CHARACTERIZATION of STRINGENT RESPONSE REGULATORS (P)Ppgpp, and Dksa in Xanthomonas Citri Subsp

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CHARACTERIZATION OF STRINGENT RESPONSE REGULATORS (p)ppGpp, AND DksA IN Xanthomonas citri subsp. citri

YANAN ZHANG

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2019

To my family

ACKNOWLEDGMENTS

First, I would like to express my thanks to my supervisor Dr. Nian Wang, a

Professor in Microbiology and Cell Science Department of University of Florida. Four years ago, he accepted me as a PhD student in his lab and opened the gate for me to science research. His diligence and enthusiasm for science is very impressive. He always encourages me to be familiar with the literature, tackle challenging questions, think deeper, try new methods and learn how to grow into an independent scientist.

Besides, I really appreciate that he gives me so much freedom so that I can carry out my research more independently following my interest. In addition, I want to give my thanks to my committee members: Drs. Tony Romeo, Julie Maupin-Furlow, Jeffrey

Jones, and Frank White. Their guidance, suggestions, scientific thought as well as encouraging words help me to be a better researcher.

My thanks also go to the former and current lab members in Dr. Nian Wang’s lab for creating an accommodating and collaborative research environment and offering so much support in many aspects. I would like to list all the lab mates who help me with my research as follows: Xiaofeng Zhou, Nadia Riera Faraone, Maxuel Andrade, Yunzeng

Zhang, Shuming Wang, Jin Xu, Doron Teper, Sheo Shankar Pandey, Donghuan Lee,

Shuo Duan, Camila Ribeiro, Yuanchun Wang, Yixiao Huang, Erica Carter, Jinyun Li,

Hongge Jia, Fernanda Nogales da Costa Vasconcelos, Xiaoen Huang, Tirtha

Lamichhane, Ali Parsaeimehr, Zhiqian Pang, Hang Su, and Wenxiu Ma. I cannot say enough thanks to Dr. Doron Teper who taught me how to do some basic experiments one on one when I entered to the lab. I am so glad that he became one of my best friends now. Dr. Monika Oli from Microbiology and Cell Science Department also gave me much confidence to overcome my language barrier.

Most importantly, I owe a lot to my family. All of this would be impossible without their unconditional love. I want to say thanks to my father, my mother, my sister, my brother in law and my lovely nephew. At last, I want to say that all my effort is worthwhile and the last four years are memorable! I believe that I have built up some skills required for research and enjoy the pleasure that research brings to me although it does not always go smoothly.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 13

CHAPTER

1 LITERATURE REVIEW ...... 15

Citrus Canker ...... 15 Secretion Systems of Xanthomonas ...... 17 Type 2 Secretion System ...... 17 Type 3 Secretion System ...... 18 Type 4 Secretion System ...... 19 Other Virulence Traits of Xanthomonas ...... 20 Extracellular Polysaccharides ...... 20 Lipopolysaccharides ...... 21 Type IV Pilus ...... 21 Flagellum ...... 22 Stringent Response Regulatory System ...... 22 (p)ppGpp Synthesis and Its Regulation ...... 23 Transcriptional Regulation by (p)ppGpp and DksA in E. coli ...... 25 Transcriptional Regulation by (p)ppGpp in B.subtilis ...... 27 Stringent Response Regulators Conjure Bacterial Virulence ...... 27 Hypothesis and Rationale ...... 29

2 TRANSCRIPTOME ANALYSES OF ∆dksA, ∆spoT∆relA AND Xcc306 ...... 33

Introduction ...... 33 Results ...... 36 Revision of the Annotation of dksA ...... 36 Sequence Analysis of dksA, relA and spoT ...... 37 RNA-seq and Functional Enrichment Analysis ...... 38 DksA and (p)ppGpp Repress tRNA and Ribosome Protein Biosynthesis and Activate Histidine Metabolism ...... 39 Discussion and Conclusion ...... 40 Materials and Methods...... 42 Bacterial Strain, Growth Conditions ...... 42 Generation of Mutant Strains and Complemented Strains ...... 42

Site-directed Mutagenesis and Western Blot ...... 43 5’ RACE ...... 44 RNA Extraction, Sequencing and Data Analysis ...... 44

3 EXPERIMENTAL ASSAYS OF VIRULENCE-ASSOCIATED TRAITS REGULATED BY (p)ppGpp AND DksA ...... 64

Introduction ...... 64 Results ...... 66 DksA and SpoT Are Required for Full Virulence ...... 66 (p)ppGpp and DksA Positively Regulate T3SS and T2SS...... 67 (p)ppGpp and DksA Repress the Expression of Motility-related Genes ...... 69 (p)ppGpp and DksA Positively Regulate TonB-dependent Transporter Genes ...... 70 Differential Regulation of Xss Gene Cluster by (p)ppGpp and DksA ...... 71 Discussion and Conclusion ...... 72 Materials and Methods...... 75 Pathogenicity and HR Test ...... 75 Bacterial Growth Assay ...... 76 Extracellular Enzyme Activity Assay ...... 76 RNA Extraction and RT-qPCR ...... 77 Motility Assay ...... 78 GUS Activity Assay ...... 78 Siderophore Production Assay ...... 79 Strains, Plasmids and Primers ...... 79

4 CONCLUDING REMARKS AND FUTURE DIRECTIONS ...... 103

LIST OF REFERENCES ...... 106

BIOGRAPHICAL SKETCH ...... 120

LIST OF TABLES

Table page

2-1 Data quality summary of RNA-seq ...... 58

2-2 Functional enrichment analysis based on the COG database ...... 59

2-3 Gene expression of tRNA-coding genes ...... 60

2-4 Gene expression of ribosome protein genes ...... 62

3-1 Gene expression profile of T3SS- and T2SS-related genes in Xcc ...... 92

3-2 Downregulated T3SS-related genes in mutant strains ...... 94

3-3 Gene expression profile of type 4 pilus biogenesis and regulatory genes...... 95

3-4 Gene expression level of TonB-dependent transporter genes of Xcc ...... 96

3-5 Differentially regulated genes by DksA and (p)ppGpp ...... 98

3-6 Strains and plasmids used in this study ...... 99

3-7 Primers used in this study ...... 100

LIST OF FIGURES

Figure page

1-1 Important virulence factors of Xcc ...... 31

1-2 A schematic diagram showing the metabolism of (p)ppGpp and working model of stringent response regulators ...... 32

2-1 Protein sequence alignment of the DksA homologues in Xanthomonas species ...... 46

2-2 Correction of the annotation of dksA ...... 47

2-3 Protein domain analyses of DksA, RelA and SpoT in Xcc ...... 48

2-4 Heatmap and PCA analysis of the sample-to-sample distances ...... 49

2-5 Comparison of gene expression by RT-qPCR and RNA-seq ...... 50

2-6 Venn diagram of differentially expressed genes identified from ∆dksA and ∆spoT∆relA ...... 50

2-7 Distribution of differentially expressed genes (DEGs) of ∆dksA and ∆spoT∆relA compared to wild type Xcc according to COG categories ...... 51

2-8 Pathway enrichment analysis of differentially expressed genes (DEGs) identified from ∆dksA...... 52

2-9 Pathway enrichment analysis of differentially expressed genes (DEGs) identified from ∆spoT∆relA...... 53

2-10 DksA and (p)ppGpp repress the expression of coding genes for tRNA and ribosome proteins...... 54

2-11 Summary of gene expression changes of coding genes for tRNA and ribosome proteins ...... 55

2-12 Downregulated DEGs involved in the histidine metabolism pathway ...... 56

2-13 Domain analysis and predicted 3D structure of reannotated DksA of Xcc...... 57

3-1 Pathogenicity test using sweet orange leaves...... 80

3-2 Bacterial growth test in vivo and in vitro ...... 81

3-3 DksA contributes to protease activity and amylase activity but inhibites xylanase activity ...... 82

3-4 DksA and (p)ppGpp negatively regulate expression of the T3SS genes...... 83

3-5 Negative regulation of flagellar assembly genes by (p)ppGpp ...... 84

3-6 Negative regulation of putative type IV pili structure and regulatory genes by DksA...... 85

3-7 TEM observation of Xcc306, ∆dksA and ∆spoT∆relA strains ...... 86

3-8 Heatmap of gene expression changes of TonB-dependent genes in ∆dksA and ∆spoT∆relA strains...... 87

3-9 Positive regulation of sux gene cluster by DksA and (p)ppGpp ...... 88

3-10 Gene expression profile of common DEGs for ∆dksA and ∆spoT∆relA...... 89

3-11 Differential regulation of the xss gene cluster by DksA and (p)ppGpp...... 90

3-12 A working model of stringent response regulators of Xcc during host colonization...... 91

LIST OF ABBREVIATIONS

CAS Chrome azurol S

CFU Colony-forming unit

COG Clusters of Orthologous Group

DEGs Differentially expressed genes

E. coli Escherichia coli

EPS Extracellular polysaccharides

ETI Effector-triggered immunity

GUS Beta-Glucuronidase

HR Hypersensitive response hrp Hypersensitive response and pathogenicity

IM Inner membrane

KEGG Kyoto Encyclopedia of Genes and Genomes

LB Luria Bertani

LPS Lipopolysaccharides

MAMP Microbe-associated molecular pattern

NA Nutrition agar

NB Nutrition broth

OD Optical density

OM Outer membrane

OMV Outer membrane vesicle

PCA Principle component analysis ppGpp Guanosine tetraphosphate pppGpp Guanosine pentaphosphate

PTI PAMP-triggered immunity

ROS Reactive oxygen species

RT-qPCR Quantitative reverse transcription PCR

RNAP RNA polymerase

T2SS Type II secretion system

T3SS Type III secretion system

T3S effector Type 3 secreted effector

T4SS Type IV secretion system

TBDTs TonB-dependent transporters

TEM Transmission electron microscopy

Xcc Xanthomonas citri subsp. citri

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

CHARACTERIZATION OF STRINGENT RESPONSE REGULATORS (p)ppGpp, AND DksA IN Xanthomonas citri subsp. citri

Yanan Zhang

August 2019

Chair: Nian Wang Major: Microbiology and Cell Science

The stringent response is a response of bacteria to nutrition deprivation and other stress conditions. This process is mediated by the small signal molecules guanosine pentaphosphate pppGpp and guanosine tetraphosphate ppGpp (collectively referred to as (p)ppGpp) and the RNA polymerase-binding transcription factor DksA.

The (p)ppGpp synthetase RelA and the (p)ppGpp synthase/hydrolase SpoT are responsible for accumulation of (p)ppGpp in bacteria. Here, we investigated the roles of

DksA and (p)ppGpp in virulence and physiology of Xanthomonas citri subsp. citri (Xcc), the causal agent of citrus canker. ∆dksA and (p)ppGpp-deficient ∆spoT∆relA Xcc mutant strains caused weaker canker symptoms and reduced growth in host plants, indicating that DksA and (p)ppGpp are required for full virulence of Xcc. To characterize the effect of stringent response regulators on gene expression, transcriptome analyses were conducted on ∆dksA and ∆spoT∆relA strains compared with the wild type strain grown in defined XVM2 medium. Transcriptome analyses showed that DksA and/or

(p)ppGpp repress the expression of coding genes for tRNAs, ribosome proteins, iron acquisition, flagellar assembly, type 4 pilus biosynthesis, and enhance the expression of coding genes for histidine metabolism, type 3 secretion system (T3SS), type 2 secretion

system (T2SS) and TonB-dependent transporters. Phenotypically, the ∆dksA and

∆spoT∆relA strains display altered motility, enhanced siderophore production and are unable to cause a hypersensitive response (HR) on the non-host plant Nicotiana benthamiana. In conclusion, stringent response regulators DksA and (p)ppGpp coordinate the expression of multiple virulence factors and positively regulate virulence and host adaptation of Xcc.

CHAPTER 1 LITERATURE REVIEW

Citrus Canker

Xanthomonas genus represents an important and ubiquitous group of plant pathogenic bacteria belonging to the Gamma subdivision of Proteobacteria. The genus is composed of 27 species and collectively causes serious diseases in about 400 plant hosts including important crops such as rice, citrus, banana, cabbage, tomato, pepper and bean (Ryan et al. 2011). Members of this genus display high host specificity and even tissue specificity and can invade either the vascular system or the mesophyll tissue of the host (Ryan et al., 2011). Each individual strain from plant pathogen

Xanthomonas is typically limited to one host plant or a few host plants from the same botanical family (Jacques et al. 2016). Xanthomonas spp. are always plant-associated although not always pathogenic which makes them different from members of

Rhizobium, Agrobacterium, Pseudomonas, Ralstonia and Erwinia (Brunings and Gabriel

2003). All these characteristics make Xanthomonas genus an ideal model to study pathogenesis and host/microbe specificity.

Xanthomonas citri subsp. citri (Xcc) is the causative agent of citrus canker. Xcc is a rod-shaped, obligately aerobic, gram-negative bacterium with a single polar flagellum.

The bacterium forms yellow colonies on solid media due to the production of the membrane-bound pigment xanthomonadin. The Xcc-A strain can invade almost all citrus cultivars of which grapefruit, lime and lemon are the most susceptible; sweet orange is moderately susceptible and kumquat and calamondin are amongst the least susceptible (Ference et al. 2017). A* group and Aw group strains are much less

commonly found in Florida and are limited in host range to Mexican lime (Brunings and

Gabriel, 2003).

The spread of Xcc is naturally driven by rain splash and Xcc presumably persists as epiphytes on the plant surface before entering the host through stomata or wounds

(Büttner and Bonas, 2010). Inside the host, Xcc localizes in the intercellular spaces and tightly adheres to the host cell wall surface with either a hrp or T4SS pilus (Brunings and

Gabriel 2003; He 1998). When Xcc reaches a certain density, bacteria shed their flagella and aggregate into a biofilm composed of the polysaccharide xanthan and other components under lesion surface (Graham et al. 2004). Xcc is a hemibiotrophic pathogen that initially feeds on living cells and kills the cells in the later infection stage

(Büttner and Bonas 2010). Therefore, the elicited symptoms will change from hypertrophy and hyperplasia to necrotic lesions.

The management of citrus canker is still challenging since all commercial citrus cultivars are susceptible to canker disease (Brunings and Gabriel 2003). In regions where citrus canker has not yet become endemic, eradication is applied (Behlau et al.

2016; Parnell et al. 2009). When eradication of the crop is no longer feasible, measures to avoid or reduce crop loss are adopted. Although copper-based bactericides have proven to be effective for the control of citrus canker, it was observed that Xcc can acquire copper-resistance genes probably by horizontal gene transfer (Behlau et al.

2011, 2013).

The key of Xcc to cause citrus canker relies on multiple virulence factors and the coordinate expression of those factors during bacterial colonization. The Figure 1-1 summarizes the important virulence factors, which are described briefly below.

Secretion Systems of Xanthomonas

Phytopathogenic bacteria have evolved highly specialized macromolecular nanomachines to secrete diversified substrates such as small molecules, proteins,

DNA, which help bacteria respond to its environment. Different substrates may play different roles in bacterial adhesion, pathogenicity, adaptation and survival (Costa et al.

2015). Depending on the secretion system, there are three destinations for these secreted substrates: 1) outer membrane-associated area, 2) the apoplast space or 3) a target cell (Gerlach and Hensel 2007). Xanthomonas spp. contain at least six types of protein secretion systems (type I to type VI) that differ significantly in their composition, function and associated substrates (Büttner and Bonas, 2010). Secretion systems that are important to virulence and pathogenicity includes the type 2 secretion system

(T2SS), type 3 secretion system (T3SS) and type 4 secretion system (T4SS).

Type 2 Secretion System

The T2SS is conserved in most Gram-negative bacteria which transport folded proteins from the periplasm into the extracellular environment (Green and Mecsas

2016). Since T2SS channel only exists in the outer membrane, the Sec or Tat secretion pathway is required to first transfer substrates across the inner membrane (Green and

Mecsas 2016). In Xanthomonas genus, two types of T2SS are encoded by xps and xcs genes. xps-T2SS genes are present in all Xanthomonas spp. and have been shown to be important to virulence (Szczesny et al. 2010; Sun et al. 2005; Wang et al. 2008; Yan and Wang 2012). However, xcs-T2SS genes are only present in certain species such as

X. campestris pv. campestris, X. euvesicatoria and Xcc, and seem to lack an obvious virulence function (Szczesny et al. 2010).

The contribution of T2SS to virulence is presumed to come from the secretion of cell wall degradative enzymes (CWDEs) such as cellulases, proteases, xylanases and lipases (Büttner and Bonas, 2010). These enzymes can degrade plant cell wall to promote the translocation of T3S effector proteins into the host (Szczesny et al. 2010).

The T2S substrates vary in different Xanthomonas spp. and outer membrane vesicles

(OMVs) also seem to be used for transport of extracellular enzymes (Solé et al. 2015).

Interestingly, mounting studies have shown that T2SS and T3SS are regulated by the same regulatory gene and work together. In one way, T2SS and T3SS are coregulated by master regulatory genes hrpG and hrpX; in the other way, T2S-dependent induction of basal plant defense can be inhibited by T3S effectors (Jha et al. 2007, 2005; Guo et al. 2011; Wang et al. 2008).

Type 3 Secretion System

Type 3 secretion system (T3SS), the key weapon of many gram-negative bacterial pathogens, can secrete numerous effectors into the host cell to inhibit signal pathway of plant immune system and/or induce plant susceptibility genes (Büttner and

Bonas 2010; Puhar and Sansonetti 2014; Tsuge et al. 2014). The syringe needle-like structure of T3SS is encoded by the hrp (hypersensitive response and pathogenicity) gene cluster, containing over 20 genes on several transcription units (Büttner and

Bonas 2002).

T3S effectors are considered as the major host-specificity determinants (Jacques et al. 2016). Up to 40 groups of related T3S effectors or putative effectors are found in

Xanthomonas and are classified into two broad groups: transcription activator-like (TAL) effectors and non-TAL effectors (White et al. 2009). TAL effector PthA4 of Xcc activates a host susceptibility gene CsLOB1 that plays a critical role in canker symptom

development (Hu et al. 2014; Li et al. 2014). Furthermore, mounting studies show that effectors can be deployed in a spatial and temporal manner depending on the infection stage (Toruño et al. 2016). Due to the functional redundancy of T3S effectors and the limited identification of plant interaction partners, functions of many effectors remain unknown (Büttner and Bonas, 2010).

Regulation of hrp genes in Xanthomonas spp. mainly depends on two key hrp regulators, HrpG and HrpX. HrpG, an OmpR-type response regulator, is supposed to be phosphorylated by a sensor kinase and then activates the expression of the regulatory gene hrpX (Wengelnik 1996). HrpX, an AraC-type transcriptional activator, can bind to the conserved motif (PIP box, plant-inducible promoter) at the promoter region of some

T3SS genes and T3S effector genes (Guo et al. 2011; Tang et al. 2006; Tsuge et al.

2014). Several other regulators have been reported to act on hrpG and/or hrpX in

Xanthomonas spp. including the post-transcriptional regulator CsrA/RsmA (Andrade et al. 2014), Lon protease (Zhou et al. 2018), sensor kinase HpaS (Li et al. 2014), LysR- type transcriptional activator GamR (Rashid et al. 2016), and small noncoding RNA sX13 (Schmidtke et al. 2013). In addition, T3SS expression is repressed in nutrient-rich media but induced when bacteria are in contact with plant cells or incubated in nutrient- deficient inducing media (Tang et al. 2006). To sum up, those regulators associated with different host and/or environmental signals highlight the complexity of T3SS regulation.

Type 4 Secretion System

Type 4 secretion system (T4SS) is a versatile multiprotein complex that spans the entire cell envelope in gram-negative and gram-positive bacteria (Grohmann et al.

2018). T4SS has diversified functions involved in contact-dependent secretion of

effectors into eukaryotic hosts, conjugative transfer of mobile DNA elements and contact-independent exchange of DNA with the extracellular milieu (Grohmann et al.

2018). The structure of T4SS is composed of 12 proteins called VirB1-VirB11 and VirD4

(Waksman and Orlova 2014). In particular, Xcc have two types of T4SSs which are encoded by VirB gene cluster located in chromosome and plasmid pXAC64 (da Silva et al. 2002). Although no evidence was shown that T4SS plays an important role in pathogen-host interaction in Xanthomonas, bacterial killing via T4SS-secreted toxin is found in Xanthomonas spp. which may give a competitive growth advantage in its niche

(Souza et al. 2015).

Other Virulence Traits of Xanthomonas

Besides bacterial secretion systems and their substrates, Xanthomonas spp. possess other virulence factors such as extracellular polysaccharides (EPS), lipopolysaccharides (LPS), type IV pili and flagella, which all contribute to host colonization.

Extracellular Polysaccharides

The mucoid appearance of Xanthomonas spp. is caused by the production of extracellular polysaccharides (EPS), also called xanthan gum. The gum gene cluster composed of 12 genes (gumB to gumM) is responsible for biosynthesis of EPS (Becker et al. 1998). Multiple studies show that EPS contributes to epiphytic fitness, bacterial growth in planta, disease symptom formation and biofilm formation (Kemp et al. 2004;

Lu et al. 2007; Rigano et al. 2007). Interestingly, EPS specifically suppresses the local plant defense via the inhibition of callose deposition (Yun et al. 2006). However, little is known about the regulation mechanism of the biosynthesis of EPS. Limited studies

indicate that the expression of gum genes is subject to positive regulation by rpfF, which encodes the diffusible signal factor (DSF) synthase (Vojnov et al. 2001).

Lipopolysaccharides

Lipopolysaccharides (LPS) are major components of the outer membrane of gram-negative bacteria and protect the cell from hostile environments (Büttner and

Bonas 2010). LPS is a tripartite molecule composed of membrane-anchored lipid A, a core oligosaccharide and polysaccharide side chains (O-antigen) (Raetz and Whitfield

2002). Whole genome sequencing indicates that a cluster of 16 genes between

XAC3587 and XAC3602 of Xcc are involved in LPS biosynthesis (da Silva et al. 2002).

Several genes including wxacO (XAC3596), rfbC (XAC3598), wzt (XAC3600) and nlxA have been characterized in the biosynthesis of LPS as well as the effect on bacterial virulence and/or host defense (Casabuono et al. 2011; Li and Wang 2011; Yan et al.

2012). Interestingly, comparative sequence analyses revealed that LPS gene clusters from different Xanthomonas spp. show high variations that may be correlated with host specificity (Lu et al. 2008; Potnis et al. 2011; Zhang et al. 2015). In addition, LPS also act as a microbe-associated molecular pattern (MAMP) and can be sensed by a lectin

S-domain receptor kinase of LORE (SD1-29) in Arabidopsis thaliana (Ranf et al. 2015).

Type IV Pilus

To survive in complex and ever-changing environments, plant bacterial pathogens need to oscillate between behaviors associated with attachment to surfaces and moving across surfaces either as individual cells or in groups. Bacterial type 4 pilus

(T4P) is a flexible surface filament and several-micrometers long, which is involved in bacterial movement, adhesion, orientation and multicellular organization (Dunger et al.

2016). The Xanthomonas T4P is composed of four subcomplexes: the outer membrane

subcomplex, the inner membrane platform, the ATPases PilB, PilT and PilU, and the pilus filament, a polymer of the major pilin (Dunger et al. 2016). The Xcc T4P is required for twitching motility, biofilm development, phage susceptibility and adherence (Dunger et al. 2014). The effect of T4P on virulence seems to vary among Xanthomonas genus.

The regulation of T4P biosynthesis is not fully understood. Mounting evidence shows that the interaction complex PilZ–FimXEAL–c-di-GMP plays a role in this process

(Guzzo et al. 2013; Chin et al. 2012).

Flagellum

Motility is often associated with chemotaxis and enables bacteria to detect and pursue nutrients, and to reach preferred niches (Josenhans and Suerbaum 2002). The most studied form of bacterial motility relies on the assembly and rotation of propeller- like flagella (Jarrell and McBride 2008). Motility seems to play a role predominantly in the early phases of infection, which is correlated with flagella-dependent chemotaxis and adherence (Josenhans and Suerbaum 2002). Xanthomonas spp. contain a single polar flagellum involved in swimming motility. The flagellum of Xcc is required for swimming motility, mature biofilm and canker development (Malamud et al. 2011). In the genus of Xanthomonas, over 40 genes organized in several operons are responsible for biosynthesis of flagellum and subject to the positive regulation of the transcription factor

FleQ, sigma factor RpoN and FliA (Hu et al. 2005; Yang et al. 2009). FleQ of

Pseudomonas aeruginosa is identified as a c-di-GMP-responsive transcription factor linking the c-di-GMP and motility regulation (Hickman and Harwood 2008).

Stringent Response Regulatory System

The term stringent response initially referred only to the bacterial response to amino acid deprivation, but now it generally refers to bacterial changes in response to

various stresses that induce the accumulation of the signaling molecules guanosine-5,

3-tetraphosphate (ppGpp) and/or guanosine-5, 3-pentaphosphate (pppGpp) (Gourse et al. 2018). During stringent response, bacteria reallocate cellular resources by reducing the synthesis of stable RNAs (tRNA and rRNA) and ribosomal proteins and rapidly promoting the expression of components crucial for stress resistance (Dalebroux and

Swanson 2012; Potrykus and Cashel 2008). This process is mainly controlled by the small signal molecules guanosine pentaphosphate (pppGpp) and guanosine tetraphosphate (ppGpp) (collectively referred to as (p)ppGpp) and the RNA polymerase- binding transcription factor DksA. Considerable variation of the stringent response regulatory system is present in different branches of bacteria, which is an elegant example of how different species adapt this system to their diverse lifestyles (Boutte and Crosson 2013; Liu et al. 2015a). Figure 1-2 summarizes the metabolism of

(p)ppGpp and the current working model of stringent response regulators. The description of the stringent response regulatory system below mainly focuses on E. coli, a model organism being heavily studied.

(p)ppGpp Synthesis and Its Regulation

(p)ppGpp metabolism is controlled by enzymes that synthesize and degrade

(p)ppGpp, including the classical RelA/SpoT homologue proteins (RSH), bearing both the synthetase and hydrolase domains, and small alarmone synthetases (SAS) and hydrolases (SAH) (Atkinson et al. 2011). Most betaproteobacteria and gammaproteobacteria contain two RSH enzymes (RelA and SpoT), which are homologous proteins and probably derived from the duplication event of the ancestral

Rel protein (Hauryliuk et al. 2015; Ronneau and Hallez 2019). RelA and SpoT typically have a hydrolase (HD) and a synthetase (SYNTH) domain in their N-terminal domain

(NTD), and a regulatory C-terminal domain (CTD) composed of TGS (ThrRS, GTPase and SpoT), helical, CC (conserved cysteine) and ACT (aspartokinase, chorismate mutase and TyrA) domains. RelA is a synthetase with hydrolytic activity being completely absent in its HD domain while SpoT is bifunctional enzyme with strong hydrolytic activity and weak synthetic activity (Hauryliuk et al. 2015). In E.coli, both RelA and SpoT can use ATP and either GDP or GTP to synthesize ppGpp or pppGpp and

AMP, as a by-product and the SpoT can degrade pppGpp/ppGpp to GTP/GDP and pyrophosphate (PPi) (Hauryliuk et al. 2015) In addition, transformation from pppGpp to ppGpp is catalyzed by pppGpp phosphatase (GppA) and other translational GTPases.

RelA-mediated synthesis of (p)ppGpp is long known to be activated by sensing an uncharged tRNA in the acceptor site of the ribosome (Haseltine and Block 1973). A cryo-EM structure of RelA bound to the ribosome suggests that direct interaction between the C-terminal autoregulatory domain of RelA and the 3’ end of uncharged tRNAs in the ribosomal A-site suppresses the autoinhibition of RelA to synthesize

(p)ppGpp (Arenz et al. 2016; Brown et al. 2016; Loveland et al. 2016). In addition, the regulation of RelA also occurs at the transcription level. The expression of relA in E. coli is upregulated by NtrC, CRP and HNS, and inhibited by 6S RNA, RpoS and HipB (Irving and Corrigan 2018). For example, the global transcriptional regulator NtrC binds to a site upstream of relA and activates its transcription to produce more (p)ppGpp in nitrogen-starved E. coli (Brown et al. 2016).

Compared with RelA, the regulation mechanism of SpoT (synthase and hydrolase) is much less clear. A key question is how bacteria achieve the shift between the hydrolase and synthase activities of SpoT (Gourse et al. 2018). Upon fatty acid or

carbon starvation, acyl carrier protein (ACP) can interact with SpoT to activate the

SpoT-dependent (p)ppGpp synthesis (Battesti and Bouveret 2009, 2006). However, the regulatory details are elusive due to the lack of structural information of SpoT.

Transcriptional Regulation by (p)ppGpp and DksA in E. coli

The realization of the important role of DksA in stringent response came much later compared with (p)ppGpp. Transcription factor DksA (DnaK suppressor A) was discovered as the cofactor of (p)ppGpp in 2004 and following studies showed that

(p)ppGpp together with DksA directly inhibits the transcription of stable RNA, ribosome proteins and activates the amino acid biosynthesis (Paul et al. 2004, 2005; Lemke et al.

2011). In most cases, DksA is assumed to increase the response of RNAP to (p)ppGpp in either positive or negative regulation of transcription initiation on specific promoters.

DksA from E. coli contains a globular domain and a long coiled-coil with aspartate residues at the tip and binds to the secondary channel of RNA polymerase (RNAP)

(Perederina et al. 2004). Interestingly, E. coli conjugative F plasmid-encoded protein

TraR is a distant homolog of DksA and regulates transcription directly in vitro by binding to the secondary channel of RNAP using interactions similar, but not identical, to those of DksA (Gopalkrishnan et al. 2017).

So far, two (p)ppGpp binding sites (site 1 and site 2) are found in the RNAP of E. coli : site 1 is at an interface of two RNAP subunits β’ and ω and site 2 is at an interface of RNAP and DksA (Ross et al. 2016, 2013). In vitro transcription assay shows that

(p)ppGpp inhibits transcription at the rRNA promoter by approximately threefold to fourfold by binding to the site 1 and another approximately fivefold by binding to a site 2 that requires DksA, accounting for the full inhibition observed in vivo (Ross et al. 2016).

Since both binding sites of (p)ppGpp require a dissociated factor (ω for site 1 and DksA

for site 2), the actual affinity of (p)ppGpp for the two sites rely on the concentration of ω and DksA (Gourse et al. 2018).

The stringent response regulates more than 10% of all E. coli genes involved in the synthesis of tRNA, ribosome proteins, flagella, fatty acids, amino acids, transporters and many other central cellular components (Åberg et al. 2009; Durfee et al. 2008;

Traxler et al. 2008). Effects of (p)ppGpp and DksA on transcription initiation, which is the main reason for global changes caused by stringent response, have been intensively studied. However, the underlying molecular mechanism is still not fully understood. The specific kinetics of the transcription initiation reaction are proposed to determine whether a promoter is regulated by (p)ppGpp and DksA (Haugen et al. 2008).

In a classical model, binding of (p)ppGpp and DksA to RNAP destabilizes all the promoter open complexes examined to date but the transcriptional outcome may differ: it is assumed that transcription at promoters that form unstable open promoter complex will be inhibited while transcription at promoters with very stable open promoter complex will be promoted (Haugen et al. 2008). Given that many proteobacteria have the conserved (p)ppGpp-binding site on their RNAP and putative DksA, it is suggested that

E.coli working model may be widely shared among proteobacteria (Liu et al. 2015a).

Besides the direct way, the decrease in occupany of RNAP by the rRNA promoter will also increase the availability of RNAP for alternative sigma factors and other promoters, which are required for the expression of some stress-related genes

(Dalebroux and Swanson 2012; Srivatsan and Wang 2008). In addition, although the synergistic effect between DksA and (p)ppGpp exists in most cases, there is evidence

showing that they may have divergent and even opposite effects (Magnusson et al.

2007; Lyzen et al. 2009, 2016).

Transcriptional Regulation by (p)ppGpp in B.subtilis

In the distantly related Firmicute Bacillus subtilis, no similar binding sites (site 1 and site 2) are found and a DksA homolog is lacking (Ross et al. 2016, 2013). Under the stringent response, B. subtilis regulates transcription such as rRNA by modulating the cellular GTP level (Krásný and Gourse 2004; Kriel et al. 2012). Strong (p)ppGpp induction under stresses significantly reduces GTP levels by two strategies: consumption of GTP due to the synthesis of pppGpp and direct inhibition of enzymes involved in GTP synthesis (Liu et al. 2015a). Combining metabolomics with biochemical demonstrations, (p)ppGpp was shown to directly inhibit the activities of multiple GTP biosynthetic enzymes including guanylate kinase (Gmk, which is proposed to convert

GMP to GDP) and HprT (which converts hypoxanthine to IMP and guanine to GMP)

(Kriel et al. 2012). Further study reveals that (p)ppGpp-GMK interaction is conserved in members of Firmicutes, Actinobacteria, and Deinococcus-Thermus, but not in

Proteobacteria, where RNAP is the major target of (p)ppGpp and DksA (Liu et al.

2015b).

Stringent Response Regulators Conjure Bacterial Virulence

Compared with DksA, (p)ppGpp has other direct targets beyond RNAP. Besides transcription initiation, direct targets of (p)ppGpp are involved in replication, translation, and cellular metabolism (Kanjee et al. 2012; Steinchen and Bange 2016; Zhang et al.

2018). Here I mainly focus on three examples selected from three different genera and show that stringent response regulators regulate bacterial virulence by controlling the transcription factors that are dedicated to virulence genes.

The genus Salmonella is composed of gram-negative, non-spore-forming, rod- shaped bacteria belonging to the Enterobacteriaceae family. Salmonella enterica serovar Typhimurium is a primary enteric pathogen infecting both humans and animals to trigger gastrointestinal disease which is often associated with ingestion of contaminated food or water (Fàbrega and Vila 2013). slyA, which encodes a multiple antibiotic resistance protein R (MarR) family transcriptional regulator SlyA that is important to virulence, is induced by the two component system PhoPQ (Norte et al.

2003). SlyA-mediated transcriptional activation requires cytoplasmic (p)ppGpp: dimerization and binding of SlyA to the target promoter are facilitated by (p)ppGpp in vivo and in vitro indicating that (p)ppGpp stimulates SlyA regulatory function by direct interaction (Zhao et al. 2008). Cumulatively, PhoP and SlyA activate transcription of genetic loci by binding to their intergenic regions containing SlyA box and Pho box simultaneously (Zhao et al. 2008).

The gram-negative bacterium Francisella tularensis is a facultative intracellular pathogen, which causes tularemia, a disease that can be fatal in humans. To survive and replicate within macrophages, stringent starvation protein MglA and SspA of F. tularensis form a complex that interact with RNAP to regulate the transcription of virulence genes (Charity et al. 2007; Lauriano et al. 2004). Later studies shows

(p)ppGpp is involved in virulence regulation by promoting the interaction between DNA- binding protein PigR and the RNAP-associated MglA-SspA complex (Charity et al.

2009). Structural and biochemical studies display that (p)ppGpp interacted with MglA-

SspA complex directly and mediated its high-affinity interactions with PigR anchoring the (MglA–SspA)–RNAP complex to the Francisella pathogenicity island (FPI) promoter

(Cuthbert et al. 2017). To sum up, F. tularensis pathogenesis is controlled by a highly interconnected molecular circuitry which is in part initiated by the small molecule stress signal (p)ppGpp induced by outside signals.

Enterohaemorrhagic E.coli (EHEC) is an emerging human pathogen that causes a wide spectrum of illnesses such as diarrhea, haemorrhagic colitis and haemolytic uraemic syndrome. Adherence capacity of EHEC and gene expression in the locus of enterocyte effacement (LEE) are enhanced by nutrient limitation or entry into the stationary growth phase, which is associated with the accumulation of (p)ppGpp

(Nakanishi et al. 2006). The stringent response regulators ((p)ppGpp and DksA)- dependent activation is further confirmed by an in vitro transcription system: two LEE transcriptional regulatory genes ler and pch are directly activated by (p)ppGpp and

DksA (Nakanishi et al. 2006).

To sum up, it seems to be a common theme that stringent response regulators regulate virulence by key virulence regulatory genes. However, the underlying mechanism varies considerably among different species.

Hypothesis and Rationale

The stringent response regulators DksA and (p)ppGpp have not been studied in

Xanthomonas genus. Little is known about the roles of DksA and (p)ppGpp in coordinating the regulation of virulence factors and their contribution to the virulence during bacterial colonization of Xcc. The hypothesis is that the stringent response of Xcc will be activated during bacteria-host interaction, DksA and rapidly accumulated

(p)ppGpp would reprogram global transcription to promote bacterial virulence and survival. Understanding how the stringent response regulatory system works will

advance our knowledge regarding how the plant pathogen Xcc prepares for host colonization.

The first objective of this study is to determine whether DksA and (p)ppGpp play a role in virulence of Xcc. Specifically, I will test whether ∆dksA and the (p)ppGpp- deficient mutant ∆relA∆spoT affect canker symptom development and bacterial growth in vitro and in vivo.

The second objective is to determine how the global transcription profile of Xcc is changed by DksA and (p)ppGpp. To be specific, I will investigate what genes or pathways are under control of DksA and (p)ppGpp; whether those genes are upregulated or downregulated and what is the biological significance of these changes in gene expression.

The third objective is to determine the phenotypes for which DksA and (p)ppGpp are responsible. In particular, I will focus on the virulence-associated phenotypes such as motility, T3SS, HR on non-host plant, and siderophore production.

To achieve these goals, RNA-seq assay is going be used to investigate the influence of DksA and (p)ppGpp on the genome-wide transcription profile. Functional enrichment analysis was conducted to identify the overrepresented gene set and reveal the potential roles of DksA and (p)ppGpp. The expression changes of virulence-related genes were further confirmed by RT-qPCR and/or GUS assay. Finally, the effect of

DksA and (p)ppGpp on specific virulence-associated traits was tested experimentally.

Figure 1-1. Important virulence factors of Xcc. The figure is adapted from Büttner and Bonas, 2010.

31 Figure 1-2. A schematic diagram showing the metabolism of (p)ppGpp and working model of stringent response regulators. Activated by different outside stimuli, RelA and SpoT synthesize the (p)ppGpp using GTP/GDP and ATP. Since SpoT is a bifunctional enzyme, (p)ppGpp can also be degraded by SpoT into GTP/GDP and pyrophosphate (PPi). Increased (p)ppGpp along with DksA can interact with RNA polymerase (RNAP) directly and positively or negatively change the transcription initiation. rrn and his is involved in rRNA and histidine synthesis, respectively Besides housekeeping sigma factor 70, other sigma factors may also participate in the process. Beyond RNAP, (p)ppGpp, as a secondary messenger, can directly interact with some enzymes and transcription factors and modulate their activities. The X-ray crystal structure of BipA is from PDB database (ID: 4ZCM) (Fan et al. 2015)

CHAPTER 2 TRANSCRIPTOME ANALYSES OF ∆dksA, ∆spoT∆relA AND Xcc306

Introduction

Xanthomonas spp. cause diseases in approximately 400 species of plant hosts including many economically important crops (Ryan et al. 2011). Xanthomonas citri subsp. citri (Xcc) is the causal agent of citrus canker, one of the most destructive bacterial diseases in citrus (Vojnov et al. 2010). Xcc can be disseminated via wind- driven rain and invades the host leaf mesophyll tissue through stomata or wounds

(Brunings and Gabriel 2003; Ference et al. 2017). To survive and multiply, Xcc needs to overcome the stresses from both the nonhost and host environments such as nutrient limitations in the phyllosphere and host immunity response (Dodds and Rathjen 2010;

Fatima and Senthil-Kumar 2015).

Gram-negative bacterial pathogens including Xcc deliver numerous effectors into the host cell via the type 3 secretion system (T3SS) to manipulate host signaling, suppress immune responses and/or induce plant susceptibility genes (Jacques et al.

2016; Tsuge et al. 2014; White et al. 2009). T3SS is encoded by hrp (hypersensitive response and pathogenicity) genes, and the regulation of hrp genes in Xanthomonas mainly depends on two key transcriptional regulators, HrpG and HrpX (Wengelnik et al.,

1996; Wengelnik and Bonas, 1996). Several virulence regulators act upstream of HrpG and/or HrpX in Xanthomonas including post-transcriptional regulator RsmA/CsrA

(Andrade et al. 2014), Lon protease (Zhou et al. 2018), sensor kinase HpaS (Li et al.

2014), LysR-type transcriptional activator GamR (Rashid et al. 2016), and small noncoding RNA sX13 (Schmidtke et al. 2013). In addition, the virulence of

Xanthomonas is also controlled by two-component systems, quorum sensing (QS), and

cyclic di-GMP (Büttner and Bonas, 2010). Beyond the T3SS induction during infection, little is known about how Xcc coordinates virulence-associated regulatory networks.

The bacterial stringent response is a response to nutrition starvation and other stress conditions. During stringent response, cellular resources are relocated from synthesis of stable RNAs (tRNA and rRNA) and ribosomal proteins to promote the expression of components crucial for stress resistance (Dalebroux and Swanson 2012;

Potrykus and Cashel 2008). This process is mainly regulated by small signal molecules guanoisine pentaphosphate pppGpp and guanosine tetraphosphate ppGpp (collectively referred to as (p)ppGpp) and the RNA polymerase-binding transcription factor DksA

(Haugen et al. 2008). DksA, a cofactor of (p)ppGpp, binds to RNA polymerase (RNAP) through the secondary channel and augments the effect of (p)ppGpp on transcription initiation (Paul et al., 2004; Perederina et al., 2004). The cellular (p)ppGpp level is controlled by RelA-SpoT homologs (RSHs), small alarmone synthetase (SAS) and small alarmone hydrolase (SAH) (Atkinson et al. 2011). Gram-negative bacteria generally have two long-RSH proteins: RelA (synthetase) and SpoT (synthase and hydrolase)

(Irving and Corrigan 2018). (p)ppGpp can be synthesized by RelA and SpoT using

GTP/GDP and ATP and degraded by SpoT into GTP/GDP and pyrophosphate (PPi)

(Hauryliuk et al. 2015). The activity of RSH homologs in E. coli can be regulated by small ligand, heterologous protein or at the transcriptional level (Irving and Corrigan

2018).

Although the specific mechanism may vary between species, at least two paradigms account for the broad effect of (p)ppGpp on cellular processes (Gourse et al.

2018). In E.coli, both (p)ppGpp and DksA can directly bind to RNAP and this interaction

positively or negatively regulates the transcription initiation depending on the kinetics of promoter open complex (Haugen et al. 2008; Ross et al. 2016). In addition, (p)ppGpp also regulates global cellular process in a RNAP-independent manner by directly interacting with enzymes or transcription factors to modulate their activities (Dalebroux and Swanson 2012).

Transcriptome analysis showed that in E. coli, the stringent response regulates more than 10% of all genes involved in synthesis of tRNA, ribosome proteins, flagella, fatty acids, amino acids, transporters and many other central cellular components

(Aberg et al., 2009; Durfee et al., 2008;Traxler et al., 2008). Although the synergistic effect between DksA and (p)ppGpp exists in most cases, evidence shows that DksA and (p)ppGpp may have divergent and even opposite effects on specific traits or gene expression (Magnusson et al. 2007; Lyzen et al. 2009, 2016). Even the basal level of

(p)ppGpp still has a regulatory effect under balanced growth conditions (Gaca et al.

2013).

Stringent response not only helps bacteria adapt to nutrient-deprivation environment, but also enhances bacterial virulence (Dalebroux et al. 2010). For plant pathogens, several reports showed the effect of stringent response regulators (p)ppGpp and/or DksA on virulence in plant pathogens like Erwinia amylovora (Ancona et al.

2015), Pectobacterium atrosepticum (Bowden et al. 2013), and Pseudomonas syringae

(Chatnaparat et al., 2015a; Chatnaparat et al., 2015b). The effect of stringent response regulators on virulence has yet to be studied in Xanthomonas genus. In this study, I examined stringent response regulators by generating single ∆dksA and (p)ppGpp- deficient ∆spoT∆relA double mutant strains of Xcc. We were unable to obtain a single

spoT deletion mutant despite multiple attempts, which is consistent with previous studies in failing to generate the ∆spoT mutant in E. coli. It was speculated that the hyper accumulation of (p)ppGpp due to disruption of spoT is lethal to bacteria (Xiao et al., 1991). RNA-seq was used to determine changes of transcription profile of ∆dksA and ∆spoT∆relA strains compared with wild type Xcc strain, which reveals the regulation roles of DksA and (p)ppGpp.

Results

Revision of the Annotation of dksA

In GC-rich genomes, up to 60% of translation start sites can be wrongly annotated (Nielsen and Krogh 2005). When analyzing the sequences and domains of dksA homologous genes from Xanthomonas spp., it was found that the annotated translation start sites of DksA were found different among Xanthomonas species.

Protein sequences alignment shows that there are three different translation start sites which correspond to predicted protein sizes: 17.4kDa, 19.0kDa and 36.5kDa (Figure 2-

1). Further DNA sequence alignment showed that there are several potential in-frame translational start sites (GTGs), which could be the reason why the translation start site was annotated differently.

To investigate the translation start site of dksA in Xcc, two pairs of primers were designed, and reverse transcription-PCR (RT-PCR) showed that the region from first

GTG to the stop codon TGA is in the same transcriptional unit (Figure 2-2A and 2B).

The 1254 bp DNA fragment upstream of the translation stop codon TGA was amplified by PCR and its 3’ end was fused in-frame with human influenza hemagglutinin (HA) tag located in the modified plasmid pBBR1MCS2. Western blot using HA antibody detected a band specific for a protein whose size was around 42 kDa, which indicated that

translation start site is likely the first GTG (Figure 2-2C). To further confirm this, three modified pBBR1MCS2 plasmids with 1 bp nucleotide deletion were constructed by site- directed mutagenesis (Figure 2-2A). If the deleted nucleotide is in the coding region of dksA, this is supposed to disrupt the expression of HA tag. Single nucleotide-deletion mutants SDM2 and SDM3 rather than SDM1 led to the disappearance of the specific band, which indicated that the first GTG is the most likely translation start site (Figure 2-

2C). These results are consistent with a proteome study in Xanthomonas euvesicatoria

(Xe) strain 85–10 using mass-spectrometry (MS)-based approach, which also indicates that the first GTG is the translation start site (Abendroth et al. 2017).

In addition, 5’ RACE was used to determine the transcription start site of dksA.

DNA sequencing and alignment of the RACE clones showed that the transcription start site is guanine nucleotide at 72 bp upstream of the first GTG (Figure 2-2A and 2D).

Furthermore, putative −10 and −35 promoter consensus motifs as well as a ribosome binding site were found upstream of the translation start site based on their characteristics (Figure 2-2A). To sum up, the above evidence show that translation start site of dksA in Xcc is further upstream of originally annotated translation start site and that the reannotated DksA of Xcc contains 346 amino acids.

Sequence Analysis of dksA, relA and spoT

Homology search showed Xcc contains stringent response regulatory genes including dksA (locus tag XAC2358), relA (XAC3113) and spoT (XAC3393). BLASTP showed that DksA, RelA and SpoT from Xcc displayed 46.88%, 43.00%, and 48.06% identity in amino acid sequence with the homologous genes from E. coli K-12 MG1655, respectively. DksA belongs to DksA/TraR family and contains a Zinc finger domain at the C-terminal region (Figure 2-3). Both RelA and SpoT contain the (p)ppGpp synthesis

(SYNTH) domain, hydrolysis (HD) domain, TGS (ThrRS, GTPase and SpoT) domain and ACT (aspartokinase, chorismate mutase and TyrA) domain (Figure 2-3).

RNA-seq and Functional Enrichment Analysis

To better understand the function of stringent response regulators in virulence and other cellular processes, RNA-seq was conducted to determine the transcription profile of wild-type Xcc306, ∆dksA, and ∆spoT∆relA strains. Bacteria were grown in the plant-mimicking XVM2 medium and total RNA was extracted from the mid-log phase

(OD600=0.35) cultures. Totally, 9 RNA samples (three biological repeats for each strain) were sent for RNA-seq (sequencing data information is provided in Table 2-1). To explore the similarities and differences between samples, principle component analysis

(PCA) was performed. The three biological repeats of each strain clustered together into distinct groups from other strains, indicating that the major variation results from the difference between each strain (Figure 2-4A). Similarly, the heatmap showed that three biological repeats are clustered together (Figure 2-4B). In addition, RT-qPCR was conducted to confirm the RNA-seq data based on selected genes, which shows log2- transformed values (∆/WT) derived from both methods are highly correlated (Figure 2-

5).

Differentially expressed genes (DEGs) were defined as genes with the absolute value of log2 fold change equals or more than 2 and adjusted p-value equals or smaller than 0.01. The differential gene expression test revealed that in the ∆spoT∆relA mutant,

873 genes were identified as DEGs and among them, 179 genes were upregulated and

694 were downregulated (Figure 2-6). In the ∆dksA mutant, 956 genes were identified as DEGs and among them, 196 genes were upregulated and 760 were downregulated

(Figure 2-6). There are approximately 4500 genes in the Xcc genome, of which 20%

were regulated in the ∆dksA and ∆spoT∆relA mutants. More than 50% of the DEGs in both mutants (482 genes) are common, which supports the notion that DksA is the cofactor of (p)ppGpp in many cases (Figure 2-6).

To discover functionality-related genes regulated by DksA and (p)ppGpp, DEGs were grouped into different functional categories based on the COG database (Figure 2-

7). Fisher exact test was conducted to identify the overrepresented functional groups (p- value < 0.05) and showed that both DksA and (p)ppGpp are involved in cell motility, inorganic ion transport and metabolism, and intracellular trafficking and secretion (Table

2-2). To identify the pathways regulated by DksA and (p)ppGpp, pathway enrichment analysis was performed based on the KEGG database (Kanehisa and Goto 2000). The results showed that the overrepresented pathways include histidine metabolism, bacterial secretion systems, ribosome biosynthesis, and degradation of aromatic compounds (Figure 2-8 and 2-9). To sum up, these results highlight the similarities in the regulons of DksA and (p)ppGpp and reveal their potential roles.

DksA and (p)ppGpp Repress tRNA and Ribosome Protein Biosynthesis and Activate Histidine Metabolism

A hallmark of stringent response is the inhibition of stable RNA and ribosome protein biosynthesis (Lemke et al. 2011; Paul et al. 2004). KEGG database-based enrichment analysis suggested that ribosome biosynthesis was overrepresented in the

DksA and (p)ppGpp regulons. We analyzed the transcript abundance of genes involved in tRNA and ribosome biosynthesis of Xcc. The relative gene expression levels of 54 tRNA-encoding genes and 55 ribosomal protein-encoding genes in the ∆dksA and

∆spoT∆relA mutants compared to the wild type Xcc were listed (Table 2-3 and 2-4).

Heatmap analysis displayed the relative gene expression profile for both ∆dksA and

∆spoT∆relA strains compared to the wild type Xcc (Fig 2-10). Specifically, 24 of the 54 tRNA genes were upregulated DEGs (log2FC > 2) in the ∆dksA strain (Figure 2-11A).

Similarly, 10 of the 54 tRNA genes were upregulated DEGs (log2FC > 2) in the

∆spoT∆relA strain (Figure 2-11A). For 55 ribosomal protein-encoding genes, 20 and 28 are upregulated DEGs (log2FC > 2) in ∆dksA and ∆spoT∆relA respectively (Figure 2-

11B).

Studies from E. coli have shown that DksA and (p)ppGpp directly stimulate certain promoters for amino acid biosynthesis and transport (Paul et al. 2005). Based on pathway enrichment analysis, we found that many genes involved in the histidine metabolism were downregulated in the ∆dksA and ∆spoT∆relA strains (Figure 2-12).

Taken together, these results indicate that DksA and (p)ppGpp of Xcc negatively regulate tRNA and ribosome protein biosynthesis and positively regulate histidine metabolism.

Discussion and Conclusion

Comparison using DNA and protein sequence of dksA showed that annotation of the translation start site varies in the different Xanthomonas spp. although DNA sequence are highly conserved. To determine the translation start site, combined experimental methods including Western blot, site-directed mutagenesis and 5’ RACE indicated that the reannotated DksA contains 346 amino acids and that transcription start site is 72 bp upstream of translation start site (GTG).These results are consistent with a previous proteogenomic study which showed the detected size of DksA is at least

255 amino acids long (Abendroth et al. 2017). Protein blast (BLASTP) indicates that this extra-long DksA is unique to the Xanthomonas genus and most DksA homologues from other species are less than 200 amino acids long. Domain detection showed that

revised DksA has the conserved Znf_DskA_TraR domain at the C terminal and low complexity region at its N terminal (Figure 2-13A). The predicted 3D structure of DksA was also generated based on structure of DksA from E. coli (Figure 2-13B). Further studies are needed to understand the effect of N terminal region on its function and structure.

By differential expression test based on RNA-seq data, I found that DksA and

(p)ppGpp regulated about 20% of total genes in Xcc and more than 50% of DEGs are common, which supports the claim that DksA is cofactor of (p)ppGpp (Potrykus and

Cashel 2008). Interestingly, near 80% of DEGs are downregulated in ∆dksA or

∆spoT∆relA strain on XVM2 medium, which seems to be in contrast to the hypothesis that (p)ppGpp and DksA are global transcription repressor (Haugen et al. 2008). It is worth noting that DEGs identified by RNA-seq here are the result of both direct and indirect effect of DksA and (p)ppGpp.

Knowledge base–driven pathway analysis is a powerful way to gain insight into the underlying biological meaning of differentially expressed genes and proteins since it reduces complexity and increases explanatory power (Khatri et al. 2012). Functional enrichment analysis based on COG database shows cell motility, inorganic ion transport and metabolism, and intracellular trafficking and secretion are under control of (p)ppGpp and DksA. Except for the common pathways, pathway enrichment analysis based on

KEGG database shows the possible difference in regulon between DksA and (p)ppGpp such as flagellum assembly genes. In summary, although functional enrichment analysis using multiple databases can give a big picture of the regulatory roles of DksA and (p)ppGpp, further analysis and experimental validation are needed.

Although the (p)ppGpp level was not measured in ∆spoT∆relA of Xcc, the following evidence strongly indicates there is very little or no (p)ppGpp in ∆spoT∆relA strain. First, spoT and relA show high sequence and domain similarity with corresponding homologous genes from E.coli. Second, repression of ribosome biosynthesis including tRNA and ribosome proteins, typical phenomena of stringent response, were clearly observed in our RNA-seq data. In addition, it is reasonable to assume that the hrp-inducing medium XVM2 can promote the accumulation of

(p)ppGpp and activate stringent response to a certain extent. However, further experiments are needed to clarify the relationship between the accumulation of

(p)ppGpp and the induction of T3SS genes in XVM2.

Materials and Methods

Bacterial Strain, Growth Conditions

E. coli cells were cultured in Lysogeny broth (LB) medium at 37℃. Xanthomonas strains were routinely cultured in nutrient broth (NB) medium and on nutrient agar (NA) plate at 28 ℃. For induction of hrp genes, Xcc strains were grown in XVM2 medium which is described elsewhere (Wengelnik and Bonas 1996). When required, growth media were supplemented with ampicillin (100µg/mL), kanamycin (50µg/mL), gentamycin (10µg/mL) and spectinomycin (100µg/mL).

Generation of Mutant Strains and Complemented Strains

Genomic DNA template was extracted using Genomic DNA Purification Kit

(Promega, Madison, WI, U.S.A). For each target gene (dksA, relA, spoT), the upstream and downstream flanking regions were amplified with two pairs of primers (Table S7).

Overlapping PCR was performed to connect two fragments. The whole fragment was subsequently inserted into multiple cloning sites of pOK1 by Rapid DNA Ligation

System (Promega). Recombinant plasmids were introduced into wild-type Xcc306 strain by electroporation. Colonies with the integration of the plasmid were selected on NA plates supplemented with spectinomycin or 5% sucrose separately. Spectinomycin- resistant and sucrose-sensitive colonies were picked up and grown in NB medium without any antibiotics. Second round selection was applied to choose sucrose-resistant and spectinomycin-sensitive colonies which were further confirmed to have a deletion mutation by PCR. The double mutant ∆spoT∆relA was generated in the background of

∆relA.

Site-directed Mutagenesis and Western Blot

Site-directed mutagenesis was performed using Q5 Site-Directed Mutagenesis

Kit according to the manufacturer’s instruction (New England Biolabs, Ipswich, MA,

U.S.A). Briefly, to delete the single nucleotide, a pair of primers designed based on the online software NEBaseChanger was used for PCR. After that, PCR products were treated by Kinase-Ligase-Dpnl (KLD) enzyme mix for circularization and template removal. Plasmids were extracted from positive colonies selected on NA plate with

Kanamycin and subsequently sequenced for confirmation.

For western blot, a fragment covering entire coding region and promoter region of dksA was used for in-frame fusion with a HA tag of pBBR1MCS2. Plasmids with or with single nucleotide deletion was transferred into wild-type strain Xcc306 by electroporation. Bacterial cells were harvested, and pellets were resuspended in phosphate-buffered saline (PBS) solution. Cells were ruptured by sonication and the supernatant was centrifuged for 15min at 4℃. Protein samples were separated by 12%

SDS-PAGE and transferred using traditional semi-dry consumables to a nitrocellulose membrane followed by immunoblotting analysis. Anti-HA antibody (Santa Cruz

Biotechnology, Dallas, TX, U.S.A) was added after blocking. After washing the membrane three times, diluted HRP conjugated anti-rabbit secondary antibody (Abcam,

Cambridge, UK) was added. Bands were detected by C-DiGit Scanner (LI-COR

Biosciences, Lincoln, NE, U.S.A.) after adding the SuperSignal West Pico

Chemiluminescent Substrate (Thermo Fisher Scientific, Waltham, MA. U.S.A).

5’ RACE

The transcription start site of dksA was determined by 3’/5’ RACE Kit (Roche,

Indianapolis, IN, U.S.A) following the manufacturer’s instructions with slight changes.

Briefly, total RNA was extracted from wild-type strain Xcc306 at the exponential stage grown in NB medium and subsequently residual DNA was removed by a TURBO DNA- free kit (Ambion, Austin, TX, U.S.A). First strand cDNA was synthesized with a gene- specific primer SP1 using qScript cDNA Synthesis Kit (Quantabio, Beverly, MA, U.S.A).

A homopolymeric A-tail was added to the 3’ end of the first strand cDNA with dATP and terminal transferase. Second round of PCR was performed with gene-specific primer

SP2 and oligo dT-Anchor primer. After dilution of PCR product, a third round PCR was carried out using the PCR Anchor primer and a nested gene-specific primer SP3.

Specific PCR band was purified and ligated into the pGEM-T vector (Promega).

Plasmids from several positive colonies were extracted using Plasmid Miniprep Kit (New

England Biolabs) and sequenced.

RNA Extraction, Sequencing and Data Analysis

Fresh colonies of Xcc306, ∆dksA and ∆spoT∆relA strains were picked up from

NA plate and grown overnight in NB medium at 28℃. Bacterial cells were harvested and washed before the inoculation into the XVM2 medium. At exponential stage

(OD600=0.35), bacterial cells were mixed with 2 volumes of RNA protect bacterial

reagent (Qiagen, Valencia, CA, U.S.A) and RNA was extracted using a RNeasy Mini Kit

(Qiagen). Residual genomic DNA was removed by a TURBO DNA-free kit (Ambion,

Austin, TX, U.S.A). The RNA concentration was determined by a Nanodrop ND-100 spectrophotometer (NanoDrop Technologies, Wilmington. DE, U.S.A). For each strain, there are three biological repeats. The ribosomal RNA was removed using the Ribo-

Zero™rRNA Removal Kit for bacteria (Illumina, Madison, USA) according to the manufacturer’s instructions. The remaining transcripts were fragmented and reverse transcribed. The messenger RNA (mRNA) libraries were constructed using the TrueSeq

Stranded mRNA Sample Prep kit (Illumina). We generated 2 X 125-bp paired end reads for all the RNA samples using Hiseq 3000 sequencer platform (Novo gene, Beijing

China). The RNA raw reads were deposited at NCBI SRA database under the bio- project accession no. PRJNA513356.

To determine the differentially expressed genes (DEGs), the quality control, mapping to the reference genome, transcript abundance quantification and differential expression analysis were performed using Rockhopper (McClure et al. 2013). The

DEGs were selected based on that the absolute value of log2 fold change (wild-type strain/mutant strain) is greater than or equal to 2 and adjusted p-value is less than or equals to 0.01. The R package DESeq2 (v1.20.0) (Love et al. 2014) and Cluster 3.0 (de

Hoon et al. 2004) were used for PCA analysis and cluster analysis. Based on KEGG pathway database, clusterProfiler (2.8.1) was used for pathway enrichment analysis (Yu et al. 2012). Enrichment analysis of all DEGs based on the Clusters of Orthologous

Groups of protein (COG) database was performed using Fisher’s exact test (Abatangelo et al. 2009).

Figure 2-1. Protein sequence alignment of the DksA homologues in Xanthomonas species. The protein sequences (Xac2358, Xcc2255, Xcv2556, Xoo2682) were extracted from KEGG database and aligned using the T-coffee (https://www.ebi.ac.uk/Tools/msa/tcoffee/). The alignment image was generated using Boxshade 3.21 (http://www.ch.embnet.org/software/BOX_form.html). Xac, Xanthomonas axonopodis pv. citri (Current name: Xanthomonas citri subsp. citri); Xcc, Xanthomonas campestris pv. campestris; Xcv, Xanthomonas campestris pv. vesicatoria; Xoo, Xanthomonas oryzae pv. oryzae

Figure 2-2. Correction of the annotation of dksA. (A) Sequence of dksA and its putative promoter region. Putative ribosome binding site (RBS) as well as −10 and −35 promoter consensus motifs are shown in bold. Transcription start site is maked by a vertical arrow. The original and potential translation start codon (GTG) were indicated in red. The underlines with arrow indicate the direction and sequence of two pairs of primers. Single nucleotide deleted by site- directed mutagenesis is shown in red color with a short deletion line. (B) PCR with designed primers and different templates. Numbers 1, and 2 indicate the shorter and longer fragment, respectively. Letters a, b, and c indicate total DNA, RNA, and cDNA samples as the templates, respectively. (C) Western blot SDM1, 2, 3 indicates three single nucleotide deletion (T, T and C), respectively. (D) Transcription sequence identified from 5’ RACE was aligned with genomic DNA sequence of Xcc.

Figure 2-3. Protein domain analyses of DksA, RelA and SpoT in Xcc. The domains of the DksA, SpoT, and RelA are shown. Protein sequence analysis was performed using online software InterPro (https://www.ebi.ac.uk/interpro/).

Figure 2-4. Heatmap and PCA analysis of the sample-to-sample distances. (A) Principal component analysis (PCA) of 9 bacterial samples. (B) Heatmap with cluster analysis gives an overview of similarities and differences between samples. Sample-to-sample distances were calculated from count matrix based on all genes. The heatmap and PCA plot were derived from the distance matrix using R package DESeq2 (v1.20.0).

Figure 2-5. Comparison of gene expression by RT-qPCR and RNA-seq. 20 genes were randomly selected for ∆dksA (A) and 16 genes for ∆spoT∆relA (B) strains. The relative expression of these genes was tested by RT-qPCR using gyrA as an endogenous control. R-squared value and p-value were calculated by R software (v3.5.1).

Figure 2-6. Venn diagram of differentially expressed genes identified from ∆dksA and ∆spoT∆relA.

Figure 2-7. Distribution of differentially expressed genes (DEGs) of ∆dksA and ∆spoT∆relA compared to wild type Xcc according to COG categories. For Xcc306, the numbers indicate the total genes in each category according to The Xanthomonas Genome Browser (http://xgb.leibniz- fli.de/cgi/cog.pl?ssi=free).

Figure 2-8. Pathway enrichment analysis of differentially expressed genes (DEGs) identified from ∆dksA. The pathways above the dash line represent overrepresented pathways (p-value < 0.05).

Figure 2-9. Pathway enrichment analysis of differentially expressed genes (DEGs) identified from ∆spoT∆relA. The pathways above the dash line represent overrepresented pathways (p-value < 0.05).

Figure 2-10. DksA and (p)ppGpp repress the expression of coding genes for tRNA and ribosome proteins.

Figure 2-11. Summary of gene expression changes of coding genes for tRNA (A) and ribosome proteins (B). Log2FC represents log2 fold change (∆/WT).

Figure 2-12. Downregulated DEGs involved in the histidine metabolism pathway. Genes in red represent downregulated DEGs in the ∆dksA and ∆spoT∆relA mutants compared to wild type Xcc306; genes in orange represent downregulated DEGs only in ∆dksA; genes in green represent downregulated DEGs only in ∆spoT∆relA. This pathway is based on KEGG pathway database (https://www.genome.jp/kegg-bin/show_pathway?xac00340).

Figure 2-13. Domain analysis and predicted 3D structure of reannotated DksA of Xcc. (A) Domain detection using SMART online software (Letunic and Bork 2018). Pink indicates low complexity region. (B) Predicted 3D structure of DksA using Phyre2 (Kelley et al. 2015).

Table 2-1. Data quality summary of RNA-seq Raw Clean Effective Error rate Sample Raw reads Clean reads base(G) base(G) rate (%) (%) Q20(%) Q30(%) GC content (%) w1 12248613 11887650 3.67 3.57 97.05 0.03 93.74 86.02 64.67 w2 12975533 12513451 3.89 3.75 96.44 0.03 93.88 86.09 63.7 w3 13026903 12399670 3.91 3.72 95.19 0.03 94.05 86.47 64.72 A1 12855657 12429821 3.86 3.73 96.69 0.03 94.29 86.75 63.31 A2 12355784 11942394 3.71 3.58 96.65 0.03 94.5 87.17 62.86 A3 11238656 10926600 3.37 3.28 97.22 0.03 93.98 86.26 63.41 AA1 12757861 12452748 3.83 3.74 97.61 0.03 94.12 86.61 63.57 AA2 13331484 12997228 4 3.9 97.49 0.03 93.77 85.94 63.53 AA3 12881446 12505742 3.86 3.75 97.08 0.03 93.84 86.09 63.35 w: wild-type Xcc306 A: ∆dksA AA: ∆spoT∆relA; 1,2,3 means the biological repeats for each strain. Raw reads: total amount of reads of raw data. Clean reads: total amount of reads of clean data. Raw bases: (Raw reads) * (sequence length), calculated in G. Clean bases: (Clean reads) * (sequence length), calculated in G. Effective Rate (%): (Clean reads/Raw reads) *100%. Error rate: base error rate. Q20, Q30: (Base count of Phred value > 20 or 30) / (Total base count). GC content: (G & C base count) / (Total base count).

Table 2-2. Functional enrichment analysis based on the COG database Functional classification (COGs) Symbol p-value (∆dksA) p-value(∆spoT∆relA) RNA processing and modification A NS NS Chromatin structure and dynamics B NS NS Energy production and conversion C NS NS Cell cycle control, mitosis and meiosis D NS NS Amino acid transport and metabolism E NS NS Nucleotide transport and metabolism F NS NS Carbohydrate transport and metabolism G 0.0432 NS Coenzyme transport and metabolism H NS NS Lipid transport and metabolism I NS NS Translation, ribosomal structure and biogenesis J NS NS Transcription K NS NS Replication, recombination and repair L NS NS Cell wall/membrane biogenesis M NS NS Cell motility N 0.0209 5.5E-05 Posttranslational modification, protein turnover, chaperones O NS NS Inorganic ion transport and metabolism P 0.0004 0.0068 Secondary metabolites biosynthesis, transport and catabolism Q NS NS General function prediction only R NS NS Function unknown S NS NS Signal transduction mechanisms T NS NS Intracellular trafficking and secretion U 0.0054 0.0056 Defense mechanisms V NS NS Extracellular structures W NS NS Cytoskeleton Z NS NS NS: no statistical significance

Table 2-3. Gene expression of tRNA-coding genes Locus tag Product description Log2FC (∆dksA/WT) Log2FC (∆spoT∆relA/WT) XAC0391 tRNA-Arg -1.06 1.69 XAC0489 tRNA-Gln 1.50 1.63 XAC0490 tRNA-Met 2.29 1.70 XAC0949 tRNA-Gln 2.33 1.31 XAC0954 tRNA-Tyr 2.74 1.40 XAC0955 tRNA-Gly 2.41 0.91 XAC0956 tRNA-Thr 2.17 0.76 XAC0958 tRNA-Trp 2.86 2.58 XAC1048 tRNA-Pro 2.44 1.94 XAC1049 tRNA-Arg 2.57 2.14 XAC1050 tRNA-His 2.19 2.53 XAC1073 tRNA-Lys 2.26 2.69 XAC1076 tRNA-Leu 0.31 0.06 XAC1082 tRNA-Val 2.04 2.03 XAC1083 tRNA-Asp 2.52 2.95 XAC1084 tRNA-Asp 1.20 1.83 XAC1092 tRNA-Ser 2.72 1.63 XAC1108 tRNA-Ser -1.66 -1.45 XAC1134 tRNA-Val -0.23 0.55 XAC1257 tRNA-Thr 0.59 1.84 XAC1447 tRNA-Leu -0.33 -0.46 XAC1565 tRNA-Leu -0.03 1.01 XAC1656 tRNA-Ser 1.65 2.03 XAC1744 tRNA-Ser 0.40 1.76 XAC1750 tRNA-Arg 1.61 1.32 XAC1751 tRNA-Arg 1.87 1.09 XAC1782 tRNA-Glu 0.85 0.84 XAC1809 tRNA-Arg 1.06 0.82 XAC2056 tRNA-Leu -1.14 1.30 XAC2058 tRNA-Glu 0.96 -0.17 XAC2059 tRNA-Ala 1.77 0.46 XAC2060 tRNA-Glu 1.39 -0.46 XAC2061 tRNA-Ala 1.78 -0.04 XAC2094 tRNA-Gly 2.92 0.72 XAC2095 tRNA-Cys 2.95 0.46 XAC2096 tRNA-Gly 2.64 0.81 XAC2104 tRNA-Leu 0.34 0.66 XAC2339 tRNA-Pro -1.00 -5.00 XAC2417 tRNA-Ala 0.52 0.74 XAC2560 tRNA-Phe 2.53 1.16 XAC2586 tRNA-Pro 0.10 -0.42 XAC2624 tRNA-Val 1.46 0.50

Table 2-3. Continued Locus tag Product description Log2FC (∆dksA/WT) Log2FC (∆spoT∆relA/WT) XAC2627 tRNA-Asn -1.00 -0.33 XAC2690 tRNA-Met 3.30 3.09 XAC2705 tRNA-Leu 2.71 2.27 XAC3138 tRNA-Lys 1.93 1.16 XAC3299 tRNA-Gly 0.97 0.67 XAC3786 tRNA-Met -1.07 0.31 XAC3894 tRNA-Ile 2.51 1.01 XAC3895 tRNA-Ala 2.57 1.02 XAC3963 tRNA-Ala 7.62 6.65 XAC4015 tRNA-Thr 0.45 1.44 XAC4289 tRNA-Ile 2.51 1.01 XAC4290 tRNA-Ala 2.57 1.02

Table 2-4. Gene expression of ribosome protein genes Gene name Locus tag Product description Log2FC Log2FC (∆dksA/WT) (∆spoT∆relA/WT) rplM XAC0487 50S ribosomal protein L13 1.41 0.87 rpsI XAC0488 30S ribosomal protein S9 1.32 0.98 rplY XAC0951 50S ribosomal protein L25 2.11 2.00 rplK XAC0961 50S ribosomal protein L11 1.55 1.77 rplA XAC0962 50S ribosomal protein L1 1.77 2.00 rplJ XAC0963 50S ribosomal protein L10 0.92 1.72 rplL XAC0964 50S ribosomal protein L7/L12 1.13 1.76 rpsL XAC0967 30S ribosomal protein S12 1.10 1.76 rpsG XAC0968 30S ribosomal protein S7 1.28 2.03 rpsJ XAC0971 30S ribosomal protein S10 1.13 1.95 rplC XAC0972 50S ribosomal protein L3 1.63 2.55 rplD XAC0973 50S ribosomal protein L4 1.53 2.61 rplW XAC0974 50S ribosomal protein L23 0.97 1.86 rplB XAC0975 50S ribosomal protein L2 1.22 2.14 rpsS XAC0976 30S ribosomal protein S19 1.13 2.38 rplV XAC0977 50S ribosomal protein L22 1.26 2.53 rpsC XAC0978 30S ribosomal protein S3 1.05 2.36 rplP XAC0979 50S ribosomal protein L16 0.67 1.71 rpmC XAC0980 50S ribosomal protein L29 0.66 1.69 rpsQ XAC0981 30S ribosomal protein S17 1.25 2.48 rplN XAC0982 50S ribosomal protein L14 0.93 2.19 rplX XAC0983 50S ribosomal protein L24 0.98 2.16 rplE XAC0984 50S ribosomal protein L5 1.01 2.15 rpsN XAC0985 30S ribosomal protein S14 1.52 2.84 rpsH XAC0986 30S ribosomal protein S8 2.08 1.75 rplF XAC0987 50S ribosomal protein L6 2.12 1.99 rplR XAC0988 50S ribosomal protein L18 2.17 2.55 rpsE XAC0989 30S ribosomal protein S5 1.88 2.50 rpmD XAC0990 50S ribosomal protein L30 1.86 2.34 rplO XAC0991 50S ribosomal protein L15 1.96 2.57 rpsM XAC0993 30S ribosomal protein S13 1.64 1.90 rpsK XAC0994 30S ribosomal protein S11 1.46 1.58 rpsD XAC0995 30S ribosomal protein S4 1.75 2.15 rplQ XAC0997 50S ribosomal protein L17 1.05 2.18 rpmF XAC1122 50S ribosomal protein L32 1.41 -0.35 rplU XAC1248 50S ribosomal protein L21 1.43 1.31 rpmA XAC1249 50S ribosomal protein L27 2.11 1.99 rpsT XAC1251 30S ribosomal protein S20 2.59 0.83 rpsP XAC1292 30S ribosomal protein S16 2.69 2.58 rplS XAC1295 50S ribosomal protein L19 2.63 2.19 rpsB XAC1422 30S ribosomal protein S2 1.80 0.60 rpsF XAC1620 30S ribosomal protein S6 2.48 2.37

Table 2-4. Continued Gene name Locus tag Product description Log2FC Log2FC (∆spoT∆relA/WT) (∆dksA/WT) rpsR XAC1621 30S ribosomal protein S18 2.39 2.26 rplI XAC1622 50S ribosomal protein L9 2.99 3.19 rpsA XAC2298 30S ribosomal protein S1 1.79 2.23 rpmJ XAC2300 50S ribosomal protein L36 3.59 2.26 rplT XAC2591 50S ribosomal protein L20 2.15 1.67 rpmI XAC2592 50S ribosomal protein L35 2.19 0.82 rpsO XAC2684 30S ribosomal protein S15 1.91 1.44 - XAC2827 30S ribosomal protein S31 2.36 2.12 rpmE XAC3389 50S ribosomal protein L31 2.83 1.00 rpsU XAC3872 30S ribosomal protein S21 3.39 0.53 rpmG XAC4158 50S ribosomal protein L33 2.55 0.93 rpmB XAC4159 50S ribosomal protein L28 2.66 0.96 rpmH XAC4374 50S ribosomal protein L34 2.00 1.51

CHAPTER 3 EXPERIMENTAL ASSAYS OF VIRULENCE-ASSOCIATED TRAITS REGULATED BY (p)ppGpp AND DksA

Introduction

Xanthomonas citri subsp. citri (Xcc) is the causal agent of citrus canker (CC) disease, an economically important disease of most commercial citrus cultivars. Xcc is disseminated via wind-driven rain and enter the intercellularcellular spaces of host leaves or fruit by stomata or wounds (Brunings and Gabriel 2003; Ference et al. 2017).

The typical canker symptoms include raised lesions surrounded by an oily, water- soaked margin and a yellow ring or halo (Brunings and Gabriel 2003; Ference et al.

2017). Before the infection starts, Xcc is assumed to be able to survive on the plant surface called phyllosphere as an epiphyte (Gent et al. 2005; Zarei et al. 2018).

The phyllosphere provides very limited nutrients and even apoplastic nutrients are not easily available for the bacterial pathogen (Fatima and Senthil-Kumar 2015). In addition to nutrient scarcity, physical factors make phyllosphere more unfavorable for bacterial pathogen, such as temperature shifts, desiccation, exposure to UV radiation and reactive oxygen species (ROS) (Delmotte et al. 2009; Vorholt 2012). Beyond physical factors, plant immunity is another layer of barrier. Plant host can recognize conserved pathogen-associated molecular patterns (PAMPs) via pattern recognition receptors (PRRs) on the plant cell surface and trigger PAMP-triggered immunity (PTI).

In addition, some T3S effectors are perceived by plant cells via nucleotide-binding leucine rich receptors (NB-LRRs) to trigger effector-triggered immunity (ETI) (Dodds and Rathjen 2010).

To survive and multiply, Xcc has evolved multiple virulence-associated factors to overcome those host and nonhost stresses. Virulence-associated genes revealed by

genome sequencing include and not limited to genes encoding the type II secretion system (T2SS), cell wall-degrading enzymes (CWDEs), rpf (regulation of pathogenicity factors) gene cluster, type III secretion system (T3SS), extracellular polysaccharide

(EPS) , lipopolysaccharide (LPS) , type IV pili and flagella (da Silva et al. 2002).

Although the importance of most virulence-associated factors to virulence and/or epiphytic fitness has been demonstrated, the underlying regulatory mechanisms remain elusive for many virulence factors (Büttner and Bonas 2010). In addition, bacterial virulence is also correlated with metabolism such as iron homeostasis (Franza and

Expert 2013), galactose metabolism (Rashid et al. 2016) and fatty acid metabolism

(Teper et al. 2019).

It is not fully understood how plant pathogens including Xcc coordinate the expression of various virulence factors and make metabolic changes for host adaptation during the colonization process. In vitro transcriptome analysis using plant intercellular environment-mimic medium XVM2 showed 38 out of 279 virulence-associated genes are differentially expressed (Astua-Monge et al. 2005). A proteomic study in planta revealed that 79 bacterial proteins accumulated more during infection compared with that in NB medium and those proteins are involved in biofilm synthesis, lipopolysaccharides biosynthesis, iron uptake and metabolism and ROS adaptation

(Moreira et al. 2017).

The stringent response is closely related to the bacterial lifestyle, which indicates that environmental niches shift can suppress or activate stringent response (Boutte and

Crosson, 2013). Accumulated evidence shows that stringent response not only helps bacteria adapt to nutrient-deprivation environment, but also enhances bacterial

virulence (Dalebroux et al., 2010). Based on our transcriptomic analysis of stringent response regulators DksA and (p)ppGpp, here I further characterized the effect of DksA and (p)ppGpp on multiple virulence traits and highlight the positive and negative interplay between DksA and (p)ppGpp. Overall, these results give insight into how Xcc utilizes stringent response regulatory system to coordinate the expression of multiple virulence traits as well as metabolic processes to promote virulence and host adaptation.

Results

DksA and SpoT Are Required for Full Virulence

Previously, a high-throughput screening for genes of Xcc involved in citrus canker symptom development in our laboratory showed that a Tn5 transposon insertion mutant (dksA:tn5) was unable to induce canker symptoms (Yan and Wang, 2012). To explore the effect of stringent response regulators on pathogenicity, three deletion mutants ∆dksA, ∆relA and double mutant ∆spoT∆relA were produced by double crossover homologous recombination.

To test the pathogenicity, wild-type strain Xcc306, ∆dksA, ∆relA, double mutant

∆spoT∆relA and complemented strains carrying recombinant plasmids were inoculated by syringe infiltration into young Valencia sweet orange (Citrus sinensis) leaves. The

∆dksA strain with/without empty plasmid did not produce canker symptoms whereas the wild-type strain and complemented strain caused typical canker symptoms characterized by necrosis with a corky appearance (Figure 3-1A). While we did not observe any difference in virulence between Xcc306 and ∆relA, ∆spoT∆relA induced substantially weaker canker symptoms including water soaking (Figure 3-1B). The complemented strain ∆spoT∆relA (spoT) partially restored the canker symptoms (Figure

3-1B). Consistent with reduced symptom development, bacterial growth of ∆dksA and

∆spoT∆relA was substantially reduced in planta compared to that of the wild-type

Xcc306 and complemented strains (Figure 3-2A).

Bacterial growth in vitro was also tested in the defined XVM2 medium. XVM2 medium was reported to mimic the environment of plant intracellular spaces and induce hrp gene expression (Astua-Monge et al., 2005; Wengelnik et al., 1996). ∆dksA growth rate was significantly lower than that of the wild-type strain in the XVM2 medium (Figure

3-2B), which might partially explain the reduced canker symptoms of the ∆dksA mutant.

Complementation of ∆dksA through plasmid expression of DksA successfully recovered the growth of ∆dksA in the XVM2 medium (Figure 3-2B). The ∆relA, and ∆spoT∆relA mutants displayed similar or slightly enhanced growth in the XVM2 and NB media

(Figure 3-2B and 2C). To sum up, our data show that stringent response regulatory genes dksA and spoT are required for the pathogenicity of Xcc.

(p)ppGpp and DksA Positively Regulate T3SS and T2SS

Xanthomonas possesses six protein secretion systems (type I to type VI). T2SS and T3SS have been shown to be important to Xanthomonas virulence (Büttner and

Bonas, 2010). Functional enrichment analysis showed that bacterial secretion systems are under control of DksA and (p)ppGpp of Xcc (Figure 2-8 and 2-9). Upon closer inspection, we identified that T3SS and the xcs T2SS genes are significantly downregulated in the ∆dksA and ∆spoT∆relA mutants compared to the wild type (Table

3-1). The hrp/hrc gene cluster, which encodes the T3SS, is composed of 24 genes distributed in several operons. 21 (87.5%) and 17 (70.8%) T3SS genes were downregulated DEGs in the ∆dksA and ∆spoT∆relA mutants compared to the wild type

Xcc306, respectively (Table 3-2). The xcs T2SS gene cluster contains 12 genes

encoded in a single operon and, 11 and 7 T2SS genes are downregulated DEGs in the

∆dksA and ∆spoT∆relA mutants compared to wild type Xcc306, respectively (Table 3-

1). It should be noted that Xcc harbors a second T2SS (xps) that was reported to be more significant to bacterial virulence (Szczesny et al. 2010). No difference was observed in the expression of the xps genes in either ∆dksA or ∆spoT∆relA compared to the wild type Xcc306.

In addition to the secretion apparatuses, we also observed significant differences in T3SS key regulatory gene hrpX, T3S effector-coding genes and T2SS-related hydrolase genes (Table 3-1 and 3-2). Plate assay of extracellular degradative enzymes showed that DksA contributed to protease activity and amylase activity but inhibited xylanase activity (Figure 3-3). Xcc encodes approximately 30 effector genes as described in The Xanthomonas Resource website (http://www.xanthomonas.org/t3e. html). Our data showed that 15 (50.0%) and 7 (23.3%) effector genes are downregulated DEGs in the ∆dksA and ∆spoT∆relA mutants compared to the wild type

Xcc306, respectively (Table 3-2).

To confirm the regulatory effect on the T3SS-related genes, RT-qPCR was used to measure the mRNA level of selected genes including two regulatory genes (hrpG, hrpX), one T3SS component gene (hrpF) and two effector genes (xopN, xopAU) in the

∆dksA and ∆spoT∆relA mutants compared to the wild type Xcc. The transcript level of selected genes in ∆dksA and ∆spoT∆relA were 2 to 5 times lower than that of wild-type

Xcc306 (Figure 3-4A). Consistent with RT-qPCR results, GUS assays using translational fusion plasmids showed that the promoter activity of four selected genes

(hrpG, hrpX, xopAU, and hrpF) in either mutant strain was substantially lower than that

of wild-type Xcc306 (Figure 3-4B). T3SS and effectors are known to be responsible for the hypersensitive response (HR) of Xcc on non-host plant Nicotiana benthamiana

(Adlung et al. 2016; Sankaranarayanan et al. 2014). To test whether the ∆dksA and

∆spoT∆relA mutants were affected in the ability to trigger HR, wild-type strain Xcc306, mutant strains including a T3SS-disrupted mutant hrcV:Tn5, and complemented strains were inoculated by syringe infiltration on N. benthamiana. In contrast to wild-type

Xcc306 and complemented strains, hrcV:Tn5, ∆dksA and ∆spoT∆relA were unable to cause HR (Figure 3-4C). Taken together, these results indicate that DksA and ppGpp positively regulate T3SS system to promote virulence.

(p)ppGpp and DksA Repress the Expression of Motility-related Genes

The bacterial flagellum biosynthesis consumes considerable metabolic energy and has been considered as a virulence trait (Josenhans and Suerbaum 2002). Both the flagellum-dependent and type IV pilus-dependent motility are present in Xcc and play important roles in adhesion, biofilm formation and virulence (Dunger et al. 2014;

Malamud et al. 2011). COG-based and KEGG-based enrichment analyses showed that motility-related genes were affected in both ∆dksA and ∆spoT∆relA strains. For flagellar assembly genes, 25 of 27 were upregulated DEGs in the ∆spoT∆relA strain while only 3 were upregulated DEGs in the ∆dksA (Figure 3-5A). Type IV pilus biogenesis-related genes displayed different expression patterns: 22 of 28 type IV pilus biogenesis-related genes including the regulatory gene fimX (XAC2398) were upregulated DEGs in ∆dksA whereas no upregulated DEGs were found in ∆spoT∆relA (Table 3-3 and Figure 3-5).

To test whether the gene expression changes affect flagellar morphology, wild- type Xcc306, ∆dksA and ∆spoT∆relA grown on the XVM2 agar medium for observation under transmission electron microscope (TEM). No obvious differences in flagellar

morphology was observed (Figure 3-7). Bacterial motility was tested on 0.25% semi- solid NA plates. Unexpectedly, ∆dksA and ∆spoT∆relA showed reduced motility compared to Xcc306, ∆relA and complemented strains (Figure 3-5B). To sum up, these results indicate that stringent response regulators DksA and (p)ppGpp inhibit the gene expression involved in flagellar and pili biosynthesis and affect motility in a flagellum- independent manner.

(p)ppGpp and DksA Positively Regulate TonB-dependent Transporter Genes

TonB-dependent transporters (TBDTs) are bacterial outer membrane proteins that are used for active uptake of nutrients such as siderophores, vitamin B12, nickel, and carbohydrates in Gram-negative bacteria (Schauer et al. 2008; Noinaj et al. 2010).

Xcc306 encodes 46 TBDTs (da Silva et al. 2002), which are all listed (Table 3-4).

Heatmaps displayed the gene expression profile of TBDTs for both ∆dksA and

∆spoT∆relA strains (Figure 3-8). To be specific, 23 out of 46 (50%) TBDTs were downregulated DEGs (log2FC > -2) in ∆dksA and ∆spoT∆relA, respectively (Table 3-4).

A previous study showed that TBDTs were overrepresented in Xanthomonas spp. and involved in the sucrose transport and metabolism as well as virulence through a conserved sux (sucrose utilization in Xanthomonas) locus in Xanthomonas campestris pv. campestris (Blanvillain et al. 2007). The same sux locus was also found in Xcc and composed of four genes encoding for a TBDT (XAC3489), an amylosucrase

(XAC3490), a sugar inner membrane transporter (XAC3488) and a regulatory protein

(XAC3487) (Figure 3-9A). Some of these genes are substantially downregulated especially in ∆dksA strain (Figure 3-9A). Compared to the wild-type strain, GUS assay showed that the promoter activity of suxA is substantially reduced in ∆spoT∆relA (Figure

3-9B). No statistical significance was observed between wild-type strain and ∆dksA

(Figure 3-9B). To sum up, these results that DksA and (p)ppGpp promote the expression of TBDTs which probably enhance the uptake of nutrients including sucrose.

Differential Regulation of Xss Gene Cluster by (p)ppGpp and DksA

Although DksA mostly acts cumulatively with (p)ppGpp, divergent and even opposite effects on gene expression and specific traits were reported as well

(Magnusson et al. 2007; Lyzen et al. 2009, 2016). We conducted cluster analysis and displayed the gene expression profile of 482 common DEGs in ∆dksA and ∆spoT∆relA strains (Figure 3-10). Among them, 426 genes were downregulated, and 40 genes were upregulated in both mutant strains compared to the wild type Xcc. Divergent regulation between ∆dksA and ∆spoT∆relA was observed for 16 genes (Table 3-5). Interestingly, we found that the xss gene cluster (composed of mhpE, xsuA and xssA-E), which is involved in biosynthesis, export, and utilization of siderophore (Pandey and Sonti 2010;

Pandey et al. 2017), was upregulated in ∆dksA and downregulated in ∆spoT∆relA compared to the wild type Xcc (Figure 3-11A).

Consistent with the RNA-seq data, GUS assay showed that the promoter activity of mphE is significantly increased in ∆dksA and slightly decreased in ∆spoT∆relA

(Figure 3-11B). The ability of these mutant strains to produce siderophore was assessed by CAS assay. The ∆dksA mutant produced a bigger halo around the colonies indicting more siderophore production (Figure 3-11C). However, no obvious difference was observed between wild-type Xcc306, ∆spoT∆relA and ∆spoT∆relA

(spoT) strains (Figure 3-11C). Taken together, these results indicate that xss gene cluster is differentially regulated by DksA and (p)ppGpp, and the DksA-mediated repression of siderophore production might be due to the inhibition of xss gene cluster expression.

Discussion and Conclusion

To adapt to host environment, plant pathogens need to rely on sensory system to monitor outside signals and therefore modulate physiological processes, metabolic pathways and virulence. It has long been known that plant pathogens can activate

T3SS in contact with host cells (Tang et al. 2006). However, beyond virulence induction, less is known about how plant pathogens prepare for infection regarding to physiological and metabolic changes. Here, our transcriptomic analyses revealed that stringent response regulators DksA and (p)ppGpp of Xcc have a profound effect on gene expression involved in biosynthesis of stable RNA, ribosome proteins, flagellum and type IV pilus, TonB-dependent transporters as well as T3SS. Further genotypic analysis showed changes in associated traits like pathogenicity, HR induction, motility and siderophore production.

Our study showed that ∆dksA, ∆spoT∆relA rather than ∆relA severely affected bacterial pathogenicity and growth in compatible host plant. Since SpoT is a bifunctional enzyme which has the capability to synthesize and degrade the (p)ppGpp, certain amount of (p)ppGpp is supposed to be present in ∆relA strain. This indicates that DksA,

(p)ppGpp are required for full virulence, which is also found in other plant pathogens including Erwinia amylovora and Pseudomonas syringae (Chatnaparat et al. 2015a,

2015b; Ancona et al. 2015). In enterohaemorrhagic E. coli (EHEC), (p)ppGpp and DksA can directly activate transcription of two transcriptional regulators Pch and Ler which control the gene expression in the locus of enterocyte effacement (LEE) encoding for

T3SS and secreted proteins (Nakanishi et al. 2006). The synergistic effect of (p)ppGpp and DksA also seems to exist in Xcc since both positively regulate hrp/hrc gene cluster encoding for T3SS. However, whether regulatory genes hrpG and/or hrpX of Xcc can be

the direct target genes of DksA and (p)ppGpp needs to be tested. In addition, to fully understand the roles of (p)ppGpp in virulence, it is necessary to establish the relationship between (p)ppGpp level of bacterial pathogen and gene expression of

T3SS in planta.

It is not surprising to see that under nutrient limitation, (p)ppGpp and DksA inhibit the biosynthesis of ribosome and cell surface organelles (flagella and pili) since high amount of energy can be consumed in synthesizing those macromolecular complexes.

The negative regulation of ribosome and/or flagella biosynthesis was also observed in

E.coli by DNA Microarray (Traxler et al. 2008; Durfee et al. 2008). Another in vitro study showed that DksA and (p)ppGpp directly regulate the flagellar cascade of E.coli by inhibiting the transcription of two regulatory genes flhDC and fliA (Lemke et al. 2009). It is possible that Xcc may adopt a similar mechanism to achieve the control of flagellar transcriptional cascade. Interestingly, our RNA-seq data indicate that in Xcc, (p)ppGpp has strong negative effect on flagellar assembly genes while DksA mainly controls gene expression of type IV pili. Since no obvious difference was observed in flagella morphology between wild-type Xcc306 and ∆dksA and ∆spoT∆relA strains, other mechanisms remain to be discovered to explain the reduced motility of the mutant strains.

TonB-dependent transporters (TBDTs) are a big family of outer membrane proteins which actively uptake outside nutrients like siderophores, vitamin B12, nickel, and carbohydrates in Gram-negative bacteria (Schauer et al., 2008; Noinaj et al., 2010).

One community proteogenomics study revealed that various TBDTs were highly expressed in phyllosphere bacteria from soybean, clover, and Arabidopsis thaliana

plants indicating the importance of TBDTs in microbial adaptation to the phyllosphere

(Delmotte et al. 2009). Although it is unclear whether overrepresented TBDTs are required for epiphytic fitness of Xanthomonas spp., the roles of TBDTs in sucrose uptake and virulence were proposed (Blanvillain et al. 2007). Given that some effectors target plant SWEET genes to cause nutrient efflux, it will be interesting to test whether effector-mediated sucrose efflux can be acquired by bacterial TBDTs (Jacques et al.

2016). In addition, based on our findings, it is possible that Xanthomonas spp. apply stringent response regulators (p)ppGpp and DksA to achieve the co-regulation of T3SS and TBDTs.

Due to the low solubility and potential toxicity of iron, bacteria evolved several iron acquisition mechanisms including siderophore-based iron transport system and iron homeostasis is tightly regulated in the cell (Andrews et al. 2003). The comparison of the regulon between DksA and (p)ppGpp indicated that xss gene cluster involved in siderophore production, transport and metabolism is differentially regulated. The following GUS assay showed that the promoter activity of mphE increased a lot in

∆dksA and slightly decreased ∆spoT∆relA. Consistent with this result, more siderophore production was observed in ∆dksA. One study from E.coli showed that iron limitation can induce SpoT-dependent accumulation, which increases the expression of iron uptake genes (Vinella et al. 2005). Another study from Salmonella enterica suggested that DksA repressed the expression of some iron homeostasis-related genes in response to nitrosative stress (Crawford et al. 2016). These studies lead us to suggest that DksA may repress the expression of the iron-uptake related genes such as xss

74 cluster at nutrient-rich condition while this repression can be covered by the positive effect of accumulated (p)ppGpp under iron-limitation condition.

Based on our findings, a hypothesis was proposed to show how DksA and

(p)ppGpp contribute to host adaptation and colonization of Xcc (Figure 3-12). To survive and multiply, Xcc in host apoplast space needs to conquer the barriers including low nutrients, less water and plant immunity defense. During this process, stringent response is supposed to be activated to a certain extent by outside unknown stimuli.

Accumulated (p)ppGpp altogether with DksA will relocate cellular resources and save biosynthetic energy by repressing the biosynthesis of stable RNA, ribosome proteins, flagella as well as type IV pili. Meanwhile, expression of coding genes for T3SS and

TonB-dependent transporters are enhanced to promotes the bacterial virulence and nutrient uptake. All these changes help the pathogen achieve a balance between fitness and virulence and contribute to host adaptation and colonization.

Materials and Methods

Pathogenicity and HR Test

Pathogenicity assays were performed in a quarantine greenhouse located at the

Citrus Research and Education Center, Lake Alfred, FL, U.S.A. Wild-type strain Xcc306,

∆dksA, ∆relA, ∆spoT∆relA and complemented strains were cultured overnight in NB medium at 28℃. After centrifugation, bacterial pellets were washed and resuspended in sterile water. The concentration of bacterial solution was adjusted to 108 CFU/mL

(OD600 = 0.2). Unmatured leaves of sweet orange (Citrus sinensis) were infiltrated using needleless syringes. Both sides of leaves were photographed after canker symptom appeared.

To test the ability to induce HR on Nicoliana Benthamiana, strains at the concentration of 108 CFU/mL were infiltrated into tabaco leaves with a needleless syringe. Leaves were photographed after HR occurred. All experiments were repeated three times in triplicates with similar results.

Bacterial Growth Assay

For bacterial growth in planta, wild-type strain Xcc306, ∆dksA, ∆relA, ∆spoT∆relA and derived strains were cultured overnight in NB medium at 28℃. After centrifugation, bacterial pellets were washed and resuspended in sterile water. The concentration of

7 bacterial solution was adjusted to 10 CFU/mL (OD600 =0.02). Then sweet orange leaves were inoculated with needleless syringes. To measure the bacterial population, three leaf disks were taken out the from inoculation sites per strain per time point. Leaf disks with a diameter of 0.6 cm were put into 1.5 mL Eppendorf tube containing sterile water and ground by a drill. The bacterial solution was serially diluted and spotted on

NA plates for incubation at 28℃. Bacterial population given as colony-forming units per milliliter (CFU/mL) was calculated after 48 hours.

For bacterial growth in vitro, NB and XVM2 media were used respectively. Fresh overnight bacterial culture was inoculated into 50 mL centrifuge tube containing 10 mL medium. The initial concentration was adjusted to OD600 = 0.05. At each time point, 200 mL bacterial culture of each strain was taken out and measured suing microplate spectrophotometer (Bio-Rad Laboratories, Hercules, CA, U.S.A). All these experiments were repeated at least twice in triplicates with similar results.

Extracellular Enzyme Activity Assay

The methods follow a previous paper (Szczesny et al. 2010) with minor changes.

Briefly, overnight bacterial culture was centrifuged down and washed once with sterile

water. The pellets were then resuspended in sterile water and adjusted to 10^8 CFU/mL

(OD600=0.4). 5µL suspension was spotted on suitable plates and diameter of halo zones was measured after 2-4 days incubation at 28℃. For the test of protease and xylanase activity, NA plates contains 0.5% skimmed milk and 0.1% remazol brilliant blue R-D-

Xylan (Sigma-Aldrich, St. Louis, MO, U.S.A), respectively. For the test of cellulase and amylase activities, plates containing 1% carboxymethyl cellulose (Sigma-Aldrich) and

1% starch were used, respectively. The plates with carboxymethyl cellulose were stained with 0.2% Congo red (Sigma-Aldrich) solution and destained with 0.5M NaCI.

Starch plates were stained with lugol’s iodine (Ricca Chemical, Arlington, TX, U.S.A) and destained with ethanol. All the experiments were repeated at least twice in triplicates

RNA Extraction and RT-qPCR

To extract bacterial total RNA, fresh colonies of each strain were picked up and grown overnight in NB medium at 28℃. Then bacterial solution was diluted 100 times in

XVM2 medium. The bacterial cells at exponential stage (OD600=0.35) were mixed with 2 volumes of RNA protect bacterial reagent (Qiagen) and RNA samples were extracted with the RNeasy Mini Kit (Qiagen). Residual genomic DNA was removed by a TURBO

DNA-free kit (Ambion). The RNA concentration was determined by a Nanodrop ND-100 spectrophotometer (NanoDrop Technologies).

For two-step RT-qPCR, 1µg RNA was used for cDNA synthesis using qScript cDNA Synthesis Kit (Quantabio, Beverly, MA, U.S.A). Primers were designed by online software Primer 3. Reaction mixture was prepared following the instruction of SYBR

Green Master Mix kit (Clontech Laboratories, Mountain View, CA, U.S.A) on a

QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA.

77 U.S.A). The expression level of gyrA was used as endogenous control for target genes.

The fold change of target genes expression was calculated using the formula 2-∆∆CT

(Livak and Schmittgen 2001). This experiment was repeated three times in triplicates with similar results.

Motility Assay

Bacterial motility was tested using semi-solid NA plate containing 0.3% agar.

Bacteria were grown in NB overnight with shaking at 200 rpm, and then centrifuged down, washed, and diluted to OD600 = 0.5 in sterile water. 5 µL suspension of each strain was spotted on the center of plates and incubated at 28℃. Plates were photographed after 48 hours. The assay was repeated three times independently in triplets with similar results.

GUS Activity Assay

To generate GUS reporter plasmid (pGUS), E. coli β-Glucuronidase (gus) gene followed by a T7 terminator were cloned in reverse orientation to the lac promoter in pBBR1MCS-5. To generate the in-frame constructs, the putative promoter regions of the tested genes were amplified and inserted into the upstream of the gus gene in pGUS. These constructs were transferred into wild-type Xcc306 and mutant strains by electroporation. The procedures was based on one previous report with minor modification (Zhou et al. 2017). Briefly, bacterial cells grown in XVM2 medium were harvested and resuspend in PBS buffer and followed by sonication. 20µL clear supernatant was added to 80µl PBS buffer containing 1.25 µM p-Nitrophenyl β-D- glucopyranoside (PNPG) in a 96-well plate. When yellow color developed during incubation at 37℃, 100µL stop buffer (0.4M Na2CO3) was added to stop the reaction immediately. Meanwhile, the reaction time was recorded. The absorbance of reaction

78 solution was measured at 405 nm and normalized by protein amount that was measured by Bradford method at 595 nm using a Bio-Rad Protein Assay Kit (Bio-Rad).

GUS activity (AU) was determined as Abs405 / (time in min × Abs595 × 0.02). The experiment was repeated at least twice in triplicates with similar results.

Siderophore Production Assay

The recipe for CAS assay was based on one previous report (Cordero et al.

2012). Briefly, 1L CAS-Fe- HDTMA dye was prepared as follows: 10 mL of a 10 mM ferric chloride (FeCl3) in 100 mM hydrochloric acid (HCl) solution was mixed with 590 mL of a 1 mM aqueous solution of CAS (Chrome Azurol S). The Fe-CAS solution was then added to 400 mL of a 2 mM aqueous solution of HDTMA. The resulting CAS-Fe-

HDTMA solution was autoclaved for 20 minutes. The solution was stored at room temperature and covered with aluminum foil. For 100 mL of CAS-agar, 10 mL of CAS-

Fe-HDTMA dye was mixed with 90 mL of NB-based media supplemented with DP (2,2’- dipyridyl). To prepare samples, bacterial strains grown on NA plate were spotted onto the CAS agar plate and incubated at 28℃ for 36 hours. Halo phenotype around colonies is indicative of siderophore production. The assay was repeated three times independently with similar results.

Strains, Plasmids and Primers

Strains and plasmids are listed in the Table 6 and all and primers see the Table

7. The two tables list all the strains, plasmids and primers used for the whole study.

79 Figure 3-1. Pathogenicity test using sweet orange leaves. (A) Inoculation of Xcc306, ∆dksA and complemented strain. (B) Inoculation of ∆relA, ∆spoT∆relA and complemented strains. The concentration of bacterial strain is 108 CFU/mL and symptoms are photographed at indicated time points. All the experiments were repeated three times independently with similar results.

80 Figure 3-2. Bacterial growth test in vivo and in vitro. A) bacterial population of Xcc306, ∆dksA, ∆relA, ∆spoT∆relA and complemented strain at indicated time points in planta. B) Bacterial growth curve in XVM2 medium. C) Bacterial growth curve in NB medium. Values represent means ± SD (n=3). One-way ANOVA with post-hoc Tukey HSD test was applied to compare multiple strains. Statistical significance means p-value < 0.01. All the experiments were repeated at least twice independently with similar results.

Figure 3-3. DksA contributes to protease activity and amylase activity but inhibites xylanase activity. A) Protease assay and summary of halo diameter. B) Amylase assay and summary of halo diameter. C) Xylanase assay and summary of halo diameter. Values represent means ± SD and star means the statistical significance using Student’s t-test (p-value < 0.05, n=3). All the experiments are repeated independently three times with similar results.

Figure 3-4. DksA and (p)ppGpp negatively regulate expression of the T3SS genes. A) mRNA abundance measurement of the selected T3SS genes by RT-qPCR. For normalization, gyrA was used as an endogenous control. B) Promoter activity of the indicated promoters was measured for Xcc306, ∆dksA and ∆spoT∆relA strains harboring translational fusion plasmids. C) Bacterial cultures (2 × 10^8 CFU/mL) of the indicated strains were infiltrated into Nicotiana benthamiana leaves. Leaves were photographed at 8 days post inoculation. (A, B) values represent means ± SD and star means the statistical significance using Student’s t-test (p-value < 0.05, n=3). All the experiments are repeated independently three times with similar results.

Figure 3-5. Negative regulation of flagellar assembly genes by (p)ppGpp. A) The schematic diagram of bacterial flagellum. Gene expression changes in the ∆dksA and ∆spoT∆relA strains compared to wild type Xcc are indicated by different colors. OM: outer membrane; PL: peptidoglycan layer; CM: cytoplasmic membrane. B) Motility test of bacteria on 0.25% nutrition agar plate. Plates were incubated for 48 hours before photographing. This experiment was repeated independently three times with similar results.

Figure 3-6. Negative regulation of putative type IV pili structure and regulatory genes by DksA.

Figure 3-7. TEM observation of Xcc306, ∆dksA and ∆spoT∆relA strains. Fresh bacteria of Xcc306, ∆dksA and ∆spoT∆relA from XVM2 solid agar medium were transferred to formvar/carbon coated 400 mesh copper grids and strained with 0.25% aq ammonium molybdate. The grids were allowed to dry for 1 hour before observation with a FEI Morgagni 268 transmission electron microscope (FEI, Oregon, U.S.A). The black arrows indicate bacterial polar flagella.

Figure 3-8. Heatmap of gene expression changes of TonB-dependent genes in ∆dksA and ∆spoT∆relA strains.

Figure 3-9. Positive regulation of sux gene cluster by DksA and (p)ppGpp. A) The organization of the sux locus and the gene expression changes in the ∆dksA and ∆spoT∆relA strains compared to wild type Xcc. B) Promoter activity test of suxA in indicated strains. Student t test was conducted and asterisk means statistical significance (n=3, p-value < 0.05).

88 Figure 3-10. Gene expression profile of common DEGs for ∆dksA and ∆spoT∆relA. Hierarchical cluster analysis shows that all common differentially expressed genes (DEGs) were grouped into four clusters I, II, III, IV. 40 genes (IV) were upregulated, and 426 genes (II) were downregulated in both mutants.12 genes (I) were upregulated in ∆dksA but downregulated in ∆spoT∆relA. 4 genes (III) were downregulated in ∆dksA but upregulated in ∆spoT∆relA.

Figure 3-11. Differential regulation of the xss gene cluster by DksA and (p)ppGpp. A) The organization of siderophore synthesis and utilization gene cluster (xss) in Xcc. B) Promoter activity of mphE in Xcc306, ∆dksA and ∆spoT∆relA strains by GUS assay. Values represent means ± SD and star indicates the statistical significance using Student’s t-test (p-value < 0.05, n = 7). C) Siderophore production by CAS assay. Fresh bacteria were inoculated on NA plates supplemented with 200 µM DP and photographed after 36 hours. The experiments were repeated independently three times with similar results.

Figure 3-12. A working model of stringent response regulators of Xcc during host colonization. Symbols: ↓, positive regulation; ⊥, negative regulation. Regulatory steps in the model are mainly at the transcription level.

Table 3-1. Gene expression profile of T3SS- and T2SS-related genes in Xcc catagory Gene Locus tag Log2FC Log2FC (∆dksA/WT) (∆spoT∆relA/WT) Key regulatory genes hrpG XAC1265 -1.90 -1.13 hrpX XAC1266 -1.90 -2.98

hrp/hrc hrcS XAC0401 -3.17 -4.98 hrcU XAC0406 -3.03 -4.61 hpa1 XAC0416 -3.21 -4.38 hrpB5 XAC0411 -2.78 -4.2 hrcR XAC0402 -3.00 -3.58 hpaP XAC0404 -2.78 -3.26 hrcT XAC0414 -3.17 -3.17 hrpB7 XAC0413 -3.04 -3.04 hrcN XAC0412 -3.17 -2.95 hrcQ XAC0403 -2.66 -2.66 hpa2 XAC0417 -3.58 -2.58 hrcJ XAC0409 -2.66 -2.44 hrpD5 XAC0399 -2.42 -2.42 hpaA XAC0400 -2.40 -2.4 hrpB4 XAC0410 -2.69 -2.24 hrpE XAC0397 -1.44 -2.21 hpaB XAC0396 -2.49 -2.17 hrpD6 XAC0398 -1.86 -1.93 hrpF XAC0394 -2.58 -1.58 hrcV XAC0405 -2.91 -1.58 hrpB2 XAC0408 -2.07 -1.39 hrpB1 XAC0407 -2.45 -1.31 hrcC XAC0415 -2.74 -0.51 hpaF XAC0393 -1.50 -0.50

effector genes avrBs2 XAC0076 -1.72 -2.72 xopR XAC0277 -0.86 -1.16 XopE1 XAC0286 -2.66 -1.17 xopS XAC0315 -1.84 -2.32 xopM XAC0418 -3.00 -0.04 xopX XAC0543 -2.47 -1.93 xopV XAC0601 -2.00 0.29 xopI XAC0754 -2.77 -3.04 xopAU XAC1171 -2.32 -0.94 xopAV XAC1172 -2.48 -1.07 xopP XAC1208 -2.27 -0.77 xopAZ XAC1358 -0.57 1.22

Table 3-1. Continued catagory Gene Locus tag Log2FC Log2FC (∆dksA/WT) (∆spoT∆relA/WT) xopZ XAC2009 -2.26 -2.58 xopF XAC2785 -2.04 -4.04 xopN XAC2786 -2.86 -2.54 xopAW XAC2949 -0.57 0.39 xopAP XAC2990 -2.66 -1.08 xopK XAC3085 -2.26 -3.43 xopL XAC3090 -2.50 -1.08 XopE3 XAC3224 -1.28 0.08 xopAI XAC3230 -1.49 -1.22 xopAK XAC3666 -1.71 -1.17 xopAD XAC4213 -1.93 -1.66 xopQ XAC4333 -2.09 -1.50 pthA1 XACa0022 -0.82 -0.79 pthA2 XACa0039 -0.96 -0.76 XopE2 XACb0011 -0.61 0.33 pthA3 XACb0015 -0.79 -0.57 pthA4 XACb0065 -0.73 -0.71 xcsC XAC0694 -2.58 -3.91 xcsD XAC0695 -2.91 -2.58

T2SS (xcs) xcsE XAC0696 -3.00 -2.19 xcsF XAC0697 -3.07 -4.39 xcsG XAC0698 -2.94 -1.72 xcsH XAC0699 -3.25 -1.25 xcsI XAC0700 -3.19 -3.00 xcsJ XAC0701 -3.10 -2.84 xcsK XAC0702 -2.89 -1.75 xcsL XAC0703 -2.85 -2.36 xcsM XAC0704 -1.77 0.00 xcsN XAC0705 -2.39 -1.22

enzymes XAC3547 -2.91 -1.58 XAC0933 -1.93 -2.25 xynB XAC4252 -2.58 -1.58 xynB XAC4254 -2.46 -1.76 xynB XAC0160 -2.46 -3.20 Note: XAC0393, which belongs to the hrp/hrc cluster, is also considered to be an effector.

Table 3-2. Downregulated T3SS-related genes in mutant strains Functional group Genes Type 3 secretion system ‡ T3SS regulatory gene hrpX hrp/hrc gene cluster hpaB hrpD5 hpaA hrcS hrcR hrcQ hpaP hpa2 hrcU hrcN hrcJ hrpB4 hrpB5 hpa1 hrpB7 hrcT † † † † hrcC hrcV hrpB1 hrpB2 † ‡ hrpF hrpE T3SS effector genes xopI xopK xopN xopZ † † † xopF xopX xopM xopAP † † † † xopE1 xopAU xopQ xopAV † † † ‡ xopL xopV xopP xopS ‡ avrBs2 †: downregulated DEGs only in ∆dksA; ‡: downregulated DEGs only in ∆spoT∆relA The other genes are downregulated DEGs in both mutant strains.

Table 3-3. Gene expression profile of type 4 pilus biogenesis and regulatory genes.

Log2 FC Log2 FC Gene Locus tag Product description (∆dksA/WT) (∆spoT∆relA/WT) type IV pilus regulatory pilZ XAC1133 protein 1.83 0.21 pilF XAC2017 fimbrial biogenesis protein 1.61 0.88 type IV pilus regulatory fimX XAC2398 protein 8.31 -0.14 pilE XAC2664 prepilin-like protein 5.87 -1.97 pilY1 XAC2665 PilY1 protein 4.38 -2.24 pilX XAC2666 prepilin-like protein 5.37 -1.52 PliW XAC2667 prepilin-like protein 8.56 -0.43 pilV XAC2668 prepilin-like protein 11.22 -0.03 fimT XAC2669 prepilin-like protein 10.77 0.55 pilU XAC2923 pilus biogenesis protein 4.60 -0.49 pilT XAC2924 pilus biogenesis protein 4.04 0.46 pilL XAC3098 PilL protein 4.36 -0.97 pilJ XAC3099 pilus biogenesis protein 6.39 -0.73 pilI XAC3100 pilus biogenesis protein 5.82 0.05 pilH XAC3101 pilus biogenesis protein 6.04 0.63 pilG XAC3102 pilus biogenesis protein 3.75 0.73 pilS XAC3237 regulatory protein 0.63 -0.57 pilR XAC3238 regulatory protein 0.86 -1.15 pilB XAC3239 pilus biogenesis protein 1.15 -0.71 fimA XAC3240 prepilin 1.79 -1.02 fimA XAC3241 prepilin 9.15 -0.16 pilC XAC3242 pilus biogenesis protein 2.94 0.01 pilD XAC3243 prepilin leader peptidase 2.34 0.80 pilQ XAC3381 pilus biogenesis protein 3.58 -1.05 pilP XAC3382 pilus biogenesis protein 3.10 -1.51 pilO XAC3383 pilus biogenesis protein 4.43 -0.51 pilN XAC3384 pilus biogenesis protein 3.46 -0.74 pilM XAC3385 pilus biogenesis protein 6.72 -0.43

Table 3-4. Gene expression level of TonB-dependent transporter genes of Xcc Locus tag Gene name Product description log2FC log2FC (∆dksA/WT) (∆spoT∆relA/WT) XAC0074 cirA TonB-dependent receptor -1.91 -3.17 XAC0144 iroN TonB-dependent receptor -1.04 0.00 XAC0653 fepA TonB-dependent receptor -1.40 -2.54 XAC0690 fecA TonB-dependent receptor -1.85 -0.71 XAC0693 fecA TonB-dependent receptor -3.25 -3.25 XAC0706 iroN TonB-dependent receptor -1.91 -0.45 XAC0716 fyuA TonB-dependent receptor -1.16 -0.95 cirA XAC0811 TonB-dependent receptor -2.81 -0.35 XAC0852 TonB-dependent receptor -2.81 -3.39 XAC1023 fecA TonB-dependent receptor -2.58 -2.58 XAC1143 fyuA TonB-dependent receptor 3.08 0.92 XAC1146 fecA TonB-dependent receptor -2.70 -1.38 XAC1276 fyuA TonB-dependent receptor -0.10 -0.86 XAC1310 btuB TonB-dependent receptor -1.65 -0.07 XAC1768 fhuA TonB-dependent receptor -2.00 -1.74 XAC1769 cirA TonB-dependent receptor -1.91 -1.32 XAC1910 cirA TonB-dependent receptor -0.30 -1.69 XAC2024 cirA TonB-dependent receptor -2.77 -3.09 XAC2193 cirA TonB-dependent receptor -2.00 -2.17 XAC2520 cirA TonB-dependent receptor -2.75 -2.43 XAC2531 btuB TonB-dependent receptor -2.91 -2.32 XAC2535 btuB TonB-dependent receptor -2.87 -2.46 XAC2600 btuB TonB-dependent receptor -2.30 -2.98 XAC2742 btuB TonB-dependent receptor -0.93 -2.25 XAC2830 fhuA TonB-dependent receptor -1.72 -3.52 XAC2941 fhuA TonB-dependent receptor 3.23 -0.29 XAC2998 fecA TonB-dependent receptor -2.74 -2.74 XAC3050 btuB TonB-dependent receptor 3.00 -1.24 XAC3071 iroN TonB-dependent receptor -2.38 -2.10 XAC3077 cirA TonB-dependent receptor -3.17 -3.17 XAC3121 fepA TonB-dependent receptor -0.21 -1.14 XAC3158 fhuA TonB-dependent receptor -2.50 -2.50 XAC3201 fyuA TonB-dependent receptor 1.42 -3.22 XAC3311 iroN TonB-dependent receptor -2.52 -2.20 XAC3334 fecA TonB-dependent receptor -2.22 0.42 XAC3366 cirA TonB-dependent receptor -1.74 0.26 XAC3427 fhuA TonB-dependent receptor -1.64 -0.56 XAC3444 btuB TonB-dependent receptor -0.17 0.49 XAC3448 btuB TonB-dependent receptor -2.09 -0.92 XAC3489 fyuA TonB-dependent receptor -2.20 -1.94 XAC3560 btuB TonB-dependent receptor 0.68 -2.83 XAC3613 btuB TonB-dependent receptor 2.13 -0.94

Table 3-4. Continued Locus tag Gene name Product description log2FC log2FC (∆dksA/WT) (∆spoT∆relA/WT) XAC4048 iroN TonB-dependent receptor -0.78 0.18 XAC4062 fhuA TonB-dependent receptor -2.10 -2.58 XAC4256 cirA TonB-dependent receptor -3.25 -4.25 XAC4368 fecA TonB-dependent receptor -2.58 -2.58

Table 3-5. Differentially regulated genes by DksA and (p)ppGpp Name Locus tag Product ∆dksA ∆spoT∆relA pilY1 XAC2665 PilY1 protein + - cysJ XAC3330 NADPH-sulfite reductase flavoprotein subunit + - cysG XAC3340 siroheme synthase + - - XAC3156 hypothetical protein + - nrtCD XAC0828 ABC transporter ATP-binding protein + - - XAC3750 hypothetical protein - + - XAC0824 hypothetical protein - + - XAC0825 hypothetical protein - + mphE XAC3175 4-hydroxy-2-oxovalerate aldolase + - fecA XAC3176 citrate-dependent iron transporter + - - XAC3177 hypothetical protein + - - XAC3178 hypothetical protein + - yceE XAC3179 transporter + - iucA XAC3180 iron transporter + - lysA XAC3181 diaminopimelate decarboxylase + - - XAC3749 hypothetical protein - + + means upregulation in mutant strain; - means downregulation in mutant strain

Table 3-6. Strains and plasmids used in this study Types Relevant characteristic Reference or source

strains

Escherichia coli HST08 F–, endA1, supE44, thi-1, recA1, relA1, gyrA96, phoA, Clontech Φ80d lacZ∆ M15, ∆ (lacZYA - argF) U169, ∆ (mrr - Laboratories Inc, hsdRMS - mcrBC), ∆mcrA, λ– Mountain View, CA

S-17-1 λpir RK2 tra regulon, pir, host for pir-dependent plasmid (Simon et al. 1983) pOK1 Xcc306 Syn. X. axonopodis pv. citri strain 306; wild type; Rifr (da Silva et al. 2002) Xcc306∆dksA dksA gene deletion strain in the background of Xcc306; This study Rifr Xcc306∆relA relA gene deletion strain in the background of Xcc306; This study Rifr Xcc306∆spoT∆relA both spoT and relA gene deletion strain in the This study background of Xcc306; Rifr plasmids pBBR1MCS-2 Broad host expression vector. KnR (Kovach et al. 1995) pBBR1MCS-2:hrpG pBBR1MCS-2 derivative for expression of HrpG fused to (Teper et al. 2019) HA tag. KnR pBBR1MCS-2:dksA pBBR1MCS-2 derivative for expression of DksA This study (XAC2358). KnR pBBR1MCS-2:spoT pBBR1MCS-2 derivative for expression of SpoT This study (XAC3393). KnR pBBR1MCS-5 Broad host expression vector. GnR (Kovach et al. 1995) pGUS pBBR1MCS-5 derivative containing gus gene followed by (Teper et al. 2019) T7 terminator cloned in reverse orientation of lac promoter. pGUS pgyrA pGUS derivative. The 189 bp upstream region of (Teper et al. 2019) XAC1631 was cloned upstream to gus. GnR pGUS phrpX pGUS derivative. The 474 bp upstream region of (Teper et al. 2019) XAC1266 was cloned upstream to gus. GnR pGUS phrpF pGUS derivative. The 831 bp upstream region of (Teper et al. 2019) XAC0394 was cloned upstream to gus. GnR pGUS pxopAU pGUS derivative. The 1000 bp upstream region of (Teper et al. 2019) XAC1171 was cloned upstream to gus. GnR pGUS pmphE pGUS derivative. The 240 bp upstream region of This study XAC1171 was cloned upstream to gus. GnR pGUS psuxA pGUS derivative. The 204 bp upstream region of This study XAC1171 was cloned upstream to gus. GnR pOK1 sacB sacQ mobRK2 oriR6K, Suicide vector. SpR (Huguet et al. 1998) pOK1:dksA pOK1 derivative contacting the flanking regions of dksA This study (XAC2358). SpR pOK1:relA pOK1 derivative contacting the flanking regions of relA This study (XAC3113). SpR pOK1:spoT pOK1 derivative contacting the flanking regions of spoT This study (XAC3393). SpR *KnR, GnR, SpR and RifR indicate resistance to kanamycin, gentamicin, rifampicin, respectively.

Table 3-7. Primers used in this study Primer name Primer sequence

for hrp genes hrpF-RTF AAGCAAGCAAAGAACTACACACAG hrpF-RTR GAAGGTATCCTGATTCAGATCGTC hrpX-RTF AGCGATCTCTGCGTTGTCCTAC hrpX-RTR ATACGCATCTTCGGCCTCTTCCTGA hrpG-RTF ATCGTGCTTGGACGTTTCGATTGC hrpG-RTR ATTGAAAGGCAGCGCAAGGACTTC xopAU-RTF GATCCCACTGACACTGTACGAC xopAU-RTR TGATCATTTGATACTCCTGGAAGA xopN-RTF AACAACAAGCTCAACCTCCTGTA xopN-RTR GTAGTTTGTCGGCCTTGATGTC gyrA-RTF GTCAAGGAAAAGAAGCTCGAAG gyrA-RTR GCTGATACAGGTTGTTGAGCAC for deletion mutant FdskABamH1 AAAGGATCCCGATCTTCCGCAGCATCGC RdskABamH1 CGTGGCCGTGACGTCCAACGCCTGGCTCGCCGAAAGA FdskAXbaI TCTT TCGGCGAGCCAGGCGTTGGACGTCACG GCCACG RdskAXbaI CCCCTCTAGAAACAGCTTGGTGCAAAACGG relABamH1F AAAGGATCCATCGAGAGCAGTCCGACGATG relABamH1R CAGGTAAGCGGCGTCCTCAG GGTAACGTGCGCGTCACCT relAXbaIF AGGTGACGCGCACGTTACC CTGAGGACGCCGCTTACCTG relAXbaIR AAATCTAGATGCGTTGGTAGAGCGGCAAC FspoTApaI AAAGGGCCCGGTAACTGGGGTGCACGA RspoTApaI ATGAACCCAG GCCCCACTGG CAACAGTCGA GCAAATCCGC FspoTXbaI GCGGATTTGCTCGACTGTTGCCAGTGGGGCCTGGGTTCAT spoT R XbaI CCCCTCTAGA CGAGACCTTCGATACGGCG for complementation FdksA(CXbaI) CCCTCTAGACATCGCGTAGTCCGAACAG RdksA(CBamHI) AAAAGGATCCCGCTAATTCGACGTATCAGG FspoT(CXbaI) CCCTCTAGATGGAAGTCGTGAACAACCGT RspoT(CBamHI) AAAAGGATCCAAAGGGTTGCCGCAGTTTAG for GUS assay mphE-gusf(XbaI) AAAATCTAGACGATAAACGCCTTGTTTCTCA mphE-gusr(EcoR1) AAAAGAATTCCATGGAAGAGGCCGCCAAGGA SuxA-gusf(XbaI) AAAATCTAGAACTGTACCTTGCCGCCGTTG SuxA-gusr(EcoR1) AAAAGAATTCCATTGCGATTCTCTCTCTCAC for validation of RNA-seq data for ∆dksA XAC0637F AGAACCCCAACATCGTTCAC XAC0637R ATTGCCCTTCATGACCGTAT XAC0823F ATGCCAACCGTAACTTCGTC XAC0823R CGCTCAGATAATCCGAAGGA XAC1008F ACGACCGTCTATCTGGAAGC

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Table 3-7. Continued Primer name Primer sequence for ∆dksA XAC1008R AGTGACCACCTGGCACATC XAC1151F CTGCAGAACGAGATCAAGCA XAC1151R GCGTACAGCACGAAGTGATT XAC1211F ATTCACCCGTTTTTCCACTG XAC1211R AGATCCCAGTTGCCTTCCTT XAC1523F GAGGAGCTGAAGAAGGCGTA XAC1523R TTCGTAGGCTTCCTTGCATT XAC1735F CGTGTCGGTGTATCTGGTCA XAC1735R GCGTGCTTGTACACCATCTG XAC1904F TACAAGTTCACCCCCATGCT XAC1904R GCGATCAGCTGTTCTGGATT XAC2013F CGGAAGTGGTCGAGATCCTA XAC2013R CAGATGGAAGGCGCTGTAGT XAC2039F GTCCACCATCAAGGAACTCG XAC2039R GCACCCATGAACTGACGATA XAC2528F GGAATACACCTCGCTGCTGT XAC2528R GCCTGGTCCATGATGAAAAC XAC2668F TTCAGTCGTACGCGATTCTG XAC2668R GATCCAATCTCGTCGGTCAT XAC2924F AAGGTGGCGCAGATGTATTC XAC2924R TCGAATATCCGCTTGTCCTT XAC3042F CAACCGTTGCTGGATTTCTT XAC3042R TTGGCCTGGATCTTGAAAAC XAC3195F CCTCCTGGCTATGTGGGTTA XAC3195R GGCATCGTCCTTTTCCTTCT XAC3242F CTGATGCTGTTCGCACTTGT XAC3242R GACCAATGATCGGCACTTTT XAC3331F CGCAAATTCAAGATCGGTTT XAC3331R GACGTTGTAGCCCAGCAACT XAC3370F CGACTACAAGCGTTCGTTCA XAC3370R GGTCTTTTCGTGGCTCACAT XAC3535F GCATCCGTTCTTCCTGTCC XAC3535R CAACGTCGCCATCTTGAAGT XAC3543F TGTTCCTGCTCGGTTATGTG XAC3543R GATCAGCACGATCCACCAGT for ∆spoT∆relA XAC0001F AAGGCGATGGACCAGTTCAA XAC0001R GCGTTGAAGGTGTGGAAGAA XAC0108F AGCACGAATACACGCATCTG XAC0108R CGGCAGGTTGGTTAGATCCT XAC0334F TTCGATTTCATCGATCAGGAC

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Table 3-7. Continued Primer name Primer sequence for ∆spoT∆relA XAC0334R GTGCCTGGAAGAAGCTGAAA XAC0952F GGCATTGGACAGCAAGTTGT XAC0952R GCCGCTGAGATTCATGAAGG XAC1521F CAAGTTCGCCAACGAGAAG XAC1521R TTGCTTGTAGGTCATGTCCAG XAC1976F ATGATCAACACGCTGCAGTC XAC1976R GGGTGACTTCGTTGGATTCG XAC1977F AGCAAGCTGGTCGGTTTTAC XAC1977R ACGGTGGTGAGTTTGGAAGA XAC2414F GGCACCAAGGAAGACTTCAA XAC2414R ACGGTGCCTCACTAATGTCC XAC2599F CCGTACCTATCCCAACTGGA XAC2599R GGGTGAACACCAGGTTGACT XAC2743F TCGACCAGCAGGAAACCTAT XAC2743R AGTGACTTCGGTTCCCACAC XAC3054F TTCGACAAGCGCTACATCAC XAC3054R GTGATGCCGATGTCCTTCTT XAC3195F CCTCCTGGCTATGTGGGTTA XAC3195R TGCAGCAGGATGTTGAAGAC XAC4055F GTACTACACCACCAGCGAAG XAC4055R GGATAACGTGCGGCATAGTG XAC4056F CAAGGAGAGCTTGGCGAAC XAC4056R GCTCGATCGATTCCCCAAAC XAC4074F CCTACGAAGACTTCATGCGC XAC4074R CGCAAAGCCGGAATAGAAGT XACb0054F TCAACATTTCGCCGTTCTCG XACb0054R TTGCTGTTGACCATGTTCGG Underline represents restriction enzyme cutting site

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CHAPTER 4 CONCLUDING REMARKS AND FUTURE DIRECTIONS

Accumulating studies in bacteria reveal that stringent response regulators DksA and (p)ppGpp regulate many cellular processes such as metabolism and virulence to promote survival under stress and host adaptation (Dalebroux et al. 2010; Dalebroux and Swanson 2012; Gourse et al. 2018; Liu et al. 2015a; Potrykus and Cashel 2008). In this study, we characterized the roles of DksA and (p)ppGpp in plant pathogen Xcc, the causal agent of citrus canker.

In the first part, we generated the ∆dksA and (p)ppGpp-deficient mutant strain

∆spoT∆relA, and analyzed the transcription profile by comparing with that of wild-type strain Xcc306 using RNA-seq. The transcriptomic analysis in vitro reveals that stringent response regulators DksA and (p)ppGpp are global transcriptional regulators and involved in regulation of many aspects at transcription level including histitine metabolism, stable RNA, ribosome proteins, flagella and type 4 pili, TonB-dependent transporters, T2SS as well as T3SS. The working model of DksA and (p)ppGpp proposed here suggests that during host colonization, stringent response can be activated by unknown stimuli and increased (p)ppGpp altogether with DksA will relocate cellular resources and save biosynthetic energy by repressing the biosynthesis of stable

RNA, ribosome proteins, flagella as well as type 4 pili. Meanwhile, expression of coding genes for T2SS, T3SS and TonB-dependent transporters is enhanced to promote the bacterial virulence and nutrient uptake. It seems that Xcc utilizes stringent response regulators to achieve a balance between fitness and virulence and favor host adaptation and colonization.

103

In the second part, based on the transcriptome analysis, the effect of (p)ppGpp and DksA on several virulence-associated traits was characterized. Both DksA and

(p)ppGpp are important to canker symptom development and bacterial growth in planta, indicating a role in bacterial pathogenicity. DksA rather than (p)ppGpp contributes to bacterial growth in defined XVM2 medium. Further tests show that both DksA and

(p)ppGpp positively regulate T3SS and the T3S effectors and are required for HR on nonhost plant. Besides, motility of ∆dksA and ∆spoT∆relA strains are reduced although no obvious changes were observed in morphology of flagellum. Interestingly, both GUS activity assay and CAS assay indicate that DksA controls the production of siderophore by inhibiting gene expression of xss gene cluster involved in siderophore biosynthesis and utilization.

Although the synergistic effect of DksA and (p)ppGpp was observed in most cases, DksA and (p)ppGpp have opposite regulation of the xss gene cluster. It has been known that iron homeostasis is subject to strict regulation and plays a critical role in virulence for both plant and animal pathogens (Franza and Expert 2013; Nairz et al.

2010) . In addition, it has been reported that (p)ppGpp and DksA have opposite effects on the regulation of pArgX (Lyzen et al. 2016). It would be of great interest to understand how DksA and (p)ppGpp control bacterial iron homeostasis as well as its correlation with virulence.

To better understand the transcription regulation of DksA and (p)ppGpp during pathogen-host interaction, analyzing gene expression profile in planta is recommended using newly developed methods (Nobori et al. 2018). In addition, the dramatic changes in transcription profile are the result of both direct and indirect effect of DksA and

104

(p)ppGpp. To identify the direct targets of (p)ppGpp beyond RNAP, DRaCALA

(differential radial capillary action of ligand assay) can be applied to systematically identify (p)ppGpp-protein interactions (Zhang et al. 2018).

Except for amino acid starvation, how other signals orchestrate the activity of

RelA and SpoT to activate stringent response remian elusive (Irving and Corrigan

2018). Given that promoters regulated by RNAP-binding transcription factors DksA and

(p)ppGpp do not have an obvious DNA signature, it is assumed the kinetic properties of the promoter complex determine the positive or negative regulation (Haugen et al.

2008). However, the underling mechanism needs further investigation. Although

(p)ppGpp are remarkedly conserved, it is expected that more mechanisms by which it is utilized for stringent response will be discovered among diversified bacterial species

(Gourse et al. 2018).

105

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BIOGRAPHICAL SKETCH

Yanan Zhang was born in Shandong province of China in 1989. In 2012, he earned his bachelor degree in plant protection from Qingdao Agricultural University. In

2015, he earned his master degree in molecular plant pathology in China Agricultural

University. During master program, he worked on rice blast disease and studied the interaction between Avr effector of Magnaporthe oryzae and R protein of rice using structural methods. In August 2015, Yanan Zhang started the PhD program in

Microbiology and Cell Science Department of University of Florida under the guidance of Dr. Nian Wang. Since that time, he has studied the virulence regulation mechanism of plant pathogen Xanthomonas citri subsp. citri and focused on stringent response regulatory system and CsrA/RsmA regulatory system.

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