the radiolabelling step (Supplementary Figure S5). EHEC str. Sakai with chromosomally tagged Hfq and YbeY were used as positive controls, while plasmid-expressed, dual-affinity tagged sfGFP was used as a negative control. Overnight cultures were subcultured into LB media or MEM-HEPES, and expression of the fusion proteins was induced after 30 minutes of growth. Cultures were UV-crosslinked with 500 mJ of 254 nm UV-C, and proteins underwent two affinity purification steps as detailed in Section 5.2.9. The 5’ ends of crosslinked RNAs were radiolabelled with 32P before elution from Ni-NTA resins. Purified proteins were precipitated and run on a polyacrylamide gel. All four proteins that were significantly enriched by TRAPP were radiolabelled above background levels (Figures 5.4A and 4C). EspY2 and ECs0604 were detected at the expected monomer size and ECs1163 and ECs1762 were approximately twice the expected monomer size, suggesting that these proteins may dimerize despite the denaturing conditions of the purification (Figure 5.4A and C). Western blots confirmed the expression of 158

Chapter 5

EspY2, ECs1184, and sfGFP, but not ECs0604, ECs1163 and ECs1762 despite an RNP radiolabelled signal for ECs1163 and ECs1762 (Figure 5.4B and D). It is possible that these proteins were not highly expressed. However, the ability to detect the radiolabelled RNA-protein complex by autoradiography but not by Western blot supports the argument that these proteins bind RNA. ECs1184 was detected on the autoradiogram in MEM-HEPES grown EHEC str. Sakai, but the signal detected was low in comparison to the signal detected on the Western blot. A similar result was observed for sfGFP, which as used as a negative control. suggesting ECs1184 is not RNA binding (Figure 5.4E). These results demonstrate that TRAPP can be used for the discovery of novel pathogen- specific RNA-binding proteins in EHEC. Furthermore, they suggest that the Stx2 phage encodes a novel RNA-binding protein and that EHEC may translocate RNA-binding effector proteins into host cells.

159

Chapter 5

Figure 5-4: PNK assays verify the RNA-binding potential of TRAPP enriched proteins. A-B. Autoradiograms of candidate RBPs overexpressed in cells grown in LB (A) or MEM-HEPES (B). C-D. Western blots to detect candidate RBPs using an anti-6x-HIS.HRP antibody. Lanes corresponding to each protein are noted above. E. Densitometry was used to quantify band intensity in both the autoradiogram and western blot. The ratio of autoradiogram to western blot intensity was used to confirm RNA-binding potential.

160

Chapter 5

5.4 Discussion RNA-binding proteins play a key role nearly all cellular processes across all kingdoms of life. The most common and most ubiquitous RNA-binding proteins are the ribosomal proteins that are essential for the translation of mRNAs. In prokaryotes, RNA-binding proteins play a significant role in gene expression and post-transcriptional regulation throughout the lifespan of a mRNA. RBPs can regulate transcription termination, translation and control the rate of RNA decay. Several well-studied RBPs in prokaryotes that are important for post- transcriptional regulation include the RNA chaperones Hfq and ProQ, which mediate interactions between sRNAs and their targets through their Sm and FinO domains, respectively (G. Chaulk et al., 2011; Vogel and Luisi, 2011; Smirnov et al., 2016). The interactions mediated by these proteins can then inhibit or enhance the expression of their target mRNA.

UV-crosslinking experiments coupled with high-throughput sequencing relies on the use of RNA binding proteins as scaffolds to discover novel sRNAs and to map out the RNA-interactome (Tree et al., 2014; Holmqvist et al., 2016, 2018; Melamed et al., 2016; Waters et al., 2017). An additional advantage of these methods is that it often reveals the sequence motifs required for ribonucleoprotein complex formation (Holmqvist et al., 2016, 2018). However, the observation that many sRNAs do not bind to Hfq or ProQ chaperones, which were used in previous crosslinking experiments, suggest that other RNA-binding proteins required for global gene regulation have yet to be discovered (Georg and Hess, 2011; Holmqvist et al., 2016). However, the first methods for global RNA-binding protein purification were not applicable for use in prokaryotes, primarily due to their dependence on oligod(T) capture of the poly(A) tails of mature eukaryotic mRNA (Castello et al., 2012). In Grad-seq, RNPs are sedimented in a glycerol gradient followed by mass spectrometry and RNA-seq. Principal component analysis with known non-coding RNAs led to the discovery of ProQ as a sRNA chaperone, and reinforced that there were undiscovered regulatory RNA-binding proteins (Smirnov et al., 2016). Recently, methods for global RBPome capture have emerged that are based on organic extraction and phase separation and so can be applied in any organism (Queiroz et al., 2019; 161

Chapter 5

Shchepachev et al., 2019; Trendel et al., 2019; Urdaneta et al., 2019). In this chapter, TRAPP is applied in enterohaemorrhagic E. coli O157:H7 str. Sakai to search for novel RNA-binding proteins that are pathogen-specific and may play a role in virulence.

TRAPP was successfully applied in EHEC, with an enrichment of known RNA- binding proteins (Figures 5.1-5.2). Mass spectrometry detected 2071 unique peptides across all the purified samples, with 795 having a statistically significant fold-change in LFQ-intensity between the UV-crosslinked and non-UV- crosslinked samples. From these, 443 were enriched at least two-fold (Supplementary Table S3). GO term analysis of the enriched peptides showed an enrichment of terms related to RNA biological processes (Figure 5.2A-B) Furthermore, domain prediction of the enriched proteins using APRICOT showed that proteins with higher enrichment values were more likely to contain an RNA- binding domain. A comparison between the list of all genes two-fold enriched by TRAPP in EHEC and in commensal E. coli showed that while there was a significant overlap between the two datasets, the number of enriched peptides recovered by TRAPP in commensal E. coli was significantly higher. SILAC labelling was used for TRAPP in commensal E. coli, while label-free quantification was used for TRAPP in EHEC due to the unavailability of a lysine and arginine auxotroph. Due to the increased amount of independent samples required for a label-free quantification experiment (3 UV-crosslinked and 3 non- UV-crosslinked) compared to those in a SILAC experiment (half the number due to mixing heavy and light isotope cultures), and the number of processing steps used in TRAPP, more variation is introduced when using a label-free quantification approach. Despite this, 35 proteins enriched by TRAPP were EHEC-specific. Of these, 16 had annotated functions related to DNA-binding, such as DNA methyltransferase, integrase, as well as the phage cI repressor. Silica beads used in TRAPP are not selective for DNA and RNA, though recovery of DNA-binding proteins is expected to be poor due to the inefficiency of DNA- protein UV-crosslinking (Angelov et al., 1988). Furthermore, previous interactome captures have suggested that DNA-binding proteins may also bind RNA (Brannan et al., 2016; Conrad et al., 2016), and protein relating to replication 162

Chapter 5

and response to DNA damage were recovered in K-12 OOPS (Queiroz et al., 2019).

Of the 19 remaining proteins, 2 were members of pOSAK1 plasmid-encoded LsoAB toxin-antitoxin system, which primarily acts as a bacterial defence system against T4 phages (Otsuka and Yonesaki, 2012). Members of the Chu operon were also found to be significantly enriched by TRAPP, specifically ChuX, ChuS, ChuW and ChuY. ChuW, ChuX and ChuY have been found to be key players in the anaerobic haem degradation pathway in EHEC (LaMattina, Nix and Lanzilotta, 2016; LaMattina et al., 2017). The reason why these proteins are enriched is unclear. ChuW is a radical-S-adenosylmethionine methyltransferase that facilitates haem degradation in anaerobic conditions (LaMattina, Nix and Lanzilotta, 2016). It has previously been demonstrated that central metabolism proteins may have dual-functions as RNA-binding proteins (Castello, Hentze and Preiss, 2015; Hentze et al., 2018; Liu et al., 2019). This suggests that the enrichment of members of the Chu operon in TRAPP is because these haem uptake proteins may moonlight as RNA-binding proteins.

Four proteins from the list of statistically significant EHEC-specific TRAPP enriched proteins (EspY2, ECs0604, ECs1163 and ECs1762), and one Stx2Φ- encoded protein with marginal statistical significance (ECs1184, p = 0.065) were chosen for further verification. The genes encoding these proteins were cloned into an arabinose-inducible plasmid and tagged with a dual-affinity HTF-tag (Supplementary Figure S5). The nucleotide-binding properties of these proteins were then verified using a polynucleotide kinase (PNK) assay. This experiment was performed on cultures grown in both rich and type 3 secretion-inducing media (used for the TRAPP assay) and provided further evidence of the potential RNA-binding properties of the four TRAPP-enriched proteins that passed the significance threshold (Figure 5.4).

ECs1163 is a 94 amino acid protein that is encoded on the Stx2 phage of EHEC and is predicted to carry an ASCH-like domain, which is characterised as containing a beta-barrel fold similar to the PUA superfamily (Iyer, Burroughs and 163

Chapter 5

Aravind, 2006). Members of this domain superfamily are represented in all kingdoms of life, having a conserved amino acid sequence motif of GxKxxxxR. In eukaryotes, they play a role in co-activation and pre-mRNA splicing. In bacteria, members of this superfamily are prevalent, being found in both gram- positive bacteria such as in Mycoplasma penetrans and Lactococcus lactis, and gram-negative bacteria such as Acinetobacter and Listeria. Typically, this domain is found fused to other RNA-binding domains, such as those in ribosomal proteins. As such, this domain is believed to play a role in prokaryotic translation regulation (Iyer, Burroughs and Aravind, 2006). A crystal structure for a 148 amino acid protein containing an ASCH-domain has been identified in Zymomonas mobilis, and evidence has been shown for weak single-strand ribonuclease activity in the presence of magnesium, and 5’ phosphatase activity (Kim et al., 2017). TraDIS data suggests that ECs1163 has a marginal effect on

EHEC fitness [Log2FC = 0.7] during bovine colonisation (Eckert et al., 2011). This suggests that TRAPP enriches for pathogen-specific RNA-binding proteins that may have a role in regulation of gene expression.

ECs0604 and ECs1762 are uncharacterized proteins encoded within S-loop 44 and the defective Sp9 prophage of EHEC strain Sakai, respectively. No characterised RNA-binding domains were found in these proteins. Both proteins showed strong radiolabelling by polynucleotide kinase, suggesting that these proteins form RNA-protein complexes. The observed band for ECs1762 on the autoradiogram was significantly larger than the predicted molecular weight for this protein, and may indicate that this protein form dimers despite the denaturing conditions of the purification. Both proteins may represent novel classes of RNA- binding proteins.

EspY2 is a type 3 secreted effector protein not encoded within the locus of enterocyte effacement (LEE). This effector has no known RNA-binding domains, but contains an N-terminal WEX5F domain which is conserved in the SopD effectors of Salmonella, and putative effectors in Edwardsiella and Sodalis (Tobe et al., 2006). SopD is an effector encoded on the SPI-1 pathogenicity island of Salmonella and may play a role in enhancing late stage virulence in mice by 164

Chapter 5

manipulating the host membrane (Bakowski et al., 2007; Giacomodonato et al., 2007). EspY3, another non-LEE encoded effector containing the same N- terminal WEX5F domain, but is 334 amino acids longer than EspY2, localizes in actin pedestals and causes elongation of polymerized actin (Larzábal et al., 2018). Comparison of TRAPP enriched proteins with attenuated mutants from a TraDIS study of EDL933 shows that EspY2 appears to be important for EHEC bovine colonisation or fitness of the cell in the gastrointestinal tract of cattle (Eckert et al., 2011).

Currently, there are no known effector proteins that have RNA-binding function. Secreted effector proteins PipB2 and Lpg2844 from Salmonella and Legionella, respectively, have been shown to possibly have nucleotide-binding capacity, though it has been stated that they are unlikely RNA-binding (Tawk et al., 2017). These proteins were hypothesised to be RNA-binding based on in silico predictions using APRICOT. However, sequence-based predictions of protein function may be more prone to false positives. While not a secreted effector protein, YopD is a component of the Yersinia T3SS that can post-transcriptionally regulate other Yop proteins during non-secretion conditions by binding to RNA (Chen and Anderson, 2011). The lack of known RNA-binding effectors make it important to further verify the ability of EspY2 to bind to RNA, and whether the RNA it binds to belongs to E. coli or the host during infection is required. Additionally, the non-LEE encoded effector EspN was also recovered from TRAPP and showed a seven-fold enrichment upon UV-crosslinking. The recovery of potentially RNA-binding effector proteins is intriguing and if proven, will help further our understanding of EHEC pathogenicity, and may represent a novel function for secreted effectors.

APRICOT analysis of the TRAPP enriched proteins showed that EspY2, ECs0604 and ECs1762 had no conserved RBDs, suggesting that they may contain novel RNA-binding domains. In an expansion of the TRAPP protocol, titanium oxide (TiO2) enrichment was used to identify the exact RNA-binding sites in the purified proteins (Shchepachev et al., 2019). TiO2 enrichment relies on the phosphate groups on RNA crosslinked to peptides to define exact RNA-protein 165

Chapter 5

binding sites. Performing this protocol in EHEC can help provide better insight into the nature of these RNA-protein interactions and may help unravel the RNA- binding domains utilised by these proteins. Additionally, the candidate RNA- binding proteins can be chromosomally tagged in EHEC with the HTF-tag. CRAC can then be performed to identify the RNAs that bind to these proteins to reveal the physiological relevance of these putative RNA-binding proteins.

In summary, performing TRAPP in EHEC led to the recovery of 443 proteins, including canonical RNA-binding proteins. From these, 19 are potential novel pathogen-specific RBPs. PNK assays were used on four of these and verified their potential to bind to RNA. These included proteins of unknown functions and domains, and proteins related to virulence, such as effector proteins. Verifying the RNA binding capacity of these proteins and identifying their putative RNA binding partners may lead to the discovery of novel RNA-binding domains, and a better understanding of EHEC pathogenesis.

166

Chapter 6

Chapter 6: Discussion

6.1 Overview of EHEC Enterohaemorrhagic E. coli is a significant foodborne pathogen that is the causative agent of haemorrhagic colitis and haemolytic uremic syndrome (Croxen et al., 2013). Approximately 2.8 million infections were reported in 2010 worldwide, and there are an estimated 341 notified cases of EHEC infection in Australia annually (Majowicz et al., 2014; Kirk et al., 2015). The primary reservoir for this pathogen are ruminants, such as cattle, and outbreaks of this disease occur due to contamination of meat products, or consumption of water contaminated with faecal matter (Michino et al., 1999; Karmali, 2004). Approximately 6-9% of all EHEC infection and 15% of paediatric EHEC infections progress to haemolytic uremic syndrome (HUS), which may lead to renal failure (Tarr, Gordon and Chandler, 2005; Fakhouri et al., 2017)

EHEC requires less than 100 cells for infection due to its ability to adapt and thrive in different niches, such as high acidity, low oxygen and low iron, all of which it would encounter as it passages through to the gastrointestinal tract (Tuttle et al., 1999; Nguyen and Sperandio, 2012; Lewis et al., 2015). Virulence factors associated with EHEC include the Locus of Enterocyte Effacement (LEE), which encodes for a type III secretion system and its associated effectors, which are required for proper adhesion and colonisation of the host (Elliott et al., 2000; Ritchie and Waldor, 2005; Stevens and Frankel, 2014). The virulence factor primarily responsible for severe disease and morbidity are the Shiga toxins, which are encoded on the lambdoid Stx phages. Shiga toxins are released from the cell upon induction of the Stx phage (Wagner et al., 2001) and the use of antibiotics for treatment is contraindicated, as they can increase the rate of Stx phage induction with a concomitant increase in toxin release (Walterspiel et al., 1992; X. Zhang et al., 2002; Pacheco and Sperandio, 2012). Because of this, treatment of EHEC infections is limited to supportive care (Goldwater and Bettelheim, 2012). The limitations in treatment options highlight the need to better understand the various mechanisms of adaptation and virulence in this significant pathogen. 167

Chapter 6

To be able to successfully infect a host, EHEC requires precise timing and coordination of gene expression for virulence, niche adaptation and fitness. Modulation of these genes can be achieved through a combination of transcriptional responses to environmental stimuli as well as post-transcriptional regulation via RNA-binding proteins and small non-coding RNAs (Luzader and Kendall, 2016; Sauder and Kendall, 2018).

6.2 Overview of small RNA regulation of EHEC pathogenicity and virulence Small RNAs (sRNA) play a key role in maintaining the post-transcriptional networks of bacteria. These are non-coding RNAs typically are around 50-300 nucleotides in length and act via base-pairing mechanisms (Waters and Storz, 2009; Wagner and Romby, 2015). Trans-acting sRNAs can either activate or repress their targets depending on the nature and location of base-pairing, but in gram-negative bacteria, these interactions are typically mediated by an RNA chaperone such as Hfq or ProQ (De Lay, Schu and Gottesman, 2013; Schu et al., 2015; Smirnov et al., 2016).

EHEC genomes are approximately 1.3 Mbp larger than commensal E. coli K-12, with the genome of EHEC O157:H7 str. Sakai being approximately 1.1 MBp larger (Hayashi et al., 2001; Perna et al., 2001). The increased genome size is due to the acquisition of additional genes and virulence factors via horizontal transfer (Gal-Mor and Finlay, 2006; Abe et al., 2008). Horizontally acquired genes can expand the post-transcriptional network of EHEC by providing additional targets for core sRNAs or encoding pathogen-specific sRNAs that can interact with the core transcriptome. Each of these expansions of the post-transcriptional network are presented here. Chapter 3 explores post-transcriptional regulation of a horizontally acquired virulence factor (ChuA) by a core-genome encoded sRNAs and in Chapter 4 a horizontally acquired sRNA, StxS, is shown to regulate the core-genome encoded stress response regulator RpoS.

6.2.1 Core genome sRNAs regulate PAI-encoded genes in EHEC

168

Chapter 6

Core genome encoded sRNAs can regulate pathogenicity island encoded genes in order to expand existing regulatory networks and confer specific advantages to the pathogen. For example, multiple mechanisms have been discovered for both direct and indirect core genome-encoded sRNA regulation of the LEE. The sRNA GlmZ represses the LEE5 operon through direct base pairing, though the exact mechanism of this repression is not understood (Gruber and Sperandio, 2014, 2015). Another sRNA, Spot42, is also able to repress LEE4 by binding to the 5’UTR of sepL adjacent to its ribosomal binding site, preventing its translation (Wang et al., 2018). Expression of Spot42 is dependent on carbon availability and is regulated by the catabolite repressor protein, CRP (Urban and Vogel, 2007; Papenfort and Vogel, 2011). Chapter 3 presents evidence that the core genome sRNA CyaR regulates a horizontally acquired haem receptor chuA.

Host organisms are able to stave off bacterial infections in part due to nutritional immunity, where the host sequesters nutrients bacteria require for growth (Hood and Skaar, 2012). Iron, a key transitional metal for a broad range of cellular processes, is sequestered by the host primarily in the form of haem. EHEC can obtain iron from the host in part due to the presence of the horizontally acquired Chu operon. Genes encoded in this operon allow for scavenging and utilisation of haem (Torres and Payne, 1997; Suits et al., 2005; LaMattina, Nix and Lanzilotta, 2016; LaMattina et al., 2017). This thesis has demonstrated the direct interaction between the CRP-cAMP activated sRNA CyaR with the outer membrane haem receptor chuA. Transcription of this gene is regulated by the iron-dependent repressor Fur, and translation is regulated by an RNA- thermometer that occludes the ribosomal binding site at 25oC (Mills and Payne, 1995, 1997; Kouse et al., 2013). Chapter 3 also suggests that chuA is subject to termination by Rho. The two layers of regulation allow EHEC to sense when it is within a host and control the expression of this receptor. Mutations preventing the formation of the chuA RNA-thermometer demonstrates that activation of chuA via CyaR is not through disruption of this regulatory structure. Truncates of the chuA-sfGFP translational fusion showed 2.1-, 1.9-, 2.9- and 9.2-fold increases in fluorescence compared to the full-length 5’UTR, and all truncates activated by

169

Chapter 6

CyaR. This suggests that CyaR-mediated activation of ChuA may not be through the disruption of an inhibitory secondary structure or sequences in the 5’UTR.

CyaR is activated by the Crp-cAMP in the presence of poor carbon sources and under energy-limited growth (Johansen et al., 2008; De Lay and Gottesman, 2009; Green et al., 2014). During colonisation of the intestine EHEC encounters gluconeogenic environments and utilises mucin-derived sugars as carbon sources (Snider et al., 2009; Pacheco et al., 2012; Carlson-Banning and Sperandio, 2016), which have previously been shown to act as signals to control expression of virulence factors. For example, glucose availability controls both the synthesis and turnover of the sRNAs CsrB and CsrC, which act as molecular decoys for the global RNA-binding protein and post-transcriptional regulator CsrA (Leng et al., 2016; Pannuri et al., 2016). CsrA in turn can regulate virulence genes in the LEE. Nascent transcripts from LEE4 are translationally inactive, and can be transiently activated by CsrA before being switched off by the Crp-cAMP repressed sRNA Spot42 (Bhatt et al., 2009; Wang et al., 2018). The T3SS in turn, can repress CsrA upon adhesion to epithelial cells through the T3SS chaperone CesT through a direct protein-protein interaction (Katsowich et al., 2017; Ye et al., 2018). FusKR is a two-component system that also regulates virulence gene expression and is expressed in response to fucose, a mucin-derived sugar. Expression of this system results in the repression of the LEE when EHEC approaches the intestinal epithelium (Pacheco et al., 2012). CyaR activation of chuA provides another example of how carbon sensing can control the expression of genes located on pathogenicity islands.

Gene expression analysis of a crp-deleted strain of commensal E. coli has shown that genes involved in iron metabolism can be subject to regulation by Fur, and that there are significant overlaps between the two regulons (Zhang et al., 2005). Activation of chuA translation by the Crp-activated sRNA CyaR is a further example of this and can provide an additional layer of regulation for EHEC to ensure this receptor is only produced when it is in a host. The demonstrated activation of ChuA by CyaR independent of RNA thermometer formation suggests that EHEC may utilise a two level AND-OR logic gate to for expression 170

Chapter 6

of the haem receptor (Figure 3-6). Expression of ChuA can only occur if EHEC is in an environment that is iron-poor (which would drive transcription) AND at least 37oC OR contains a poor carbon source to activate translation. This environment is what EHEC would experience in the gastrointestinal tract, and so using this logic gate for chuA expression may allow EHEC to better compete for haem in the gastrointestinal tract. The coordination of these signals highlights the ability of EHEC to utilise environmental signals to tightly control gene expression in order to effectively infect a host with a low dose.

6.2.2 The phage-encoded sRNA StxS modulate the host stress response

The low infectious dose of EHEC can partially be attributed to its ability to tolerate a broad range of stresses (Nguyen and Sperandio, 2012; Foster, 2013). This is in part due to its acquisition of multiple bacteriophages by horizontal gene transfer and have played a key role in facilitating its evolution and pathogenesis (Brüssow et al., 2004; Asadulghani et al., 2009; Boyd, 2012). Once lysogenised, bacteriophages can contribute to the gene regulatory networks of their hosts. Cryptic prophages have been found to significantly contribute to antibiotic resistance and stress resistance of commensal E. coli (Wang et al., 2010). This is mediated in part by prophage encoded sRNAs that contribute to changes in stress tolerance. For example, DicF, a sRNA encoded on the cryptic Qin prophage, regulates cell division in commensal E. coli K-12 (Bouché and Bouché, 1989; Tétart and Bouché, 1992). Enterohaemorrhagic E. coli have three additional copies of this sRNA, which enhances virulence by activating expression of the T3SS. The low oxygen levels required for expression of this sRNA are indicative of the near anaerobic conditions of the colon (Melson and Kendall, 2019). EHEC-specific prophage encoded sRNAs are also important post-transcriptional regulators. AgvB and AsxR are prophage encoded anti- sRNAs that sponge GcvB and FnrS, respectively, and contribute to regulation of amino acid biosynthesis and iron homeostasis (Tree et al., 2014). In Chapter 4, a novel regulatory small RNA within the Shiga toxin-encoding bacteriophages was identified and characterised. The sRNA is generated by early termination of the Shiga toxin transcript and is termed StxS (Figure 4.1-2). This sRNA is a by-

171

Chapter 6

product of premature transcript termination by the late phage terminator tR’. Induction of Stx2Φ results in the anti-termination of this transcript, allowing for transcription of the Shiga toxin and lysis genes. This sRNA is accumulated in both minimal and virulence-inducing media upon entry into stationary phase

(Figure 4.3). The terminated PR’ transcript StxSL, is 255 nucleotides and is processed by RNase E into a functional 74 nt trans-acting sRNA termed StxSS (Figure 4.4).

RNase E-CLASH was used to recover the RNA interactome of EHEC (Waters et al., 2017). Analysis of this dataset revealed interactions with the general stress and stationary phase sigma factor RpoS (Figure 4.8B). This was confirmed using MS2-affinity purification and sequencing (Figure 4.9). The use of rpoS-GFP translational fusions in DH5α and rpoS-GFP transcriptional fusions in EHEC str. Sakai demonstrated a direct interaction between the two RNAs (Figure 4.6 D and

F). Alignments of StxSS with known rpoS-interacting sRNAs DsrA, RprA and ArcZ show that the four sRNAs utilise the same seed region to base pair with RpoS (Figure 4.6E). This suggests that StxS, like DsrA, RprA and ArcZ, may activate RpoS by destabilising secondary structures formed along the 5’UTR that occlude the ribosomal binding site and through anti-termination of Rho (Lease and Woodson, 2004; Updegrove et al., 2008; Mandin and Gottesman, 2010; McCullen et al., 2010; Sedlyarova et al., 2016).

RpoS is the stationary phase and general stress sigma factor in Enterobacteria and plays key roles in the virulence of pathogens such as Salmonella, Vibrio and E. coli (Dong and Schellhorn, 2009, 2010). Deletion of RpoS in EHEC causes changes in the expression of 1135 genes, including genes required for tolerance to acute acid shock, oxidative stress, and changes in osmolarity (Gottesman, 2019). The roles of RpoS in the regulation of EHEC virulence genes such as those in the LEE and in the hemolysin operon have also been investigated (Iyoda and Watanabe, 2005; Li et al., 2008). StxS activation of RpoS did not appear to influence the ability of EHEC to tolerate acute acid shock or osmotic stress (Figure 4.10C-D). This is likely due to the presence of other RpoS-activating sRNAs such as DsrA and RprA, which are expressed when the cell is exposed 172

Chapter 6

to high acid and osmolarity, respectively (Lease et al., 2004; Madhugiri, Basineni and Klug, 2010). For future experiments, the use of a DsrA/RprA/ArcZ knockout, which has been generated by other laboratories, combined with overexpression of StxS, could help understand the contribution of StxS to different stresses. The presence of StxS was necessary for EHEC to grow to higher cell densities in minimal M9 medium, regardless of the supplied carbon source, in an rpoS- dependent manner (Figure 4.8E-F). The reduced levels of RpoS in ΔstxS1ΔstxS2 EHEC str. Sakai may also explain the increased expression of Shiga toxin production in this mutant (Figure 4.11A). The results from Chapter 4 suggest that in nutrient-poor conditions, accumulation of StxS provides a constitutively activating signal for RpoS, allowing for growth to higher cell densities, and may indirectly prevent needless toxin expression.

An intriguing StxS target identified by RNase E-CLASH was the bi-functional acetaldehyde-CoA and alcohol dehydrogenase AdhE (Figure 4.8B and 4.9). This interaction was also confirmed using MS2-affinity purification. StxS appears to bind within the coding region of AdhE, suggesting that StxS may repress its expression through recruitment of RNase E (Bandyra et al., 2012). This may be facilitated by the 5’ monophosphate end of StxS that was generated by RNase E processing of the 255 nt StxSL. Deletion of AdhE in EHEC has previously been shown to strongly suppress the T3SS and have reduced motility and virulence (Beckham et al., 2014). High expression of StxS in virulence-inducing growth media suggests that it may play a role in regulation of the LEE through regulation of AdhE. This would warrant an investigation into the StxS-AdhE interaction, and the role it may play in regulating EHEC virulence.

Anti-termination is a key process in the lytic-lysogenic decision of bacteriophage. The expression of the StxS sRNA suggests that RNAs subject to anti-termination may be an untapped reservoir for novel sRNA discovery. The short IsrK sRNA from the Gifsy-1 prophage in Salmonella is transcribed from the same promoter as a longer IsrK isoform and promotes its translation, leading to the activation of the AntQ anti-terminator (Hershko-Shalev et al., 2016). Lysogens carrying phage λ have also been shown to accumulate a transcript arising from the late phage 173

Chapter 6

PR’ promoter called the 6S RNA (Lebowitz, Weissman and Radding, 1971; J Sklar, Yot and Weissman, 1975; Grayhack et al., 1985). There is no currently identified function for this RNA. The discovery of StxS as a functional sRNA suggests that λ 6S, and equivalent regions of other lambdoid Stx phages may have unappreciated regulatory functions when lysogenised in a host.

6.3 Rho-termination is important in EHEC gene regulation

Approximately 20-30% of all termination events in E. coli are facilitated by the Rho terminator (Peters et al., 2009, 2012). Rho is an ATP-dependent RNA- helicase that binds to nascent RNAs at Rho utilisation (rut) sites. A rut site is typically around 60-80 nt in length, contains regularly spaced cytosines, and is rich in cytosine while poor in guanine (Allfano et al., 1991; Mitra et al., 2017). Rho travels along the transcript until it reaches the RNA polymerase elongation complex and causes its dissociation (Grylak-Mielnicka et al., 2016). Rho termination ensures proper gene expression, maintains genome integrity, and suppresses transcription of foreign DNA and antisense transcripts (Ray-Soni, Bellecourt and Landick, 2016).

Rho has previously been found to terminate gene transcription intragenically in ~100 genes in E. coli (Peters et al., 2012), and out of the 1,200 5’UTRs longer than 80 nt, at least half of these are subject to Rho termination (Sedlyarova et al., 2016). sRNA mediated translation inhibition has previously been found to result in increased transcription termination by Rho (Bossi et al., 2012; Wang et al., 2015). The opposite was also found to occur. The sRNA SraL regulates rho mRNA by preventing premature termination by its own protein product (Silva et al., 2019). The 567 nucleotide 5’UTR of rpoS was also found to be subject to termination by Rho, and activation of rpoS by DsrA, RprA and ArcZ was found to be in part, due to anti-termination mediated by these sRNAs (Sedlyarova et al., 2016). StxS was demonstrated in Chapter 4 to share the same seed region and rpoS binding site as these sRNAs, and so may also activate RpoS through Rho anti-termination.

174

Chapter 6

In EHEC, the use of RhoTermPredict revealed a putative rut site beginning at +35, and potential RNA polymerase pause sites between truncates T3 and T4 of the chuA 5’UTR (Figure 3.5A) (Di Salvo et al., 2019). This is supported by findings that demonstrate exposure of E. coli EDL933 to the Rho inhibitor bicyclomycin resulted in the increased expression of EHEC-specific genes, including the haem receptor chuA (Cardinale et al., 2008) (Figure 3.5B). Based on differential RNA- seq, the 5’UTR of chuA was found to be 290 nucleotides in length. The 5’UTR showed an RNA-seq profile typical of a Rho terminated transcript, with a proximal read to distal read ratio (with respect to the +1 site) greater than 1.5. Exposure of DH5α cells containing the chuA-GFP translational fusion to bicyclomycin benzoate, a semisynthetic derivative of bicyclomycin, resulted in a 1.3-fold increase in fluorescence. This derivative has only 10% of the potency of unmodified bicyclomycin and was used due to unavailability of bicyclomycin (Muller et al., 1979). A Rho-deletion mutant cannot be constructed due to the essentiality of this gene (Yamamoto et al., 2009). Nevertheless, treatment with bicyclomycin benzoate demonstrated that the 5’UTR of chuA was subject to Rho termination (Figure 3.5C). The use of bicyclomycin on the ΔstxS strains of Sakai can be done to confirm the anti-termination effect of StxS on RpoS and uncover other possible StxS-mediated antitermination events. Alternatively, Rho can be chromosomally tagged with the dual-affinity HTF-tag for use in CRAC experiments to identify transcripts bound to Rho at different stages of growth or in different stress conditions.

6.4 Pathogen-specific RNA-binding proteins may be important for post- transcriptional regulation in EHEC

RNA-binding proteins have a central role in maintaining cell metabolism and post- transcriptional networks across all kingdoms of life. The development of chemical or UV-crosslinking techniques for discovery of novel regulatory RNAs and their interactomes were spurred by an understanding of the role and function of specific RBPs (Wong et al., 2018).

175

Chapter 6

In Chapters 3 and 4, in vivo UV-crosslinking of Hfq facilitated the discovery of chuA as a sRNA-regulated transcript, and StxS as functional regulatory sRNA transcribed from the late phage promoter PR’. Further characterisation and target discovery for StxS was driven by UV-crosslinking of RNase E. This thesis has showcased the importance of RBPs as scaffolds for studying bacterial RNA biology. Small RNAs that do not bind to either Hfq or ProQ were observed upon gradient profiling of RNAs and RNPs in Salmonella (Smirnov et al., 2016), and a sRNA chaperone RocC was found to bind to only one trans-encoded sRNA in Legionella despite transcribing hundreds of sRNAs (Attaiech et al., 2016). This suggests that there likely to be more undiscovered RBPs that bind to sRNAs and facilitate their function. The first methods developed to identify the global RNA- binding proteome was limited to eukaryotes as they required poly(A) tails found on mature eukaryotic mRNAs (Castello et al., 2012; Beckmann et al., 2015). High-throughput methods have recently been developed to purify the total RBPome of prokaryotes in a non-poly(A) dependent manner (Queiroz et al., 2019; Shchepachev et al., 2019; Trendel et al., 2019; Urdaneta et al., 2019). In Chapter 5, one of these methods, Total RNA-Associated Protein Purification (TRAPP) was used to purify the total RNA-binding proteome of EHEC O157:H7 str. Sakai grown in virulence-inducing media. The Sakai strain of EHEC has a genome approximately 1.3 MBp larger than the commensal K-12 strain, and the pathogenicity islands could potentially code for EHEC-specific RNA-binding proteins. (Hayashi et al., 2001). Analysis of the mass spectrometry output from TRAPP revealed the two-fold enrichment of 443 statistically significant proteins after UV-crosslinking, including canonical RBPs such as Hfq, ProQ, and CsrA. GO term analysis and APRICOT predictions of RNA-binding domains on the 443 proteins confirmed that the use of TRAPP in EHEC enriched for RBPs.

Of the 443 two-fold enriched proteins, 35 were specific to EHEC (Table 5.4). Of these, 16 had functions associated with DNA-binding, such as methyltransferases, integrases and the phage cI repressor. This is not surprising given that silica beads used for the RBP isolation steps in TRAPP can enrich for both DNA and RNA-binding proteins. UV-crosslinking and acidic GTC-phenol however, is expected to be significantly more efficient for RNA-protein complexes 176

Chapter 6

compared to DNA-protein complexes (Angelov et al., 1988; Shchepachev et al., 2019; Urdaneta et al., 2019). The DNA-binding transcriptional regulators Crp and Fur were both enriched in TRAPP performed in K-12 and EHEC, and was also recovered in OOPS (Supplementary Table S3). While DNA-binding proteins can be recovered using TRAPP, these do not represent a significant proportion of the enriched proteins. Five enriched EHEC specific proteins were tagged with the HTF dual affinity tag and cloned into an arabinose inducible vector including ECs1184, a protein located on the Stx2Φ. This protein was enriched 10-fold in UV-crosslinked samples but failed to reach the significance cutoff (p=0.065) and did not form an RNP when tested using a PNK assay. UV-crosslinked complexes of EspY2, ECs0604, ECs1163 and ECs1762 were labelled with γ32P-ATP using polynucleotide kinase suggesting that they are bona fide RBPs (Figure 5.4).

ECs0604 and ECs1762 are uncharacterised proteins of unknown function located on S-loop 44 and the defective Sp9 prophage in EHEC str. Sakai, respectively. Both proteins have no conserved domains, and were strongly radiolabelled following the PNK reaction, demonstrating their capacity to bind to RNA. The observed signal on the autoradiogram for ECs1762 was approximately 17 kDa, which is approximately twice the estimated size for an ECs1762 monomer (8.9 kDa) . This suggests that this protein may form a dimer despite the denaturing conditions of the assay.

ECs1163 is a protein-encoded on the Stx2Φ that is predicted to contain the RNA- binding ASCH-domain, which have been associated with regulation of both transcription and translation, although an exact function has not been identified (Iyer, Burroughs and Aravind, 2006; Kim et al., 2017). This protein is located between the StxΦ genes for integration and recombination and has also been found in Escherichia and Salmonella phages, suggesting it may play a role in lysogeny. The RNA-binding partners of this protein need to be identified to better understand its significance in EHEC physiology.

A computational approach followed by a PNK assay has previously been taken to uncover potential RNA-binding effectors. PipB2 and Lpg2844 from Salmonella 177

Chapter 6

and Legionella, respectively, were proposed to bind to mono- or di-nucleotides, but not RNA (Tawk et al., 2017). Two non-LEE encoded effector proteins, EspN and EspY2, were enriched by TRAPP in UV-crosslinked cultures seven and six- fold, respectively. These proteins do not contain any known RNA-binding domains, and so were missed in the previous study. The significant enrichment of EspY2 and EspN by TRAPP suggest that these may represent a novel class of RNA-binding effectors. The nucleotide-binding properties of EspY2 were confirmed using a PNK assay. Effector proteins are injected into host cells by the T3SS system and influence their host cell processes and physiology to benefit the pathogen. EHEC contains 62 putative effectors, and the translocation of 39 effectors (including EspN) has been confirmed (Tobe et al., 2006). EspN shares sequence similarity to the cytotoxic necrotizing factor CNF from E. coli, but this does not include the C-terminal catalytic domain, and so it has been suggested to have a different function. Results presented here indicate that effector proteins injected into host cells may be RNA-binding and are relevant for host infection.

EspY2 was not confirmed to be type III secreted, but translocation of effectors in the same family: EspY1, EspY3 and EspY4, has been confirmed. EspY2 also shares an N-terminal WEX5F domain with several confirmed effectors of Salmonella (Tobe et al., 2006). While EspY1 and EspY3 carry the same N- terminal WEX5F domain, they have significantly different functions. EspY1 is involved in apoptosis and regulation of the cell cycle, while EspY3 had a role in actin pedestal formation (Blasche et al., 2014; Larzábal et al., 2018). A TraDIS study of EHEC str. EDL933 suggests that EspY2 and EspN are required for EHEC colonisation of cattle (Figure 5.3B) (Eckert et al., 2011). Confirmation of RNA-binding properties of both effectors could indicate that RNA-binding effectors may be critical for virulence. Currently, the only known T3SS protein known to bind RNA is YopD, which is a part of the injectisome and regulates virulence in Yersinia enterocolitica (Chen and Anderson, 2011; Kopaskie, Ligtenberg and Schneewind, 2013). There are currently no known effector proteins that bind to host RNA. The necessity of EspY2 for bovine colonisation suggests that the ability of this effector to bind to nucleic acids is important for EHEC pathogenicity. 178

Chapter 6

A previous screen for RNA-binding effectors using in silico domain predictions contained a large number of false positives (Tawk et al., 2017). For the putative EHEC-specific RNA-binding proteins, their experimental discovery using TRAPP makes this less of a concern. However, for future work on this, controls need to be incorporated into the assay to verify the capacity of the putative RBPs to bind to RNA. Inclusion of a non-UV-crosslinked control in the PNK assay can indicate that the observed signal on the autoradiogram is due to radiolabelling of RNA UV-crosslinked to the protein. Additionally, omitting polynucleotide kinase from the assay can control for direct protein-ATP interactions or autophosphorylation events (Tawk et al., 2017). The proteins can also be chromosomally tagged with the dual-affinity HTF-tag and UV-crosslinked to RNA in vivo to identify the classes of RNAs that bind to the putative RBPs. The verification of the candidate RBPs can provide insight into the physiological significance of these proteins, novel RNA-binding domains, and the effects these proteins may have in EHEC virulence.

6.5 Conclusion The studies covered by this thesis have helped achieve a better understanding of the post-transcriptional networks in enterohaemorrhagic E. coli through small RNAs and RNA-binding proteins. First, this thesis has shown that the core genome small RNA CyaR activates the pathogenicity island encoded outer membrane haem receptor chuA. This may potentially link carbon sensing and iron scavenging to optimise infection in the host. Further, a horizontally acquired, phage-encoded sRNA StxS was found to regulate the host stress response. StxS activates the general stress and stationary phase sigma factor RpoS and allows for maximal growth in starvation conditions. Significantly, StxS was found to repress production of the Shiga toxin independent of phage induction. This sRNA may also represent a novel class of sRNAs that arise from antiterminated transcripts. Finally, a global view of RNA-binding proteins was captured using TRAPP, and novel pathogen-specific putative RNA-binding proteins have been discovered. Evidence that suggests that these proteins have bona fide RNA- binding capabilities have been provided using a polynucleotide kinase assay. 179

Chapter 6

Two effectors, EspN and EspY2, may represent a novel class of RNA-binding effectors, which can help further the understanding of virulence not only in E. coli, but other pathogens capable of type III secretion. These studies have all demonstrated the importance of post-transcriptional regulation in EHEC, and the findings may have implications for the post-transcriptional networks of other organisms.

180

References

1. Abe, H. et al. (2008) ‘Global regulation by horizontally transferred regulators establishes the pathogenicity of Escherichia coli’, DNA Research, 15(1), pp. 13–23. doi: 10.1093/dnares/dsm028. 2. Allfano, P. et al. (1991) ‘A consensus motif common to all rho-dependent prokaryotic transcription terminators’, Cell, 64(3), pp. 553–563. doi: 10.1016/0092-8674(91)90239-U. 3. Allison, H. E. et al. (2003) ‘Immunity profiles of wild-type and recombinant Shiga-like toxin-encoding bacteriophages and characterization of novel double lysogens’, Infection and Immunity, 71(6), pp. 3409–3418. doi: 10.1128/IAI.71.6.3409-3418.2003. 4. Allison, H. E. (2007) ‘Stx-phages: drivers and mediators of the evolution of STEC and STEC-like pathogens.’, Future microbiology, 2(2), pp. 165–174. doi: 10.2217/17460913.2.2.165. 5. Altuvia, S. et al. (1997) ‘A small, stable RNA induced by oxidative stress: Role as a pleiotropic regulator and antimutator’, Cell, 90(1), pp. 43–53. doi: 10.1016/S0092-8674(00)80312-8. 6. Altuvia, S., Storz, G. and Papenfort, K. (2018) ‘Cross-regulation between bacteria and phages at a posttranscriptional level’, Regulating with RNA in Bacteria and Archaea, pp. 501–514. doi: 10.1128/microbiolspec.rwr-0027-2018. 7. Andersen, J. and Delihas, N. (1990) ‘micF RNA Binds to the 5′ End of ompF mRNA and to a protein from Escherichia coli’, Biochemistry, 29(39), pp. 9249– 9256. doi: 10.1021/bi00491a020. 8. Andrews, S. et al. (2010) FastQC. A quality control tool for high throughput sequence data. Available at: http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/. 9. Angelov, D. et al. (1988) ‘Protein - DNA crosslinking in reconstituted nucleohistone, nuclei and whole cells by picosecond UV laser irradiation’, Nucleic Acids Research, 16(10), pp. 4525–4538. doi: 10.1093/nar/16.10.4525. 10. Anzaldi, L. L. and Skaar, E. P. (2010) ‘Overcoming the heme paradox: Heme toxicity and tolerance in bacterial pathogens’, Infection and Immunity, 78(12), pp. 4977–4989. doi: 10.1128/IAI.00613-10. 11. Argaman, L. et al. (2001) ‘Novel small RNA-encoding genes in the intergenic regions of Escherichia coli’, Current Biology, 11(12), pp. 941–950. doi:

181

References

http://dx.doi.org/10.1016/S0960-9822(01)00270-6. 12. Asadulghani, M. et al. (2009) ‘The defective prophage pool of Escherichia coli O157: prophage-prophage interactions potentiate horizontal transfer of virulence determinants’, PLoS Pathogens, 5(5). doi: 10.1371/journal.ppat.1000408. 13. Ashburner, M. et al. (2000) ‘Gene Ontology : tool for the unification of biology’, Nature Genetics, 25(may), pp. 25–29. doi: 10.1038/75556. 14. Attaiech, L. et al. (2016) ‘Silencing of natural transformation by an RNA chaperone and a multitarget small RNA’, Proceedings of the National Academy of Sciences, 113(31), pp. 8813–8818. doi: 10.1073/pnas.1601626113. 15. Azam, M. S. and Vanderpool, C. K. (2018) ‘Translational regulation by bacterial small RNAs via an unusual Hfq-dependent mechanism’, Nucleic Acids Research. Oxford University Press, 46(5), pp. 2585–2599. doi: 10.1093/nar/gkx1286. 16. Babitzke, P. and Kushner, S. R. (1991) ‘The Ams (altered mRNA stability) protein and ribonuclease E are encoded by the same structural gene of Escherichia coli.’, Proceedings of the National Academy of Sciences, 88(1), pp. 1–5. doi: 10.1073/pnas.88.1.1. 17. Backofen, R. and Hess, W. R. (2010) ‘Computational prediction of sRNAs and their targets in bacteria’, RNA Biology, 7(1). doi: 10.4161/rna.7.1.10655. 18. Bags, A. and Neilands, J. B. (1987) ‘Ferric uptake regulation protein acts as a repressor, employing iron(ii) as a cofactor to bind the operator of an iron transport operon in Escherichia coli’, Biochemistry, 26(17), pp. 5471–5477. doi: 10.1021/bi00391a039. 19. Bai, X. et al. (2018) ‘Identification and pathogenomic analysis of an Escherichia coli strain producing a novel Shiga toxin 2 subtype’, Scientific Reports. Springer US, 8(1), pp. 1–11. doi: 10.1038/s41598-018-25233-x. 20. Baichoo, N. and Helmann, J. D. (2002) ‘Recognition of DNA by Fur: A reinterpretation of the Fur box consensus sequence’, Journal of Bacteriology, 184(21), pp. 5826–5832. doi: 10.1128/JB.184.21.5826-5832.2002. 21. Bak, G. et al. (2014) ‘Roles of rpoS-activating small RNAs in pathways leading to acid resistance of Escherichia coli’, MicrobiologyOpen, 3(1), pp. 15–28. doi: 10.1002/mbo3.143. 22. Baker, C. S. et al. (2007) ‘CsrA inhibits translation initiation of Escherichia coli hfq by binding to a single site overlapping the Shine-Dalgarno sequence’, Journal of Bacteriology, 189(15), pp. 5472–5481. doi: 10.1128/JB.00529-07. 182

References

23. Bakowski, M. A. et al. (2007) ‘SopD acts cooperatively with SopB during Salmonella enterica serovar Typhimurium invasion’, Cellular Microbiology, 9(12), pp. 2839–2855. doi: 10.1111/j.1462-5822.2007.01000.x. 24. Balasubramanian, D. et al. (2016) ‘A prophage-encoded small RNA controls metabolism and cell division in Escherichia coli’, mSystems, 1(1), pp. 1–18. doi: 10.1128/mSystems.00021-15.Editor. 25. Baltz, A. G. et al. (2012) ‘The mRNA-bound proteome and its global occupancy profile on protein-coding transcripts’, Molecular Cell, 46(5), pp. 674–690. doi: 10.1016/j.molcel.2012.05.021. 26. Bandyra, K. J. et al. (2012) ‘The seed region of a small rna drives the controlled destruction of the target mRNA by the endoribonuclease RNase E’, Molecular Cell, 47(6), pp. 943–953. doi: 10.1016/j.molcel.2012.07.015. 27. Bao, X. et al. (2018) ‘Capturing the interactome of newly transcribed RNA’, Nature Methods, 15(3), pp. 213–220. doi: 10.1038/nmeth.4595. 28. Barquist, L. and Vogel, J. (2015) ‘Accelerating discovery and functional analysis of small RNAs with new technologies’, Annual Review of Genetics, 49(1), pp. 367–394. doi: 10.1146/annurev-genet-112414-054804. 29. Basu, D. et al. (2015) ‘The A1 subunit of Shiga toxin 2 has higher affinity for ribosomes and higher catalytic activity than the A1 subunit of Shiga toxin 1’, Infection and Immunity, 84(1), pp. 149–161. doi: 10.1128/IAI.00994-15. 30. Beckham, K. S. H. et al. (2014) ‘The metabolic enzyme AdhE controls the virulence of Escherichia coli O157: H7’, Molecular Microbiology, 93(1), pp. 199– 211. doi: 10.1111/mmi.12651. 31. Beckmann, B. M. et al. (2015) ‘The RNA-binding proteomes from yeast to man harbour conserved enigmRBPs’, Nature Communications, 6. doi: 10.1038/ncomms10127. 32. Beisel, C. L. et al. (2012) ‘Multiple factors dictate target selection by Hfq-binding small RNAs’, EMBO Journal. Nature Publishing Group, 31(8), pp. 1961–1974. doi: 10.1038/emboj.2012.52. 33. Beisel, C. L. and Storz, G. (2011) ‘The base-pairing RNA Spot42 participates in a multioutput feedforward loop to help enact catabolite repression in Escherichia coli’, Molecular Cell. Elsevier Inc., 41(3), pp. 286–297. doi: 10.1016/j.molcel.2010.12.027. 34. Bertin, Y. et al. (2013) ‘Carbohydrate utilization by enterohaemorrhagic Escherichia coli O157: H7 in bovine intestinal content’, Environmental Microbiology, 15(2), pp. 610–622. doi: 10.1111/1462-2920.12019. 183

References

35. Betz, J. et al. (2016) ‘Shiga toxin of enterohemorrhagic Escherichia coli directly injures developing human erythrocytes’, Cellular Microbiology, 49(0), p. n/a-n/a. doi: 10.1111/cmi.12592. 36. Bhatt, S. et al. (2009) ‘The RNA binding protein CsrA is a pleiotropic regulator of the locus of enterocyte effacement pathogenicity island of enteropathogenic Escherichia coli’, Infection and Immunity, 77(9), pp. 3552–3568. doi: 10.1128/IAI.00418-09. 37. Biesen, T. van and Frost, L. S. (1994) ‘The FinO protein of IncF plasmids binds FinP antisense RNA and its target, traJ mRNA, and promotes duplex formation’, Molecular Microbiology, 14(3), pp. 427–436. doi: 10.1111/j.1365- 2958.1994.tb02177.x. 38. Blasche, S. et al. (2014) ‘The EHEC-host interactome reveals novel targets for the translocated intimin receptor’, Scientific Reports, 4, pp. 22–26. doi: 10.1038/srep07531. 39. Blattner, F. R. et al. (1997) ‘The complete genome sequence of Escherichia coli K-12’, Science, 277(5331), pp. 1453–1462. doi: 10.1126/science.277.5331.1453. 40. Boisen, N. et al. (2009) ‘Short report: High prevalence of serine protease autotransporter cytotoxins among strains of enteroaggregative Escherichia coli’, American Journal of Tropical Medicine and Hygiene, 80(2), pp. 294–301. 41. Boisset, S. et al. (2007) ‘Staphylococcus aureus RNAIII coordinately represses the synthesis of virulence factors and the transcription regulator Rot by an antisense mechanism’, Genes and Development, 21(11), pp. 1353–1366. doi: 10.1101/gad.423507. 42. Bolivar, F. et al. (1977) ‘Construction and characterization of new cloning vehicle. II. A multipurpose cloning system’, Gene, 2(2), pp. 95–113. doi: 10.1016/0378-1119(77)90000-2. 43. Bonanno, L. et al. (2016) ‘Heterogeneity in induction level, infection ability, and morphology of shiga toxin-encoding phages (Stx phages) from dairy and human shiga toxin-producing Escherichia coli O26:H11 isolates’, Applied and Environmental Microbiology, 82(7), pp. 2177–2186. doi: 10.1128/aem.03463- 15. 44. Bossi, L. et al. (2012) ‘A role for Rho-dependent polarity in gene regulation by a noncoding small RNA’, Genes and Development, 26(16), pp. 1864–1873. doi: 10.1101/gad.195412.112. 45. Bossi, L. and Figueroa-Bossi, N. (2007) ‘A small RNA downregulates LamB 184

References

maltoporin in Salmonella’, Molecular Microbiology, 65(3), pp. 799–810. doi: 10.1111/j.1365-2958.2007.05829.x. 46. Bossi, L. and Figueroa-Bossi, N. (2016) ‘Competing endogenous RNAs: A target-centric view of small RNA regulation in bacteria’, Nature Reviews Microbiology. Nature Publishing Group, 14(12), pp. 775–784. doi: 10.1038/nrmicro.2016.129. 47. Bouché, F. and Bouché, J. ‐P (1989) ‘Genetic evidence that DicF, a second division inhibitor encoded by the Escherichia coli dicB operon, is probably RNA’, Molecular Microbiology, 3(7), pp. 991–994. doi: 10.1111/j.1365- 2958.1989.tb00249.x. 48. Bouvier, M. et al. (2008) ‘Small RNA Binding to 5′ mRNA coding region inhibits translational initiation’, Molecular Cell, 32(6), pp. 827–837. doi: 10.1016/j.molcel.2008.10.027. 49. Boyd, E. F. (2012) Bacteriophage-Encoded Bacterial Virulence Factors and Phage-Pathogenicity Island Interactions. 1st edn, Advances in Virus Research. 1st edn. Elsevier Inc. doi: 10.1016/B978-0-12-394621-8.00014-5. 50. Brannan, K. W. et al. (2016) ‘SONAR discovers RNA-binding proteins from analysis of large-scale protein-protein interactomes’, Molecular Cell. Elsevier Inc., 64(2), pp. 282–293. doi: 10.1016/j.molcel.2016.09.003. 51. Brigotti, M. et al. (2006) ‘Shiga toxins present in the gut and in the polymorphonuclear leukocytes circulating in the blood of children with hemolytic-uremic syndrome’, Journal of Clinical Microbiology, 44(2), pp. 313– 317. doi: 10.1128/JCM.44.2.313-317.2006. 52. Brigotti, M. et al. (2008) ‘Interactions between Shiga toxins and human polymorphonuclear leukocytes’, Journal of Leukocyte Biology, 84(4), pp. 1019– 1027. doi: 10.1189/jlb.0308157. 53. Brigotti, M. et al. (2011) ‘Change in conformation with reduction of α-helix content causes loss of neutrophil binding activity in fully cytotoxic shiga toxin’, Journal of Biological Chemistry, 286(40), pp. 34514–34521. doi: 10.1074/jbc.M111.255414. 54. Brigotti, M. et al. (2013) ‘Identification of TLR4 as the receptor that recognizes Shiga toxins in human neutrophils’, The Journal of Immunology, 191(9), pp. 4748–4758. doi: 10.4049/jimmunol.1300122. 55. Brimacombe, R. et al. (1988) ‘Intra-RNA and RNA—protein cross-linking techniques in escherichia coli ribosomes’, Methods in Enzymology, 164(C), pp. 287–309. doi: 10.1016/S0076-6879(88)64050-X. 185

References

56. Brown, L. and Elliott, T. (1997) ‘Mutations that increase expression of the rpoS gene and decrease its dependence on hfq function in Salmonella typhimurium’, Journal of Bacteriology, 179(3), pp. 656–662. doi: 10.1128/jb.179.3.656- 662.1997. 57. Brüssow, H. et al. (2004) ‘Phages and the evolution of bacterial pathogens : from genomic rearrangements to lysogenic conversion phages and the evolution of bacterial pathogens : from genomic rearrangements to lysogenic conversion’, Microbiology and molecular biology reviews, 68(3), pp. 560–602. doi: 10.1128/MMBR.68.3.560. 58. Bunger, J. et al. (2015) ‘Reduced Toxicity of Shiga Toxin (Stx) Type 2c in Mice Compared to Stx2d Is Associated with Instability of Stx2c Holotoxin’, Toxins, 7(6), pp. 2306–2320. doi: 10.3390/toxins7062306. 59. Busch, A., Richter, A. S. and Backofen, R. (2008) ‘IntaRNA: Efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions’, Bioinformatics, 24(24), pp. 2849–2856. doi: 10.1093/bioinformatics/btn544. 60. Bustamante, V. H. et al. (2001) ‘Transcriptional regulation of type III secretion genes in enteropathogenic Escherichia coli: Ler antagonizes H-NS-dependent repression’, Molecular Microbiology, 39, pp. 664–678. doi: 10.1046/j.1365- 2958.2001.02209.x. 61. de Buys Roessingh, A. S. et al. (2007) ‘Gastrointestinal complications of post- diarrheal hemolytic uremic syndrome’, Eur J Pediatr Surg, 17(5), pp. 328–334. doi: 10.1055/s-2007-965013. 62. Calderwood, S. B. et al. (1987) ‘Nucleotide sequence of the Shiga-like toxin genes of Escherichia coli.’, Proceedings of the National Academy of Sciences of the United States of America, 84(July), pp. 4364–4368. doi: 10.1073/pnas.84.13.4364. 63. Campellone, K. G., Robbins, D. and Leong, J. M. (2004) ‘EspFU is a translocated EHEC effector that interacts with Tir and N-WASP and promotes Nck-independent actin assembly’, Developmental Cell, 7(2), pp. 217–228. doi: 10.1016/j.devcel.2004.07.004. 64. Caprioli, A. et al. (1992) ‘Hemolytic-uremic syndrome and vero cytotoxin- producing Escherichia coli infection in italy’, Journal of Infectious Diseases, 166(1), pp. 154–158. doi: https://doi.org/10.1093/infdis/166.1.154. 65. Carbon, S. et al. (2019) ‘The Gene Ontology Resource: 20 years and still GOing strong’, Nucleic Acids Research. Oxford University Press, 47(D1), pp. 186

References

D330–D338. doi: 10.1093/nar/gky1055. 66. Cardinale, C. J. et al. (2008) ‘Termination factor Rho and its cofactors NusA and NusG silence foreign DNA in E. coli’, Science, 320(May), pp. 935–938. doi: 10.1126/science.1152763. 67. Carlson-Banning, K. M. and Sperandio, V. (2016) ‘Catabolite and oxygen regulation of enterohemorrhagic Escherichia coli virulence’, mBio, 7(6), pp. 1– 11. doi: 10.1128/mBio.01852-16. 68. Castello, A. et al. (2012) ‘Insights into rna biology from an atlas of mammalian mRNA-binding proteins’, Cell. Elsevier Inc., 149(6), pp. 1393–1406. doi: 10.1016/j.cell.2012.04.031. 69. Castello, A., Hentze, M. W. and Preiss, T. (2015) ‘Metabolic enzymes enjoying new partnerships as RNA-binding proteins’, Trends in Endocrinology & Metabolism. Elsevier Ltd, 26(12), pp. 746–757. doi: 10.1016/j.tem.2015.09.012. 70. Celia, H. et al. (2016) ‘Structural insight into the role of the Ton complex in energy transduction’, Nature. Nature Publishing Group, 538(7623), pp. 60–65. doi: 10.1038/nature19757. 71. Chao, Y. et al. (2012) ‘An atlas of Hfq-bound transcripts reveals 3′ UTRs as a genomic reservoir of regulatory small RNAs’, EMBO Journal, 31(20), pp. 4005– 4019. doi: 10.1038/emboj.2012.229. 72. Chao, Y. et al. (2017) ‘In vivo cleavage map illuminates the central role of RNase E in coding and noncoding RNA pathways’, Molecular Cell, 65, pp. 39– 51. doi: 10.1016/j.molcel.2016.11.002. 73. Chao, Y. and Vogel, J. (2010) ‘The role of Hfq in bacterial pathogens’, Current Opinion in Microbiology, 13(1), pp. 24–33. doi: 10.1016/j.mib.2010.01.001. 74. Chao, Y. and Vogel, J. (2016) ‘A 3′ UTR-derived small RNA provides the regulatory noncoding arm of the inner membrane stress response’, Molecular Cell, pp. 352–363. doi: 10.1016/j.molcel.2015.12.023. 75. Chareyre, S. and Mandin, P. (2018) ‘Bacterial Iron Homeostasis Regulation by sRNAs’, Microbiology Spectrum, 6(2), pp. 1–15. doi: 10.1128/microbiolspec.RWR-0010-2017. 76. Chen, C. et al. (2013) ‘Identification and characterization of spontaneous deletions within the SP11-SP12 prophage region of Escherichia coli O157:H7 Sakai’, Applied and Environmental Microbiology, 79(6), pp. 1934–1941. doi: 10.1128/aem.03682-12. 77. Chen, H. et al. (2015) ‘Genome-wide study of mRNA degradation and transcript elongation in Escherichia coli’, Molecular Systems Biology, 11(5), pp. 808–808. 187

References

doi: 10.15252/msb.20159000. 78. Chen, J., Morita, T. and Gottesman, S. (2019) ‘Regulation of transcription termination of small RNAs and by small RNAs: Molecular mechanisms and biological functions’, Frontiers in Cellular and Infection Microbiology, 9(June), pp. 1–9. doi: 10.3389/fcimb.2019.00201. 79. Chen, Y. and Anderson, D. M. (2011) ‘Expression hierarchy in the Yersinia type III secretion system established through YopD recognition of RNA’, Molecular Microbiology, 80(4), pp. 966–980. doi: 10.1111/j.1365-2958.2011.07623.x. 80. Chen, Z., Yang, H. and Pavletich, N. P. (2008) ‘Mechanism of homologous recombination from the RecA-ssDNA/dsDNA structures’, Nature, 453(7194), pp. 489–494. doi: 10.1038/nature06971. 81. Cherepanov, P. P. and Wackernagel, W. (1995) ‘Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant’, Gene, 158(1), pp. 9–14. doi: 10.1016/0378- 1119(95)00193-A. 82. Colon, M. et al. (2016) ‘Mechanisms that Determine the Differential Stability of Stx+ and Stx− Lysogens’, Toxins, 8(4), p. 96. doi: 10.3390/toxins8040096. 83. Conrad, T. et al. (2016) ‘Serial interactome capture of the human cell nucleus’, Nature Communications, 7, pp. 1–11. doi: 10.1038/ncomms11212. 84. Corcoran, C. P. et al. (2012) ‘Superfolder GFP reporters validate diverse new mRNA targets of the classic porin regulator, MicF RNA’, Molecular Microbiology, 84(3), pp. 428–445. doi: 10.1111/j.1365-2958.2012.08031.x. 85. Cox, J. and Mann, M. (2008) ‘MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.’, Nature biotechnology, 26(12), pp. 1367–72. doi: 10.1038/nbt.1511. 86. Croxen, M. A. et al. (2013) ‘Recent advances in understanding enteric pathogenic Escherichia coli’, Clinical Microbiology Reviews, 26(4), pp. 822– 880. doi: 10.1128/CMR.00022-13. 87. Curtis, M. M. et al. (2014) ‘The gut commensal Bacteroides thetaiotaomicron exacerbates enteric infection through modification of the metabolic landscape’, Cell Host and Microbe. Elsevier Inc., 16(6), pp. 759–769. doi: 10.1016/j.chom.2014.11.005. 88. Dahan, S. et al. (2004) ‘Transcriptome of enterohemorrhagic Escherichia coli O157 adhering to eukaryotic plasma membranes’, Infection and Immunity, 72(9), pp. 5452–5459. doi: 10.1128/IAI.72.9.5452. 188

References

89. Dar, D. and Sorek, R. (2018) ‘Bacterial noncoding RNAs excised from within protein-coding transcripts’, mBio, 9(5), pp. 1–7. doi: 10.1128/mbio.01730-18. 90. Davis, B. M. et al. (2005) ‘Characterization of the small untranslated RNA RyhB and its regulon in Vibrio cholerae’, Journal of Bacteriology, 187(12), pp. 4005– 4014. doi: 10.1128/JB.187.12.4005-4014.2005. 91. Dean, P. and Kenny, B. (2009) ‘The effector repertoire of enteropathogenic E. coli: ganging up on the host cell’, Current Opinion in Microbiology. Elsevier Ltd, 12(1), pp. 101–109. doi: 10.1016/j.mib.2008.11.006. 92. Deana, A. and Belasco, J. G. (2005) ‘Lost in translation: The influence of ribosomes on bacterial mRNA decay’, Genes and Development, 19(21), pp. 2526–2533. doi: 10.1101/gad.1348805. 93. Deng, W. et al. (2004) ‘Dissecting virulence: Systematic and functional analyses of a pathogenicity island’, Proceedings of the National Academy of Sciences, 101(10), pp. 3597–3602. doi: 10.1073/pnas.0400326101. 94. Deng, Z. et al. (2012) ‘Two sRNA RyhB homologs from Yersinia pestis biovar microtus expressed in vivo have differential Hfq-dependent stability’, Research in Microbiology. Elsevier Masson SAS, 163(6–7), pp. 413–418. doi: 10.1016/j.resmic.2012.05.006. 95. Desnoyers, G. et al. (2009) ‘Small RNA-induced differential degradation of the polycistronic mRNA iscRSUA’, EMBO Journal, 28(11), pp. 1551–1561. doi: 10.1038/emboj.2009.116. 96. Dimastrogiovanni, D. et al. (2014) ‘Recognition of the small regulatory RNA RydC by the bacterial Hfq protein.’, eLife, 3, pp. 1–19. doi: 10.7554/eLife.05375. 97. Ding, Y., Davis, B. M. and Waldor, M. K. (2004) ‘Hfq is essential for Vibrio

cholerae virulence and downregulates σE expression’, Molecular Microbiology, 53(1), pp. 345–354. doi: 10.1111/j.1365-2958.2004.04142.x. 98. Dodd, I. B. et al. (2001) ‘Octamerization of λ cI repressor is nedded for effective

repressions of PRM and efficient switching from lysogeny’, Genes Dev., 15, pp. 3013–3022. doi: 10.1101/gad.937301.presses. 99. Dodd, I. B. et al. (2004) ‘Cooperativity in long-range gene regulation by the λ CI repressor’, Genes and Development, 18(3), pp. 344–354. doi: 10.1101/gad.1167904. 100. Dodt, M. et al. (2012) ‘FLEXBAR—Flexible Barcode and Adapter Processing for Next-Generation Sequencing Platforms’, Biology, 1(3), pp. 895– 905. doi: 10.3390/biology1030895. 189

References

101. Donaldson, G. P., Lee, S. M. and Mazmanian, S. K. (2015) ‘Gut biogeography of the bacterial microbiota’, Nature Reviews Microbiology. Nature Publishing Group, 14(1), pp. 20–32. doi: 10.1038/nrmicro3552. 102. Dong, T., Kirchhof, M. G. and Schellhorn, H. E. (2008) ‘RpoS regulation of gene expression during exponential growth of Escherichia coli K12’, Molecular Genetics and Genomics, 279(3), pp. 267–277. doi: 10.1007/s00438- 007-0311-4. 103. Dong, T. and Schellhorn, H. E. (2009) ‘Global effect of RpoS on gene expression in pathogenic Escherichia coli O157:H7 strain EDL933’, BMC Genomics, 10, pp. 1–17. doi: 10.1186/1471-2164-10-349. 104. Dong, T. and Schellhorn, H. E. (2010) ‘Role of RpoS in virulence of pathogens’, Infection and Immunity, 78(3), pp. 887–897. doi: 10.1128/IAI.00882-09. 105. Dubey, A. K. et al. (2005) ‘RNA sequence and secondary structure participate in high-affinity CsrA-RNA interaction’, RNA, 11(10), pp. 1579–1587. doi: 10.1261/rna.2990205. 106. Ducey, T. F. et al. (2009) ‘Transcript analysis of nrrF, a Fur repressed sRNA of Neisseria gonorrhoeae’, Microbial Pathogenesis. Elsevier Ltd, 46(3), pp. 166–170. doi: 10.1016/j.micpath.2008.12.003. 107. Dugar, G. et al. (2013) ‘High-resolution transcriptome maps reveal strain-specific regulatory features of multiple Campylobacter jejuni isolates’, PLoS Genetics. doi: 10.1371/journal.pgen.1003495. 108. Eckert, S. E. et al. (2011) ‘Retrospective application of transposon- directed insertion site sequencing to a library of signature-tagged mini-Tn5Km2 mutants of Escherichia coli O157:H7 screened in cattle’, Journal of Bacteriology, 193(7), pp. 1771–1776. doi: 10.1128/JB.01292-10. 109. Edgar, R. C. (2004) ‘MUSCLE: multiple sequence alignment with high accuracy and high throughput.’, Nucleic acids research, 32(5), pp. 1792–7. doi: 10.1093/nar/gkh340. 110. Ellermann, M. and Arthur, J. C. (2017) ‘Siderophore-mediated iron acquisition and modulation of host-bacterial interactions’, Free Radical Biology and Medicine. Elsevier B.V., 105(August 2016), pp. 68–78. doi: 10.1016/j.freeradbiomed.2016.10.489. 111. Elliott, S. J. et al. (2000) ‘The locus of enterocyte effacement (LEE)- encoded regulator controls expression of both LEE- and non-LEE-encoded virulence factors in enteropathogenic and enterohemorrhagic Escherichia coli’, 190

References

Infection and Immunity, 68(11), pp. 6115–6126. doi: 10.1128/IAI.68.11.6115- 6126.2000. 112. Endo, Y. et al. (1988) ‘Site of action of a Vero toxin (VT2) from Escherichia coli O157 : H7 and of Shiga toxin on eukaryotic ribosomes’, European Journal of Biochemistry, 171(1–2), pp. 45–50. doi: 10.1111/j.1432- 1033.1988.tb13756.x. 113. Eriksson, K. J., Boyd, S. G. and Tasker, R. C. (2001) ‘Acute neurology and neurophysiology of haemolytic-uraemic syndrome.’, Archives of disease in childhood, 84(5), pp. 434–5. doi: 10.1136/adc.84.5.434. 114. Ernst, F. D., Homuth, G., et al. (2005) ‘Iron-Responsive Regulation of the Helicobacter pylori Iron-Cofactored Superoxide Dismutase SodB Is Mediated by Fur’, Society, 187(11), pp. 3687–3692. doi: 10.1128/JB.187.11.3687. 115. Ernst, F. D., Bereswill, S., et al. (2005) ‘Transcriptional profiling of Helicobacter pylori Fur- and iron-regulated gene expression’, Microbiology, 151(2), pp. 533–546. doi: 10.1099/mic.0.27404-0. 116. Escolar, L., Pérez-Martín, J. and De Lorenzo, V. (1998) ‘Binding of the Fur (ferric uptake regulator) repressor of Escherichia coli to arrays of the GATAAT sequence’, Journal of Molecular Biology, 283(3), pp. 537–547. doi: 10.1006/jmbi.1998.2119. 117. Etienne-Mesmin, L. et al. (2011) ‘Interactions with M cells and macrophages as key steps in the pathogenesis of enterohemorragic Escherichia coli infections’, PLoS ONE, 6(8). doi: 10.1371/journal.pone.0023594. 118. Evfratov, S. A. et al. (2017) ‘Application of sorting and next generation sequencing to study 5’-UTR influence on translation efficiency in Escherichia coli’, Nucleic Acids Research, 45(6), pp. 3487–3502. doi: 10.1093/nar/gkw1141. 119. Evstatiev, R. and Gasche, C. (2012) ‘Iron sensing and signalling’, Gut, 61(6), pp. 933–952. doi: 10.1136/gut.2010.214312. 120. Fakhouri, F. et al. (2017) ‘Haemolytic uraemic syndrome’, The Lancet, 390(10095), pp. 681–696. doi: 10.1016/S0140-6736(17)30062-4. 121. Fender, A. et al. (2010) ‘RNAs actively cycle on the Sm-like protein Hfq, Genes Develop (2010).pdf’, Genes & Development, 3(Table 1), pp. 2621–2626. doi: 10.1101/gad.591310.Structures. 122. Figueroa-Bossi, N. et al. (2009) ‘Caught at its own game: Regulatory small RNA inactivated by an inducible transcript mimicking its target’, Genes 191

References

and Development, 23(17), pp. 2004–2015. doi: 10.1101/gad.541609. 123. Fischbach, M. A. and Sonnenburg, J. L. (2011) ‘Eating for two: How metabolism establishes interspecies interactions in the gut’, Cell Host and Microbe. Elsevier, 10(4), pp. 336–347. doi: 10.1016/j.chom.2011.10.002. 124. Flagler, M. J. et al. (2007) ‘Comparative analysis of the abilities of Shiga toxins 1 and 2 to bind to and influence neutrophil apoptosis’, Infection and Immunity, 75(2), pp. 760–765. doi: 10.1128/IAI.01594-06. 125. Fleckenstein, J. M. et al. (2010) ‘Molecular mechanisms of enterotoxigenic Escherichia coli infection’, Microbes and Infection. Elsevier Masson SAS, 12(2), pp. 89–98. doi: 10.1016/j.micinf.2009.10.002. 126. Foster, D. B. (2013) ‘Modulation of the enterohemorrhagic E. coli virulence program through the human gastrointestinal tract’, Virulence, 4(4), pp. 315–323. doi: 10.4161/viru.24318. 127. Fournier, C., Smith, A. and Delepelaire, P. (2011) ‘Haem release from haemopexin by HxuA allows Haemophilus influenzae to escape host nutritional immunity’, Molecular Microbiology, 80(1), pp. 133–148. doi: 10.1111/j.1365- 2958.2011.07562.x. 128. Fredrickson, J. et al. (2014) ‘A pause sequence enriched at translation start sites drives transcription dynamics in vivo’, Science, 344(6187), pp. 1042– 1047. doi: 10.1126/science.1251871. 129. Fröhlich, K. S. et al. (2013) ‘A small RNA activates CFA synthase by isoform-specific mRNA stabilization’, EMBO Journal, 32(22), pp. 2963–2979. doi: 10.1038/emboj.2013.222. 130. Fromont-racine, M. et al. (1993) ‘A highly sensitive method for mapping the 5′ termini of mRNAs’, Nucleic Acids Research, 21(7), pp. 1683–1684. doi: 10.1093/nar/21.7.1683. 131. Fuller, C. A. et al. (2011) ‘Shiga toxin subtypes display dramatic differences in potency’, Infection and Immunity, 79(3), pp. 1329–1337. doi: 10.1128/IAI.01182-10. 132. G. Chaulk, S. et al. (2011) ‘ProQ is an RNA chaperone that controls ProP levels in Escherichia coli’, Biochemistry, 50(15), pp. 3095–3106. doi: 10.1021/bi101683a. 133. Gal-Mor, O. and Finlay, B. B. (2006) ‘Pathogenicity islands: A molecular toolbox for bacterial virulence’, Cellular Microbiology, 8(11), pp. 1707–1719. doi: 10.1111/j.1462-5822.2006.00794.x. 134. Galán, J. E. and Wolf-Watz, H. (2006) ‘Protein delivery into eukaryotic 192

References

cells by type III secretion machines.’, Nature, 444(7119), pp. 567–73. doi: 10.1038/nature05272. 135. Galkin, V. E. et al. (2009) ‘Cleavage of Bacteriophage λ cI Repressor Involves the RecA C-Terminal Domain’, Journal of Molecular Biology. Elsevier Ltd, 385(3), pp. 779–787. doi: 10.1016/j.jmb.2008.10.081. 136. Garcia, E. C., Brumbaugh, A. R. and Mobley, H. L. T. (2011) ‘ Redundancy and Specificity of Escherichia coli Iron Acquisition Systems during Urinary Tract Infection ’, Infection and Immunity, 79(3), pp. 1225–1235. doi: 10.1128/iai.01222-10. 137. Garmendia, J. et al. (2004) ‘TccP is an enterohaemorrhagic Escherichia coli O157:H7 type III effector protein that couples Tir to the actin-cytoskeleton’, Cell Microbiol, 6, pp. 1167–1183. doi: 10.1111/j.1462-5822.2004.00459.x. 138. Garred, O., Van Deurs, B. and Sandvig, K. (1995) ‘Furin-induced cleavage and activation of Shiga toxin’, Journal of Biological Chemistry, 270(18), pp. 10817–10821. doi: 10.1074/jbc.270.18.10817. 139. Gauthier, A., Thomas, N. A. and Finlay, B. B. (2003) ‘Bacterial injection machines’, Journal of Biological Chemistry, 278(28), pp. 25273–25276. doi: 10.1074/jbc.R300012200. 140. Geelen, J. M. et al. (2007) ‘Lack of specific binding of Shiga-like toxin (verocytotoxin) and non-specific interaction of Shiga-like toxin 2 antibody with human polymorphonuclear leucocytes’, Nephrology Dialysis Transplantation, 22(3), pp. 749–755. doi: 10.1093/ndt/gfl688. 141. Georg, J. and Hess, W. R. (2011) ‘cis-Antisense RNA, another level of gene regulation in bacteria’, Microbiology and Molecular Biology Reviews, 75(2), pp. 286–300. doi: 10.1128/mmbr.00032-10. 142. Gerstle, K. et al. (2012) ‘The small RNA RybA regulates key-genes in the biosynthesis of aromatic amino acids under peroxide stress in E. coli’, RNA Biology, 9(4), pp. 458–468. doi: 10.4161/rna.19065. 143. Giacomodonato, M. N. et al. (2007) ‘SipA, SopA, SopB, SopD, and SopE2 effector proteins of Salmonella enterica serovar Typhimurium are synthesized at late stages of infection in mice’, Microbiology, 153(4), pp. 1221– 1228. doi: 10.1099/mic.0.2006/002758-0. 144. Goldberg, M. D. et al. (2001) ‘Role of the nucleoid-associated protein fis in the regulation of virulence properties of enteropathogenic Escherichia coli’, Molecular Microbiology, 41(3), pp. 549–559. doi: 10.1046/j.1365- 2958.2001.02526.x. 193

References

145. Goldwater, P. N. and Bettelheim, K. A. (2012) ‘Treatment of enterohemorrhagic Escherichia coli (EHEC) infection and hemolytic uremic syndrome (HUS)’, BMC Medicine, 10. doi: 10.1186/1741-7015-10-12. 146. Gonzalez, G. M. et al. (2017) ‘Structural insights into RapZ-mediated regulation of bacterial amino-sugar metabolism’, Nucleic Acids Research, 45(18), pp. 10845–10860. doi: 10.1093/nar/gkx732. 147. Göpel, Y. et al. (2013) ‘Targeted decay of a regulatory small RNA by an adaptor protein for RNase E and counteraction by an anti-adaptor RNA’, Genes and Development, 27(5), pp. 552–564. doi: 10.1101/gad.210112.112. 148. Gorski, S. A., Vogel, J. and Doudna, J. A. (2017) ‘RNA-based recognition and targeting: Sowing the seeds of specificity’, Nature Reviews Molecular Cell Biology. Nature Publishing Group, 18(4), pp. 215–228. doi: 10.1038/nrm.2016.174. 149. Gottesman, S. (2019) ‘Trouble is coming: Signaling pathways that regulate general stress responses in bacteria’, Journal of Biological Chemistry, p. jbc.REV119.005593. doi: 10.1074/jbc.REV119.005593. 150. Grabowicz, M., Koren, D. and Silhavy, T. J. (2016) ‘The CpxQ sRNA negatively regulates skp to prevent mistargeting of β-barrel outer membrane proteins into the cytoplasmic membrane’, mBio, 7(2), pp. 1–8. doi: 10.1128/mBio.00312-16. 151. Grabowicz, M. and Silhavy, T. J. (2017) ‘Envelope Stress Responses: An Interconnected Safety Net’, Trends in Biochemical Sciences. Elsevier Ltd, 42(3), pp. 232–242. doi: 10.1016/j.tibs.2016.10.002. 152. Granneman, S., Petfalski, E. and Tollervey, D. (2011) ‘A cluster of ribosome synthesis factors regulate pre-rRNA folding and 5.8S rRNA maturation by the Rat1 exonuclease’, EMBO Journal. Nature Publishing Group, 30(19), pp. 4006–4019. doi: 10.1038/emboj.2011.256. 153. Grayhack, E. J. et al. (1985) ‘Phage lambda gene Q antiterminator recognizes RNA polymerase near the promoter and accelerates it through a pause site.’, Cell, 42(1), pp. 259–269. doi: 10.1016/S0092-8674(85)80121-5. 154. Green, J. et al. (2014) ‘Cyclic-AMP and bacterial cyclic-AMP receptor proteins revisited: Adaptation for different ecological niches’, Current Opinion in Microbiology. Elsevier Ltd, 18(1), pp. 1–7. doi: 10.1016/j.mib.2014.01.003. 155. Griener, T. P. et al. (2007) ‘Differential binding of Shiga toxin 2 to human and murine neutrophils’, Journal of Medical Microbiology, 56(11), pp. 1423– 1430. doi: 10.1099/jmm.0.47282-0. 194

References

156. Gruber, C. C. and Sperandio, V. (2014) ‘Posttranscriptional control of microbe-induced rearrangement of host cell actin’, Mbio, 5(1), pp. 1–14. doi: 10.1128/mBio.01025-13.Editor. 157. Gruber, C. C. and Sperandio, V. (2015) ‘Global analysis of posttranscriptional regulation by GlmY and GlmZ in enterohemorrhagic Escherichia coli O157:H7’, Infection and Immunity, 83(4), pp. 1286–1295. doi: 10.1128/IAI.02918-14. 158. Grylak-Mielnicka, A. et al. (2016) ‘Transcription termination factor Rho: A hub linking diverse physiological processes in bacteria’, Microbiology (United Kingdom), 162(3), pp. 433–447. doi: 10.1099/mic.0.000244. 159. Gulliver, E. L. et al. (2018) ‘Determination of the small RNA GcvB regulon in the Gram-negative bacterial pathogen Pasteurella multocida and identification of the GcvB seed binding region’, RNA, p. rna.063248.117. doi: 10.1261/rna.063248.117. 160. Guo, M. S. et al. (2014) ‘MicL, a new σE-dependent sRNA, combats envelope stress by repressing synthesis of Lpp, the major outer membrane lipoprotein’, Genes and Development, 28(14), pp. 1620–1634. doi: 10.1101/gad.243485.114. 161. Gyles, C. L. (2007) ‘Shiga toxin-producing Escherichia coli: an overview.’, Journal of animal science, 85(13 Suppl). doi: 10.2527/jas.2006-508. 162. Habdas, B. J. et al. (2010) ‘The LysR-type transcriptional regulator QseD alters type three secretion in enterohemorrhagic Escherichia coli and motility in K-12 Escherichia coli’, Journal of Bacteriology, 192(14), pp. 3699– 3712. doi: 10.1128/JB.00382-10. 163. Hagan, E. C. and Mobley, H. L. T. (2009) ‘Haem acquisition is facilitated by a novel receptor Hma and required by uropathogenic Escherichia coli for kidney infection’, Molecular Microbiology, 71(1), pp. 79–91. doi: 10.1111/j.1365- 2958.2008.06509.x. 164. Hale, C. R. et al. (2012) ‘Estimates of enteric illness attributable to contact with animals and their environments in the United States.’, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 54 Suppl 5(Suppl 5), pp. S472-9. doi: 10.1093/cid/cis051. 165. Han, K., Tjaden, B. and Lory, S. (2016) ‘GRIL-seq provides a method for identifying direct targets of bacterial small regulatory RNA by in vivo proximity ligation’, Nature Microbiology. Nature Publishing Group, 16239(December), pp. 1–10. doi: 10.1038/nmicrobiol.2016.239. 195

References

166. Hansen, A. M. and Kaper, J. B. (2009) ‘Hfq affects the expression of the LEE pathogenicity island in enterohaemorrhagic Escherichia coli’, Molecular Microbiology, 73(3), pp. 446–465. doi: 10.1111/j.1365-2958.2009.06781.x. 167. Harrington, S. M., Dudley, E. G. and Nataro, J. P. (2006) ‘Pathogenesis of enteroaggregative Escherichia coli infection’, FEMS Microbiology Letters, 254(1), pp. 12–18. doi: 10.1111/j.1574-6968.2005.00005.x. 168. Hayashi, T. et al. (2001) ‘Complete genome sequence of enterohemorrhagic Escherichia coli O157 : H7 and genomic comparison with a laboratory strain K-12’, DNA Research, 8(1), pp. 11–22. 169. Hébrard, M. et al. (2012) ‘sRNAs and the virulence of Salmonella enterica serovar Typhimurium’, RNA Biology, 9(4), pp. 437–445. doi: 10.4161/rna.20480. 170. Heidrich, N. et al. (2006) ‘The small untranslated RNA SR1 from the Bacillus subtilis genome is involved in the regulation of arginine catabolism’, Molecular Microbiology, 62(2), pp. 520–536. doi: 10.1111/j.1365- 2958.2006.05384.x. 171. Heidrich, N., Moll, I. and Brantl, S. (2007) ‘In vitro analysis of the interaction between the small RNA SR1 and its primary target ahrC mRNA’, Nucleic Acids Research, 35(13), pp. 4331–4346. doi: 10.1093/nar/gkm439. 172. Helwak, A. et al. (2013) ‘Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding’, Cell. Elsevier Inc., 153(3), pp. 654–665. doi: 10.1016/j.cell.2013.03.043. 173. Henderson, C. A. et al. (2013) ‘Hfq binding changes the structure of Escherichia coli small noncoding RNAs OxyS and RprA , which are involved in the riboregulation of rpoS’, RNA, pp. 1089–1104. doi: 10.1261/rna.034595.112.(sRNAs). 174. Hentze, M. W. et al. (2018) ‘A brave new world of RNA-binding proteins’, Nature Reviews Molecular Cell Biology. Nature Publishing Group, 19(5), pp. 327–341. doi: 10.1038/nrm.2017.130. 175. Hernandez-Doria, J. D. and Sperandio, V. (2018) ‘Bacteriophage transcription factor Cro regulates virulence gene expression in enterohemorrhagic Escherichia coli’, Cell Host and Microbe. Elsevier Inc., 23(5), pp. 607-617.e6. doi: 10.1016/j.chom.2018.04.007. 176. Hershko-Shalev, T. et al. (2016) ‘Gifsy-1 prophage IsrK with dual function as small and messenger RNA modulates vital bacterial machineries.’, PLoS genetics, 12(4), p. e1005975. doi: 10.1371/journal.pgen.1005975. 196

References

177. Ho, Y. S. and Rosenberg, M. (1985) ‘Characterization of a third, cII- dependent, coordinately activated promoter on Phage λ involved in lysogenic development’, Journal of Biological Chemistry, 260(21), pp. 11838–11844. 178. Hoekzema, M. et al. (2019) ‘Hfq‐dependent mRNA unfolding promotes sRNA‐based inhibition of translation’, The EMBO Journal, 38(7), p. e101199. doi: 10.15252/embj.2018101199. 179. Höfer, K. et al. (2016) ‘Structure and function of the bacterial decapping enzyme NudC’, Nature Chemical Biology. Nature Publishing Group, 12(9), pp. 730–734. doi: 10.1038/nchembio.2132. 180. Holmqvist, E. et al. (2010) ‘Two antisense RNAs target the transcriptional regulator CsgD to inhibit curli synthesis’, EMBO Journal, 29(11), pp. 1840–1850. doi: 10.1038/emboj.2010.73. 181. Holmqvist, E. et al. (2016) ‘Global RNA recognition patterns of post- transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo.’, The EMBO journal, 35(9), p. e201593360. doi: 10.15252/embj.201593360. 182. Holmqvist, E. et al. (2018) ‘Global maps of ProQ binding in vivo reveal target recognition via rna structure and stability control at mRNA 3′ Ends’, Molecular Cell, 70(5), pp. 971-982.e6. doi: 10.1016/j.molcel.2018.04.017. 183. Holmqvist, E. and Vogel, J. (2018) ‘RNA-binding proteins in bacteria’, Nature Reviews Microbiology. Springer US, 16(10), pp. 601–615. doi: 10.1038/s41579-018-0049-5. 184. Holmqvist, E. and Wagner, E. G. H. (2017) ‘Impact of bacterial sRNAs in stress responses.’, Biochemical Society transactions, 45(6), pp. 1203–1212. doi: 10.1042/BST20160363. 185. Hood, M. I. and Skaar, E. P. (2012) ‘Nutritional immunity: Transition metals at the pathogen-host interface’, Nature Reviews Microbiology. Nature Publishing Group, 10(8), pp. 525–537. doi: 10.1038/nrmicro2836. 186. Hoopes, B. C. and McClure, W. R. (1985) ‘A cII-dependent promoter is located within the Q gene of bacteriophage lambda.’, Proceedings of the National Academy of Sciences, 82(10), pp. 3134–3138. doi: 10.1073/pnas.82.10.3134. 187. Hör, J., Gorski, S. A. and Vogel, J. (2018) ‘Bacterial RNA biology on a genome scale’, Molecular Cell, pp. 785–799. doi: 10.1016/j.molcel.2017.12.023. 188. Hu, J. et al. (2018) ‘Cryo-EM analysis of the T3S injectisome reveals the structure of the needle and open secretin’, Nature Communications, 9(1), pp. 1–11. doi: 10.1038/s41467-018-06298-8. 197

References

189. Hu, J. and Torres, A. G. (2015) ‘Enteropathogenic Escherichia coli: Foe or innocent bystander?’, Clinical Microbiology and Infection, 21(8), pp. 729– 734. doi: 10.1016/j.cmi.2015.01.015. 190. Huang, R. et al. (2018) ‘Capture and identification of RNA-binding proteins by using Click Chemistry-assisted RNA-interactome Capture (CARIC) Strategy’, Journal of Visualized Experiments, (140), pp. 1–8. doi: 10.3791/58580. 191. Huang, S. H. et al. (2012) ‘Role of the small RNA RyhB in the Fur regulon in mediating the capsular polysaccharide biosynthesis and iron acquisition systems in Klebsiella pneumoniae’, BMC Microbiology, 12. doi: 10.1186/1471-2180-12-148. 192. Hudault, S., Guignot, J. and Servin, a L. (2001) ‘Escherichia coli strains colonising the gastrointestinal tract protect germfree mice against Salmonella typhimurium infection.’, Gut, 49(1), pp. 47–55. doi: 10.1136/gut.49.1.47. 193. Hughes, D. T. et al. (2009) ‘The QseC adrenergic signaling cascade in enterohemorrhagic E. coli (EHEC)’, PLoS Pathogens, 5(8). doi: 10.1371/journal.ppat.1000553. 194. Hunke, S., Keller, R. and Müller, V. S. (2012) ‘Signal integration by the Cpx-envelope stress system’, FEMS Microbiology Letters, 326(1), pp. 12–22. doi: 10.1111/j.1574-6968.2011.02436.x. 195. Ikeda, Y. et al. (2011) ‘Hfq binding at RhlB-recognition region of RNase E is crucial for the rapid degradation of target mRNAs mediated by sRNAs in Escherichia coli’, Molecular Microbiology, 79(2), pp. 419–432. doi: 10.1111/j.1365-2958.2010.07454.x. 196. Imamovic, L. et al. (2016) ‘Heterogeneity in phage induction enables the survival of the lysogenic population’, Environmental Microbiology, 18(3), pp. 957–969. doi: 10.1111/1462-2920.13151. 197. Ingle, D. J. et al. (2019) ‘Emergence and divergence of major lineages of Shiga-toxin-producing Escherichia coli in Australia’, Microbial Genomics, (May), pp. 1–9. doi: 10.1099/mgen.0.000268. 198. Isaac, D. D. et al. (2005) ‘The extracytoplasmic adaptor protein CpxP is degraded with substrate by DegP’, Proceedings of the National Academy of Sciences, 102(49), pp. 17775–17779. doi: 10.1073/pnas.0508936102. 199. Islam, M. R. et al. (2012) ‘A sensitive and simple plaque formation method for the Stx2 phage of Escherichia coli O157:H7, which does not form plaques in the standard plating procedure’, Plasmid. Elsevier Inc., 67(3), pp. 198

References

227–235. doi: 10.1016/j.plasmid.2011.12.001. 200. Ivarsson, M. E., Leroux, J. C. and Castagner, B. (2012) ‘Targeting bacterial toxins’, Angewandte Chemie - International Edition, 51(17), pp. 4024– 4045. doi: 10.1002/anie.201104384. 201. Iyer, L. M., Burroughs, A. M. and Aravind, L. (2006) ‘The ASCH superfamily: Novel domains with a fold related to the PUA domain and a potential role in RNA metabolism’, Bioinformatics, 22(3), pp. 257–263. doi: 10.1093/bioinformatics/bti767. 202. Iyoda, S. et al. (2006) ‘The GrlR-GrlA regulatory system coordinately controls the expression of flagellar and LEE-encoded type III protein secretion systems in enterohemorrhagic Escherichia coli’, Journal of Bacteriology, 188(16), pp. 5682–5692. doi: 10.1128/JB.00352-06. 203. Iyoda, S. and Watanabe, H. (2005) ‘ClpXP protease controls expression of the type III protein secretion system through regulation of RpoS and GrlR levels in enterohemorrhagic Escherichia coli’, Journal of Bacteriology, 187(12), pp. 4086–4094. doi: 10.1128/JB.187.12.4086-4094.2005. 204. Jagodnik, J., Chiaruttini, C. and Guillier, M. (2017) ‘Stem-loop structures within mRNA coding sequences activate translation initiation and mediate control by small regulatory RNAs’, Molecular Cell. Elsevier Inc., 68(1), pp. 158- 170.e3. doi: 10.1016/j.molcel.2017.08.015. 205. Jensen, B. H. et al. (2014) ‘Epidemiology and clinical manifestations of enteroaggregative Escherichia coli’, Clinical Microbiology Reviews, 27(3), pp. 614–630. doi: 10.1128/CMR.00112-13. 206. Jiang, Y. et al. (2015) ‘Multigene editing in the Escherichia coli genome via the CRISPR-Cas9 system’, Applied and Environmental Microbiology, 81(7), pp. 2506–2514. doi: 10.1128/aem.04023-14. 207. Jiménez, R. et al. (2010) ‘Molecular characterization of GrlA, a specific positive regulator of ler expression in enteropathogenic Escherichia coli’, Journal of Bacteriology, 192(18), pp. 4627–4642. doi: 10.1128/JB.00307-10. 208. Jinneman, K. C., Yoshitomi, K. J. and Weagant, S. D. (2003) ‘Multiplex real-time PCR method to identify Shiga toxin genes stx1 and stx2 and Escherichia coli O157:H7/H- serotype’, Applied and Environmental Microbiology, 69(10), pp. 6327–6333. doi: 10.1128/AEM.69.10.6327- 6333.2003. 209. Johansen, J. et al. (2006) ‘Conserved small non-coding RNAs that

belong to the σE regulon: role in down-regulation of outer membrane proteins’, 199

References

Journal of Molecular Biology, 364(1), pp. 1–8. doi: 10.1016/j.jmb.2006.09.004. 210. Johansen, J. et al. (2008) ‘Down-regulation of outer membrane proteins

by noncoding RNAs: Unraveling the cAMP-CRP- and σE-Dependent CyaR- ompX Regulatory Case’, Journal of Molecular Biology, 383(1), pp. 1–9. doi: 10.1016/j.jmb.2008.06.058. 211. Johnson, Z., Ochsner, U. A. and Vasil, M. L. (2000) ‘Genetics and regulation of two distinct haem-uptake systems, phu and has, in Pseudomonas aeruginosa’, Microbiology, 146(1), pp. 185–198. doi: 10.1099/00221287-146-1- 185. 212. Jores, J., Rumer, L. and Wieler, L. H. (2004) ‘Impact of the locus of enterocyte effacement pathogenicity island on the evolution of pathogenic Escherichia coli’, International Journal of Medical Microbiology, 294(2–3), pp. 103–113. doi: 10.1016/j.ijmm.2004.06.024. 213. Jung, Y. S. and Kwon, Y. M. (2008) ‘Small RNA ArrF regulates the expression of sodB and feSII genes in Azotobacter vinelandii’, Current Microbiology, 57(6), pp. 593–597. doi: 10.1007/s00284-008-9248-z. 214. Kacharia, F. R., Millar, J. A. and Raghavan, R. (2017) ‘Emergence of enw sRNAs in enteric bacteria is associated with low expression and rapid evolution’, Journal of Molecular Evolution. Springer US, 84(4), pp. 204–213. doi: 10.1007/s00239-017-9793-9. 215. Kakoschke, T. et al. (2014) ‘The RNA chaperone Hfq impacts growth, metabolism and production of virulence factors in Yersinia enterocolitica’, PLoS ONE, 9(1). doi: 10.1371/journal.pone.0086113. 216. Kambara, T. K., Ramsey, K. M. and Dove, S. L. (2018) ‘Pervasive targeting of nascent transcripts by Hfq’, Cell Reports. ElsevierCompany., 23(5), pp. 1543–1552. doi: 10.1016/j.celrep.2018.03.134. 217. Kaper, J. B., Nataro, J. P. and Mobley, H. L. (2004) ‘Pathogenic Escherichia coli.’, Nature reviews. Microbiology, 2(2), pp. 123–140. doi: 10.1038/nrmicro818. 218. Karch, H., Tarr, P. I. and Bielaszewska, M. (2005) ‘Enterohaemorrhagic Escherichia coli in human medicine’, International Journal of Medical Microbiology, 295(6–7), pp. 405–418. doi: 10.1016/j.ijmm.2005.06.009. 219. Karmali, M. A. et al. (1985) ‘The association between idiopathic hemolytic uremic syndrome and infection by verotoxin-producing Escherichia coli’, Journal of Infectious Diseases, 151(5), pp. 775–782. doi: 10.1093/infdis/151.5.775. 200

References

220. Karmali, M. A. (2004) ‘Infection by Shiga toxin-producing Escherichia coli: an overview’, Molecular biotechnology, 26. 221. Katsowich, N. et al. (2017) ‘Host cell attachment elicits posttranscriptional regulation in infecting enteropathogenic bacteria’, Science, 355(6326), pp. 735–739. doi: 10.1126/science.aah4886. 222. Kawano, K. et al. (2008) ‘Relationship between pathogenicity for humans and stx genotype in Shiga toxin-producing Escherichia coli serotype O157’, European Journal of Clinical Microbiology and Infectious Diseases, 27(3), pp. 227–232. doi: 10.1007/s10096-007-0420-3. 223. Kendall, M. M. et al. (2011) ‘Hfq virulence regulation in enterohemorrhagic Escherichia coli O157:H7 Strain 86-24’, Journal of Bacteriology, 193(24), pp. 6843–6851. doi: 10.1128/JB.06141-11. 224. Kendall, M. M., Rasko, D. A. and Sperandio, V. (2010) ‘The LysR-type regulator QseA regulates both characterized and putative virulence genes in enterohaemorrhagic Escherichia coli O157:H7’, Molecular Microbiology, 76(5), pp. 1306–1321. doi: 10.1111/j.1365-2958.2010.07174.x. 225. Kery, M. B. et al. (2014) ‘TargetRNA2: Identifying targets of small regulatory RNAs in bacteria’, Nucleic Acids Research, 42(W1), pp. 124–129. doi: 10.1093/nar/gku317. 226. Kim, B. N. et al. (2017) ‘Crystal structure of an ASCH protein from Zymomonas mobilis and its ribonuclease activity specific for single-stranded RNA’, Scientific Reports. Springer US, 7(1), pp. 1–10. doi: 10.1038/s41598- 017-12186-w. 227. Kim, D. et al. (2012) ‘Comparative Analysis of Regulatory Elements between Escherichia coli and Klebsiella pneumoniae by Genome-Wide Transcription Start Site Profiling’, PLoS Genetics, 8(8). doi: 10.1371/journal.pgen.1002867. 228. Kirk, M. D. et al. (2015) ‘World Health Organization estimates of the global and regional disease Burden of 22 foodborne bacterial, protozoal, and viral diseases, 2010: a data synthesis’, PLoS Medicine, 12(12), pp. 1–21. doi: 10.1371/journal.pmed.1001921. 229. Knutton, S. et al. (1989) ‘Adhesion to tissue culture cells : basis of a new actin accumulation at sites of bacterial adhesion to tissue culture cells : basis of a new diagnostic test for enteropathogenic and enterohemorrhagic Escherichia coli’, 57(4), pp. 1290–1298. 230. Knutton, S. et al. (1998) ‘A novel EspA-associated surface organelle of 201

References

enteropathogenic Escherichia coli involved in protein translocation into epithelial cells’, EMBO Journal, 17(8), pp. 2166–2176. doi: 10.1093/emboj/17.8.2166. 231. Konczy, P. et al. (2008) ‘Genomic O island 122, locus for enterocyte effacement, and the evolution of virulent verocytotoxin-producing Escherichia coli’, Journal of Bacteriology, 190(17), pp. 5832–5840. doi: 10.1128/JB.00480- 08. 232. Kopaskie, K. S., Ligtenberg, K. G. and Schneewind, O. (2013) ‘Translational regulation of Yersinia enterocolitica mRNA encoding a type III secretion substrate’, Journal of Biological Chemistry, 288(49), pp. 35478– 35488. doi: 10.1074/jbc.M113.504811. 233. Kouse, A. B. et al. (2013) ‘RNA-mediated thermoregulation of iron- acquisition genes in Shigella dysenteriae and pathogenic Escherichia coli’, PLoS ONE, 8(5). doi: 10.1371/journal.pone.0063781. 234. Krinke, L., Mahoney, M. and Wulff, D. L. (1991) ‘The role of the OOP antisense RNA in coliphage λ development’, Molecular Microbiology, 5(5), pp. 1265–1272. doi: 10.1111/j.1365-2958.1991.tb01900.x. 235. Krüger, A. and Lucchesi, P. M. A. (2015) ‘Shiga toxins and stx phages : highly diverse entities’, pp. 451–462. doi: 10.1099/mic.0.000003. 236. Kubori, T. et al. (1998) ‘Supramolecular structure of the Salmonella typhimurium type III protein secretion system.’, Science (New York, N.Y.), 280(April), pp. 602–605. 237. Kulesus, R. R. et al. (2008) ‘Impact of the RNA chaperone Hfq on the fitness and virulence potential of uropathogenic Escherichia coli’, Infection and Immunity, 76(7), pp. 3019–3026. doi: 10.1128/IAI.00022-08.

238. Lacour, S. and Landini, P. (2004) ‘σS-dependent gene expression at the onset of stationary phase in Escherichia coli: Function of σS-dependent genes and identification of their promoter sequences’, Journal of Bacteriology, 186(21), pp. 7186–7195. doi: 10.1128/JB.186.21.7186-7195.2004. 239. Lalaouna, D. et al. (2015) ‘A 3’ external transcribed spacer in a tRNA transcript acts as a sponge for small RNAs to prevent transcriptional noise’, Molecular Cell, 58(3), pp. 393–405. doi: 10.1016/j.molcel.2015.03.013. 240. Lalaouna, D. et al. (2017) ‘Identification of unknown RNA partners using MAPS’, Methods. Elsevier Inc., 117, pp. 28–34. doi: 10.1016/j.ymeth.2016.11.011. 241. Lalaouna, D. and Massé, E. (2015) ‘Identification of sRNA interacting 202

References

with a transcript of interest using MS2-affinity purification coupled with RNA sequencing (MAPS) technology’, Genomics Data, 5, pp. 136–138. doi: 10.1016/j.gdata.2015.05.033. 242. LaMattina, J. W. et al. (2017) ‘Anaerobic Heme Degradation: ChuY Is an Anaerobilin Reductase That Exhibits Kinetic Cooperativity’, Biochemistry, 56(6), pp. 845–855. doi: 10.1021/acs.biochem.6b01099. 243. LaMattina, J. W., Nix, D. B. and Lanzilotta, W. N. (2016) ‘ Radical new paradigm for heme degradation in Escherichia coli O157:H7 ’, Proceedings of the National Academy of Sciences, 113(43), pp. 12138–12143. doi: 10.1073/pnas.1603209113. 244. Larzábal, M. et al. (2018) ‘Novel Effector Protein EspY3 of Type III Secretion System from Enterohemorrhagic Escherichia coli Is Localized in Actin Pedestals’, Microorganisms, 6(4), p. 112. doi: 10.3390/microorganisms6040112. 245. De Lay, N. and Gottesman, S. (2009) ‘The crp-activated small noncoding regulatory RNA CyaR (RyeE) links nutritional status to group behavior’, Journal of Bacteriology, 191(2), pp. 461–476. doi: 10.1128/JB.01157- 08. 246. De Lay, N., Schu, D. J. and Gottesman, S. (2013) ‘Bacterial small RNA- based negative regulation: Hfq and its accomplices’, Journal of Biological Chemistry, 288(12), pp. 7996–8003. doi: 10.1074/jbc.R112.441386. 247. Lease, R. A. et al. (2004) ‘The small noncoding DsrA RNA is an acid resistance regulator in Escherichia coli’, Journal of Bacteriology, 186(18), pp. 6179–6185. doi: 10.1128/JB.186.18.6179-6185.2004. 248. Lease, R. A., Cusick, M. E. and Belfort, M. (1998) ‘Riboregulation in Escherichia coli: DsrA RNA acts by RNA:RNA interactions at multiple loci’, Proceedings of the National Academy of Sciences of the United States of America, 95(21), pp. 12456–12461. doi: 10.1073/pnas.95.21.12456. 249. Lease, R. A. and Woodson, S. A. (2004) ‘Cycling of the Sm-like protein Hfq on the DsrA small regulatory RNA’, Journal of Molecular Biology, 344(5), pp. 1211–1223. doi: 10.1016/j.jmb.2004.10.006. 250. Lebowitz, P., Weissman, S. M. and Radding, C. M. (1971) ‘Nucleotide sequence of a ribonucleic acid transcribed in vitro from lambda phage deoxyribonucleic acid.’, The Journal of biological chemistry, 246(16), pp. 5120– 39. 251. Lee, H. J. et al. (2003) ‘Regulation of fur expression by RpoS and fur in 203

References

Vibrio vulnificus’, Journal of Bacteriology, 185(19), pp. 5891–5896. doi: 10.1128/JB.185.19.5891-5896.2003. 252. Leenanon, B., Elhanafi, D. and Drake, M. A. (2003) ‘Acid adaptation and starvation effects on Shiga toxin production by Escherichia coli O157:H7’, Journal of Food Protection, 66(6), pp. 970–977. doi: 10.4315/0362-028X- 66.6.970. 253. Leng, Y. et al. (2016) ‘Regulation of CsrB/C sRNA decay by EIIAGlc of the phosphoenolpyruvate: Carbohydrate phosphotransferase system’, Molecular Microbiology, 99(4), pp. 627–639. doi: 10.1111/mmi.13259. 254. Leskinen, K., Varjosalo, M. and Skurnik, M. (2015) ‘Absence of YbeY RNase compromises the growth and enhances the virulence plasmid gene expression of Yersinia enterocolitica O:3’, Microbiology (United Kingdom), 161(2), pp. 285–299. doi: 10.1099/mic.0.083097-0. 255. Lewis, S. B. et al. (2015) ‘Enterohemorrhagic Escherichia coli colonization of human colonic epithelium in vitro and ex vivo’, Infection and Immunity, 83(3), pp. 942–949. doi: 10.1128/IAI.02928-14. 256. Li, H. et al. (2008) ‘RpoS, H-NS, and DsrA influence EHEC hemolysin operon (ehxCABD) transcription in Escherichia coli O157:H7 strain EDL933’, FEMS Microbiology Letters, 285(2), pp. 257–262. doi: 10.1111/j.1574- 6968.2008.01240.x. 257. Li, H. et al. (2009) ‘The Sequence Alignment/Map format and SAMtools’, Bioinformatics, 25(16), pp. 2078–2079. doi: 10.1093/bioinformatics/btp352. 258. Li, H. and Durbin, R. (2010) ‘Fast and accurate long-read alignment with Burrows-Wheeler transform’, Bioinformatics, 26(5), pp. 589–595. doi: 10.1093/bioinformatics/btp698. 259. Li, L. et al. (2013) ‘BSRD: A repository for bacterial small regulatory RNA’, Nucleic Acids Research, 41(D1), pp. 233–238. doi: 10.1093/nar/gks1264. 260. Ling, H. et al. (1998) ‘Structure of the Shiga-like toxin I B-pentamer complexed with an analogue of its receptor Gb3’, Biochemistry, 37(7), pp. 1777–1788. doi: 10.1021/bi971806n. 261. Link, T. M., Valentin-Hansen, P. and Brennan, R. G. (2009) ‘Structure of Escherichia coli Hfq bound to polyriboadenylate RNA.’, Proceedings of the National Academy of Sciences of the United States of America, 106(46), pp. 19292–7. doi: 10.1073/pnas.0908744106. 262. Liu, L. et al. (2019) ‘Insight into novel RNA-binding activities via large- scale analysis of lncRNA-bound proteome and IDH1-bound transcriptome’, 204

References

Nucleic Acids Research. Oxford University Press, 47(5), pp. 2244–2262. doi: 10.1093/nar/gkz032. 263. Liu, M. Y. et al. (1997) ‘The RNA molecule CsrB binds to the global regulatory protein CsrA and antagonizes its activity in Escherichia coli’, Journal of Biological Chemistry, 272(28), pp. 17502–17510. doi: 10.1074/jbc.272.28.17502. 264. Liu, X. and Gorovsky, M. A. (1994) ‘Mapping the 5′ and the 3′ ends of Tetrahymena thermophila mRNAs using RNA ligase mediated amplification of cDNA ends (RLM-RACE)’, Nucleic Acids Research, 22(3), p. 551. doi: 10.1093/nar/22.3.551. 265. Livny, J. and Friedman, D. I. (2004) ‘Characterizing spontaneous induction of Stx encoding phages using a selectable reporter system’, Molecular Microbiology, 51(6), pp. 1691–1704. doi: 10.1111/j.1365- 2958.2003.03934.x. 266. Lorenz, R. et al. (2011) ‘ViennaRNA Package 2.0’, Algorithms for Molecular Biology, 6(1), pp. 122–128. doi: 10.1186/1748-7188-6-26. 267. Luk, K. . and Szybalski, W. (1983) ‘Tandem transcription-termination sites in the late rightward operon of bacteriophage lambda’, Molecular and General Genetics MGG, 189(2), pp. 289–297. 268. Lukjancenko, O., Wassenaar, T. M. and Ussery, D. W. (2010) ‘Comparison of 61 sequenced Escherichia coli genomes’, Microbial Ecology, 60(4), pp. 708–720. doi: 10.1007/s00248-010-9717-3. 269. Luzader, D. H. and Kendall, M. M. (2016) ‘Commensal “trail of bread crumbs” provide pathogens with a map to the intestinal landscape’, Current Opinion in Microbiology. Elsevier Ltd, 29, pp. 68–73. doi: 10.1016/j.mib.2015.11.005. 270. Lynn, R. M. et al. (2005) ‘Childhood hemolytic uremic syndrome, United Kingdom and Ireland’, Emerging Infectious Diseases, 11(4), pp. 590–596. doi: 10.3201/eid1104.040833. 271. Madhugiri, R., Basineni, S. R. and Klug, G. (2010) ‘Turn-over of the small non-coding RNA RprA in E. coli is influenced by osmolarity’, Molecular Genetics and Genomics, 284(4), pp. 307–318. doi: 10.1007/s00438-010-0568- x. 272. Maere, S., Heymans, K. and Kuiper, M. (2005) ‘BiNGO: A Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks’, Bioinformatics, 21(16), pp. 3448–3449. doi: 205

References

10.1093/bioinformatics/bti551. 273. Majdalani, N. et al. (1998) ‘DsrA RNA regulates translation of RpoS message by an anti-antisense mechanism, independent of its action as an antisilencer of transcription’, Proceedings of the National Academy of Sciences, 95(21), pp. 12462–12467. doi: 10.1073/pnas.95.21.12462. 274. Majdalani, N. et al. (2001) ‘Regulation of RpoS by a novel small RNA: The characterization of RprA’, Molecular Microbiology, 39(5), pp. 1382–1394. doi: 10.1046/j.1365-2958.2001.02329.x. 275. Majowicz, S. E. et al. (2014) ‘Global incidence of human Shiga toxin- producing Escherichia coli infections and deaths: a systematic review and knowledge synthesis.’, Foodborne pathogens and disease, 11(6), pp. 447–55. doi: 10.1089/fpd.2013.1704. 276. Maki, K. et al. (2008) ‘RNA, but not protein partners, is directly responsible for translational silencing by a bacterial Hfq-binding small RNA’, Proceedings of the National Academy of Sciences, 105(30), pp. 10332–10337. doi: 10.1073/pnas.0803106105. 277. Malagon, F. (2013) ‘RNase III is required for localization to the nucleoid of the 5’ pre-rRNA leader and for optimal induction of rRNA synthesis in E. coli’, RNA, 19(9), pp. 1200–1207. doi: 10.1261/rna.038588.113. 278. Mandin, P. and Gottesman, S. (2010) ‘Integrating anaerobic/aerobic sensing and the general stress response through the ArcZ small RNA.’, The EMBO journal. Nature Publishing Group, 29(18), pp. 3094–107. doi: 10.1038/emboj.2010.179. 279. Mann, M., Wright, P. R. and Backofen, R. (2017) ‘IntaRNA 2.0: Enhanced and customizable prediction of RNA-RNA interactions’, Nucleic Acids Research, 45(W1), pp. W435–W439. doi: 10.1093/nar/gkx279. 280. Marbaniang, C. N. and Vogel, J. (2016) ‘Emerging roles of RNA modifications in bacteria’, Current Opinion in Microbiology, 30(Box 1), pp. 50– 57. doi: 10.1016/j.mib.2016.01.001. 281. Massé, E. et al. (2007) ‘Small RNAs controlling iron metabolism’, Current Opinion in Microbiology, 10(2), pp. 140–145. doi: 10.1016/j.mib.2007.03.013. 282. Massé, E., Escorcia, F. E. and Gottesman, S. (2003) ‘Coupled degradation of a small regulatory RNA and its mRNA targets in Escherichia coli Coupled degradation of a small regulatory RNA and its mRNA targets in Escherichia coli’, pp. 2374–2383. doi: 10.1101/gad.1127103. 206

References

283. Massé, E. and Gottesman, S. (2002) ‘A small RNA regulates the expression of genes involved in iron metabolism in Escherichia coli.’, Proceedings of the National Academy of Sciences of the United States of America, 99(7), pp. 4620–4625. doi: 10.1073/pnas.032066599. 284. Massé, E., Vanderpool, C. K. and Gottesman, S. (2005) ‘Effect of RyhB small RNA on global iron use in Escherichia coli’, Journal of Bacteriology, 187(20), pp. 6962–6971. doi: 10.1128/JB.187.20.6962-6971.2005. 285. McAteer, S. P. et al. (2018) ‘Ribosome maturation by the endoribonuclease YbeY stabilizes a type 3 secretion system transcript required for virulence of enterohemorrhagic Escherichia coli’, Journal of Biological Chemistry, 293(23), pp. 9006–9016. doi: 10.1074/jbc.RA117.000300. 286. McCullen, C. A. et al. (2010) ‘Mechanism of positive regulation by DsrA and RprA small noncoding RNAs: Pairing increases translation and protects rpoS mRNA from degradation’, Journal of Bacteriology, 192(21), pp. 5559– 5571. doi: 10.1128/JB.00464-10. 287. Melamed, S. et al. (2016) ‘Global mapping of small RNA-target interactions in bacteria’, Molecular Cell. The Authors, 63(5), pp. 884–897. doi: 10.1016/j.molcel.2016.07.026. 288. Mellin, J. R. et al. (2007) ‘A novel fur- and iron-regulated small RNA, NrrF, is required for indirect fur-mediated regulation of the sdhA and sdhC genes in Neisseria meningitidis’, Journal of Bacteriology, 189(10), pp. 3686– 3694. doi: 10.1128/JB.01890-06. 289. Melson, E. M. and Kendall, M. M. (2019) ‘The sRNA DicF integrates oxygen sensing to enhance enterohemorrhagic Escherichia coli virulence via distinctive RNA control mechanisms’, Proceedings of the National Academy of Sciences, p. 201902725. doi: 10.1073/pnas.1902725116. 290. Melton-Celsa, A. R. (2014) ‘Shiga Toxin (Stx) Classification, Structure, and Function.’, Microbiology spectrum, 2(4), p. EHEC-0024-2013. doi: 10.1128/microbiolspec.EHEC-0024-2013. 291. Melton-Celsa, A. R., Kokai-Kun, J. F. and O’Brien, A. D. (2002) ‘Activation of Shiga toxin type 2d (Stx2d) by elastase involves cleavage of the C-terminal two amino acids of the A2 peptide in the context of the appropriate B pentamer’, Molecular Microbiology, 43(1), pp. 207–215. doi: 10.1046/j.1365- 2958.2002.02733.x. 292. Merlin, C., Mcateer, S. and Masters, M. (2002) ‘Tools for Characterization of Escherichia coli Genes of Unknown Function’, Journal of 207

References

Bacteriology, 184(16), pp. 4573–4581. doi: 10.1128/JB.184.16.4573. 293. Mi, H. et al. (2019) ‘PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools’, Nucleic Acids Research. Oxford University Press, 47(D1), pp. D419–D426. doi: 10.1093/nar/gky1038. 294. Michaux, C. et al. (2017) ‘RNA target profiles direct the discovery of virulence functions for the cold-shock proteins CspC and CspE’, Proceedings of the National Academy of Sciences, 114(26), pp. 6824–6829. doi: https://doi.org/10.1073/pnas.1620772114. 295. Michino, H. et al. (1999) ‘Massive Outbreak of Escherichia coli O157:H7 Infection in Schoolchildren in Sakai City, Japan, Associated with Consumption of White Radish Sprouts’, American Journal of Epidemiology, 150(8), pp. 787– 796. doi: http://dx.doi.org/10.1093/oxfordjournals.aje.a010082. 296. Mills, M. and Payne, S. M. (1995) ‘Genetics and regulation of heme iron transport in Shigella dysenteriae and detection of an analogous system in Escherichia coli O157:H7’, Journal of Bacteriology, 177(11), pp. 3004–3009. doi: 10.1128/jb.177.11.3004-3009.1995. 297. Mills, M. and Payne, S. M. (1997) ‘Identification of shuA, the gene encoding the heme receptor of Shigella dysenteriae, and analysis of invasion and intracellular multiplication of a shuA mutant’, Infection and Immunity, 65(12), pp. 5358–5363. 298. Mirhoseini, A., Amani, J. and Nazarian, S. (2018) ‘Review on pathogenicity mechanism of enterotoxigenic Escherichia coli and vaccines against it’, Microbial Pathogenesis. Elsevier Ltd, 117(January), pp. 162–169. doi: 10.1016/j.micpath.2018.02.032. 299. Mitra, P. et al. (2017) ‘Rho protein: roles and mechanisms’, Annual Review of Microbiology, 71(1), pp. 687–709. doi: 10.1146/annurev-micro- 030117-020432. 300. Miyakoshi, M., Chao, Y. and Vogel, J. (2015) ‘Cross talk between ABC transporter mRNAs via a target mRNA-derived sponge of the GcvB small RNA.’, The EMBO journal, 34(11), pp. 1478–1492. doi: 10.15252/embj.201490546. 301. Mohanty, B. K. and Kushner, S. R. (2016) ‘Regulation of mRNA Decay in Bacteria’, Annual Review of Microbiology, 70(1), pp. 25–44. doi: 10.1146/annurev-micro-091014-104515. 302. Møller, T. et al. (2002) ‘Spot 42 RNA mediates discoordinate expression 208

References

of the E. coli galactose operon’, Genes and Development, 16(13), pp. 1696– 1706. doi: 10.1101/gad.231702. 303. Mondal, S. I. et al. (2016) ‘Genes essential for the morphogenesis of the Shiga toxin 2-transducing phage from Escherichia coli O157:H7’, Scientific Reports. Nature Publishing Group, 6(September), pp. 1–12. doi: 10.1038/srep39036. 304. Morgenthau, A. et al. (2013) ‘Bacterial receptors for host transferrin and lactoferrin: Molecular mechanisms and role in host-microbe interactions’, Future Microbiology, 8(12), pp. 1575–1585. doi: 10.2217/fmb.13.125. 305. Morita, T. et al. (2004) ‘Enolase in the RNA degradosome plays a crucial role in the rapid decay of glucose transporter mRNA in the response to phosphosugar stress in Escherichia coli’, Molecular Microbiology, 54(4), pp. 1063–1075. doi: 10.1111/j.1365-2958.2004.04329.x. 306. Morita, T., Mochizuki, Y. and Aiba, H. (2006) ‘Translational repression is sufficient for gene silencing by bacterial small noncoding RNAs in the absence of mRNA destruction’, Proceedings of the National Academy of Sciences, 103(13), pp. 4858–4863. doi: 10.1073/pnas.0509638103. 307. Morita, T., Nishino, R. and Aiba, H. (2017) ‘Role of the terminator hairpin in the biogenesis of functional Hfq-binding sRNAs’, Rna, 23(9), pp. 1419–1431. doi: 10.1261/rna.060756.117. 308. Muller, B. et al. (1979) ‘Semisynthetic bicyclomycin derivatives: Preparation and antibacterial evaluation.’, The Journal of Antibiotics, 32(7), pp. 689–705. doi: 10.7164/antibiotics.32.689. 309. Murphy, E. R. and Payne, S. M. (2007) ‘RyhB, an iron-responsive small RNA molecule, regulates Shigella dysenteriae virulence’, Infection and Immunity, 75(7), pp. 3470–3477. doi: 10.1128/IAI.00112-07. 310. Nagy, G. et al. (2001) ‘Expression of hemin receptor molecule chuA is influenced by rfaH in uropathogenic E. coli strain 356’, Infection and Immunity, 69(3), pp. 1924–1928. doi: 10.1128/IAI.69.3.1924. 311. Naikare, H. et al. (2013) ‘Campylobacter jejuni ferric-enterobactin receptor CfrA is TonB3 dependent and mediates iron acquisition from structurally different catechol siderophores’, Metallomics, 5(8), pp. 988–996. doi: 10.1039/c3mt20254b. 312. Naville, M. et al. (2011) ‘ARNold: A web tool for the prediction of rho- independent transcription terminators’, RNA Biology, 8(1), pp. 10–13. doi: 10.4161.rna.8.1.13346. 209

References

313. Nevesinjac, A. Z. and Raivio, T. L. (2005) ‘The Cpx envelope stress response affects expression of the type IV bundle-forming pili of enteropathogenic Escherichia coli’, Journal of Bacteriology, 187(2), pp. 672– 686. doi: 10.1128/JB.187.2.672-686.2005. 314. Nguyen, Y. and Sperandio, V. (2012) ‘Enterohemorrhagic E. coli (EHEC) pathogenesis’, Frontiers in Cellular and Infection Microbiology, 2(July), pp. 1–7. doi: 10.3389/fcimb.2012.00090. 315. Njoroge, J. W. et al. (2012) ‘Virulence meets metabolism: Cra and KdpE gene regulation in enterohemorrhagic Escherichia coli.’, mBio, 3(5), pp. 1–12. doi: 10.1128/mBio.00280-12. 316. Notti, R. Q. and Erec Stebbins, C. (2016) ‘The structure and function of type iii secretion systems’, Microbiology Spectrum, 4(1). doi: doi:10.1128/microbiolspec.VMBF-0004-2015. 317. Nowicki, D. et al. (2015) ‘Defects in RNA polyadenylation impair both lysogenization by and lytic development of Shiga toxin-converting bacteriophages’, Journal of General Virology, 96(7), pp. 1957–1968. doi: 10.1099/vir.0.000102. 318. Obata, F. et al. (2008) ‘Shiga toxin 2 affects the central nervous system through receptor globotriaosylceramide localized to neurons.’, The Journal of infectious diseases, 198(9), pp. 1398–1406. doi: 10.1086/591911. 319. Obrig, T. G. (2010) ‘Escherichia coli shiga toxin mechanisms of action in renal disease’, Toxins, 2(12), pp. 2769–2794. doi: 10.3390/toxins2122769. 320. Ogino, T. et al. (2006) ‘Assembly of the type iii secretion apparatus of enteropathogenic Escherichia coli’, Society, 188(8), pp. 2801–2811. doi: 10.1128/JB.188.8.2801. 321. Ogura, Y. et al. (2007) ‘Extensive genomic diversity and selective conservation of virulence-determinants in enterohemorrhagic Escherichia coli strains of O157 and non-O157 serotypes’, Genome Biology, 8(7). doi: 10.1186/gb-2007-8-7-r138. 322. Van Der Oost, J. et al. (2014) ‘Unravelling the structural and mechanistic basis of CRISPR-Cas systems’, Nature Reviews Microbiology. Nature Publishing Group, 12(7), pp. 479–492. doi: 10.1038/nrmicro3279. 323. Opdyke, J. A. et al. (2011) ‘RNase III participates in gadY-dependent cleavage of the gadX-gadW mRNA’, Journal of Molecular Biology. Elsevier B.V., 406(1), pp. 29–43. doi: 10.1016/j.jmb.2010.12.009. 324. Opdyke, J. A., Kang, J. and Storz, G. (2004) ‘GadY, a Small-RNA 210

References

Regulator of Acid Response Genes in Escherichia coli GadY’, Journal of Bacteriology, 186(20), pp. 6698–6705. doi: 10.1128/JB.186.20.6698. 325. Oppenheim, A. B. et al. (2005) ‘Switches in bacteriophage lambda development.’, Annual review of genetics, 39, pp. 409–429. doi: 10.1146/annurev.genet.39.073003.113656. 326. Orth, D. et al. (2007) ‘The Shiga toxin genotype rather than the amount of Shiga toxin or the cytotoxicity of Shiga toxin in vitro correlates with the appearance of the hemolytic uremic syndrome’, Diagnostic Microbiology and Infectious Disease, 59, pp. 235–242. doi: 10.1016/j.diagmicrobio.2007.04.013. 327. Otaka, H. et al. (2011) ‘PolyU tail of rho-independent terminator of bacterial small RNAs is essential for Hfq action’, Proceedings of the National Academy of Sciences, 108(32), pp. 13059–13064. doi: 10.1073/pnas.1107050108. 328. Otsuka, Y. et al. (2007) ‘A role of RnlA in the RNase LS activity from Escherichia coli.’, Genes & genetic systems, 82(4), pp. 291–9. Available at: http://www.ncbi.nlm.nih.gov/pubmed/17895580. 329. Otsuka, Y. and Yonesaki, T. (2012) ‘Dmd of bacteriophage T4 functions as an antitoxin against Escherichia coli LsoA and RnlA toxins’, Molecular Microbiology, 83(4), pp. 669–681. doi: 10.1111/j.1365-2958.2012.07975.x. 330. OzFoodNet (2015) ‘Monitoring the incidence and causes of diseases potentially transmitted by food in Australia: Annual Report of the OzFoodNet Network, 2011’, Communicable Diseases Intelligence, 39(2), pp. E236-64. 331. Pacheco, A. R. et al. (2012) ‘Fucose sensing regulates bacterial intestinal colonization’, Nature. Nature Publishing Group, 492(7427), pp. 113– 117. doi: 10.1038/nature11623. 332. Pacheco, A. R. and Sperandio, V. (2012) ‘Shiga toxin in enterohemorrhagic E.coli: regulation and novel anti-virulence strategies’, Frontiers in Cellular and Infection Microbiology, 2(June), pp. 1–12. doi: 10.3389/fcimb.2012.00081. 333. Padalon-Brauch, G. et al. (2008) ‘Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence’, Nucleic Acids Research, 36(6), pp. 1913–1927. doi: 10.1093/nar/gkn050. 334. Padavannil, A. et al. (2013) ‘Structure of GrlR-GrlA complex that prevents GrlA activation of virulence genes’, Nature Communications, 4, pp. 2– 11. doi: 10.1038/ncomms3546. 211

References

335. Panja, S., Schu, D. J. and Woodson, S. A. (2013) ‘Conserved arginines on the rim of Hfq catalyze base pair formation and exchange’, Nucleic Acids Research, 41(15), pp. 7536–7546. doi: 10.1093/nar/gkt521. 336. Pannuri, A. et al. (2016) ‘Circuitry linking the catabolite repression and Csr global regulatory systems of Escherichia coli’, Journal of Bacteriology, 198(21), pp. 3000–3015. doi: 10.1128/JB.00454-16.

337. Papenfort, K. et al. (2006) ‘σE-dependent small RNAs of Salmonella respond to membrane stress by accelerating global omp mRNA decay’, Molecular Microbiology, 62(6), pp. 1674–1688. doi: 10.1111/j.1365- 2958.2006.05524.x. 338. Papenfort, K. et al. (2013) ‘Small RNA-mediated activation of sugar phosphatase mRNA regulates glucose homeostasis’, Cell, 153(2), pp. 426–437. doi: 10.1016/j.cell.2013.03.003. 339. Papenfort, K. and Vogel, J. (2011) ‘Sweet Business: Spot42 RNA networks with CRP to modulate catabolite repression’, Molecular Cell, 41(3), pp. 245–246. doi: 10.1016/j.molcel.2011.01.011. 340. Pennington, T. H. (2014) ‘E. coli O157 outbreaks in the United Kingdom: past, present, and future.’, Infection and drug resistance, 7, pp. 211–22. doi: 10.2147/IDR.S49081. 341. Perez-Reytor, D. et al. (2017) ‘Role of non-coding regulatory RNA in the virulence of human pathogenic vibrios’, Frontiers in Microbiology, 7(JAN), pp. 1–13. doi: 10.3389/fmicb.2016.02160. 342. Perna, N. T. et al. (2001) ‘Genome sequence of enterohaemorrhagic Escherichia coli O157:H7’, Nature, 409(6819), pp. 529–533. doi: 10.1038/35054089. 343. Persson, S. et al. (2007) ‘Subtyping method for Escherichia coli Shiga toxin (Verocytotoxin) 2 variants and correlations to clinical manifestations’, Journal of Clinical Microbiology, 45(6), pp. 2020–2024. doi: 10.1128/JCM.02591-06. 344. Peters, J. M. et al. (2009) ‘Rho directs widespread termination of intragenic and stable RNA transcription’, Proceedings of the National Academy of Sciences, 106(36), pp. 15406–15411. doi: 10.1073/pnas.0903846106. 345. Peters, J. M. et al. (2012) ‘Rho and NusG suppress pervasive antisense transcription in E. coli’, Genes & Development, 26, pp. 2621–2633. doi: 10.1101/gad.196741.112.The. 346. Polayes, D. A. et al. (1988) ‘Cyclic AMP-cyclic AMP receptor protein as 212

References

a repressor of transcription of the spf gene of Escherichia coli.’, Journal of Bacteriology, 170(7), pp. 3110–3114. 347. Prévost, K. et al. (2007) ‘The small RNA RyhB activates the translation of shiA mRNA encoding a permease of shikimate, a compound involved in siderophore synthesis’, Molecular Microbiology, 64(5), pp. 1260–1273. doi: 10.1111/j.1365-2958.2007.05733.x. 348. Price, M. N., Dehal, P. S. and Arkin, A. P. (2010) ‘FastTree 2 - Approximately maximum-likelihood trees for large alignments’, PLoS ONE, 5(3). doi: 10.1371/journal.pone.0009490. 349. Price, S. B. et al. (2000) ‘Role of rpoS in acid resistance and fecal shedding of Escherichia coli O157:H7’, Applied and Environmental Microbiology, 66(2), pp. 632–637. doi: 10.1128/AEM.66.2.632-637.2000. 350. Ptashne, M. (2004) A genetic switch: phage lambda revisited, Genetical Research. 351. Puente, J. L. et al. (2005) ‘A positive regulatory loop controls expression of the locus of enterocyte effacement-encoded regulators Ler and GrlA’, Journal of Bacteriology, 187(23), pp. 7918–7930. doi: 10.1128/jb.187.23.7918- 7930.2005. 352. Pulvermacher, S. C., Stauffer, L. T. and Stauffer, G. V. (2009a) ‘Role of the Escherichia coli Hfq protein in GcvB regulation of oppA and dppA mRNAs’, Microbiology, 155(1), pp. 115–123. doi: 10.1099/mic.0.023432-0. 353. Queiroz, R. M. L. et al. (2019) ‘Comprehensive identification of RNA– protein interactions in any organism using orthogonal organic phase separation (OOPS)’, Nature Biotechnology. Springer US, 37(2), pp. 169–178. doi: 10.1038/s41587-018-0001-2. 354. Raivio, T. L., Popkin, D. L. and Silhavy, T. J. (1999) ‘The Cpx envelope stress response is controlled by amplification and feedback inhibition’, Journal of Bacteriology, 181(17), pp. 5263–5272. 355. Rappsilber, J., Ishihama, Y. and Mann, M. (2003) ‘Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics.’, Analytical chemistry, 75(3), pp. 663–70. doi: 10.1021/ac026117i. 356. Rappsilber, J., Mann, M. and Ishihama, Y. (2007) ‘Protocol for micro- purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips’, Nature Protocols, 2(8), pp. 1896–1906. doi: 10.1038/nprot.2007.261. 213

References

357. Rasmussen, A. A. et al. (2005) ‘Regulation of ompA mRNA stability: The role of a small regulatory RNA in growth phase-dependent control’, Molecular Microbiology, 58(5), pp. 1421–1429. doi: 10.1111/j.1365-2958.2005.04911.x. 358. Ray-Soni, A., Bellecourt, M. J. and Landick, R. (2016) ‘Mechanisms of bacterial transcription termination: All good things must end’, Annual Review of Biochemistry, 85(1), pp. 319–347. doi: 10.1146/annurev-biochem-060815- 014844. 359. Recktenwald, J. and Schmidt, H. (2002) ‘The nucleotide sequence of Shiga toxin (Stx) 2e-encoding phage φP27 is not related to other Stx phage genomes, but the modular genetic structure is conserved’, Infection and Immunity, 70(4), pp. 1896–1908. doi: 10.1128/IAI.70.4.1896-1908.2002. 360. Reichenbach, B. et al. (2008) ‘The small RNA GlmY acts upstream of the sRNA GlmZ in the activation of glmS expression and is subject to regulation by polyadenylation in Escherichia coli’, Nucleic Acids Research, 36(8), pp. 2570–2580. doi: 10.1093/nar/gkn091. 361. Ren, G. X., Guo, X. P. and Sun, Y. C. (2017) ‘Regulatory 3’ untranslated regions of bacterial mRNAs’, Frontiers in Microbiology, 8(JUL), pp. 1–6. doi: 10.3389/fmicb.2017.01276. 362. Reuter, J. S. and Mathews, D. H. (2010) ‘RNAstructure: Software for RNA secondary structure prediction and analysis’, BMC Bioinformatics, 11. doi: 10.1186/1471-2105-11-129. 363. Révet, B. et al. (1999) ‘Four dimers of λ repressor bound to two suitably spaced pairs of λ operators form octamers and DNA loops over large distances’, Current Biology, 9(3), pp. 151–154. doi: 10.1016/S0960- 9822(99)80069-4.

364. Rhodius, V. A. et al. (2006) ‘Conserved and variable functions of the σE stress response in related genomes’, PLoS Biology, 4(1), pp. 0043–0059. doi: 10.1371/journal.pbio.0040002. 365. Rice, J. B., Balasubramanian, D. and Vanderpool, C. K. (2012) ‘Small RNA binding-site multiplicity involved in translational regulation of a polycistronic mRNA’, Proceedings of the National Academy of Sciences, 109(40), pp. E2691–E2698. doi: 10.1073/pnas.1207927109. 366. Ridley, K. A. et al. (2006) ‘Heme utilization in Campylobacter jejuni’, Journal of Bacteriology, 188(22), pp. 7862–7875. doi: 10.1128/JB.00994-06. 367. Righetti, F. et al. (2016) ‘Temperature-responsive in vitro RNA structurome of Yersinia pseudotuberculosis’, Proceedings of the National 214

References

Academy of Sciences of the United States of America, 113(26), pp. 7237–7242. doi: 10.1073/pnas.1523004113. 368. Ritchie, J. M. and Waldor, M. K. (2005) ‘The locus of enterocyte effacement-encoded effector proteins all promote enterohemorrhagic Escherichia coli pathogenicity in infant rabbits’, Infection and Immunity, 73(3), pp. 1466–1474. doi: 10.1128/IAI.73.3.1466-1474.2005. 369. Roberts, J. W. (1975) ‘Transcription termination and late control in phage lambda.’, Proceedings of the National Academy of Sciences, 72(9), pp. 3300–3304. doi: 10.1073/pnas.72.9.3300. 370. Robinson, J. T. et al. (2011) ‘Integrative genomics viewer’, Nature Biotechnology, 29(1), pp. 24–26. doi: 10.1038/nbt.1754. 371. Robinson, K. E. et al. (2014) ‘Mapping Hfq-RNA interaction surfaces using tryptophan fluorescence quenching’, Nucleic Acids Research, 42(4), pp. 2736–2749. doi: 10.1093/nar/gkt1171. 372. Rochat, T. et al. (2018) ‘The conserved regulatory RNA RsaE downregulates the arginine degradation pathway in Staphylococcus aureus’, Nucleic Acids Research. Oxford University Press, (August), pp. 1–14. doi: 10.1093/nar/gky584. 373. Roe, A. J. et al. (2004) ‘Co-ordinate single-cell expression of LEE4- and LEE5-encoded proteins of Escherichia coli O157:H7’, Molecular Microbiology, 54(2), pp. 337–352. doi: 10.1111/j.1365-2958.2004.04277.x. 374. Romilly, C., Deindl, S. and Wagner, E. G. H. (2019) ‘The ribosomal protein S1-dependent standby site in tisB mRNA consists of a single-stranded region and a 5′ structure element’, Proceedings of the National Academy of Sciences, p. 201904309. doi: 10.1073/pnas.1904309116. 375. Rossi, M. S. et al. (2001) ‘Identification and characterization of the hemophore-dependent heme acquisition system of Yersinia pestis’, Infection and Immunity, 69(11), pp. 6707–6717. doi: 10.1128/IAI.69.11.6707-6717.2001. 376. Ruggenenti, P., Noris, M. and Remuzzi, G. (2001) ‘Thrombotic microangiopathy, hemolytic uremic syndrome, and thrombotic thrombocytopenic purpura’, Kidney International, 60(3), pp. 831–846. doi: 10.1046/j.1523-1755.2001.060003831.x. 377. Ryu, S. et al. (1995) ‘Effect of the FruR regulator on transcription of the pts operon in Escherichia coli’, The Journal of biological chemistry, 270(6), pp. 2489–2496. doi: 10.1074/jbc.270.6.2489. 378. Salvail, H. et al. (2013) ‘Antagonistic functions between the RNA 215

References

chaperone Hfq and an sRNA regulate sensitivity to the antibiotic colicin’, EMBO Journal, 32(20), pp. 2764–2772. doi: 10.1038/emboj.2013.205. 379. Di Salvo, M. et al. (2019) ‘RhoTermPredict: An algorithm for predicting Rho-dependent transcription terminators based on Escherichia coli, Bacillus subtilis and Salmonella enterica databases’, BMC Bioinformatics. BMC Bioinformatics, 20(1), pp. 1–11. doi: 10.1186/s12859-019-2704-x. 380. Sambrook, J. and Russell, D. (2000) Molecular Cloning, 3-Volume Set : A Laboratory Manual, Cold Spring Harbor Laboratory Press. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24532655. 381. Santangelo, T. J. and Artsimovitch, I. (2011) ‘Termination and antitermination: RNA polymerase runs a stop sign.’, Nature reviews. Microbiology. Nature Publishing Group, 9(5), pp. 319–29. doi: 10.1038/nrmicro2560. 382. Santiago-Frangos, A. et al. (2016) ‘C-terminal domain of the RNA chaperone Hfq drives sRNA competition and release of target RNA’, Proceedings of the National Academy of Sciences, 113(41), pp. E6089–E6096. doi: 10.1073/pnas.1613053113. 383. Santiago-Frangos, A. et al. (2017) ‘Acidic C-terminal domains autoregulate the RNA chaperone Hfq’, eLife, 6, pp. 1–25. doi: 10.7554/eLife.27049. 384. Santiago-Frangos, A. and Woodson, S. A. (2018) ‘Hfq chaperone brings speed dating to bacterial sRNA’, Wiley Interdisciplinary Reviews: RNA, (January), pp. 1–16. doi: 10.1002/wrna.1475. 385. Sauder, A. B. and Kendall, M. M. (2018) ‘After the fact(or): Posttranscriptional gene regulation in enterohemorrhagic Escherichia coli O157:H7’, Journal of Bacteriology, 200(19), pp. 1–16. doi: 10.1128/JB.00228- 18. 386. Sauer, E., Schmidt, S. and Weichenrieder, O. (2012) ‘Small RNA binding to the lateral surface of Hfq hexamers and structural rearrangements upon mRNA target recognition.’, Proceedings of the National Academy of Sciences of the United States of America, 109(24), pp. 9396–401. doi: 10.1073/pnas.1202521109. 387. Sauer, R. T., Ross, M. J. and Ptashne, M. (1982) ‘Cleavage of the lambda and P22 repressors by recA protein.’, Journal of Biological Chemistry, 257(8), pp. 4458–4462. 388. Scheutz, F. et al. (2012) ‘Multicenter evaluation of a sequence-based 216

References

protocol for subtyping shiga toxins and standardizing Stx nomenclature’, Journal of Clinical Microbiology, 50(9), pp. 2951–2963. doi: 10.1128/JCM.00860-12. 389. Schiano, C. A., Bellows, L. E. and Lathem, W. W. (2010) ‘The small RNA chaperone Hfq is required for the virulence of Yersinia pseudotuberculosis’, Infection and Immunity, 78(5), pp. 2034–2044. doi: 10.1128/IAI.01046-09. 390. Schmidt, H. (2001) ‘Shiga-toxin-converting bacteriophages’, Research in Microbiology, 152, pp. 687–695. doi: 10.1016/S0923-2508(01)01249-9. 391. Schu, D. J. et al. (2015) ‘Alternative Hfq-sRNA interaction modes dictate alternative mRNA recognition.’, The EMBO journal, 34(20), pp. 2557–73. doi: 10.15252/embj.201591569. 392. Schumacher, M. A. et al. (2002) ‘Structures of the pleiotropic translational regulator Hfq and an Hfq-RNA complex: A bacterial Sm-like protein’, EMBO Journal, 21(13), pp. 3546–3556. doi: 10.1093/emboj/cdf322. 393. Sears, C. L. and Kaper, J. B. (1996) ‘Enteric bacterial toxins: mechanisms of action and linkage to intestinal secretion.’, Microbiological reviews, 60(1), pp. 167–215. 394. Sedlyarova, N. et al. (2016) ‘sRNA-mediated control of transcription termination in E. coli’, Cell, 167(1), pp. 111-121.e13. doi: 10.1016/j.cell.2016.09.004. 395. Shakhnovich, E. A., Davis, B. M. and Waldor, M. K. (2009) ‘Hfq negatively regulates type III secretion in EHEC and several other pathogens’, Molecular Microbiology, 74(2), pp. 347–363. doi: 10.1111/j.1365- 2958.2009.06856.x. 396. Shankar, S., Hatoum, A. and Roberts, J. W. (2007) ‘A transcription antiterminator constructs a NusA-dependent shield to the emerging transcript’, Molecular Cell, 27(6), pp. 914–927. doi: 10.1016/j.molcel.2007.07.025. 397. Sharan, M. et al. (2017) ‘APRICOT: An integrated computational pipeline for the sequence-based identification and characterization of RNA- binding proteins’, Nucleic Acids Research, 45(11), p. e96. doi: 10.1093/nar/gkx137. 398. Sharma, C. M. et al. (2010) ‘The primary transcriptome of the major human pathogen Helicobacter pylori’, Nature, 464(7286), pp. 250–255. doi: 10.1038/nature08756. 399. Sharma, C. M. et al. (2011) ‘Pervasive post-transcriptional control of 217

References

genes involved in amino acid metabolism by the Hfq-dependent GcvB small RNA’, Molecular Microbiology, 81(5), pp. 1144–1165. doi: 10.1111/j.1365- 2958.2011.07751.x. 400. Sharp, F. C. and Sperandio, V. (2007) ‘QseA directly activates transcription of LEE1 in enterohemorrhagic Escherichia coli’, Infection and Immunity, 75(5), pp. 2432–2440. doi: 10.1128/IAI.02003-06. 401. Shchepachev, V. et al. (2019) ‘Defining the RNA interactome by total RNA‐associated protein purification’, Molecular Systems Biology, 15(4), p. e8689. doi: 10.15252/msb.20188689. 402. Shepherd, J. and Ibba, M. (2015) ‘Bacterial transfer RNAs’, FEMS Microbiology Reviews, 39(3), pp. 280–300. doi: 10.1093/femsre/fuv004. 403. Shevchenko, A. et al. (2007) ‘In-gel digestion for mass spectrometric characterization of proteins and proteomes’, Nature Protocols, 1(6), pp. 2856– 2860. doi: 10.1038/nprot.2006.468. 404. Shi, T. and Friedman, D. I. (2001) ‘The operator-early promoter regions of Shiga-toxin bearing phage H-19B’, Molecular Microbiology, 41(3), pp. 585– 599. doi: 10.1046/j.1365-2958.2001.02507.x. 405. Silva, I. J. et al. (2019) ‘ SraL sRNA interaction regulates the terminator by preventing premature transcription termination of rho mRNA ’, Proceedings of the National Academy of Sciences, 116(8), pp. 3042–3051. doi: 10.1073/pnas.1811589116. 406. Sittka, A. et al. (2007) ‘The RNA chaperone Hfq is essential for the virulence of Salmonella typhimurium’, Molecular Microbiology, 63(1), pp. 193– 217. doi: 10.1111/j.1365-2958.2006.05489.x. 407. Sittka, A. et al. (2008) ‘Deep sequencing analysis of small noncoding RNA and mRNA targets of the global post-transcriptional regulator, Hfq’, PLoS Genetics, 4(8). doi: 10.1371/journal.pgen.1000163. 408. Sklar, Jeffrey, Yot, P. and Weissman, S. (1975) ‘Determination of genes, restriction sites, and DNA sequences surrounding the 6S RNA template of bacteriophage lambda’, Proceedings of the National Academy of Sciences of the United States of America, 72(May), pp. 1817–1821. doi: 10.1073/pnas.72.5.1817. 409. Smirnov, A. et al. (2016) ‘Grad-seq guides the discovery of ProQ as a major small RNA-binding protein.’, Proceedings of the National Academy of Sciences of the United States of America, 113(41), pp. 11591–11596. doi: 10.1073/pnas.1609981113. 218

References

410. Smirnov, A. et al. (2017) ‘Molecular mechanism of mRNA repression in trans by a ProQ‐dependent small RNA’, The EMBO Journal, 36(8), pp. 1029– 1045. doi: 10.15252/embj.201696127. 411. Smith, D. L. et al. (2012) ‘Comparative genomics of Shiga toxin encoding bacteriophages’, BMC Genomics, 13(1), p. 311. doi: 10.1186/1471- 2164-13-311. 412. Smith, W. E. et al. (2003) ‘Shiga toxin 1 triggers a ribotoxic stress response leading to p38 and JNK activation and induction of apoptosis in intestinal epithelial cells.’, Infection and immunity, 71(3), pp. 1497–504. doi: 10.1128/IAI.71.3.1497. 413. Snider, T. A. et al. (2009) ‘E. coli O157:H7 catabolism of intestinal mucin-derived carbohydrates and colonization’, Veterinary Microbiology, 136(1– 2), pp. 150–154. doi: 10.1016/j.vetmic.2008.10.033. 414. Somasekhar, G. and Szybalski, W. (1983) ‘Mapping of the Q-utilization site (qut) required for antitermination of late transcription in bacteriophage lambda’, Gene, 26(2–3), pp. 291–294. doi: 10.1016/0378-1119(83)90199-3. 415. Somasekhar, G. and Szybalski, W. (1987) ‘The functional boundaries of the Q-utilization site required for antitermination of late transcription in bacteriophage λ’, Virology, 158(2), pp. 414–426. doi: 10.1016/0042- 6822(87)90213-3. 416. Song, T., Sabharwal, D. and Wai, S. N. (2010) ‘VrrA mediates Hfq- dependent regulation of OmpT synthesis in Vibrio cholerae’, Journal of Molecular Biology. Elsevier Ltd, 400(4), pp. 682–688. doi: 10.1016/j.jmb.2010.05.061. 417. Song, T. and Wai, S. N. (2009) ‘A novel sRNA that modulates virulence and environmental fitness of Vibrio cholerae.’, RNA biology, 6(3), pp. 254–258. doi: 10.4161/rna.6.3.8371. 418. Sonnleitner, E. et al. (2018) ‘Interplay between the catabolite repression control protein Crc, Hfq and RNA in Hfq-dependent translational regulation in Pseudomonas aeruginosa’, Nucleic Acids Research, 46(3), pp. 1470–1485. doi: 10.1093/nar/gkx1245. 419. Soper, T. et al. (2010) ‘Positive regulation by small RNAs and the role of Hfq’, Proceedings of the National Academy of Sciences, 107(21), pp. 9602– 9607. doi: 10.1073/pnas.1004435107. 420. Soper, T. J., Doxzen, K. and Woodson, S. A. (2011) ‘Major role for mRNA binding and restructuring in sRNA recruitment by Hfq Major role for 219

References

mRNA binding and restructuring in sRNA recruitment by Hfq’, RNA, pp. 1544– 1550. doi: 10.1261/rna.2767211. 421. Soper, T. J. and Woodson, S. A. (2008) ‘The rpoS mRNA leader recruits Hfq to facilitate annealing with DsrA sRNA’, RNA, 14(9), pp. 1907–1917. doi: 10.1261/rna.1110608. 422. Sorek, R., Kunin, V. and Hugenholtz, P. (2008) ‘CRISPR - A widespread system that provides acquired resistance against phages in bacteria and archaea’, Nature Reviews Microbiology, 6(3), pp. 181–186. doi: 10.1038/nrmicro1793. 423. Sperandio, V., Li, C. C. and Kaper, J. B. (2002) ‘Quorum-sensing Escherichia coli regulator A: A regulator of the LysR family involved in the regulation of the locus of enterocyte effacement pathogenicity island in enterohemorrhagic E. coli’, Infection and Immunity, 70(6), pp. 3085–3093. doi: 10.1128/IAI.70.6.3085-3093.2002. 424. Ståhl, A. L. et al. (2009) ‘Shiga toxin and lipopolysaccharide induce platelet-leukocyte aggregates and tissue factor release, a thrombotic mechanism in hemolytic uremic syndrome’, PLoS ONE, 4(9). doi: 10.1371/journal.pone.0006990. 425. Ståhl, A. lie et al. (2015) ‘A novel mechanism of bacterial toxin transfer within host blood cell-derived microvesicles’, PLoS Pathogens, 11(2), pp. 1–22. doi: 10.1371/journal.ppat.1004619. 426. Stead, M. B. et al. (2011) ‘Analysis of Escherichia coli RNase E and RNase III activity in vivo using tiling microarrays’, Nucleic Acids Research, 39(8), pp. 3188–3203. doi: 10.1093/nar/gkq1242. 427. Stearns-Kurosawa, D. J. et al. (2010) ‘Distinct physiologic and inflammatory responses elicited in baboons after challenge with Shiga toxin type 1 or 2 from enterohemorrhagic Escherichia coli’, Infection and Immunity, 78(6), pp. 2497–2504. doi: 10.1128/IAI.01435-09. 428. Steuten, B., Schneider, S. and Wagner, R. (2014) ‘6S RNA: Recent answers - future questions’, Molecular Microbiology, 91(4), pp. 641–648. doi: 10.1111/mmi.12484. 429. Stevens, M. P. and Frankel, G. A. D. M. (2014) ‘The Locus of Enterocyte Effacement and associated virulence factors of Enterohemorrhagic Escherichia coli’, 2(4), pp. 1–26. doi: 10.1128/microbiolspec.EHEC-0007- 2013.Correspondence. 430. Steyert, S. R. et al. (2012) ‘Comparative Genomics and stx Phage 220

References

Characterization of LEE-Negative Shiga Toxin-Producing Escherichia coli’, Frontiers in Cellular and Infection Microbiology, 2(November), pp. 1–18. doi: 10.3389/fcimb.2012.00133. 431. Sudo, N. et al. (2014) ‘A novel small regulatory RNA enhances cell motility in enterohemorrhagic Escherichia coli’, The Journal of General and Applied Microbiology, 60(1), pp. 44–50. doi: 10.2323/jgam.60.44. 432. Sudo, N. et al. (2018) ‘Small RNA Esr41 inversely regulates expression of LEE and flagellar genes in enterohaemorrhagic Escherichia coli’, Microbiology (United Kingdom), 164(5), pp. 821–834. doi: 10.1099/mic.0.000652. 433. Suits, M. D. L. et al. (2005) ‘Identification of an Escherichia coli O157:H7 heme oxygenase with tandem functional repeats’, Proceedings of the National Academy of Sciences of the United States of America, 102(47), pp. 16955– 16960. doi: 10.1073/pnas.0504289102. 434. Supek, F. et al. (2011) ‘REVIGO summarizes and visualizes long lists of gene ontology terms’, PLoS ONE, 6(7). doi: 10.1371/journal.pone.0021800. 435. Svenningsen, S. L. et al. (2005) ‘On the role of Cro in prophage induction’, Proceedings of the National Academy of Sciences, 102(12), pp. 4465–4469. doi: 10.1073/pnas.0409839102. 436. Tarr, P. I., Gordon, C. a. and Chandler, W. L. (2005) ‘Shiga-toxin- producing Escherichia coli and haemolytic uraemic syndrome’, Lancet, 365(9464), pp. 1073–1086. doi: 10.1016/S0140-6736(05)71144-2. 437. Tawk, C. et al. (2017) ‘A systematic analysis of the RNA-targeting potential of secreted bacterial effector proteins’, Scientific Reports, 7(1), pp. 1– 14. doi: 10.1038/s41598-017-09527-0. 438. Taylor, R. G., Walker, D. C. and Mclnnes, R. R. (1993) ‘E. coli host strains significantly affect the quality of small scale plasmid DNA’, Nucleic acids research, 21(7), pp. 1677–1678. 439. Tenaillon, O. et al. (2010) ‘The population genetics of commensal Escherichia coli.’, Nature reviews. Microbiology. Nature Publishing Group, 8(3), pp. 207–217. doi: 10.1038/nrmicro2298. 440. Tesh, V. L. and O’Brien, a D. (1991) ‘The pathogenic mechanisms of Shiga toxin and the Shiga-like toxins.’, Molecular microbiology, 5(8), pp. 1817– 1822. doi: 10.1111/j.1365-2958.1991.tb00805.x. 441. Tétart, F. and Bouché, J. ‐P (1992) ‘Regulation of the expression of the cell‐cycle gene ftsZ by DicF antisense RNA. Division does not require a fixed 221

References

number of FtsZ molecules’, Molecular Microbiology. doi: 10.1111/j.1365- 2958.1992.tb01508.x. 442. Thorvaldsdóttir, H., Robinson, J. T. and Mesirov, J. P. (2013) ‘Integrative Genomics Viewer (IGV): High-performance genomics data visualization and exploration’, Briefings in Bioinformatics, 14(2), pp. 178–192. doi: 10.1093/bib/bbs017. 443. Tobe, T. et al. (2006) ‘An extensive repertoire of type III secretion effectors in Escherichia coli O157 and the role of lambdoid phages in their dissemination.’, Proceedings of the National Academy of Sciences of the United States of America, 103(40), pp. 14941–6. doi: 10.1073/pnas.0604891103. 444. Torres, A. G. et al. (2001) ‘TonB-dependent systems of uropathogenic Escherichia coli: Aerobactin and heme transport and TonB are required for virulence in the mouse’, Infection and Immunity, 69(10), pp. 6179–6185. doi: 10.1128/IAI.69.10.6179-6185.2001. 445. Torres, A. G. and Payne, S. M. (1997) ‘Haem iron-transport system in enterohaemorrhagic Escherichia coli O157:H7’, Molecular Microbiology, 23(4), pp. 825–833. doi: 10.1046/j.1365-2958.1997.2641628.x. 446. Tree, J. J. et al. (2011) ‘Transcriptional regulators of the GAD acid stress island are carried by effector protein-encoding prophages and indirectly control type III secretion in enterohemorrhagic Escherichia coli O157:H7’, Molecular Microbiology, 80(5), pp. 1349–1365. doi: 10.1111/j.1365- 2958.2011.07650.x. 447. Tree, J. J. et al. (2014) ‘Identification of bacteriophage-encoded anti- sRNAs in pathogenic Escherichia coli’, Molecular Cell. The Authors, 55(2), pp. 199–213. doi: 10.1016/j.molcel.2014.05.006. 448. Trendel, J. et al. (2019) ‘The Human RNA-Binding Proteome and Its Dynamics during Translational Arrest’, Cell. Elsevier Inc., 176(1–2), pp. 391- 403.e19. doi: 10.1016/j.cell.2018.11.004. 449. Tsui, H. C. T., Leung, H. C. E. and Winkler, M. E. (1994) ‘Characterization of broadly pleiotropic phenotypes caused by an hfq insertion mutation in Escherichia coli K-12’, Molecular Microbiology, 13(1), pp. 35–49. doi: 10.1111/j.1365-2958.1994.tb00400.x. 450. Turner, P. C. et al. (1998) ‘Neisseria gonorrhoeae heme biosynthetic mutants utilize heme and hemoglobin as a heme source but fail to grow within epithelial cells’, Infection and Immunity, 66(11), pp. 5215–5223. 451. Tuttle, J. et al. (1999) ‘Lessons from a large outbreak of Escherichia coli 222

References

O157:H7 infections: Insights into the infectious dose and method of widespread contamination of hamburger patties’, Epidemiology and Infection, 122(2), pp. 185–192. doi: 10.1017/S0950268898001976. 452. Tyanova, S. et al. (2016) ‘The Perseus computational platform for comprehensive analysis of (prote)omics data’, Nature Methods, 13(9), pp. 731– 740. doi: 10.1038/nmeth.3901. 453. Tyler, J. S., Mills, M. J. and Friedman, D. I. (2004) ‘The operator and early promoter region of the Shiga toxin type 2-encoding bacteriophage 933W and control of toxin expression’, Journal of Bacteriology, 186(22), pp. 7670– 7679. doi: 10.1128/JB.186.22.7670-7679.2004. 454. Tyrrell, G. J. et al. (1992) ‘Alteration of the carbohydrate binding specificity of verotoxins from Gal alpha 1-4Gal to GalNAc beta 1-3Gal alpha 1- 4Gal and vice versa by site-directed mutagenesis of the binding subunit.’, Proceedings of the National Academy of Sciences of the United States of America, 89(2), pp. 524–8. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=48271&tool=pmcentr ez&rendertype=abstract. 455. Udekwu, K. I. and Wagner, E. G. H. (2007) ‘Sigma E controls biogenesis of the antisense RNA MicA’, Nucleic Acids Research, 35(4), pp. 1279–1288. doi: 10.1093/nar/gkl1154. 456. Unkmeir, A. and Schmidt, H. (2000) ‘Structural analysis of phage-borne stx genes and their flanking sequences in Shiga toxin-producing Escherichia coli and Shigella dysenteriae type 1 strains’, Infection and Immunity, 68(9), pp. 4856–4864. doi: 10.1128/IAI.68.9.4856-4864.2000. 457. Updegrove, T. et al. (2008) ‘Effect of Hfq on RprA-rpoS mRNA pairing: Hfq-RNA binding and the influence of the 5′ rpoS mRNA leader region’, Biochemistry, 47(43), pp. 11184–11195. doi: 10.1021/bi800479p. 458. Updegrove, T. B., Zhang, A. and Storz, G. (2016) ‘Hfq: the flexible RNA matchmaker’, Current Opinion in Microbiology. Elsevier Ltd, 30, pp. 133–138. doi: 10.1016/j.mib.2016.02.003. 459. Urban, J. H. et al. (2007) ‘A conserved small RNA promotes discoordinate expression of the glmUS operon mRNA to activate GlmS synthesis’, Journal of Molecular Biology, 373(3), pp. 521–528. doi: 10.1016/j.jmb.2007.07.035. 460. Urban, J. H. and Vogel, J. (2007) ‘Translational control and target recognition by Escherichia coli small RNAs in vivo’, Nucleic Acids Research, 223

References

35(3), pp. 1018–1037. doi: 10.1093/nar/gkl1040. 461. Urban, J. H. and Vogel, J. (2008) ‘Two seemingly homologous noncoding RNAs act hierarchically to activate glmS mRNA translation’, PLoS Biology, 6(3), pp. 0631–0642. doi: 10.1371/journal.pbio.0060064. 462. Urbanowski, M. L., Stauffer, L. T. and Stauffer, G. V. (2000) ‘The gcvB gene encodes a small untranslated RNA involved in expression of the dipeptide and oligopeptide transport systems in Escherichia coli’, Molecular Microbiology, 37(4), pp. 856–868. doi: 10.1046/j.1365-2958.2000.02051.x. 463. Urdaneta, E. C. et al. (2019) ‘Purification of cross-linked RNA-protein complexes by phenol-toluol extraction’, Nature Communications. Springer US, 10(1), pp. 1–17. doi: 10.1038/s41467-019-08942-3. 464. Vanderpool, C. K. and Armstrong, S. K. (2001) ‘The Bordetella bhu locus is required for heme iron utilization’, Journal of Bacteriology, 183(14), pp. 4278–4287. doi: 10.1128/JB.183.14.4278. 465. Vanderpool, C. K. and Gottesman, S. (2004) ‘Involvement of a novel transcriptional activator and small RNA in post-transcriptional regulation of the glucose phosphoenolpyruvate phosphotransferase system’, Molecular Microbiology, 54(4), pp. 1076–1089. doi: 10.1111/j.1365-2958.2004.04348.x. 466. Vercruysse, M. et al. (2014) ‘The highly conserved bacterial RNase YbeY Is essential in Vibrio cholerae, playing a critical role in virulence, stress regulation, and RNA processing’, PLoS Pathogens, 10(6). doi: 10.1371/journal.ppat.1004175. 467. Vogel, J. and Luisi, B. F. (2011) ‘Hfq and its constellation of RNA.’, Nature reviews. Microbiology, 9(8), pp. 578–589. doi: 10.1038/nrmicro2615. 468. Vvedenskaya, I. O. et al. (2014) ‘Interactions between RNA polymerase and the “core recognition element” counteract pausing’, Science, 344(6189), pp. 1285–1289. doi: 10.1126/science.1253458. 469. Wadler, C. S. and Vanderpool, C. K. (2007) ‘A dual function for a bacterial small RNA: SgrS performs base pairing-dependent regulation and encodes a functional polypeptide’, Proceedings of the National Academy of Sciences, 104(51), pp. 20454–20459. doi: 10.1073/pnas.0708102104. 470. Wagner, E. G. H. and Romby, P. (2015) Small RNAs in Bacteria and Archaea: Who They Are, What They Do, and How They Do It, Advances in Genetics. Elsevier Ltd. doi: 10.1016/bs.adgen.2015.05.001. 471. Wagner, P. L. et al. (2001) ‘Role for a phage promoter in Shiga toxin 2 expression from a pathogenic Escherichia coli strain’, J Bacteriol, 183(6), pp. 224

References

2081–2085. doi: 10.1128/JB.183.6.2081. 472. Wagner, P. L. et al. (2002) ‘Bacteriophage control of Shiga toxin 1 production and release by Escherichia coli’, Molecular Microbiology, 44(4), pp. 957–970. doi: 10.1046/j.1365-2958.2002.02950.x. 473. Waldminghaus, T. et al. (2007) ‘FourU: A novel type of RNA thermometer in Salmonella’, Molecular Microbiology, 65(2), pp. 413–424. doi: 10.1111/j.1365-2958.2007.05794.x. 474. Walterspiel, J. N. et al. (1992) ‘Effect of subinhibitory concentrations of antibiotics on extracellular Shiga-like toxin I’, Infection, 20(1), pp. 25–29. doi: 10.1007/BF01704889. 475. Wang, D. et al. (2018) ‘An RNA-dependent mechanism for transient expression of bacterial translocation filaments’, Nucleic Acids Research. Oxford University Press, 46(7), pp. 3366–3381. doi: 10.1093/nar/gky096. 476. Wang, X. et al. (2010) ‘Cryptic prophages help bacteria cope with adverse environments’, Nature Communications. Nature Publishing Group, 1(9), pp. 147–149. doi: 10.1038/ncomms1146. 477. Wang, X. et al. (2015) ‘ Two-level inhibition of galK expression by Spot 42: Degradation of mRNA mK2 and enhanced transcription termination before the galK gene ’, Proceedings of the National Academy of Sciences, 112(24), pp. 7581–7586. doi: 10.1073/pnas.1424683112. 478. Wassarman, K. M. and Saecker, R. M. (2006) ‘Synthesis-mediated release of a small RNA inhibitor of RNA polymerase.’, pp. 1601–1604. 479. Waters, L. S. and Storz, G. (2009) ‘Regulatory RNAs in Bacteria’, Cell. Elsevier Inc., 136(4), pp. 615–628. doi: 10.1016/j.cell.2009.01.043. 480. Waters, S. A. et al. (2017) ‘ Small RNA interactome of pathogenic E. coli revealed through crosslinking of RNase E ’, The EMBO Journal, 36(3), pp. 374– 387. doi: 10.15252/embj.201694639. 481. Webb, S. et al. (2014) ‘PAR-CLIP data indicate that Nrd1-Nab3- dependent transcription termination regulates expression of hundreds of protein coding genes in yeast.’, Genome biology, 15(1), p. R8. doi: 10.1186/gb-2014- 15-1-r8. 482. Weber, G. G. et al. (2014) ‘RNA thermometer controls temperature- dependent virulence factor expression in Vibrio cholerae’, Proceedings of the National Academy of Sciences of the United States of America, 111(39), pp. 14241–14246. doi: 10.1073/pnas.1411570111. 483. Weber, H. et al. (2005) ‘Genome-wide analysis of the general stress 225

References

response network in Escherichia coli : σS -Dependent Genes , Promoters , and Sigma Factor Selectivity’, Journal of Bacteriology, 187(5), pp. 1591–1603. doi: 10.1128/JB.187.5.1591. 484. Weinberg, E. D. (1975) ‘Nutritional Immunity: Host’s Attempt to Withhold Iron From Microbial Invaders’, The Journal of the American Medical Association, 231(1), pp. 39–41. doi: doi:10.1001/jama.1975.03240130021018. 485. Welch, R. A. et al. (2002) ‘Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli’, Proceedings of the National Academy of Sciences of the United States of America, 99(26), pp. 17020–17024. doi: 10.1073/pnas.252529799. 486. Westermann, A. J. et al. (2016) ‘Dual RNA-seq unveils noncoding RNA functions in host-pathogen interactions’, Nature. Nature Publishing Group, 529(7587), pp. 496–501. doi: 10.1038/nature16547. 487. Westermann, A. J. et al. (2019) ‘ The major rna-binding protein ProQ impacts virulence gene expression in Salmonella enterica Serovar Typhimurium ’, mBio, 10(1), pp. 1–21. doi: 10.1128/mbio.02504-18. 488. Wilderman, P. J. et al. (2004) ‘Identification of tandem duplicate regulatory small RNAs in Pseudomonas aeruginosa involved in iron homeostasis’, Proceedings of the National Academy of Sciences of the United States of America, 101(26), pp. 9792–9797. doi: 10.1073/pnas.0403423101. 489. Winther, K. S., Roghanian, M. and Gerdes, K. (2018) ‘Activation of the Stringent Response by Loading of RelA-tRNA Complexes at the Ribosomal A- Site’, Molecular Cell. Elsevier Inc., 70(1), pp. 95-105.e4. doi: 10.1016/j.molcel.2018.02.033. 490. Wong, A. R. C. et al. (2011) ‘Enteropathogenic and enterohaemorrhagic Escherichia coli: Even more subversive elements’, Molecular Microbiology, 80(6), pp. 1420–1438. doi: 10.1111/j.1365-2958.2011.07661.x. 491. Wong, G. T. et al. (2017) ‘Genome-wide transcriptional response to varying RpoS levels in Escherichia coli K-12’, Journal of Bacteriology, 199(7), pp. 1–17. doi: 10.1128/JB.00755-16. 492. Wong, J. et al. (2018) Systems-Level Analysis of Bacterial Regulatory Small RNA Networks, Systems Biology. doi: 10.1007/978-3-319-02904-7. 493. Woodson, S. A., Panja, S. and Santiago-Frangos, A. (2018) ‘Proteins that chaperone RNA regulation’, in Microbiology Spectrum, pp. 1–13. doi: 10.1128/microbiolspec.rwr-0026-2018. 494. Wright, P. R. et al. (2014) ‘CopraRNA and IntaRNA: Predicting small 226

References

RNA targets, networks and interaction domains’, Nucleic Acids Research, 42(W1), pp. 119–123. doi: 10.1093/nar/gku359. 495. Wulff, D. I. and Krinke, L. (1987) ‘OOP RNA, produced from multicopy plasmids, inhibits lambda cII gene expression through an RNase III-dependent mechanism’, Genes & Development, 1, pp. 1005–1013. 496. Wyckoff, E. E. et al. (1998) ‘Structure of the Shigella dysenteriae haem transport locus and its phylogenetic distribution in enteric bacteria’, Molecular Microbiology, 28(6), pp. 1139–1152. doi: 10.1046/j.1365-2958.1998.00873.x. 497. Xu, J. et al. (2003) ‘A genomic view of the human-Bacteroides thetaiotaomicron symbiosis’, Science, 299(March), pp. 2074–2076. 498. Xu, X. et al. (2012) ‘Lysogeny with Shiga toxin 2-encoding bacteriophages represses type III secretion in enterohemorrhagic Escherichia coli’, PLoS Pathogens, 8(5). doi: 10.1371/journal.ppat.1002672. 499. Yamamoto, N. et al. (2009) ‘Update on the Keio collection of Escherichia coli single-gene deletion mutants’, Molecular Systems Biology, 5(335). doi: 10.1038/msb.2009.92. 500. Yang, X. J. et al. (1987) ‘Transcription antitermination by phage lambda gene Q protein requires a DNA segment spanning the RNA start site.’, Genes & development, 1(3), pp. 217–226. doi: 10.1101/gad.1.3.217. 501. Yarnell, W. S. and Roberts, J. W. (1992) ‘The phage lambda gene Q transcription antiterminator binds DNA in the late gene promoter as it modifies RNA polymerase’, Cell, 69(7), pp. 1181–1189. doi: 10.1016/0092- 8674(92)90639-T. 502. Ye, F. et al. (2018) ‘Molecular basis of binding between the global post- transcriptional regulator CsrA and the T3SS chaperone CesT’, Nature Communications. Springer US, 9(1), pp. 1–11. doi: 10.1038/s41467-018-03625- x. 503. Yoon, J. H., Srikantan, S. and Gorospe, M. (2012) ‘MS2-TRAP (MS2- tagged RNA affinity purification): Tagging RNA to identify associated miRNAs’, Methods. Elsevier Inc., 58(2), pp. 81–87. doi: 10.1016/j.ymeth.2012.07.004. 504. Zambolin, S. et al. (2016) ‘Structural basis for haem piracy from host haemopexin by Haemophilus influenzae’, Nature Communications, 7(May), pp. 1–12. doi: 10.1038/ncomms11590. 505. Zhang, A. et al. (2002) ‘The Sm-like Hfq protein increases OxyS RNA interaction with target mRNAs’, Molecular Cell, 9(1), pp. 11–22. doi: 10.1016/S1097-2765(01)00437-3. 227

References

506. Zhang, A. et al. (2013) ‘Mutations in interaction surfaces differentially impact E. coli Hfq association with small RNAs and their mRNA targets’, Journal of Molecular Biology. Elsevier B.V., 425(19), pp. 3678–3697. doi: 10.1016/j.jmb.2013.01.006. 507. Zhang, X. et al. (2002) ‘Quinolone antibiotics induce Shiga toxin– encoding bacteriophages, toxin production, and death in mice’, The Journal of Infectious Diseases, 181(2), pp. 664–670. doi: 10.1086/315239. 508. Zhang, Y. F. et al. (2017) ‘Probing the sRNA regulatory landscape of P. aeruginosa: post-transcriptional control of determinants of pathogenicity and antibiotic susceptibility’, Molecular Microbiology, 106(6), pp. 919–937. doi: 10.1111/mmi.13857. 509. Zhang, Z. et al. (2005) ‘Functional interactions between the carbon and iron utilization regulators, Crp and Fur, in Escherichia coli’, Journal of Bacteriology, 187(3), pp. 980–990. doi: 10.1128/JB.187.3.980-990.2005. 510. Zhang, Z., Aboulwafa, M. and Saier, M. H. (2014) ‘Regulation of crp gene expression by the catabolite repressor/activator, Cra, in Escherichia coli’, Journal of Molecular Microbiology and Biotechnology, 24(3), pp. 135–141. doi: 10.1159/000362722. 511. Zheng, A., Panja, S. and Woodson, S. A. (2016) ‘Arginine patch predicts the RNA annealing activity of Hfq from Gram-negative and Gram-positive bacteria’, Journal of Molecular Biology. Elsevier Ltd, 428(11), pp. 2259–2264. doi: 10.1016/j.jmb.2016.03.027. 512. Zhou, Z. et al. (2010) ‘Derivation of Escherichia coli O157:H7 from its O55:H7 precursor’, PLoS ONE, 5(1). doi: 10.1371/journal.pone.0008700. 513. Zuker, M. (2003) ‘Mfold web server for nucleic acid folding and hybridization prediction.’, Nucleic acids research, 31(13), pp. 3406–15. doi: 10.1093/nar/gkg595. 514. Zwiefka, A., Kohn, H. and Widger, W. R. (1993) ‘Transcription Termination Factor rho: The Site of Bicyclomycin Inhibition in Escherichia coli’, Biochemistry, 32(14), pp. 3564–3570. doi: 10.1021/bi00065a007.

228

Appendix

Appendix: Supplementary Figures and Tables

229

Appendix

Supplementary Figure S1: Genomic DNA sequencing of StxS mutants to confirm deletion or repair in Stx1Φ. Shown is the coverage from genomic DNA sequencing for StxS1 region confirming deletion of the sRNA.

230

Appendix

Supplementary Figure S2: Genomic DNA sequencing of StxS mutants to confirm deletion or repair in Stx2Φ. Shown is the coverage from genomic DNA sequencing for StxS2 region confirming deletion of the sRNA.

231

Appendix

Supplementary Table 1: GBlocks used in this study

Sequence name Sequence Purpose Chapter 4 GCCTTTACCGGAACAATCGCGATCTGCATGACTTGCTG GTTGATTATTACGTGTTGGGGGAGACGTTCATGGCGTT GGCACGGAAACATGGGTGCTCTGACACCTGTATAGGT AAACGCCTTCACAAAGCGGAGGGGATTGTTGAAGGCA TGCTGATGATGCTGGGAGTGAGGCTTGAGATGGATCG GTATGTTGAGCGTGAATTGCCGGGAGGGAGAACCTCT GTATTTTATCAGCGAAAAAATAGTTTACGATCGTAAAAA TCTGCATATCATGATAAGAGTGGTTACATTGCCGTATTT TAAGTATTGCAGGATAACCCTGTAACGAAGTTTGCGTA ACAGCATTTTGCTCTACGAGTTTGCCAGCCTCCCCCAG TGGCTGGCTTTTTTATGTCCGTAGCGTCAAAGCAGCAA repair TGGCGCTAGGGCGTCGTGCAATTGGCGTTGAGCTGGA template GAGCGGGCGTTTTGAGCAGACGGTCAGGGAAGTTCAG for AATGTAGTCAGTCAGAACGGATGATATTGCAGGATTAG generating TTACGTACCGTTATTATCCTGCGCCCGGCCCTTTAGCT partial CAGTGGTGAGAGCGAGCGACTCATAATCGCCAGGTCG StxS2 BS_stxSS_only CTGGTTCAAATCCAG deletion Chapter 5 GTTTTTTTGGGCTAGCAGGAGGAATTCATGAAAGAGAT TGATTTATTATATGAAAATATTTATCAGCTTTTAATTAAA CCTTACCTACTCGATCTTTCTAGCCAGTCTGGAAAAAA AATAGAATTAAATTACACATGCAAAATAAAAGATGCTGC TGATGAAATAAAGGGTAGTATGATTTTTAATGATGTAGA TGGTAAACAAAAAGCAACTTGCACTATCAGGGTGTTAA TACTAAAAACATTTCACGATGGTCGTTATAGATTTGTAA TTGAATCTGTGATTTATGATTTAATTAATAATTACAGTG GATTTATTTTAACGGGGAGATTGTTTTGGCAAGGGGAA GGTTTTGGCCATGAATTATTCCCCGTGACAAACAAATA cloning TAATGCTTGGAGATGGAAAAATAAAAAAATAATAGATAT ECs0604 TTCTTGGTCTAGAGTCGACCTGCAGGCATGCAAGCTTG into ECs0604 GCTGTTTTGGCAAAAAC pBAD24 GTTTTTTTGGGCTAGCAGGAGGAATTCATGTTAAAATC AACTCTTATTGCTAAATGCCTTTATCAAAATCGCATGGT AAGCAGCATTTCAATAGGCGAGTCTGCAGTTAAAAGTA TTTTCGAAGAGTACTTTCCCGGGCATGATTTTAATAAAT GGAATACCAAATTACCGCCAGCAGTTTCAACGCGTATT cloning CTGAAAGCAACCGAAAGAGCAAGTACAATTCGCGTTAA ECs0604 CTATTTCATTAAAGATTTGTGGGATCTTTCTAGAGTCGA into ECs1762 CCTGCAGGCATGCAAGCTTGGCTGTTTTGGCAAAAAC pBAD24 GTTTTTTTGGGCTAGCAGGAGGAATTCATGAAAGTAAG AAACCCAGAACAGATTAGTATCCCTGCTAGTAATACCA CTAAAGATCCAGGCCTTACTAATTCTCAGATTATCAGG ATGGCTAACTTGGTCAAAAAGACCGAGAATATGAATAT CTTTGAAGAGCTATGGGAGACGTTACGCAATCTCTTCC AGTCAGATAAACATTCCCAGACTGCGGCAAGGCAAATT CTGAAGGATGCATTTTATTTCCAGAATAGTGATGACTA CTCAAAATACTTTACCGGGGCTGTCGACGGGAAGGCG CGTGATAAGTTAACTCACTGGCTGATAAAATTTAATGAA CTCAAAGAATACGCAAAAGATCCGGAAAATATGGCCGC GAAGGCCTCACTCTCGCCAGAAGGTACGTTATGCGTC cloning AGTTTTTTTATTGGCGATGAGGCGATATTTACTCTGGA ECs0604 GTTGCAGCTGAAAAAGAGCACCAGGACCGGGGGGATT into ECs0073 GATCTGAGTAATGCTTACTTTAACGGGGTTGTCATTTG pBAD24 232

Appendix

TGGTATTGACTGTCTCGAGGTTGACCTGAGCAATGCTG AAACGAACAATAGCCGTTGGTATGACTCTAGAGTCGAC CTGCAGGCATGCAAGCTTGGCTGTTTTGGCAAAAAC GTTTTTTTGGGCTAGCAGGAGGAATTCATGGTATTGAG CAAGCAACTCACTGGATGCCGCTACCAGAACCGCCGC AGGAGGTTAACCGTGGCTAACCTGCAACTTGCCGTTAA AGGTGAATACTTCGATGCCATGATTCGCGGAGAGAAAA CGGAAGAGTATCGCCTGTTTAATGACTACTGGAATAAG CGAATTATGTTCCGCGAGTATGACCGCCTGATTATCAC AAAGGGATATCCGAAGCGCGACGATTCCAGCCGAAGA ATTGATGTTCCGTATGACGGATATGAAATCAAGACAAT CACACATCCGCACTTCGGTGATAAACCGGTAAAGGTGT cloning TCGCGATAAAGGTGAATATCGGCAATGAATCTAGAGTC ECs0604 GACCTGCAGGCATGCAAGCTTGGCTGTTTTGGCAAAA into ECs1163 AC pBAD24 GTTTTTTTGGGCTAGCAGGAGGAATTCATGGCAAAGTC AAACGTTAGCGTGCAGGCATTCAAGGACTTCCTTGAAG AGCTTATGTCGCTGAACATAATGAAGGAGGCCACCGC TCGAAATTTAAAAAACTCATCCGCTCGCCTCTTAACGG TAGTCCAAGAAGAGGAAATGGGTGATGTTACTCAGCTT GATGTGAATGAGCTTGCCGAGCGATACATCAACGCAA CTGAGCCGAAGCCTAGCGACAGCAGCATTACTGCATA TAAAAGCCGCATGGAAAGTGCAATCAAAAAGTTTGTAG CTTTCCAGTCTGGTGAAGAAATCCCATACACTCCGATT GACAAAGAATCCAGTGAGGAAAAAGATTTGACTGGCG AACCAACAAAAGTCGAAGGCAAGGCTAATGCACTTCAT ACCTATGATCTTCCAGTAGTTCTTCGACCTGAATCAGG GGTTACAGTAACGATTAAAGGCATTCCTAACGATATCA cloning CAAACGAAGAAGCCGAACGCATCTCTTCAATTCTGAAG ECs0604 GTTTACGTTCGGCCTCAATCTAGAGTCGACCTGCAGG into ECs1184 CATGCAAGCTTGGCTGTTTTGGCAAAAAC pBAD24

233

Appendix

Supplementary Table S2: Secondary SNPs detected in constructed Sakai mutants

Strain Coordinate Original Alternate Gene Stx(-) WT 2427339 C A astB JJT378 (Stx(-) ΔstxS1) Allelic exchange deletion 2427339 C A astB JJT379 (Stx(-) ΔstxS2) Allelic exchange deletion 2427339 C A astB JJT380 (Stx(-) ΔstxS1 ΔstxS2) Allelic exchange deletion 2427339 C A astB JJT384 (Stx(+) ΔstxS1) Allelic exchange deletion 1915367 A G abgR-smrA 2926542 G A Stx1 Q 2926555 C T Stx1 Q 2926694 C T Stx1 Q 4400616 A G gadE 5'UTR JJT385 (Stx(+) ΔstxS2) Allelic exchange deletion 1915367 A G abgR-smrA 4400616 A G JJT438 (Stx(+) ΔstxS1ΔstxS2) Allelic exchange deletion 1915367 A G abgR-smrA 2926542 G A Stx1 Q 2926555 C T Stx1 Q 2926694 C T Stx1 Q 4400616 A G JJT701 CRISPR (Stx(+)ΔstxS1::stxS1) Allelic exchange deletion repair 1915367 A G abgR-smrA 2926694 C T Stx1 Q 4400616 A G gadE 5'UTR JJT702 CRISPR (Stx(+)ΔstxS2::stxS2) Allelic exchange deletion repair 1915367 A G abgR-smrA 4400616 A G gadE 5'UTR 5297927 G A tamA 5'UTR JJT953 (Stx(+) ΔstxS2) CRISPR deletion ECs3262 (phage DNA 3195526 A G injection protein) JJT1027 CRISPR (Stx(+)ΔstxS2::stxS2) CRISPR deletion repair 1266407 C T pre-StxS2 SNP

234

Appendix

Supplementary Table S3: Secondary indels detected in constructed Sakai mutants

insertion/deletio Strain Start End n gene (s) Stx(-) WT 2186268 2224869 deletion Sp11-Sp12 2237235 2242512 deletion Sp12 Allelic exchange JJT378 (Stx(-) ΔstxS1) deletion 2180367 2224920 deletion Sp11-Sp12 2237194 2242502 deletion Sp12 Allelic exchange JJT379 (Stx(-) ΔstxS2) deletion 1594711 1610171 deletion Sp7 2186330 2224900 deletion Sp11-Sp12 2237327 2242543 deletion Sp12 JJT380 (Stx(-) ΔstxS1 Allelic exchange ΔstxS2) deletion 1594711 1610171 deletion Sp7 2186330 2224900 deletion Sp11-Sp12 2237327 2242543 deletion Sp12 JJT437 Allelic exchange (Stx(-) ΔstxS1 ΔstxS2 ΔrpoS) deletion 1594711 1610171 deletion Sp7 2186330 2224900 deletion Sp11-Sp12 2237327 2242543 deletion Sp12 Allelic exchange JJT385 (Stx(+) ΔstxS2) deletion 2186300 2224901 deletion Sp11-Sp12 Allelic exchange CRISPR JJT702 (Stx(+)ΔstxS2::stxS2) deletion repair 2186300 2224901 deletion Sp11-Sp12 JJT957 (Stx(+)Δpre-StxS2) CRISPR deletion ECs2835- 2774902 2797339 deletion ECs2853 ECs4385- 4395371 4402559 deletion ECs4393 JJT953 (Stx(+)ΔStxS2) CRISPR deletion ECs2835- 2774902 2797339 deletion ECs2853 ECs4385- 4395371 4402559 deletion ECs4393 JJT993 CRISPR (Stx(+)Δpre-stxS2::stxS2) CRISPR deletion repair ECs2835- 2774902 2797339 deletion ECs2853 ECs4385- 4395371 4402559 deletion ECs4393 JJT1027 CRISPR (Stx(+)ΔstxS2::stxS2) CRISPR deletion repair ECs2835- 2774902 2797339 deletion ECs2853 ECs4385- 4395371 4402559 deletion ECs4393

235

Appendix

Supplementary Table S4: Statistically significant twofold enriched proteins recovered with TRAPP

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS truD 33 0.001444 9.3287 A0A0H3PLU8 tRNA pseudouridine synthase D T T T F truB 25 0.004912 7.9089 A0A0H3PSN1 tRNA pseudouridine synthase B T T T F artP 3 0.003560 7.8700 A0A0H3PQ14 Arginine transport ATP-binding protein ArtP T T F F

yedK 20 0.000985 7.4717 Q8XEH0 T F F F rsmA 19 0.002290 7.3479 A0A0H3PVN2 Ribosomal RNA small subunit methyltransferase A T T T F rpsG 35 0.001417 7.2574 A0A0H3Q2E3 30S ribosomal protein S7 T T T F rpsU 11 0.001472 7.1899 A0A0H3PSC3 30S ribosomal protein S21 T T T F entB 24 0.004058 7.1268 A0A0H3PZT4 Enterobactin synthase component B;Isochorismatase;Aryl carrier protein T T F F rplP 14 0.001671 7.0547 A0A0H3Q2L2 50S ribosomal protein L16 T T T F rapZ 20 0.000702 6.7986 A0A0H3Q0P2 RNase adapter protein RapZ T T F F trmB 11 0.000985 6.7328 A0A0H3PTJ3 tRNA (guanine-N(7)-)-methyltransferase T T F F yceD 8 0.004391 6.6944 A0A0F6F4M1 Uncharacterized protein YceD T T T T smpB 14 0.002205 6.5773 A0A0H3PIQ4 SsrA-binding protein T T F F rpsR 10 0.003717 6.5729 A0A0H3PUJ3 30S ribosomal protein S18 T T T F infA 6 0.004224 6.4328 A0A0H3PNF8 Translation initiation factor IF-1 T T F F yaaA 23 0.006987 6.4079 A0A0H3Q2X9 UPF0246 protein YaaA T T F F bcp 17 0.000985 6.3611 A0A0H3PM77 Putative peroxiredoxin bcp T T T F nusG 21 0.003009 6.2333 A0A0H3PKN3 Transcription termination/antitermination protein NusG T T T F nusB 9 0.001109 6.2274 A0A0H3PRM2 N utilization substance protein B;N utilization substance protein B homolog T T T F rmf 5 0.006893 6.2202 A0A0F6F3L4 Ribosome modulation factor T F T F nei 15 0.002205 6.1589 A0A0H3PY62 Endonuclease 8 T T F F rplD 36 0.006159 6.1469 A0A0H3PTZ9 50S ribosomal protein L4 T T T F yjbJ 13 0.003009 6.0713 A0A0H3PZD7 UPF0337 protein YjbJ T T T F ssb 26 0.001224 6.0673 A0A0H3PYY5 Single-stranded DNA-binding protein T T T F

236

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS cspC 9 0.001151 6.0068 A0A0H3PMN4 Cold shock-like protein CspC T T T F cspE 10 0.001838 5.9978 A0A0H3PRE4 Cold shock-like protein CspE T T T F dctR 11 0.001708 5.9026 A0A0H3PU53 HTH-type transcriptional regulator DctR T F F F cspD 3 0.001417 5.8898 A0A0H3PJP5 Cold shock-like protein CspD T T T F rpmB 14 0.001394 5.8353 A0A0H3PU29 50S ribosomal protein L28 T T T F rob 16 0.004483 5.7839 A0A0H3Q3T5 Right origin-binding protein T T T F

lhr 72 0.004224 5.7596 Q8X626 T F F F rplW 14 0.003545 5.6763 A0A0H3PX51 50S ribosomal protein L23 T T T F fmt 13 0.001623 5.6352 A0A0H3PSZ7 Methionyl-tRNA formyltransferase T T F F csrA 4 0.002967 5.6265 A0A0H3PM07 Carbon storage regulator T T F F

yciV 14 0.002082 5.5442 Q8X7B9 T F F F yeaZ 8 0.007629 5.5203 Q8XDR4 T F T F rplR 13 0.001224 5.5097 A0A0H3PTR6 50S ribosomal protein L18 T T T F acpP 4 0.002585 5.4976 A0A0H3PK35 Acyl carrier protein T T T F cpxR 15 0.001151 5.4632 A0A0H3PUD3 Transcriptional regulatory protein CpxR T T F F truA 18 0.002239 5.4615 A0A0H3PNS6 tRNA pseudouridine synthase A T T F F

xthA 21 0.011384 5.4348 Q8XDY5 T T F F rpmE 7 0.008569 5.3600 A0A0H3PZ52 50S ribosomal protein L31 T T T F ung 18 0.007242 5.3340 A0A0H3JMX3 Uracil-DNA glycosylase T T F F ugd 15 0.002082 5.3301 A0A0H3PLP7 UDP-glucose 6-dehydrogenase T F F T rsuA 17 0.002889 5.3262 A0A0H3PKC6 Ribosomal small subunit pseudouridine synthase A T T T F recG 24 0.005600 5.2982 A0A0H3PT60 ATP-dependent DNA helicase RecG T T F F

ybgA 17 0.000985 5.2847 Q8X9D6 T F F F lsoB 17 0.000985 5.2843 Q7DKW4 Antitoxin LsoB F F F F mug 14 0.007116 5.2069 A0A0H3PT14 G/U mismatch-specific DNA glycosylase T T T F lsoA 23 0.005600 5.1909 O82881 mRNA endoribonuclease LsoA F F F F 237

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS rluC 20 0.009860 5.1752 A0A0F6F4L9 Ribosomal large subunit pseudouridine synthase C T T T F iscR 11 0.007875 5.1471 A0A0H3PW81 HTH-type transcriptional regulator IscR T T F F

ECs1574 19 0.003775 5.1390 A0A0H3JFP3 F F F F ECs0274 10 0.018148 5.1277 Q8X7M5 F F F T miaA 16 0.006744 5.1104 A0A0H3PV31 tRNA dimethylallyltransferase T T F F dgt 21 0.002804 5.0770 A0A0H3PT01 Deoxyguanosinetriphosphate triphosphohydrolase T T F F sfsA 14 0.001151 5.0386 A0A0H3PKE6 Sugar fermentation stimulation protein A T T F F hfq 11 0.001866 4.9812 A0A0H3PUX0 RNA-binding protein Hfq T T T F A0A0H3PWX rpsK 12 0.000985 4.9757 2 30S ribosomal protein S11 T T T F nfo 16 0.007625 4.9513 A0A0H3PL11 Endonuclease 4;Probable endonuclease 4 T T T F

uvrD 37 0.003775 4.9505 Q8X8P5 T T T F lrp 17 0.006755 4.9345 A0A0H3PKK3 Leucine-responsive regulatory protein T T F F yajQ 15 0.007311 4.9198 A0A0H3PVK1 UPF0234 protein YajQ T T T F ybaB 7 0.002303 4.9072 A0A0H3PQU5 Nucleoid-associated protein YbaB T T F F infC 19 0.005600 4.9054 P0A709 Translation initiation factor IF-3 T T T F rpsH 12 0.001896 4.9034 A0A0H3PV72 30S ribosomal protein S8 T T T F

yciF 15 0.005037 4.8961 Q7DBI4 T T F T ECs1072 9 0.005275 4.8924 A0A2Z6FL91 F F F F rluE 14 0.002994 4.8850 A0A0H3PS88 Ribosomal large subunit pseudouridine synthase E T T F F yebC 14 0.002082 4.8680 A0A0H3PM39 Probable transcriptional regulatory protein YebC T T T F rplV 15 0.001224 4.8134 A0A0H3PUC6 50S ribosomal protein L22 T T T F hpf 8 0.031035 4.7695 A0A0H3PSH5 Ribosome hibernation promoting factor T T F F mutM 10 0.006662 4.7495 A0A0H3PVF5 Formamidopyrimidine-DNA glycosylase T T F F pcnB 38 0.009479 4.7409 A0A0F6F0X5 Poly(A) polymerase I T T T F rpmH 3 0.010587 4.7020 A0A0H3PR50 50S ribosomal protein L34 T T F F cra 16 0.006987 4.6996 A0A0H3PMJ8 Catabolite repressor/activator T T T F 238

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS rbfA 14 0.001612 4.6814 A0A0H3PW41 Ribosome-binding factor A T T T F

ECs1196 9 0.004305 4.6485 Q8XEH5 F F F T mutY 9 0.002773 4.6343 Q8XCS8 T F F F ECs1664 12 0.034897 4.6060 A0A0H3PS95 F F F F modE 8 0.002278 4.5883 A0A0H3PPY3 Transcriptional regulator ModE T T F F hchA 12 0.001224 4.5725 A0A0H3PMZ8 Molecular chaperone Hsp31 and glyoxalase 3 T T T F rplN 16 0.001199 4.5685 A0A0H3PXU7 50S ribosomal protein L14 T T T F dam 17 0.002092 4.5569 A0A0H3PX42 DNA adenine methylase T T T F nfi 13 0.007808 4.5255 A0A0H3PQD0 Endonuclease V T T F F lysU 33 0.002109 4.5252 A0A0H3PVV8 Lysine--tRNA ligase, heat inducible T T T T astE 14 0.006798 4.5057 A0A0H3PMS5 Succinylglutamate desuccinylase T F F F

ECs0285 17 0.008569 4.4954 Q8X7L0 F F F F rng 39 0.001612 4.4808 A0A0H3Q177 Ribonuclease G T T T F rsmH 15 0.011441 4.4761 A0A0H3PMD6 Ribosomal RNA small subunit methyltransferase H T T T F rpsA 108 0.001612 4.4709 A0A0H3PNM8 30S ribosomal protein S1 T T T F

ECs4465 18 0.007001 4.4323 Q8XDJ0 F F F F ihfB 8 0.002239 4.4256 A0A0H3PSL2 Integration host factor subunit beta T T T F

kdgR 17 0.007380 4.4069 Q8XCN4 T T F F rpsE 20 0.001232 4.3745 A0A0H3PV53 30S ribosomal protein S5 T T T F

ybjD 19 0.005600 4.3517 Q8X6K5 T T F F yheO 11 0.040551 4.3464 A0A0H3PU83 T T F F ybaK 12 0.023996 4.3383 Q8XD27 T T F F selA 37 0.002278 4.3066 A0A0H3PVK5 L-seryl-tRNA(Sec) selenium transferase T T T F tyrS 41 0.001708 4.2851 A0A0H3PI15 Tyrosine--tRNA ligase T T T F

ECs3001 9 0.001199 4.2693 Q7DBB9 F F F F rplM 22 0.002671 4.2625 A0A0H3PSI3 50S ribosomal protein L13 T T T F 239

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS yeaO 9 0.002092 4.2606 A0A0H3PQQ1 T T F F rpsC 34 0.000985 4.2454 A0A0F6FD23 30S ribosomal protein S3 T T T F aroK 13 0.011822 4.2229 A0A0H3PUC3 Shikimate kinase 1 T T T F

hflX 46 0.009585 4.2226 Q8XDN1 T T T F gcvH 5 0.005652 4.2054 A0A0H3PSS1 Glycine cleavage system H protein T F F F argR 8 0.003009 4.2046 A0A0H3PSU0 Arginine repressor T T F T murI 12 0.002082 4.1863 A0A0H3PQ50 Glutamate racemase T T T F queA 13 0.021952 4.1722 A0A0H3PZF8 S-adenosylmethionine:tRNA ribosyltransferase-isomerase T T F F

ECs0604 7 0.005212 4.1710 A0A0H3JDX7 F F F F yebE 8 0.009585 4.1585 Q8XCK2 T T F F cspA 7 0.005169 4.1523 A0A0H3PVF3 Cold shock protein CspA T T T T

ECs1654 13 0.036476 4.1465 A0A0H3PRX6 F F F T yciE 12 0.013144 4.1380 Q7DBI3 T F F F stpA 14 0.006662 4.1300 A0A0H3PMN7 DNA-binding protein StpA T T F T

btuE 10 0.003819 4.1009 Q8X5V9 T T T F yajC 4 0.006798 4.0933 A0A0H3PVH8 UPF0092 membrane protein YajC T T F F rcsB 17 0.003447 4.0782 J9JEG3 Transcriptional regulatory protein RcsB T T F F matP 6 0.006798 4.0536 A0A0H3PSK5 Macrodomain Ter protein T T F F ispH 6 0.000985 4.0212 A0A0H3PYS8 4-hydroxy-3-methylbut-2-enyl diphosphate reductase T T F F A0A0H3PWD rsmI 15 0.047613 3.9807 1 Ribosomal RNA small subunit methyltransferase I T T F F rplK 14 0.001612 3.9798 A0A0H3PQD6 50S ribosomal protein L11 T T T F A0A0H3PWB iscU 10 0.046490 3.9596 8 Iron-sulfur cluster assembly scaffold protein IscU T T T F trxA 8 0.018542 3.9251 A0A0H3PQJ3 Thioredoxin-1 T T T T tusE 5 0.001417 3.9085 A0A0H3PX19 Sulfurtransferase TusE T T F F

recB 25 0.004094 3.8979 Q8X6M9 T F F F yggX 8 0.004408 3.8933 A0A0H3PS66 Probable Fe(2+)-trafficking protein T T F F 240

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS nth 6 0.000985 3.8850 Q8X652 Endonuclease III T T F T minE 9 0.002205 3.8604 A0A0H3PMK6 Cell division topological specificity factor T T F F grxC 6 0.001224 3.8584 A0A0H3PU47 Glutaredoxin-3 T T F F cobB 6 0.006987 3.8533 A0A0H3PH72 NAD-dependent protein deacylase T T F F rsmF 21 0.033100 3.8226 A0A0H3PMF3 Ribosomal RNA small subunit methyltransferase F T T F F gmd 14 0.028224 3.8171 A0A0H3PLK8 GDP-mannose 4,6-dehydratase T F F F

wcaG 12 0.007423 3.8103 Q8X4R4 T F F F rimM 11 0.012475 3.7882 P0A7X8 Ribosome maturation factor RimM T T F F nsrR 6 0.002082 3.7757 A0A0H3PZ43 HTH-type transcriptional repressor NsrR T T F F

ydaL 13 0.008610 3.7727 Q8X8Q2 T F F F rplO 14 0.003390 3.7592 A0A0H3PXL5 50S ribosomal protein L15 T T T F rluD 12 0.004100 3.7404 A0A0H3PJN0 Ribosomal large subunit pseudouridine synthase D T T T F uvrY 15 0.009080 3.7389 A0A0H3PGP2 Response regulator UvrY T T F T

ECs1353 15 0.002248 3.7025 Q52355 F F F F treR 12 0.005283 3.6997 A0A0H3PUQ2 T T F F wcaK 13 0.011604 3.6637 Q8X7P4 T F F F intC 13 0.024963 3.6478 A0A0H3JH97 T F F T tus 11 0.001664 3.6375 A0A0H3PI02 DNA replication terminus site-binding protein T T F T

ybiB 20 0.019054 3.6302 Q8X7X6 T T F F glyQ 15 0.015661 3.6294 A0A0H3PUG7 Glycine--tRNA ligase alpha subunit T T T F

ECs1862 10 0.004912 3.6059 Q8X7E0 F F F F rpsN 10 0.003869 3.6032 A0A0H3PUM5 30S ribosomal protein S14 T T T F

ybcJ 7 0.011084 3.5868 A0A0H3PSQ4 T T F F recC 22 0.005248 3.5806 Q8X6M7 T F F F ECs5307 14 0.002040 3.5791 Q8XB70 F F F F proB 8 0.018360 3.5740 A0A0H3PJ57 Glutamate 5-kinase T T F F 241

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS thiI 47 0.002671 3.5338 A0A0H3PR86 tRNA sulfurtransferase T T T F

yffB 6 0.002278 3.5259 A0A0H3PN10 T T F F rluB 28 0.029901 3.5197 A0A0H3PLB7 Ribosomal large subunit pseudouridine synthase B T T T F

holA 8 0.002130 3.5189 Q8X523 T T F F rpsJ 13 0.002239 3.5139 A0A0H3Q1T5 30S ribosomal protein S10 T T T F

ECs1089 5 0.009485 3.4972 Q7DBL5 F F F F ECs1330 6 0.002278 3.4794 A0A0H3PLC3 50S ribosomal protein L31 type B 2 F F F F rpsI 17 0.002479 3.4777 A0A0H3Q183 30S ribosomal protein S9 T T T F allR 8 0.003009 3.4761 A0A0F6F268 HTH-type transcriptional repressor AllR T T T F rpsM 15 0.007380 3.4331 A0A0H3PS78 30S ribosomal protein S13 T T T F

polA 88 0.001224 3.4074 Q8X8H1 T T T F menB 14 0.025592 3.4001 Q8XDY1 1,4-dihydroxy-2-naphthoyl-CoA synthase T T T F ychF 34 0.004013 3.3975 A0A0H3PP42 Ribosome-binding ATPase YchF T T T T proQ 31 0.000985 3.3823 A0A0H3PQK7 RNA chaperone ProQ T T T F

helD 13 0.035045 3.3779 Q8XD92 T T T F yhbY 2 0.005212 3.3684 A0A0H3PW88 RNA-binding protein YhbY T T F F A0A0H3PMW slyA 9 0.008234 3.3672 0 Transcriptional regulator SlyA T T F F rnt 9 0.011014 3.3664 A0A0H3PJ21 Ribonuclease T T T F F entE 25 0.013390 3.3624 A0A0H3PRQ9 Enterobactin synthase component E T F F F yghA 11 0.001896 3.3535 A0A0H3Q090 Uncharacterized oxidoreductase YghA T F F T

malZ 14 0.015327 3.3500 A0A0F6F1U3 T T F T rplT 12 0.005037 3.3438 A0A0H3PL95 50S ribosomal protein L20 T T T F ompR 21 0.002721 3.3427 A0A0H3PV82 Transcriptional regulatory protein OmpR T T T F rplB 45 0.005037 3.3343 A0A0H3Q1K4 50S ribosomal protein L2 T T T F rpmC 9 0.030324 3.3335 A0A0H3PUN0 50S ribosomal protein L29 T T T F cysQ 10 0.011404 3.3222 A0A0H3PZ31 3(2),5-bisphosphate nucleotidase CysQ T T T F 242

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS yceH 5 0.016017 3.3181 A0A0H3PRB7 UPF0502 protein YceH T T T F kdsD 7 0.041966 3.3070 A0A0H3Q0U2 Arabinose 5-phosphate isomerase KdsD T T F F truC 10 0.002670 3.3016 A0A0H3PNC8 tRNA pseudouridine synthase C T T F F rlmB 16 0.017381 3.2835 A0A0H3Q3H1 23S rRNA (guanosine-2-O-)-methyltransferase RlmB T T F F rplQ 11 0.003299 3.2766 A0A0H3PS60 50S ribosomal protein L17 T T T F glnS 68 0.006226 3.2721 A0A0H3PS00 Glutamine--tRNA ligase T T T F

yggL 7 0.024763 3.2690 A0A0H3PT70 T T F F ytfP 4 0.018578 3.2690 A0A0H3PVA5 Gamma-glutamylcyclotransferase family protein YtfP T T F F

yccU 9 0.047297 3.2455 Q8XD87 T T F F A0A0H3PMQ recA 37 0.001151 3.2322 4 Protein RecA T T T F

gpt 4 0.005278 3.2211 A0A0H3PLK3 T T T F ybeL 7 0.003775 3.2189 A0A0H3PRL0 Uncharacterized protein YbeL T T T F rplL 9 0.015666 3.2153 A0A0H3PHH7 50S ribosomal protein L7/L12 T T T F cmk 9 0.002082 3.1921 A0A0H3PSL7 Cytidylate kinase T T T F rnpA 5 0.005278 3.1816 A0A0H3PZ66 Ribonuclease P protein component T T F F fklB 9 0.042587 3.1790 A0A0H3PUN2 Peptidyl-prolyl cis-trans isomerase T T T F yeaK 13 0.016445 3.1656 A0A0H3PQN5 Uncharacterized protein YeaK T T F F ssb 10 0.004100 3.1650 A0A0H3PL38 Single-stranded DNA-binding protein T T T F accD 27 0.012896 3.1512 A0A0F6F9U8 Acetyl-coenzyme A carboxylase carboxyl transferase subunit beta T T T F pth 12 0.005354 3.1483 A0A0H3PNJ9 Peptidyl-tRNA hydrolase T T F F

ECs2766 9 0.040721 3.1347 A0A0H3JGC9 F F F F selU 8 0.002348 3.1322 A0A0H3PZS2 tRNA 2-selenouridine synthase T T T F

ycdY 5 0.006333 3.1238 Q8X9J5 T T F F yaeO 3 0.000702 3.1178 A0A0H3PNB4 T F F F DinI-like protein Z3305/ECs2939 in prophage CP-933V;DinI-like protein ECs3483 5 0.005278 3.1084 A0A0F6F923 Z3916/ECs3483 F F F F

yggJ 11 0.005671 3.0614 A0A0H3PTH1 T F F F 243

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS kdsC 6 0.004099 3.0590 A0A0H3PTA5 3-deoxy-D-manno-octulosonate 8-phosphate phosphatase KdsC T T F F

ECs1762 4 0.016058 3.0551 Q8XCA9 F F F T yfcZ 3 0.002228 3.0543 A0A0H3PXR9 T T T F rppH 7 0.012606 3.0296 A0A0H3PMT0 RNA pyrophosphohydrolase T T F F

ybgK 7 0.005283 3.0064 Q8X9D0 T T F F Dihydrolipoyllysine-residue succinyltransferase component of 2-oxoglutarate sucB 25 0.005037 2.9923 A0A0H3PY09 dehydrogenase complex T T T F ygaC 8 0.004490 2.9869 A0A0H3PMK2 Uncharacterized protein YgaC T F F F

nadR 13 0.005054 2.9789 A0A0H3PZB3 T T F F erpA 5 0.008791 2.9723 A0A0H3PSZ8 Iron-sulfur cluster insertion protein ErpA T T F F

recJ 13 0.006744 2.9593 Q8XD43 T T F F ygiM 11 0.032404 2.9531 A0A0H3PU11 Uncharacterized protein YgiM T T T F dapE 3 0.019414 2.9469 A0A0H3PNQ9 Succinyl-diaminopimelate desuccinylase T T F F

fpr 10 0.016036 2.9456 Q8X7A2 T T F F ECs5262 80 0.005277 2.9452 Q8XC72 F F F T pnp 72 0.001515 2.9309 A0A0H3Q192 Polyribonucleotide nucleotidyltransferase T T T F phnA 5 0.010110 2.9241 A0A0F6FFR6 Protein PhnA T T F F hslR 6 0.016184 2.9173 A0A0H3Q265 Heat shock protein 15 T T F F ruvA 4 0.013558 2.9001 A0A0H3PM71 Holliday junction ATP-dependent DNA helicase RuvA T F F F engB 13 0.008901 2.8824 A0A0H3PRM7 Probable GTP-binding protein EngB T T T F

yjgF 7 0.002092 2.8517 A0A0H3PZK0 T F T T cysB 21 0.002479 2.8490 A0A0F6F5P6 HTH-type transcriptional regulator CysB T T F F ygfZ 22 0.001151 2.8406 A0A0H3PS01 tRNA-modifying protein YgfZ T T T F rhlB 32 0.012325 2.8301 Q8XAT4 ATP-dependent RNA helicase RhlB T T T F lysS 53 0.003978 2.8245 A0A0H3Q0D8 Lysine--tRNA ligase T T T F rpsD 29 0.004234 2.8163 A0A0H3Q0X2 30S ribosomal protein S4 T T T F rpoE 8 0.007141 2.8116 A0A0H3PKK7 ECF RNA polymerase sigma-E factor T T F T 244

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS fruK 9 0.004391 2.8072 A0A0H3PKF4 1-phosphofructokinase T T F F

ECs1561 65 0.005192 2.8030 A0A0H3PPJ9 F F F T accB 2 0.024686 2.7930 A0A0H3PSF5 Biotin carboxyl carrier protein of acetyl-CoA carboxylase T T F F

ECs1662 12 0.006744 2.7872 Q8XDP4 F F F T ECs4384 12 0.002929 2.7772 Q8X5N5 F F F T aidB 12 0.015666 2.7480 A0A0H3PVG0 T F F F rpsT 11 0.030994 2.7447 A0A0H3Q3B8 30S ribosomal protein S20 T T T F

rpoN 19 0.038256 2.7247 Q8X9I9 T T T F cbpA 13 0.016843 2.7095 A0A0H3PSP0 Curved DNA-binding protein T T F T rpsO 5 0.038380 2.6825 A0A0H3PSV6 30S ribosomal protein S15 T T T F hemC 8 0.019970 2.6804 A0A0H3PYP8 Porphobilinogen deaminase T T F F sucC 32 0.001612 2.6775 A0A0H3PR92 Succinyl-CoA ligase [ADP-forming] subunit beta T T T F ycgL 8 0.005712 2.6706 A0A0H3PSA9 Protein YcgL T T F F sopB 21 0.002364 2.6608 A0A0H3PHR7 Protein SopB F F F F galM 14 0.015406 2.6408 A0A0H3PTZ8 Aldose 1-epimerase T T F F mukE 7 0.017994 2.6399 A0A0H3PNL4 Chromosome partition protein MukE T T F F

cpsG 28 0.028007 2.6261 Q8X4R5 T F F F pgl 20 0.003128 2.6056 A0A0H3PY88 6-phosphogluconolactonase T T T F tilS 9 0.009166 2.5976 A0A0H3PSZ3 tRNA(Ile)-lysidine synthase T T T F grxD 7 0.003479 2.5789 A0A0H3PG25 Glutaredoxin-4 T T T F uvrB 48 0.000985 2.5788 A0A0H3PQF9 UvrABC system protein B T T T F rpsB 33 0.002092 2.5739 A0A0H3PK71 30S ribosomal protein S2 T T T F ibpA 7 0.024405 2.5679 A0A0H3PXD1 Small heat shock protein IbpA T T F F yfcN 5 0.003560 2.5641 A0A0H3PPB9 UPF0115 protein YfcN T T F F Multifunctional CCA protein;CCA-adding enzyme;2-nucleotidase;2,3-cyclic cca 9 0.013979 2.5621 A0A0H3PST0 phosphodiesterase;Phosphatase T T T F rsgA 16 0.003515 2.5596 A0A0H3PWT3 Putative ribosome biogenesis GTPase RsgA T T T F 245

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS leuS 78 0.001873 2.5428 A0A0H3PRT7 Leucine--tRNA ligase T T T F

seqA 11 0.010189 2.5387 Q8X9G7 T T F F espY2 13 0.002092 2.5144 Q8XA03 F F F T ECs4379 30 0.002889 2.5089 Q8X5N8 F F F F ECs1587 7 0.021742 2.4998 Q8X757 Single-stranded DNA-binding protein F F F F rdgC 19 0.038432 2.4635 A0A0H3PVD7 Recombination-associated protein RdgC T T F F rimJ 5 0.008235 2.4516 A0A0H3PLG3 Ribosomal-protein-alanine acetyltransferase T T F F gcvT 11 0.001151 2.4465 A0A0H3PSB4 Aminomethyltransferase T T T T

ECs1653 8 0.018052 2.4459 Q8X589 F F F T osmC 13 0.001374 2.4438 Q8XAT8 T T T F ygdI 2 0.005054 2.4381 A0A0H3PMP9 T F F F argS 51 0.005192 2.4342 A0A0H3PH89 Arginine--tRNA ligase T T T F

yjaG 8 0.010549 2.4306 Q8X6X4 T T F F crp 21 0.002509 2.4166 A0A0H3PTU4 cAMP-activated global transcriptional regulator CRP T T T F ftsE 5 0.005671 2.4081 A0A0H3PT85 Cell division ATP-binding protein FtsE T T T F

bolA 5 0.017169 2.4021 A0A0H3PRE3 T T F F recQ 10 0.019118 2.3731 Q8X8N1 T F F F hupA 8 0.024405 2.3706 A0A0H3PFQ7 DNA-binding protein HU-alpha T T F F katG 64 0.007629 2.3587 A0A0H3PR37 Catalase-peroxidase 1 T T T F frr 13 0.001224 2.3542 A0A0H3PJM5 Ribosome-recycling factor T T T F

yhiN 5 0.031511 2.3409 Q8X5R3 T T F T fdhE 14 0.004094 2.3142 A0A0H3PRQ2 Protein FdhE T T F F fldA 7 0.031511 2.3026 A0A0F6F2N1 Flavodoxin-1 T T T F gloA 3 0.019054 2.3010 A0A0H3PSK4 Lactoylglutathione lyase T T F T rpmA 10 0.003232 2.3006 A0A0H3PSC2 50S ribosomal protein L27 T T T F

nfnB 10 0.048135 2.2972 Q8XBX3 T F F F 246

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS dapF 5 0.007311 2.2817 A0A0H3PZ08 Diaminopimelate epimerase T T F F yhhW 4 0.024405 2.2800 A0A0H3PTK7 Quercetin 2,3-dioxygenase T T F T serB 8 0.007808 2.2668 A0A0H3PVY2 Phosphoserine phosphatase T T F F Multiphosphoryl transfer protein;Phosphocarrier protein HPr;Fructose-specific fruB 19 0.009999 2.2623 A0A0H3PP04 phosphotransferase enzyme IIA component T T F F

acrR 4 0.002081 2.2575 Q8X5D3 T F F F uup 16 0.026062 2.2570 A0A0H3PPN4 T T F F rsd 4 0.041071 2.2547 A0A0H3PFP8 Regulator of sigma D T T F F folX 3 0.005278 2.2485 A0A0H3PTD7 D-erythro-7,8-dihydroneopterin triphosphate epimerase T T F F

ybdZ 2 0.013282 2.2469 Q8X498 T F F F mntR 5 0.001374 2.2452 A0A0H3PPH3 Transcriptional regulator MntR T T F F gadX 7 0.002889 2.2339 A0A0H3PTU0 HTH-type transcriptional regulator GadX T F F F rplJ 18 0.003501 2.2283 A0A0H3PGI7 50S ribosomal protein L10 T T T F

murF 8 0.029680 2.2277 Q8X9Z1 T T F F ECs1163 6 0.030760 2.2263 A0A0H3JER3 F F F T ogt 7 0.020505 2.2229 Q8X8N5 T F F F ECs4998 6 0.002585 2.2214 A0A0H3JIG9 F F F F greB 9 0.019119 2.2160 A0A0H3PX31 Transcription elongation factor GreB T T F F cysS 34 0.011658 2.2096 A0A0H3PS27 Cysteine--tRNA ligase T T T F hns 18 0.003560 2.1941 A0A0H3PNM3 DNA-binding protein;DNA-binding protein H-NS T T T T A0A0H3PQW astB 13 0.037313 2.1938 4 N-succinylarginine dihydrolase T F F F

yciO 11 0.004224 2.1786 Q8X7C0 T T T T prs 23 0.011730 2.1739 A0A0H3PNS3 Ribose-phosphate pyrophosphokinase T T T F hscB 3 0.012745 2.1427 A0A0H3PNT3 Co-chaperone protein HscB T T F F

fcI 18 0.011395 2.1329 Q7DBF6 T F F T purC 19 0.011658 2.1212 A0A0H3PW87 Phosphoribosylaminoimidazole-succinocarboxamide synthase T T T F sbcB 20 0.005450 2.1173 A0A0H3PL52 Exodeoxyribonuclease I T T F F 247

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS rsmC 24 0.001151 2.1102 A0A0H3PVJ7 Ribosomal RNA small subunit methyltransferase C T T T F mprA 8 0.021473 2.0959 A0A0H3PLX4 Transcriptional repressor MprA T T T F

ygiF 18 0.004113 2.0927 Q8XBM2 T T T F pssA 28 0.001224 2.0868 Q8X9F5 T T T F rsmG 8 0.008780 2.0804 A0A0H3PYR0 Ribosomal RNA small subunit methyltransferase G T T F F

otsB 6 0.007001 2.0742 Q8XCE6 T F F F yhhF 9 0.007808 2.0695 Q8X6R0 T F F F ihfA 15 0.007263 2.0673 A0A0H3PM29 Integration host factor subunit alpha T T T F rnb 70 0.003545 2.0631 A0A0H3PUU1 Exoribonuclease 2 T T T F hisS 38 0.004912 2.0581 A0A0H3PM42 Histidine--tRNA ligase T T T F recD 6 0.019119 2.0471 Q8X6N0 RecBCD enzyme subunit RecD T F F F yqgF 6 0.026608 2.0393 A0A0H3PWZ6 Putative Holliday junction resolvase T T F F rpoB 184 0.001224 2.0369 A0A0H3PHI1 DNA-directed RNA polymerase subunit beta T T T F tsaE 4 0.009449 2.0364 A0A0H3Q2Z2 tRNA threonylcarbamoyladenosine biosynthesis protein TsaE T T F F der 41 0.016625 2.0354 A0A0H3PN58 GTPase Der T T T F trmH 5 0.014679 2.0341 A0A0H3PUX9 tRNA (guanosine(18)-2-O)-methyltransferase T T F F purD 19 0.006427 2.0327 A0A0H3PHH0 Phosphoribosylamine--glycine ligase T T T F luxS 15 0.001224 2.0090 A0A0H3PNB5 S-ribosylhomocysteine lyase T T F F

dppF 7 0.010984 2.0031 Q8X4K8 T T F F yfbU 10 0.039839 2.0008 A0A0H3PXS2 UPF0304 protein YfbU T T F F

yjjV 4 0.032972 1.9916 A0A0H3PZA8 T F F F tufA 53 0.000985 1.9903 A0A0H3PXA6 Elongation factor Tu T T T F ypdB 7 0.001885 1.9169 A0A0H3PP77 Transcriptional regulatory protein YpdB T T F F

ptsN 6 0.016184 1.9103 Q8X9I8 T T F F birA 6 0.026062 1.9071 Q8X709 T T F F selB 29 0.003627 1.9002 Q8XDI8 T T T F 248

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS lepA 42 0.000985 1.8761 A0A0F6FAT9 Elongation factor 4 T T T F rluF 4 0.033458 1.8708 A0A0H3PYV5 Ribosomal large subunit pseudouridine synthase F T T F F rfaH 4 0.029819 1.8703 A0A0H3PYP3 Transcription antitermination protein RfaH T F F F A0A0H3PWE yjbR 3 0.001374 1.8569 7 Uncharacterized protein YjbR T T F F trmD 4 0.047475 1.8307 A0A0H3PIU2 tRNA (guanine-N(1)-)-methyltransferase T T T F rpmD 5 0.042912 1.8284 A0A0H3PUF0 50S ribosomal protein L30 T T F F dcm 25 0.003976 1.8268 A0A0H3PIE5 DNA-cytosine methyltransferase T T F F A0A0H3PVW pyrI 6 0.002642 1.8259 3 Aspartate carbamoyltransferase regulatory chain T T F F rho 49 0.007556 1.8221 A0A0H3PR08 Transcription termination factor Rho T T T F pyrH 12 0.019119 1.8078 A0A0H3PJG6 Uridylate kinase T T F F tig 69 0.001151 1.8008 A0A0H3PZT8 Trigger factor T T T F fnr 13 0.008908 1.7917 P0A9E7 Fumarate and nitrate reduction regulatory protein T F F F prfB 28 0.002628 1.7857 A0A0F6FBT7 Peptide chain release factor 2 T T T F ghrB 8 0.005169 1.7856 A0A0H3PXG0 Glyoxylate/hydroxypyruvate reductase B T T F F gcvA 8 0.015661 1.7845 A0A0H3PVR4 Glycine cleavage system transcriptional activator T T F F yifE 17 0.005336 1.7832 A0A0H3PQL4 UPF0438 protein YifE T T T F glyS 64 0.007398 1.7611 A0A0H3PTR5 Glycine--tRNA ligase beta subunit T T T T ptsH 7 0.002290 1.7600 A0A0H3PN19 Phosphocarrier protein HPr T T T F

topA 114 0.000985 1.7596 Q8X7C5 T T T F mobA (pOSAK1) 4 0.045508 1.7413 A0A0T7C0W0 F F F F astA 21 0.011051 1.7406 A0A0H3PLA6 Arginine N-succinyltransferase T F F F dksA 6 0.003447 1.7324 A0A0H3PKD3 RNA polymerase-binding transcription factor DksA T T T F rpoC 168 0.003128 1.7284 A0A0H3PHG3 DNA-directed RNA polymerase subunit beta T T T T rsmB 21 0.041904 1.7138 A0A0H3PT94 Ribosomal RNA small subunit methyltransferase B T T T F tnaA 8 0.048522 1.7100 A0A0H3PQX1 Tryptophanase T F T T

249

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS ppiC 4 0.013011 1.6915 A0A0H3PYM9 Peptidyl-prolyl cis-trans isomerase C T T T F pepA 39 0.003680 1.6758 A0A0H3PZI5 Cytosol aminopeptidase;Probable cytosol aminopeptidase T T F F

ygdL 10 0.014873 1.6729 Q8X6P5 T F F T folC 12 0.012173 1.6714 Q8XCR3 T T F F valS 96 0.002325 1.6695 A0A0H3PYQ9 Valine--tRNA ligase T T T F

yijO 6 0.005899 1.6624 Q8X746 T F F F A0A0H3PVW fimB 8 0.005212 1.6619 0 Type 1 fimbriae regulatory protein FimB T F F T

fes 16 0.008654 1.6584 A0A0H3PRH5 T F F F ybeD 4 0.027617 1.6517 A0A0H3PS70 UPF0250 protein YbeD T T F F metG 66 0.002732 1.6385 A0A0H3PJX0 Methionine--tRNA ligase T T T F narX 5 0.029901 1.6345 A0A0H3PSB6 Nitrate/nitrite sensor protein NarX T F F F rplY 11 0.034084 1.6263 A0A0H3PTN6 50S ribosomal protein L25 T T T F murE 17 0.002082 1.6112 A0A0H3PMF6 UDP-N-acetylmuramoyl-L-alanyl-D-glutamate--2,6-diaminopimelate ligase T T F T rpsS 10 0.002364 1.6066 A0A0H3PUG9 30S ribosomal protein S19 T T F F rlmF 4 0.007808 1.6046 A0A0H3PPR3 Ribosomal RNA large subunit methyltransferase F T T T F

hrpA 65 0.011384 1.6011 A0A0H3PMH2 T T T T yecA 7 0.011604 1.5787 Q8XCD3 T T F F pheT 57 0.001612 1.5631 A0A0H3PMP3 Phenylalanine--tRNA ligase beta subunit T T T F greA 12 0.004973 1.5554 A0A0H3PSE2 Transcription elongation factor GreA T T T F nadK 4 0.041071 1.5115 A0A0H3PS91 NAD kinase T F F F

yciK 15 0.015827 1.5111 Q8X7C3 T T F F rplS 13 0.019304 1.4989 A0A0H3PIP9 50S ribosomal protein L19 T T T F rpsP 6 0.029680 1.4952 A0A0H3PJJ0 30S ribosomal protein S16 T T T F

ECs4383 15 0.016625 1.4941 Q8X5N6 F F F F gltX 38 0.002607 1.4907 A0A0H3PPZ3 Glutamate--tRNA ligase T T T T ygaF 8 0.008901 1.4823 A0A0H3PHR6 L-2-hydroxyglutarate oxidase LhgO T F F F 250

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS efp 10 0.049925 1.4694 A0A0H3PVX1 Elongation factor P T T T F

yrdD 3 0.008741 1.4578 A0A0H3PTU1 T F F F dnaE 37 0.007666 1.4576 A0A0H3PNC5 DNA polymerase III subunit alpha T F F F yeeN 9 0.040721 1.4562 A0A0H3PN15 Probable transcriptional regulatory protein YeeN T T F F

gabD 27 0.004548 1.4555 Q8X950 T T T F rpsL 10 0.030063 1.4424 A0A0H3PXX8 30S ribosomal protein S12 T T T F

rfaF 5 0.010587 1.4267 Q8XDD5 T T F F thiJ 5 0.038045 1.4254 A0A0H3PS98 T F F F nemA 7 0.030063 1.4209 Q8X630 T T F F codA 16 0.024686 1.4145 Q8X690 T T F T rlmI 22 0.010259 1.4053 A0A0H3PP55 Ribosomal RNA large subunit methyltransferase I T T T F purE 7 0.023221 1.3964 A0A0H3PZZ8 N5-carboxyaminoimidazole ribonucleotide mutase T T F F narL 14 0.008569 1.3773 A0A0H3PP93 Nitrate/nitrite response regulator protein NarL T T F F dps 26 0.005278 1.3746 A0A0H3PY13 DNA protection during starvation protein T T T T

asnB 56 0.023341 1.3738 Q8XBJ9 T T T T manB 28 0.010439 1.3678 A0A0H3PLS3 Phosphomannomutase T F F T

ECs1955 62 0.013885 1.3600 Q8X8S8 F F F T secB 8 0.040551 1.3376 A0A0H3PUT8 Protein-export protein SecB T T F F proS 50 0.005212 1.3306 A0A0H3PN87 Proline--tRNA ligase T T T F alaS 86 0.003775 1.3130 A0A0F6FB60 Alanine--tRNA ligase T T T F

waaD 7 0.015827 1.3109 Q8XDC3 T F T F srmB 26 0.042530 1.2909 A0A0H3PQB6 ATP-dependent RNA helicase SrmB T T T F ileS 85 0.001515 1.2884 A0A0H3PZA1 Isoleucine--tRNA ligase T T T F rplF 15 0.028629 1.2823 A0A0H3PTB1 50S ribosomal protein L6 T T T F Ribosomal RNA large subunit methyltransferase K/L;23S rRNA m2G2445 rlmL 45 0.003798 1.2817 A0A0H3PXB1 methyltransferase;23S rRNA m7G2069 methyltransferase T T F F pcm 11 0.004159 1.2711 A0A0H3PMS4 Protein-L-isoaspartate O-methyltransferase T T F F 251

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS astC 31 0.040721 1.2622 A0A0H3PLX7 Succinylornithine transaminase T F F F pckA 30 0.012969 1.2454 A0A0H3Q2T3 Phosphoenolpyruvate carboxykinase [ATP] T T T F rplE 31 0.015219 1.2396 A0A0F6FD17 50S ribosomal protein L5 T T T F aroC 10 0.027903 1.2163 A0A0H3PXM3 Chorismate synthase T T F F yejK 22 0.018079 1.2136 A0A0F6F9G4 Nucleoid-associated protein YejK T T F F hflC 26 0.019049 1.2060 A0A0H3PYF7 Modulator of FtsH protease HflC T T T F sucA 43 0.004094 1.2022 A0A0H3PXZ4 2-oxoglutarate dehydrogenase E1 component T T T T

icd 46 0.001224 1.1999 Q8X722 T T T F ftsY 27 0.013594 1.1764 Q8X6R2 T T T F rpoS 34 0.012539 1.1759 Q7ABA5 RNA polymerase sigma factor RpoS;RNA polymerase sigma factor T F T F

ECs5259 27 0.012173 1.1757 Q8XC76 F F F T dnaN 9 0.023466 1.1686 A0A0F6FEG2 DNA polymerase III subunit beta T T F F hemN 11 0.024686 1.1615 A0A0H3PQG3 Coproporphyrinogen-III oxidase T T F F katE 64 0.009118 1.1550 A0A0H3PVK6 Catalase T T T F sodB 8 0.029302 1.1533 A0A0H3PPX5 Superoxide dismutase [Fe] T T T F glnK 16 0.022581 1.1502 A0A0H3PUZ4 Nitrogen regulatory protein P-II 2 T T F F

yiiS 4 0.048519 1.1231 Q8X7A3 T F F T mutS 37 0.027884 1.0966 A0A0H3PN69 DNA mismatch repair protein MutS T T T T rpsF 18 0.009449 1.0942 A0A0H3Q3X0 30S ribosomal protein S6 T T T F map 19 0.006662 1.0934 A0A0H3PIL2 Methionine aminopeptidase T T F F fusA 77 0.008501 1.0930 A0A0H3PU63 Elongation factor G T T T F dnaA 25 0.036570 1.0929 A0A0H3PRY9 Chromosomal replication initiator protein DnaA T T T F ligA 16 0.039835 1.0820 A0A0F6FAC8 DNA ligase T T T F ffh 34 0.005450 1.0795 A0A0H3PJM6 Signal recognition particle protein T T T F kbl 16 0.035940 1.0700 A0A0H3PUS4 2-amino-3-ketobutyrate coenzyme A ligase T F T F

yahK 23 0.024729 1.0555 Q8X6A3 T T F F 252

Appendix

Gene Log2F K K-12 OOPS (3 names Pep p-adj C UniProt Acc Protein names 12 TRAPP reps) TraDIS ppiB 10 0.003730 1.0370 Q8XCU0 T T F F arcA 24 0.003009 1.0262 A0A0H3PVC2 Aerobic respiration control protein ArcA T T T F ahpC 23 0.005054 1.0143 A0A0H3PS97 Alkyl hydroperoxide reductase subunit C T T T T fur 10 0.026272 1.0011 A0A0H3PSJ7 Ferric uptake regulation protein T T T F

253

Appendix

A. B. BINGO UV- Panther UV- 2.2 10 proteolysis biological process ) 2.0 glutamine transport ) 8

value 1.8 value 6 organonitrogen compound metabolic process - - p p ( ( 1.6 mannose transport 4 10 10 macromolecule localization carbohydrate import across plasma membrane proteolysis log log - 1.4 Gram-negative-bacterium-type cell outer membrane assembly - 2 lipid transport cellular response to bacteriocin 1.2 0 0 5 10 15 20 25 0 2 4 6 8 fold-change fold-change

Supplementary Figure S3: GO term analysis on twofold enriched proteins recovered by TRAPP in non-crosslinked samples. Enrichment of GO terms associated with the 238 proteins from non-crosslinked samples were analysed using BINGO (A) or Panther (B). Fold-change enrichment of GO terms were plotted against -log10 transformed p-value.

254

Appendix

Supplementary Table S5: List of RNA-binding domains used as search terms for APRICOT

RRM La XRN_N RGG PUF THUMP DEAD PUA RNB zf-CCCH ZnFC2HC RrnaAD KH SWAP Tap-RNA_bind GTP_EFTU RAP tRNA-synt_1b GTP_EFTU_D2 pumilio APOBEC_N GTP_EFTU_D3 Ribosomal Surp dsrm MMR_HSR1 PAP_assoc zf-CCHC Brix PAZ LSM WD40 Piwi OB_NTP_bind Nop zf-C2H2 HA2 YTH zf-C3HC4 G-patch zf-CCHC Alba IBN_N LSM FtsJ SAP PurA Pept_tRNA_hydro TUDOR RNase_PH PseudoU_synth_1 RnaseA RNase_PH_C PseudoU_synth_2 zf-C2H2_jaz S4 RNA_bind MMR_HSR1 GTP_EFTU_D2 RNase_P_pop3 KOW GTP_EFTU RTC RNase_T Nol1_Nop2_Fmu RTC_insert MIF4G R3H SAM zf-RanBP RNase_T SpoU_sub_bind NTF2 MIF4G SpoU_sub_bind PAZ Btz SRP14 RBM1CTR Helicase_RecD SRP72 PAM2 RNase_P_p30 TRM Xpo1 SURF6 tRNA_anti S1 UPF1_Zn_bind tRNA_m1G_MT HGTP_anticodon SAP tRNA_U5-meth_tr tRNA-synt_2b eRF1_3 tRNA-synt_2 Piwi Fibrillarin TROVE CSD Gar1 TrpBP HABP4_PAI- Ribosomal_L7Ae RBP1 TruB_N RNase_Zc3h12a S10_plectin zf-RNPHF Anticodon_1 TruD Helicase_C R3H

255

Appendix

2 Anti-6X-His 2 0604 1163 1184 1762 Seeblue Plus EspY ECs ECs ECs ECs sfGFP

28 19 14

Supplementary Figure S4: Western blot confirming expression of candidate RBPs from pBAD24. Anti-6X-His tag antibody was used to detect HTF-tagged candidate RBPs. Lanes corresponding to each protein are noted above.

256