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Theses and Dissertations--Plant Pathology Plant Pathology

2015

ESTABLISHMENT OF BIOTROPHY BY THE MAIZE ANTHRACNOSE ESTABLISHMENT OF BIOTROPHY BY THE MAIZE ANTHRACNOSE

PATHOGEN PATHOGEN COLLETOTRICHUM GRAMINICOLA: USE OF : USE OF

BIOINFORMATICS AND TRANSCRIPTOMICS TO ADDRESS THE BIOINFORMATICS AND TRANSCRIPTOMICS TO ADDRESS THE

POTENTIAL ROLES OF SECRETION, STRESS RESPONSE, AND POTENTIAL ROLES OF SECRETION, STRESS RESPONSE, AND

SECRETED PROTEINS SECRETED PROTEINS

Ester Alvarenga Santos Buiate University of Kentucky, esterbuiate@uky.edu

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Ester Alvarenga Santos Buiate, Student

Dr. Lisa J. Vaillancourt, Major Professor

Dr. Lisa J. Vaillancourt, Director of Graduate Studies

ESTABLISHMENT OF BIOTROPHY BY THE MAIZE ANTHRACNOSE PATHOGEN COLLETOTRICHUM GRAMINICOLA: USE OF

BIOINFORMATICS AND TRANSCRIPTOMICS TO ADDRESS THE POTENTIAL ROLES OF SECRETION, STRESS RESPONSE, AND

SECRETED PROTEINS

______________________________________

DISSERTATION ______________________________________

A dissertation submitted in partial fulfillment of the requirements

for the degree of Doctor of Philosophy in the College of Agriculture,

Food and Environment at the University of Kentucky

By

Ester Alvarenga Santos Buiate

Lexington, Kentucky

Director: Dr. Lisa J. Vaillancourt, Professor of Plant Pathology

Lexington, Kentucky 2015

Copyright © Ester Alvarenga Santos Buiate 2015

ABSTRACT OF DISSERTATION

ESTABLISHMENT OF BIOTROPHY BY THE MAIZE ANTHRACNOSE PATHOGEN COLLETOTRICHUM GRAMINICOLA: USE OF

BIOINFORMATICS AND TRANSCRIPTOMICS TO ADDRESS THE POTENTIAL ROLES OF SECRETION, STRESS RESPONSE, AND

SECRETED PROTEINS

Colletotrichum graminicola is a hemibiotrophic pathogen of maize that causes anthracnose leaf and stalk rot diseases. The pathogen penetrates the host and initially establishes an intracellular biotrophic infection, in which the hyphae are separated from the living host cell by a membrane that is elaborated by the host, apparently in response to pathogen signals. A nonpathogenic mutant (MT) of C. graminicola was generated that germinates and penetrates the host normally, but is incapable of establishing a normal biotrophic infection. The mutated gene is Cpr1, conserved in eukaryotes and predicted to encode a component of the signal peptidase complex. How can we explain why the MT is normal in culture and during early stages of pathogenicity, but is deficient specifically in the ability to establish biotrophy? To address this, first I characterized the insertion in the 3’ UTR of the MT strain in detail, something that had not been done before. The wild-type (WT) transcript did not differ from predictions, but the MT produced several aberrant transcript species, including truncated and non-spliced transcripts, and the normal one. Aberrant splicing of MT cpr1 was observed both in RNAseq transcriptome data and reverse-transcription polymerase chain reaction (RT-PCR), under different growth conditions and in planta. I also conducted a bioinformatic analysis of other conserved components of the secretory pathway in the MT and WT in planta. One explanation for nonpathogenicity of the MT is that it cannot cope with an increase in secretory activity during infection, and fails to produce necessary pathogenicity factors. With the transcriptome data, I was able to identify effector proteins that were expressed in the WT but not in the MT. Another possible explanation for the MT phenotype is that the MT can’t adapt to stress imposed by the plant. I developed a growth assay to characterize the effect of chemical stressors in vitro. The MT was more sensitive to most stressors, when compared to the WT. The transcriptome data indicates that the genes involved in different stress pathways are expressed in planta in both WT and MT, although very few genes are differentially expressed across the different growth stages.

KEYWORDS: corn anthracnose, secretion pathway, fungal stress pathway, fungal effectors, bioinformatics.

Copyright © Ester Alvarenga Santos Buiate 2015

ESTABLISHMENT OF BIOTROPHY BY THE MAIZE ANTHRACNOSE PATHOGEN COLLETOTRICHUM GRAMINICOLA: USE OF

BIOINFORMATICS AND TRANSCRIPTOMICS TO ADDRESS THE POTENTIAL ROLES OF SECRETION, STRESS RESPONSE, AND

SECRETED PROTEINS

by

Ester Alvarenga Santos Buiate

Lisa J. Vaillancourt

Director of Dissertation

Lisa J. Vaillancourt

Director of Graduate Studies

March 23, 2015

Date

Aos meus pais, que foram meus primeiros professores com seu exemplo de

força e caráter. O constante apoio e amor incondicional de vocês se refletem

nas minhas conquistas. Obrigada!

Für Hermann, der mir Stärke gab als ich selbst keine hatte.

iii

Acknowledgements

I’ve known Dr. Lisa Vaillancourt for almost nine years, and she has not only

been a great mentor and a colleague, but a great friend. Her patience, concern

and dedication to her work and her students, and her faith in me are what

motivated me during those years. I am forever grateful for all that you taught

me. I would like to thank my dissertation committee, Dr. Mark Farman, Dr. Peter

Nagy and Dr. Arthur Hunt, for their academic support and insightful advice over

the years. In addition, my gratitude is extended to the faculty members of the

department, from whom I learned much through classes and our conversations.

I am extremely grateful to Etta Nuckles and Doug Brown not only for their great

work, essential for this dissertation to be completed, but for their

encouragement and friendship. To all the staff of the Department, especially

(my maid of honor) Alicia Landon and Shirley Harris, many thanks for always

been a tremendous help and for the laughs when needed.

During these years at Vaillancourt Lab, I was extremely lucky to have had

incredible PhD students as my colleagues. I would like to thank Dr. Sladana

Bec, you taught me so many things in the lab, as well as to live life in a sweeter

way. Your friendship has been invaluable inside and outside the lab. Dr. Maria

Torres, we’ve shared benches, too many corn sheaths, a few worries and

incredibly funny moments. I am grateful to have met such an amazing scientist

and a life-long friend. Many thanks to Katia Xavier, for always providing the

much-needed coffee, encouragement and for being a tremendous help

whenever I needed. Your friendship made the completion of this dissertation

easier. To all the other past and current members of the Vaillancourt Lab whom

I’ve had the pleasure to work with, thank you for your help, conversations and

support. Finally, I would like to thank the friends I made over the years at UK,

most now scattered around the world. Your support, near or far, was essential.

iv

Table of contents

Acknowledgements ......................................................................................... iii

Table of contents ............................................................................................. iv

List of Figures ................................................................................................ viii

List of Files ...................................................................................................... xi

Chapter 1 – Colletotrichum graminicola, the causal agent of maize anthracnose

........................................................................................................................ 1

1.1 Overview ................................................................................................ 1

1.2 The economic impact of maize anthracnose .......................................... 1

1.3 Disease cycle of anthracnose ................................................................ 3

1.4 Hemibiotrophy and its role in the disease cycle in Colletotrichum .......... 4

1.5 Resistance to maize anthracnose .......................................................... 7

1.6 The Cpr1 mutant of C. graminicola......................................................... 8

1.7 What is the role of Cpr1 in C. graminicola pathogenicity? .................... 10

1.8 Hypotheses and objectives of this dissertation ..................................... 11

Chapter 2 – Transcriptional analysis of Cpr1, and other components of the

signal peptidase complex and secretory pathway, in wild type and mutant

strains of Colletotrichum graminicola ............................................................. 18

2.1 Overview .............................................................................................. 18

2.2 Introduction .......................................................................................... 19

2.3 Material and Methods ........................................................................... 24

2.3.1 Defining the putative secretory pathway in C. graminicola ............. 24

2.3.2 Sequence analysis of signal peptidase complex proteins .............. 24

2.3.3 Fungal strains ................................................................................ 25

2.3.4 In planta visualization of the endomembrane system and Cpr1

expression ............................................................................................... 25

2.3.5 DNA Extraction .............................................................................. 26

2.3.6 Plant growth and inoculations ........................................................ 27

2.3.7 RNA extraction ............................................................................... 28

2.3.8 Transcriptome: Illumina RNA sequencing and data analysis ......... 29

2.3.9 Heatmaps ....................................................................................... 31

2.3.10 Characterization of the MT Cpr1 allele ......................................... 31

2.3.11 Southern Blots ............................................................................. 32

v

2.3.12 Transcriptome analysis to characterize transcripts of Cpr1 and other

SPC genes in planta ............................................................................... 32

2.3.13 Reverse transcription (RT)-PCR .................................................. 33

2.4 Results ................................................................................................. 35

2.4.1 Genomics and comparative transcriptomics of the proposed secretory

pathway in C. graminicola ....................................................................... 35

2.4.2 The SPC proteins in C. graminicola ............................................... 37

2.4.3 Structure of the mutant and wild type Cpr1 alleles ......................... 38

2.4.4 MT and WT Cpr1 transcripts in planta ............................................ 40

2.4.6 RT-PCR amplification and sequencing of alternative transcripts from

C. graminicola mutant strain ................................................................... 41

2.4.7 Analysis of 3’UTR sequences of C. graminicola MT and WT ......... 41

2.4.8 In planta analysis of the endomembrane system and expression and

localization of CPR1 ................................................................................ 42

2.5 Discussion ............................................................................................ 44

Chapter 3 – Comparative genomics of the Colletotrichum graminicola

secretome and effectorome ........................................................................... 66

3.1 Overview .............................................................................................. 66

3.2 Introduction .......................................................................................... 67

3.2.1 Effectors and fungal pathogenicity to plants ................................... 67

3.2.2 The role of effectors in Colletotrichum ............................................ 68

3.2.3 Identification of putative effectors ................................................... 69

3.2.4 Use of transcriptome data to identify candidate effectors ............... 70

3.2.5 The role of effectors in non-host resistance ................................... 71

3.2.6 Rationale for identification of C. graminicola candidate effectors ... 73

3.3 Material and Methods ........................................................................... 75

3.3.1 Fungal strains and genome data used in this study ....................... 75

3.3.2 DNA extraction protocol ................................................................ 75

3.3.3 Sequencing and assembly ............................................................. 76

3.3.4 Gene annotation ............................................................................ 77

3.3.5 Transcriptome data ........................................................................ 77

3.3.6 Genome synteny ............................................................................ 77

3.3.7 Identification of putative orthologous genes ................................... 78

3.3.8 Gene categorization ....................................................................... 78

vi

3.3.9 Identification of candidate effector proteins .................................... 79

3.3.10 Identification of non-annotated putative effector proteins in C.

graminicola .............................................................................................. 81

3.3.11 Expression analysis of the BAS3 homolog in the WT in vitro and in

planta ...................................................................................................... 82

3.4 Results ................................................................................................. 84

3.4.1 Comparison of Colletotrichum genomes ........................................ 84

3.4.2 Comparative analysis of Colletotrichum proteins ........................... 85

3.4.3 Comparative genomics of Colletotrichum secretomes and

effectoromes ........................................................................................... 88

3.4.4 Effector diversity among isolates ................................................... 94

3.4.5 Transcriptome analysis: expression of putative effector genes in MT

vs WT C. graminicola in planta ............................................................... 94

3.4.6 Identification of non-annotated effectors ........................................ 99

3.4.7 Expression of the C. graminicola BAS3 homolog in vitro and in planta.

.............................................................................................................. 100

3.4.8 Identification of candidate effectors for future studies .................. 101

3.5 Discussion .......................................................................................... 102

Chapter 4 – Genomic and functional analysis of stress response in wild type

and mutant strains of Colletotrichum graminicola in vitro and in planta ...... 124

4.1 Overview ............................................................................................ 124

4.2 Introduction ........................................................................................ 124

4.2.1. Fungal Response to Stress ......................................................... 124

4.2.2 The Role of Stress Response in Pathogenicity ............................ 126

4.2.3 Evidence that Cpr1 and the signal peptidase may play a role in stress

response ............................................................................................... 132

4.3 Material and Methods ......................................................................... 133

4.3.1 Strains and culture conditions ...................................................... 133

4.3.2. Pharmacological analysis of WT and MT stress responses in vitro

.............................................................................................................. 133

4.3.3 In silico identification of C. graminicola stress response genes and

pathways ............................................................................................... 134

4.3.4 Transcriptome analysis ................................................................ 135

4.3.5 Heatmaps ..................................................................................... 136

4.3.6 Expression analysis of selected stress response genes in the WT

under conditions of stress in vitro and in planta .................................... 136

vii

4.4 Results ............................................................................................... 140

4.4.1 The MT was more sensitive than the WT or MT-C strains to most

stress-inducing chemicals and treatments ............................................ 140

4.4.2 About one-fifth of the C. graminicola genome encodes genes that are

predicted to be involved in stress response .......................................... 140

4.4.3 Colletotrichum spp. encode homologs for a majority of the genes in

the yeast secretory, oxidative, osmotic, and cell wall integrity stress

response pathways. .............................................................................. 140

4.4.4 Presence of UPRE in stress genes .............................................. 142

4.4.5 Genes involved in response to stress, and particularly to secretion

stress, are highly expressed in planta. .................................................. 143

4.4.6 Patterns of differentially expressed genes in planta suggest that

stress responses are most active early during infection by both the WT and

the MT. .................................................................................................. 144

4.4.7 Relatively few stress responsive genes are differentially expressed in

planta. ................................................................................................... 145

4.4.8 Expression of selected stress response genes in vitro in response to

chemically induced stress. .................................................................... 148

4.4.9 Stress altered the structure of the MT Cpr1 transcripts in vitro

compared with in planta ........................................................................ 149

4.4.10 In planta visualization of the BiP stress response protein .......... 149

4.4.11 In vitro response of endomembrane system to stress chemicals149

4.5 Discussion .......................................................................................... 151

Chapter 5 – Concluding remarks ................................................................. 177

Appendix I – Verification of transcriptome sequencing results .................... 181

AI.1 Background ....................................................................................... 181

AI.2 Material and Methods ....................................................................... 182

AI.3 Results .............................................................................................. 183

Appendix III – Optimization of the protocol for race tube growth assays ..... 192

AIII.1 Background ..................................................................................... 192

AIII.2 Material and Methods ..................................................................... 193

AIII.3 Results ............................................................................................ 194

References .................................................................................................. 200

Vita .............................................................................................................. 239

viii

List of Figures

Figure 1.1 Disease cycle of C. graminicola. ......................................................... 15

Figure 1.2 Sequential biotrophy of C. graminicola ............................................... 16

Figure 1.3 Growth in media (A) and in planta (B) of the three strains used in

the experiments. ........................................................................................................ 17

Figure 2.1 Illustration of Cpr1 and the signal peptidase complex ..................... 50

Figure 2.2 Illustration of the pFPL Gateway vector construct. ........................... 51

Figure 2.3 Degree of conservation of Colletotrichum secretory pathway based

on yeast proteins ....................................................................................................... 52

Figure 2.4 Model of the Colletotrichum graminicola secretory pathway

together with normalized reads counts for each gene across growth stages . 53

Figure 2.5 Alignment of amino acid sequences of the four signal peptidase

subunits ....................................................................................................................... 54

Figure 2.6 Map of the MT 3’downstream region .................................................. 55

Figure 2.7 Southern Blots performed using MT DNA .......................................... 56

Figure 2.8 Transcript prediction for WT and MT Cpr1 gene using FGENESH 57

Figure 2.9 Illustration of Cpr1 reads for WT and MT strains .............................. 58

Figure 2.10 Intron splicing in the four signal peptidase subunit transcripts ..... 59

Figure 2.11 Cpr1 intron pattern of WT and MT strains ....................................... 60

Figure 2.12 Cpr1 transcript intron pattern of WT, MT and MT-C mycelia grown

in Fries medium ......................................................................................................... 61

Figure 2.13 Prediction of the MT cpr1 transcript with the intron retained done

by Geneious ............................................................................................................... 62

Figure 2.14 Sequencing of the 3’UTR of the WT and MT strains ..................... 63

Figure 2.15 WT and MT-HDEL tagged isolates ................................................... 64

Figure 2.16 Expression of CPR1-RFP in planta ................................................... 65

Figure 3.1 Bioinformatic pipeline for prediction of the Colletotrichum

effectorome .............................................................................................................. 107

Figure 3.2 Synteny between C. graminicola and C. sublineola ....................... 108

Figure 3.3 Amino acid similarity of orthologous proteins between C.

graminicola and other sequenced Colletotrichum species ............................... 109

Figure 3.4 Orthology by OrthoMCL ...................................................................... 110

Figure 3.5 Orthology by Reciprocal Best Hit ....................................................... 111

Figure 3.6 Carbohydrate-degrading enzymes (CAZymes) in C. graminicola

(Cgram), C. sublineola (Csub) and C. higginsianum (Chig), separated by

different classes....................................................................................................... 112

Figure 3.7 Blast results of proteins shared between C. graminicola and the

other two species using NCBI database ............................................................. 113

Figure 3.8 Orthology among the small secreted proteins between all 3

Colletotrichum species. .......................................................................................... 114

ix

Figure 3.9 Amino acid similarity of orthologous proteins between C.

graminicola and the other two species, C. sublineola (A) and C. higginsianum

(B) .............................................................................................................................. 115

Figure 3.10 Amino acid similarity of orthologous proteins between C.

graminicola and the other two species, C. sublineola (A) and C. higginsianum

(B) .............................................................................................................................. 116

Figure 3.11 Alignment of effector protein families. ............................................ 117

Figure 3.12 Sequence of motifs discovered on SSPs of C. graminicola using

MEME. ...................................................................................................................... 118

Figure 3.13 Comparison between secreted proteins present in the genome

and secreted proteins present in differentially expressed RNAseq transcripts,

showing the over-representation of secreted proteins in planta ...................... 119

Figure 3.14 Timing of the differentially expressed SSPs based on their

homology to other Colletotrichum species .......................................................... 120

Figure 3.15 Identification of non-annotated effectors in C. graminicola using

FGENESH prediction programs ............................................................................ 121

Figure 3.16 Expression of C. graminicola BAS3 on in planta and in vitro

conditions.................................................................................................................. 122

Figure 3.17 Orphan candidate effectors highly expressed in the early stages

of infection... ............................................................................................................. 123

Figure 4. 1 Illustration of the pFPL Gateway vector construct used to create

protein fusions between C. graminicola BiP promoter region and the red

fluorescent protein reporter gene. ........................................................................ 157

Figure 4. 2 Growth of C. graminicola strains during chemical stress ............. 158

Figure 4.3 Degree of conservation of Colletotrichum stress proteins versus

their putative yeast homologs................................................................................ 163

Figure 4.4 Stress pathway models ....................................................................... 164

Figure 4.5 Placement of response elements in the 5’ upstream region of

genes involved in stress ......................................................................................... 168

Figure 4.6 Heat maps ............................................................................................. 169

Figure 4.7 Analysis of stress-related genes present in the laser capture data

with a logFC higher than 1 ..................................................................................... 171

Figure 4.8 RT-PCR with BiP, Hac and PDI during in vitro and in planta

conditions.................................................................................................................. 172

Figure 4.9 Effect of stress on Cpr1 transcripts in vitro ...................................... 173

Figure 4.10 Transcript sequencing results of Cpr1 gene in the WT and MT

strains ........................................................................................................................ 174

Figure 4.11 Expression of BiP (GLRG_10629) chaperone reporter in vitro and

in planta… ................................................................................................................ 175

Figure 4.12 WT-HDEL tagged isolate visualized in the confocal

microscope ............................................................................................................... 176

x

Figure AI. 1 Wild-type (WT) Cpr1 gene (green) is represented with flanking

genes (pink).. ........................................................................................................... 185

Figure AI. 2 Technical replicates (Lanes), treatments and biological replicates

(Rep) from the transcriptome sequencing project .............................................. 186

Figure AI. 3 Transcriptome reads showing matches to the Cpr1 gene and the

two flanking genes in the WT (blue) and the MT (red) strains. ........................ 187

Figure AI. 4 Hypothesized mutant sequence with reads matching the plasmid

regions…... ............................................................................................................... 188

Figure AI. 5 Map of pAN56-1-sGFP-HDEL plasmid .......................................... 189

Figure AI. 6 Correlation analysis of fold changes by RNA sequencing and RT-

PCR.. ......................................................................................................................... 190

Figure AI. 7 Corrected map of pCB1636. ............................................................ 191

Figure AIII.1 Variability in radial growth during chemical stress. ..................... 196

Figure AIII.2 Race tubes growth.. ......................................................................... 197

Figure AIII. 3 Alignment between S. cerevisiae Kar2/BiP (YJL034W) and C.

graminicola homolog GLRG_10629 ..................................................................... 198

Figure AIII. 4 Primers and sequencing results of the promoter region used to

produce a reporter construct for BiP using Gateway ......................................... 199

xi

List of Files

EsterBuiate_Dissertation_Files.xlsb File size 3.89 MB

1

Chapter 1

Colletotrichum graminicola, the causal agent of maize anthracnose

1.1 Overview

Anthracnose stalk rot, caused by the fungus Colletotrichum graminicola (Ces.)

Wilson, is one of the most important diseases of maize and causes significant

losses worldwide. The most effective control for the disease is the use of

resistant cultivars, but the success of this approach depends not only on the

host genetics, but also on the plant physiological state and environmental

conditions that can change from year to year.

C. graminicola is a hemibiotroph which initially establishes an asymptomatic

biotrophic infection that is followed later by a switch to necrotrophy when

symptoms are produced. Our laboratory produced a mutant strain of C.

graminicola several years ago that is completely nonpathogenic. The mutation

is in a gene encoding one of the components of the signal peptidase complex,

known to be important for protein processing and secretion. This mutant fails to

establish biotrophy. Our long-term goal is to understand the function of the

mutated gene and its relation to disease development. The goal of my

dissertation research was to use a genomic and bioinformatics approach to

address several hypotheses related to understanding the difference in the

ability of the wild type (WT) and the mutant (MT) to establish biotrophy.

1.2 The economic impact of maize anthracnose

Maize (Zea mays L.) is one of the most important cereal crops in the world, and

is the most valuable crop cultivated in the United States (USA), which is the

biggest producer. In 2012, worldwide production of maize was 872 million tons,

more than any other cereal. The USA alone produced around 31.4% of the total

(FAO, 2014). Besides being a staple food in some regions of the world, maize

can be used for the production of oil, syrup, alcohol, livestock feed, and more

recently for biofuel. Maize stalks can also be a source for cellulosic ethanol

production. The increase in production of ethanol biofuel from maize in the USA

2

has resulted in corresponding increases in the value of the crop and in the

acreage grown. Continuous cropping has become more common. One of the

major factors limiting maize production is disease. It is estimated that diseases

cause losses from 2 to 15% annually, and anthracnose in particular is a disease

of worldwide importance on maize (Balint-Kurti and Johal, 2009; White, 1999).

Anthracnose can affect all parts of the plant, but it is most commonly associated

with leaf blight (ALB) and stalk rot (ASR). ASR is by far the more important of

the two. Maize stalk rots can be caused by several fungi other than C.

graminicola, including Fusarium graminearum and Stenocarpella maydis (Denti

and Reis, 2003; Ribeiro et al., 2005). In the USA, C. graminicola is considered

to be the most important stalk rot pathogen although it was considered a minor

problem prior to 1970. In the early 1970s several severe ASR epidemics

occurred that caused lodging and in some cases complete crop loss (Bergstrom

and Nicholson, 1999) and now ASR is the most common and damaging of the

stalk rots (Denti and Reis, 2003; Ribeiro et al., 2005). Although there is

relatively little information available on anthracnose disease severity, Jirak-

Peterson and Esker (2011) reported disease levels in Wisconsin of around 5%

during a two year trial, while work done by Costa et al. (2010) recorded disease

severities ranging from 30 to 60% in Brazil. Both studies suggested that the

hybrid chosen was an important factor for the occurrence of anthracnose stalk

rot.

ASR disease management involves deployment of resistant cultivars, control

of the European corn borer and corn rootworms, and cultural practices such as

balanced soil fertility, stress reduction and proper planting rates (Bergstrom and

Nicholson, 1999). Occasionally, fungicides are used, especially in maize seed

production fields in Brazil. Increases in disease pressure and in grain prices

have led to more fungicide use for maize production in the last decade (Wise

and Mueller, 2011). Although the levels of control achieved are not large, the

small increase in production caused by strobilurin fungicides (due to delay of

plant senescence) is an incentive for farmers to apply the chemical

(Byamukama et al. 2013; Vincelli et al. 2013). Anthracnose disease levels

depend largely on plant maturity and environmental conditions, which can vary

from year to year, making it hard to develop a management strategy based

3

solely on genetic resistance. Thus, chronic losses of between 5-10% are

believed to occur annually, and epidemics resulting in losses of up to 100%

occur sporadically and without warning (Frey et al. 2011; Bergstrom and

Nicholson 1999). A better understanding of how the pathogen infects and

colonizes maize is essential for more consistent and effective control of the

disease (Ceasar and Ignacimuthu, 2012; Tripathi et al. 2014).

1.3 Disease cycle of anthracnose

The causal agent of maize anthracnose is the hemibiotrophic fungus

Colletotrichum graminicola (Ces.) G.W. Wilson (Sutton, 1980). Until recently,

C. graminicola was thought to infect sorghum and other grasses in addition to

maize, but isolates from these other grass species are now known to belong to

different, related species (Vaillancourt and Hanau, 1992; Crouch et al. 2009).

C. graminicola differs in morphology from the closely related species that infects

sorghum (C. sublineola): attempts to cross them did not produce progeny, and

results of DNA analyses demonstrated they are reproductively isolated sibling

species (Crouch et al., 2009; Vaillancourt and Hanau, 1992). Wheeler et al.

(1974) reported that maize isolates could infect sorghum in a growth chamber

when the inoculated plants were incubated for 24 hours at 100% moisture.

Venard and Vaillancourt (2007b) showed that C. sublineola could complete its

life cycle in maize stalks, although colonization was confined to the epidermal

cells. However, the majority of reports suggest that the maize and sorghum

isolates of Colletotrichum are host-specific, and cross-inoculation has never

been reported to occur in the field (Jamil and Nicholson, 1987; Snyder et al.

1991; Torres et al. 2013).

The anthracnose disease cycle (Figure 1.1) starts with acervuli produced on

overwintering crop debris on the soil surface, from which conidia are

disseminated by rain splash to nearby maize leaves or stalks. Production of

secondary inoculum happens in the contaminated tissues, usually on the lower

leaves. Many secondary infection cycles can happen in the same season

(Bergstrom and Nicholson, 1999; Crouch et al., 2014). The pathogen can

overwinter in the residues as a saprophyte and starts sporulating in the spring,

where it serves as a source of primary infection during the next season (Naylor

4

and Leonard, 1977). Reports showed that burying plant residue from the

previous year may result in more stalk rot (Jirak and Esker, 2009) and that the

incidence of disease was 78% higher when corn was planted continuously than

when rotated with soybeans (Jirak-Peterson and Esker, 2011). If infested

cornstalks were buried at least 10-14 cm deep for eight months, only 2% of

them produced sporulating acervuli, whereas sporulation occurred on all of the

stalks left on the soil surface (Lipps 1983).

In highly susceptible maize cultivars, it was observed that planting infected

kernels resulted in root decay (Warren and Nicholson, 1975). Bergstrom and

Nicholson (1999) suggested that the pathogen could spread to stalk tissues

from root infections via the vascular tissue. The pathogen can be readily

recovered from isolated vascular bundles (Bergstrom and Bergstrom, 1987).

Sukno et al. (2008) reported the systemic spread of the pathogen from infected

roots through the xylem, but Venard and Vaillancourt (2007b) did not find

evidence for systemic movement of the pathogen in vascular bundles, although

they did find that the hyphae could colonize the associated nonliving fiber cells.

The fungus was able to move quickly along the vascular bundles via these fiber

cells, and emerge to attack cells far from the infection point (Venard and

Vaillancourt, 2007a). The pathogen grows through the bundle sheath and fiber

cells surrounding vascular bundles asymptomatically (Mims and Vaillancourt,

2002; Venard and Vaillancourt, 2007a, 2007b).

1.4 Hemibiotrophy and its role in the disease cycle in Colletotrichum

C. graminicola, like other Colletotrichum species, is a hemibiotroph. Conidia

adhere to the plant surface, germinate and produce a melanized appressorium

that facilitates penetration of the plant cell wall, a process helped by the

production of cell wall degrading enzymes (Bergstrom and Nicholson, 1999;

Nicholson et al.1976). Penetration occurs via the penetration pore, a circular

zone at the base of the appressorium that is not melanized from which the

penetration peg emerges. The epidermal cells are invaded by primary hyphae

that grow to colonize neighboring cells. The primary infection hyphae are

multinucleate, swollen, and irregularly shaped and they are surrounded by a

membrane that separates them from the living host cells (Bergstrom and

5

Nicholson, 1999; Mims and Vaillancourt, 2002; Venard and Vaillancourt

2007a,b). Growth of the primary hyphae from cell to cell is via narrow cell

connections through extremely thin hyphal connections (Venard and

Vaillancourt, 2007a,b). Newly colonized cells are still alive while the cells behind

the infection front quickly become granulated and die, a phenomenon that we

have called sequential biotrophy (Figure 1.2). Secondary hyphae, which are

narrower, uninucleate, and not surrounded by a membrane, are produced as

branches from the primary hyphae in the invaded cells after the cells die and

the pathogen enters its necrotrophic phase of growth (Venard and Vaillancourt,

2007a,b, Torres et al., 2013). At some point after the pathogen switches to

necrotrophy, host cell walls become degraded and symptoms of anthracnose

become evident. Similar processes occur both in leaves and in stalks (Cadena-

Gomez and Nicholson, 1987; Mims and Vaillancourt, 2002; Tang et al. 2006;

Venard and Vaillancourt, 2007a, 2007b).

The issue of whether the C. graminicola had a biotrophic phase or not was

raised by O’Connell et al. (2000). Politis and Wheeler (1973) reported that the

plasma membrane remained intact and surrounded the base of the

appressorium, suggesting a biotrophic stage, but they also saw disrupted

plasma membrane in some cells, which they attributed to the tissue preparation

for microscopy. The issue was settled by Mims and Vaillancourt (2002) when

they observed the presence of a membrane surrounding biotrophic hyphae

inside living host cells. This has since been further supported by work done by

two independent research groups that confirmed plasmolysis in maize cells

containing C. graminicola biotrophic hyphae (Tang et al. 2006; Torres et al.,

2013).

Besides sequential hemibiotrophy, which is typical of C. graminicola and C.

sublineola, there are two other types of Colletotrichum hemibiotrophy (Crouch

et al. 2014). In C. higginsianum, primary hyphae occur only in the epidermal

cells, and then these produce secondary hyphae that kill the neighboring cells

in advance, prior to necrotrophic invasion. In C. orbiculare, the primary hyphae

colonize several layers of neighboring cells biotrophically, but at some point,

like C. higginsianum, this fungus also makes a complete switch to necrotrophic

hyphae that begin to kill cells in advance of colonization. Thus, the main feature

6

that differentiates hemibiotrophy in C. graminicola and C. sublineola from

hemibiotrophy in other Colletotrichum species is the persistence of the

biotrophic phase at the colony edges, and the resulting co-existence of

necrotrophy and biotrophy in expanding lesions (Torres et al., 2013). In all three

types of hemibiotrophy, the biotrophic step appears to be critical for the

establishment of a successful infection in the living host (Crouch et al., 2014).

To successfully infect a host, hemibiotrophic and biotrophic fungi deploy

secreted effector proteins that promote virulence, and allow the establishment

of biotrophy (Dou and Zhou, 2012; Kleemann et al., 2012; Wit et al. 2009).

Necrotrophic fungi often produce secondary metabolites that act as

phytotoxins, leading to death of the host cells and allowing them to colonize the

dead cells (Vleeshouwers and Oliver, 2014). The genomes of Colletotrichum

species encode expanded repertoires of secreted proteases, CAZymes,

secondary metabolites, and secreted effector proteins, and the primary hyphae

seem to function mainly as secretory cells for these products (O'Connell et al.,

2012; reviewed by Crouch et al. 2014). The effectors and secondary

metabolites produced during biotrophy are assumed to suppress plant

defenses and cell death, at least temporarily, and help to establish compatibility

(Kleemann et al., 2012; O’Connell et al., 2012; Rafiqi et al. 2012).

During the interaction with the plant it is thought that the fungus encounters

various stresses, including oxidative, nutritional and secretion stress, to which

it must adapt (Torres, 2010). Maize plants react to fungal invasion by the

production of various defensive compounds and by the construction of lignified

papillae in the outer walls of epidermal cells (Bergstrom and Nicholson, 1999;

Mims and Vaillancourt, 2002; Politis and Wheeler, 1973). Cells surrounding

infected cells within lesions are also fortified by lignification, as well as by the

deposition of polymers that make cell wall more resistant to CWDE and

induction of chitinases that attack the fungal cell wall (Cadena-Gomez and

Nicholson, 1987). None of these measures seems to prevent the establishment

of C. graminicola in a susceptible host (Mims and Vaillancourt, 2002; Politis and

Wheeler, 1973).

7

1.5 Resistance to maize anthracnose

Maize anthracnose is managed primarily by planting resistant hybrids. The

resistance to C. graminicola currently utilized in commercial hybrids is primarily

quantitative, aka. partial, resistance. This type of resistance has been quite

effective: thus, current hybrids are much more resistant than those that were

commonly planted in the early 1970s, when several major epidemics of

anthracnose occurred (Bergstrom and Nicholson, 1999; Leonard and

Thompson, 1976). However, these quantitative sources can fail if populations

of the pathogen are high (as occurs in continuous maize cropping, or with no-

till or reduced tillage management systems), and/or if the plants are stressed

(as occurs during drought, significant insect or pathogen damage, or high plant

populations that result in nutritional or light stress). These quantitative

resistance sources also become less effective during flowering and grain fill,

which can lead to late-season stalk damage and lodging (Dodds and

Schwechheimer, 2002). It is complicated and time-consuming for breeders to

introduce new quantitative resistance sources by traditional techniques; thus,

we are unable to react quickly to the shifts that occur in the pathogen population

in response to currently deployed sources of resistance.

A major-gene source of resistance for anthracnose stalk rot, Rcg1 (Resistance

to Colletotrichum graminicola), was described by Frey et al. (2011). Rcg1 is a

complex locus that contains two LRR-type R genes, both of which are required

for the expression of resistance (Frey et al. 2011). Only about 6% of the maize

lines that are used for breeding in North America contain Rcg1 (Broglie et al.

2011). Inbred lines containing the Rcg1 locus were highly resistant to stalk rot

disease and delivered a higher yield when compared with near isogenic lines

that did not contain the resistance genes that were exposed to the same

amount of disease pressure (Frey et al. 2011). Pioneer is currently developing

commercial hybrids containing Rcg1. Other major-gene sources of resistance

for ASR and ALB have been described, but none of these are currently in

commercial development, to my knowledge (Matiello et al. 2012; Badu-Apraku

et al. 1987; Badu-Apraku personal communication). Resistance to ALB is not

always correlated with ASR resistance, and resistance to anthracnose usually

does not confer resistance to other stalk rot pathogens, e.g. Stenocarpella

8

maydis and Gibberella zeae (Nyhus et al. 1989; Sweets and Wright, 2008;

Zuber et al.1981). Additionally, there is concern that wide deployment of major

gene sources of resistance could lead to selection of pathogenic races and

“boom-bust” epidemics, similar to those that occur in sorghum infected by the

closely related pathogen C. sublineola. Unlike maize anthracnose sorghum

anthracnose is managed mainly by the use of vertical (aka. major gene, or

qualitative) resistance, and the C. sublineola population contains a very large

number of different races (Casela et al. 2004; Prom et al. 2012; Valério et al.

2005). Until recently, races were not believed to exist in the population of C.

graminicola (Forgey et al. 1978; Nicholson and Warren, 1981), but a study last

year confirmed the existence of races for the first time, occurring rarely in the

population of C. graminicola in Brazil (Costa et al., 2014).

Long-term management of anthracnose by using either quantitative or

qualitative sources of resistance developed by traditional breeding will remain

challenging. A better understanding of the mechanisms that are critical for

infection by C. graminicola might suggest novel targets for chemical or

biotechnological therapies that could provide a more durable and effective

solution.

1.6 The Cpr1 mutant of C. graminicola

A non-pathogenic C. graminicola MT was produced several years ago in our

laboratory by restriction enzyme-mediated insertional (REMI) mutagenesis

(Thon et al. 2002; Thon et al. 2000). This MT is normal in culture, except for a

slightly reduced growth rate, but it is unable to cause any symptoms in either

maize leaves or stalks (Figure 1.3, Thon et al., 2002; Mims and Vaillancourt,

2002; Venard and Vaillancourt, 2007a,b). The insertional mutation occurred in

the 3’UTR region of a homolog of the yeast Spc3 gene, 19 bp downstream from

the stop codon. The gene was named Cpr1, for Colletotrichum pathogenicity

related gene 1. Transformation of the MT with a sub-clone containing the Cpr1

gene resulted in complementation, demonstrating that the mutation in Cpr1 was

responsible for the nonpathogenic phenotype (Thon et al., 2002). The MT is

leaky, apparently producing a small quantity of normal transcript, which seems

9

to be enough for near-normal in vitro growth but not for a successful plant

infection (Thon et al., 2002).

The yeast Spc3 gene encodes a non-catalytic component of the endoplasmic

reticulum (ER)-localized signal peptidase complex (SPC), which is essential for

protein processing and secretion (Fang et al. 1997). In yeast the SPC consists

of four subunits. Sec11p and Spc3p are essential for signal peptidase activity

in yeast, and the deletion of either of the genes encoding these proteins is lethal

(Fang et al., 1997; Paetzel et al. 2002). Sec11p is required for signal peptide

cleavage and signal peptidase-dependent protein degradation. The function of

Spc3 is currently unknown, but in yeast it interacts with Sec11p, as

demonstrated by co-immunoprecipitation experiments (Fang et al., 1997;

VanValkenburgh et al.1999). The other subunits of the yeast SPC are Spc1p

and Spc2p. These perform auxiliary and non-redundant roles, and they are both

non-essential for cell growth and enzyme activity (Fang et al., 1997). Spc2 was

found to be necessary for growth in high temperatures in yeast (Mullins et al.

1996).

Cytological and ultrastructural observations of infection in leaves of susceptible

maize plants demonstrated that the MT produces normal appressoria and

penetrates epidermal cells, and also mesophyll and bundle sheath cells to a

very limited degree (Thon et al., 2000; Mims and Vaillancourt, 2002). Both the

MT and the WT caused cell death, but the MT produced very few dead cells,

and failed to switch to necrotrophic growth in mesophyll cells (Thon et al., 2000;

Mims and Vaillancourt, 2002).

A subsequent, more detailed cytological study in living maize leaf sheaths

confirmed normal production of appressoria by the MT, but revealed that the

production of primary invasive hyphae is delayed by approximately 24 hours

relative to the WT strain. The MT was mostly unable to progress from the first

invaded cell to establish the normal biotrophic phase of development (Torres et

al., 2013). The authors reported that when the host cells were killed, the MT

colonized the tissues at the same rate as the WT and progressed to sporulation,

showing it has the ability to grow saprophytically in the maize tissues. Even

more interesting, these authors showed that when the MT and WT strains are

inoculated close together, the MT is able to grow normally. This suggested the

10

hypothesis that the WT is able to produce some kind of a diffusible substance

or signal that renders nearby host cells receptive to fungal colonization, and

that the MT is unable to produce these substances or signals.

1.7 What is the role of Cpr1 in C. graminicola pathogenicity?

To successfully infect a plant, fungal pathogens must process and secrete

many proteins that are necessary for inducing susceptibility, adapting to the

stressful plant environment, and utilizing plant tissues for nutrients (Coaker,

2014; de Jonge et al. 2011; Dickman and de Figueiredo, 2013; Gan et al., 2012;

Kamoun, 2007; Kombrink and Thomma, 2013; Tang et al., 2006; Torres et al.,

2013; Valent and Khang, 2010). The SPC is critical for processing and secretion

of proteins. The fact that the MT strain can grow when the plant tissue is dead,

or when it is grown in close proximity to the WT strain in living sheath tissues,

(Torres et al., 2013) suggests that it is deficient specifically in its ability to induce

susceptibility and/or adapt to conditions in a living host that is actively defending

itself.

Bacterial Type 1 signal peptidases have been directly implicated in

pathogenicity. Signal peptidases of Listeria monocytogenes, Escherichia coli

and Staphylococcus aureus occur as a series of paralogs, some of which are

specifically involved in the secretion of virulence factors (Choo et al. 2008;

Kavanaugh et al. 2007; Raynaud and Charbit, 2005). In Streptococcus

pneumoniae, mutation of the signal peptidase reduced pathogenicity in a

mammalian host (Khandavilli et al. 2008).

I propose two hypotheses: a) the MT can’t adapt to the stressful environment

resulting from active plant defenses, and/or b) it can’t produce or secrete

proteins necessary for induction of susceptibility. Unfortunately, even though it

appears to be universally conserved in eukaryotic organisms, very little is

known about the precise role of the CPR1 protein and its homologs in SPC

function. We know that it is an essential protein, because knockouts are lethal

in yeast and Candida albicans, and presumably also in C. graminicola where

attempts to obtain a viable knockout strain failed (De la Rosa et al. 2004; Fang

et al., 1997; Thon et al., 2002). The C. graminicola MT is unique because the

leaky insertional mutation results in a conditional effect during in vitro versus in

11

planta growth. Understanding the specific nature of this mutation and its role in

establishment of biotrophy could lead to a novel means to disrupt and prevent

biotrophic establishment, and thus potentially to new and highly effective

disease management tools.

1.8 Hypotheses and objectives of this dissertation

My dissertation is divided into three chapters, each focused on a different

hypothesis related to the putative role of Cpr1 in pathogenicity. I have utilized

genomics and bioinformatics approaches for my dissertation research. Our

laboratory, with our collaborators, sequenced the C. graminicola strain M1.001

(O’Connell et al. 2012), a second C. graminicola strain (M5.001), and a C.

sublineola strain (CgSl1). We also sequenced the transcriptomes of the M1.001

WT strain during different stages of growth in planta (O’Connell et al., 2012)

and of the cpr1 MT strain at comparable stages.

Genomics and transcriptomics provide essential bases for future work. These

are fast, high-throughput, and relatively inexpensive techniques. Analysis of the

genomes and transcriptomes helps to identify gene candidates for future

functional analyses of their potential role in pathogenesis. The transcriptome

provides data that can be used to surmise regulatory interactions, including

coordination of signaling pathways and gene clusters. Comparative genomics

can reveal differences between closely related pathogen strains that vary in

virulence, and identify candidate genes that are potentially important for host or

cultivar specificity (Bhadauria et al. 2007; Nemri et al., 2014).

I am aware that genomic data also have limitations. Although some studies

show a good correlation between transcriptomes and proteomes (Barker et al.

2012), others suggest they are only weakly correlated (Haider and Pal, 2013;

Washburn et al., 2003). Levels of proteins involved in translation or stress

response, in particular, were poorly correlated with transcript levels in

pathogenic Staphylococcus (Carvalhais et al. 2015). Transcript structure, e.g.

3’UTR sequence, can have a significant impact on translation efficiency and

transcript stability, and thus on protein levels (Horgan and Kenny, 2011). Many

transcripts occur in very low abundance, or have short half-lives, and thus are

under-represented in a transcriptome study. Because the plant to fungal tissue

12

ratio is high, fungal transcripts are much less abundant than plant transcripts in

mixed extracts from infected plant tissues, thus many fungal transcripts may be

overlooked in these combined transcriptome studies. In spite of these

limitations, genomics and transcriptomics represent an essential starting point

for developing new hypotheses about C. graminicola pathogenicity that can be

tested in the future.

1.8.1 Hypothesis 1: the mutation in the Cpr1 gene results in abnormal splicing

of the transcript sequence.

Northern blots suggested that the MT strain made very little of the normal

transcript, and instead produced a variety of aberrant transcripts that were both

longer and shorter than normal in vitro (Thon et al., 2002). These data

suggested the hypothesis that the Cpr1 transcript sequence is altered by

differential splicing during some phases of development in the WT and/or the

MT, and that this leads to changes in the quality or quantity of the CPR1 protein.

The 3’UTR sequence, where the Cpr1 mutation occurred, has been implicated

in regulating transcript splicing and polyadenylation, nuclear export, transcript

stability, translation efficiency, and mRNA targeting (reviewed by Grzybowska

et al. 2001). In chapter 2, I addressed this hypothesis by investigating whether

transcript sequences differed in the MT and WT strains in vitro and during

development in planta.

1.8.2 Hypothesis 2: The MT is nonpathogenic because it fails to secrete

effectors that are necessary for infection and establishment of biotrophy in

maize.

The literature suggests that secreted proteins, and particularly a class of highly

divergent, small secreted proteins known as effectors, are very important in the

establishment of infection by plant pathogenic fungi. A study with Medicago

truncatula demonstrated that a plant orthologue of Cpr1 was essential for

establishment of nodule formation (Wang 2010), apparently because it was

specifically required for secretion of small protein effectors necessary for

conversion of the bacteria to bacteroids (Van den Velde et al., 2010). Because

the MT is affected in a putative component of the secretory pathway, and

because cytological evidence suggests that the MT may fail to produce

13

diffusible factors necessary for establishment of biotrophy in the living host, it is

logical to investigate the nature of the C. graminicola secretome and its

expression in planta. This is a first step toward testing the hypothesis that an

inability to produce important secreted proteins is responsible for the MT

phenotype, by identifying the most likely candidates for those critical proteins.

Chapter 3 contains the results of my comparative bioinformatics analysis of the

genomes and secretomes of C. graminicola and of the very closely related

fungus C. sublineola, which is not a pathogen of maize. My rationale for this

study was that idea that secreted proteins that are directly relevant to the

establishment of biotrophy in maize are likely to be under strong selection

pressure, and thus comparison with C. sublineola, which is closely related but

fails to establish a successful biotrophic interaction with maize, may identify the

most likely candidates for effectors with this role. Additionally, I reasoned that

transcriptome analysis of the WT vs MT would help to identify effectors that are

expressed at the right time (that is, early) to be involved in biotrophic

establishment. My goal for this work was to identify a list of the most promising

such candidate effectors that can be tested by future researchers in functional

studies, and to test my prediction that effectors that are most divergent between

C. graminicola and C. sublineola would also be those that were expressed

early, during the establishment of biotrophy.

1.8.3 Hypothesis 3: the MT is nonpathogenic because it cannot adapt to

secretion stress and/or other stresses that are encountered in planta.

There is evidence in the literature that plant tissues that are actively defending

themselves produce a stressful environment for the pathogen (Dou and Zhou,

2012; Lowe and Howlett, 2012; O’Connell and Panstruga, 2006; Vleeshouwers

and Oliver, 2014). The MT is able to grow normally in maize tissues that are not

alive and actively defensive (Torres et al., 2013). The question arises, when C.

graminicola establishes a biotrophic infection, does it induce plant defenses and

activate its stress response pathways? And might the MT be deficient in these

activities? Some evidence to suggest a role for Cpr1 in stress response came

from a study on responses to secretion stress by Aspergillus niger. Guillemette

et al. (2007) reported that the Cpr1 homolog in A. niger was transcriptionally

up-regulated by 2-fold during chemically-induced secretion stress. Moreover, it

14

was post-transcriptionally up-regulated by 7-fold in response to the stress, more

than any other gene in their study! The Sec11 orthologue and the other

components of the SPC were not similarly up-regulated in A. niger. This

observation suggested the hypothesis that Cpr1 could play an important and

specific role in helping the pathogen deal with secretion stress or other stresses

occurring during the establishment of infection. To address this hypothesis, in

chapter 4 I tested whether the MT is deficient in the ability to adapt to stress in

vitro, and I investigated expression of genes involved in stress response in the

WT versus MT in vitro and in planta. I also expected to receive some insights

from this work into the types of stress that are being experienced by the WT

during various phases of development in planta.

Copyright © Ester Alvarenga Santos Buiate 2015

15

Figure 1.1 Disease cycle of C. graminicola. Fungus overwinter in plant stalks (A), producing primary inoculum during spring (B and C). Spores are disseminated by rain (D) and infect corn plants (E). Pathogen produce secondary inoculum on infected stalks (F) and leaves (G). Figure B courtesy of Dr. Lisa Vaillancourt. Figure 4 from USDA Natural Resources Conservation Service (http://conservationdistrict.org/2015/the-power-of-a-raindrop.html).

A

BC

3

D

E

F G

16

Figure 1.2 Sequential biotrophy of C. graminicola. The pathogen penetrates the epidermal cell (1) via an appressorium (asterisk). The primary hyphae branches and sequentially infects other host cells (2, 3 and 4). Granulation of the plant cytoplasm is observed in the first invaded cell.

* 1

2

3

4

17

Figure 1.3 Growth in media (A) and in planta (B) of the three strains used in the experiments: WT (wild-type strain), MT (mutant strain) and MT-C (complemented strain). The whitish flecks on the maize leaves are caused by thrips damage, not disease.

WT

MT

MT-C

BA

18

Chapter 2

Transcriptional analysis of Cpr1, and other components of the signal

peptidase complex and secretory pathway, in wild type and mutant

strains of Colletotrichum graminicola

2.1 Overview

Our laboratory produced a MT strain of C. graminicola that is nonpathogenic to

maize. The mutation is in a gene called Cpr1. The CPR1 protein is a homolog

of one component of the microsomal signal peptidase complex, which

comprises the first step in the canonical secretory pathway in eukaryotic

organisms. The precise function of the conserved CPR1 subunit of the

eukaryotic signal peptidase is unknown, and there is very little published

research. However, it appears to be universally conserved in eukaryotes, and

it is essential for viability in yeast. C. graminicola has only a single copy of this

essential housekeeping gene. Lack of the CPR1 protein should be highly

debilitating. How can we explain why the MT is apparently normal in culture,

and during early stages of pathogenicity, but is deficient specifically in the ability

to establish biotrophy? The conditional nature of the mutation could be related

to qualitative or quantitative changes in the transcript and/or the protein. The

MT has an insertion in the 3’UTR of the Cpr1 gene, and the 3’UTR is known to

regulate various post-transcriptional processes, including transcript stability,

transcript splicing, and rates of translation. One possibility is that C. graminicola

needs more of the CPR1 protein specifically during biotrophy, and that the MT

is unable to produce adequate amounts to support this transition due to

transcript instability, aberrant splicing, or reduced translation rates. Another

possibility is that the WT produces different proteins via alternative splicing, one

of which is specifically functional in planta, and that the MT is unable to undergo

this alternative splicing. In this chapter I explore some of these possibilities by

characterizing the Cpr1 transcripts produced by the MT, WT, and

complemented MT (MT-C) strains in planta and in vitro. To accomplish this, it

was first necessary for me to characterize the insertion in the 3’ UTR of the MT

strain in detail, something that had not been done previously. I also conducted

19

a broader bioinformatic and comparative transcriptomic analysis of other

putative conserved components of the secretory pathway in the MT and WT in

planta, in order to address some alternate hypotheses about the conditional

nature of the Cpr1 mutation, and to determine whether there were differences

in the expression of other secretory pathway genes between the MT and WT

strains.

2.2 Introduction

In an effort to identify novel C. graminicola genes critical for pathogenicity, our

laboratory produced a collection of random mutants by using the restriction

enzyme-mediated insertional (REMI) mutagenesis technique, and screened

them for loss of virulence on maize leaves and stalks (Thon et al., 2000; Thon

et al., 2002). The REMI mutagenesis experiment was done using EcoRI. A

plasmid called pCB1636 (Sweigard et al., 1997), that had also been linearized

with EcoRI, was inserted randomly into the genome (Thon et al., 2000; Thon et

al., 2002). One mutant identified from this study was completely non-pathogenic

to living maize leaves and stalks, but capable of near-normal growth in culture

(Thon et al., 2002; Mims and Vaillancourt 2002). This mutant is also able to

complete its life cycle in killed maize tissues, and it germinates and penetrates

living maize epidermal cells normally (Torres et al., 2013). However, it ultimately

fails to establish a successful biotrophic infection once it enters the host cells

(Torres et al., 2013). The mutation is in a gene we called Cpr1 (Colletotrichum

pathogenicity related gene 1). Introduction of a subclone containing the wild-

type Cpr1 gene at an ectopic site complemented the mutant and restored

pathogenicity (Figure 1.3C; Thon et al., 2002).

The REMI plasmid sequence was inserted into an EcoRI site 19 base pairs

downstream from the predicted stop codon of Cpr1, interrupting the 3’UTR

(untranslated region) of the gene (Figure 2.1.A) (Thon et al., 2000; Thon et al.,

2002). It was proposed that one complete and one incomplete copy of the REMI

plasmid were integrated in tandem at this site. This was determined by a

combination of Southern hybridization analysis and sequencing the upstream

end and flank of the insertion, which were rescued in Escherichia coli

20

(Genbank: AF263837.1) (Thon et al., 2000; Thon et al., 2002). The downstream

end and flank of the insertion were not successfully rescued.

A postdoctoral researcher in our laboratory, Dr. Eunyoung Park, used 3’ and 5’

RACE to confirm the predicted sequence of the complete WT Cpr1 transcript in

vitro (Figure 2.1.A, E. Park unpublished data). However, Dr. Park was

unsuccessful when she tried to use a similar approach to characterize the MT

transcript. A graduate student in our laboratory, Dr. Maria Torres, used real-

time quantitative reverse-transcription (RT)-PCR to demonstrate that the total

amount of Cpr1 transcript produced by the MT was the same as the WT or MT-

C strains in planta (Torres, 2013). Northern blots published by Thon et al.

(2002) indicated that the mutant had a severe reduction in the amount of normal

Cpr1 transcript in vitro, compared with the WT and MT-C strains, but that it also

produced a variety of additional transcript species that were both larger and

smaller than the normal size.

The CPR1 protein is homologous to Spc3p in Saccharomyces cerevisiae.

Spc3p is one subunit of the signal peptidase complex (SPC), an integral

endoplasmic reticulum (ER) membrane protein complex that is important for

cleaving signal peptides of proteins destined for the secretory pathway (Figure

2.1.B). The SPC in S. cerevisiae is comprised of four subunits: Sec11p, Spc1p,

Spc2p, and Spc3p. Sec11p and Spc3p appear to be universally conserved in

all eukaryotic organisms (Paetzel et al. 2002; Antonin et al. 2000; Liang et al.

2003).

Sec11p is the catalytic subunit, and it is essential for signal peptide cleavage

and for viability (Böhni et al., 1988). The residues that are directly involved in

the catalytic function in Sec11p appear to be conserved across a range of

organisms studied (VanValkenburgh et al. 1999). Spc1p and Spc2p are not

essential for cell growth in yeast, and they appear to have auxiliary roles. Spc2

increases the enzymatic activity of the complex, and is also thought to interact

with proteins from the translocon pore and facilitate cleavage of the nascent

polypeptide chain (Antonin et al. 2000). Spc2p has been shown to be important

for optimal growth and function at high temperatures (Mullins et al. 1996). The

Spc3p subunit, like Sec11p, is essential for viability and for activity of the

complex, although it does not appear to have a direct catalytic function (Fang

21

et al. 1997). Work with temperature sensitive Sec11 and Spc3 yeast mutants

has shown that they accumulate misfolded proteins (Meyer and Hartmann

1997; Böhni et al. 1988). This results in activation of the conserved Unfolded

Protein Response (UPR) (aka. secretion stress) pathway (Travers et al. 2000),

which functions to maintain viability when protein transport is interrupted, and

the ERAD (ER-associated degradation) pathway, that works to remove

misfolded proteins from the cell. Work by Fang and collaborators (1997) shows

that overexpression of Spc3p in yeast can suppress the Sec11 mutation, but

that the opposite is not true. Although the precise function of Spc3p is unknown,

it is proposed that it serves to stabilize the catalytic Sec11p subunit of the SPC

(Meyer and Hartmann 1997).

Lack of the CPR1 protein in C. graminicola should be highly debilitating, yet the

MT is nearly normal in culture and during the early stages of pathogenicity. How

can we explain its deficiency specifically in the ability to establish biotrophy? In

some organisms, there is more than one paralog of some subunits of the SPC.

For example, in mammals there are two homologs of the catalytic Sec11

subunit, SPC18 and SPC21. Their sequences are extremely similar (Shelness

and Blobel 1990) and although they have overlapping substrate specificity, they

show different efficiencies in processing the same transcript (Liang et al. 2003).

In the model legume M. truncatula, there are two proteins, DNF1L and DNF1,

both annotated as homologs of the Spc3p subunit. The two paralogs share 82%

identity at the amino acid level. The first one is important for normal plant

function, while the other is expressed only in nodule cells infected with

Rhizobium, and is essential for nodulation (Wang et al. 2010). It appears that

this second protein functions specifically in processing of plant secreted

proteins that are needed to induce transformation of Rhizobium bacteria into

bacteroids (Van de Velde et al. 2010). Although the paper by Thon et al. (2002)

included Southern blot evidence for a potential paralog of Cpr1, subsequent

analyses of the sequenced C. graminicola genome have failed to confirm this.

In the absence of multiple isoforms, post-transcriptional or post-translational

regulation could explain the conditional behavior of the cpr1 mutant.

There is evidence in the literature for post-transcriptional regulation of genes

encoding components of the SPC. Guillemette et al. (2007) reported that the

22

Cpr1 homolog in Aspergillus niger was transcriptionally up-regulated by 2-fold

during chemically-induced secretion stress. Moreover, it was post-

transcriptionally up-regulated by 7-fold in response to the stress, more than any

other gene in their study. The mechanism of this is unknown, but could involve

features of the 3’UTR, which has been implicated in regulating translation

efficiency and mRNA targeting (reviewed by Grzybowska et al. 2001). Post-

transcriptional regulation could also involve alternate splicing of the transcript,

leading to production of different proteins or alterations in transcript stability and

translation efficiency during different phases of development. In humans, the

SPC18 transcript is subject to alternative splicing that results in production of

six different protein isoforms (Oh et al. 2005). The roles of these isoforms, and

particularly whether they have different functions in protein processing, have

not been investigated in the literature, to my knowledge.

Yeast Spc3p contains an N-glycosylation post-translational modification (PTM)

in vivo (Meyer and Hartmann 1997). However, mutation of the glycosylated

residues in yeast had no discernable effects on viability in normal culture

conditions (VanValkenburgh et al. 1999). The mammalian homolog of CPR1,

SPC22/23 occurs in vivo as two versions with different sedimentation

coefficients, due to differences in glycosylation (Shelness et al., 1994). Protein

glycosylation is associated with functions in protein stability, localization, and

complex formation (Freeman, 2000; Roth et al., 2012). During pathogenesis, it

is possible that the post-translational modifications lead to conformational shifts

in the structure and function of CPR1 in C. graminicola.

In this chapter, my goal was to characterize the precise nature of the MT and

WT Cpr1 gene transcripts in vitro and in planta. I tested three predictions related

to the hypothesis that alternate splicing plays a role in CPR1 function and the

MT phenotype: i) alternative splicing of the Cpr1 transcripts occurs during

development in planta versus in culture in WT, and the MT fails to undergo this

splicing normally; ii) alternate splicing occurs in the MT but not the WT in planta;

or iii) alternate splicing does not occur in either strain in planta: MT and WT

transcripts differ only in the 3’UTR sequence. I also conducted a bioinformatic

and comparative transcriptomics study of the conserved SPC and secretory

pathway genes in C. graminicola, as well as a comparative analysis of the SPC

23

proteins of C. graminicola which included identification of conserved PTM and

catalytic residues.

This chapter contains a detailed description of the methods that were used to

produce and analyze the C. graminicola in planta transcriptome data that I have

used throughout my dissertation. As is typical for such studies, the generation

of these data was a collaborative effort. Some of the methods and an earlier

analysis of the data have been published in O’Connell et al., 2012. I have tried

to make it clear in the description that follows what I did and what my

collaborators did, and also what has been published previously.

24

2.3 Material and Methods

2.3.1 Defining the putative secretory pathway in C. graminicola

The hypothetical secretory pathway of C. graminicola was reconstructed based

on similarities to the secretory pathway genes from yeast (Delic et al., 2013;

Kienle et al., 2009; Schekman and Rothman, 2002) and filamentous fungal

pathogens (Giraldo et al. 2013; Yi et al. 2009; Petre and Kamoun 2014). To

identify homologs of these genes in the Colletotrichum species, protein

sequences obtained from the Saccharomyces Genome Database (SGD)

(www.yeastgenome.org) or the NCBI database (for sequences from other

filamentous fungi) were subjected to standalone BLASTP with an e-value cutoff

of 1e-5. Genes were considered to be orthologs only when the two proteins

were reciprocal best hits (RBH) (Nikolaou et al., 2009; Wall et al., 2003).

The genomes of C. graminicola and of C. higginsianum, published in O’Connell

et al. (2012), were downloaded from the Colletotrichum Comparative

Sequencing Project (http://www.broadinstitute.org/). The genomes of C.

gloeosporioides and C. orbiculare were published by Gan et al. (2012), and

both were downloaded from the NCBI database (C. gloeosporioides accession

number: PRJNA225509; C. orbiculare accession number: PRJNA171217). The

genome of C. sublineola strain CgSl1 was generated in the University of

Kentucky AGTC and is currently housed on our laboratory server. Details about

these genome assemblies are presented in Table AII.1 of this dissertation.

2.3.2 Sequence analysis of signal peptidase complex proteins

The protein sequences for the four components of the yeast SPC (Spc1p,

Spc2p, Spc3p, and Sec11p) were retrieved from the SGD. Homologous

sequences in C. graminicola, Magnaporthe oryzae, Medicago truncatula, Canis

familiaris and Gallus gallus were identified by using BLASTP against the NCBI

website platform, using the non-redundant (NR) protein sequences database

for each species. Alignments of the sequences were made by using the default

parameters of the Muscle platform in Geneious. Alignments were not manually

adjusted. Sites for glycosylation, myristoylation, and phosphorylation were

predicted by using web prediction tools as follows: NetOGlyc 4.0 Server

25

(http://www.cbs.dtu.dk/services/NetOGlyc) for glycosylation; Expasy

myristoylator (http://web.expasy.org/myristoylator) and NMT-The MYR

Predictor (http://mendel.imp.ac.at/myristate/SUPLpredictor.htm) for

myristoylation, and for phosphorylation, the Group-based Phosphorylation

Score Method (http://csbl.bmb.uga.edu/~ffzhou/gps_web/predict.php), the

NetPhos 2.0 Server (http://www.cbs.dtu.dk/services/NetPhos), and the

KinasePhos website (http://kinasephos.mbc.nctu.edu.tw).

2.3.3 Fungal strains

The C. graminicola strain M1.001 (aka. M2), isolated from diseased maize

(Forgey et al., 1978) was obtained from the late Dr. Robert Hanau (Purdue

University, West Lafayette, IN, U.S.A.). The nonpathogenic mutant strain (MT)

and its complement (MT-C), described in Thon et al. (2002), were both derived

from M1.001 (WT). All isolates were routinely cultured on Potato Dextrose Agar

(PDA, Difco Laboratories, Detroit) at 23o C under continuous fluorescent light.

2.3.4 In planta visualization of the endomembrane system and Cpr1

expression

The endomembrane system of C. graminicola was labeled by transforming the

WT, MT, and MT-C strains with the plasmid pgpdA_Gla514::sGFPhdel,

containing GFP linked to an HDEL membrane anchor driven by a constitutive

glucoamylase promoter (Vinck et al. 2005). The plasmid was obtained from Dr.

C. Van den Hondel. There was no map so I produced one with a combination

of restriction digestions and sequencing (Figure AI.5 in Appendix I of this

dissertation). I sequenced part of the promoter, the terminator and the plasmid

backbone, as well as the region containing the engineered GFP that has a

modification in a serine that improves GFP expression in plants (Chiu et al.

1996).

I used the pSITE vectors (Chakrabarty et al. 2007), as modified by Gong et al.

(2015) for Gateway technology (Invitrogen), to produce a construct to

investigate expression and localization of CPR1 in planta. A sequence of 1747

bp comprising the Cpr1 ORF and its promoter region was PCR-amplified and

introduced upstream of the red fluorescent protein (RFP) ORF (Figure 2.2). I

introduced the clones into WT C. graminicola by Agrobacterium-mediated

26

transformation (Flowers and Vaillancourt 2005). I recovered several

independent transformants and single-spored them before use.

Living hyphae were visualized in vitro after growing them on sterile glass slides

in a thin film of Fries complete medium (30 g sucrose, 5 g ammonium tartrate,

1.0 g ammonium nitrate, 1.0 g potassium phosphate, 0.48 g magnesium sulfate

anhydrous, 1.0 g sodium chloride, 0.13 g calcium chloride, 1.0 g yeast extract/

liter of H2O). Transformants were also observed in vivo, in leaf sheaths

inoculated as described below. Transformants were observed with the Olympus

FV1000 (Olympus America Inc., Melville, NY, USA) laser-scanning confocal

microscope using 543 nm laser line.

2.3.5 DNA Extraction

Fungal biomass for DNA extraction was produced from 5 X 105 spores

inoculated into 500 ml of Potato Dextrose Broth (Difco Laboratories, Detroit,

PDB) in a 1 liter Erlenmeyer flask grown for 3 days on a rotary shaker at 200

rpm at 23° C. The mycelial mat was collected by vacuum filtration, and 2 grams

of the fresh mycelium was ground in liquid nitrogen, until the consistency of

talcum powder. The mycelium was mixed with 4 mls of CTAB extraction buffer

(20 mls 1 M Tris pH 7.0; 28 mls 5 M NaCl; 4 mls 500 mM EDTA pH 8; 2 g CTAB;

2 mls mercaptoethanol per 100 mls) and incubated at 65° C for 1 hour. After the

samples cooled to room temperature, an equal volume of

phenol:chloroform:isoamyl alcohol (PCI|25:24:1) was added and the sample

was rolled on the orbital mixer table for 5 min, followed by centrifugation at 6000

rpm for 15 min. The upper aqueous phase was removed to a new tube and the

PCI extraction was repeated, followed by an extraction with chloroform. The

upper aqueous phase was removed to a new tube and the DNA was

precipitated with 1 volume of isopropanol. The DNA was spooled from the

isopropanol/aqueous mix interface using a bent glass rod. DNA was rinsed

several times in 95% ethanol to remove CTAB and dissolved in 1 ml of Tris-

EDTA with 5 μl of RNase A solution (10 mg/ml). The sample was incubated at

room temperature in the orbital mixer for 30 minutes. A half volume of

autoclaved 7.5 M ammonium acetate was added to denature and precipitate

proteins, and incubated at room temperature for 30 min. The sample was

27

centrifuged in a microfuge at top speed, the aqueous phase transferred to

another tube and 2 volumes of cold 95% ethanol was added to precipitate DNA.

Samples were centrifuged for 30 min in a microfuge and the pellet was rinsed

twice with 70% ethanol. After being air dried, DNA was resuspended with 100

μl of sterile Milli-Q water.

2.3.6 Plant growth and inoculations

Maize leaf sheaths of the maize inbred Mo940, or of the hybrid sweet corn

Golden Jubilee (West Coast Seeds, Canada, product #CN361E), were used to

produce the RNA for transcriptome and reverse transcription (RT)-polymerase

chain reaction (PCR) analysis. The samples for the transcriptome analysis were

prepared by me and a former Ph.D. student in our laboratory, Dr. Maria Torres,

and the method has already been published in O’Connell et al., 2012. Other

samples for this dissertation were prepared by me using the same protocols.

Plants were grown in the greenhouse, in plastic Conetainers (Super SC-10 UV

stabilized Stuewe & Sons, Inc. Oregon, USA) in a growth medium composed

of 60% Pro-Mix BX (Premiere Horticulture, Ltd, Riviere du Loup, PQ, Canada)

and 40% sterile topsoil. Plants were watered daily or as needed. Beginning one

week after germination, a solution of 150 ppm of 20-10-20 fertilizer (Scotts-

Sierra Horticultural Products Co., Marysville, OH) was applied two or three

times per week. Leaf sheaths were removed from the V2 leaves at the V3 stage

of plant growth and cut into segments approximately 3 inches in length.

Spores from two- to three-week-old PDA cultures were used for inoculations.

Two mL of sterile water was added to each plate and the surface was rubbed

gently with a sterile plastic minipestle to dislodge the spores. The conidial

suspension was filtered through sterile glass wool, and centrifuged for 10 mins

at 3000 rpm in a table top centrifuge. The conidial suspension was washed 3

times and the concentration was adjusted to 5 x 105 spores/ml. Inoculations

were done as described in O’Connell et al. (2012). The leaf sheaths were

supported with the midrib sides downward inside Petri dishes lined with wet

filter paper. Two 20-µl spore drops were applied to the inside epidermal surface

of each sheath, approximately 1 cm apart. The Petri plates were placed inside

a clear plastic box lined with moistened germination paper at 23°C under

28

continuous light. The sheaths were sampled at three stages: ~20 hours after

inoculation (hpi) for the pre-penetration appressorial phase (AP); ~36 hpi for

the intracellular biotrophic hyphal phase (BT); and ~60 hpi for the necrotrophic

hyphal phase (NT). For the MT, only the AP and BT phases were collected,

because it doesn’t progress to NT. All sheaths were inspected under the

microscope to verify the developmental stage, and trimmed to remove as many

of the surrounding uninfected plant cells as possible. For the AP and BT

samples, the mesophyll layers below the infected epidermal cells were carefully

trimmed away as well, in order to increase the fungal/plant ratio. The NT

samples were too fragile to be subjected to this last step. For BT and NT

samples, the sheaths were brushed gently with a moistened sterile cotton swab

to remove any superficial mycelia. Approximately 8 individual trimmed sheath

pieces from each developmental phase were pooled into a microfuge tube,

flash-frozen in liquid nitrogen, and maintained at -80°C until RNA extraction.

The entire process, from initial observation to flash freezing, took no more than

2 minutes per sheath. Three biological replicates for each of eight treatments

(WTAP; WTBT; WTNT; MTAP; MTBT; MT-CAP; MT-CBT; MT-CNT) were

prepared for RNA extraction. The strains are WT (wild-type), MT (mutant) and

MT-C (complemented) during AP (appressoria), biotrophic (BT) and

necrotrophic (NT) stages.

2.3.7 RNA extraction

Total RNA was extracted using the protocol that has been published previously,

in O’Connell et al. (2012), following the methods described by Metz et al. (2006)

with modifications. Briefly, frozen plant or fungal tissue samples were ground

with a plastic minipestle, and RNA was extracted with Trizol reagent

(Invitrogen). To increase RNA yields, samples were incubated overnight in

isopropanol, followed by 100% ethanol, both maintained at -20°C. Trizol

extraction was followed by DNAse treatment using the RNeasy Plant Mini Kit

(Qiagen). Samples for the transcriptome study were extracted by me and Dr.

Maria Torres: the other samples for this dissertation were prepared and

extracted by myself using the same method.

29

2.3.8 Transcriptome: Illumina RNA sequencing and data analysis

Only the WT and MT samples (WTAP; WTBT; WTNT; MTAP; MTBT) were

included in the transcriptome study. The preparation and sequencing of the WT

samples has been published previously, in O’Connell et al. (2012). Briefly, 300

µg of total RNA from each of three biological replicates of each treatment were

submitted to the Texas AgriLife Genomes and Bioinformatics Service Center

(Texas A&M System). They prepared libraries by using the TruSeq™ RNA and

DNA sample preparation kit (Illumina ®). A 7 bp barcode adaptor was added to

differentiate the biological replicates, and data were generated from 10 lanes

using the Illumina Genome Analyzer IIx. A total of four lanes (i.e. technical

replicates) of data were generated for WTAP; one lane each was produced for

WTBT and WTNT; and two lanes each were produced for MTAP and MTBT.

For the MTBT samples, only two of the three biological replicates produced

usable data: the third was discarded (see Appendix I for more details). The

technical replicates for each treatment were pooled. Data were processed using

the Illumina software CASAVA-1.7.0 for base calling and de-multiplexing, and

the final read results were stored as individual files for each sample in FASTQ

format. Results from a previous analysis of these data were reported in

O’Connell et al. (2012), and have been deposited in Genbank (PRJNA151285).

For the work described in this dissertation, a new analysis of the data was

performed by Dr. Noushin Ghaffari of the Texas AgriLife Genomics and

Bioinformatics Service Center. This re-analysis was prompted in part by my

discovery of an error in the original transcriptome analysis that was most likely

due to a sample mix-up at Texas Agrilife (this error is described in more detail

in Section AI.1, and Figures AI.1-A1.4, in Appendix I, of this dissertation). Dr.

Ghaffari first re-mapped the individual reads onto the C. graminicola M1.001

supercontigs (NCBI Biosample: SAMN02953757) by using the CLC Genomics

Workbench (GWB) RNA-Seq analysis tool (http://www.clcbio.com/). I used

these mapping data to calculate normalized read counts for each gene for each

treatment, following the protocol published in O’Connell et al., 2012. The

equation was as follows: normalized read count for gene X in treatment Y =

(mapped read count for gene X in treatment Y) / (total read count for treatment

Y) * (average read count across all conditions) (Tables 2.1, 2.2). Data for the

30

WT and MT were normalized separately because the total number of reads was

relatively low for the MT in comparison with the WT.

A total of 2.2 X 107 out of 3.5 X 108 sequencing reads (6.3%) were mapped by

Dr. Ghaffari to the fungal genome (Table 2.3). The remapping resulted in a

larger percentage of mapped reads than in the original version reported in

O’Connell et al. (2012) (Table 2.3). More than 95% of the annotated C.

graminicola genes were expressed at some point during infection (defined as

surpassing five total reads) (Table AI.1). Eighty-four percent of the genes

(10,028/12,006) had sufficient reads to pass the two-stage SRBFF filtering

process. After applying the edgeR coefficient parameters (including all the

coefficients in the glmLRT function) this number was reduced to 4250 genes

that had an FDR of at least 0.5. A total of 3723 genes from this selected gene

set were identified as statistically differentially expressed at α = 0.05 and with a

log2FC bigger than 2, and this set of genes was included in my study (Tables

2.1, 2.2).

To validate the new RNAseq analysis, I utilized some data previously reported

by Dr. Maria Torres (2013) who used quantitative real-time reverse transcription

polymerase chain reaction (qRT-PCR) to measure the expression levels of

fourteen different C. graminicola genes. I calculated and plotted the Log2

transcript fold-changes (WTAP:WTBT, WTBT:WTNT, and WTAP:WTNT),

measured by both RNAseq and qRT-PCR, across 38 individual differential

comparisons to measure the correlation between the gene expression profiles.

A linear regression value of R2=0.8604, and a slope of y=1.036, indicated that

the data were relatively consistent for the two analyses (Figure AI.6 in

Appendix I).

I also summarized the occurrence of stress-response genes among sequences

that were identified in the microarray experiment described by Tang et al.

(2006). This study generated a list of differentially expressed sequence tags

from biotrophic hyphae recovered from maize stalks by laser microdissection,

which were compared with hyphae growing in culture. The complete data set

was never published, but the authors generously shared it with me for my study

(Drs. W. Tang and J. Duvick, personal communication). I used the logFC values

31

provided by the authors, and I considered only values that they considered to

be significant. I used BLAST (e-value 1e-5) to match the sequence tags from

their dataset with C. graminicola genes, which had not yet been annotated

when they did their study.

2.3.9 Heatmaps

Gene expression patterns were visualized by creating heatmaps using log2 fold

changes of genes generated from the transcriptome data. Those data were

calculated as described in O’Connell et al. (2012). The expression ratio

between the normalized counts of a gene in a developmental stage and the

geometrical mean number of normalized reads across all the stages was

calculated (Table 2.1, 2.2). The log2FC is derived from this expression ratio and

it was used to generate heatmaps of secretory pathway gene expression

profiles with the Genesis tool (Sturn et al. 2002).

2.3.10 Characterization of the MT Cpr1 allele

The insertion in the 3’UTR of the Cpr1 gene in the MT strain had never been

characterized in detail. In order to determine the sequence of the MT Cpr1

allele, I used genomic DNA from the MT and WT as a template for PCR. I

designed PCR primers based on my initial hypotheses about the nature of the

insertion, including my understanding of the sequence of the REMI plasmid

pCB1636 (Figure AI.7 in Appendix I of this chapter) which I then progressively

modified and refined in accordance with the results of the PCR amplification

and sequencing. PCR amplification was carried out using Phusion High-Fidelity

DNA polymerase (Thermo Scientific, Waltham, MA, USA) in 20 µl reactions

consisting of 0.75 µM of each primer, 0.2 mM dNTP and 0.5 units of Phusion.

Thermal cycling was performed as follows: 98° C for 30 sec followed by 30

cycles of amplification at 98° C for 10 sec, annealing temperature of X° C (“X”

was set independently for each primer combination) for 30 sec, followed by

synthesis at 72° C for 30 sec per kb. All of the primers that were used for this

analysis are included in Table AI.1 in Appendix I of this dissertation. Some

PCR products were cloned in the pGEM®-T Easy Vector from Promega

(Madison, WI, USA).

32

Sequencing of PCR products or clones was done by using the BigDye® Direct

Sanger sequencing kit (Life Technologies). All sequencing was performed by

the Advanced Genetic Technologies Center (AGTC) at the University of

Kentucky by using the 3730 DNA Analyzer (Applied Biosystems). Sequence

alignments were done by using Geneious v.6 (Biomatters Ltd.).

2.3.11 Southern Blots

Southern blots were used to further test and refine my hypotheses about the

structure of the MT Cpr1 allele. Approximately 1 µg of genomic DNA from the

MT strain was digested with restriction enzymes overnight and fragments were

separated by electrophoresis on a 1.0% agarose gel for 24 h at 30 V. Restriction

enzymes used were EcoRI, XhoI, XbaI, XmnI, StuI, ClaI, SphI and EcoRV (New

England Biolabs). Genomic DNA fragments were transferred from the gel to a

nylon membrane (GE Healthcare Life Sciences) by using an electroblotter (Idea

Scientific, Minneapolis). Probes for Cpr1, ampicillin (Amp) and the hygromycin

phosphotransferase selectable marker gene (Hyg) were produced by PCR

amplification with Taq DNA Polymerase (Life Technologies) and the MT DNA

as a template, using the primer pairs CPR1intF3 and CPR1intR3 for Cpr1,

5NEWF4 and 5NEWR4 for Amp, and 2NEWF3 and 2NEWR3 for Hyg (Table

AI.1 in Appendix I). Probes were purified by using the Qiaquick PCR

purification kit (Qiagen), and labeled by using the DNA Polymerase I Large

(Klenow) Fragment kit (Promega) for random primer 32P-labeling. A Typhoon

PhosphorImager (GE Healthcare) was used for imaging.

2.3.12 Transcriptome analysis to characterize transcripts of Cpr1 and other

SPC genes in planta

Reads obtained from the RNAseq data were used to identify alternatively

spliced regions in the genes encoding the four proteins that were predicted to

comprise the C. graminicola SPC. RNAseq reads were mapped against the

genome of C. graminicola using TopHat version 1.3.1 and default settings. The

percentages of fungal reads that aligned to the supercontigs for each condition

are included in Table 2.3. The alignments and the splice junctions list were

visualized by using the Integrated Genome Browser

(http://bioviz.org/igb/index.html ©UNC Charlotte). To identify each of the 4

33

genes, I used C. graminicola supercontigs as my reference sequences and I

focused my analysis on the regions surrounding and including the positions of

the four SPC genes. Supercontig sequences and information about the location

of each gene are available from the Broad Institute Colletotrichum Database

site (http://www.broadinstitute.org/annotation/genome/ colletotrichum_group).

2.3.13 Reverse transcription (RT)-PCR

I used RNA extracted from leaf sheaths and from fungal cultures to characterize

the Cpr1 transcripts from WT, MT and MT-C strains by RT-PCR. RT-PCR was

performed according to the protocol of Venard et al. (2008), with a few

modifications. For each reaction, 2 µg of RNA diluted in 10 µl of water was

incubated with 1 µl of oligodT primer for 15 mins at 65°C. Four µl of 5x RT

buffer, 2 µl of 0.1 M DTT, 1 µl of 10 mM dNTP, 40 units of RNAseout

(Invitrogen), and 1 µl (200 units) of Superscript II (Invitrogen) were added and

incubated at 42°C for 1 h, followed by denaturation at 65°C for 15 min. One µl

of cDNA was used for each RT-PCR reaction. Two different primer

combinations were used for amplification of sequences spanning the Cpr1

intron: CPR1F and CPR1R; and CPR1F2 and CPR1R2 (Table AI.1 in

Appendix I). Some PCR products were cloned in the pGEM®-T Easy Vector

from Promega (Madison, WI, USA). Sequencing of clones was done by using

the BigDye® Direct Sanger sequencing kit (Life Technologies). All sequencing

was performed by the Advanced Genetic Technologies Center (AGTC) at the

University of Kentucky by using the 3730 DNA Analyzer (Applied Biosystems).

Sequence alignments were done by using Geneious v.6 (Biomatters Ltd.).

To characterize the poly(A) tails, RNA extracted from WT and MT strains grown

in culture was used to produce cDNA. The RT-PCR reaction was performed as

above, but with a different oligodT primer, which was designed based on the

protocol in Ma and Hunt (2015) with modifications. The oligodTPolyA primer

with degenerated bases included a specific sequence that, after the production

of cDNA, can be used as a binding site for the reverse primer. The gene of

interest, Cpr1, is amplified by using a specific forward primer designed close to

the stop codon of the gene (Table AI.1 in Appendix I). The cDNA was used in

a PCR with Phusion polymerase to amplify the poly(A) tail regions of the Cpr1

34

transcripts. The primers used for the poly(A) tail amplification were 2NEWF2

and TailR2EB (Table AI.1 in Appendix I). Sequencing was performed by the

AGTC as described above.

35

2.4 Results

2.4.1 Genomics and comparative transcriptomics of the proposed secretory

pathway in C. graminicola

The genes encoding proteins that comprise the putative secretory pathway of

C. graminicola are listed in Table 2.4.

Two of the genes, SEC61 and UFE1, appear to be absent from one of five

Colletotrichum species, C. gloeosporioides (Figure 2.3). When I used BLASTP

against the NR database of NCBI, I found a match to SEC61, but the gene did

not appear to be complete. This suggests that the rest of the gene was not

sequenced, or that there is a mistake in the annotation. I also found a match to

UFE1 in a different isolate of C. gloeosporioides in the NCBI database. Thus,

this gene may be missing only from the strain I downloaded, or else it wasn’t

sequenced or annotated in that strain. The latter seems most probable since

the quality of that sequence annotation is not particularly high (see Table AII.1

in Appendix II).

The proposed secretory pathway of C. graminicola is presented

diagrammatically in Figure 2.4, together with the normalized read counts for

each gene across the three WT developmental phases and the two MT phases,

obtained from the RNAseq data.

The secretory pathway begins with proteins that are involved in processing and

translocation of the pre-proteins across the ER membrane and into the ER

lumen. These include the SPC, the translocon pore, and various chaperones

including BIP/Kar2, which stabilize the proteins and assist with folding in the

lumen (Gething 1999; Yi et al. 2009; Delic et al. 2013). The translocon pore

allows the entrance of nascent proteins being produced by the ribosome into

the ER. BIP is located at the translocon pore and binds to the polypeptide as it

enters the ER (Seppä and Makarow 2005). Chaperones bind to proteins to

stabilize them, facilitating proper folding (Vitale and Denecke 1999). The SPC

removes the signal peptide and the protein is released into the ER lumen.

Proteins exit the ER by the COPII pathway, also called anterograde transport,

a coated vesicle protein transport path to the Golgi (Schekman and Rothman

2002). The formation of transport vesicles starts with Sar1 that, when catalyzed

36

by Sec12, becomes activated and recruits the heterodimeric complex Sec23-

Sec24 to initiate vesicle formation (Valkonen 2003). This complex than interacts

with another Sec13/Sec31 complex to form a coat around the proteins that will

be transported. The fusion of the vesicles to the Golgi membrane is mediated

by Sar1, catalyzed by Sec23 (Yoshihisa, Barlowe, and Schekman 1993). In the

retrograde COP1 transport pathway from the Golgi to the ER, proteins that have

ER-retention signals (e.g. HDEL) will return to the ER after processing,

including glycosylation, in the Golgi. The primary protein in this pathway is Arf1,

which interacts with the Golgi membranes to retrieve the proteins (Paczkowski

and Fromme 2014). This pathway is dependent on two SNARE proteins, Sec20

and Ufe1 (Ballensiefen et al., 1998; Schleip et al., 2001).

Several proteins are important for the export of proteins across the plasma

membrane. In Figures 2.3 and 2.4 I have included some of those. Sso1 is a

SNARE protein localized in the plasma membrane that will interact with Snc1

to allow the secretory vesicles to fuse to the membrane. Yeast syntaxins Sso1p

and Sso2p belong to a family of related membrane proteins that function in

vesicular transport (Aalto et al., 1993). In a recent paper by Giraldo et al. (2013)

on the secretion of effectors in M. oryzae, the Sso1 homolog has a role in hyphal

development and effector secretion, and localizes at the Biotrophic Interface

Complex (BIC). Sso1 mutants were reduced in virulence.

The exocyst is a complex of eight proteins that is usually localized to areas of

active secretion (Munson and Novick, 2006), represented in Figure 2.4. The

exocyst complex transport is independent from the Golgi, and was required for

secretion of apoplastic effectors (Giraldo et al., 2013). Mutants in Exo70 and

Sec5, two components of the complex, have reduced virulence in M. oryzae

(Giraldo et al., 2013).

Proteins shown between the Golgi and the vacuole are important for transport

between the two organelles and endosome-endosome fusion. There are

several vacuole protein sorting genes (Vps) involved in this pathway. Both Chc1

and Clc1, represented in Figure 2.4, are subunits of the coat protein involved

intracellular protein transport. Pep12 and Vps1 are involved in fusion events at

the endosome (Bowers and Stevens, 2005). Vps33 and Vps34 are important

for transport from Golgi to vacuole, and mutants in yeast exhibit vacuole defects

37

(Banta et al., 1988). In the human pathogen C. albicans the phosphatidylinositol

3-kinase Vps34 is required for virulence (Bruckmann et al., 2000). There are

several proteins involved in the transport to endosome and to vacuoles in yeast

(Schekman and Rothman 2002). Ypt7, Ypt52 and Ypt53 are some of the

regulators of fusion in the vacuole, involved in endocytosis (Arlt et al., 2011).

The transcriptome data suggest that all of the genes included in the putative

pathway are expressed in planta during all stages, with the BiP chaperone the

most highly expressed (Table 2.5). Only two genes in Figure 2.4 appear to be

differentially expressed among different treatments. In the WT, the homolog of

Arf1 (GLRG_01625) is more highly expressed during the AP stage. The Sec12

homolog (GLRG_10268) is more highly expressed during WTNT, and also

during AP in the MT. It should be noted that overall expression levels for this

gene are low. In yeast, Sec12p is an ER-membrane associated protein that is

necessary for the initiation of COPII vesicle formation during ER to Golgi

transport (Barlowe and Schekman, 1993). The LCD microarray data included

33 of the putative secretory pathway genes: only three (two in the exocyst

complex and one in the COPII pathway) were differentially expressed in

biotrophic hyphae versus in vitro, according to the microarray analysis.

2.4.2 The SPC proteins in C. graminicola

Genes encoding putative homologs of the four SPC proteins from yeast were

identified from C. graminicola and M. oryzae (both Pezizomycotina in the

kingdom Mycota) Medicago truncatula (plants) Gallus gallus (birds) and Canis

familiaris (mammals) C. familiaris had two paralogs of the Sec11p, and M.

truncatula had two of the Spc3p, but Gallus gallus and the two fungal species

had just one homolog of each of the yeast genes (Table 2.5).

The Sec11 protein (Figure 2.5) is the most highly conserved. A mutational

analysis of this gene (VanValkenburgh et al. 1999) identified amino acids that

were essential for viability/function, including S44, G67, H83, D103, and D109

(Figure 2.5, Sec11, asterisks). These sites are conserved in the Sec11 proteins

of all six species I looked at. The same group identified glycosylation of D109

of Sec11 (Figure 2.5, Sec11, grey box), and this site is also conserved in all six

species.

38

In yeast Spc3, the transmembrane domain is predicted to be between residues

15-34, and the region in contact with the ER lumen to be between residues 35-

184. In the paper by Meyer and Hartmann (1997), two glycosylation sites are

predicted. These are not conserved in the Spc3 homologs of the other species,

and neither match the glycosylation sites predicted for the proteins from these

other species (Figure 2.5, Spc3, grey boxes). Mutations in conserved residues,

including glycosylation sites, in the Spc3 gene didn’t cause loss of viability, even

though in yeast loss of this gene is lethal (VanValkenburgh et al. 1999; Meyer

and Hartmann 1997). Relatively few amino acids of the Spc3 proteins are

conserved among all species (Figure 2.5, Spc3). Also, it is interesting to notice

that the C. graminicola and M. oryzae proteins have a 25 amino-acid insertion

in the domain that is predicted to extend into the ER lumen, which the other

species don’t have. These two species also share two glycosylation sites in this

region.

The other two proteins of the SPC have very low levels of similarity across the

six species, with few regions in common (Figure 2.5, Spc1 and Spc2).

2.4.3 Structure of the mutant and wild type Cpr1 alleles

The upstream flank of the REMI insertion in the MT had already been rescued

and sequenced (NCBI Biosample: SAMN02953757), but the downstream flank

was not successfully recovered (Thon et al., 2000). I was able to identify several

individual reads in the transcriptome data that spanned the junction between

the inserted plasmid DNA and the downstream flanking DNA, as well as

confirming the upstream flank as it had been published (Figure 2.6C). There

were five overlapping reads that crossed the downstream junction, and five that

spanned the upstream junction. The overlapping sequences included intact

EcoRI sites at both junctions of the insertion.

I used this information, together with the Southern blot data of Dr. Thon (Thon

et al., 2002) and the sequence of the REMI plasmid pCB1636 (provided to me

by Dr. Jim Sweigard) to develop a hypothesis about the structure of the REMI

insertion. I developed primers based on that hypothesis and, using a

combination of PCR amplification, cloning, and sequencing, I was able to “walk”

through most of inserted DNA in the MT Cpr1 locus, including the upstream and

39

downstream flanks, the intact EcoRI sites at the upstream and downstream

junctions, and into the ORF that is immediately downstream of Cpr1

(GLRG_04963, a 5’-3’ exonuclease) (Figure 2.6B). In the process of doing this

work I discovered that the plasmid sequence provided by Dr. Sweigard was

incorrect, and that the hygromycin cassette in the REMI plasmid was actually

inverted compared with his version. The correct map and sequence of the

plasmid is presented in Figure AI.7, in Appendix I of this dissertation.

I used the same techniques to confirm the structure of the WT allele, which

matched the predicted structure (Thon et al., 2002) and the results obtained by

Dr. Park by using 3’ and 5’ RACE (Figure 2.6B).

The insertion in the MT 3’UTR consists of two nearly complete copies of the

REMI plasmid in an inverse (head-to-head) orientation. I was unable to

sequence across the head-to-head connection between the two copies of the

REMI plasmid, although I tried numerous approaches to amplify and/or clone

this region. I did some Southern blots (SB) with eight different restriction

enzymes and three different probes, one corresponding to Cpr1, another to Hyg

and the third to Amp (Figure 2.7). My goal was to confirm my map, and estimate

the size of the fragment that was still missing from my sequence. The results of

the SB showed that the map is accurate, since the bands hybridizing to the Hyg

probe (Figure 2.7A), the Cpr1 probe (Figure 2.7B), and the Amp probe (Figure

2.7C) matched the predicted numbers and sizes. The results of the SB suggest

that there is not an intact EcoRI site at the head-to-head junction between the

two plasmid copies, but that the two copies are nearly complete. It seems that

I am missing approximately 300 bp from my sequencing of the insertion, based

on the sequence I do have and the estimated size of the Xmn1 fragment probed

with Amp. My inability to clone or to sequence this region could be related to its

expected inverse repeat structure.

Based on these genomic sequences, I predicted the transcript sequences of

MT and WT Cpr1 using the FGENESH online gene prediction tool. For the WT

sequence, the transcript was identical to the Broad prediction, and to Dr. Park’s

prediction based on RACE analysis. The WT transcript is predicted to encode

a 229 amino-acid protein. But the transcript predicted for the MT is much longer

40

(3312 bp), and has the potential to encode a 1103 amino-acid protein (Figure

2.8).

2.4.4 MT and WT Cpr1 transcripts in planta

I mapped individual reads from the transcriptome data to my confirmed

sequences of the WT and MT Cpr1 alleles, including the 5’ and 3’UTRs (Figure

2.9).

The MT Cpr1 transcript appears to be the same as the WT transcript from the

5’UTR through to the first intron splice junction. However, although the

transcripts from the WT matched the predicted intron splice junctions, there

were several reads in the MT transcriptome that spanned those junctions,

suggesting that read-throughs were occurring in those samples. I mapped

reads to the splice junctions of the introns in the other putative SPC genes

(GLRG_10877; GLRG_03901; and GLRG_04022). There were a few variants

(although no read-throughs) in both WT and MT for all of these, but only the MT

Cpr1 transcripts showed evidence of frequent aberrant intron splicing resulting

in read-throughs (Figure 2.10). A recent paper confirmed that Cpr1 and the

other three SPC genes are expressed in maize leaf blades during infection, and

also confirmed the predicted WT intron splicing patterns for all of these SPC

transcripts (Schliebner et al., 2014).

Transcripts mapped to the Cpr1 3’UTR sequence matched the predicted

transcript and the 3’RACE results in the WT, but the 3’UTR sequence of the MT

was much more complex. Reads were found matching sequences from the

entire insertion (Figure 2.9). The pattern of reads suggested that the Hyg genes

and areas of the pBluescript in the inserted DNA were being expressed in the

MT in planta. It is important to point out, though, that it is difficult to interpret

results of my transcript mapping to the MT 3’UTR because it was an inverted

repetitive sequence and I was unable to differentiate transcripts from the

positive versus negative strands.

41

2.4.6 RT-PCR amplification and sequencing of alternative transcripts from C.

graminicola mutant strain

I used RT-PCR to amplify the region spanning the Cpr1 intron from the WT and

MT strains in planta during all stages of development, as well as from WT

appressoria produced in vitro. I consistently amplified at least three different

products from the MT, whereas the WT produced only one major amplicon of

the expected size (Figure 2.11). One of the MT products was the expected size,

but there appeared to be less of this amplicon compared with the WT. Some of

the amplified fragments were cloned and sequenced. The WT strain produced

only one major amplicon, and only one transcript variant was cloned, which

corresponded to the predicted sequence with the intron removed (Figure 2.11).

A cloned fragment from the MT that was the same size as the WT amplicon

was also the expected transcript with the intron properly removed. One cloned

MT fragment was the same size as the genomic band (470 bp), and in this

fragment the intron was retained (Figure 2.11A). One cloned fragment from the

MT was smaller than the normal transcript size, and it seemed like a larger

intron (close to 370 bp in size) had been removed. I was unable to clone or

sequence any bands that were larger than the 470 bp amplicon, so I am not

sure what those are. The MT strain produced the same variant amplicons in

vitro, suggesting that it is a feature of the strain and not dependent on the

environment (Figure 2.12).

I used GENEIOUS to predict the ORF that would result if the MT transcript with

the intron retained was translated. There is an in-frame stop codon at the

beginning of the intron, thus intron retention is predicted to result in production

of a 140 aa protein consisting only of the CPR1 N-terminal region (Figure 2.13).

This shorter protein still encodes the ER transmembrane region, but it lacks the

entire ER luminal domain, and 3 out of 4 predicted glycosylation sites.

2.4.7 Analysis of 3’UTR sequences of C. graminicola MT and WT

Cloning and sequencing of the 3’UTR from samples of the MT and WT in planta

revealed that in both the MT and the WT, it seems to occur as several nested

versions (Figure 2.14). I was able to sequence three variants of the WT 3’UTR

(Figure 2.14: 1, 2, 3), and two of the MT 3’UTR (Figure 2.14: 4,5). None of the

42

WT variants that I sequenced matched the predicted transcript that was also

confirmed by RACE from in vitro samples. One poly(A) for the MT occurs right

after the EcoRI site, and another a few base pairs later. Some of my results

from the MT matched areas more than 2000 bp beyond the stop codon so it is

possible that in some cases, the 3’UTR in the MT is extremely long. I was not

able to obtain continuous sequence for these putative 3’UTRs however, so they

are not included in my figure.

2.4.8 In planta analysis of the endomembrane system and expression and

localization of CPR1

HDEL is an amino acid motif that anchors proteins in the ER membrane. A

construct was produced by Dr. C. Von den Hondel that encodes GFP linked to

the HDEL anchor, driven by a strong constitutive promoter. I used this construct

to transform the WT, MT, and MT-C strains, and I used the transformants to

inoculate maize leaf sheaths. Transformation with this construct allowed me to

visualize the putative endomembrane system in the living fungi in planta (Figure

2.15). A similar pattern of fluorescence, which was “netlike”, was seen in all

three strains, reminiscent of the appearance of the endomembrane network as

reported in the literature (Hickey et al., 2004). Because the hyphae were alive I

could see dynamic movement in the system, including apparent vesicle

transport. There were no obvious differences between the strains in the

structure or behavior of the putative endomembrane system, except that the

fluorescence seemed to be somewhat more intense in the MT when growing in

the plant. I anticipate that these strains could be valuable for future analyses of

the movement of proteins through the secretory system of C. graminicola.

The CPR1 protein is predicted to be located in the ER membrane. I made a

chimeric construct to express the CPR1 protein complexed with RFP, and

transformed it into the WT strain. Because the MT strain is already resistant to

the selectable marker that was available for the vector system, I did not

transform the MT. Examination of the transformants in planta showed very little

red fluorescence, indicating very low levels of the chimeric protein in the WT

strain. However, faint fluorescence could be seen in some cases that seemed

to correspond with large vacuolar structures (Figure 2.16). Unfortunately,

43

because the expression was so low, it was not possible to tell if the pattern of

fluorescence was similar to that in the GFP-labeled endomembrane system. In

future it will be good to transform the MT strain, because it is possible that the

WT CPR1 protein “outcompetes” the RFP chimeric version in the SPCs of the

WT transformants, causing the low fluorescence that was observed.

44

2.5 Discussion

The ability to secrete proteins is crucial for pathogenicity in fungi. Yet, we know

relatively little about fungal secretory pathways outside of a few, mostly non-

pathogenic, model systems. A lack of the critical CPR1 signal peptidase protein

in C. graminicola should be incapacitating, and yet the MT is nearly normal in

culture and during the early stages of pathogenicity. It appears to be deficient

specifically in the establishment of biotrophy. How can we explain this? The

goal of the work I have described in this chapter of my dissertation was to help

me to address this question.

In this chapter I characterized the putative secretory pathway of C. graminicola,

and evaluated its expression in planta. C. graminicola seems to have homologs

for all of the proteins in the canonical secretory pathway, and they seem to be

expressed at similar levels across all stages of development in planta. My

analysis of the laser capture microdissection (LCD) data from Tang et al. (2006)

suggested that expression of secretory protein genes is generally similar in vitro

and in planta. There was one gene, Sec23, a COPII subunit that was highly

expressed in the biotrophic hyphae when compared to in vitro hyphae. In yeast

this protein is necessary for vesicle budding and transport from the ER

membranes (Schekman and Rothman, 2002). The homolog of Arf1, in COP1,

was differentially expressed during WTAP in the RNAseq data. Differential

expression of these two genes indicates that vesicle trafficking between the ER

and the Golgi is very active during in planta infection. Two genes in the exocyst

complex, homologs of Sec3 and Sec5, were differentially regulated in the LCD

data. Sec3 was more highly expressed in planta while Sec5 was more highly

expressed in vitro. In M. oryzae, Giraldo et al. (2013) found that, while

apoplastic effectors were secreted by the conventional ER-Golgi secretion

pathway, apoplastic effectors appeared to be exported via the exocyst complex.

I detected very few differences in the expression of secretory pathway genes

between the WT and the MT, even though the MT is believed to be affected in

the function of the signal peptidase, the first step in that pathway. Apparently,

even if the function of the pathway is affected by the mutation, the relative

expression of the pathway genes, including of Cpr1 itself, is not. We might

expect the need for secretion to be greater during necrotrophy when fungus is

45

producing lots of cell wall degrading enzymes, but if this is so it also isn’t

reflected at the transcriptional level, at least not so I can detect it.

Some organisms have multiple copies of Cpr1 and/or other components of the

SPC. For example, mammals have more than one paralog encoding the

catalytic Sec11 protein, and they also produce additional versions by alternative

splicing in some conditions of the transcripts. If C. graminicola also has the

potential to produce multiple isoforms of these proteins, and some of them

function specifically in planta, this could explain the conditional nature of the

MT. I investigated CPR1 and the other SPC proteins in C. graminicola, and I

determined that each exists as only a single copy. Furthermore, I found no

evidence, either in the literature (Schliebner et al. 2014), or from my own work,

that the transcripts for any of the SPC genes, including Cpr1, undergo

alternative splicing in the WT in planta to produce additional proteins. Thus, this

does not seem to be a likely explanation for the behavior of the MT.

The removal of introns from transcripts is necessary for the production of

functional proteins. Splicing usually begin co-transcriptionally and is completed

before the poly(A) tail is added, but there are also many genes where splicing

happens post-transcriptionally (Brugiolo et al., 2013). Alternative splicing is

common, and it provides a mechanism by which the same protein-coding region

of the DNA can produce different protein isoforms, potentially with different

functions. Additionally, some transcript variants do not encode proteins but play

important regulatory roles. Around 95% of the human protein-coding genes

appear to undergo alternative splicing (Chen and Manley, 2009). Intron splicing,

performed by a protein and RNA complex called the spliceosome, is regulated

by both trans-acting proteins and cis-acting regulatory sites in mRNAs. The

3’UTR region, in particular, has an important role in regulation of translation by

alternative splicing. The fact that the MT has an altered 3’UTR sequence led

me to speculate that splicing of the Cpr1 mRNA could also vary in the MT

compared with the WT.

I set out to test three possibilities related to the hypothesis that alternative

splicing of the Cpr1 intron played a role in the function of CPR1 and the MT

phenotype: i) alternative splicing of Cpr1 occurs during development in planta

versus in culture in WT, and the MT fails to undergo this splicing normally; ii)

46

alternate (aberrant) splicing occurs in the MT but not the WT in planta; or iii)

alternate splicing does not occur in either strain in planta: MT and WT

transcripts differ only in the 3’UTR sequence. Analysis of the WT suggested

that it never underwent alternate splicing in any condition, whether in vitro or in

planta. Thus, I reject the first possibility. I did find evidence for alternate splicing

of the MT Cpr1 transcript, both in vitro and in planta, thus the third possibility

can also be rejected and the second possibility is supported. Furthermore, it

appears that the MT undergoes alternative splicing constitutively, under all

conditions that I examined, based on the RT-PCR results, not just in planta.

Thus, it is unlikely to be related to specific regulatory conditions in planta, but

rather to an innate characteristic of the mutant.

I determined that the MT does encode different cpr1 3’UTR sequences than the

WT, some of which appear to be shorter, and others much longer. These results

are consistent with the Northern blots of M. Thon that showed the presence of

multiple transcript species that were both larger and smaller than the WT in

vitro. They are also consistent with the results of M. Torres, who showed no

statistical difference in the amounts of transcript between different treatments

when she used primers at the 5’ end of the gene for real-time RT-PCR. Defects

in intron splicing or changes in 3’UTRs would not affect the 5’ ends of the

transcripts. Since the amount of transcript appears similar, based on both the

real-time results of Dr. Torres and the RNAseq analysis, it appears that there is

no deficiency in the transcriptional activation of the cpr1 gene in the MT.

It is possible that the aberrant transcripts produced by the MT could be unstable

and quickly degraded (although the RT-PCR and Northern results seem to

argue against this), or they might be targeted incorrectly, and thus fail to be

translated. If the aberrant transcripts are translated, they are predicted to

produce an alternate version of the protein that lacks the entire C-terminus,

including the ER luminal portion. If this variant protein is stable and can be

inserted into the ER membrane, this would certainly affect its ability to bind to

other proteins in the lumen including SEC11p, which could destabilize the SPC.

The yeast SPC3p homolog of CPR1 has been shown to contain an N-

glycosylation post-translational modification (PTMs) in vivo. The CPR1

sequence also includes strongly predicted sites for glycosylation. Protein

47

glycosylation is predominantly associated with stability, localization and

complex formation. During biotrophy and stress response, it is possible that

post-translational modifications lead to conformational shifts in the structure of

CPR1 that are important for its function. The MT protein lacks 2 of the 3

predicted glycosylation sites, and it’s function could also be affected by that

difference. The MT does make some normal transcript, but apparently in

reduced amounts. It is possible that there is enough of the normal CPR1 protein

to support growth in vitro, but not during pathogenicity. In the future it will be

important to examine the CPR1 protein directly in both the MT and WT strains.

Why is intron splicing altered in the mutant? Intron splicing is regulated by

elements that can be located within the open reading frames (ORFs), but more

frequently are found in the untranslated regions (UTRs) i.e. the 5’cap and the

3’poly(A) tail (Mignone et al., 2002). These two structures are essential for

efficient processing of the mRNA, and their removal or alteration can cause

rapid degradation of the molecule.

Although the 5’UTR contains many important cis-acting elements that can affect

translation rates, including e.g. secondary structures and alternative start sites,

the 3’UTR is regarded as the main factor regulating mRNA stability. This region

is subject to a variety of different regulatory mechanisms, including: a) poly(A)

tail length and positioning, b) RNA transport and subcellular localization of the

transcript c) initiation of translation, by interacting with the 5’UTR (Mazumder et

al., 2003), d) presence of cis-elements where proteins can bind and block the

ribosome, e) Adenosine-Uridine-rich elements (AREs) can cause translation

inhibition and decay and f) microRNAs (miRNAs) can bind to sequences in the

3’UTR and block translation (Hughes 2006).

Polyadenylation involves two steps in mRNA processing; first is the recognition

of specific cleavage site, which is followed by the polymerization of the

adenosine tail (Lutz and Moreira 2011). There are five cis-acting DNA elements

that are involved in polyadenylation, and they have roles in mRNA stability,

tissue-specific expression, translation, export and cellular localization, and

miRNA targeting. Defects in polyadenylation are implicated in some human

diseases, such as cancer (Lutz and Moreira 2011; Paillard and Osborne 2003).

Polyadenylation has been widely studied in oncogenes. Some studies show

48

that cancer cell lines with shorter mRNA isoforms have higher stability and

translation rates due to loss of regulatory miRNA binding sites (Mayr and Bartel,

2009). Studies in lymphocytes show that longer 3’UTRs decreased protein

translation efficiencies (Sandberg et al., 2008). In fungi the signal for

polyadenylation contains an A-rich sequence (usually AAUAA), 13 to 30

nucleotides upstream from the cleavage site. It appears that the change in the

3’UTR of our mutant fungus has caused the creation and removal of cleavage

and polyadenylation points (Ozsolak et al. 2010), creating alternative

polyadenylated forms. Both the MT and WT Cpr1 transcripts seemed to occur

in planta as multiple polyadenylated forms. In yeast it was observed that 72%

of the genes had multiple polyadenylation sites (Ozsolak et al. 2010). It is

possible that the different UTR sequences play important regulatory roles in the

WT, which are no longer functional in the MT with its different UTRs.

The mRNAs are exported from the nucleus to the cytoplasm for protein

synthesis. Specific sequence elements like splicing signals, 5’cap and poly(A)

tails are important for RNA transport. Different mRNA types form complexes

with different RNA-binding proteins to be exported thru the nuclear pore

complex (Cullen 2000). That could also be a factor in the MT, as the alterations

in the poly(A) tail might significantly affect the transport of the transcripts in the

cell.

Proteins that bind to cis-elements in the 3’UTR might be involved directly in

fungal pathogenicity. Franceschetti et al. (2011) studied a trans-acting RNA

recognition motif (RRM) protein in M. oryzae that, when mutated, caused

alterations in the 3’UTR processing of target mRNAs, leading to a lack of

virulence and overall defects in fungal development, secondary metabolism,

protein secretion and cell wall biosynthesis. Other regulatory elements that bind

specifically to 3’UTRs include microRNAs (miRNAs). MicroRNAs are small ~21

noncoding RNAs that regulate gene expression postranscriptionally by binding

to cis-elements in the 3’UTR of genes and either cleaving the mRNA or

repressing the target gene (Fabian et al., 2010). Kang and collaborators (2013)

identified 13 miRNAs candidates and reported that their expression patterns

were associated with cellulase production in Trichoderma reesei. The targets

for these miRNAs included 3’UTRs of genes involved in transportation, and

49

enzymatic transcriptional and translational regulation, among others (Kang et

al., 2013). It is possible that the changes in the 3’UTR sequence of the MT

have resulted in alterations in patterns of gene regulation due to a lack of

binding sites for protein or miRNA regulators.

The work in this chapter has led to the development of a new hypothesis, that

the alteration in the 3’UTR sequence of the MT C. graminicola Cpr1 gene

results in a reduction in the amount of CPR1 protein produced in planta, and

that this results in an inability to establish biotrophy. An alternative hypothesis

is the change in the 3’UTR leads to aberrant splicing, resulting in production of

alternative protein isoforms that are not functional for establishment of biotrophy

in planta. These hypotheses need to be tested by direct investigations of the

CPR1 protein, something that is planned for the future. My work in this chapter

also developed a model for the secretory pathway in C. graminicola, and

developed some fungal strains that can be used to visualize the

endomembrane system and CPR1 protein expression and localization in the

living plant-fungal interaction. These tools will be valuable for future studies of

this interesting and important pathogenicity mutant.

Copyright © Ester Alvarenga Santos Buiate 2015

50

Figure 2.1 Illustration of Cpr1 and the signal peptidase complex. A) Representation of Cpr1 (dark green, intron in light green) in the WT and MT strains, with the mutation site 19 bp after the stop codon. Dark blue boxes show the 5’UTR and 3’UTR, characterized by Dr. Eunyoung Park using RACE. Dark arrows show primers used in Torres et al. 2013 and lighter arrows show primers used in Thon et al. 2002. B) Representation of the signal peptidase complex as well as other structures, such as the translocon, involved in pre-protein processing. SEC11 is dark green and the mutated subunit SPC3 is represented in red. Both SPC1 and SPC2 are represented in lighter green. The drawing also depicts a nascent polypeptide chain and how it is guided through the translocon inside the ER to have the signal peptide removed by the SPC.

~8300 bp19 bp 372 bp

Mutation3´ UTR

Next gene

B)

Signal

peptidase

complex

ER

SR

P re

cepto

r

Tra

nslo

con

BiP

Cytoplasm

ER lumen

Signal

peptide

Signal peptide

cleavage

SRP

3´ UTR

59 bp

A)

5´ UTR

82 bp 117 bp

749 bp 391 bp

5´ UTR

Next gene

WT-

MT-

51

Figure 2.2 Illustration of the pFPL Gateway vector construct. It was used to create protein fusions between C. graminicola Cpr1 promoter region and open reading frame to the red fluorescent protein reporter gene. Modified from figure kindly provided by Dr. Mark Farman.

52

Yeast Cgram Csub Chig Corb Cglo BIP 100 76 73 76 75 72

SAR1 100 75 73 71 74 75 YPT7 100 67 59 67 66 66

SEC61 100 63 63 63 64 0 VPS1 100 60 59 61 61 61 SNC1 100 60 59 60 59 60

YPT52 100 59 53 52 57 58 ARF1 100 59 57 45 58 77

SEC13 100 58 58 58 57 58 CHC1 100 55 55 60 55 56 SBH1 100 55 55 55 57 55

SEC11 100 53 47 54 53 53 SEC23 100 53 53 53 53 53 SEC24 100 47 47 46 46 47 YPT53 100 46 44 47 46 46 SSS1 100 46 48 47 46 46

VPS34 100 39 39 41 39 40 SED5 100 38 38 38 38 39 BET1 100 38 37 38 34 41 UFE1 100 34 34 34 21 0 LHS1 100 34 34 34 33 32

SEC62 100 34 31 34 34 34 SEC31 100 33 32 30 29 32

SPC3 100 32 32 35 29 33 SSO1 100 32 31 32 31 32 SPC2 100 32 33 34 32 29 SEC3 100 31 33 33 34 32

SEC63 100 30 28 27 31 30 PEP12 100 29 28 27 29 28 SEC10 100 27 28 28 25 29

CLC1 100 27 26 29 32 30 SPC1 100 24 23 26 30 25

VPS33 100 24 24 24 24 23 SEC15 100 24 25 26 23 26 EXO70 100 23 23 24 22 22 EXO84 100 23 23 23 24 24 SEC20 100 22 22 21 21 19

SEC8 100 22 22 21 21 22 SEC6 100 21 21 20 20 20 SEC5 100 21 22 21 21 21

Figure 2.3 Degree of conservation of Colletotrichum secretory pathway based on yeast proteins. Values represent percent sequence identity using BLASTP. Cgram = C. graminicola, Csub = C. sublineola, Chig = C. higginsianum, Corb = C. orbiculare and Cglo = C. gloeosporioides.

53

Figure 2.4 Model of the Colletotrichum graminicola secretory pathway together with normalized reads counts for each gene across growth stages. The stages are, in order: WTAP, WTBT, WTNT, MTAP and MTBT. Reads in red mean the gene was differentially expressed between different stages. Reads with light pink or light yellow in the WTBT mean they were differentially expressed in the laser capture data (LCD): pink means higher in planta and yellow means higher in vitro.

Secretory pathway

629 896 856 223 189

267 289 290 74 78

392 416 381 113 116

211 294 250 42 52

299 339 248 52 58

318 303 184 85 82

160 136 112 25 22

217 197 161 42 46

279 249 241 25 38

31403089302811751156

89813357171428782398

59 83 80 20 20

33 51 49 9 9

284 344 481 126 146

569 727 587 91 104

416 529 606 96 130

399 535 573 126 149

141 206 179 37 39

894 962 799 152 168

625 792 865 145 158

ER membrane

Translocon pore

Signal peptidase complex

Chaperones

Lhs1

Bip

Sec63

Sec62

Sec61

Sss1

Sbh1

Spc3

Spc1

Sec11

Spc2

Ve

sicl

e t

raff

icki

ng

COPIICOPI

Sec13

Sec31

Sec23

Sec24

Sar1

Arf1

Bet1

Sed5

Sec20

Ufe1

Exocyst complex

Sec3

Sec5

Sec6

Sec8

Sec10

Exo70

Sec15

Exo84

Golgi

230 261 289 62 60 80 105 104 31 17

Vacuole

Plasma membrane

Pep12

Chc1

Clc1

Vps1

83 118 86 11 18

Snc1 Sso1

Vps33

Vps34

Ypt52

Ypt53

Ypt7

Sec12 2 4 13 10 5

148 205 230 29 32

456 747 733 104 129

435 606 494 76 71

470 588 626 94 101

160 192 230 37 47

890 511 363 275 226

33 17 34 6 9

60 96 151 26 24

251 295 323 40 45

194 235 277 36 40

64 126 156 20 26

42 72 84 10 13

87 148 187 28 24

76 92 113 21 22

189 198 221 43 35

69 122 137 19 28

71 101 111 18 19

Endosomes

54

Figure 2.5 Alignment of amino acid sequences of the four signal peptidase subunits. Boxes mark conserved residues of Sec11 in different eukaryotic species. Catalytic residues identified in yeast by VanValkenburgh et al. 1999 are shown with asterisks. Glycosylation sites from Sec11 (Bohni et al. 1988) and Spc3 (Hellmuth and Hartmann, 1997) are indicated by the grey boxes. Glycosylation sites in C. graminicola Spc3 are indicate by orange boxes and were identified using NetOGlyc 4.0 Server.

55

Figure 2.6 Map of the MT 3’downstream region. A) Linearized map of plasmid pCB1636 and EcorRI sites. B) Putative MT 3’downstream region with two copies of the plasmid in opposite directions. C) RNA seq reads matching the EcoRI junction sites. Red nucleotides show where the stop codon is and blue show the plasmid sequences. EcoRI sites are represented in bold letters. RNAseq reads where found aligning to both sites of the mutation. D) Representation of sequencing data for the MT 3’ downstream, showing that all sequences overlapped. I actually sequenced a lot more PCR products across this region, and every part of the insertion except for the area covered by the yellow box was sequenced at least twice in both directions. The shaded light yellow box shows the junction region between the two plasmids that I was not able to sequence.

56

Figure 2.7 Southern Blots performed using MT DNA. They were probed with A) Hygromycin gene, B) Cpr1 gene and C) Ampicillin gene. D) Map of the restriction sites for each enzyme used in this study.

57

Figure 2.8 Transcript prediction for WT and MT Cpr1 gene using FGENESH. Figures are drawn to scale. Green boxes represent the WT Cpr1 exons. Pink boxes represent the predicted exons based on the sequencing of the downstream region of the MT.

WT-

MT-

58

Figure 2.9 Illustration of Cpr1 reads for WT and MT strains. The lanes in blue are from the WT and in green from MT. Below the transcripts there is a representation of the genomic map comprising the 3 genes (GLRG_04963, GLRG_04964 and GLRG_04965) that were used to search for transcripts. The peaks show the number of RNAseq reads for the area and the numbers on the left are an approximation of the number of reads that were mapped.

MT

Ap

pre

sso

ria

MT

Bio

tro

ph

y

WT

Ap

pre

sso

ria

WT

Bio

troph

y

WT

Necro

trop

hy

59

Figure 2.10 Intron splicing in the four signal peptidase subunit transcripts. Blue bars show the mapped RNAseq reads from appressoria and biotrophic stages to each gene and its adjacent sequences. Green bar below Cpr1 represents gene model, with the light green area being the intron. It is possible to observe several reads across the MT-cpr1 intron region, as well as a longer poly(A).

60

Figure 2.11 Cpr1 intron pattern of WT and MT strains. DNA control is shown for both strains, followed by in vitro appressoria (IVAP), appressoria (AP), biotrophic (BT) and for the WT also necrotrophic (NT). Sequencing results from representative clones illustrated here with WT-Cpr1 always showing correct predicted intron and MT-cpr1 with three different versions sequenced from in planta growth: normal intron, intron retention and short reads.

61

Figure 2.12 Cpr1 transcript intron pattern of WT, MT and MT-C mycelia grown in Fries medium. Both WT and MT-C shown here only in minimal Fries medium as they had the same pattern in all conditions. MT strain treatments are 1. complete Fries medium, 2. complete Fries medium blended, 3. minimal Fries medium and 4. minimal Fries medium blended.

62

Figure 2.13 Prediction of the MT cpr1 transcript with the intron retained done by Geneious. There is an in-frame stop codon right at the beginning of the intron. Prediction of the poly(A) site was done as in Thon et al. (2000), by looking for consensus polyadenylation signals. The predicted site in the WT is also marked in the figure, as well as the confirmed site based on RACE results.

5´ UTR

82 bpMT- AATAAA

AATAAT

3´ UTR

59 bp

5´ UTR

82 bp

WT-AATAAT

749 bp 149 bp

450 bp 1452 bp 450 bp

63

Figure 2.14 Sequencing of the 3’UTR of the WT and MT strains. WT includes three variants, MT shows two, different variants. RACE results from Dr. Eunyong Park are shown here for comparison.

3´ UTR

59 bp

5´ UTR

82 bp 126 bpWT-

TAAAATGGACGAGGAAGTATGTGAATTCGGCGGAAATGAATATAACTTCCTAAACATTCTTACCTCATTTTGCCAGTCATTCATACAAAGTTTTTTTTTATATATATAAGAAGGTCAACTTGAAAAAAARACE

TAAAATGGACGAGGAAGTATGTGAATTCGGCGGAAATGAATATAACTTCCTAAACATTCTTACCTCATTTTGCCAGTCATTCATACAAAGTTTTTTTTTATATATATAAGAAGGTCAACTAATGCTGCGTATGTGTATCGTCTCCTAATAATGACCAGGTACACTGAGTTGCCCCCTGAAAAAAAAAAAAGAAAAAAAAAAAAAAAAAA

TAAAATGGACGAGGAAGTATGTGAATTCGGCGGAAATGAATATAACTTCCTAAACATTCTTACCTCATTTTGCCAGTCATTCATACAAAGTTTTTTTTTATATATTAAAAAAAAAAAAAAAAA

TAAAATGGACGAGGAAGTATGTGAATTCGGCGGAAATGAATATAACTTCCTAAACATTCTTACCCCAAAAAAAAAAAAAAAAA

TAAAATGGACGAGGAAGTATGTGAATTCGGCGGAAATGAATATAACTTCCTAAACATTCTTACCTCATTTTGCCAGTCATTCATACAAAGTTTTTTTTTATATATATAAGAAGGTCAACTAATGCTGCGTATGTGTATCGTCTCCTAATAATGACCAGGTACACTGAGTTGCCCCCTGAAGACTAGAGCAGACGCCTCGATAGTCCAAAAAGenomic

1

2

3

Mutation5´ UTR

MT-

Genomic

TAAAATGGACGAGGAAGTATGTGAATTCGATATCAAGCTTATCGATACCGTCCAAAAAAAAAAAAA

TAAAATGGACGAGGAAGTATGTGAATTCGATATCAAGCTTATCGATACCGTCGACGTTAACTGGTTCCCGGTCGGCATCTACTCTATTCCTTTGCCCTCGGACGAGTGCTGGGGCGTCGGTTTCCACTATCGGCGAGTACTTCTACACAGCCATCGGTCCAGACGGCCGCGCTTCTGCGGGCGATTTGTGTACGCCCGACAGTCCCGGCT

TAAAATGGACGAGGAAGTATGTGAATTCGACAAAAAAAAAAAAA4

5

64

Figure 2.15 WT and MT-HDEL tagged isolates. They were visualized in the epifluorescence microscope during in vitro growth and when inoculated in the plant. Pictures were taken at 400x magnification and 12 and 24 hpi for in vitro and in planta, respectively.

65

Figure 2.16 Expression of CPR1-RFP in planta. All pictures were taken at 24 hpi with the confocal microscope at 520V/543 nm. Magnification at 400x.

66

Chapter 3

Comparative genomics of the Colletotrichum graminicola secretome and

effectorome

3.1 Overview

The literature suggests that secreted proteins, and particularly a class of highly

divergent, small secreted proteins known as effectors, are very important in the

establishment of infection by plant pathogenic fungi (Stergiopoulos and de Wit

2009; Cantu et al. 2013; Djamei et al. 2011; Hogenhout et al. 2009; Kamoun

2007; Bozkurt et al. 2012). One hypothesis to explain the behavior of the C.

graminicola cpr1 mutant is that it fails to produce secreted effectors that are

necessary for the successful establishment of biotrophy. So that this

hypothesis can be addressed in the long-term, my goal for this chapter of my

dissertation was to identify putative C. graminicola effectors that are most likely

to have a role in the establishment of biotrophy. I used two criteria. First, I

assumed that effectors necessary for biotrophy would be expressed early,

during pre-penetration and early biotrophic stages of development (Mosquera

et al. 2009; Hacquard et al. 2012), versus late in infection, during necrotrophy,

when the host tissues are already dead. Thus, in this chapter I have catalogued

the putative secreted effector protein genes (the “effectorome”) of C.

graminicola, and used the in planta transcriptome data to identify effector genes

that are expressed early during infection. My second assumption was that

effectors that played a role in the establishment of biotrophy in living cells would

be lineage-specific, because they would be under strong selective pressure

(Win et al. 2007; Valent and Khang 2010; van der Hoorn and Kamoun 2008; Ali

et al. 2014). Thus, the other part of my work for this chapter involved a

comparative analysis of the C. graminicola and C. sublineola effectoromes. C.

sublineola is very closely related to C. graminicola, but it fails to establish a

successful biotrophic infection in maize. Similarly, C. graminicola is unable to

infect sorghum, which is the host of C. sublineola. If both of my assumptions

are accurate, then I would expect there to be some overlap between the list of

effectors that are expressed early in C. graminicola, and the list of effectors that

67

are divergent between C. graminicola and C. sublineola. Thus, in this chapter

I tested the hypothesis that the effector genes that are highly divergent in C.

graminicola when compared with C. sublineola will also be expressed early

during the infection of maize by C. graminicola.

3.2 Introduction

3.2.1 Effectors and fungal pathogenicity to plants

There are two levels of host defense that pathogens must overcome when

infecting a plant. The first one is known as pathogen-associated molecular

patterns (PAMPs)-triggered immunity (PTI). PAMPs are typically essential

structural molecules that can’t be easily modified, such as flagellin in bacteria,

or chitin in fungi (de Jonge et al., 2011; Koeck et al., 2011; Zipfel et al., 2004).

PAMPs are recognized by plant recognition receptors present in the plant

plasma membrane. Recognition induces PTI which includes a variety of

generalized defense responses. This basal defense is believed to be the reason

why microbes are unable to infect the majority of potential hosts (Göhre and

Robatzek, 2008). In order to cause disease, the pathogens must secrete

various types of molecules (aka. effectors) that overcome PTI (Chisholm et al.

2006; Okmen and Doehlemann 2014). Recognition of these specific effectors

by the host can lead to a secondary level of resistance known as effector-

triggered immunity (ETI) (Thomma, Nürnberger, and Joosten 2011).

Effectors can be broadly defined as pathogen-secreted proteins that have an

effect on host cells, either by altering host-cell structure or by modulating their

function to facilitate infection (effector-triggered susceptibility, ETS) (Ellis et al.,

2009; van der Hoorn and Kamoun, 2008). It is known that some effectors are

translocated and function in the host cytoplasm, where they can target different

host cell compartments (Djamei et al., 2011; Dou et al., 2008; Kemen et al.,

2005; Khang et al., 2010). Other effectors operate in the plant cell apoplast.

Examples include cell-wall degrading enzymes, necrosis and ethylene-inducing

protein (NEP)-like proteins, and small cysteine-rich secreted proteins such as

LysM (de Jonge et al., 2011; de Jonge and Thomma, 2009). One example of

68

an effector in which the molecular function is understood is Avr2 from

Cladosporium fulvum, which inhibits host cysteine proteases in the apoplast.

There are effectors that can suppress rapid host cell death, deposition of

callose, and accumulation of reactive oxygen species (ROS), which are

common defense responses that occur during PTI (S. Chen et al., 2013;

Gawehns et al., 2014; Gilroy et al., 2011; Hemetsberger et al., 2012; Mengiste,

2012). Other effectors are targeted to the host nucleus, and seem to interfere

with transcription or gene regulation (McLellan et al. 2013; Caillaud et al. 2013).

The Cmu1 effector (chorismate mutase) of the biotrophic smut fungus Ustilago

maydis blocks the salicylic acid (SA) pathway in maize plants (Djamei et al.

2011). The activation of the SA pathway normally results in localized cell death,

which could block the growth of a biotrophic pathogen. Some effectors that

function in the apoplast seem to be NEP-like proteins, which induce host

programmed cell death (PCD) and favor necrotrophic growth of the pathogens

(Gijzen and Nürnberger, 2006; Kleemann et al., 2012). Some effectors are

thought to help the fungus avoid triggering PTI. For example, some mask fungal

chitin and thus prevent the induction of chitin-triggered host defenses (de

Jonge, Bolton, and Thomma 2011). In general, it is difficult to establish exactly

how most effectors facilitate fungal pathogenicity. Effector mutants often don’t

have an obvious phenotype, probably due to functional redundancy (Birch et

al., 2008; Lawrence et al., 2010; Mosquera et al., 2009). Localization

experiments using fluorescent proteins have been done, but the location of the

protein doesn’t reveal the specific function of the fungal secreted proteins in the

plant (Khang et al. 2010; Kleemann et al. 2012).

3.2.2 The role of effectors in Colletotrichum

Both C. graminicola and C. sublineola are hemibiotrophs, which means that

they infect initially as biotrophs and then switch to necrotrophic development.

True biotrophs (e.g. rusts, smuts, and powdery and downy mildews) reprogram

living host cells by producing small secreted protein (SSP) effectors that

suppress PTI and host cell death (Doehlemann et al. 2008; Eichmann et al.

2004; Niks and Marcel 2009, Stergiopoulos and de Wit 2009). Some

necrotrophs take advantage of plant defense responses to enhance

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pathogenicity, and induce PCD by secreting phytotoxic host-specific (HST)

effectors (Amselem et al., 2011; Govrin and Levine, 2002) or toxic metabolites

(Navarre and Wolpert 1999). It is suggested that hemibiotrophic Colletotrichum

fungi first suppress, and then later induce, host PCD (Gan et al., 2012;

Kleemann et al., 2012; O’Connell et al., 2012; Stephenson et al. , 2000; Yoshino

et al., 2012). Arrays of small secreted protein (SSP) effectors that are

presumably involved in the initial step, suppression of PCD, are produced by

the appressoria and the primary biotrophic hyphae of Colletotrichum (Gan et

al., 2012; Kleemann et al., 2012; O’Connell et al., 2012).

3.2.3 Identification of putative effectors

Putative effector protein genes can be identified from genome data by using a

bioinformatics approach. Effectors in fungi are usually classified as small

secreted proteins (SSP), and sometimes more specifically as cysteine-rich SSP

(Doehlemann et al. 2009; Ellis et al. 2009). The primary characteristic for

bioinformatic identification of an effector is that the protein has an N-terminal

sequence that targets it for processing and secretion. Effector proteins are

usually described as small, but sources have defined “small” differently, ranging

from < 400 amino acids (Bowen et al. 2009) to < 100 amino acids (Kleemann

et al. 2012). Some families of oomycete effectors have been identified by the

presence of conserved amino acid motifs. Known oomycete effectors contain

an RXLR (Arg-X-Leu-Arg) motif, sometimes followed by the dEER (Asp-Glu-

Glu-Arg) motif, near the N-terminus of the proteins. The RXLR motif is

proposed to be involved in translocation into the host cell (Dou et al., 2008;

Whisson et al., 2007; Yaeno et al., 2011). Recently it was found that some

oomycetes also contain effectors with additional W, Y and L motifs (WYL) in the

C-terminus of the protein (Win et al. 2012; Dou et al. 2008). The WYL confers

hydrophobicity to the molecule, but its function in effector activity (if any) is not

yet known. Oomycetes also have a second class of effectors, called crinkle

effectors, with a conserved motif LxLFLAK that mediates transport into the host.

The crinkle effectors were demonstrated to accumulate in the host nucleus, and

some of them are required for virulence (Schornack et al. 2010). Some fungal

effectors from rusts and powdery mildews have a Y/F/WxC tripeptide motif in

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the N-terminal (Rafiqi et al. 2012). This motif appears to be necessary for

translocation of the protein from the pathogen to the plant, but not for activity

inside the host cell. The Y/F/WxC motif is not universal in fungal effectors, and

no other motifs have been confirmed that would facilitate their bioinformatic

identification or classification.

Because effectors are assumed to be subject to diversifying selection

(Kaschani et al., 2010; Koeck et al., 2011; Maor and Shirasu, 2005;

Sperschneider et al., 2014) another common definition of an effector is a

sequence that shows no similarity with other sequences in the genetic

databases (O’Connell et al., 2012). Because new sequences are added to the

databases daily, this definition is something of a moving target. Evaluation of

presence/absence polymorphisms or signatures of diversifying selection in

proteins in closely related species or strains is another way to identify potential

effectors (Rech et al., 2014). The Pathogen Host Interaction database (PHI)

catalogues effectors in a variety of pathogenic microbes (Baldwin et al. 2006;

Urban et al. 2014). It is possible to use this database to identify conserved or

novel members of shared effector families. Another characteristic that can be

taken in consideration, if transcriptome data is available, is the timing of gene

expression. Effectors that function in pathogen establishment and suppression

of PTI during biotrophy would be expected to be expressed early during the

infection (Ipcho et al. 2012; Cantu et al. 2013; Duplessis et al. 2011).

Furthermore, it appears that many fungal effectors are induced by plant signals

and are expressed only in planta, so this can be an additional clue (Marshall et

al. 2011).

3.2.4 Use of transcriptome data to identify candidate effectors

There have been several reports on the use of transcriptome analysis for fungal

effector identification. In the M. oryzae/rice pathosystem, transcriptome data

was used to identify 59 candidate effectors, including the biotrophic-associated

proteins (BAS1-4) that accumulate in the biotrophic interfacial complex (aka

BIC) (Mathioni et al., 2011; Mosquera et al., 2009). BAS1 was shown to diffuse

out ahead of the hyphae into neighboring host cells (Khang et al. 2010).

Transcriptome data also helped to identify 437 candidate effectors expressed

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in haustoria of the wheat stripe rust Puccinia striiformis f.sp. tritici (Garnica et

al., 2013) and 725 in haustoria and infected leaf tissues of the flax rust

Melampsora lini (Nemri et al. 2014). Much less is known about effectors in

necrotrophic fungi. Transcriptome data generated from tissues infected by the

white rot fungus Sclerotinia sclerotiorum (Guyon et al., 2014) led to the

recognition of 78 candidate effectors expressed in planta that might function in

blocking host immune responses and inducing PCD. Transcriptome analyses

have also been used to characterize oomycete effectors. Thus, Stassen and

co-authors (2012) identified 78 RXLR candidate effectors, and a cluster of

genes encoding other putative secreted proteins, that were expressed in planta

by the lettuce downy mildew pathogen Bremia lactucae.

The transcriptome data provide a “snapshot” of gene expression at a given time

point in the infection. However, transcript levels are not necessarily correlated

with protein levels (e.g. Müller et al. 2012). Post-transcriptional regulation,

translational controls, and protein stability also significantly affect protein levels.

Nonetheless, the transcriptome study is a valuable and necessary first step in

designing and interpreting proteomics studies and in designing experiments to

functionally characterize individual candidate proteins.

3.2.5 The role of effectors in non-host resistance

Effectors can have a positive or negative effect on the disease outcome, from

the pathogen’s perspective, depending on the host genotype. Effectors that

function in ETI and ETS include a vast array of SSPs in biotrophic and

hemibiotrophic pathogens (Giraldo and Valent, 2013), and HSTs in necrotrophs

(van der Does and Rep, 2007; Vleeshouwers and Oliver, 2014). ETS is critical

early in the interaction, when the pathogen is establishing itself in the host cell,

and effector expression usually peaks during early infection (Vleeshouwers and

Oliver, 2014). Transcription of effectors is typically induced in response to

unknown host signals (de Jonge et al., 2011; Stergiopoulos and de Wit, 2009).

Evidence suggests that inducible non-host resistance in many agriculturally-

important pathosystems, particularly in closely related hosts, is actually due to

ETI versus PTI: the latter operates more frequently in more distantly related

plants. In these cases all members of the non-host plant species contain the

72

same R gene(s), while all members of the nonpathogenic microbial species

contain the corresponding avr gene(s) (Schulze-Lefert and Panstruga, 2011;

Tosa, 1992). For example, M. oryzae is divided into a large number of host

specific forma speciales (f.sp.). Laboratory crosses have demonstrated that a

small number of genes control host specificity among these f.sp. (Chuma et al.,

2011; Murakami et al., 2000; Nga et al., 2009; Takabayashi et al., 2002; Tosa,

Tamba et al., 2006; Valent et al., 1991). Some of the genes have been cloned,

including members of the PWL gene family that controls pathogenicity to

weeping lovegrass. They encode highly divergent SSP that meet the definition

of effectors/avr gene products (Kang et al., 1995). Crosses have demonstrated

gene-for-gene regulation of non-host resistance in both M. oryzae and E.

graminis (Matsumura and Tosa, 1995; Takabayashi et al., 2002; Tosa et al.,

2006). Comparative genomics has revealed that the majority of differences

among host-adapted species and f.sp. in several different biotrophic and

hemibiotrophic fungal and oomycete pathosystems are in effector proteins,

encoded by rapidly evolving genes (Raffaele et al., 2010; Maryam Rafiqi et al.,

2012; Spanu et al., 2010). Work with bacteria showed that type three effectors

are involved in host specificity to plants (Hajri et al., 2009; Lindeberg et al.,

2009). Effectors in rust have been identified as potentially important for host

specificity when compared to closely related rust species (Nemri et al. 2014;

Dong et al. 2014). A recent paper by Lee et al. (2014) suggests that

Phytophthora infestans effectors might contribute to nonhost resistance. It is

proposed that host specificities are determined in many agriculturally-significant

pathosystems by repertoires of microbial effectors that have undergone

diversifying selection during adaptive co-evolution (Win et al. 2007).

Recent studies that have applied comparative genomics to closely related

pathogens have shown that non syntenic regions are enriched with genes

encoding polymorphic secreted effector proteins, some of which were shown to

be involved in host specificity (P. J. G. M. de Wit et al. 2012; Dong et al. 2014;

Nemri et al. 2014; Cantu et al. 2013; Spanu et al. 2010). Work comparing two

closely related species of corn smut fungi, Ustilago maydis and Sporisorium

reilianum, revealed clusters encoding groups of divergent secreted proteins in

the two species, and when the divergent proteins of the clusters were deleted,

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some had a negative effect on U. maydis virulence in maize (Schirawski et al.

2010). More recent work in U. maydis (Brefort et al. 2014) described a cluster

containing genes encoding 24 secreted effectors, 12 of which were unique

when compared with S. reilianum (evalue 1e-10). When these were deleted

there was a major negative impact on tumor formation, although the pathogen

could still complete its life cycle in the plant. In other work the host ranges of

two different species of Phytophthora were determined by a single amino acid

polymorphism in a protease inhibitor protein (EPIC1) (Dong et al. 2014).

When we inoculate C. graminicola in sorghum, it rapidly elicits a visible defense

response in the form of accumulation of anthocyanin, a red pigment that is

involved in plant defense (Nicholson and Hammerschmidt, 1992). C.

graminicola is able to germinate and form appressoria on the nonhost, but it

cannot establish biotrophic hyphae (Katia Xavier, personal communication).

Likewise, when we inoculate C. sublineola on maize, the pathogen also cannot

establish a biotrophic infection (Torres et al. 2013). However, both C.

graminicola and C. sublineola can complete their life cycles in non-host tissues

that have been killed by treatment with herbicides or dry ice (Torres et al. 2013;

Katia Xavier, personal communication). This suggests the possibility that

effectors produced during the early stages of infection are recognized and

trigger non-host resistance (ETI) in the living plant cells.

3.2.6 Rationale for identification of C. graminicola candidate effectors

Timely secretion of effector proteins in the plant is important for successful

infection. For example, when a M. oryzae chaperone protein (LHS1) involved

in protein folding in the ER was mutated, the fungus was unable to infect rice

plants (Yi et al. 2009). The C. graminicola cpr1 mutant (MT) has an insertion

into a gene encoding a putative component of the signal peptidase complex

(Thon et al., 2000; Thon et al. 2002). This complex, described in more detail in

the previous chapter of this dissertation, is responsible for the first step in the

secretory pathway, and regulates entry of proteins into the ER (Fang, Mullins,

and Green 1997). Even though Cpr1 is an essential gene, the MT grows nearly

normally in culture (Thon et al., 2002; Venard and Vaillancourt, 2007b).

However, when inoculated in maize leaf sheaths, the vast majority (96%) of the

74

mutant hyphae remain in the first cell and do not establish successful biotrophic

infections, or complete their life cycles (Torres et al., 2013). The MT is able to

grow on killed maize tissue, and it can also develop apparently normal

biotrophic infections if it is inoculated in very close proximity to the WT (Torres

et al., 2013). The suggestion was made that one or more diffusible factors

produced by the WT strain promotes susceptibility in neighboring cells, and

allows the non-pathogenic strain to grow (Torres et al., 2013). These diffusible

factors could be effector proteins, some of which have been shown in M. oryzae

to diffuse out ahead of the infection front (Khang et al. 2010). The MT may be

unable to secrete enough of these effectors to establish a compatible

interaction with the host.

In order to identify the most likely candidates for C. graminicola effectors

involved in the establishment of biotrophy, I used a bioinformatics approach and

made two assumptions based on the literature. First, I assumed that these

effectors would be expressed early, during the pre-penetration and early

biotrophic phases of development. And second, I assumed that these effectors

would target specific host proteins to suppress PMI and PCD during

establishment of biotrophy, and thus would be under selective pressure. If both

of my assumptions were true, I would expect some overlap between these two

groups of effectors. Thus, for this chapter, I tested the prediction that effectors

that are most divergent between C. graminicola and C. sublineola will also be

those that are expressed early during infection. I included C. higginsianum,

which is proposed to be basal to the clade containing C. graminciola and C.

sublineola (O’Connell et al., 2012; Crouch et al., 2014) in my comparisons. My

reasoning was that effectors shared by C. higginsianum and by either C.

graminicola or C. sublineola, but not both, might have been lost in one of the

lineages due to selection pressure.

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3.3 Material and Methods

3.3.1 Fungal strains and genome data used in this study

The C. graminicola strain M1.001 was collected in Missouri, USA, in the 1970s

(Forgey, Blanco, and Loegering 1978). Strain M5.001 was collected in Brazil in

1989. C. sublineola strain CgSl1 was obtained from R. Nicholson of Purdue

University, and was collected from grain sorghum in Indiana in the 1970s. The

genome assemblies of C. graminicola and of C. higginsianum, published in

O’Connell et al. (2012), were downloaded from the Colletotrichum Comparative

Sequencing Project (http://www.broadinstitute.org/). The genomes of C.

gloeosporioides and C. orbiculare were published by Gan et al. (2012) and are

used in some of my comparisons. Both of these genome assemblies were

downloaded from the NCBI database (C. gloeosporioides accession number:

PRJNA225509; and C. orbiculare accession number: PRJNA171217). The

genome sequences of M5.001 and CgSl1 were generated by the University of

Kentucky (U.K.) Advanced Genetic Technologies Center (AGTC), and they

were assembled and annotated by U.K. computational bioinformaticians Dr.

Neil Moore, Dr. Jola Jaromczyk, and Dr. Jerzie Jaromczyk. Another C.

sublineola strain, TX430BB, which was collected from grain sorghum in Texas

in the 1980s, was recently sequenced (Baroncelli et al. 2014) and those data

were downloaded from NCBI (accession number: PRJNA246670).

3.3.2 DNA extraction protocol

High-molecular weight genomic DNA of strains M1.001, M5.001, and CgSl1

were obtained from cultures grown in 500 ml of liquid Fries Complete Medium

(30 g sucrose, 5 g ammonium tartrate, 1.0 g ammonium nitrate, 1.0 g potassium

phosphate, 0.48 g magnesium sulfate anhydrous, 1.0 g sodium chloride, 0.13

g calcium chloride, 1.0 g yeast extract/ liter of H2O) inoculated with 1 X 105

spores in a 1 liter Erlenmeyer flask on a rotary shaker at 200 rpm for 3 days at

23oC. The mycelial mat was collected by vacuum filtration and 2 grams of the

mycelium was ground in liquid nitrogen, until the consistency of talcum powder.

The powdered mycelium was mixed with 4 mls of warm CTAB extraction buffer

(20 mls 1 M Tris pH 7.0; 28 mls 5 M NaCl; 4 mls 500 mM EDTA pH 8; 2 g CTAB;

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2 mls mercaptoethanol perl 100 mls) and incubated at 65oC for 1 hour. After

the samples were cooled to room temperature, an equal volume of

phenol:chloroform:isoamyl alcohol (PCI/25:24:1) was added and the sample

was rolled on the orbital mixer table for 5 min, followed by centrifugation at 6000

rpm for 15 min. The upper aqueous phase was removed to a new tube and the

PCI extraction was repeated, followed by an extraction with chloroform. The

upper aqueous phase was removed to a new tube and the DNA was

precipitated with 1 volume of isopropanol. The DNA was spooled from the

isopropanol/aqueous interface using a bent glass rod. The DNA was rinsed

several times in 95% ethanol to remove CTAB, and dissolved in 1 ml of Tris-

EDTA amended with 5 μl of RNase A solution (10 mg/ml). The sample was

incubated at room temperature in the orbital mixer for 30 minutes. A half volume

of 7.5 M ammonium acetate was added to denature and precipitate proteins,

and incubated at room temperature for 30 minutes. The sample was centrifuged

in a microfuge at top speed, the aqueous phase transferred to another tube and

2 volumes of cold 95% ethanol was added to precipitate DNA. Samples were

centrifuged for 30 minutes in a microfuge and pellet rinsed twice with 70%

ethanol. After being air dried, DNA was resuspended with 100 μl of autoclaved

Milli-Q water.

3.3.3 Sequencing and assembly

The genome of M1.001 was sequenced as part of a collaboration with our lab

by the Broad Institute of MIT and Harvard (http://www.broadinstitute.org/) to a

depth of 9X using a combination of Sanger and 454 sequencing technologies

(O’Connell et al., 2012). The genomes of M5.001 and CgSl1 were sequenced

by using 454 technology in the U.K. AGTC to 10X and 43X coverage,

respectively. Shotgun libraries were prepared according to the "Rapid Library

Preparation Method Manual" (Rev 2010). Paired-End 3000 Libraries were

prepared according to the "GS FLX Titanium 3kb Span Paired End Library

Preparation Method Manual", using a Library Prep Kit, General Library

Reagents, and The GS FLX Titanium Paired End Adaptor Set (Roche).

Emulsion PCR and enrichment was performed according to the "GS FLX

emPCR Method Manual" using the emPCR Kit Reagents (Lib-L) (Roche).

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Beads were loaded onto a PicoTiterPlate (70x75) for sequencing with the

Sequencing Kit Reagents XLR70 (Roche). Genome assembly was done by Dr.

Jola Jaromczyk using Newbler.

3.3.4 Gene annotation

Different annotation methods were used for the different strains. The C.

graminicola M1.001 genome was annotated by the Broad Institute as described

in O’Connell et al. (2012), using a proprietary program called Calhoun that

includes a combination of FGENESH (Softberry Inc.), GENEID, and GeneMark,

and is trained by using EST data. The C. graminicola M5.001 and C. sublineola

CgSl1 genomes were annotated by using Maker (http://www.yandell-

lab.org/software/maker.html). Maker does ab initio prediction, as well as using

previous data to train the program to increase the gene prediction confidence.

The FGENESH gene prediction program, which is ab initio only (Ohm et al.

2010; Salamov and Solovyev 2000), was also used to predict genes in the

assemblies of C. graminicola and C. sublineola. Gene annotations other than

those done by Broad were done by Dr. Neil Moore (Computer Science

Department – University of Kentucky).

3.3.5 Transcriptome data

Sample preparation, RNA extraction and sequencing and data manipulation for

the transcriptome dataset that I used for my analysis are described in Chapter

2 of this dissertation, and in O’Connell et al. (2012). I also used the laser-

capture microarray data from the work of Tang et al. (2006), which was

described in more detail in Chapter 2.

3.3.6 Genome synteny

Analysis of genome synteny between C. graminicola and C. sublineola was

done by using the Synteny Mapping and Analysis Program (Symap) v4.2 and

default settings (Soderlund et al., 2006). The 13 chromosomes of C.

graminicola (O’Connell et al., 2012) were used as a backbone to align and

identify syntenic blocks in scaffolds of C. sublineola CgSl1 and C. higginsianum.

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3.3.7 Identification of putative orthologous genes

Two different methods were used to identify putative orthologous genes among

the different species. In the first approach I used results generated by the

programs Ortho-MCL and Coco-CL (C-CL) (COrrelation COefficient-based

CLustering) (Jothi et al., 2006; Li et al., 2003). The program OrthoMCL groups

proteins into groups of orthologs and recent paralogs by using a BLAST-based

algorithm, and allows for simultaneous analysis across multiple genomes by

incorporating the Markov Cluster algorithm (MCL). C-CL is a hierarchical

clustering method that does not rely on pairwise sequence comparisons as

OrthoMCL does, but utilizes a more global approach that can ‘refine’ the results

so that distant paralogs are more accurately excluded from the orthology

groups. The species used for OrthoMCL and C-CL comparisons were C.

graminicola, C. higginsianum, C. sublineola, M. oryzae, Epichlöe festucae,

Fusarium graminearum, F. oxysporum, Trichoderma reesei, Verticillium dahliae

and Aspergillus flavus. The OrthoMCL and C-CL analyses were done by N.

Moore.

The second approach I used to identify putative orthologous proteins was the

Reciprocal Best Hit (RBH) method. This is a common and very simple

computational method (Moreno-Hagelsieb and Latimer, 2008; Wall et al.,

2003). In RBH, a protein from one organism will be considered to be an ortholog

of a protein in another organism if both are the best BLAST hit for one other. A

significant weakness of this approach is that it can inaccurately classify distant

paralogs as orthologs. RBH is the first step in the OrthoMCL method, but

OrthoMCL goes on to apply a weighting protocol to exclude these distant

paralogs. However, a major advantage of RBH for my work was that it could

be used to characterize all genes. OrthoMCL and C-CL failed to predict

orthology groups for some genes, and this was particularly true for the genes

that I classified as effectors (see below), many of which don’t appear to have

orthologs in the other species that were included in the analysis.

3.3.8 Gene categorization

For both genomes, I used the same programs and parameters to categorize

annotated genes.

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Genome-wide amino acid similarity analysis of proteins among five different

Colletotrichum species was performed as described in De Wit et al. (2012). The

predicted protein sequences from C. graminicola were compared with the other

species by BLASTp. Two proteins were considered homologous if they were

each other’s best hits. Further, they were only included as homologs if the

predicted similarity spanned at least 70% of their lengths, and if difference in

length between them was no more than 20%.

To identify members of protein families, I used the Protein Family (Pfam)

database (http://pfam.sanger.ac.uk/), with an e-value cutoff of 1e-5 (Punta et

al. 2012).

Functional characterization and gene ontology (GO) categories for cellular

functions, cellular components, and biological processes, were assigned using

the Blast2Go suite (Conesa and Götz, 2008). The GOSSIP function was utilized

to determine GO term enrichment in different comparisons (Blüthgen et al.

2005).

To predict secreted proteins, I used Wolf-Psort for fungi, a program that predicts

the most likely locations for proteins (www.genscript.com/psort/wolf_psort.html)

(Horton et al. 2007). I compared the performance of this program with SignalP

(http://www.cbs.dtu.dk/services/SignalP/) by evaluating the ability of both to

predict localization of a set of proteins that had been previously functionally

characterized as secreted by using the Yeast Sequence Trap Analysis (Krijger

et al. 2008).

To identify carbohydrate active enzymes (CAZymes) I used the web resource

dbCAN (http://csbl.bmb.uga.edu/dbCAN/annotate.php), an automated

CAZyme annotation that is based on the classification scheme of CAZyDB

(Cantarel et al. 2009; Yin et al. 2012).

To predict and characterize proteases, I used the MEROPS peptidase

database (http://merops.sanger.ac.uk/).

3.3.9 Identification of candidate effector proteins

The way that effectors are defined varies in different bioinformatics studies. For

example, in the earlier analysis of C. graminicola (O’Connell et al., 2012),

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effectors were defined as secreted proteins (of any size) that appeared to be

unique to C. graminicola or to the Colletotrichum genus, based on comparisons

with the NCBI database at that time. Since 2012 many additional Colletotrichum

and other fungal genomes have been sequenced, so the list of effectors of C.

graminicola by that definition has changed and presumably will continue to

change. For my work, I defined putative protein effector genes more broadly

(and more permanently!) as open reading frames (ORFs) predicted to encode

small secreted proteins (SSPs). This was the approach used by Gan et al.

(2014) in their analyses of the genomes of C. orbiculare and C. gloeosporioides.

My identification pipeline is shown in Figure 3.1. I utilized the same pipeline to

identify putative effectors from C. graminicola, C. sublineola, and C.

higginsianum. My first step was to identify predicted secreted proteins by using

WolfPsort. My next step was to filter that list to include only proteins that were

between 40 amino acids and 300 amino acids in size. Other researchers have

had different definitions of SSP, ranging from < 400 amino acids (Bowen et al.

2009) to less than 100 amino acids (Kleemann et al. 2012). I used the same

parameters as the ones (Lowe and Howlett 2012) used to identify putative

effectors of Leptosphaeria maculans. Since there are relatively few functional

studies of putative fungal effectors, any decision on what sizes to include is

somewhat arbitrary.

Because many fungal effectors have been described as being cysteine-rich

(Kleemann et al., 2008; Amaral et al., 2012; Raffaele et al., 2010), I calculated

the percentage of cysteines for each SSP, and identified all that had more than

3% cysteine as cysteine-rich (SSP-CR) (Gan et al. 2012).

All the putative protein effector genes were compared with the Pathogen-Host

Interaction (PHI database). This database contains “curated molecular and

biological information on genes proven to affect the outcome of pathogen-host

interactions” from fungi, oomycetes and bacterial pathogens (Baldwin et al.

2006). I used BLAST to identify candidate Colletotrichum effector proteins with

similarity to any proteins present in the PHI database, with an e-value cutoff of

1e-5.

To further characterize the effectors, I used Pfam (http://pfam.sanger.ac.uk/) to

identify potential functional motifs. Many fungal effectors lack these

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recognizable functional domains, and are typically annotated as “hypothetical

proteins” (Sperschneider et al. 2014).

To identify effectors with homologs among the three species, or that appeared

to occur in only one of the three, I used a combination of my own RBH analysis,

and the OrthoMCL results generated by Dr. Moore. The OrthoMCL results were

also used to identify effector homologs that were shared with the other fungal

species included in that analysis. In cases where the results of the RBH and

OrthoMCL did not agree (there were relatively few of these), I used RBH as the

default. Since many of the effectors were not included in the OrthoMCL

analysis, this seemed to be the most consistent way to compare across the

entire group. The effectors that were found only in C. graminicola (aka “non-

conserved” effectors), or that were shared by only two of the three

Colletotrichum species, were further evaluated by using BLASTP against the

non-redundant protein sequences NCBI database (downloaded in July 2014).

Hits with an e-value of below 1e-5 were considered to be homologs (Camacho

et al. 2009). In a few cases, a different e-value was used and these are

explained on a case-by-case basis.

The lists of candidate effector protein sequences for each species were used

for standalone BLASTP analysis against five Colletotrichum species (C.

graminicola, C. sublineola, C. higginsianum, C. orbiculare, and C.

gloeosporioides). The lists were also used to identify putative homologs in M.

oryzae 70-15 (MG8) by BLASTP using the the Magnaporthe Comparative

Sequencing Project website (http://www.broadinstitute.org/). For the

Colletotrichum effectors that had Magnaporthe homologs, a further BLASTP

search of the non-redundant protein sequences NCBI database (downloaded

in July 2014) was performed to identify those that were only found in

Magnaporthe and Colletotrichum and no other genera.

3.3.10 Identification of non-annotated putative effector proteins in C.

graminicola

Even though C. graminicola has a very high quality genome assembly, effector

proteins can still be difficult to identify, because they are quite small, they often

lack functional domains, and they frequently they don’t look like any other genes

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in the databases. Annotation programs are trained by using available data sets

from previous sequencing projects, and for these reasons they can easily miss

effector genes. One solution is to use an ab initio annotation method

(FGENESH) which does not rely on training with previous datasets. Thus, I

used FGENESH to predict additional effector proteins.

3.3.11 Expression analysis of the BAS3 homolog in the WT in vitro and in

planta

3.3.11.1. In vitro analysis: The C. graminicola WT strain was cultured in 500 ml

of Fries complete liquid medium (30 g sucrose, 5 g ammonium tartrate, 1.0 g

ammonium nitrate, 1.0 g potassium phosphate, 0.48 g magnesium sulfate

anhydrous, 1.0 g sodium chloride, 0.13 g calcium chloride, 1.0 g yeast extract/

liter of H2O). Washed spores were added to produce a final concentration of

1x106 spores/ml, and the culture was incubated at 23°C on a rotary platform

shaker at 15 rpm. After 5 days, the cultures were blended and 5 mls of the slurry

was added to a new flask containing 50 ml of Fries minimal liquid medium and

returned to the shaker. Mycelia were harvested 36 hours later under vacuum

filtration and flash frozen in liquid nitrogen, then wrapped in aluminum foil

packets and kept at -80°C until RNA extraction.

3.3.11.2. In planta analysis: Appressoria of the WT were produced in vitro on

polystyrene Petri dishes as described by Kleemann et al. (2008), with some

modifications. C. graminicola spores were collected and washed three times,

and 40 ml of a spore suspension at a concentration of 1 x 104 spores/ml was

added to each Petri dish. Twenty hours later, each plate was inspected under

the microscope to verify the presence of mature melanized appressoria. Trizol

was added and appressoria were broken and scraped from the bottom using a

sterile culture spreader. The slurry was recovered from 30 Petri plates in a total

of nine ml of TRIzol per replicate.

Infection of maize leaf sheaths by C. graminicola and processing of the tissue

samples to obtain RNA was done as described in Chapter 2 of this dissertation.

The RNA extraction was performed essentially as described in O’Connell et al.

(2012), with a few modifications. Frozen mycelia were ground while still

contained inside of the foil packet using a pestle. Around 100 mg of the

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powdered mycelia was added to a 2 ml Eppendorf tube with 1 µl of Trizol

reagent (Invitrogen) for extraction. The cleanup step in the RNeasy Plant Mini

Kit (Qiagen) was performed on the supernatant according to the manufacturer’s

instructions, including the DNase A treatment.

For the first-strand cDNA synthesis, I used one µg of total RNA and the

Superscript II reverse transcriptase kit (Invitrogen) with an oligodT primer.

Semi-quantitative RT-PCRs were carried out in 25 µl reactions and consisted

of 0.1 µM of each primer, 0.2 mM each dNTP, 0.25 units of Taq DNA

Polymerase (Life Technologies) and 1.5 nM MgCl2. Thermal cycling was

performed as follows: 94°C for 3 minutes followed by 30 cycles of amplification

at 94°C for 45 s, 60°C for 30 sec and 72°C for 1 min. The primers for BAS3

(EBBAS3F and EBBAS3R) are included in Table AI.1 in Appendix I of this

dissertation. Actin (GLRG_03056) was used as an internal control. Sequential

dilutions of cDNA were used as template, with the concentrations determined

by using amplification of the control gene to normalize across samples, and

diluting appropriately so that the control gene was in an exponential range

(Choquer et al. 2003).

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3.4 Results

3.4.1 Comparison of Colletotrichum genomes

3.4.1.1 Genome sequencing and assembly. Genome characteristics of the

Colletotrichum species used in this chapter are summarized in Table AII.1, in

Appendix II. Some of these data come from the literature, and some was

generated by Dr. Moore and some by me. The total contig length of C.

graminicola, C. higginsianum, and C. gloeosporioides was ~50 Mb in each

case, whereas C. sublineola was a bit larger at ~65 Mb. C. orbiculare was the

largest, with ~90 Mb. C. sublineola has 13,331 predicted genes, a little bit more

than the 12,006 predicted in C. graminicola. However, when Dr. Moore used

MAKER to re-annotate C. graminicola, it actually predicted 14,419 genes, more

than in C. sublineola. C. higginsianum is predicted to have more genes than C.

graminicola and C. sublineola, which may be partly due to the fragmented

nature of the currently available genome assembly of this species. I estimated

that close to 9% of conserved genes were either split or truncated in this C.

higginsianum assembly (O’Connell et al., 2012). All three genome assemblies

contained homologs for most or all of a set of phylogenetically conserved genes

(CEGMA) (Parra, Bradnam, and Korf 2007) and a set of conserved fungal

genes (Liu et al., 2006) (Table AII.1 in Appendix II), suggesting that all of the

assemblies are similarly complete.

3.4.1.2 Synteny analysis. Gene order (synteny) is relatively highly conserved

between C. graminicola and C. sublineola. I was able to align 83% of the C.

graminicola genome assembly with C. sublineola scaffolds, and 79% with C.

higginsianum scaffolds, based on the relative arrangement of conserved genes

(Figure 3.2A, Table 3.1). Much of what could not be aligned was comprised of

the three C. graminicola minichromosomes, which seem to be largely unique to

this strain of C. graminicola (Rollins 1996). Although the percentages of

scaffolds that could be aligned were similar, the number of syntenous genes

contained within the aligned sequences was very different. Between C.

graminicola and C. sublineola 85% of the genes were syntenous, but that

number dropped to only 50% for C. higginsianum genes (Table 3.1). In

comparing C. graminicola with C. sublineola, regions that appear to be inverted,

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and regions that appear to lack synteny, could be discerned embedded within

the largely co-linear assemblies (Figure 3.2B).

The relative similarity of C. graminicola and C. sublineola can also be seen in

the degree of amino acid identity among predicted proteins (Figure 3.3).

Genome-wide amino acid similarity analysis was performed as described in De

Wit et al. (2012). Among the proteins shared by C. graminicola and C.

sublineola, 66.4% have more than 81% similarity. Only 44% of the shared

proteins of C. graminicola and C. higginsianum, on the other hand, are that

similar. The other two genomes, C. gloeosporioides and C. orbiculare share

even less similarity with C. graminicola, with less than 30% of the proteins

having more than 81% similarity.

3.4.2 Comparative analysis of Colletotrichum proteins

3.4.2.1 Identification of orthologous proteins. Results from OrthoMCL analysis

including ten other species of Ascomycete fungi indicated that the three

Colletotrichum species shared the majority of their proteins, comprising 8799

orthologous groups (Figure 3.4). C. graminicola and C. sublineola had more

groups and more proteins in common than either species shared with C.

higginsianum. OrthoMCL identified 134 proteins as present in only C.

graminicola, and 456 that were found only in C. sublineola (, Figure 3.4). The

majority of these non-conserved proteins had no identifiable domains or

predicted functions. However, nearly all of them matched sequences in other

species, typically identified as hypothetical proteins, in the NCBI databases.

Only one protein in C. graminicola, and 61 in C. sublineola, appeared to be

species- or strain-specific orphans.

Approximately 9% of C. graminicola genes and 16% of C. sublineola genes

could not be included in the OrthoMCL analysis. OrthoMCL also failed to

characterize 29% of C. higginsianum genes. For this reason, I also utilized the

Reciprocal BLAST Hits (RBH) approach to analyze orthologous proteins (Wall

et al. 2003). With this approach, all proteins can be accounted for. For more

than 90% of the proteins, the two methods gave the same result. Results of

RBH agreed with results of OrthoMCL analysis in suggesting that a majority of

proteins are shared among the three species, and that C. graminicola shares

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more proteins with C. sublineola than either does with C. higginsianum (Figure

3.5). The RBH identified more non-conserved proteins than OrthoMCL: 1,164

were found only in C. graminicola and not the other two species, and 2,502

were found only in C. sublineola (Figure 3.5).

3.4.2.2 Protein families analysis. The Protein Family Database (Pfam) (Punta

et al. 2012) was used to characterize and compare the predicted proteins from

C. graminicola, C. sublineola, and C. higginsianum (Table 3.2). Only 67% of C.

graminicola proteins, 62% of C. sublineola proteins, and 58% of C.

higginsianum proteins could be categorized into Pfam families. A majority of

these families were shared by all three isolates, with relatively few differences

in the number of family members across the strains. There were 35 exceptions

in which there was at least a three-fold expansion in one or two of the three

species (Table 3.2). In cases of apparent gene family expansion, nearly all were

expanded in C. higginsianum relative to the other two species, or less frequently

in C. higginsianum and C. sublineola relative to C. graminicola. One of these

differentially expanded families was PF03211, a family of pectate lyases,

represented by 14 members in C. higginsianum but only four in C. graminicola

and three in C. sublineola. Another family of pectate lyases, PF00544, had

twice as many members in C. higginsianum than in the other two species. This

increased representation of pectin degrading enzymes was confirmed by an

analysis of the proteins with the Cazymes database (www.cazy.org) (Figure

3.6). Previously, an expansion of pectate degrading enzyme genes was noted

in C. higginsianum in comparison to C. graminicola, and postulated to relate to

differences in dicot versus monocot cell wall structure (O’Connell et al., 2012).

Another family expanded in C. higginsianum (PF00668) was related to

polyketide synthases, which have previously been shown to be more abundant

in C. higginsianum than in C. graminicola (O’Connell et al., 2012). In one case,

there was an expansion only in C. sublineola, for family PF14529, which is likely

to be a retrotransposon-associated gene. In no case was a gene family notably

expanded in C. graminicola relative to the other two strains.

There were 18 Pfam families that were found only C. graminicola and not in the

other two species, while 29 were found only in C. sublineola (Table 3.2). There

were also 121 families that were found in both C. graminicola and C. sublineola,

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but not in C. higginsianum (Table 3.2). Almost all of these species-specific or

clade-specific families contained only a single protein. To my knowledge, none

of the species- or clade-specific families have been implicated directly in

pathogenicity.

3.4.2.3 BLAST analysis. The C. graminicola and C. sublineola predicted

proteins were further evaluated by using BLAST against the NCBI database

(downloaded in July 2014). Of the C. graminicola proteins that were conserved

among all three Colletotrichum species, 64 of them appear to be specific to just

these three species, having no significant similarity to other sequences from the

database. The majority of the proteins (547 of 560) that were shared only

between C. graminicola and C. higginsianum were present in other organisms

(Figure 3.7). Among the proteins shared only by C. graminicola and C.

sublineola most were also present in other organisms (880 of 1018). In contrast,

about 50% (550/1164) of the non-conserved proteins in C. graminicola that

were identified by RBH, and about 40% (1029/2502) of the C. sublineola non-

conserved proteins, had no matches in the database outside of those species.

Among the nonconserved C. graminicola proteins with hits, the majority

(337/614) were to hypothetical proteins in other species. In C. sublineola,

867/1473 of the non-conserved proteins had hits to hypothetical proteins.

3.4.2.4 Characterization of non-conserved proteins. The average size of the

predicted non-conserved proteins for both C. graminicola and C. sublineola was

smaller than the average for all proteins [186 amino acids (aa)] vs. 466 aa; and

256 aa vs. 462 aa, respectively). If the analysis was limited to only those

proteins with no matches in the database (orphans), the average size was even

smaller (109 aa and 180 aa, respectively). Forty percent of the non-conserved

proteins of C. graminicola, including 37% of orphans, had transcript evidence

(defined as a minimum of five normalized reads in at least one sample)

(O’Connell et al., 2012) (Table 2.1). We don’t have transcript data for C.

sublineola so I could not do a similar analysis for those predicted proteins.

More than half of the non-conserved proteins in both C. graminicola and C.

sublineola were predicted to localize to mitochondria or nucleii. About 2/3 of the

proteins that had no matches in the database (orphans) in each case were also

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predicted to be mitochondrial or nuclear. Somewhat surprisingly to me, only

about 10 percent in each case were predicted to be secreted.

A majority of the non-conserved proteins in both species did not have Pfam

categories. Among those with Pfam classifications, the largest groups were

transporters, cytochrome P450s, carbohydrate-active enzymes (Cazymes),

transcription factors, and secondary metabolism enzymes. There was also a

large group of proteins in each case that were categorized as heterokaryon

incompatibility factors, and a number of proteins that were potentially involved

in signaling (i.e. protein kinases and protein phosphatases) and pathogenicity

[i.e. proteins with necrosis inducing NPP domains (Gijzen and Nürnberger,

2006; Kleemann et al., 2012), NUDIX domains (Bhadauria et al., 2013), and

CFEM domains (Kulkarni et al., 2003)]. About a quarter of the annotated genes

in each of the three Colletotrichum species had matches in the PHI database.

In the group of non-conserved proteins in C. graminicola, 125 matched the PHI

database, and 279 matched the PHI database in C. sublineola.

3.4.3 Comparative genomics of Colletotrichum secretomes and effectoromes

3.4.3.1 The Colletotrichum secretome. To predict the secretome of each

species, I used the WolfPsort protein subcellular localization prediction

program. In O’Connell et al. (2012) it was stated that this program classified

known extracellular proteins better than other programs that are commonly

used. I also found that it did a better job than SignalP of predicting localization

for a list of C. graminicola proteins that had previously been identified as

secreted proteins by using a yeast secretion signal trapping technique (Krijger

et al. 2008). WolfPsort predicted that are 1,690 secreted proteins encoded by

C. graminicola and 1,891 by C. sublineola, accounting for about 14% of the

proteins for each species.

3.4.3.2 The Colletotrichum effectorome. My criteria for calling a protein a

putative effector were that it was predicted to be secreted, and that it was

between 40 and 300 amino acids in size. I identified 687 small secreted proteins

(SSPs) in C. graminicola (5.7% of all proteins); 824 in C. sublineola (6.2% of all

proteins); and 1178 in C. higginsianum (7.3% of all proteins). About 40% of the

C. graminicola and C. sublineola secreted proteins have fewer than 300 amino

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acids. Based on the results from RBH, approximately 400 SSPs are shared

among all three Colletotrichum species, and there are more proteins shared

between C. graminicola and C. sublineola than C. graminicola and C.

higginsianum (Figure 3.8).

It is interesting that the level of amino acid similarity of homologous secreted

proteins is smaller than that of non-secreted proteins (Figure 3.9). If we

consider only SSPs versus all secreted proteins, there’s even less amino acid

similarity (Figure 3.10). There is consistently less similarity between C.

graminicola and C. higginsianum proteins, versus between C. graminicola and

C. sublineola proteins.

I used the Pfam database to classify the SSPs into functional protein families.

Almost half of the SSPs shared by the three species, or shared only by C.

higginsianum and C. graminicola, could be classified. In contrast, only 30% of

the SSPs shared between C. graminicola and C. sublineola could be classified,

and that dropped to less than 10% of the non-conserved C. graminicola SSPs.

The trend is similar in C. sublineola.

The C. graminicola and C. sublineola effectoromes are comprised mainly of

hypothetical proteins. Only 36% of all of the C. graminicola SSPs, and 31% of

the C. sublineola SSPs, can be classified by Pfam. The majority of the SSPs

have hits on the NCBI database (89% in C. graminicola, and 87% in C.

sublineola) but most in each case hit hypothetical proteins.

I analyzed the cysteine content of the SSPs, as effectors are often described

as being cysteine rich (SSP-CR). There are 251 SSP-CR in C. graminicola, and

306 in C. sublineola. The majority of those are homologous to hypothetical

proteins in the NCBI database. Among the SSP-CR, 62 and 53 are

characterized by Pfam, and 20 and 13 have similarities to proteins in the PHI

database, in C. graminicola and C. sublineola, respectively.

3.4.3.3 Conserved effector classes in C. graminicola. Several classes of fungal

pathogenicity effectors described in the literature from other organisms have

homologs in C. graminicola and C. sublineola.

The Common in Fungal Extracellular Membrane (CFEM) proteins have an eight

cysteine-containing domain of around 66 amino acids (Kulkarni, Kelkar, and

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Dean 2003). Some CFEM proteins have important roles in pathogenesis. For

example, PTH11 and ACI1 from M. oryzae are required for appressorium

development (Choi and Dean, 1997; DeZwaan et al., 1999). All three

Colletotrichum species encode numerous secreted and membrane-bound

CFEM proteins. Pfam identified 24 CFEM-domain proteins in C. graminicola,

and 11 of those are SSP-CRs. C. sublineola has 22, 10 of which are SSP-CRs.

Homologs of another conserved cysteine-rich secreted effector protein, cerato-

platanin, are also found in all three Colletotrichum species. Cerato-platanin acts

as a toxin in the wilt fungus Ceratocystis fimbriata (Pazzagli et al. 2009) and is

a known as a general fungal elicitor and inducer of PCD.

Chitin-binding proteins contain one or more chitin-binding domains, and they

also bind to various complex glycoconjugates (Raikhel et al., 1993). In plants,

these proteins are assumed to have a role in host defense, but in fungi they are

believed to bind to chitin present in fungal cell walls, thus protecting the

pathogen from plant chitinases. The Avr4 protein of Cladosporium fulvum is a

chitin-binding domain effector that, when mutated, resulted in decreased

virulence (van Esse et al., 2007). C. graminicola has two genes identified with

the chitin binding domains (GLRG_06483 and GLRG_10441). C. sublineola

and C. higginsianum also have two chitin binding domain proteins.

The lysin motifs (LysM) were identified as a class of conserved effectors in

pathogenic and nonpathogenic fungi (Kombrink and Thomma 2013). All three

Colletotrichum genomes contain an expanded family of genes encoding LysM

proteins. These genes appear to be highly divergent among the species, and

thus to be evolving rapidly (Kleemann et al. 2012). LysM effectors, eg. Ecp6

from C. fulvum, are believed to sequester fungal chitin fragments, thus avoiding

host detection (Jonge et al. 2010). The same function was ascribed to M.

oryzae Slp1 and Mycosphaerella graminicola Mg3 LysM effectors (Marshall et

al. 2011; Mentlak et al. 2012). In C. lindemuthianum, a LysM protein called CIH1

was localized specifically to the surface of biotrophic hyphae by using a

monoclonal antibody (Pain et al., 1994; Perfect et al., 1998). All three

Colletotrichum species have homologs of CIH1. There are six LysM-protein

genes in C. graminicola, including two SSPs, one of which is the ClH1 homolog.

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The Nudix hydrolase, CtNUDIX, was identified in a transcriptome study of the

C. truncatum-lentil interaction. CtNUDIX was proposed to induce cell death

during the switch to necrotrophy (Bhadauria et al., 2011). There are 17 proteins

with the NUDIX domain identified by Pfam in C. graminicola. Homologs of the

Nudix effector are also present in other hemibiotrophic pathogens including M.

oryzae, and P. infestans, but absent in biotrophic and necrotrophic pathogens,

prompting the suggestion that it might be important specifically for this lifestyle.

CtNudix homologs are also present in C. higginsianum, but interestingly, not in

C. sublineola.

There are several known fungal effector families that induce PCD in plant

assays. NIS1 is an effector that is expressed in biotrophic hyphae of C.

orbiculare, and induces host cell death in the model plant N. benthamiana

(Yoshino et al. 2012). There is one homolog in C. graminicola (GLRG_05338).

Homologs of Necrosis Inducing Proteins (NIP), described from biotrophic

hyphae of Fusarium, are found in all three Colletotrichum species. Six genes

encoding members of the necrosis- and ethylene- inducing peptide (NEP) 1-

like protein family (Gijzen and Nürnberger, 2006) were identified in C.

higginsianum (Kleemann et al. 2012). However, only three of these homologs

actually caused cell death in N. benthamiana: the others lacked crucial amino

acids and were not able to induce necrosis (Kleemann et al. 2012). Homologs

of all but one of the C. higginsianum proteins were present C. graminicola and

C. sublineola. It is interesting to note that there are two C. sublineola proteins

that match ChNLP3 and 3 that match ChNLP5, but C. graminicola has only a

single homolog for each of these proteins (Table 3.3).

Of the four biotrophy-associated secreted (BAS) proteins described in M.

oryzae (Mosquera et al. 2009), BAS2 and BAS3 are present in all three

Colletotrichum species and C. higginsianum also has an homolog of BAS4.

CgDN3 is a small secreted protein that is required for the successful

establishment of C. gloeosporioides on Stylosanthes guianensis leaves

(Stephenson et al. 2000). Homologs of CgDN3 were found in the genome of C.

higginsianum, but not in C. graminicola or C. sublineola.

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3.4.3.4 Orphan genes. Genes encoding secreted proteins involved directly in

host-pathogen recognition are frequently highly divergent, because they are

subject to rapid adaptive evolution due to selection pressure (Stergiopoulos and

de Wit, 2009). Using comparative genomics, I was able to identify lineage-

specific proteins (“orphans”) that lack similarity to other proteins in the

databases. Although these orphan genes potentially have homologs that could

be identified in the future as new species are sequenced every day, using a

very closely related pathogen in my comparisons should increase the chance

that the genes I identified as orphans really are species-specific.

I did a BLAST analysis of all of the proteins from C. graminicola, C. sublineola,

and C. higginsianum against the NCBI non redundant database. It is interesting

to notice that out of the 9264 genes shared among the three species, 64 appear

to be specific only to those three, including four SSPs. Of the genes that were

shared only between C. graminicola and C. higginsianum, all but 13 genes,

three of which were SSPs, had hits to other species in the database. Most of

the C. graminicola and C. sublineola homologous genes also had hits on NCBI;

there were 138 that did not have homologs, out of 1,018 shared genes. Twenty-

two of these are SSPs.

It was among the sets of non-conserved genes that I found most of the genes

with no homology with any other proteins in the databases. Out of 1,164 non-

conserved genes in C. graminicola, 583 (50%) had hits in the NCBI database,

with 337 of those being to hypothetical proteins in other species. The 581

remaining genes, including 49 predicted to encode SSPs, appear to be C.

graminicola-specific. In C. sublineola, out of the 2,502 non-conserved genes,

1400 (56%) have hits in the NCBI database, 824 of those to hypothetical

proteins in other species. Among the 1102 orphan genes, 117 are predicted to

encode SSPs.

3.4.3.5. Effector families and clusters. OrthoMCL classified 579 C. graminicola

SSPs out of the 687 in groups. Most groups included just one gene copy from

each fungus, but a few contained up to 3 paralogs from one or more species.

This suggests that there has been relatively little duplication and diversification

of these effector proteins. Most of the effectors that were included in OrthoMCL

are shared with one or more of the other genera that were incorporated in the

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analysis. It was interesting to see that 47 SSPs were found only in

Colletotrichum and M. oryzae, and not in the other Ascomycetes included in the

analysis. M. oryzae causes rice blast disease and it is a hemibiotroph like C.

graminicola, with a very similar mode of infection and development in its host.

There are two SSPs that are found ONLY in C. graminicola and M. oryzae,

based on BLAST searches of NCBI.

I used the Broad Institute Colletotrichum Database

(http://www.broadinstitute.org/

annotation/genome/colletotrichum_group/MultiHome.html) to further identify

conserved and non-conserved protein families in C. graminicola and C.

higginsianum. Using this tool, I found that 468 C. graminicola SSPs are grouped

into families in one or both of these species. I used BLAST analysis to

determine whether C. sublineola also contained members of these families.

There were no families that were specific to C. graminicola, without members

in the other two species. There are seven families in C. graminicola that have

members only in C. sublineolum and not in C. higginsianum. An example of one

of these families is shown in Figure 3.11A. Most families had members in all

three species. For example, I found that the gene GLRG_04750 is part of a

family with three paralogs in C. graminicola and one in C. higginsianum. Using

BLAST, I identified four genes in C. sublineola that also belong in that family

(Figure 3.11B). Some C. graminicola effector families also had members in

more distantly related species. Thus, there is a family that has members in C.

sublineola and M. oryzae (Figure 3.11C).

I used the program MEME to identify potential protein motifs shared among the

SSPs. MEME identified several 10-bp motifs in the C. graminicola putative

effectors, shared among at least 11 of the proteins (Figure 3.12). Several of the

motifs were cysteine rich. I also analyzed the 105 SSPs that are found only in

the three species of Colletotrichum, but I could not identify any motifs that were

consistently shared by these proteins. None of the motifs that I found in the C.

graminicola SSPs matched any of those that have been reported in the

literature (e.g RXLR, dEER, crinkle motifs, etc.). I found no evidence for these

motifs in the C. graminicola putative effectors.

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3.4.4 Effector diversity among isolates

For this dissertation, I had access to genome sequences of two strains of C.

graminicola, and two of C. sublineola (Table AII.1 in Appendix II). Having two

strains of each species made it possible for me to compare effector diversity

within species.

Only 73 out of 12006 genes (~1%) are not shared between the two C.

graminicola isolates, and only five of those are SSPs. Of those 73 genes

present only in M1.001, 41 of them were found only in C. graminicola and not

in C. sublineola and C. higginsianum: 15 were shared with C. sublineola only:

2 were shared with C. higginsianum only: and 14 were found in all three

species. Ninety-nine percent of the genes shared between M1.001 and M5.001

had more than 90% similarity by BLAST. Among the 41 proteins from C.

graminicola that were not shared with M5.001 or with C. sublineola or C.

higginsianum, 25 had hits to other species using the NCBI non redundant

database, mainly C. gloeosporioides, C. fioriniae and Fusarium species. The

remaining 16 proteins appeared to be unique to C. graminicola M1.001. Two of

these were SSP-CRs.

The C. sublineola isolate CgSl1 has 117 genes (less than 1%) that are not

shared with TX430BB, and 23 of those are SSPs. Of the 117 genes present

only in CgSl1, seven are shared with both C. graminicola and C. higginsianum:

nine are shared with C. graminicola only: eight with C. higginsianum only: and

the majority, 93, were not shared with either species. Using BLAST against the

non-redundant nucleotide database from NCBI, I saw that 37 of these non-

conserved genes had hits to sequences in other species, mostly C.

gloeosporiodes, C. orbiculare, M. oryzae, F. oxysporum, Ophiostoma pieceae

and Podospora anserina. The remaining 56 genes appear to be unique to

CgSl1, including ten SSPs.

3.4.5 Transcriptome analysis: expression of putative effector genes in MT vs

WT C. graminicola in planta

3.4.5.1. Secreted proteins among the most highly-expressed genes. I

generated lists of the 100 most highly expressed genes for each of the

treatments (Table AII.2 in Appendix II). Analysis of GO-Terms using Blast2GO

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suggested that the majority of the genes in each case were involved in primary

metabolism, growth, and signal transduction (Tables AII.3 in Appendix II).

About 20% of the most highly expressed genes in each condition were related

to stress response. Putative SSP effectors comprised between 9% (WTNT) and

19% (WTAP) of the lists.

When comparing the lists between WTAP and WTBT, there was relatively little

overlap, with only 24/100 genes that were shared. In contrast most genes

(81/100) were shared between the MTAP and MTBT top 100 lists. A majority of

genes (65/100) were also shared between WTBT and WTNT.

During AP, 67/100 of the most highly expressed genes in the MT and the WT

were the same in the two strains. Most of the genes (17/33) that were found

only on the WTAP top 100 list were ribosomal proteins. Seven others encoded

putative SSP effectors, and five had homologs in the PHI database of

pathogenicity-associated proteins. Only seven of these 33 genes were

statistically more highly expressed in WTAP than MTAP, including five of the

SSP effector genes. Many more of the genes found only on the MTAP top 100

list were found in the PHI database (21/33). Two of the genes encoded putative

SSP effectors. Only two of the 33 genes were statistically more highly

expressed in the MTAP then the WTAP. Neither of these was an SSP.

During BT, 64 genes were shared between the WT and MT top 100 lists. A

majority of the genes unique to the top 100 list of the WTBT (22/36) encoded

ribosomal proteins. There were also four putative SSP genes, and six that had

putative homologs in the PHI database. Only four of the genes were statistically

more highly expressed in the WTBT, including one SSP. Among the orphan

genes on the MTBT top 100 list were six putative SSP effector genes, and 18

with homologs in the PHI database. Only eight of the genes were statistically

more highly expressed in the MTBT, none of which were SSP effectors.

3.4.5.2 Differentially expressed genes. A total of 2412 differentially regulated

genes had a log2 fold change ≥ 2.00. There were 760 genes that were

differentially expressed during the transition from appressoria to biotrophy

(WTAP:WTBT), and 992 during the shift from biotrophy to necrotrophy

(WTBT:WTNT)(Tables 2.1, 2.2) . A total of 228 genes were differentially

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expressed in both comparisons. Among the genes that were different in only

one comparison, 159 “early genes” were significantly higher only in AP, and

440 “late genes” were significantly higher only during NT.

For the MT, only two phases of development occurred in leaf sheaths (AP and

BT) (Torres et al. 2013). In contrast with the large change in gene expression

in the WT during the transition from AP to BT (760 genes), only 20 genes were

differentially expressed between these two phases in the MT, all of them more

highly expressed during BT (MTAP:MTBT_down) (Tables 2.1, 2.2). One-third

of these genes encoded carbohydrate-active enzymes (CAZymes). Four

encoded putative SSP effectors, and ten encoded putative homologs of

proteins included in the PHI database.

When comparing the WTAP and MTAP treatments, 218 genes were

differentially expressed: 74 were higher in the WTAP (WTAP:MTAP_up); and

144 were higher in the MTAP (WTAP:MTAP_down) (Tables 2.1, 2.2). There

were 714 genes that were differentially expressed between the WTBT and

MTBT treatments, including 192 that were higher in WTBT (WTBT:MTBT_up)

and 522 that were higher in MTBT (WTBT:MTBT_down) (Tables 2.1, 2.2).

Several classes of proteins that could be important in pathogenicity appeared

to be over-represented or under-represented, relative to their abundance in the

genome, among the differentially-expressed genes. These included genes

encoding SSP and SSP-CR, secreted proteases, and carbohydrate-active

enzymes (CAZymes) (Table 3.4).

3.4.5.3. Secreted proteins are over-represented among differentially expressed

genes. Looking at the secreted proteins RNAseq data, I noticed that there is an

over representation of secreted proteins among the differentially expressed

genes. Overall in the genome, 14% of the proteins are predicted to be secreted,

but among the differentially expressed genes, the secreted proteins represent

30% (Figure 3.13). Among all the SSPs, 250 (~36%) were differentially

expressed, and they can be separated into “early” genes versus “late” genes

(Table 3.4). This indicates that the effectors are transcribed in “waves”, as seen

in other Colletotrichum species (O’Connell et al. 2012; Gan et al. 2013). Of the

C. graminicola early genes that are specifically expressed in appressoria, 114

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are SSPs. My theory is that the effectors that are expressed early are most

likely the ones involved in establishment of biotrophy and host specificity.

3.4.5.4 Comparison of species and genus specific secreted proteins/effectors.

Using the genomes from C. higginsianum (O’Connell et al., 2012) and

C.sublineola, the 250 differentially expressed C. graminicola SSP genes were

classified as shared by all the three species; shared between C. graminicola

and C. sublineola; shared between C. graminicola and C. higginsianum; or

species specific to C. graminicola. The genes that were species specific to C.

graminicola were expressed in the early stages (appressoria and/or biotrophy)

65% of the time, but the genes shared between two or three species were

expressed during the early stages less than 50% of the time (Figure 3.14).

3.4.5.5 Expression Patterns of Conserved SSP Effectors. Homologs of

previously described effector proteins in other organisms have been identified

in C. graminicola. The BAS2 and BAS3 homologs are among the most highly

expressed genes throughout development in planta in both the MT and WT, but

they are both expressed at significantly lower levels in WTNT when compared

to either WTAP or WTBT (Tables 2.1, 2.2).

Two of the CFEM SSP proteins (GLRG_02673 and GLRG_06605) have an

early pattern of expression, while GLRG_09687 is expressed later (NT/AP

comparison) (Tables 2.1, 2.2).

The two LysM domain SSP proteins, including the homolog of ClH1, are

expressed during the early stages of fungal colonization in the WT strain

(Tables 2.1, 2.2). Also, the expression of this in MTAP is significantly different

than from the WT at the same stage (MTAP/AP comparison) (Tables 2.1, 2.2).

C. graminicola has two chitin-binding domain SSPs (GLRG_06483 and

GLRG_10441), and the first one is differentially expressed in the WTBT and

WTNT (Tables 2.1, 2.2).

Besides effectors that are thought to be involved in the establishment of

biotrophy, hemibiotrophic pathogens also secrete proteins that induce PCD,

which are thought to be involved in the switch to necrotrophy. I identified

homologs in C. graminicola for all but one of the six NEP proteins identified in

C. higginsianum (Table 3.3). In C. higginsianum, ChNLP1 and ChNLP2 are

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expressed during the switch from biotrophy to necrotrophy, ChNLP3 and

ChNLP5 are expressed in appressoria, and ChNLP4 is not expressed. The

ability of ChNLP1 to cause PCD was functionally confirmed, while ChNLP3 did

not trigger necrosis. C. graminicola homologs of ChNLP2, ChNLP3 and

ChNLP5 are differentially expressed. Only CgNLP1 and CgNLP2 in C.

graminicola share the amino acids residues crucial for NEP activity. CgNLP2 is

most highly expressed during WTNT. This gene is also more highly expressed

in WTBT vs MTBT. In contrast, the CgNLP3 and CgNLP5 transcripts are more

abundant during WTAP, similar to the expression patterns of their homologs in

C. higginsianum. Neither of these proteins has all the amino acid residues that

are essential for induction of PCD (Ottmann et al., 2009). Expression of

CgNLP4 was very low in planta, similar to its homolog in C. higginsianum

(Kleeman et al., 2012) (Table 3.3).

There are 17 proteins with the NUDIX domain identified by Pfam in C.

graminicola. Only one of them (GLRG_05582) was highly expressed in WTBT

and WTNT (Tables 2.1, 2.2). None of the NUDIX domain proteins was classified

as an SSP.

The homolog of the NIS1 gene in C. graminicola (GLRG_05338), was very

highly expressed at the same level across all developmental stages of the

pathogen, including biotrophy.

3.4.5.6. Effectors differentially expressed in the mutant. There were 20 genes

that were differentially expressed in the transition from MTAP and MTBT, all

more highly expressed in MTBT. Ten of the 20 have homologs in the PHI

database. Ten of the 20 are predicted to encode secreted proteins, including 4

SSPs.

There is one glycosyl hydrolase SSP effector that was differentially expressed

in the MT, a homolog of XYL2 from C. carbonum (GLRG_05524). Work done

by Nguyen and collaborators (2011) in M. oryzae showed a reduction in the

virulence when the homolog of this gene was knocked out in that fungus.

The gene GLRG_06286 encodes a secreted metalloprotease that is

homologous to MEP1 from Coccidioides posadasii (Tables 2.1, 2.2). These

types of protease enzymes require a metal for activity. In C. posadasii, MEP1

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is secreted by the fungi and prevents host detection by digesting surface

antigens from mice cells. When this gene was mutated, virulence was reduced

(Hung et al. 2005). There are two additional secreted metalloproteases among

the differentially expressed secreted proteins, although they are larger than the

300 amino acid upper cutoff to be called SSP.

One SSP-CR (GLRG_11440) that is found in multiple Colletotrichum species,

and one orphan SSP (GLRG_03485), are differentially expressed in the MT.

GLRG_03485 is also differentially expressed specifically in WTAP. (This gene

appears to be missing the 3’end, and is very short – 66 aa/198nt –

supercontig1.10, start at 726614).

3.4.6 Identification of non-annotated effectors

Kleemann and his collaborators (2012) used data from RNA sequencing to

identify 54 putative C. higginsianum effector genes that had not been included

in the Broad genome annotation. It’s possible that these genes were missed by

the Broad annotation because they do not match known genes in the

databases, and Broad uses prediction tools that take these prior data into

account.

For all my analysis I used the Broad predictions from C. graminicola and C.

higginsianum, but for the C. sublineola strain CgSL1 and the C. graminicola

strain M5.001 we used the Maker prediction since we did not have access to

Broad’s proprietary annotation program Calhoun. The newly published C.

sublineola TX430BB strain also used the Maker pipeline for genome annotation

(Baroncelli et al. 2014). Different gene prediction programs use different

algorithms and therefore often yield different annotations.

Broad’s Calhoun protocol uses EST evidence and BLAST homology against

Genbank’s NR database, among other criteria, to identify genes. They train the

gene prediction program with a combination of other programs such as

FGENESH, GENEID, and GeneMark, and EST-based automated and manual

gene models. After that, they apply an in-house pipeline to improve their

annotation of the genome. A full description can be found at

(http://www.broadinstitute.org/annotation/genome/colletotrichum_group/Gene

Finding.html). This is the prediction used for the published genomes of C.

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graminicola and C. higginsianum. Our lab did not have access to Broad

annotations, so we used MAKER, a program from GMOD, to predict C.

sublineola putative genes. We also used FGENESH as a third prediction

method.

Table 3.5 summarizes and compares the results from each prediction program.

The numbers do vary, but when I compared the different C. graminicola

predictions using BLASTP, 90.5% of the genes were the same. There were

1,653 new genes predicted by FGENESH that did not match the Broad

annotation. Out of those, 411 proteins had less than 30 amino acids and were

removed from any further consideration. Some authors use an arbitrary lower

cutoff of 100 amino acids to avoid including non-protein coding RNAs in their

analyses (Dinger et al., 2008), but recently smaller proteins have been

associated with important roles in biological processes (Hanada et al., 2007;

Yang et al., 2011), so I chose to only remove proteins smaller than 30 amino

acids. This left 1,242 proteins with no matches to the BROAD annotation,

including 113 secreted protein genes, 79 of which were SSPs. The majority of

those genes were orphan C. graminicola genes, but some had hits in the other

two Colletotrichum species (Figure 3.15). Out of those 79, 55 had RNAseq

transcript evidence, and some of them were expressed early. The

transcriptome data helped me to identify and characterize the expression

patterns of these effectors and the genes with transcript evidence will be

particularly interesting to include in future research.

3.4.7 Expression of the C. graminicola BAS3 homolog in vitro and in planta.

The expression of the BAS3 homolog of C. graminicola was tested by

semiquantitative RT-PCR (Figure 3.16). The housekeeping gene actin was

used as the RT-PCR normalization control. I used the same amount of total

RNA to make cDNA, but each in planta treatment had different amounts of

fungal biomass, so normalizing the data was crucial but, it turned out, rather

complicated in practice. There is still some room for optimization in my

experimental design. The results showed that BAS3 was not expressed in

mycelium in vitro, but that there did appear to be expressed in appressoria

produced on Petri dishes. Furthermore, it was expressed in planta during

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WTAP and WTBT, but expression was reduced in WTNT (Figure 3.16). The in

planta results are consistent with our transcriptome data that also show higher

expression of this effector early. Many effectors are known to be plant-induced

including BAS3 in M. oryzae (Mosquera et al., 2009), and so it is not surprising

to find that the C. graminicola homolog is not expressed in vitro in mycelium. It

was a little bit surprising to see expression in appressoria produced in vitro,

which suggests a degree of developmental regulation of this gene in addition

to response to plant signals. This method has potential for evaluating relative

expression patterns of other candidate effectors in future.

3.4.8 Identification of candidate effectors for future studies

Based on my assumption that effectors most likely to be involved in biotrophy

would be expressed early, and would be highly divergent, I chose a group of

effectors that met these criteria as promising candidates for further study

(Figure 3.17). There are 11 orphan C. graminicola effectors that are specifically

expressed early during infection (Figure 3.17). One of those genes was also

identified in the LCD data as being highly expressed in planta versus in vitro.

Some of these effectors are very highly expressed. It would be good to confirm

their expression patterns using semi-quantitative PCR and in planta reporter

gene and localization studies, as well as knockout analyses.

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3.5 Discussion

In the work described in this chapter, I have used a bioinformatic (“in silico”)

approach to characterize the putative effectorome of C. graminicola. In the first

part I used a comparative approach to identify putative effectors that are

divergent, and thus most likely to be under strong selection pressure, and in the

second part of the work I made use of the transcriptome data to identify putative

effectors that are expressed early, during the establishment of biotrophy.

Finally, I combined these two lists to identify the most promising early, divergent

candidate effectors, so that in the future the hypotheses that these are involved

in the establishment of biotrophy in C. graminicola, and that they are deficient

in our CPR1 MT, can be addressed. I had postulated that there would be

significant overlap between these two lists if my assumptions were valid, and in

fact I did see a trend in which more divergent effectors tended to be expressed

earlier while more conserved ones were expressed later. This was not,

however, an absolute rule and there were many exceptions, suggesting that my

assumptions are probably over-simplified.

No less important, I believe, than generating this list of candidate effectors, was

that I also organized and summarized a very large amount of genetic and

transcriptomic data in a format that will hopefully make it much more convenient

for future researchers (especially those who may not be experienced with

programming) to access and use.

C. graminicola was one of the last fungal genomes to be sequenced by using

primarily Sanger technology. When compared to next generation sequencing

(NGS) methods, Sanger sequencing offers the possibility for a higher quality

assembly because of the longer sequences that can be generated (Liu et al.,

2012). However, Sanger sequencing is relatively expensive and as assembly

programs for NGS have improved, microbial genome sequencing projects have

largely moved away from Sanger and even 454 in favor of Illumina and other,

even cheaper alternatives. The high quality of the C. graminicola genome

makes it valuable as a reference sequence for other Colletotrichum genomes

that are sequenced using NGS (e.g. Rech et al., 2014).

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The genomes I compared in my work were prepared using different methods

for sequencing, as well as for assembly and annotation. Inevitably this will have

resulted in some errors in my comparisons, in which genes that appear to be

highly divergent may simply have been mis-annotated, mis-assembled, or

missing from the sequencing data. I found some apparent examples of this in

the secretory pathway of C. gloeosporioides (Chapter 2), but it’s harder to

recognize this kind of error for effector genes, which are much less conserved

in general than other types of genes. I saw that using different annotation

protocols produces different results, sometimes VERY different, for the same

genome assembly. For example, by using a different gene prediction program,

I identified 79 additional putative effector proteins that had not been annotated

by the Broad prediction program. I was able to validate most of these novel

effectors by using the transcriptome data.

One major challenge in comparative genomics studies is to identify homologous

proteins. It is important that proteins being analyzed are truly orthologs rather

than paralogs, if the assumptions of the analysis are to be met. I used two

different approaches. OrthoMCL is more commonly used, but it failed to include

proteins that were more divergent, and the effectors are over-represented in

that group (Figure 3.4). Therefore, I also used RBH, which gave me more

homology results than OrthoMCL, but has the potential for assigning orthology

status to genes that are actually distant paralogs instead. I was encouraged

that the two methods agreed more than 90% of the time. When they did not

agree, it is likely that OrthoMCL, with its additional weighting steps gives more

accurate results. However, for the large number of proteins, many of them

putative effectors that OrthoMCL did not classify, RBH was the best method I

could use.

Errors like these seem to be an inevitable problem of comparative

bioinformatics studies. Unfortunately, there is very little validation or

comparison among methods in the literature that can help us to evaluate which

is likely to perform the best, and even the best still have weaknesses. It is

important to emphasize that all results of my work here should be treated as

hypothetical models to be tested by experimentation.

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Having said that, comparative bioinformatics, particularly of closely related

species, is still a powerful tool to develop new hypotheses regarding species-

specific characteristics. In closely related species that differ in pathogenicity,

regions of the genome that are unique or highly divergent could be important

for those differences (Stukenbrock 2013). My comparative synteny analysis

revealed that the genomes of C. graminicola and C. sublineola are largely co-

linear, reflecting their very close evolutionary relationship. I observed some

chromosome inversions that seem to have occurred between the two species

(Figure 3.2B). Examples include regions of C. graminicola chromosome 1 and

C. sublineola scaffold 5, or of chromosome 3 and scaffold 8. This phenomenon

is called mesosynteny (Hane et al. 2011). It has been hypothesized that

mesosynteny can be important in speciation (Stukenbrock 2013). I also

observed small regions that lacked synteny embedded in the larger co-linear

segments. Some of these regions contained putative effector genes. The

Symap platform that I used for these comparisons has been set up to allow

detailed analysis and visualization of these microsyntenies for future

researchers.

The overall degree of similarity of proteins between C. graminicola and C.

sublineola was very high, compared to other species. However, the secretome

was less conserved, and SSPs in particular were even less conserved (Figure

3.9, 3.10). Schirawski et al. (2010), comparing closely related corn smut fungi,

also found that secreted proteins are more divergent than total proteins. Genes

under a high rate of evolution show less amino acid similarity, so I hypothesize

that secreted proteins, and especially SSPs, are changing faster. Some of the

orphan genes among the SSPs could be involved in the host specificity we

observe between C. graminicola and C. sublineola, and also in early events in

pathogenicity in their hosts including establishment of biotrophy. Given this, I

was quite surprised to find that relatively few of the proteins (<10%) that were

not conserved between C. graminicola and C. sublineola were predicted to be

secreted. Instead, most seemed to be targeted either to the nucleus or to the

mitochondria. I’m not sure about the significance of this, and it could simply be

due to incorrect calls by the Wolf PSORT protein localization prediction

program. It would be good in future to test this experimentally.

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The effectorome of C. graminicola is not terribly large, by the standard of most

plant pathogenic fungi, and it is smaller than that of its close relative C.

sublineola. There appears to be relatively little evidence for the presence of

large effector gene families in C. graminicola. One rare example, with five

members in C. graminicola, is depicted in Figure 3.11A. I identified five genes

from C. sublineola that appear to belong to the same family. The family is

characterized by the presence of six conserved cysteine-rich regions.

Interestingly, there is also a family with members limited to C. graminicola, C.

sublineola and M. oryzae (Figure 3.11C). These three fungi share a very similar

hembiotrophic lifestyle on graminaceous hosts, so it is possible that this gene

family may play a unique role in that lifestyle.

Comparison of two strains of C. graminicola, one from North America and one

from South America, collected more than a decade apart, revealed very little

genome diversity, including among the effectors: the two strains differed in only

five SSP proteins. This might suggest that there is little selective pressure

driving effector diversification in C. graminicola, perhaps because its host,

maize, is a cultivated crop with a relatively low level of genetic diversity, in which

resistance to C. graminicola is primarily due to quantitative trait loci rather than

major “R” genes. A recent paper by Rech et al. (2014) also reported very low

levels of diversity among effector proteins across seven different strains of C.

graminicola from a worldwide collection. However, they found evidence for

diversifying selection in the 5’UTR regions of the effector genes, and they

suggested that differences in effector expression may be more important than

differences in protein sequence in considering effector evolution and selection.

I did not investigate 5’UTR sequences in my study, but it would be a good thing

to do in future.

I found no evidence for a widely-conserved sequence motif like RXLR (Birch et

al. 2008; Morgan and Kamoun 2007) among my effectors (Figure 3.12). There

were a few motifs, including several that were cysteine-rich, that were shared

among smaller groups of proteins. Cysteine forms disulfide bonds, thus

potentially stabilizing protein tertiary structures important for function, or

protecting them from host proteases. Effectors with the same motifs might have

similar functions. The transcriptome data shows that some groups of proteins

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that share the same motif are similarly regulated. For example, among the 10

sequences with Motif07, eight are differentially regulated, and seven are

expressed preferentially early during infection. However, most of the motifs

don’t seem to match particular patterns of expression. The significance of these

motifs, if any, would need to be tested experimentally in mutagenesis studies.

I used all the data from my genome comparison of C. graminicola and its close

relative C. sublineola, together with the transcriptome data from pathogenic and

non-pathogenic strains of C. graminicola, to identify a group of effector

candidates that I hypothesize are most likely to be involved in the species-

specific establishment of biotrophy and suppression of PCD. Dr. M. Torres, in

her dissertation research, reported that the development of the MT is stopped

very early during biotrophy in the host tissues, and thus genes that are

differentially expressed in MTBT are likely to be those that are turned on first

during the transition from appressoria to biotrophy (Torres, 2013). These genes

are also good candidates for biotrophy determinants..

Copyright © Ester Alvarenga Santos Buiate 2015

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Figure 3.1 Bioinformatic pipeline for prediction of the Colletotrichum effectorome. The pipeline is composed of the major steps used in the characterization of the proteins.

Potential effectors

Secreted proteins (Wolf Psort)

≤ 300YesNo > 300

Protein size (aa)

Genome sequencing and assemblyC. graminicolaC. sublineola

Gene Annotation1. Broad Institute2. Maker (GMOD)

3. FGenesh (Salamov & Solovyev, 2000)

Standalone BlastIdentify orthologous genes in other Colletotrichum species not in NCBI

PHIPathogen Host

Interaction Database

NCBI BLASTBasic Local Alignment

Search Tool

PfamCollection of protein

families

YesNo YesNo

HitsNo hits

RNAseqTranscriptome used to confirm expression of putative and unique

effector genes

Unique effectors

Cysteine-rich> 3%

Potential effectors Broad/Maker

Potential effectors Fgenesh

Cons

ensu

s lis

t

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Figure 3.2 Synteny between C. graminicola and C. sublineola. A) Global view of syntenic alignments between C. graminicola chromosomes (green), and scaffolds of C.sublineola (purple) and C. higginsianum (grey). B) Micro synteny between each C. graminicola chromosome (vertical axis) and C. sublineola supercontigs (horizontal axis). Homologous regions are identified inside the boxes.

chr01

chr02

chr03

chr04

chr05

chr06

chr07

chr08

chr09

chr10

A

B

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Figure 3.3 Amino acid similarity of orthologous proteins between C. graminicola and other sequenced Colletotrichum species.

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Figure 3.4 Orthology by OrthoMCL. Phylogenetic tree (unscaled) based on NCBI Taxonomy Browser. OrthoMCL data provided by Dr. Neil Moore showing the number of shared OrthoMCL groups among all species, as well the number of non-conserved genes that had no homologs between the ten species.

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Figure 3.5 Orthology by Reciprocal Best Hit. Each diagram shows the orthology between the main species (on the top) with the other two.

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Figure 3.6 Carbohydrate-degrading enzymes (CAZymes) in C. graminicola (Cgram), C. sublineola (Csub) and C. higginsianum (Chig), separated by different classes.

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Figure 3.7 Blast results of proteins shared between C. graminicola and the other two species using NCBI database.

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Figure 3.8 Orthology among the small secreted proteins between all 3 Colletotrichum species. Each diagram shows the orthology between the main species (on the top) with the other two.

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Figure 3.9 Amino acid similarity of orthologous proteins between C. graminicola and the other two species, C. sublineola (A) and C. higginsianum (B). Proteins were separated into not secreted versus secreted proteins (SP) categories.

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Figure 3.10 Amino acid similarity of orthologous proteins between C. graminicola and the other two species, C. sublineola (A) and C. higginsianum (B). Only secreted proteins were considered, separated between those that had more than 300 aa (SP) and the small secreted proteins (SSP).

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Figure 3.11 Alignment of effector protein families.

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Figure 3.12 Sequence of motifs discovered on SSPs of C. graminicola using MEME. Number of proteins with the motif sites identified are shown in each figure.

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Figure 3.13 Comparison between secreted proteins present in the genome and secreted proteins present in differentially expressed RNAseq transcripts, showing the over-representation of secreted proteins in planta.

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Figure 3.14 Timing of the differentially expressed SSPs based on their homology to other Colletotrichum species.

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Figure 3.15 Identification of non-annotated effectors in C. graminicola using FGENESH prediction programs.

C. graminicola

Csub Chig

58

5

15 1

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Figure 3.16 Expression of C. graminicola BAS3 on in planta and in vitro conditions. AP: appressoria, BT: biotrophic, NT: Necrotrophic, IVAP: in vitro appressoria, Fries: minimal Fries medium, PQ: Paraquat, TUN: tunycamycin.

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Figure 3.17 Orphan candidate effectors highly expressed in the early stages of infection.

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Chapter 4

Genomic and functional analyses of stress response in wild type and

mutant strains of Colletotrichum graminicola in vitro and in planta

4.1 Overview

There is evidence in the literature that plant tissues that are actively defending

themselves produce a stressful environment for the pathogen ( Torres 2010).

In this chapter, I investigated the nature of stress responses in WT C.

graminicola in planta, and under chemically imposed stress in vitro. I expected

to receive some insights from this work into the types of stress that are being

experienced by the WT during various phases of development in planta. The

MT is able to grow normally in maize tissues that are not alive and actively

defensive. In this chapter I addressed the hypothesis that C. graminicola is

exposed to stresses in planta, and that the MT is deficient in its ability to adapt

to those stresses by testing three predictions related to this hypothesis. My first

prediction was that the MT would be more sensitive than the WT to stress in

vitro. My second prediction was that WT and MT strains would express stress

response genes in planta. My third prediction was that the MT would differ from

the WT in the expression of stress response genes in planta and in vitro under

stress conditions.

4.2 Introduction

4.2.1. Fungal Response to Stress

All organisms, including fungi, have the capacity to adapt to environmental

stresses via the activation of a variety of stress response signaling pathways

(Kültz 2003). The functions of these pathways and their component stress

response proteins have been characterized in detail in several model

organisms, including bacteria, mammals, plants, and fungi. Recent

comparisons of these models with related species that have been characterized

by genomic analyses have shown a generally high level of conservation of the

pathways, although they often differ in some details (Smith et al. 2010). The

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general strategy of the stress response pathways is activation of a variety of

sensors in response to stress, which in turn initiate secondary messenger

signaling pathways, typically MAP kinase cascades, which serve to amplify the

signals, and finally triggering transcription factors that regulate multiple genes

involved in protection and adaptation to the individual stressors.

Results of a comparative genomics study of a large number of species across

several different kingdoms showed that 67 of the 368 phylogenetically most

highly conserved proteins (or almost 1 in 5) were involved in stress response

(Kültz 2003; Kültz 2005). In addition to conservation of the coding regions,

stress response genes typically also have conserved upstream regulatory

elements that bind to the transcription factors that are controlled by stress

response pathways. Stress responsive upstream elements are conserved in a

wide range of species, from yeast (Estruch 2000; Hohmann 2002) to humans

(Papadakis and Workman, 2014) and plants (Naika et al., 2013). Some

conserved sequences located in the 3’UTR have been shown to be involved in

post-transcriptional regulation of gene expression in response to stress

(Spicher et al. 1998). Alternative splicing of transcripts is known to occur in

response to stress. In yeast, alternative splicing was especially common in the

transcripts of ribosomal proteins in response to nutritional stress (Bergkessel et

al., 2011). Stress response can also be regulated post-translationally by control

of protein synthesis rates, release of membrane-bound regulatory molecules,

and changes in locations of proteins as compartments are disrupted by stress

(Kaufman et al. 2002; Kaufman 1999; Guerra et al. 2015; Bernales et al. 2006;

Kültz 2005).

Different stress conditions frequently elicit a response from the same genes,

demonstrating that stress pathways are highly interconnected (Chasman et al.

2014; Bergkessel et al. 2011; Breitkreutz et al. 2010). The crosstalk among

different stress pathways allows a stress signal to be amplified, and activation

of multiple pathways in response to stresses can increase pathogen survival

(Fuchs and Mylonakis, 2009; Hayes et al., 2014).

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4.2.2 The Role of Stress Response in Pathogenicity

Evidence suggests that the ability to cope with external stresses is crucial for a

pathogen to successfully colonize and complete its life cycle in a host (Chung,

2012; Doehlemann and Hemetsberger, 2013). The host environment is

believed to be very stressful, due to pre-formed and inducible defensive

mechanisms deployed by the host to protect itself from pathogen attacks.

Several stress response pathways have been specifically implicated in

pathogenicity.

4.2.2.1 Secretion Stress: Transported proteins are processed by the signal

peptidase complex (SPC) and enter the lumen of the endoplasmic reticulum

(ER), where they are folded into their proper conformations through the

activities of various chaperone proteins and enzymes. They are then

transported in vesicles from the ER to the Golgi, where they are often further

modified, e.g. by adding glycosyl groups, before being directed to their final

internal or external locations (reviewed by Vitale and Denecke 1999: also see

Chapter 2 of this dissertation). Secretion stress occurs when this process of

protein transport is perturbed, resulting in a potentially lethal accumulation of

misfolded proteins in the lumen of the ER. The Unfolded Protein Response

(UPR) is a conserved stress response pathway that helps the cell to maintain

essential transport functions and viability in the presence of secretion stress.

Activation of UPR results in removal of misfolded proteins, decreases in the

production and transport of non-essential proteins, and adaptive increases in

overall secretory capacity (Heimel, 2015; Hollien, 2013; Lai et al., 2007).

The UPR pathway has been most closely studied in the budding yeast

Saccharomyces cerevisiae, but it seems to be highly conserved in all

eukaryotes (Arvas et al., 2006; Lin et al., 2008; Richie et al., 2009; Travers et

al., 2000). The signal to activate the UPR originates in the ER and travels to the

nucleus, resulting in an increase in the expression of chaperones and folding

proteins (Patil and Walter, 2001). Ire1 is an ER transmembrane protein that

interacts with BiP, (aka Kar2 in yeast), a member of the Hsp70 family, on the

luminal side of the membrane. BiP is the most abundant chaperone protein in

the ER and is highly conserved across a wide range of organisms (Pincus et al.

2010; Gething 1999). BiP is involved in stabilization of nascent proteins as they

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pass through the translocon into the ER lumen, and it binds to proteins to

facilitate their correct folding. Currently there are two proposed models of how

UPR is activated (Guerriero and Brodsky 2012). In the first one, when misfolded

proteins accumulate, Ire1 transduces a UPR signal across the membrane,

causing BiP to dissociate from it and to bind to unfolded proteins. This also

activates the endoribonuclease function of Ire1 which catalyzes the splicing of

the transcription factor Hac1 pre-mRNA. Splicing of the Hac1 transcript

activates its regulatory activity and allows Hac1 to switch on genes that have a

conserved UPR element in their promoters (Mori et al., 1996; Mori et al., 1998).

These genes are involved in moderating secretion stress, and include various

heat shock proteins (Miskei et al., 2009; Schröder and Kaufman, 2005; Gülow

et a., 2002). The other model was proposed by Pincus and co-authors (2010).

In this model, BiP binds to and inactivates Ire1 during low stress conditions.

Binding of unfolded proteins to Ire1 causes the formation of Ire1 complexes,

releasing it from BiP, and resulting in its activation to process the Hac1 pre-

mRNA (reviewed by Gardner et al. 2013).

The UPR is a central stress response pathway that is integrated closely with

responses to a large number of other environmental stresses, many of which

directly or indirectly cause perturbation of protein transport and an accumulation

of misfolded proteins. In plants it appears that proteins that detect secretion

stress and trigger UPR are adapted as general stress surveillance molecules

that also activate other stress response pathways (Liu and Howell 2010; Takato

et al. 2013). UPR can be induced in vitro by treatment with chemicals that

interfere with protein folding or modification (Denecke et al., 2012; Guillemette

et al., 2007; Iwata et al., 2010; Satpute-Krishnan et al., 2014; Yi et al., 2009).

Two commonly used chemicals are Dithiothreitol (DTT) and tunicamycin. DTT

reduces disulfide bond formation, while tunicamycin blocks the synthesis of N-

linked glycoproteins.

Numerous human diseases are linked to pathologic conditions that trigger

secretion stress, including diabetes, some types of cancer and viral infections,

and neurodegenerative diseases such as Alzheimer’s (Hutt et al., 2009;

Kadowaki and Nishitoh, 2013; Lin et al., 2008; Myung et al., 2001; Schröder

and Kaufman, 2005).

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The UPR and the secretory system have been directly implicated in fitness and

virulence of filamentous fungi. For example, virulence of the opportunistic

human pathogen Aspergillus fumigatus has been associated with activation of

the UPR and the endoplasmic reticulum-associated degradation pathway

(ERAD), both related to secretion stress (Richie et al. 2011; Feng et al. 2011).

Disruption of Hac1, the major transcriptional regulator of UPR, produced an A.

fumigatus mutant that was reduced in virulence in mouse models, with

increased sensitivity to heat stress and fungicides, and an inability to assimilate

nutrients from complex substrates (Richie et al. 2009). Growth of the Hac1

mutant was comparable to the wild-type strain under normal conditions, but

conidiation was reduced (Richie et al. 2009). In the necrotrophic Brassica

pathogen Alternaria brassicicola the disruption of a homolog of Hac1 resulted

in a nonpathogenic mutant which also had a cell wall defect and a reduced

capacity for secretion (Joubert et al. 2011). In a study in Magnaporthe oryzae,

the Lhs1 chaperone which is important for protein transport into the ER and

proper folding, was essential for pathogenicity (Yi et al. 2009).

4.2.2.2 Oxidative Stress: The production of reactive oxygen species (ROS), the

so-called “oxidative burst”, is one of the earliest observable plant defense

responses to pathogen attack (Apel and Hirt 2004; Dickman and de Figueiredo,

2013; Moye-Rowley, 2003). ROS are generated during normal cellular

metabolism, but they are transient and tightly regulated in order to maintain

them at tolerable levels (Angelova et al., 2005). Exposure to high levels of ROS

during the oxidative burst damages proteins, lipids and nucleic acids of both

plant and pathogen, and can result in programmed cell death (PCD) of the plant

cells (Ikner and Shiozaki, 2005). PCD is beneficial to necrotrophic pathogens

that can only colonize dead host cells (Chung, 2012; Dickman and de

Figueiredo, 2013). Fungi react to ROS exposure by activating the oxidative

stress response pathway, leading to the production of protective antioxidant

molecules such as glutathione and thioredoxin, and enzymes such as

catalases, peroxidases, and superoxide dismutases that specifically degrade

and detoxify ROS (Montibus et al., 2013). Oxidative stress can be induced in

vitro by treatment with various chemicals including menadione, hydrogen

peroxide (H2O2), or the herbicide Paraquat (Kavitha and Chandra 2014;

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Angelova et al. 2005). Menadione induces oxidative stress by forming

superoxide anions, and Paraquat generates reactive oxygen species (ROS) by

the induction of electron transfer in multiple subcellular compartments (Abegg

et al., 2011; Angelova et al., 2005; Fernandes et al., 2007). Different chemical

treatments sometimes induce different responses: thus H2O2 induced

production of both superoxide dismutases and catalases in several different

fungi, but Paraquat only induced superoxide dismutases (Angelova et al.,

2005). However, in a different study, genes induced by menadione and H2O2

showed significant overlap (Jamieson 1992). Moreover, in M. oryzae,

transcription factors upregulated during in vitro oxidative stress were similar to

ones induced in planta (Park et al. 2013).

Several fungal genes related to oxidative stress and ROS detoxification were

induced during infection of chickpea by the necrotrophic fungus Ascochyta

rabiei (Singh et al., 2012). A glutathione peroxidase mutant of M. oryzae

produced lesions in barley that were smaller than those produced by the WT

(Huang et al., 2011a, 2011b). Mutation of an ortholog of the yeast oxidative

stress transcription factor Yap1 in Alternaria alternata, a necrotroph that causes

citrus brown spot, resulted in a loss of pathogenicity and reduced expression of

catalases, peroxidases, and superoxide dismutases (Lin et al., 2009). Oxidative

stress has been linked to the production of toxic secondary metabolites in fungi

including Fusarium graminearum (Ponts et al., 2006) and M. oryzae (Forlani et

al. 2011).

4.2.2.3 Osmotic Stress: Osmotic stress occurs due to an imbalance in the

solute concentration between the internal and external cell environment. If the

cell occupies a hyperosmotic environment, it will lose water resulting in

plasmolysis. In hypo-osmotic conditions the cell will absorb water, and can

eventually burst. Osmotic stress can be induced in vitro by exposure of the fungi

to high levels of an osmolyte such as sorbitol or sodium chloride (Kovács et al.,

2013; Rispail and Pietro, 2010). Osmotic stress causes impairment of cell

function, ultimately resulting in cell death. Cells protect themselves by activating

the osmotic stress pathway, which allows them to regulate concentrations of

internal solutes such as glycerol to maintain osmotic balance (Posas et al.

1996). Fungi can adapt even to sudden changes in external osmolarity. In

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Candida albicans, which causes Candidiasis in mammals, this pathway has

been shown to be very important for pathogenicity. The Hog1 kinase is a highly

conserved protein that when mutated causes inability to grow under osmotic

stress conditions in S. cerevisiae (Brewster et al., 1993). Deletion of the Hog1

homolog in C. albicans results in a reduction in virulence (Cheetham et al.

2011). Interestingly, the C. albicans Hog1 mutant does not become more

sensitive to osmotic stress, suggesting that it has an additional osmotic stress

response pathway that yeast lacks (Enjalbert et al. 2003).

In some filamentous fungi, activation of Hog1 results in accumulation of

molecules, including saccharides and glycerol, which diminish osmotic stress.

However, in these fungi Hog1 is also an important regulator of the oxidative

response pathway (Bilsland et al., 2004). When the Hog1 ortholog of

Aspergillus nidulans was deleted, it resulted in reduced conidiospore viability

and increased sensitivity to oxidative stress and heat shock (Kawasaki et al.,

2002). Similar defects were found in Hog1 mutants in F. graminearum (Zheng

et al. 2012), which also became less pathogenic to wheat. In Alternaria

alternata, deletions of Hog1 resulted in increased susceptibility to oxidative and

salt stress and loss of pathogenicity on tangelo leaves (Chung 2012). Ssk1 is

a critical activator of Hog1 in yeast but when Ssk1 was mutated in Aspergillus

there were no changes in sensitivity to osmotic stress (Duran et al. 2010). This

further illustrates differences that exist between yeast and other fungi in the

osmotic stress response pathway (Kültz and Burg 1998; Miskei et al. 2009).

4.2.2.4 Cell Wall Stress: Fungi have wall sensors that detect external stresses

and respond to them via the cell wall integrity pathway, which triggers adaptive

changes in the composition and strength of the wall (reviewed by Fuchs and

Mylonakis 2009). The fungal cell wall serves as the primary sensory connection

between the fungal organism and the external environment, and the cell wall

integrity pathway thus serves as a central regulatory pathway that interfaces

with other response pathways to several different external stresses including

osmotic, pH, thermal, oxidative, and nutrient stresses. This ensures that the

critical functions of the cell wall can be maintained and optimized under a range

of stress conditions (Fuchs and Mylonakis 2009; Nikolaou et al. 2009). The

fluorescent dye Calcofluor can be used to induce cell wall stress in fungi.

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Calcofluor interacts with chitin, and interferes with cell wall assembly (Kovács

et al. 2013).

In the cell wall integrity pathway, cell wall stress is first detected by membrane

receptors. Mutation of the Wsc1 cell wall stress sensor gene in yeast results in

cell rupture at elevated temperatures (Gray 1997). The wall sensors recruit

Rom, which in turn triggers the small G protein Rho, which initiates activation

of a MAP kinase cascade. Mutations in either Rom or Rho cause wall and

growth defects in yeast (Schmelze 2002). Mutations of the kinases also result

in sensitivity to cell wall stress and growth defects in yeast and in C. albicans

(reviewed by Levin 2005, Blankenship 2010; Navarro-Garcia 1995). Homologs

of these kinases in filamentous fungi have been shown to be involved in cell

wall structure, conidiation, and pathogenicity (Zhao et al., 2007). The cell wall

integrity pathway terminates with activation of several transcriptional regulators

that have multiple targets, including genes involved in chitin synthesis. The GFA

(fructose-6-phosphate amidotransferase) enzyme, which is responsible for the

first, rate-limiting step in chitin synthesis, is highly induced under cell wall stress

conditions in yeast (Lagorce et al. 2002) as well as in filamentous fungi like A.

niger and F. oxysporum (Ram et al. 2004). The result of activation of the cell

wall integrity pathway is an increase in chitin accumulation, and stiffening and

strengthening of the cell wall (Dallies 1998).

There is evidence that the cell wall integrity pathway plays a role in

pathogenicity. Disruption of the homolog of the Slt2 MAPK in the

entomopathogenic fungus Beauvera bassania resulted in alterations in the cell

wall, hypersensitivity to the cell wall inhibitory compound Congo Red, and a

reduction in conidiation and virulence (Luo et al., 2012). Disruption of the Bck1

MAPKKK homolog of the mycoparasite Coniothyrium minitans also resulted in

wall and conidiation defects, and reduced virulence of this parasite to its host

Sclerotinia sclerotiorum (Zeng et al., 2012). A F. oxysporum mutant with a

defect in the Rho1 GTPase also showed cell wall defects, and was less virulent

to tomato plants (Martínez-Rocha et al. 2008).

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4.2.3 Evidence that Cpr1 and the signal peptidase may play a role in stress

response

To successfully infect a plant, a pathogenic fungus must secrete an array of

proteins that promote susceptibility and facilitate nutrient uptake from the host

(Tang et al. 2006). The ER plays a central role in protein secretion, and the

SPC is the gateway to the ER for secreted proteins. A sudden increase in

secretory activity can be triggered during differentiation of specific secretory

cells, or in response to various stresses that may be encountered in planta

(Kaufman et al., 2002). An increase in secretory activity during the

establishment of biotrophy and the switch to necrotrophy could lead to secretion

stress in C. graminicola, which would be expected to trigger the UPR (Richie et

al., 2009; Schröder and Kaufman, 2005).

Cpr1 has been specifically connected to secretion stress responses in fungi. In

A. niger, a 7-fold increase in the rate of translation of the Cpr1 homolog was

reported in response to secretion stress induced by chemicals (Guillemette et

al., 2007). Another study reported that several genes encoding proteins in the

secretory pathway, including Cpr1, were up-regulated in A. niger under

conditions of carbon starvation stress in vitro (Jørgensen et al., 2009). Other

members of the signal peptidase complex (SPC) have also been implicated in

stress response. The Spc2 protein of the yeast SPC is not essential, but it is

required for full enzymatic activity of the SPC in vitro (Wolfram Antonin, Meyer,

and Hartmann 2000). Mullins and colleagues found that an Spc2 mutant

accumulated unfolded and unprocessed proteins in the ER under temperature

stress (Mullins et al. 1996), suggesting that Spc2 is required for optimization of

protein transport during stress.

In this chapter, I explore a possible connection between Cpr1 and stress

response in C. graminicola. My hypothesis is that C. graminicola is exposed to

stresses in planta, and that the MT is deficient in its ability to adapt to those

stress. To address this hypothesis I tested three predictions: A) the MT will be

more sensitive than the WT to stress in vitro; B) WT and MT strains express

stress response genes in planta; and C) the MT will differ from the WT in the

expression of stress response genes in planta and in vitro under stress

conditions.

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4.3 Material and Methods

4.3.1 Strains and culture conditions

The C. graminicola strain M1.001 isolated from diseased maize was obtained

from the late Dr. Robert Hanau (Purdue University, West Lafayette, IN, U.S.A.).

The nonpathogenic mutant strain (MT) and its complement (MT-C), described

in Thon et al. (2002), were both derived from M1.001 (WT). All isolates were

routinely cultured on Potato Dextrose Agar (Difco Laboratories, Detroit, PDA)

at 23oC under continuous fluorescent light. Spores were collected and used for

inoculations as described in Chapter 2 of this dissertation.

4.3.2. Pharmacological analysis of WT and MT stress responses in vitro

The effect of various stresses on linear growth of the MT, WT, and MT-C strains

was determined by using race tube assays as described in detail in Appendix

III of this dissertation. The tubes contained minimal Fries medium (30 g

sucrose, 5 g ammonium tartrate, 1.0 g ammonium nitrate, 1.0 g potassium

phosphate, 0.48 g magnesium sulfate anhydrous, 1.0 g sodium chloride, 0.13

g calcium chloride/ liter of H2O) solidified with 1.5% agar. After autoclaving and

cooling, the medium was amended with stress-inducing chemicals as described

below. Controls in each case contained the same minimal Fries medium

amended with the same concentrations of the dilution buffer only. Linear growth

was measured after 10 days of incubation at 23°C.

DTT and tunicamycin were used to induce secretion stress. DTT was diluted in

water and added to the media to produce final assay concentrations of 2.5, 5,

10, 15 and 20 mM. A stock of 1 mg/ml tunicamycin was prepared in DMSO,

and the stock was diluted and added to the media to produce the final assay

concentrations of 2, 4, 6, 8 and 10 µg/ml. Paraquat and menadione were used

to induce oxidative stress. Paraquat assay concentrations were 0.5, 1.0, 3.0,

5.0 and 10 mM, and menadione assay concentrations were 50, 100, 200, 300

and 500 µM. Sorbitol and sodium chloride (NaCl) were used to induce osmotic

stress. Sorbitol was assayed at concentrations of 0.4, 0.8, 1.2, 1.6 and 2 M,

and NaCl concentrations were 0.2, 0.4, 0.8, 1.2, and 1.6 M. To induce cell wall

stress, I used Calcofluor at 1, 1.5, 2, 2.5 and 3 mg/ml.

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To evaluate the effect of temperature stress, growth on unamended minimal

Fries medium at 18, 30 and 37°C was compared with the control that was

cultured at the optimum temperature of 23°C.

To evaluate the effect of nutritional stress, the strains were grown in a carbon

and nitrogen-limited minimal Fries medium. The control was minimal Fries

medium as described above, and the treatments consisted of Fries minimal

medium containing one half, one quarter, and one eighth the normal

concentration of carbon (sucrose) and nitrogen (ammonium nitrate and

ammonium tartrate) present in the original formula. For pH experiments,

minimal Fries medium was adjusted to pH values of 4, 5.5, 8, and 10. The

control was the unadjusted pH 6.0 medium.

The effect of each treatment on fungal growth was assessed by calculating the

percentage of growth of each strain relative to their non-stress control for each

chemical. Each treatment had four repetitions and most experiments were

repeated at least three times. Experiments were not repeated if the first

repetition clearly showed that there was no difference in the WT vs MT

response. To compare the stress sensitivity of each fungal strain the mean

relative growth (%) was calculated for each species under the conditions tested.

To measure relative growth, the amount of growth in presence of stress was

divided by the amount of growth observed for unstressed cells of the same

species, and expressed as a percentage.

4.3.3 In silico identification of C. graminicola stress response genes and

pathways

I compiled a list of candidate stress response genes in C. graminicola by using

BLASTP, with an e-value of 1e-5, to identify putative homologs of stress-

associated sequences that had been described in the literature from S.

cerevisiae, A. niger, Trichoderma reesei and M. oryzae (Guillemette et al. 2007;

Arvas et al. 2006; Mathioni et al. 2011; Jørgensen et al. 2009; Nikolaou et al.

2009). Additional stress response genes were identified by searching for

homologs of genes included in the Fungal Stress Response Database (FSRD)

(www.http://internal.med.unideb.hu/fsrd/). The FSRD contains 1985 genes with

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verified functions in stress response in fungi, including pathogens of humans

and plants, as well as species of industrial significance (Karányi et al., 2013).

To identify UPRE (UPR elements), I searched for motifs that had previously

been described in other organisms (Kokame et al. 2001; Fordyce et al. 2012;

Gilchrist et al. 2006; Roy and Lee 1999) in the upstream region of selected

genes involved in stress response (Table 4.1). The motifs included sequences

that were identified in both mammalian systems and yeast: 5’-

CCAATN9CCACG-3’; 5’-ATTGGN9CCACG-3’; 5’-GGCCAGCTG-3’; 5’-

CAGcGTG-3’; 5’-TACGTG-3’; 5’-AGGACAAC-3’.

Putative stress response pathways for C. graminicola were constructed based

on pathways that had been described for model fungi in the literature (Chen

and Dickman, 2004; Geysens et al., 2009; Ikner and Shiozaki, 2005; Jørgensen

et al., 2009; Mathioni et al., 2011; Miskei et al., 2009; Moye-Rowley, 2003;

Nikolaou et al., 2009). Putative C. graminicola homologs of stress response

genes included in each pathway were identified by BLASTP as described

above. To identify the degree of conservation of the proteins associated with

these pathways, five species of Colletotrichum were compared with S.

cerevisiae, as described in Chapter 2.

4.3.4 Transcriptome analysis

A description of the experimental and statistical analysis protocol for the in

planta transcriptome study was presented in Chapter 2 of this dissertation.

I identified genes predicted to be associated with stress from among the 100

most highly expressed transcripts for each treatment. As detailed in Chapter

2, the in planta treatments were: WT pre-penetration appressoria (WTAP); WT

biotrophic phase (WTBT); WT necrotrophic phase (WTNT); MT pre-penetration

appressoria (MTAP); and MT biotrophic phase (MTBT). I also identified stress

genes from among the list of genes that were differentially expressed in each

comparison. As detailed in Chapter 2, the comparisons were WTAP:WTBT;

WTAP:WTNT; WTBT:WTNT; MTAP:MTBT; WTAP:MTAP; and WTBT:MTBT.

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I also summarized the occurrence of stress-response genes among sequences

that were identified in the microarray experiment described by Tang et al.

(2006) which was described in more detail in Chapter 2 of this dissertation.

The GOSSIP function of BLAST2GO 2.8 (https://www.blast2go.com/) was

utilized to determine GO term enrichment for the entire set of differentially

expressed genes in different comparisons (Blüthgen et al. 2005). I also used

the online tool FungiFun2 (Priebe et al., 2014) to perform a functional

annotation specifically of stress genes from the lists of differentially expressed

genes using both the Illumina transcriptome and the microarray data sets

(https://elbe.hki-jena.de/fungifun/).

4.3.5 Heatmaps

Gene expression patterns were visualized by creating heatmaps using log2 fold

changes of genes generated from the transcriptome data. Those data were

calculated as described in O’Connell et al. (2012). The expression ratio

between the normalized counts of a gene in a developmental stage and the

geometrical mean number of normalized reads across all the stages was

calculated (Table 2.1 and 2.2). The log2FC is derived from this expression ratio

and it was used to generate heatmaps of stress response gene expression

profiles with the Genesis tool (Sturn et al. 2002).

4.3.6 Expression analysis of selected stress response genes in the WT under

conditions of stress in vitro and in planta

4.3.6.1. In vitro analysis: The C. graminicola WT strain was cultured in 500 ml

of Fries complete liquid medium (30 g sucrose, 5 g ammonium tartrate, 1.0 g

ammonium nitrate, 1.0 g potassium phosphate, 0.48 g magnesium sulfate

anhydrous, 1.0 g sodium chloride, 0.13 g calcium chloride, 1.0 g yeast extract/

liter of H2O). Washed spores were added to produce a final concentration of

1x105 spores/ml, and the culture was incubated at 23°C on a rotary platform

shaker at 15 rpm. After 5 days, the cultures were blended and 5 mls of the slurry

was added to a new flask containing 50 ml of Fries minimal liquid medium and

returned to the shaker. After 24 hours of recovery, the chemical treatments

were added, and the mycelium was collected 12 hours later. The treatments

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used and their final concentrations were tunicamycin (10 µg/ml) and Paraquat

(3 mM). Mycelia were harvested under vacuum filtration and flash frozen in

liquid nitrogen, then wrapped in aluminum foil packets and kept at -80°C until

RNA extraction.

Appressoria of the WT were produced in vitro on polystyrene Petri dishes as

described by Kleemann et al. (2008), with some modifications. C. graminicola

spores were collected and washed three times, and 40 ml of a spore

suspension at a concentration of 1 x 104 spores/ml was added to each Petri

dish. Twenty hours later, each plate was inspected under the microscope to

verify the presence of mature melanized appressoria. Trizol was added and

appressoria were broken and scraped from the bottom using a sterile culture

spreader. The slurry was recovered from 30 Petri plates in a total of 9ml of

Trizol per replicate.

The RNA extraction was performed essentially as described in O’Connell et al.

(2012), with a few modifications. Frozen mycelia were ground while still

contained inside of the foil packet using a pestle. Around 100 mg of the

powdered mycelia was added to a 2 ml Eppendorf tube with 1 µl of Trizol

reagent (Invitrogen) for extraction. The cleanup step in the RNeasy Plant Mini

Kit (Qiagen) was performed on the supernatant according to the manufacturer’s

instructions, including the DNase A treatment.

For the first-strand cDNA synthesis, I used one µg of total RNA and the

Superscript II reverse transcriptase kit (Invitrogen) with an oligodT primer.

Semi-quantitative RT-PCRs were carried in 25 µl reactions and consisted of 0.1

µM of each primer, 0.2 mM each dNTP, 0.25 units of Taq DNA Polymerase

(Life Technologies) and 1.5 nM MgCl2. Thermal cycling was performed as

follows: 94°C for 3 minutes followed by 30 cycles of amplification at 94°C for 45

s, 60°C for 30 sec and 72°C for 1 min. Actin (GLRG_03056) was used as an

internal control. Sequential dilutions of cDNA were used as template, with the

concentrations determined by using amplification of the control gene to

normalize across samples, and diluting appropriately so that the control gene

was in an exponential range (Choquer et al. 2003).

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Three stress-associated genes were tested: GLRG_10629 (BiP); GLRG_02684

(HAC1); and GLRG_01327 (PDI) (Table 4.1). The primers were designed to

produce an amplicon that spanned at least one intron, so that any DNA

contamination of the RNA could be easily detected. Primer sequences all had

an annealing temperature of 60°C, except for the control actin (GLRG_00649),

for which the annealing temperature was 56°C.

4.3.6.2. In planta visualization of expression of the BiP homolog of C.

graminicola. I used the pSITE vectors (Chakrabarty et al. 2007), modified by

Gong et al. (2015) for the Gateway technology (Invitrogen) to produce a

reporter construct to investigate expression of the BiP protein in C. graminicola.

The upstream 433 bp of the yeast Kar2 (Bip) homolog (GLRG_10629) was

introduced upstream of the RFP coding region (Figure 4.1, Figures AIII.3 and

AIII.4 in Appendix III). Primers used to amplify the promoter region of

GLRG_10629 included adaptors as described in the protocol by Gong et al.

(2015). The GLRG_10629 gene is highly expressed in planta, in both MT and

WT, at all stages of development. I introduced the clones into C. graminicola by

Agrobacterium-mediated transformation (Flowers and Vaillancourt 2005). I

recovered five independent transformants and single-spored them before use.

Two of these transformants were observed with the Olympus FV1000 (Olympus

America Inc., Melville, NY, USA) laser-scanning confocal microscope using 543

nm laser line. Response to in vitro stresses were evaluated by growing hyphae

on sterile glass slides in a thin film of media, as described in Chapter 2 of this

dissertation, amended with DTT (20 mM), tunicamycin (10 µg/ml), menadione

(125 μM), and Paraquat (3 mM). Leaf sheath inoculations, as described in

Chapter 2 of this dissertation, were also performed.

4.2.6.3 Visualization of the endomembrane system in vitro under stress. The

endomembrane system of C. graminicola was labeled by transforming with the

plasmid pAN56-1-sGFP-HDEL, containing GFP linked to an HDEL membrane

anchor driven by a constitutive glucoamylase promoter (Vinck et al. 2005) as

described in Chapter 2 (Figure AI.5).

Response to in vitro stresses were evaluated by growing the hyphae on sterile

glass slides in a thin film of media, as described in Chapter 2 of this dissertation,

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amended with DTT (20 mM), tunicamycin (10 µg/ml), menadione (125 μM), and

Paraquat (3 mM).

Transformants were observed with the Olympus FV1000 (Olympus America

Inc., Melville, NY, USA) laser-scanning confocal microscope using 543 nm laser

line.

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4.4 Results

4.4.1 The MT was more sensitive than the WT or MT-C strains to most stress-

inducing chemicals and treatments

The MT was significantly more sensitive than the WT or MT-C strains to

tunicamycin (secretion stress); Paraquat and menadione (oxidative stress);

Calcofluor (cell wall stress); sorbitol and NaCl (osmotic stress); and low

temperatures (Figure 4.2A-D, Table 4.2). The MT did not appear to be more

sensitive to DTT (secretion stress) (Figure 4.2, Table 4.2); high temperatures

(Figure 4.2E); nutrient limitation (Figure 4.2E); or high or low pH (Figure 4.2E).

4.4.2 About one-fifth of the C. graminicola genome encodes genes that are

predicted to be involved in stress response

I compiled a list of 2730 putative stress response genes in C. graminicola based

on similarity to known stress response genes in other fungi (Table 4.3). The

genes were classified into secretion, osmotic, oxidative, and cell wall stress

related categories (Guillemette et al. 2007; Mathioni et al. 2011; Nikolaou et al.

2009; Karányi et al. 2013; Jørgensen et al. 2009). An additional category, “other

stresses”, included miscellaneous genes that matched the FSRD or other

literature sources, and genes that were linked to general stress responses.

Some genes occupied more than one category. The genes related to stress

response were generally highly conserved. Considering only the top hit in the

NCBI database (excluding C. graminicola itself), only 98 genes out of 2730 had

less than 50% identity to their homolog in the model fungi. The average percent

identity of the proteins in each the four pathways to their homologs ranged from

88 to 90%.

4.4.3 Colletotrichum spp. encode homologs for a majority of the genes in the

yeast secretory, oxidative, osmotic, and cell wall integrity stress response

pathways.

The secretion, oxidative, osmotic, and cell wall integrity stress response

signaling pathways in S. cerevisiae are very well characterized, but filamentous

fungi do not always have homologs of all of the yeast genes in the pathways

(Nikolaou et al. 2009). Five sequenced Colletotrichum species, including C.

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graminicola, had putative homologs for most, but not all, of the yeast proteins

in these four pathways (Figure 4.3; Table 4.3). It was a general theme that the

components that act as sensors and the transcription factors that regulate

genes responsive to stress were the least highly conserved, whereas the

components of the secondary messenger signaling pathways that connected

them were more highly conserved.

Yeast has more redundancy in many of the stress response genes than the

filamentous fungi. For example, the cyclic AMP protein kinase A Tpk1p, which

functions in the oxidative stress response pathway, has two isoforms (Tpk2p

and Tpk3p) in yeast, but there appears to be only one homologous gene in each

of the Colletotrichum species. Similarly, in the secretion stress pathway,

putative homologs for only 58 of 63 yeast genes were found in C. graminicola

(Figure 4.3A; Table 4.3). Only nine out of the total of 129 genes potentially

involved in secretion stress response in C. graminicola occur as more than one

copy in the genome (Table 4.3). The increased redundancy in yeast could be

related to a genome duplication that occurred in the yeast lineage some time in

its evolutionary history (Wolfe and Shields, 1997).

Homologs of twenty-eight of the 33 yeast genes in the osmotic stress response

pathway were present in C. graminicola (Figure 4.3B; Table 4.3). One gene,

HOT1, was not found in C. graminicola, C. sublineola or C. higginsianum, but

was present in C. orbiculare and C. gloeosporioides. This transcription factor in

yeast is necessary for induction of the glycerol biosynthetic genes GPD1 (NAD-

dependent glycerol-3-phosphate dehydrogenase) and GPP2 (glycerol-3-

phosphate phosphatase) (Rep et al., 1999). It is possible that a different gene

performs this function in C. graminicola, since C. graminicola does have

homologs of GPD1 and GPP2.

C. graminicola and the other Colletotrichum species had putative homologs for

all of the genes in the yeast oxidative stress response pathways except one,

YBP1 (Figure 4.3C; Table 4.3). The Ybp1p oxidizes cysteine residues of the

transcription factor Yap1p, which results in it relocalizing to the nucleus in

response to stress. All five Colletotrichum species have a homolog of YAP1,

although it is not very highly conserved with yeast in any of the Colletotrichum

species. If this putative YAP1 homolog plays the same role in oxidative stress

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response as the yeast protein, then it must be activated by some other

mechanism in Colletotrichum.

C. graminicola had 21 of the 24 genes in the yeast cell wall integrity pathway.

Three of the genes are shared with other pathways (Figure 4.3D; Table 4.3).

One of the genes that C. graminicola didn’t share, WSC3, was found in its close

relative C. sublineola, and also C. gloeosporioides, although the level of

similarity was very low. The Wsc3p is a sensor that responds to heat shock and

other stresses affecting wall integrity by activating the PKC-MPK1 signaling

pathway and in yeast has two other paralogs, Wsc1 and Wsc2. C. higginsianum

just has one paralog of this gene, while the others species have at least two.

The components of that signaling pathway were conserved in C. graminicola

and the other four species, so different paralogs must function in each

Colletotrichum.

I assembled the homologs from C. graminicola into proposed osmotic,

oxidative, and cell wall stress signaling pathways by using templates from

Nikolaou et al. (2009) that are based on yeast (Figure 4.4B, Figure 4.4C, Figure

4.4D, Table 4.3). For the secretion stress pathway I used the model presented

by Guillemette et al. (2007) instead, amended with additional information from

Jørgensen et al. (2009) (Figure 4.4A). These two papers describe the secretion

stress response pathway of A. niger. The pathway of the filamentous fungi,

including A. niger, differs in some respects from the yeast pathway, and is likely

to be a better model for filamentous fungi like C. graminicola (Miskei et al. 2009;

Duran et al. 2010). The other pathways have not been as well studied in

filamentous fungi, so yeast remains the best model for those. These models

provided me with a framework for interpretation of the transcriptome data

(below).

4.4.4 Presence of UPRE in stress genes

The unfolded protein response elements (UPRE) are cis acting elements found

in the 5’ upstream region of genes, where transcription factors will bind to

activate the UPR. In yeast, those elements were found to be important for

appropriate activation of the UPR pathway (Yoshida et al., 1998). The

transcription factor Hac1 binds to these UPRE and this leads to activation of

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the genes (Fordyce et al. 2012). Hac1 itself also has a UPRE, and a mutant

with a defect in this region had increased sensitivity to secretion stress (Ogawa

and Mori, 2004). In the upstream regions of the homologs of Hac1 and Sln1 in

C. graminicola I found the canonical UPRE 5’-CAGcGTG-3’ (Fordyce et al.,

2012; Ogawa and Mori, 2004) (Figure 4.5). In Hog1 I identified the ER stress

response element (ERSE) sequence CCAATN8CCACG, which differs by only

one nucleotide from the motif identified in yeast (Roy and Lee, 1999). I did not

identify classic UPRE sequences in Ire1 (GLRG_10691), BIP (GLRG_10629),

Tsa1 (GLRG_10121), Wsc1 (GLRG_06481), MFS transporter (GLRG_06379),

Catalase (GLRG_05821), glucose-repressible protein (grg – GLRG_03168),

RHO1 (GLRG_05224), PTC2 (GLRG_04244), PDI (GLRG_01327), MNT2

(GLRG_00793), BAS3 (GLRG_00201), or Actin (GLRG_00649).

4.4.5 Genes involved in response to stress, and particularly to secretion stress,

are highly expressed in planta.

There are 58 genes in the entire stress list that are part of the top 100 most

expressed genes in at least one stage in the WT or MT. Three of the genes that

I had mapped to C. graminicola secretion stress response pathways were also

among the 100 most highly expressed genes in planta (Table 4.3). All three are

components of the secretion stress response pathway, involved in protein

folding. BiP, the ER chaperone that has an important function in activating the

UPR, was found among the top 100 in every in planta treatment (WTAP, WTBT,

WTNT, MTAP, and MTBT) (Table 4.3). Homologs of PDI1 and CLX1, also

included in the secretion stress pathway, were among the top 100 in MTAP and

MTBT, and PDI1 was also among the most highly expressed genes in WTAP

and WTNT. PDI1 is a protein disulfide isomerase that makes and breaks

disulfide bonds between cysteine residues during folding. CLX1 (or CNE1) is a

calnexin ER chaperone protein involved in folding and stability of glycoproteins.

PDI1 and BiP were both induced during in vitro induced secretion stress in

Trichoderma reesei (Pakula et al. 2003). The other characterized pathways had

no genes that were included in the top 100 most highly expressed genes.

There were 18 stress-related genes that were included among the top 100 most

highly expressed genes across every treatment (WTAP, WTBT, WTNT, MTAP

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and MTBT) (Table 4.3). As mentioned above, one of these was BiP, involved

in secretion stress response. The other genes were from the “other stresses”

category, and included heat shock proteins involved in protein stabilization,

ribosomal proteins and ubiquitin responsible for turnover of protein populations,

a homolog of the Neurospora crassa cpc-2 gene, involved in response to

nitrogen starvation (Müller et al., 1995), and genes encoding glyceraldehyde 3-

phosphate dehydrogenase and other proteins potentially related to repair and

protection of DNA (Takaoka et al. 2014) (Table 4.3).

I used FungiFun2 program to make a functional annotation of the 58 stress-

associated genes among the top 100 most highly expressed genes in at least

one developmental stage (Table 4.4). Out of those, 40 genes were annotated

into categories. The most highly represented categories were involved in

protein binding, folding and stabilization, and 11 genes involved in protein

synthesis including a ribosome protein (GLRG_06907). The UPR pathway and

peroxidase reaction (related to oxidative stress response) account for 9 genes.

There is one gene (GLRG_02292), described as being involved in prevention

of apoptosis, that was highly expressed during biotrophic and necrotrophic

stages. Anti-apoptotic genes were implicated in full pathogenicity of the

necrotroph Botrytis cinerea (Shlezinger et al. 2011).

Among these 58 highly expressed stress-related genes, 16 were found only on

the top-100 lists in the MT strain (Table 4.3). Three of them were annotated as

part of the UPR pathway, two as involved in protein folding and stabilization,

and one in oxidative stress reaction. The other three genes annotated were

categorized as involved in energy (Table 4.4).

4.4.6 Patterns of differentially expressed genes in planta suggest that stress

responses are most active early during infection by both the WT and the MT.

It is know that global stress responses can result in down-regulation of genes

involved in growth, RNA metabolism and protein synthesis, and up-regulation

of others that are important for adapting to the stress (Nadal et al. 2011; Al-

Sheikh et al. 2004). A majority of the most highly expressed genes, both in the

WT and the MT during all phases of development in planta were involved in

primary metabolism, growth, and signal transduction, with about 20% in each

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case involved in stress response. Genes involved in antioxidant activities were

among those that were overrepresented in WTAP relative to WTBT in the

molecular function category, suggesting there might be a higher level of

oxidative stress response during WTAP vs WTBT. The transition to WTBT

correlated with enrichment in primary metabolism genes, suggesting an

increase in primary metabolic activity during WTBT vs WTAP. Categories

associated with stress response and PCD were overrepresented during WTBT

compared with WTNT, suggesting higher levels of stress response during

WTBT. These data suggest that stress responses occur throughout

development, but that they are more active during early phases, when the host

cells are alive and actively defending themselves. In comparisons of the MT

and the WT, categories of genes involved in stress response were not enriched

in either case, either during AP or BT, suggesting that transcriptional activity

related to stress response was similar in the two strains.

4.4.7 Relatively few stress responsive genes are differentially expressed in

planta.

Most of the C. graminicola putative stress response genes, including those that

were located in the putative response pathways, were relatively poorly

expressed in planta (Table 4.3, Figure 4.6A-D). Very few genes mapped to

these known stress pathways were significantly differentially expressed (Table

4.3, and Figure 4.6). Out of the 2730 genes in the stress table, only 17% (490,

almost all in the “other stress genes” category) were differentially expressed in

at least one comparison (Table 4.3, Figure 4.6E). This supports the possibility

that stress response is deployed at a relatively constant level during all phases

of in planta growth, both by the WT and by the MT.

In the secretion stress pathway, only two genes were differentially expressed

(Figure 4.6A). Homologs of GLRG_07837 (Mns1) and GLRG_00793 (Mnt2) are

involved in protein glycosylation. Mns1 is more highly expressed during WTBT

and WTNT, and Mnt2 is more highly expressed earlier, during WTAP. Mns1 is

also involved in the ERAD (Delic et al. 2013), which might indicate that during

later developmental stages, more proteins are being misfolded and subjected

to degradation. In the microarray data, there are six genes that are part of the

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secretion stress pathway that are increased in expression in biotrophic hyphae

compared with cultured mycelium. These genes are mostly involved in protein

folding and metabolism of energy reserves. Two genes are more highly

expressed during in vitro conditions, homologs of the amino acid permease

Hnm1 (GLRG_10760) and the Rud3 protein involved in structural organization

of the Golgi (GLRG_08651).

The putative osmotic stress pathway in C. graminicola (Figure 4.6B) has only

one differentially expressed gene, which is more highly expressed during

WTNT (Table 4.3). The yeast homolog of GLRG_03441 (Sln1) is a

transmembrane protein that functions as an osmosensor (Rodriguez-Pena et

al., 2010). It is also important for cell wall integrity. The homolog of PTC2

(GLRG_04244) has >2 fold change in the LCD, indicating it’s highly expressed

in planta versus in vitro. In yeast, PTC2 together with PTC1 and PTC3

negatively regulate the HOG pathway (Young et al., 2002). The homolog of

Ssk1 (GLRG_04594) has a <3 fold change in the LCD, indicating that it is up-

regulated in vitro versus in planta. Ssk1 activates HOG1 in yeast under

conditions of extreme osmotic stress. The Sln1 sensor only functions in lower

osmolarities (Posas et al. 1996).

The model for the oxidative stress pathway in C. graminicola (Figure 4.6C) has

two differentially expressed genes, Sln1 and Tsa1 (GLRG_03441). The first one

is also present in the osmotic pathway. The second, Tsa1, encodes a

peroxiredoxin, an important antioxidant in yeast that binds to YAP1 and

participates in ROS detoxification. Yeast Tsa1 mutants are hypersensitive to

secretion stress and to ROS (Weids and Grant, 2014; Wong et al., 2002).

GLRG_03441 is less highly expressed in the C. graminicola MT versus the WT,

during both AP and BT.

Only one of the genes present in the cell wall integrity pathway (Figure 4.6D),

is differentially expressed. GLRG_06481 is a homolog of Wsc1, a plasma-

membrane sensor that responds to changes in the cell wall and activates Rom,

the first step in the activation of the MAP kinase cascade (Igual and Estruch,

2000; Nikolaou et al., 2009). This gene is more highly expressed during WTNT

than during earlier stages of development. According to the LCD, the homolog

of Sec3 is more highly expressed in planta versus in vitro. Sec3 functions in

147

transport of secretory vesicles from the Golgi to the plasma membrane, in a

pathway dependent on the Rho protein but independent of the normal secretory

pathway or the actin cytoskeleton (Levin 2005).

For the “other stress genes” category there also appeared to be groups of

genes that were more highly expressed either during AP or during NT (Figure

4.6E; Table 4.5). Out of 2,543 genes, 491 of them had a >2-fold change (log2)

in at least one condition. More than half (214) of the annotated genes were

categorized by FunCat as primarily involved in metabolism, followed by 76

(19%) involved in cellular transport. There were 69 genes that were classified

as cell cycle, DNA processing, cell rescue, defense and virulence, accounting

for 17.5 % of the annotated genes, identified in Figure 4.6E by the name stress

related. These included stress response [19 genes including catalases (2),

Hsp70 (2) and superoxide dismutase (1)]; DNA repair (16 genes); detoxification

by export (13 genes); and other classes with fewer genes like nutrient starvation

response (5), and pH stress response (3).

I examined a microarray dataset produced by Tang et al (2006), representing

differentially expressed transcripts from biotrophic hyphae captured by laser

capture and compared with hyphae that were cultured in vitro. These authors

were kind enough to share these unpublished data with me (Table 4.3 - LCD

column). There are 797 stress-related genes represented in this dataset.

Among those, 127 were more highly expressed in planta (logFC > 1), and 136

were more highly expressed in vitro (logFC of < -1). In the secretion stress

pathway, ten genes were more highly expressed in planta and three were

higher in vitro (Table 4.3, Figure 4.4A: genes present in the pathway are marked

in pink for higher during in planta and yellow during in vitro). The osmotic

pathway included one gene that was higher in planta (Ptc2) and one that was

lower (Ssk1) (Figure 4.4B). The oxidative pathway has two genes that were

higher in vitro: one is Ssk1, shared with osmotic pathway, and the other is Bcy1

(Figure 4.4C). In the cell wall integrity pathway, only one gene, the exocyst

complex gene Sec3, is differentially regulated, being more highly expressed in

planta (Figures 4.4D).

Among the 127 genes with higher expression in planta, 57 could be assigned

to 12 different categories by using FunCat (Figure 4.7A). These included

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categories relevant to UPR, DNA damage response and DNA repair, protein

folding, and general stress response, indicating that the biotrophic hyphae were

responding to these stresses more actively than hyphae in culture. Among the

136 genes with higher in vitro expression, only 14 could be classified by

FunCat, and all were either in detoxification or drug/toxin transport categories

(Figure 4.7B). These 14 genes were predicted to encode either major facilitator

superfamily transporters or ABC-2 type transporters.

4.4.8 Expression of selected stress response genes in vitro in response to

chemically induced stress.

Selected genes were tested by semi-quantitative RT-PCR in response to

chemical stress (Figure 4.8). The housekeeping gene actin was used as the

RT-PCR normalization control. I used the same amount of total RNA to make

cDNA, but each in planta treatment had different amounts of fungal biomass

(Table 2.3). Unfortunately the dilutions that I tried did not give consistently good

results, and so it is difficult to quantify the expression of the genes relative to

one another in every treatment. I can tell that the stress genes I selected (BiP,

Hac1 and PDI) are being expressed in all the tissues. In the in vitro appressoria

(IVAP) HAC1 seems to be expressed at a high level while BiP and PDI seem

to have lower expression. In yeast, HAC1 is constitutively expressed in all

tissues, being regulated post-transcriptionally by differential splicing, and

transcript levels vary relatively little (Schröder et al., 2003). I found no evidence

for differential splicing of the putative C. graminicola HAC1 homolog. Both BiP

and PDI seem to be more highly expressed in in planta AP versus IVAP. HAC1

also seems to be expressed at relatively high levels in planta compared with in

vitro (Fries medium). All three genes seem to be expressed at lower levels

during BT versus in AP and NT. Because of the issues with dilutions, it was not

possible for me to tell whether treatment with chemical inducers of stress

(Paraquat and tunicamycin) induced the expression of any of these three

genes. These experiments must be repeated before I can make any firm

conclusions about the identities of these three genes and their roles in stress

response in C. graminicola.

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4.4.9 Stress altered the structure of the MT Cpr1 transcripts in vitro compared

with in planta

RT-PCR revealed the presence of the same transcript variants of Cpr1 in the

MT in vitro, in the presence or absence of stress, as were previously observed

in planta and in vitro in Chapter 2 (Figure 4.9). Sequencing results of several

different clones confirmed that only the normal transcript was detectable in the

WT, either with or without stress (Figure 4.10A). In the mutant, sequencing

revealed the same intron retention variants that were identified previously (in

Chapter 2) in all treatments, including the control without stress. Interestingly,

a novel pattern of intron splicing was identified in the MT treated with

tunicamycin (Figure 4.10B). An aberrant intron is spliced from positions 391-

CG^GTTGGG to ATGGCAG^CA-445, whereas the normal intron is from 446-

CG^GTAAGA to ACTTCGCAG^TG-505. There are no conserved splice motifs

at the junctions of this novel intron in the “plus” strand, but there are in the

“minus” strand.

4.4.10 In planta visualization of the BiP stress response protein

Fluorescent reporter constructs with the C. graminicola BiP chaperone were

used as another approach to visualize stress response in planta. The

transcriptome results show BiP as one of the most highly-expressed stress

genes in all stages of development in planta. Hyphae of transformants

containing reporter constructs in which RFP was linked to the promoter of the

BiP gene were treated in vitro with chemicals known to induce stress. RFP

accumulated in hyphae and appressoria in response to DTT, tunicamycin,

Paraquat, and especially menadione (Figure 4.11). There was no RFP visible

in the untreated controls. I could detect strong red fluorescence in spores, and

particularly in primary hyphae in planta. These results suggest that BiP

expression is induced in response to stress in vitro and in planta.

4.4.11 In vitro response of endomembrane system to stress chemicals

I used a WT-HDEL transformed isolate to visualize the fungal endomembrane

system, already described in Chapter 2, during in vitro stress. Analysis of the

transformant grown in minimal Fries medium amended with the chemical

150

tunicamycin show an increase of fluorescence when compared to the control

sample, grown only in minimal Fries liquid medium (Figure 4.12). It is important

to point out that all treatments were analyzed at the same exposure. The

hyphae grown in the presence of stress became swollen, and produced multiple

branches. The appearance was quite reminiscent of the growth of primary

hyphae like those that are found inside the plant (Panel C and D). At 1000x

magnification (Panel E) it is possible to clearly identify the ER membrane

pattern surrounding the nuclei.

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4.5 Discussion

In this chapter, I explored the hypothesis that defects in pathogenicity displayed

by the C. graminicola cpr1 MT were related to deficiencies in stress response.

This hypothesis was prompted by reports that the Cpr1 homolog in some other

fungi is induced during in vitro conditions that result in secretion or nutritional

stress (Guillemette et al. 2007; Jørgensen et al. 2009). It is assumed that the

living plant imposes a stressful environment on the pathogen, and if the MT is

unable to adapt to stress, this could result in its being unable to establish a

successful infection. For example, mutants of M. oryzae deficient in oxidative

stress response were non-pathogenic to rice (Guo et al. 2011).

My first prediction based on this hypothesis was that the MT would be more

sensitive than the WT to chemically-induced stresses in vitro. The cpr1 MT

strain has no apparent differences from the WT strain in vitro, other than a

somewhat slower rate of radial growth (Thon et al. 2000; Thon et al. 2002;

Torres et al. 2013; Venard and Vaillancourt 2007). My experiments showed that

the mutant is more sensitive that the WT to a range of stress-inducing

chemicals, including compounds reported to induce secretion stress, oxidative

stress, and especially cell wall stress (Figures 4.2). The mutant was also more

sensitive to cold temperatures, but its sensitivity to heat, and to nutritional and

pH stress, appeared to be unaltered. There is a lot of cross-talk that occurs

between stress pathways, so this may explain why the MT appears to have a

relatively broad deficiency in stress response.

To induce secretion stress, I used two different chemicals: tunicamycin, and

DTT (Guillemette et al. 2007). The MT was sensitive to tunicamycin but not

DTT. DTT not only causes secretion stress, it is also an antioxidant (Liu et al.

1999). Secretion stress is known to cause the accumulation of ROS and thus,

oxidative stress (Haynes et al., 2004). So one possibility is that DTT, acting as

an antioxidant, modulates the toxicity of the secretion stress that it presumably

induces in C. graminicola. It is important to point out that, even though all of the

chemicals I used had negative effects on the growth and development of C.

graminicola, I do not have direct evidence that they were inducing the stress

pathways that have been linked to them in the literature. It will be important to

investigate the expression of known stress pathway genes, to confirm that they

152

are induced by these chemicals as expected. Unfortunately I was not able to

accomplish this, although I did develop some tools for semi-quantitative PCR

analysis that would help to answer this question. Northern blots would be the

best type of experiment to do, at some point.

My in silico analysis of the C. graminicola genome suggested that 22% of the

predicted proteins are stress-related. I developed detailed proposals for several

putative stress response pathways in C. graminicola, based on comparisons

with the literature, particularly reports related to S. cereviseae. Most of the

references I found describing stress responses in filamentous fungi used yeast

as a model, even though there seem to be some differences between pathways

in yeast and filamentous fungi. For example, A. nidulans requires two genes,

Hog1 and Pbs2, to activate osmotic stress response, whereas yeast only

requires Hog1 (Furukawa et al., 2005). Target genes of the pathways were also

found to differ in some cases from those in yeast, as shown in this paper about

cell wall stress signaling (Fujioka et al. 2007).

I was able to organize stress gene homologs into putative pathways involved in

secretion, osmotic, oxidative, and cell wall stress. The proteins that are key

regulators in the pathways were the most highly conserved between yeast and

Colletotrichum (Figure 4.3). For example, BIP in the secretion stress pathway,

Hog1 in the osmotic stress and oxidative stress pathways, and Rho1 in the cell

wall stress pathway had greater than 70% sequence identity with the yeast

proteins. Nothing had been done with stress response before in C. graminicola,

so this work will be valuable for developing hypotheses for future studies related

to stress.

Despite the high level of conservation, I was not able to find evidence for

conservation of promoter elements that are present in stress genes and

regulate their expression in yeast. The promoter region of the gene in the 5’UTR

of some stress genes show what is called ER stress response elements (ERSE)

or Unfolded Protein Response Elements (UPRE). ERSE, with a consensus of

CCAATN9CCACG, is suggested to be a sequence that coregulate stress genes

during stress conditions by a common factor, activating different genes involved

in the Unfolded Protein Response (UPR). In yeast, it is interesting to notice that

while ERSE leads to a increase in translation and protein synthesis of genes

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involved in stress response, expression of genes involved in protein synthesis

are actually reduced, maybe in an effort to protect the cell while trying to adapt

to the new environment (Berry and Gasch, 2008). Although this activates some

genes, it is a mild response in preparation for what type of stress might actually

be happening. Then other stress genes will be activated, involved in each type

of stress. In C. graminicola, I found those elements in the upstream region of

three out of 16 genes involved in stress response that I checked (Figure 4.5),

althought all of them have parts of the ERSE sequence, but not the complete

sequence. Only Hog1 in C. graminicola has an intact ERSE. Sln1 has a UPRE

sequence in the promoter region, being two components of the osmotic stress

pathway. The other gene is Hac1, a transcription factor important in the

secretion stress and UPR. It could be that in C. graminicola activation of ERSE

doesn’t depend on the entire consensus element sequence from yeast or that

regulation is not controlled by that at all. The presence of these elements in the

entire set of putative stress-related genes needs further study.

My second prediction for this chapter was that WT and MT strains would

express stress response genes in planta, indicating that they were

experiencing, and reacting to, stress. All of the genes that I included in the

stress response pathways were expressed in planta. Although there were a few

exceptions (e.g. the gene encoding the BiP homolog), most of the genes were

not expressed at very high levels. Signaling genes are frequently not highly

expressed, and the expression of stress genes is sometimes transient (López-

Maury et al. 2008). Both of these factors could account for the generally low

expression levels. Alternatively, the stress response genes might be regulated

post-transcriptionally (Lackner and Bähler, 2008). The paper by Guilllemette et

al. (2007) found a transcriptional increase of only 2-fold for the Cpr1 homolog,

but they reported that there was a 7-fold increase in translation efficiency during

secretion stress conditions, due to differential polysome loading.

According to my interpretation of the laser capture microarray data of Tang et

al. (2006), numerous stress genes were induced in planta versus in vitro,

suggesting that the biotrophic hyphae are experiencing, and responding to,

greater levels of stress. In the paper by Vargas et al. (2012), it was reported

that the maize plant expresses defense genes at a high level during biotrophy,

154

and ROS production during biotrophy has also been demonstrated by

cytological assays (Vargas et al. 2012; Torres et al. 2013).

It has been suggested that the biotrophic primary hyphae of hemibiotrophs like

C. graminicola could be good models for the haustoria of obligate biotrophs like

rusts and powdery mildews, which cannot be cultured. It has generally been

believed that obligate biotrophs secrete effectors that suppress host defenses,

and thus they don’t need to express stress response pathways, in contrast to

necrotrophs that actually induce plant cell death PCD (Glazebrook, 2005;

Schulze-Lefert and Panstruga, 2003). But several recent papersi are changing

this idea. For example, work in Ustilago maydis found that to successfully infect

the plant the pathogen must induce antioxidant pathways (Doehlemann et al.

2008). Proteomic studies identified several heat shock proteins expressed in

the haustoria of powdery mildew (L V Bindschedler et al. 2009). Several

transcriptome studies of isolated haustoria of rust and powdery mildew fungi

also contain evidence for activity of various stress response pathways (Garnica

et al. 2013; Weßling et al. 2012; Link et al. 2014).

My third prediction was that the MT would differ from the WT in the expression

of stress response genes in planta and in vitro under stress conditions.

However, very few of the stress response genes I identified were differentially

expressed in the transcriptome data, either between MT and WT, or between

different stages of development in the WT. Stress response genes can be

regulated post-transcriptionally (Schröder and Kaufman, 2005), so the lack of

differences could mean that the regulation of the genes is primarily post-

transcriptional. Another possibility is that the in planta environment is uniformly

stressful. The up-regulation of many genes in the LCD microarray data supports

the idea that even the biotrophic hyphae are experiencing significant levels of

stress.

Characterization of the induced stress response genes from the LCD

microarray data suggests that C. graminicola biotrophic hyphae are responding

to secretion stress by activation of the UPR response. There is also evidence

in the transcriptome data for activity of the UPR during all developmental stages

(Figure 4.7, Table 4.4, Table 4.5). It is expected that C. graminicola would need

to secrete a variety of different proteins to successfully infect, colonize, and

155

finally rot the host tissues, and this high requirement for secretory activity could

certainly lead to secretion stress. So it is not surprising to find UPR activity.

Apparently the MT can express these genes, and therefore an inability to

transcribe the genes is not the reason for its increased sensitivity to

tunicamycin, or for its non-pathogenic phenotype.

Preliminary data from my semi-quantitative RT-PCR experiments (Figure 4.8)

supports the idea that UPR-associated stress genes are induced in planta

during all developmental phases. There seemed to be less expression in vitro,

in appressoria produced on Petri plates. Unfortunately I could not confirm that

tunicamycin and menadione induced expression of these genes in vitro using

this technique. However, both chemicals, as well as DTT and Paraquat,

induced expression of an RFP reporter linked to the BiP promoter (Figure 4.11).

Furthermore, the reporter was also strongly expressed in biotrophic hyphae in

planta. One effect of the induction of the UPR is an increase in secretory

capacity, which can be visualized as an increase in the volume of the

endomembrane system in yeast (Bernales et al., 2006). Visualization of the

GFP_HDEL endomembrane system in the WT strain treated with tunicamycin

seemed to show an increase in fluorescence when compared to the control

(Figure 4.12).

As I described in Chapter 2 of this dissertation, I found that the MT produced

several variant cpr1 transcripts in vitro and in planta, while the WT produced

only the predicted transcript (Figure 4.9). I wanted to see if the same variants

were produced in the MT or WT exposed to stress. I observed that only the MT

produced variants, which appeared to be similar in size and number to those

that I had observed in Chapter 2 (Figure 4.10). However, cloning of the

amplicons revealed a new variant that I had not seen before, which had a novel

intron removed that was just upstream of the normal intron, which was retained

(Figure 4.10B). There were relatively few reads that matched any of the splice

variants, so I can’t say for sure that this variant is specific to the tunicamycin

treatment. Further studies are needed to confirm the frequencies of the different

splice variants that I saw, and whether any are specific to any particular

condition. I am currently doing an Illumina sequencing experiment to try to

address this.

156

Interestingly, the putative intron in the tunicamycin-associated variant did not

have canonical splice signals on the positive strand, but it did have them on the

minus strand, suggesting that these reads could be from a transcript that was

being produced from the opposite strand. None of the gene prediction programs

I used predicted any ORFs on the minus strand, but that doesn’t necessarily

mean there isn’t a gene there. Although I have been assuming that the stress

response and pathogenicity phenotypes of the MT are linked, it’s also possible

that the stress response phenotype is due to a deficiency in an unknown stress

response gene on the minus strand, and not Cpr1 itself. Unfortunately I was not

able to determine which strand the transcriptome reads originated from, so this

question remains unanswered for now.

In summary, the work I did for this chapter indicated that the MT and WT are

both experiencing and responding to stress, particularly secretion stress, in

planta; that there is no difference between them at the transcriptional level; and

that stress appears to be induced in planta in comparison with in vitro, equally

across all developmental phases. In addition to these findings, I produced

descriptive models of major secretory stress pathways in C. graminicola, and

fungal strains that can potentially be used to monitor stress response in planta

in future studies.

Copyright © Ester Alvarenga Santos Buiate 2015

157

Figure 4. 1 Illustration of the pFPL Gateway vector construct used to create protein fusions between C. graminicola BiP promoter region and the red fluorescent protein reporter gene. Figure kindly provided by Dr. Mark Farman and modified to show where the BiP promoter was added.

BiP promoter - 433 bp

158

Figure 4. 2 Growth of C. graminicola strains during chemical stress. A) Sensitivity of wild-type (WT), mutant (MT) and complement (MT-C) strains to secretion stress chemicals Tunicamycin and DTT. Values are expressed as cm (left), or percentage growth in the absence of the chemical (%) (right).

159

Figure 4.2 Growth of C. graminicola strains during chemical stress. B) Sensitivity of wild-type (WT), mutant (MUT) and complement (C) strains to oxidative stress chemicals Paraquat and Menadione. Values are expressed as cm (left), or percentage growth in the absence of the chemical (%) (right).

160

Figure 4.2 Growth of C. graminicola strains during chemical stress. Sensitivity of wild-type (WT), mutant (MUT) and complement (C) strains to osmotic stress chemicals Sorbitol and NaCl. Values are expressed as cm (left), or percentage growth in the absence of the chemical (%) (right).

161

Figure 4.2 Growth of C. graminicola strains during chemical stress. D) Sensitivity of wild-type (WT), mutant (MUT) and complement (C) strains to cell wall stress chemical Calcofluor. Values are expressed as cm (left), or percentage growth in the absence of the chemical (%) (right).

162

Figure 4.2 Growth of C. graminicola strains during chemical stress. E) Sensitivity of wild-type (WT), mutant (MUT) and complement (C) strains to temperatures. Sensitivity of wild-type (WT), mutant (MUT) and complement (C) strains to nutritional and pH changes. Values are expressed as cm (left), or percentage growth in the absence of the chemical (%) (right).

163

Figure 4.3 Degree of conservation of Colletotrichum stress proteins versus their putative yeast homologs. Values represent the percent sequence identity when using BLASTP. A) The secretion stress pathway. B) The osmotic stress pathway. C) Oxidative stress pathway. D) Cell wall stress pathway.

A

B

C

D

164

Figure 4.4 Stress pathway models. A) Model of the secretion stress pathway together with mean normalized read counts for each gene across the following stages: wild-type appressoria (WTAP), wild-type biotrophic (WTBT), wild-type necrotrophic (WTNT), mutant appressoria (MTAP) and mutant biotrophic (MTBT). Reads in red mean that the gene was differentially expressed between different stages. Reads in pink means they were significantly up-regulated in biotrophic hyphae in the LCD. Genes in blue mean that they are unique to that pathway, pink means that they are shared with other pathways.

chiA

Secretion stress

GAS1

CRH1

TIR3

UTR2

RUD3

YPT31

nsfA

Plasma membrane

ER membrane

SEC61

SEC63

SEC71

SBH2

SEC65

SRP72

SPC2

SPC3

ALG2

ALG3

ALG5

ALG7

OSTA

OST2

OST3

STT3

ERG24

BIP

PDI

PRP

CLX

ERO

SCJ1

TIGA

CWH43

SCW10

treA

DSK2

HRD3

UBP13

ERD2

LHS1

CYPB

SEC11SS1

OPI3

MCR1ITR2

HNM1ERG28

KRE5

WBP1

RER2

ROT2

CWH41

RFT1

DPMA

MNT2

HTM1

HUT1

MNS1

SEC27

SEC20

ERV41

EMP47

YIP1

USO1

SLY1

ERV46

NSFA

44 37 34 13 12

129 135 98 22 20

66 67 60 23 21

36 24 18 9 5

254 278 268 55 75

201 368 347 66 66

11 16 10 10 7

94 103 150 15 30

246 331 358 50 54

176 200 196 49 53

489 663 581 110 128

154 206 180 37 48

482 720 652 128 139

58 42 47 15 13

5 142 9 9

180 283 274 62 66

94 103 150 15 30

158 108 32 32 40

Golgi

64 52 98 20 21

42 72 84 10 13SEC6

167 166 155 34 38

468 590 568 77 87

629 896 856 223 189

392 416 381 113 116

168 256 223 50 51

211 294 250 42 52

299 339 248 52 58

117 161 178 20 27

243 383 340 41 43

10 13 25 1 2

149 170 175 73 66

89 131 112 37 37

253 254 265 49 63

42 72 84 10 13

31403089302811751156

33782679283412701412

169 243 295 64 77

139616951889 437 461

284 344 481 126 146

324 543 435 54 54

102 105 144 26 34

109 116 90 12 11

210 281 192 50 47

25 45 53 11 8

352 466 422 64 80

156 195 194 33 32

64 52 98 20 21

563 767 781 148 141

59 83 80 20 20

378 385 371 99 96

352 423 376 119 118

110 100 170 48 36

161 277 406 42 75

76 62 81 9 17

50 55 66 11 13

2 1 6 1 1

14612200 2004 279 373

32 54 89 4 5

126 157 232 22 37

268 275 306 61 77

158 187 272 36 36

125 115 129 42 46

10 14 16 3 3

199 278 305 39 36

217 197 161 42 46

279 249 241 25 38

318 303 184 85 82

Vacuole

EndosomeMembrane fusion

Cell wall

Exocytic pathway

Ve

sic

le tra

ffic

kin

g

Translocation

Lipid metabolism

Folding

Glycosylation

ER-associated degradation

259 280 241 34 44

165

FIGURE 4.4 Stress pathway models. B) Model of the osmotic stress pathway together with mean normalized read counts for each gene across the following stages: WTAP, WTBT, WTNT, MTAP and MTBT. Reads in red mean that the gene was differentially expressed between different stages. Genes in blue mean that they are unique to that pathway, pink means that they are shared with other pathways.

Smp1

Rlm1

Msb2Sho1

Ste11

Ste20

Cla4Cdc42

Ste50

Hog1

Sln1

Hog1

Hog1

Hog1

Hog1

Hog1

Hog1

Ypd1

Ssk1

Ssk2

Ssk22

Pbs2

Rck1

Rck2

Ptp2

Ptp3

Osmotic stress

B) Sln1-branch

Osmosensing

histidine kinase

A) Sho1-branch

Role not determined

C) ? UNKNOWN

pathway in

and

filamentous fungi

not dependent

on Hog1

Plasma membrane

Nuclear membrane

Msn1

Sko1

Msn2

Msn4

Rck1

Rck2

128 209 463 39 51

23 58 13 14

118 134 217 18 28

147 148 236 31 37

77 131 141 13 12

119 212 515 46 59

71 127 222 21 32

110 196 485 26 45

Ptc1

Ptc2/3864 11171303 174 184

125 173 401 49 52

38 32 35 6 7

135 193 467 49 63

245 366 300 82 80

119 212 515 46 59

66 101 97 18 14

1 2 1 1 2

119 181 227 30 31229 320 450 60 49

322 370 411 108 105

136 94 109 34 28

265 239 267 66 88

62 93 129 20 16

166

FIGURE 4.4 Stress pathway models. C) Model of the oxidative stress pathway together with mean normalized read counts for each gene across the following stages: WTAP, WTBT, WTNT, MTAP and MTBT. Reads in red mean that the gene was differentially expressed between different stages. Genes in blue mean that they are unique to that pathway, pink means that they are shared with other pathways.

Cdc25Plasma membrane

Nuclear membrane

Sln1

Ssk1

Pbs2

Hog1

Rck2

Hog1CTT1 (Catalase)

GLR1 (Glutathione)

A)

Oxidative stress

B)Gpx3

Yap1

Yap1 Skn7GLR1 (Glutathione)

GPX2 (Glutathione)

TRX2 (H2O2 tolerance)

C)

Skn7 Hsf1Heat Shock proteins

Metallothionein

Crm1

Ira1

Cyr1

Ras1

cAMP

Tpk

D)

Msn4

Msn4

Activate stress genes

CTT1, GRX1, etc

Glucose

23 58 13 14

147 148 236 31 37

71 127 222 21 32

110 196 485 26 45

119 212 515 46 59

222 308 293 70 86

151 188 304 53 68

224 311 383 43 48

368 454 495 88 85

47 132 231 15 20

Tsa1 485 680 231 33

245 366 300 82 80

73 107 106 14 19

199 297 450 28 42

415 541 601 104 119

191 255 271 30 33

222 296 432 47 41

Bcy1 134 192 266 31 44

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FIGURE 4.4 Stress pathway models. D) Model of the cell wall stress pathway together with mean normalized read counts for each gene across the following stages: WTAP, WTBT, WTNT, MTAP and MTBT. Reads in red mean that the gene was differentially expressed between different stages. Genes in blue mean that they are unique to that pathway, pink means that they are shared with other pathways.

Cell wall stress

Plasma membrane

Nuclear m

embrane

Rom1

Rho1

Pkc1

Swi4

Smp1

Rlm1

Skn7

Bck1

Mkk1

Slt2

Wsc118 11 10 7

595 989 1047 109 145

638 379 403 170 152

66 101 97 18 14

247 416 576 72 88

124 215 322 52 55

47 132 231 15 20

40 45 58 11 6

134 119 239 35 32

151 232 266 51 52

Slt2Ptp2

Ptp3125 173 401 49 52

38 32 35 6 7

Fks1

Sec3

1295 1747 2217 326 336

87 148 187 28 24

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Figure 4.5 Placement of response elements in the 5’ upstream region of genes involved in stress. UPRE: unfolded protein response elements; ERSE: ER stress response element.

Hac1

Hog1

Sln1

CCACG N8 A TTGG

CAGCGTG

CAGCGTG

-87

-459

-675

UPRE

ERSE

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Figure 4.6 Heat maps. A) Secretion stress pathway, B) Osmotic stress pathway, C) Oxidative stress pathway, D) cell wall stress pathway.

A B

C

D

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FIGURE 4.6 Heat maps. E) Heat maps of gene expression of those categorized as “Other stresses” (Table 4.3) with a two-fold log2 in at least one condition. Genes were annotated in different categories by FungiFun2.

Metabolism Transporters

Stress related

Others

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Figure 4.7 Analysis of stress-related genes present in the laser capture data with a logFC higher than 1. A) Genes with higher expression in vitro and B) genes with higher expression in planta.

All (2) significa ntly enriched categories

Method: FunCat

14

12

drug/toxin transport

detoxification by export

A

All (12) significantly enriched categories

Method: FunCat

25

7

9

13

1320

12

21

12

8

ATP binding

Ca2+ mediated signal transduction

unfolded protein response (e.g. ER quality control)

DNA damage response

stress response

C-4 compound metabolism

DNA conformation modification (e.g. chromatin)

phosphate metabolism

DNA repair

transcriptional control

organization of chromosome structure

protein folding and stabilization

B

172

Figure 4.8 RT-PCR with BiP, Hac and PDI during in vitro and in planta conditions. Actin (Act) is used as a control. IVAP: In vitro appressoria, AP: appressoria, BT: biotrophic, NT: necrotrophic, Fries: minimal Fries medium, PQ: Paraquat, TUN: tunicamycin. The numbers on top of the lanes represent the ratio between that lane and the control actin, as calculated by the program GelQuantNET (BiochemLabSolutions).

173

Figure 4.9 Effect of stress on Cpr1 transcripts in vitro. RT-PCR amplification of material grown in planta and in vitro from WT and MT strains. Treatments are in vitro appressoria (IVAP), appressoria (AP), biotrophic (BT) and necrotrophic (NT), as well as in vitro fries control (Fries), Paraquat (PQ) and tunicamycin (TUN). Ladder (L) is 1 kb Plus DNA ladder from Life Technologies. Primers used CPR1intF4xCPR1intR4.

174

Figure 4.10 Transcript sequencing results of Cpr1 gene in the WT and MT strains. A) While WT maintains the predicted intron in all the conditions tested, the MT shows intron retention and alternative splicing, as well as the normal intron. B) Close up of the three possible intro variants of Cpr1 gene. The first one is the predicted intron, here represented by a sequencing of WT during Paraquat treatment. The second line shows the intron retention, in this example shown by MT control on minimal Fries medium. And last, the third version of the intron found only in MT during tunicamycin treatment.

Mutation

Exon1 Exon259 bp

Exon1 Exon259 bp

WT sequencing

1

1 - MT

MT sequencing

175

Figure 4.11 Expression of BiP (GLRG_10629) chaperone reporter in vitro and in planta. All pictures were taken in the confocal microscope at 520 V/ 543 nm. Magnification of 400x or 1200x.

WT WT-BiP-RFP

Contr

ol

DT

TT

unic

am

ycin

Menad

ione

Para

quat

with c

hem

ical str

ess

maiz

e inocula

tions

12 h

pi

24 h

pi

176

Figure 4.12 WT-HDEL tagged isolate visualized in the confocal microscope at 520 V/ 543 nm during in vitro growth with the chemical tunicamycin.

177

Chapter 5

Concluding remarks

I have focused in my dissertation research on a very interesting non-pathogenic

mutant of the maize anthracnose fungus Colletotrichum graminicola. This

mutant was produced in our laboratory by insertional mutagenesis quite a few

years ago, and it has been the subject of study by various laboratory members

ever since (Mims and Vaillancourt, 2002; Thon et al., 2002, 2000; Torres et al.,

2013; Venard and Vaillancourt, 2007a). The thing that is very interesting about

this mutant is that it is conditional: it grows normally in culture; and it germinates,

produces appressoria, and penetrates the host normally. It specifically fails to

establish biotrophic hyphae, and thus to produce a successful infection. The

previous work in the laboratory has taught us much, but so far it has been

unable to explain the conditional nature of this mutation.

My arrival in the Vaillancourt laboratory coincided with a major effort to obtain

new genomic resources for C. graminicola, and to apply those to understanding

its pathogenicity to maize. A major part of my work has been on generating and

analyzing the genome and transcriptome of C. graminicola. I first came to the

Vaillancourt lab for a short research sabbatical right after I finished my

undergraduate degree. While I was here, I helped to extract the DNA of M1.001

(aka WT) that was sent for sequencing at the Broad Institute. Little did I know

that I would come back and work so intensively with this strain and this genome!

When I joined the lab as a PhD student, one of the first things I did was to

extract DNA from C. graminicola M5.001 and from C. sublineola CgSl1, both of

which were sequenced by the AGTC and also used in my analysis. Later, I

prepared RNA samples for the transcriptome study, together with my fellow

graduate student at the time, Dr. Maria Torres. I have focused a lot of my time

and effort on organizing, summarizing, and analyzing all of these different

datasets. In the process of doing this, I have developed various resources that

will make it easier for future researchers to access and interpret these data.

178

In spite of the fact that the mutant had been in the lab for more than ten years,

nobody had ever done a comprehensive analysis of the insertion site in the MT

Cpr1 allele. The genome made this an easier task (kind of!), and so I used a

combination of PCR amplification, sequencing, and Southern blotting to

characterize the mutation in detail. Mapping of the transcriptome reads to the

map of the mutant allele revealed that intron splicing appeared to be altered in

the MT, compared with the WT. I confirmed by RT-PCR, cloning, and

sequencing, the presence of at least three variant splice forms in the MT that

don’t seem to occur in the WT. To further characterize this phenomenon, I

prepared RNA from 60 samples, representing MT, WT, and MT-C, in planta,

and in vitro in the presence or absence of stress, for high-throughput Illumina

sequencing. These samples are currently being processed by the AGTC.

The insertion in the MT was known to be in the 3’UTR, 19 bp downstream from

the stop codon of the Cpr1 ORF. This certainly implied that the 3’UTR sequence

of the MT and WT would be different, but nothing was known about the precise

nature of the MT 3’UTR length or sequence. I found several different “nested”

versions of the 3’UTR for both strains in planta by using a PCR protocol to

amplify the poly(A) regions. A former postdoc in the lab had used RACE to

characterize the 3’UTR of the WT in vitro, and she found only a single version,

which was a bit longer than the one predicted in Thon et al., (2002). None of

the versions I cloned looked exactly like hers. One possibility is that my

experiment was flawed: additional methods should be applied to characterizing

the 3’UTRs of both strains in planta to confirm my results. Another, more

interesting possibility is that the WT 3’UTR varies in planta, and that these

variations have some significance in function. The MT 3’UTR differs from the

WT in both length and sequence, and this could affect those functions. For

example, the 3’UTR can regulate transcript stability, transcript localization and

translation efficiency, and intron splicing, among other things. It is possible that

the variation in intron splicing in the MT is a result of the altered 3’UTR

sequence.

The CPR1 protein is predicted to comprise part of the signal peptidase complex,

which is the first step in the canonical eukaryotic secretory pathway. I used the

genome and transcriptome data to undertake comprehensive analyses of the

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putative secretory pathway of C. graminicola. I also developed a WT strain

expressing a RFP-CPR1 chimeric protein, and developed I developed some

transformants that expressed an HDEL-GFP anchored in the ER membrane to

visualize the endomembrane system in living cells of the WT, MT, and MT-C

strains. I did not observe any differences between the WT and MT in the

apparent expression or activity of the secretory pathway. Nonetheless, the tools

that I have developed will help for future studies designed to test the hypothesis

that secretion activity varies in the MT vs WT strain.

One hypothesis to explain why the MT is nonpathogenic is that it fails to secrete

necessary effector proteins. I did not address this hypothesis directly, but I

undertook a comprehensive “in silico” comparative genomic and transcriptomic

analysis to characterize the effectorome of C. graminicola, and to identify the

most likely candidates for effectors that could be involved in the establishment

of biotrophy. My analysis is already being applied by a visiting scientist in our

laboratory, who is investigating polymorphisms in these effectors among

different strains of C. graminicola. I am confident that the tools and the data I

developed will continue to facilitate future research in our laboratory on the

mechanisms of pathogenicity in C. graminicola.

Even though it’s undeniable that the signal peptidase complex is critical for

secretion, the idea that all secreted effectors have a signal peptide and are

secreted thru the ER-Golgi canonical secretory pathway has recently been

challenged in fungi. A recent study on yeast showed that UPR can trigger an

unconventional secretion pathway for misfolded and excessive proteins to be

delivered into the extracellular space (Miller et al., 2010). Several studies in

human fungal pathogens show that two thirds of the proteins known to be

secreted by them are exported via alternative pathways (reviewed by Rodrigues

et al. 2013). A recent study with the plant pathogens Phytophthora sojae and

Verticillium dahliae showed a protein that affected salycilic acid response

pathway in plants and that did not have the canonical signal peptide even

though they were translocated to the plant cytoplasm (Liu et al. 2014). In M.

oryzae Giraldo et al., (2013) found that cytoplasmic and apoplastic effectors are

secreted by two different secretion pathways: the exocyst complex and the the

conventional ER-Golgi secretion pathway, respectively (Giraldo et al. 2013).

180

Thus, it is important to keep an open mind about the possible function of the

CPR1 protein, and think beyond a possible role in secretion.

Reports from work with other fungi suggested that the CPR1 protein could be

involved in adaptation to secretion stress. To test this possibility, I characterized

the reaction of the mutant to different stresses in vitro. The race tube test that I

developed to test the sensitivity of the fungi to stress-inducing chemicals is

already being used by other student in the lab to characterize the reaction of

other strains to fungicides. I used the genome and transcriptome data to

develop models for the major stress pathways in C. graminicola. To my

knowledge stress response has not been characterized in Colletotrichum

previously. My analyses did not reveal any major differences in the expression

of stress response genes in planta between the MT and WT, or across the

various WT developmental stages in planta. On the other hand, the laser-

capture microdissection dataset that I analyzed strongly suggested that stress

genes were induced in planta in comparison to culture, supporting the idea that

the plant is a stressful environment. My analysis using a WT strain transformed

with a BiP-RFP reporter construct also suggested that hyphae are reacting to

stress when growing inside the plant. This strain may provide a useful tool for

monitoring stress response in the living host-pathogen interaction.

My data generally support the idea that post-transcriptional differences play an

important role in gene regulation in the C. graminicola-maize interaction, and

future work should be focused at the protein level. There have been relatively

few proteomics studies for pathogens during infection of plants, partly because

there are many technical difficulties in obtaining and identifying the full range of

proteins from the interaction. There have been a few proteomics studies that

have implicated stress response (Bindschedler et al., 2009), and the production

of small secreted proteins (Rep et al., 2004) in pathogenicity. Such studies will

certainly become more accessible, and more common, in the future. It is

important to emphasize that the genomic, bioinformatics, and transcriptomics

analyses I’ve done for this dissertation will play an essential role in designing

and interpreting future protein and proteomics studies of the C. graminicola WT

and MT.

Copyright © Ester Alvarenga Santos Buiate 2015

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Appendix I

Supplemental Material for Chapter 2

Verification of Transcriptome Sequencing Results

AI.1 Background

Dr. Maria Torres and I prepared the RNA samples that were sent to the Texas

AgriLife Genomes and Bioinformatics Service Center for transcriptome

sequencing. We inoculated leaf sheaths with two C. graminicola isolates, the

M.1001 wild-type (WT) and the cpr1 mutant (MT). For the WT strain, we

collected appressoria (WTAP), biotrophic (WTBT), and necrotrophic (WTNT)

developmental stages, and for the MT we collected appressoria (MTAP), and

biotrophic (MTBT) stages. A summary of the WT transcriptome data was

published in O’Connell et al. (2012).

The MT has an insertion in the 3’UTR of Cpr1 (Colletotrichum pathogenicity

related gene 1), a homolog of the Spc3 gene in yeast. The MT is unique

appears to produce a very small quantity of normal transcript, which however

is sufficient for normal growth in vitro (Thon et al. 2002). 3’UTR sequences are

implicated in regulating transcript cleavage and polyadenylation, controlling

alternative polyadenylation, nuclear export, transcript stability, translation

efficiency, and mRNA targeting (Grzybowska, Wilczynska, and Siedlecki 2001).

The insertion in the 3’UTR of Cpr1 might explain the reduced transcript levels

in the MT.

In my work, described in Chapter 2, to analyze the MT Cpr1 transcript, I wanted

to A) discover if intron splicing in the MT differs from the WT; and B)

characterize the sequence of the MT 3’UTR. I was given the sequence of the

plasmid pCB1636 that was used in the Restriction-Enzyme Mediated

Integration (REMI) mutation by Dr. Jim Sweigard, who created the plasmid, so

I could identify transcripts from the insertion plasmid in the transcriptome, if they

existed. The work by Thon and collaborators (Thon et al. 2002) described the

mutant as containing 1.5 copies of the plasmid inserted 19 bp downstream of

182

the stop codon in the Cpr1 gene. I used this to create a hypothetical map of the

insertion (Figure AI.1. Mutant). After that, I used BLAST to find reads in the

transcriptome data that matched. In the process of doing this mapping, I

discovered an error in our transcriptome data, which had probably been caused

by a sample mix-up at Texas Agrilife. Below I will describe how I found out about

this error and what we did about it.

AI.2 Material and Methods

Sample preparation and RNA extraction are detailed in the supplemental data

in O’Connell et al. (2012) and in Chapter 2 of this dissertation. In short, maize

leaf sheaths were inoculated with 5x105 spores/ml of C. graminicola, either the

wild-type or the mutant strain. The sheaths were collected at appressoria stage,

intracellular biotrophic hyphae and necrotrophic stages with secondary hyphae

visible. Each leaf sheath was confirmed with a light microscope and they were

trimmed to include only the inoculated area. Appressoria and biotrophic leaf

sheaths were also shaved to remove uninfected cells, trying to increase our

fungal reads. The sheath pieces were maintained at -80oC and RNA was

extracted by combining Trizol reagent (Invitrogen) and purification protocol from

RNeasy Plant Mini Kit (Qiagen) with DNase A digestion. RNA integrity and

quantity were measure with an Agilent 2010 Bioanalyzer before sequencing.

RNA was sequenced using the Illumina Genome Analyzer IIx. The sequences

obtained for the 24 libraries were converted to fastq format, and that is the

format I used in my analysis.

The proposed MT sequence I generated was based on data from previous

researchers in the laboratory (Thon et al., 2000; Thon et al., 2002), and the

sequence of the plasmid used in the mutation experiments, pCB1636. I mapped

the RNAseq reads from all the libraries to the proposed WT and MT sequences

using BLAST (Basic Local Alignment Search Tool). The mapping of the

sequences that matched was done using Gnumap with a 75% alignment score

and the figures showing the hits were created using Integrated Genome

Browser (IGB). Other figures were manually created using a vector graphics

editor Inkscape.

183

AI.3 Results

Each of the five treatments that were sent for sequencing (WTAP, WTBT,

WTNT, MTAP, MTBT) had three biological replicates, each in a separate,

identical, labeled microfuge tube. Two technical replicates (lanes) were done

for the WTAP, MTAP, and MTBT stages, which had very low fungal:plant ratios.

WTBT and WTNT had only one technical replicate (lane). During my mapping,

I discovered that one of the MTAP biological replicates contained no reads that

matched the Hyg gene or the plasmid sequences, while one WTAP biological

replicate did match those areas.

On Figure AI.2, the “X” marks where the plasmid had matches when using

BLAST. The grey areas indicate the lanes that appear to be swapped. In

biological replicate 1 for WTAP, in both technical replicates, I found reads that

matched the plasmid sequence. The non-transformed WT strain should not

contain any plasmid or Hyg sequences. PCR using primers against the Hyg

gene confirmed that the WT does not contain it. Neither of the other two

biological replicates of WTAP contains plasmid or Hyg sequences.

Furthermore, although two biological replicates of MTAP did contain Hyg and

plasmid sequences, biological replicate 3 had no matches to these sequences.

I have concluded that these two samples were probably switched, most likely

due to a mixup with the tubes at Texas. Additionally, biological replicate 3 of the

MTBT treatment also did not contain plasmid or hygromycin reads. Although

this could just be a function of the relatively low number of hits I found overall

in these regions, it could also be due to a mistake made by me and Dr. Torres

in identifying samples here. It is possible that we used sheaths inoculated with

WT instead of MT.

To address this problem, both lanes of biological replicate 3 of MTAP, and of

biological replicate 1 of WTAP, were removed from the dataset. Furthermore,

both lanes of biological replicate 3 of MTBT were also removed. The data were

re-analyzed for my study as described in Chapter 2.

The corrected reads for the Cpr1 gene and the two flanking genes are shown

in Figure AI.3 for both fungal strains. The genes are numbered, and introns are

184

represented by the areas with no color. Reads from the WT are blue and from

the MT are red.

Using the hypothesized mutant sequence (Figure AI.4 - Mutant), I mapped MT

reads to the plasmid. Figure AI.4 shows the reads mapped to my original

hypothesized mutant sequence (I ended up modifying this, as described in

Chapter 2, based on my results). Identifying this error was very important for

my analysis and for our future work with the transcriptome. However, our

analyses of the WT data before and after the error was corrected revealed

only a few, very minor differences. This is because it appears that,

transcriptionally, the WTAP and MTAP stages are, statistically, virtually

identical. Thus we do not believe that our original publication is going to cause

any issues for anyone who may have already used the data for their own

work.

185

Figure AI. 1 Wild-type (WT) Cpr1 gene (green) is represented with flanking genes (pink). This map of the mutant sequence shows the EcoRI sites (red boxes) and the Hygromycin and plasmid sequence areas as I believed them to be at the time.

186

Figure AI. 2 Technical replicates (Lanes), treatments and biological replicates (Rep) from the transcriptome sequencing project

187

Figure AI. 3 Transcriptome reads showing matches to the Cpr1 gene and the two flanking genes in the WT (blue) and the MT (red) strains.

188

Figure AI. 4 Hypothesized mutant sequence with reads matching the plasmid regions.

189

Figure AI. 5 Map of pAN56-1-sGFP-HDEL plasmid. Figure made with Geneious (version 6.0) created by Biomatters. Available from http://www.geneious.com

190

Figure AI. 6 Correlation analysis of fold changes by RNA sequencing and qRT-PCR. Log2 fold changes by qRT-PCR are plotted on the x-axis and RNAseq are plotted on the y-axis.

y = 1.036x - 0.2391R² = 0.8604

-10

-8

-6

-4

-2

0

2

4

6

8

-8 -6 -4 -2 0 2 4 6 8

191

Figure AI. 7 Corrected map of pCB1636. Figure made with Geneious (version 6.0) created by Biomatters. Available from http://www.geneious.com.

192

Appendix III

Supplemental Material for Chapter 4

Optimization of the protocol for race tube growth assays

AIII.1 Background

For my experiments in Chapter 4, I wanted to compare the growth of the MT

and WT strains during exposure to a wide range of concentrations and types of

chemical inducers of stress. Growth assays commonly described in the

literature include liquid cultures (Angelova et al. 2005; Pakula et al. 2003); radial

growth measurements (D. Li et al. 1995); and spore germination assays

(Angelova et al. 2005). Spore germination assays seemed to be too labor-

intensive to be feasible for the very large number of treatments I wanted to do

(Slawecki et al.,2002). Liquid cultures required me to use too much of the

chemicals, some of which were quite expensive. Thus I settled on radial growth

measurements for my experiments. Radial growth is commonly used for

comparing different fungal species and/or strains on different substrates or

treatments (P. J. G. M. de Wit et al. 2012; Reeslev and Kjoller 1995), including

comparisons of resistance to different chemicals (Zheng et al. 2012).

In my first experiments, I grew the cultures on Petri plates on which I had

overlaid a thin layer of agar containing the stress-inducing chemical. I used the

agar overlays because it allowed me to use less of the chemicals. I took three

measurements of mycelial growth for each plate at different locations, and then

compared the average for the control with the averages for the different

treatments. Unfortunately I found that the variation in my measurements with

this technique was unacceptably large. I considered that the problem was

probably the overlay: it was difficult to obtain an even layer of agar, and so

some areas may have had more chemical than others.

Race tubes have long been used for studies of hyphal growth and circadian

rhythms in Neurospora crassa (Ryan et al., 1943; Sargent, Briggs et al., 1966;

Davis and Perkins 2002). The races tubes are made of Pyrex glass. They are

193

not readily available, and they are also rather difficult to clean out after use.

White and Woodward (1995) proposed a different method of producing race

tubes by using plastic pipettes, which allows easy filling with media as well as

being disposable. I needed an assay that would be accurate and repeatable,

so I decided to compare the performance of standard Petri dishes to the race

tubes for radial growth assays.

AIII.2 Material and Methods

AIII.2.1 Fungal strains

I used M1.001 Colletotrichum graminicola wild-type strain (L. J. Vaillancourt and

Hanau 1991), as well as the 6-2 MT and MT-C complement strain derived from

M1.001 (Thon et al. 2002), as described in Chapter 2.

AIII.2.2. Assays

Two assays were compared, one using Petri plates and the other using race

tubes. Modified Fries Minimal (FM) agar media (Tuite 1969) was used for both

assays. The medium consisted of 30 g of sucrose, 5 g ammonium tartrate, 1 g

NH4NO3, 1 g KH2PO4, 0.48g MgSO4.7H20, 1 g NaCl and 0.13 g CaCl2.2H20.

For the experiments to compare the performance of the Petri plates with the

race tubes, I added tunicamycin, a chemical that causes secretion stress, to the

media (as described in Chapter 4). Concentrations used were 0, 5, 10, 15 and

20 µg/ml. For the experiments to optimize the performance of the race tubes,

un-amended FM was used, with no chemicals added.

For the Petri plates, 20 ml of FM was poured and solidified in the hood. A 5 ml

overlay of molten agar containing tunicamycin was added and allowed to

solidify. A small plug of mycelia was put in the middle of the plate, and it was

incubated at 23oC. After 7 days I took three measurements of the colony radius

on each Petri plate and calculated the average. For the race tube assays, I

followed the protocol described by White and Woodward (1995). Twenty-five

ml sterile disposable polystyrene pipettes (USA Scientific) were completely

filled with molten FM medium (with or without tunicamycin) until it reached the

maximum volume (approximately 36 ml). Then, the media was released until

only 10 ml remained and the pipette was braced in a horizontal position until

194

the media solidified. The tip was removed, and the pipette was cut in half, by

using a heated scalpel blade.

I did several experiments to optimize the performance of the race tubes.

Different materials were tested for closing the tips of the race tubes. I used

aluminum foil, aluminum foil held with a rubber band, Parafilm M (Pechiney

Plastic Packaging Company) and Breathe-Easy sealing membrane (Sigma-

Aldrich). For the race tubes, I also tested other parameters including location

(open shelves, or contained inside a box with wet paper towel inside to maintain

humidity); light and dark (transparent box, or in one covered with aluminum foil);

volume of media (10 or 13 ml). I also evaluated the effect of different shelves

inside the 23oC room, and also the region in the Petri dish from which the agar

plug was removed (the edge of the colony or the middle).

To inoculate the race tubes, I used a 4mm cork borer to remove mycelial plugs

from a plate colony, and with a scalpel I removed as much of the agar as I could.

The mycelia plug was then inserted into one of the open end of the tube,

approximately 2 mm from the end. The tubes were then sealed in some manner

(see below) and incubated for 7 days. Linear growth was measured in just one

direction, lengthwise along the tube.

The petri plates and race tubes were maintained under continuous light at 23oC

for 14 days, when measurements of the linear mycelial growth were made. As

the MT strain grows slightly slower than WT and C strains (Figure 1.3A), the

difference in growth on the different treatments was always compared to the

control of each strain, therefore creating a percentage of growth rate.

AIII.3 Results

In the comparison Petri plate assay, the main problem I had was the culture

radius was not homogenous (Figure AIII.1A). That caused a lot of variation

between the three different measurements (Figure AIII.1B), and produced a

high standard deviation (Figure AIII.1C). This variation, together with the

difficulty in producing an even overlay of the chemical medium, made this

method problematic.

The race tubes, on the other hand, using the same chemical concentrations,

showed less variation between the repetitions (Figure AIII.2A). Additionally the

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race tubes allowed for a much longer incubation time, 14 days when compared

to 7 days using the Petri plates. I noted that the fungus was more sensitive to

the chemicals in the race tubes, compared with the Petri plates, for all

treatments tested (DTT, tunicamycin, Paraquat, menadione).

Once I decided to use the race tubes for the growth experiments, I addressed

several parameters in an effort to further diminish variation.

In two independent tests, any of several different ways to close the open sides

of the tubes produced similar results. Aluminum foil, aluminum foil with rubber

band, Parafilm M, and Breath-right were not statistically different by the Waller-

Duncan test. Parafilm was chosen because it was the easiest to seal, avoiding

the contamination that occasionally happened when using aluminum foil.

In regard to where the tubes were placed, either on open shelves in the 23o C

room, or inside an open box or a closed one in the same room, there was also

no difference between the treatments using the Waller-Duncan test. For

convenience, I decided to put the tubes on the open shelves in the 23o C

chamber. There was no difference in fungal growth or percentage media weight

loss whether I used 10 ml or 13 ml, so I choose to use 10 ml, reducing the

amount of chemicals needed. Only one of the factors I tested, the area of the

Petri dish from which the mycelial plug was removed, had a significant effect

on mycelial growth. Based on those experiments, I consistently used mycelial

plugs removed from the edges of the colonies.

Based on all the optimization experiments I came up with the following for the

race tubes: i) 10 mls of media in each pipette, producing 2 race growth tubes

per pipette; ii) removing the mycelial plug with a cork borer from the edge of the

colony to guarantee uniformity; iii) close both open sides of the tubes with

parafilm; and iv) setting the tubes always on the same shelf of the 23o C growth

chamber. Those parameters were used on the stress growth assays described

in Chapter 3.

196

Figure AIII.1 Variability in radial growth during chemical stress. A) Mutant strain with the tunicamycin treatment (Control, 5, 10, 15 and 20 ug/ml), B) Drawing showing how the 3 measurements were taken, C) Measurement means in mm and standard deviation of in vitro growth for each strain in different tunicamycin concentrations.

A B

C

197

Figure AIII.2 Race tubes growth. A) Linear growth means in cm and standard deviation of in vitro growth results for each strain in different tunicamycin concentrations using race tubes, B) Race tube with M1001 in control media.

A

B

198

Identities = 484/687 (70%), Positives = 559/687 (81%), Gaps = 26/687 (3%)

GLRG_10629 1 MSRSRNSMAFGFGLLAWMVLLFTPLAFVQTAQ----------AQDTEDYGTVIGIDLG 48

M N ++ G L+ V+L+ + Q A D E+YGTVIGIDLG

BIP yeast 1 MFFNRLSAGKLLVPLSVVLYALFVVILPLQNSFHSSNVLVRGADDVENYGTVIGIDLG 58

GLRG_10629 49 TTYSCVGVMQKGKVEILVNDQGNRITPSYVAFTEEERLVGDAAKNQAAANPQNTIFDIKR 108

TTYSCV VM+ GK EIL N+QGNRITPSYVAFT++ERL+GDAAKNQ AANPQNTIFDIKR

BIP yeast 59 TTYSCVAVMKNGKTEILANEQGNRITPSYVAFTDDERLIGDAAKNQVAANPQNTIFDIKR 118

GLRG_10629 109 LIGQKFSDKSVQSDIKHFPYKVIEKDGKPIVEVQVAGSPKRFTPEEVSAMILGKMKEVAE 168

LIG K++D+SVQ DIKH P+ V+ KDGKP VEV V G K FTPEE+S MILGKMK++AE

BIP yeast 119 LIGLKYNDRSVQKDIKHLPFNVVNKDGKPAVEVSVKGEKKVFTPEEISGMILGKMKQIAE 178

GLRG_10629 169 SYLGKKVTHAVVTVPAYFNDNQRQATKDAGIIAGLNVLRIVNEPTAAAIAYGLDKTDGER 228

YLG KVTHAVVTVPAYFND QRQATKDAG IAGLNVLRIVNEPTAAAIAYGLDK+D E

BIP yeast 179 DYLGTKVTHAVVTVPAYFNDAQRQATKDAGTIAGLNVLRIVNEPTAAAIAYGLDKSDKEH 238

GLRG_10629 229 QIIVYDLGGGTFDVSLLSIDHGVFEVLATAGDTHLGGEDFDQRIINYFAKSYNKKNSVDI 288

QIIVYDLGGGTFDVSLLSI++GVFEV AT+GDTHLGGEDFD +I+ K++ KK+ +D+

BIP yeast 239 QIIVYDLGGGTFDVSLLSIENGVFEVQATSGDTHLGGEDFDYKIVRQLIKAFKKKHGIDV 298

GLRG_10629 289 TKDLKAMGKLKREAEKAKRTLSSQMSTRIEIEAFFEGKDFSETLTRAKFEELNMDLFKKT 348

+ + KA+ KLKREAEKAKR LSSQMSTRIEI++F +G D SETLTRAKFEELN+DLFKKT

BIP yeast 299 SDNNKALAKLKREAEKAKRALSSQMSTRIEIDSFVDGIDLSETLTRAKFEELNLDLFKKT 358

GLRG_10629 349 MKPVEQVLKDAKVKKEDVDDIVLVGGSTRIPKVVSLIEEYFGGKKASKGINPDEAVAFGA 408

+KPVE+VL+D+ ++K+DVDDIVLVGGSTRIPKV L+E YF GKKASKGINPDEAVA+GA

BIP yeast 359 LKPVEKVLQDSGLEKKDVDDIVLVGGSTRIPKVQQLLESYFDGKKASKGINPDEAVAYGA 418

GLRG_10629 409 AVQGGVLSNEVGAEDIVLMDVNPLTLGIETTGGVMTKLIQRNTPIPTRKSQIFSTAADNQ 468

AVQ GVLS E G EDIVL+DVN LTLGIETTGGVMT LI+RNT IPT+KSQIFSTA DNQ

BIP yeast 419 AVQAGVLSGEEGVEDIVLLDVNALTLGIETTGGVMTPLIKRNTAIPTKKSQIFSTAVDNQ 478

GLRG_10629 469 PVVLIQVFEGERSLTKDNNQLGKFELTGIPPAPRGVPQIEVSFELDANGILKVSAHDKGT 528

P V+I+V+EGER+++KDNN LGKFELTGIPPAPRGVPQIEV+F LDANGILKVSA DKGT

BIP yeast 479 PTVMIKVYEGERAMSKDNNLLGKFELTGIPPAPRGVPQIEVTFALDANGILKVSATDKGT 538

GLRG_10629 529 GKQESITITNDKGRLTQEEIDRMVAEAEKYAEEDKATRERIEARNGLENYAFSLKNQVND 588

GK ESITITNDKGRLTQEEIDRMV EAEK+A ED + + ++E+RN LENYA SLKNQVN

BIP yeast 539 GKSESITITNDKGRLTQEEIDRMVEEAEKFASEDASIKAKVESRNKLENYAHSLKNQVNG 598

GLRG_10629 589 DEGLGGKIDDEDKETILEAVKETTSWLEENSGTATTEDFEEQKEKLSNVAYPITSKMYQG 648

D LG K+++EDKET+L+A + WL++N TA EDF+E+ E LS VAYPITSK+Y G

BIP yeast 599 D--LGEKLEEEDKETLLDAANDVLEWLDDNFETAIAEDFDEKFESLSKVAYPITSKLYGG656

GLRG_10629 649 AGGAGG---DDEPPS-------HDEL* 665

A G+G DDE HDEL*

BIP yeast 657 ADGSGAADYDDEDEDDDGDYFEHDEL* 683

Figure AIII.3 Alignment between S. cerevisiae Kar2/BiP (YJL034W) and C. graminicola homolog GLRG_10629.

199

Identities = 434/3278 (13%), Positives = 434/3278 (13%), Gaps = 2844/3278 (86%)

Sequencing BiP -----------------------------------------------------------

BIP +1kb upstream 301 TTTTATTAAGGATAATTAATATAACTTAAAAGTACTATTTTAGTTAATTAGTTATAAAAT 360

Sequencing BiP -----------------------------------------------------------

BIP +1kb upstream 361 AAAGAATTAATAACCTTAAGCTAAGATCTCTATATATAGTATTAAGTACTCTAAATTATA 420

Sequencing BiP -----------------------------------------------------------

BIP +1kb upstream 421 TATTTATAGGCTTCTAGCTTTTATATTATAGTAGTTAGCCTTAAATAAGCTTGTAAACGT 480

Sequencing BiP -----------------------------------------------------------

BIP +1kb upstream 481 AGTAAGTTTTAGTGTAGAACTAGGGATTAAGCTACACTTTTGCTTACCTACTGTACATAC 540

Sequencing BiP 1 --------------------------GCAGTGCAGGTTGCCCGCCTCTGGGGACCACAC 33

GCAGTGCAGGTTGCCCGCCTCTGGGGACCACAC

BIP +1kb upstream541 TCCGTAGTGTCCATTATACAACACGATGCAGTGCAGGTTGCCCGCCTCTGGGGACCACAC 600

Sequencing BiP 34 AGTAGGGATTCACCACTGGTACCCTCCACAGCCACGGTCCACCAAAAGCAACCGATCCCT 93

AGTAGGGATTCACCACTGGTACCCTCCACAGCCACGGTCCACCAAAAGCAACCGATCCCT

BIP +1kb upstream601 AGTAGGGATTCACCACTGGTACCCTCCACAGCCACGGTCCACCAAAAGCAACCGATCCCT 660

Sequencing BiP 94 GTCACGTGTGGCGCCTCTCAACGTGAAATCTGGCCTTTTGTGCAATCTGGACACCTCAAT 15

GTCACGTGTGGCGCCTCTCAACGTGAAATCTGGCCTTTTGTGCAATCTGGACACCTCAAT

BIP +1kb upstream661 GTCACGTGTGGCGCCTCTCAACGTGAAATCTGGCCTTTTGTGCAATCTGGACACCTCAAT 720

Sequencing BiP 154 CGTTTTTTGGAGAAGCCATTCATAAAGCCGACAGATCTTCCCTCCCACCGCTTAACCTTC 213

CGTTTTTTGGAGAAGCCATTCATAAAGCCGACAGATCTTCCCTCCCACCGCTTAACCTTC

BIP +1kb upstream721 CGTTTTTTGGAGAAGCCATTCATAAAGCCGACAGATCTTCCCTCCCACCGCTTAACCTTC 780

Sequencing BiP 214 AACCTCTCCCAATCCAACACCTGCCAAACCACCTCAATTCGATTGGGTGCGCAGCTAGTA 273

AACCTCTCCCAATCCAACACCTGCCAAACCACCTCAATTCGATTGGGTGCGCAGCTAGTA

BIP +1kb upstream781 AACCTCTCCCAATCCAACACCTGCCAAACCACCTCAATTCGATTGGGTGCGCAGCTAGTA 840

Sequencing BiP 274 GAAGGGAATCTCGTCAACCTTCTCTCCCACGTCTTCCATTGACGGCTTTTTGTTTTTTAT 333

GAAGGGAATCTCGTCAACCTTCTCTCCCACGTCTTCCATTGACGGCTTTTTGTTTTTTAT

BIP +1kb upstream841 GAAGGGAATCTCGTCAACCTTCTCTCCCACGTCTTCCATTGACGGCTTTTTGTTTTTTAT 900

Sequencing BiP 334 TTTCTCCCCTATTTTTTTCATTATTGCCCAAAAGCTCGAGATACCACACGCGCGCAAACC 393

TTTCTCCCCTATTTTTTTCATTATTGCCCAAAAGCTCGAGATACCACACGCGCGCAAACC

BIP +1kb upstream901 TTTCTCCCCTATTTTTTTCATTATTGCCCAAAAGCTCGAGATACCACACGCGCGCAAACC 960

Sequencing BiP 394 ATCGTCGCAAGGTAGCTTCATAGCACCAAAATCTCCCACAA------------------- 434

ATCGTCGCAAGGTAGCTTCATAGCACCAAAATCTCCCACAA

BIP +1kb upstream961 ATCGTCGCAAGGTAGCTTCATAGCACCAAAATCTCCCACAATGTCGAGATCCAGAAACTC 1020

Sequencing BiP -----------------------------------------------------------

BIP +1kb upstream1021AATGGCCTTTGGCTTCGGCCTCCTGGCCTGGATGGTTCTCCTCTTCACCCCCCTGGCTTT 1080

Figure AIII.4 Primers and sequencing results of the promoter region used to produce a reporter construct for BiP using Gateway. Primer regions are underlined and the start codon of the gene is in bold.

200

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Vita

ESTER A. S. BUIATE

PLACE OF BIRTH

Lavras, Brazil

EDUCATION

B.S. Agronomy Engineer. 2006. Universidade Federal de Uberlandia. Brazil

M.Sc. Genetics and Plant Breeding. 2009. Universidade Federal de Lavras.

Brazil.

PROFESSIONAL EXPERIENCE

Aug/2009-Apr2015. Graduate Research Assistant, University of Kentucky.

USA.

2007. Visiting Scholar, University of Kentucky. USA.

2006. Field Assistant, Monsanto do Brasil Ltda. Brazil.

2005-2006. Fellowship Student, Universidade Federal de Uberlândia and

Syngenta Seeds. Brazil.

2004-2006. Fellowship Student, Embrapa Maize and Sorghum Research

Center. Brazil.

2002-2003. Student worker, Universidade Federal de Uberlândia. Brazil.

RESEARCH PUBLICATIONS

O’Connell, R.J.; Thon, M.R.; Hacquard, S.; Amyotte, S.G.; Kleeman, J.; Torres,

M.F.; Damn, U.; BUIATE, E.A.; …; Ma, L.; Vaillancourt, L. J. Lifestyle

transitions in plant pathogenic Colletotrichum fungi deciphered by genome and

transcriptome analyses. Nature Genetics, v. 44, p. 1-6, 2012.

BUIATE, E.A.S.; Souza, E.A.; Vaillancourt, L.; Resende, I.; Klink, U.P.

Evaluation of resistance in sorghum genotypes to the causal agent of

anthracnose. Crop Breeding and Applied Biotechnology, v. 10, p. 166-172,

2010.

BUIATE, E.A.S.; Brito, C.H.; Batistella, R.A., Brandão, A.M. Reação de

híbridos

de milho e levantamento dos principais fungos associados ao complexo de

240

patógenos causadores de “grão ardido” em Minas Gerais. Horizonte Científico,

v.1, p.1 - 14, 2008.

Book Chapters

Crouch, J., O’Connell, R., Gan, P., BUIATE, E., Torres, M., Beirn, L., Shirasu,

K.;

Vaillancourt, L. The Genomics of Colletotrichum. In. “Genomics of Plant

associated

Fungi and Oomycetes”, A. Lichens-Park, C. Kole, and R. Dean, editors.

Springer Berlin Heidelberg, 2014.

BUIATE, E.A.S. ; Barcelos, Q.L.; Pinto, J.M.; Souza, E.A.; Venard, C.;

Vaillancourt, L. O gênero Colletotrichum em plantas cultivadas. In: Wilmar

Cório

da Luz. (Org.). Revisão Anual de Patologia de Plantas, 2009, v. 17, p. 1-42.

PUBLISHED CONFERENCE PROCEEDINGS

Conference Proceedings (8 total)

Abstracts (19 total)

Meeting Presentation with no published proceedings (1 total)

PROFESSIONAL SERVICE

Student representative on the Department of Plant Pathology Academic

Program Committee, Jun/2013 – Apr/2015.

PROFESSIONAL ACTIVITIES

Member of GEN (Genetic Studies Group) at UFLA. Brazil. 2007-2009.

Member of APPS (Association of Plant Pathology Scholars) at UK. USA. 2010-

2015.

Member of professional societies: The American Phytopathological Society and

Mycological Society of America.

Ester Alvarenga Santos Buiate