in-depth secretome analysis of puccinia striiformis f. sp ......jan 13, 2020 · puccinia...

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In-depth secretome analysis of Puccinia striiformis f. sp. tritici in 2

infected wheat uncovers effector functions 3

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(Short Title: In-depth secretome analysis of Pst) 5

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Ahmet Caglar Ozketen1*, Ayse Andac-Ozketen1, Bayantes Dagvadorj1,2, Burak 7

Demiralay1, Mahinur S. Akkaya3* 8

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1 Middle East Technical University, Biotechnology Program, Ankara 06800, Turkey. 10

2 Division of Plant Sciences, Research School of Biology, The Australian National 11

University, Canberra, Australian Capital Territory 2601, Australia. 12

3 School of Bioengineering, Dalian University of Technology, No. 2 Linggong Road, 13

Dalian, Liaoning 116023, China. 14

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*Corresponding authors: Mahinur S. Akkaya and Ahmet Caglar Ozketen 17

E-mails: [email protected] & [email protected] 18

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Keywords 21

Transcriptome, secretome, effectors, yellow/stripe rust, pathogenesis, localization, 22

function, Puccinia striiformis f. sp. tritici 23

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.CC-BY-NC-ND 4.0 International licenseperpetuity. It is made available under apreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in

The copyright holder for thisthis version posted January 13, 2020. ; https://doi.org/10.1101/2020.01.11.897884doi: bioRxiv preprint

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https://doi.org/10.1101/2020.01.11.897884

http://creativecommons.org/licenses/by-nc-nd/4.0/

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ABSTRACT 25

The importance of wheat yellow rust disease, caused by Puccinia striiformis f. sp. tritici 26

(Pst), has increased substantially due to the emergence of aggressive new Pst races in 27

the last couple of decades. In an era of escalating human populations and climate 28

change, it is vital to understand the infection mechanism of Pst in order to develop 29

better strategies to combat wheat yellow disease. This study focuses on the 30

identification of small secreted proteins (SSPs) and candidate-secreted effector proteins 31

(CSEPs) that are used by the pathogen to support infection and control disease 32

development. We generated de novo assembled transcriptomes of Pst collected from 33

wheat fields in central Anatolia. We inoculated both susceptible and resistant seedlings 34

with Pst and analyzed haustoria formation. At 10 days post-inoculation (dpi), we 35

analyzed the transcriptomes and identified 10,550 Differentially Expressed Unigenes 36

(DEGs), of which 6,220 were Pst-related. Among those Pst-related genes, 230 were 37

predicted as PstSSPs. In silico characterization was performed using an approach 38

combining the transcriptomic data and data mining results to provide a reliable list to 39

narrow down the ever-expanding repertoire of predicted effectorome. The 40

comprehensive analysis detected 14 Differentially Expressed Small-Secreted Proteins 41

(DESSPs) that overlapped with the genes in available literature data to serve as the best 42

CSEPs for experimental validation. One of the CSEPs was cloned and studied to test 43

the reliability of the presented data. Biological assays show that the randomly selected 44

CSEP, Unigene17495 (PSTG_10917), localizes in the chloroplast and is able to 45

suppress cell death induced by INF1 in a Nicotiana benthamiana heterologous 46

expression system. 47



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INTRODUCTION 48

Puccinia striiformis f. sp. tritici (Pst) is an obligate biotrophic fungus, which is the 49

causative agent of wheat stripe (yellow) rust disease, a disease that can drastically 50

reduce the production of wheat worldwide. The use of fungicides is a suitable solution 51

for combatting Pst, but using resistant wheat strains to control Pst genetically would be 52

better for the environment and more cost-effective. In order to achieve host resistance 53

against rapid evolving virulent strains of Pst, understanding the molecular basis of the 54

disease is crucial. 55

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Pst infects the leaf of the wheat plant by penetrating the interior through the stomata 57

and later differentiating into various developmental entities before finally entering into 58

the mesophyll cells in which it can generate a feeding structure called a haustorium. 59

The main interface for pathogen-host interactions is in the haustorium that expands 60

between the plant cell wall and the cell membrane. This interface can activate primary 61

host defenses when pathogen-associated molecular patterns (PAMPs) are detected by 62

pattern recognition receptors (PRRs), resulting in PAMP-triggered immunity (PTI) [1]. 63

Virulence is achieved by Pst through the deployment of secreted proteins, termed 64

effectors, into the apoplastic fluid or inside the host [2]. The effectors are useful 65

weapons in the fungal arsenal that allow Pst i) to overcome PTI, ii) to establish a 66

suitable environment, and iii) to help proliferation [3]. Particular effectors are 67

recognized by cytoplasmic receptor proteins in the host cell, triggering a defense 68

response called effector-triggered immunity (ETI). Compatible and incompatible 69

interactions depend on whether the effectors can achieve virulence by evading 70

Resistance (R) proteins, while the R proteins evolve to detect the effectors. Therefore, 71



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evolutionary pressure imparts a driving force on the race between host resistance factors 72

and pathogen virulence factors [4]. 73

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Investigating the secreted effectorome of the fungus guides us towards a better 75

comprehension of the nature of pathogenic virulence and determinants that can lead to 76

avirulence. The combination of next-generation sequencing (NGS) and data mining 77

approaches represent a great opportunity to determine candidate effectors secreted by 78

the fungus. Several studies attempting to construct a secretome of either Pst or its 79

closest relatives (stem rust pathogen Puccinia graminis f. sp. tritici (Pgt) and leaf rust 80

pathogen Puccinia triticina (Ptt) have been published. Yin and colleagues reported a 81

set of secreted proteins generated from expressed sequence tag (EST) sequences of a 82

cDNA library generated from Pst haustoria [5]. A repertoire of small-secreted proteins 83

(SSPs) was reported using a pipeline for effector mining in the genome sequence 84

information of Pgt and Melampsora larici populina (the pathogen that causes poplar 85

leaf rust) [6]. The isolate PST-130 was sequenced and assembled to discover Pst genes 86

and identify candidate effectors [7]. The genome sequences of Pgt (CRL 75-36-700-3), 87

Ptt (BBDD), PST-78 (2K-041), PST-1 (3-5-79), PST-127 (08-220), PST-CYR-32 (09-88

001) were released by the Broad Institute (http://www.broadinstitute.org/). Cantu and 89

colleagues used comprehensive genome analysis to identify candidate secreted 90

effectors in two United Kingdom isolates (PST-87/7 and PST-08/21) and two United 91

States isolates (PST-21 and PST-43) [8]. They also released data of infection-expressed 92

genes (6 and 14 dpi) and haustorial-expressed genes (7 dpi) for PST-87/7. Another 93

report was published using transcriptome sequencing on Pst isolate 104E137A to 94

elucidate haustorial secreted proteins (HSPs) and expose the best candidate effectors 95

[9]. A pioneer correlation analysis between genomes of seven existing races and seven 96



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new races of Pst was pursued to predict avirulence (Avr) determinants among SSPs 97

[10]. In this study, 7 new races of Pst were sequenced with NGS and then combined 98

with 7 older published genomes for a total of 14 races of Pst that were then subjected 99

to correlation analysis to point out Avr candidates [10]. 100

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Data mining methodology is a promising strategy to identify candidate secreted effector 102

proteins (CSEPs) in genome and transcriptome data, as the incorporation of newly 103

generated data continually improves the prediction algorithms using deep machine 104

learning. Likewise, our understanding of the infection and disease progression of the 105

pathogen is steadily expanding. Therefore, new information (candidate effectors) can 106

be generated by exhausting presently available sequence data. A combined effort of 107

using both comparative transcriptomics and data mining to narrow down the ever-108

expanding sets of CSEPs will simplify and focus the work being done to discover the 109

function of each CSEP experimentally. Only a few reports have focused on integrating 110

both in silico prediction and transcriptome sequencing data on particular Pst races at 111

the defined time intervals of haustoria formation and infection [8,9]. 112

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In this study, our objective is to identify differentially expressed gene sequences 114

(DEGs) of Pst during the interaction with both susceptible and resistant host genotypes. 115

We acquired de novo transcriptome sequencing data from both susceptible and resistant 116

wheat cultivars infected with Pst for 10 days in order to discover differentially 117

expressed secretome repertoires composed of small secreted proteins at a unique time 118

point of the infection process after haustoria formation. Detected PstDEGs were then 119

subjected to in silico prediction analysis to identify differentially expressed small 120

secreted proteins (PstDESSPs). Then, the cataloged secretome candidates were 121



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analyzed in-depth via annotation and prediction programs to sort them into classified 122

functions. Furthermore, a comparison of published Pst expression data [8, 9] indicated 123

the presence of 14 overlapping mutual CSEPs as the most promising subset for 124

experimental validation. To test the reliability of our work, cDNA of one of the CSEP 125

genes (Unigene17495, homologous to PSTG10917), was generated from Pst-infected 126

wheat leaves (10 dpi) and cloned into Agrobacterium tumefaciens-compatible 127

expression vectors. Heterologous expression of the CSEP in Nicotiana benthamiana 128

revealed it was localized to the chloroplast in plant cells. Furthermore, we observed the 129

biological function of Unigene17495 to be suppression of the INF1-mediated cell death 130

response to N. benthamiana. 131

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MATERIALS AND METHODS 133

Plant materials, growth, and pathogen inoculation 134

Near-isogenic wheat lines of Avocet-S and Avocet-YR10 were used to study both 135

compatible and incompatible interactions upon Pst inoculation. Pst isolates were 136

collected from the fields of Anatolia, Turkey and obtained from the 'General Directorate 137

of Agricultural Research and Policies (TAGEM) of The Ministry of Food, Agriculture, 138

and Livestock. Avocet-S cultivars are susceptible to PstTR (which has virulence 139

towards Yr2, 6, 7, 8, 9, 25, A, EP, Vic, Mich), whereas Avocet-Yr10 is resistant. Seeds 140

were planted in soil and grown in a growth chamber under ideal conditions (relative 141

humidity 60%, 22°C, 16 h light, and 8 h dark). The Pst inoculations were performed 142

when the seedlings reached the two-leaf stage by spraying freshly collected 143

urediniospores directly onto the leaves. Before the inoculation, urediniospores (100 144

mg) were germinated for 5 min at 42°C and treated with mineral oil (1 mL) to enhance 145

their attachment to the leaf blade. For the control samples (mock inoculations), only 146



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mineral oil was used. After air-drying for 20 min, the plants were kept in the dark at 147

10°C overnight in the presence of continuous mist in order for infection to take place. 148

After overnight infection, the standard growth conditions were resumed. The wheat 149

leaves were collected at 10 days post-inoculation (10 dpi) when the disease symptoms 150

were visible on the susceptible cultivar. 151

152

RNA extraction, de novo transcriptome sequencing, assembly, and annotation 153

The flow chart in Fig 1 describes the overall protocol followed for collecting infected 154

leaf samples using homogenization with liquid nitrogen and a sterile mortar and pestle. 155

For 0.1 g leaf sample, 0.02 g Poly(vinylpolypyrrolidone) (PVPP) was added during 156

mortar and pestle grinding. For each condition, three biological replicates were 157

homogenized together. Total RNA isolation was carried out using QIAzol Lysis 158

Reagent (Qiagen) by following the manufacturer’s protocol. A NanoDrop was used for 159

the spectrophotometric quantification of the total RNA samples. The samples were 160

shipped to Beijing Genomics Institute (BGI) in absolute ethanol for sequencing. BGI 161

measured the RNA quality of the samples on an Agilent 2100 Bioanalyzer, and the 162

samples with a RIN value above 8 were allowed to proceed to mRNA enrichment and 163

cDNA synthesis using random hexamers. Single nucleotide adenine was added to the 164

purified and end-repaired short cDNA fragments. After adapter ligation, size selection 165

was used to obtain templates for PCR amplification. The Illumina HiSeq™ 2000 166

platform was used to perform de novo transcriptome sequencing. BGI-Shenzhen 167

(Shenzhen, China) conducted all of the protocol steps from mRNA enrichment to 168

sequencing. All quality controls were evaluated during sequencing using the Agilent 169

2100 Bioanalyzer and ABI StepOnePlus Real-Time PCR System for quantification and 170

qualification of the sample libraries. 171



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172

Before assembly, clean reads were obtained from raw reads using internal BGI software 173

(filter_fq) that follows three main parameters: (1) elimination of reads with adaptors, 174

(2) removal of reads with unidentified nucleotides larger than 5%, (3) elimination of 175

low-quality reads (if the percentage of the low-quality value (Q ≤ 10) reads are higher 176

than 20%, they are removed). For de novo transcriptome assembly, clean reads of each 177

library were assembled into contigs and unigenes by the Trinity program (release-178

20130225, http://trinityrnaseq.sourceforge.net/) [11]. 179

180

Unigenes were aligned using the Blastx program (e-value < 0.00001) and the following 181

protein databases: NR (NCBI, non-redundant database) [12], Swiss-Prot (EMBL 182

protein database) [13], KEGG (Kyoto Encyclopedia of Genes and Genomes) [14], and 183

COG (Clusters of Orthologous Groups) [15]. The direction of the unigene sequences 184

was determined using the best alignment scores. NR, Swiss-Prot, KEGG, and COG 185

were sourced to predict the coding region (CDS) of unigenes in the subsequent order 186

of priority if a conflict in sequence orientation arose. ESTScan software was performed 187

on unigenes with no successive alignments [16]. The results for NR, NT, Swiss-Prot, 188

KEGG, and COG database alignments were also used for unigene function annotation. 189

To obtain gene ontology (GO) annotation from NR annotations, the Blast2GO program 190

(v2.5.0) was performed (e-value < 0.00001) on fungi databases (taxa, 4751) [17]. Web 191

Gene Ontology Annotation Plot (WEGO) software was used for the classification of 192

GO annotations [18]. The KEGG database was employed to investigate the metabolic 193

pathway analysis of unigenes [14]. 194

195



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Expression difference analysis 196

Differentially expressed genes (DEGs) were identified using the FPKM method also 197

known as RPKM method [19]. In this method, FPKM stands for fragments per kb per 198

million reads, and RPKM stands for reads per kb per million reads. We used the 199

following calculation formula: 200

𝐹𝑃𝐾𝑀 =106𝐶

𝑁𝐿/103 201

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FPKM signifies the expression of Unigene A, C is the number of fragments that 203

uniquely aligned to Unigene A, N is the total number of fragments that uniquely aligned 204

to all unigenes, and L is the base number in the CDS of Unigene A. As we obtained 205

FPKM values for each unigene, we calculated FPKM ratios between two samples at a 206

time. We defined differentially expressed genes (DEGs) to have a FPKM ratio ≥ 2 and 207

a false discovery rate (FDR) ≤ 0.001. We executed GO functional analysis (P-value ≤ 208

0.05), including GO functional enrichment and functional classification on DEGs. 209

Furthermore, we carried out the KEGG pathway analysis (Q-value ≤ 0.05) for pathway 210

enrichment to further understand biological functions [20]. 211

212

Identification of pathogen-associated DEGs 213

We identified our DEGs through transcriptome data obtained from pathogen-inoculated 214

wheat leaves. However, we needed to eliminate any possible contaminating sequences 215

from the host. To accomplish this, we constructed a local database (named ‘The 216

Pucciniale database’) by merging protein data of Puccinia striiformis f. sp. tritici 217

(isolate 2K41-Yr9, race PST-78), Puccinia graminis f. sp. tritici (strain CRL 75-36-218

700-3, race SCCL), and Puccinia triticina (isolate 1-1, race 1-bbbd). All protein data 219



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was obtained from the Broad Institute Puccinia website 220

(http://www.broadinstitute.org/). We also merged protein data of the WheatD genome 221

of Aegilops tauschii (wheatD_final_43150.gff.cds, downloaded from GIGA_DB 222

‘http://gigadb.org/’) [21] to ‘The Pucciniale database’. We performed a BlastX analysis 223

(e-value cut-off ≤ e-10) on DEGs using our local database of Pucciniale and wheat [22]. 224

If the hit matched with the wheat proteome or even if it produced a significant hit with 225

the Pucciniale proteome, we discarded the sequences following the decision rule. The 226

best hits within the Pucciniale proteome were evaluated as PstDEGs. The sequences 227

with no significant hits against neither the pathogen nor host were assessed as unknown 228

novel DEGs. 229

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Construction and characterization of secretome data 231

We predicted the ORFs and protein sequences from the PstDEG data using the 232

ORFPredictor web tool (http://bioinformatics.ysu.edu/tools/OrfPredictor.html). We 233

followed a widely accepted secretome prediction pipeline to construct our own 234

differentially expressed secretome database [6, 23, 24]. The predictions were made 235

following three criteria: 1) presence of N-terminus signal peptide region, 2) absence of 236

transmembrane helices, and 3) mature protein length should be smaller than 300 amino 237

acids. SignalP (version 4.1) was used to predict the presence of a secretion signal [25]. 238

We estimated transmembrane helices using the TMHMM webtool (version 2.0, 239

http://www.cbs.dtu.dk/services/TMHMM/) [26]. Filtered sequences were termed as 240

differentially expressed small-secreted proteins of Puccinia striiformis f. sp. tritici 241

(PstDESSPs). The same filtering pipeline was applied to total proteins obtained from 242

the 'Broad Institute database’ for Pst and Pst relatives (Pgt and Ptt) 243

(http://www.broadinstitute.org/). Four secretome data sets (PstDESSPs, PstSSPs, 244



http://www.broadinstitute.org/

http://bioinformatics.ysu.edu/tools/OrfPredictor.html

http://www.cbs.dtu.dk/services/TMHMM/


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PgtSSPs, PttSSPs) were subjected to effector prediction via the EffectorP 1.0 and 2.0 245

programs (http://effectorp.csiro.au/) [27, 28]. The Blastp program was used (E-value < 246

e-5) for characterization of PstDESSPs by scanning against the following databases: the 247

lipase engineering database (http://www.led.uni-stuttgart.de/), the protease and 248

protease inhibitors (MEROPs) database (http://merops.sanger.ac.uk/) [29], the 249

carbohydrate-active enzymes (CAZymes) database (http://csbl.bmb.uga.edu/dbCAN/) 250

[30], the fungal peroxidase database (fPoxDB, http://peroxidase.riceblast.snu.ac.kr/) 251

[31], and the pathogen-host interaction database (http://phi-blast.phi-base.org/) [32]. 252

The ClustalW program was executed for multiple alignment analysis of PstDESSPs 253

(https://www.ebi.ac.uk/Tools/msa/clustalo/) [33]. The iTOL web-tool was used to 254

construct a phylogenetic tree using multiple alignment results (https://itol.embl.de/) 255

[34]. Small secreted cysteine-rich effector proteins were assessed as cysteine-rich if the 256

protein possessed four cysteine residues or more. A comparison between infection- and 257

haustoria-expressed genes was done using a similarity search using Blastp (e-value cut-258

off ≤ e-10) on publicly available data [8, 9]. We scanned for PstDESSPs overlapping 259

with Avr determinant candidates originally interpreted using correlation analysis by Xia 260

and colleagues [10]. Subcellular localization predictions were done on mature proteins 261

(without N-terminus signal peptides) to forecast translocation sites in the host plant 262

using four different tools: TargetP 1.1 (http://www.cbs.dtu.dk/services/TargetP/) [35], 263

Localizer (http://localizer.csiro.au/) [36], WolfPSORT (https://wolfpsort.hgc.jp/) [37], 264

and ApoplastP (http://apoplastp.csiro.au/) [38]. All of the analyses, annotations, and 265

predictions regarding PstDESSPs are displayed in Table S3. 266

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http://effectorp.csiro.au/

http://www.led.uni-stuttgart.de/

http://csbl.bmb.uga.edu/dbCAN/

http://peroxidase.riceblast.snu.ac.kr/

http://phi-blast.phi-base.org/

https://www.ebi.ac.uk/Tools/msa/clustalo/

https://itol.embl.de/

http://localizer.csiro.au/

https://wolfpsort.hgc.jp/

http://apoplastp.csiro.au/

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Cloning of Unigene17495 (Pstg10917) 268

The cloning primers were designed using the Pstg10917 sequence with an N-terminus 269

CACC site in forward primers (with and without signal peptide (SP)) and no stop codon 270

in reverse primers (Table S4). Total RNA samples of infected Avocet-S leaves (10 dpi) 271

used in transcriptome sequencing were sourced to synthesize cDNA after DNase 272

treatment. The Transcriptor First Strand cDNA Synthesis Kit (Roche) was used 273

following the manufacturer’s protocol, except that we preferred using a mixture of both 274

random hexamer primers and oligo(dT) primers at equal volumes to the reaction primer. 275

The effector of interest, with and without SP, was amplified from cDNA, cloned into 276

the pENTR/D-TOPO vector (Invitrogen), and then subsequently cloned into the 277

pK7FWG2 expression vector [39] utilizing Gateway Cloning. Both colony PCR and 278

sequencing analysis validated the cloning of constructs at the proper positions. Then, 279

the plasmid constructs were electroporated into Agrobacterium tumefaciens GV3101. 280

281

Agrobacterium tumefaciens-mediated gene transfer 282

A. tumefaciens infiltration experiments were performed using the previously described 283

protocol [40] with slight alterations. A. tumefaciens GV3101 strains carrying 284

expression vectors were plated on selective agar media. After two days, bacteria were 285

scratched and suspended in sterile water. Bacteria were collected by centrifugation at 286

4,000 rpm for 4 min at room temperature (24-25°C). Bacteria were washed two times 287

with sterile water and two times with A. tumefaciens-mediated gene transfer-induction 288

medium (10 mM MES, pH 5.6, 10 mM MgCl2). The final cell concentration was 289

adjusted to 0.4 at A600nm for infiltration unless otherwise stated. The inoculums were 290

injected into Nicotiana benthamiana leaves using a needleless syringe. The expressions 291

of the gene of interests were observed in the next 2-4 d. 292



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293

Confocal microscopy 294

A. tumefaciens GV3101 cells carrying pK7FWG2/Pstg_10917 and pK7FWG2/ΔSP-295

Pstg_10917 were injected into N. benthamiana leaves (4-5 weeks old) for transient 296

expression. After 2-3 d, leaves were cut into small pieces of 3-5 mm length and soaked 297

in tap water. A Leica 385 TCS SP5 confocal microscope (Leica Microsystems, 298

Germany) was used for subcellular localization imaging. The wavelengths used for 299

imaging GFP fluorescence were excitation at 488 nm and emission at 495-550 nm. 300

Alternatively, the excitation and emission wavelengths for chloroplast autofluorescence 301

were in the far-infrared (>800 nm). 302

303

Cell death suppression assay 304

The pTRBO/GFP plasmid for GFP expression (courtesy of the Kamoun Lab, Sainsbury 305

Laboratory, Norwich, UK) and the pK7FWG2/SP-GFP plasmid [41] for SP-GFP were 306

used as negative controls in the cell death suppression assays. The A. tumefaciens 307

GV3101 cells carrying the negative control plasmids (GFP and SP-GFP) or the effector 308

of interest (with and without SP) were injected into a N. benthamiana leaf. After 24 h, 309

A. tumefaciens GV3101 carrying cell death elicitor pGR106/Inf1 (a gift from the 310

Bozkurt lab, Imperial College, London, UK) was injected in the same spot without 311

damaging leaf tissue. Cell death was measured at 4 dpi. 312

313



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RESULTS AND DISCUSSION 314

Sequencing of Pst-infected wheat leaves and de novo transcriptome assembly 315

We have collected total RNA from Pst-infected wheat leaves of susceptible and 316

resistant seedlings to analyze gene expression differences between the compatible and 317

incompatible pathogen-host interactions. The sequence reads have been deposited in 318

the NCBI BioProject database under the accession number PRJNA545454. A total of 319

9,204,786 clean reads for Pst-infected susceptible wheat (Avocet-S_PST), and 320

6,899,596 clean reads for Pst-infected resistant wheat cultivar (Avocet-YR10_PST) 321

were obtained after quality filtering. Their Q20 values were 98.16% and 97.90%, 322

respectively (Table 1). 323

324

The clean reads were assembled into more than 100,000 contigs with an average length 325

of greater than 200 nucleotides using Trinity software (Table 2). Unigenes were 326

clustered based on their level of similarity with each other. A cluster was defined as 327

containing unigenes with more than 70% similarity. We generated 12,956 distinct 328

unigene clusters for Avocet-YR10_PST and 13,564 for Avocet-S_PST (Table 2). A 329

total of 42,275 and 55,004 unigenes came from single genes within the two sets, 330

respectively. This data is represented in Table 2 as the distinct singleton. The length 331

distribution of assembled contigs and unigenes is provided in Fig S1. 332

333

Functional annotation and classification of de novo transcriptome 334

The unigenes obtained from both Avocet-S_PST and Avocet-YR10_PST were merged 335

into a single integrated data set to conduct an overall interpretation of transcriptome 336

sequencing. The unigenes were aligned against the following databases: NR, NT, 337

Swiss-Prot, KEGG, COG, and GO for annotation via the Blastx program (e-value < 338



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0.00001). Overall, 26,843 of 61,105 unigenes, corresponding to 43.9%, were annotated 339

in one or more databases (Table 3). The detailed annotation results are listed in Table 340

S1. The NR database provided the highest number of annotations. Approximately half 341

of the annotated unigenes shared high similarity (>60%) and aligned to either Pst genes 342

(41.4%) or genes of other fungal species (Fig 2). The results of COG classification and 343

GO functional annotation of the unigenes are presented in Fig S2. 344

345

Discovery of differentially expressed unigenes (DEGs) 346

We used the FRKM (RPKM) method [19] to determine DEGs between compatible and 347

incompatible interactions. We identified 10,550 DEGs with a false discovery rate 348

(FDR) of less than 0.001, and an expression difference of at least two-fold (Fig 3). 349

350

We mapped the DEGs to our custom database constructed by merging the pathogen 351

and host genomes. Local Blastx analysis (e-value < 0.00001) revealed that 6,220 of the 352

DEGs aligned best to the Pucciniales genome and 2,880 of the DEGs aligned best to 353

the wheat genome. The remaining DEGs yielded no significant homology to either the 354

pathogen or the host; hence, they are considered to be unknown novel DEGs. The 6,220 355

Pst-mapped DEGs (PstDEGs) were selected as the differentially expressed, pathogen-356

associated candidate genes to proceed with for secretome analysis. 357

358

The identified PstDEGs were subjected to Blast2GO analysis to elucidate their 359

functional classification (Table S2). A graphical view of the molecular functions and 360

biological processes of the PstDEGs is displayed in Fig 4. We were able to annotate the 361

majority (75.8%) of PstDEGs with their corresponding functional classification. The 362

distribution of molecular function between proteins with catalytic activity (61.73%) and 363



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binding activity (61.07%) attributes were practically equal in the Blast2GO level 2 364

analysis (Gene Ontology (GO) specificity increases as the “level” of the analysis gets 365

higher). During level 3 analysis of the PstDEGs, we observed that the proteins with 366

catalytic activity belonged to the hydrolase, transferase, and oxidoreductase categories 367

(Fig 4A). The Blast2GO analysis illustrates that the pathogen undergoes radical 368

changes during disease progression. A large number of PstDEGs (6,220 genes 369

compared to the total predicted protein number of Pst genes which is over 20,000) were 370

sorted into various categories of biological processes (Fig 4B). Therefore, we wanted 371

to determine the number of PstDEGs that sorted as enzymes. In total, we cataloged 372

1,824 PstDEGs as enzymes. Among them, hydrolases were the most common, followed 373

by transferases and oxidoreductases (Fig 4C). 374

375

Identification and characterization of the differentially expressed secretome 376

We made use of a previously constructed pipeline defined and validated by several 377

research groups (6, 23, 24). We extracted the secretome repertoire of 230 small-secreted 378

proteins of Puccinia striiformis, which are referred to as PstDESSPs, by selecting all 379

of the differentially expressed genes identified in our transcriptome data (6,220 380

differentially expressed genes (DEGs)). We identified other small-secreted proteins by 381

applying the same pipeline on all of the available genome sequences of Puccinia 382

species: Pst, Pgt, and Ptt (http://www.broadinstitute.org/). We then compared all of the 383

identified secretomes in a method similar to that used in the study published by Xia et 384

al., 2017 [10]. Among 230 small secreted proteins, 94 were predicted as effectors using 385

‘Effector 2.0’ software, and were then classified as Pst-Small Secreted Candidate 386

Effectors (PstSSCEs) (Fig 5A). The percentage of PstDESSPs within the data set of all 387

differentially expressed genes was 3.7% (230 out of 6,220). This ratio appeared 388




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significantly smaller when compared to the hypothetical prediction of small secreted 389

proteins within PST-78 (6.5%, 1,332 out of 20,482) and the other Pucciniale genomes. 390

Therefore, we concluded that the number of actively involved small secreted proteins 391

at 10 dpi is indeed much lower than what was hypothetically expected. This indicates 392

that the hypothetical number of PstDESSPs can be exaggerated when obtained using 393

only transcriptome predictions from genome sequences. 394

395

Certain types of gene functions are useful for the fungus in pathogen-host interactions, 396

including Carbohydrate-Active Enzymes (CAZymes), proteases, peroxidases, and 397

lipases. The PstDESSPs were compared against several distinguished public databases 398

(MEROPs, dbCAN, LED, fPoxDB) in order to predict their functions. We identified 4 399

proteases and protease inhibitors, 4 CAZymes, 11 lipases, and 9 peroxidases among the 400

PstDESSPs (Fig 5B). We searched the Pathogen-Host Interaction (PHI) database to 401

predict the virulence attribute of each PstDESSP based on their sequence similarity. A 402

total of 24 of the PstDESSPs exhibited significant similarity to at least one entry in the 403

PHI database. For example, Unigene31938 (Pstg04010) had a predicted ‘copper-zinc 404

superoxide dismutase’ domain match with PHI383 (C. albicans) and PHI6412 (P. 405

striiformis) (Table S3). Although the level of similarity barely exceeded the cutoff 406

value, both of these entries have a superoxide dismutase domain. PHI6412 (PsSOD1) 407

has been reported to increase the resistance of the pathogen against host-derived 408

oxidative stress [42]. The results suggest that all of the PstDESSPs that matched with 409

entries in the PHI database are worthy enough of further exploration employing in vivo 410

systems. Hence, a comparison against multiple databases provides a better way to 411

discover CSEPs and understand the importance of the PstDESSPs in Table S3. 412

Furthermore, a considerable percentage (80%) of the PstDESSPs remain 413



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uncharacterized (Fig 5B), due to the tendency of the candidate effectors to only show 414

homology to the pathogenicity related domains and not to other typical functional 415

domains. 416

417

To understand the relationship between PstDESSPs, we generated a phylogenetic tree 418

using an analysis of multiple sequence alignments. The outcome suggests that there are 419

no significant conserved sequences among the 230 PstDESSPs. In general, we 420

identified three main branches (or classes) of PstDESSPs based on sequence 421

similarities. The purple branch in Fig 5C possesses a substantial amount of the 422

PstDESSPs and is also subdivided into two smaller arms. No significant conserved 423

motifs were detected among the PstDESSPs, except for the Y/F/WxC motif (Table S3), 424

which is common to the powdery mildew [43]. 425

426

The PstDESSPs determined in this study were compared with other published 427

expression data sets of Pst to investigate whether the SSPs in this study were unique or 428

whether they were similar to haustorial differentially expressed effector candidates. 429

Cantu and colleagues studied the expression data for effectors expressed in infected 430

leaf and haustoria samples and provided a pioneer list of SSP tribes in their analysis (7, 431

8). We analyzed the similarity between tribes of SSPs via the Blastp program (e-value 432

< 0.00001). A total of 116 PstDESSPs were found to share significant homology to 433

haustoria-expressed secreted effector proteins (HESPs), and 133 of PstDESSPs were 434

found to be homologous to infected leaves-expressed secreted effector proteins (IESPs) 435

(Table S3). The remaining 96 PstDESSPs that did not match with HESPs or IESPs were 436

found to be differentially expressed at 10 dpi in this study. Comparison with the list of 437

haustorial secreted proteins (HSPs) published by Garnica et al. indicated that 94 of the 438



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PstDESSPs are drastically similar to HSPs [9]. Overall, the number of PstDESSPs that 439

we identified that are not similar to any of those in previously reported data sets is 32, 440

signifying their exclusivity to the present study. Xia and colleagues scrutinized the 441

effector candidates for avirulence (Avr) by comparing the expression data sets of Cantu 442

et al., 2013 [8], [10]. We compared our list of PstDESSPs to the list of Avr candidates 443

and the results suggest that three previously overlooked Avr candidates and six known 444

Avr candidates are present among our PstDESSP data set (Table S5). 445

446

The cysteine content of a candidate effector protein has long been a determinant for 447

making effector predictions [8–10, 23, 24]. Although several effectors have been 448

discovered to be not rich in their cysteine composition, such as AvrM [44] and AvrL567 449

[45], we examined the PstDESSPs in terms of cysteine possession. We identified 64 450

cysteine-rich small-secreted effector proteins (SCEPs) that have four or more cysteine 451

amino acid residues in their mature protein length (not including the N-terminal signal 452

peptide) (Table S5). 453

454

To find the most promising subset of candidate effectors, we focused on the PstDESSPs 455

that produced positive results in the EffectorP analysis, had cysteine residue counts of 456

more than three, and had homologous matches within the previously mentioned reports 457

(HESPs, IESPs, and HSPs). A total of 14 PstDESSPs met all five criteria and were 458

defined as reliable CSEPs for further investigations (Fig 5D). A more detailed 459

description of the features of the 14 CSEPs is provided in Table 4. 460

461



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Subcellular localization of Unigene17495 (Pstg_10917) 462

One of the CSEPs from Table 4 was chosen at random to test the reliability of the 463

presented PstDESSPs data. Unigene17495 (Pstg_10917) was predicted to have a transit 464

peptide sequence following the N-terminus secretion signal. Thus, if the effector 465

candidate succeeds in translocation within the host cell, it may be able to partially target 466

the chloroplast, the site of control for programmed cell death (PCD) against both biotic 467

and abiotic stressors. The heterologous expression of Unigene17495 with a GFP tag on 468

N. benthamiana leaves showed chloroplast localization (Fig 6). Its localization to the 469

chloroplast despite being predicted for secretion outside the cell was unexpected. 470

However, re-checking the predictions by other algorithms; ‘TargetP’ in addition to 471

predicting a secretion signal, only after its removal, it also detected a transit peptide 472

within the remainder sequence (111 a.a.). On the other hand, ‘Localizer’ analysis 473

predicted a transit peptide overlapping the secretion signal region. Therefore, the 474

experimentally shown chloroplast localization, instead of originally predicted secretion 475

outside of the cell, can be explained by the presence of a transit peptide and a possible 476

disruption of the secretion signal due to a particular condition in the vicinity of the 477

molecule. Alternatively, in the natural host, trafficking in both locations may be 478

possible or there is a slight change in the physiological condition that may direct a 479

preference of one target location to another. 480

481

Unigene17495 (Pstg10917) suppresses INF1-induced cell death 482

Unigene17495 (Pstg10917) is conserved among Pst relatives. However, none of the 483

homologs have been previously studied for their function. Interestingly, an effector 484

candidate of the soybean rust pathogen (Phakopsora pachyrhizi), PpEC82 [46], 485

resembles Unigene17495 (Pstg_10917) with an e-value of e-14. PpEC82 was reported 486



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to localize to the cytosol and nucleus with strong aggregation [47] properties, unlike 487

the chloroplast-targeting Unigene17495 (Pstg_10917) as we have demonstrated. 488

Moreover, Qi et al. stated that PpEC82 was able to suppress BAX-induced cell death 489

when expressed in yeast cells [47]. Correspondingly, we conducted cell death 490

suppression assays to test whether Unigene17495 (Pstg_10917) has suppression 491

abilities similar to its distant homolog, PpEC82. We used a cell death elicitor (INF1) as 492

a cell death inducer and categorized the resulting suppression of cell death observations 493

into four levels: strong suppression, moderate suppression, weak suppression, or no 494

suppression at all. Across 15 replicates of Unigene17495-treated N. benthamiana 495

leaves, we observed three cases of strong, three cases of moderate, and three cases of 496

weak cell death suppression. The other six leaves presented no significant level of cell 497

death suppression. In all 15 trials, leaves treated with the negative controls (GFP and 498

SP-GFP) resulted in tissue collapse and cell death in the infiltrated area. Therefore, we 499

concluded that Unigene17495 (Pstg_10917) without SP, was able to partially suppress 500

programmed cell death triggered by the INF1 elicitor (Fig 7). We need to emphasize 501

that not all attempts produced equal levels of cell death suppression. Although the 502

expression of the effector was verified by microscope analysis at 2 dpi for all of the 503

leaves, 6 out of the 15 leaves revealed no detectable suppression of INF1-induced cell 504

death. Fisher’s exact test calculated a P-value of 0.0007 for the findings, and thus the 505

results were significant (P < 0.05). Together, these data show that Pstg10917ΔSP-GFP 506

(Unigene17495) is capable of significantly suppressing PCD induced by INF1. 507

508

It should be noted that the complex nature of heterologous expression systems serves 509

as a double-edged sword due to the oscillations in cell death response with N. 510

benthamiana. However, the versatility of the system provides a fast and effective way 511



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to study candidate effectors. Other candidate effectors of Pst have been studied using 512

heterologous expression systems. For example, the Pst effector PstHa5a23 was 513

reported to suppress PCD triggered by BAX, INF1, and two mitogen-activated protein 514

kinases (MKK1 and NPK1) involved in host cell resistance [48]. PstHa5a23 is a 515

homolog to Pstg_00676 identified in our PstDESSPs list. In a recent publication, 516

Pstg_14695 and Pstg_09266 were able to suppress PCD as part of effector-triggered 517

immunity [49]. Furthermore, Petre et al. utilized mass spectroscopy analysis to decipher 518

host cell factors that interact with GFP-tagged candidate effectors in co-519

immunoprecipitation samples [50]. Eleven of the twenty candidate effectors reported 520

in that study are homologous to our PstDESSPs. These recent reports support the 521

reliability of our PstDESSPs data as a trustworthy collection of candidate effectors that 522

can be used for effector function studies in the future. 523

524

Conclusion 525

The obligate biotrophic fungus Pst causes yellow rust disease by establishing an 526

interface between the host and pathogen using small secreted proteins. Transcriptome 527

sequencing has proven a useful strategy in elucidating the crosstalk of proteins between 528

the host and pathogen, including the interaction between wheat and Pst. We compared 529

compatible and incompatible interaction points in order to identify the differentially 530

expressed genes (PstDEGs) at 10 dpi. In silico predictions highlighted the SSPs within 531

the group of PstDEGs. The annotations, comparisons, and characterizations contributed 532

to the identification of a list of the most promising subset of SSPs that function as 533

phytopathogen effectors. The presented SSP data represent candidates for the 534

functional analysis to better understand yellow rust disease in wheat. In particular, we 535



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were able to determine that Unigene17495 (Pstg10917) of our PstDESSPs list is 536

localized to the chloroplasts of infected plant cells and is capable of halting 537

programmed cell death. 538

539

ACKNOWLEDGMENTS 540

This study was funded by COST-113Z350 (call FA1208), PI: MSA. BD (Dagvadorj), 541

ACO, and AA-O were supported by TUBITAK-BIDEB: 2215 and 2211/C, 542

respectively. The authors want to thank the Kamoun Lab, Sainsbury Lab, and Tolga 543

Bozkurt of Imperial College London, Department of Life Sciences, London, SW7 544

2AZ, the UK for vectors used in the study. 545

546

AUTHOR CONTRIBUTIONS 547

Ahmet Caglar Ozketen: Methodology, Investigation, Data Curation, Validation, 548

Writing – original draft preparation, Writing – review & editing 549

Ayse Andac-Ozketen: Investigation, Validation 550

Bayantes Dagvadorj: Investigation 551

Burak Demiralay: Data Curation 552

Mahinur S. Akkaya: Methodology, Funding acquisition, Supervision, Writing – review 553

& editing 554

555

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724

725



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FIGURE LEGENDS 726

Fig 1. Flowchart to summarize sampling, sequencing, and in silico analysis. 727

Pipeline for the steps performed to achieve de novo assembly, expression analysis, and 728

mapping of Pst genes. * = FDR (False Discovery Rate). 729

730



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Table 1. Sequencing statistics for Pst inoculated/infected wheat transcriptome. 731

Samples Avocet-S_Pst Avocet-Yr10_Pst

Total Clean Reads 9,204,786 6,899,596

Total Clean

Nucleotides (nt) 828,430,740 620,963,640

Q20 98.16 % 97.90 %

GC 51.44 % 54.04 %

Q20 is the proportion of nucleotides with a quality value larger than 20. 732 GC is the proportion of guanine and cytosine nucleotides among total nucleotides. 733 734

735

Table 2. De novo assembly statistics for Pst inoculated/infected wheat transcriptome. 736

Samples Number Length (nt) Mean Length (nt) N50 Distinct

Clusters

Distinct

Singleton

Contig Avocet-Yr10_Pst 105,866 23,189,064 219 249 - -

Avocet-S_Pst 128,680 29,848,685 232 272 - -

Unigene Avocet-Yr10_Pst 55,231 18,753,197 340 371 12,956 42,275

Avocet-S_Pst 68,568 24,329,132 355 394 13,564 55,004

All 61,105 26,978,874 442 490 15,016 46,089

Distinct clusters are similar (more than 70%) unigenes, and these unigenes may originate from the same 737 gene or homologous gene; whereas distinct singletons represent the unigene come from a single gene. 738 739

740

Table 3. Annotation statistics of the assembled unigenes of the transcriptome 741

sequencing project 742

Sequence file NR NT Swiss-Prot KEGG COG GO All

All-Unigene.fa 24,933 9,945 16,318 16,275 13,278 10,015 26,843

743

744



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Fig 2. Statistics of the NR classification of unigenes. (A) The e-value distribution of 745

the alignment results of NR annotation. (B) The similarity distribution and (C) the 746

species distribution of the results of NR annotation. 747

748

749

750



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34

Fig 3. Distribution of differentially expressed unigenes (DEGs). Red: Up-regulated 751

genes (6851), green: downregulated genes (3699), and blue: no differential expression 752

observed with the parameters FDR ≤ 0.001 and │log2ratio│≥ 1. 753

754

755

756



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35

Fig 4. Functional annotations of PstDEGs via the Blast2Go program. (A) 757

Molecular function analysis (Level 3) of PstDEGs. (B) Biological process classification 758

(Level 2) of PstDEGs. (C) Enzyme class distribution of PstDEGs. 759

760



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36

761



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37

Fig 5. Characterization of the PstDESSPs. (A) The number of total proteins, small 762

secreted proteins, and small secreted candidate effectors among our data (PstDEGs) 763

and three wheat rust fungi genomes: Pst, Pgt (P. graminis f. sp. tritici), and Ptt (P. 764

triticina). (B) Distribution of PstDESSPs for functional annotations. (C) Phylogenetic 765

analysis based on the protein sequence similarity of PstDESSPs. (D) Characterization 766

and comparison of PstDESSPs in five categories: small cysteine-rich effector proteins 767

(SCEPs), predicted to be an effector candidate by a machine learning algorithm 768

(EffectorP 2.0-positive), and consistent with previous sequencing reports in the 769

literature of candidate rust effectors in haustorial (HESPs) or infection (IESPs) 770

expressed secreted proteins [8], and haustorial secreted proteins (HSPs) [9]. 771

772

773



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38

Table 4: List of the most promising CSEPs overlapped in all five classes in Figure 5D. 774

aLength represents the length of mature proteins without N-terminus signal peptide part. 775 b Cysteine residue number in the mature protein. 776 c Differential expression compatible interaction over incompatible interaction. 777 d The results of TargetP (TP) program,e Localizer (Loc.) Program. Abbreviations are C: Chloroplast, M: 778 mitochondria, N: nucleus, G: Golgi, E.R.: endoplasmic reticulum, Cy: cytosolic, AP: apoplastic, NonAP: non-779 apoplastic. All predictions were performed using mature protein sequences under the assumption that mature 780 proteins translocate into the host cell. 781

782

783

CSEP Properties Subcellular Localization Prediction

ID Corresponding

Pst78 Homologs

Conserved

Domains

Length

(a.a.)a

Cysb DEc TPd Loc.e WolfPSORT ApoplastP

CL887.Contig1 PSTG_16009 138 10 Up - - C AP

CL887.Contig2 PSTG_16009 138 10 Up - - C AP

Unigene13023 PSTG_03450 100 10 Up - - C AP

Unigene17495 PSTG_10917 111 6 Up C C M AP

Unigene28760 PSTG_14090 DPBB_1

superfamily 112 6 Up - - C AP


Unigene31294 PSTG_00994 254 10 Up - N N NonAP

CL5364.Contig2 PSTG_01880 205 6 Up - - ER NonAP

CL5364.Contig3 PSTG_01881 205 6 Up - - N AP

Unigene33801 PSTG_05079 92 6 Up - -

C AP

Unigene36354 PSTG_08969 196 8 Up C - C AP

Unigene36901 PSTG_03879,

partial 49 6 Up - - N AP

Unigene38807 PSTG_14378 191 10 Up - - G AP




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Fig 6. Subcellular localization of Unigene17495 (Pstg10917) tagged with GFP at 784

C-terminus. (A) Expression of Pstg10917ΔSP-GFP (without N-terminus secretion 785

signal). (B) and (E) Chloroplast autofluorescence. (C) and (F) Overlaid images. (D) 786

Expression of Pstg10917 (with N-terminus secretion signal). The expression on N. 787

benthamiana leaves was visualized under a confocal microscope at 2 dpi. 788

789

790



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40

Fig 7. Suppression of cell death mediated by Inf1 expression. (A) Daylight and (B) 791

UV light exposure. Constructs were expressed with Agrobacterium tumefaciens; (1) 792

GFP, (2) SP-GFP, (3) Unigene17495 without SP (Pstg10917ΔSP-GFP) and (4) 793

Unigene17495 (Pstg10917-GFP). Cell death elicitor (Inf1) was infiltrated into the same 794

region after 24 hours. Photos were taken at 4 days after Inf1 challenge. 795

796

797



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41

SUPPORTING INFORMATION 798

Fig S1. Length distribution of contigs and unigenes after the assembly process. 799

Graphical representation of the length distribution of A) contigs of AvocetS_PST, B) 800

unigenes of AvocetS_PST, C) contigs of AvocetYR10_PST and D) unigenes of 801

AvocetS_PST. 802

803

Fig S2. Function annotation of the unigene data. The function of each of the unigenes 804

was determined using the A) COG or B) GO databases. 805

806

Table S1. Annotation of the assembled unigenes discovered in transcriptome 807

sequencing. 808

809

Table S2. Annotation of the PstDEGs mapped to the Pucciniales local database. 810

811

Table S3. In-depth characterization of the differentially expressed small 812

secretome profile of Pst (PstDESSPs). 813

814

Table S4. Primers used in this study for cloning. 815

Table S5. Matching PstDESSPs to the YR candidates identified in the study by 816

Xia et al., 2017. 817

818

819

820



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in-depth secretome analysis of puccinia striiformis f. sp ......jan 13, 2020 · puccinia...

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