MOLECULAR RESPONSES OF PLANTS TO SYMBIOTIC FUNGI
by
Isadora Louise Alves da Costa Ribeiro Quintans
M.Sc., Universidade Federal de Pernambuco, 2010 B.Sc., Universidade Federal da Paraíba, 2007
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
in
THE COLLEGE OF GRADUATE STUDIES
(Biology)
THE UNIVERSITY OF BRITISH COLUMBIA
(Okanagan)
October, 2019
© Isadora Louise Alves da Costa Ribeiro Quintans, 2019 The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Molecular responses of plants to symbiotic fungi submitted by Isadora Louise Alves da Costa Ribeiro Quintans in partial fulfillment of the requirements of the degree of Doctor of Philosophy .
Dr. Michael K. Deyholos, Biology, Irving K. Barber School of Arts and Sciences Supervisor
Dr. Soheil Mahmoud, Irving K. Barber School of Arts and Sciences Supervisory Committee Member
Dr. Richard Plunkett, Irving K. Barber School of Arts and Sciences Supervisory Committee Member
Dr. Nasser Yalpani, Irving K. Barber School of Arts and Sciences University Examiner
Dr. Thuy Dang, Irving K. Barber School of Arts & Sciences External Examiner
Dr. Stephen Strelkov, University of Alberta Additional Committee Members include:
Supervisory Committee Member
Supervisory Committee Member
ii Abstract Plants and fungi interact in complex ways that can benefit or harm a plant. To better understand how plants defend against pathogens and enhance interactions with beneficial fungi, I used several different approaches to identify genes and pathways involved in these processes.
First, I developed a bioinformatics pipeline to search for antimicrobial peptides (AMPs) in public DNA sequence databases. I found 16,870 novel candidate AMPs from 1,003 species, and demonstrated that transcripts of heveins and cyclolinopeptides (CLPs), especially, increased abundance in response to a pathogenic fungus (Fusarium oxysporum f. sp. lini) but not a mutualistic fungus (Rhizoglomus irregulare).
Second, I developed an in vitro system to evaluate the effects of the interaction of two mutualistic fungi (Rhizoglomus irregulare and Clonostachys rosea) on flax roots infected by F. oxysporum. I found that both R. irregulare and C. rosea have bio-protective effects against F. oxysporum. R. irregulare also increased flax biomass production, while mitigating the negative effects caused by F. oxysporum on shoot length and biomass.
Third, I used RNA-Seq to compare the pre-colonization responses of flax roots to inoculation with R. irregulare or F. oxysporum, separately and in combination. I found distinct classes of genes that responded uniquely to the pathogen and the mutualist.
Finally, I tested the activity of flax CLPs, polyamines (PAs), and carbendazim against three pathogenic fungi: F. oxysporum, S. linicola, and Alternaria sp. I found that CLPs and PAs have antifungal activity in vitro against the fungi studied at a concentration range that might be biologically relevant. We suggest that CLPs and PAs should be tested for the mitigation of Alternaria sp. in vivo, since these compounds had better effects on the growth inhibition of this fungus in comparison to a commercial antifungal agent.
iii Together, these data will improve the understanding of mechanisms underlying pathogenicity and mutualism, and will lead to the development of better strategies to control fungal diseases.
iv Lay Summary
Every day, plants and other forms of life face survival threats. Plants are at a disadvantage because they are unable to quickly escape from unfavourable environmental conditions. Despite their frequent interactions with pathogenic microorganisms, plants are frequently protected from disease by production of many different chemicals and proteins (such as antimicrobial proteins). Plants also form beneficial associations with some soil microorganisms, enhancing the plant’s nutrition and resistance to pathogens. We therefore were interested in comparing the associations of plants with beneficial and pathogenic fungi, and to understand how beneficial fungi enhance plant defenses against pathogens. To understand these interactions, we used mycorrhizal fungi, which form beneficial associations with flax, and a pathogenic fungus, which causes Fusarium wilt in flax. Our study revealed several genes important for the establishment of beneficial relationships between plants and fungi and as well as for plant defense against pathogens.
v Preface
This project was initially developed by me (Professor, The Federal Rural University of semi-arid, Brazil) and my supervisor Dr. Michael Deyholos (Professor, UBC Okanagan, Canada) aiming to identify novel plant defense mechanisms/molecules, with an initial focus on antimicrobial peptides (AMPs) and cyclolinopeptides (CLPs) and later on, polyamines (PAs). For the first chapter, all the bioinformatics experiments were designed by myself with guidance from my supervisor (Dr. Michael Deyholos) and Taffarel Melo Torres (Professor, The Federal Rural University of semi-arid, Brazil). The in vitro system using flax and two symbiotic fungi was designed by me, with help from two other PhD students at the time, Eric Vukicevich (UBC Okanagan) and Vasilis Kokkoris (UBC Okanagan), and with advice from our supervisors Dr. Michael Deyholos and Dr. Miranda Hart (Professor, UBC Okanagan, Canada). We expanded this experiment by analysing the harvested root tissues using RNA-Seq, Chapter 4, and investigated specific compounds in Chapter 5, in experiments designed by me and Juliana Alves da Costa Ribeiro Souza (microbiologist, Universidade Federal da Paraiba, Brazil) and my supervisor. I was responsible for all the laboratory work and received assistance from several students, especially Vasilis and Eric, who helped with the experiment set ups, harvests, readings at the winRHIZO program and preparing root slides (for Chapter 3); Erica Packard (undergraduate, UBC Okanagan), which helped with the harvests, RNA extractions and PCR (for Chapters 2, 3 and 4); and Dinesh Adhikary (then a PhD student, UBC Okanagan) who helped with the real time PCR design. I completed the statistical analysis of my data with assistance from Vasilis, Eric, and my supervisor. My thesis was written with guidance from Dr. Deyholos, and reviewed by my supervisory committee Dr. Soheil Mahmoud, Dr. Richard Plunkett, and Dr. Nasser Yalpani. Vasilis and Eric reviewed Chapter 3.
vi Table of Contents Abstract ...... iii Lay Summary ...... v Preface ...... vi Table of Contents ...... vii List of Tables ...... xi List of Figures ...... xiii List of abbreviations ...... xvi Acknowledgements ...... xx Dedication ...... xxii Chapter 1 : Introduction ...... 1 1.1 Natural and agricultural systems for plant defense ...... 1 1.1.1 Plant molecular responses to symbiosis ...... 2 1.1.2 Host colonization by fungi ...... 5 1.1.3 Fusarium oxysporum ...... 7 1.1.4 Agricultural protective measures ...... 9 1.1.5 Antimicrobial peptides ...... 10 1.1.5.1 Thionins ...... 11 1.1.5.2 Defensins ...... 13 1.1.5.3 Lipid Transfer Proteins ...... 15 1.1.5.4 Heveins ...... 17 1.1.5.5 Snakins ...... 19 1.1.5.6 Cyclic peptides ...... 21 1.1.5.6.1 Cyclotides ...... 22 1.1.5.6.2 Cyclolinopeptides (Orbitides) ...... 26 1.1.5.7 AMPs distribution among databases ...... 28 1.1.5.8 AMPs mode of action ...... 29 1.1.5.9 AMP applications ...... 30 1.1.6 Polyamines ...... 33 1.2 Flax ...... 38 1.3 Research objectives and outline ...... 40
vii Chapter 2 : In silico prospecting for plant antimicrobial peptides (AMPs) in the transcriptomes of over 1000 species ...... 43 2.1 Background ...... 43 2.2 Material and Methods ...... 46 2.2.1 Computational methods ...... 46 2.2.1.1 Seed sequences ...... 46 2.2.1.2 Local databases ...... 47 2.2.1.3 AMPs prediction using psiBLAST ...... 47 2.2.1.4 AMPs prediction using HMMER ...... 47 2.2.1.5 Conserved domains search ...... 48 2.2.1.6 Pattern recognition ...... 48 2.2.1.7 Clustering the sequences ...... 48 2.2.2 Differential expression of AMPs in response to pathogens ...... 49 2.2.2.1 Biotic stress assay ...... 49 2.2.2.2 RNA extraction ...... 50 2.2.2.3 Primer design ...... 50 2.2.2.4 Real time PCR assay ...... 51 2.3 Results ...... 52 2.3.1 AMP prediction and conserved domain analysis ...... 52 2.3.1.1 psiBLAST ...... 52 2.3.1.2 Conserved domains search and Pattern recognition ...... 55 2.3.1.3 HMMER ...... 57 2.3.1.4 Combined sequences ...... 58 2.3.1.5 Distribution of AMPs by plant taxon ...... 60 2.4 Discussion ...... 70 Chapter 3 : The effect of non-pathogenic versus pathogenic fungi on flax growth ...... 74 3.1 Background ...... 74 3.2 Material and Methods ...... 77 3.2.1 Fungal material ...... 77 3.2.2 Plant material ...... 77
viii 3.2.3 Culture conditions ...... 78 3.2.4 Disease symptoms analysis and plant growth assessment ...... 78 3.2.5 Statistical analysis of growth parameters ...... 79 3.2.6 Root colonization assessment ...... 79 3.2.6.1 Fungal isolation ...... 80 3.2.6.2 Microscopy ...... 80 3.3 Results ...... 80 3.3.1 Growth responses and disease assessment ...... 81 3.3.1.1 Harvest 1 ...... 81 3.3.1.2 Harvest 2 ...... 81 3.3.2 Root colonization assessment ...... 87 3.4 Discussion ...... 87 Chapter 4 : Comparative transcriptomics of root responses to pathogenic (Fusarium oxysporum f.sp. lini) and non-pathogenic (Rhizoglumus irregulare) fungi ...... 93 4.1 Background ...... 93 4.2 Material and Methods ...... 97 4.2.1 Experimental design ...... 97 4.2.2 RNA extraction and cDNA synthesis ...... 98 4.2.3 RNA sequencing ...... 98 4.2.3.1 Bioinformatic analysis ...... 99 4.2.4 Real time PCR ...... 99 4.3 Results ...... 101 4.3.1 RNA-Seq ...... 101 4.3.2 Definition of differentially expressed genes ...... 102 4.3.3 Validation of RNA-Seq data by qRT-PCR ...... 103 4.3.4 Visualization and clustering of DEGs ...... 105 4.3.5 Principal component analysis of all treatments ...... 107 4.3.6 Global patterns of gene expression of all treatments ...... 108 4.3.7 Identification of DEGs responsive to inoculation by either F. oxysporum or R. irregulare ...... 110
ix 4.3.8 GO Functional Enrichment – Functions of F. oxysporum DEGs ...... 111 4.3.9 Functions of R. irregulare responsive DEGs ...... 115 4.3.10 DEGs with opposite responses to F. oxysporum and R. irregulare ...... 115 4.3.11 Simultaneous inoculation with a combination of F. oxysporum and R. irregulare ...... 120 4.4. Discussion ...... 123 4.4.1. Mycorrhization and Pathogenesis ...... 123 4.4.1.1 Mutualism ...... 126 4.4.1.1.1 Calcium signaling ...... 127 4.4.1.1.2 Lateral root development ...... 128 4.4.1.1.3 Nutrient transporters ...... 128 4.4.1.1.4 Cell wall modifications ...... 129 4.4.1.2 Defense responses ...... 130 4.4.1.2.1 Hormone signaling ...... 131 4.4.1.2.2 Secondary metabolism ...... 132 4.4.2. Bio-protective effects of R. irregulare in flax roots ...... 133 Chapter 5 : Orbitides and free polyamines have similar fungicidal activity against three common pathogens of flax in vitro...... 138 5.1 Background ...... 138 5.2 Material and Methods ...... 140 5.2.1 Fungal sources and culture ...... 140 5.2.2 Test compound sources and dilutions ...... 141 5.2.3 Fungal growth inhibition assay ...... 142 5.3 Results ...... 143
5.3.1 Measurement of EC50 in a fungal inhibition assay...... 143 5.4 Discussion ...... 146 Chapter 6 Concluding remarks ...... 150 Bibliography ...... 156
x List of Tables Table 1.1 Sequence pattern for each AMP class ...... 11 Table 1.2 Thionin features ...... 13 Table 1.3 Defensin features ...... 15 Table 1.4 LTP features ...... 17 Table 1.5 Hevein features ...... 19 Table 1.6 Snakin features ...... 21 Table 1.7 Cyclotide features ...... 26 Table 1.8 Number of AMPs sequences deposited on databases ...... 29 Table 1.9 Models of antimicrobial activity of membrane-active peptides ...... 30 Table 1.10 Transgenic plants developed using plant AMPs ...... 32 Table 2.1 Antimicrobial peptide databases ...... 45 Table 2.2 Primers designed for the real time PCR assay ...... 51 Table 2.3 Comparison of the number of sequences with unique IDs retrieved with psiBLAST search against Uniprot and 1KP databases ...... 53 Table 2.4 Number of AMPs from Uniprot reviewed database retrieved using keyword queries ...... 54 Table 2.5 Percentage of AMP candidates with CDD domains after rpsBLAST search . 54 Table 2.6 Percentage of AMPs retrieved from Uniprot using psiBLAST according with annotation ...... 54 Table 2.7 Sequence pattern of AMP classes identified with sequences retrieved from 1KP database ...... 55 Table 2.8 The most frequent domains among AMP classes ...... 56 Table 2.9 List of AMP domains found among AMP classes ...... 56 Table 2.10 Number of sequences with unique IDs retrieved from Uniprot and 1KP databases using the HMMER program ...... 58 Table 2.11 Number of sequences with Pfam conserved AMP domains using the program ‘hmmscan’ ...... 58 Table 2.12 Combined sequences for each AMP class from Uniprot and 1KP ...... 59 Table 2.13 Frequency of the most conserved domain at Uniprot and 1KP databases after rpsBLAST search ...... 59
xi Table 2.14 Frequency of the most conserved domain at Uniprot and 1KP databases after ‘hmmscan’ search ...... 60 Table 2.15 Number of sequences for each AMP class from AMP candidates identified from 1KP for the top 30 plants with the greater total number of AMPs retrieved ...... 62 Table 4.1 Primers used for qRT-PCR analysis ...... 100 Table 4.2 RNA-Seq statistics ...... 102 Table 4.3 Comparison of RNA-Seq and qRT-PCR log2 expression ratios (treatment/control) for selected genes at 9 dpi ...... 104 Table 4.4 Comparison of RNA-Seq and qRT-PCR log2 expression ratios (treatment/control) for selected genes at 14 dpi ...... 105 Table 4.5 Gene Ontology (GO term) enrichment analysis for DEGs ...... 114 Table 4.6 Comparison of genes up-regulated by R. irregulare that were oppositely down-regulated by F. oxysporum ...... 117 Table 4.7 Comparison of genes down-regulated by R. irregulare at any time point that were oppositely up-regulated by F. oxysporum...... 118 Table 5.1 Composition of CLP mixture. For calculation of molecular mass, the sulfoximine forms of the CLPs were used, where applicable...... 141 Table 5.2 Summary of EC50 from fungal growth inhibition assays (mass/volume basis) ...... 146 Table 5.3 Summary of EC50 from fungal growth inhibition assays (molar basis) ...... 146
xii List of Figures Figure 1.1 Simplified diagram of plant defense responses pathway...... 4 Figure 1.2 Establishment and persistence of AMF in plant roots...... 5 Figure 1.3 Root sections of flax CDC Bethune plants after a month of colonization by Rhizoglomus irregulare...... 7 Figure 1.4 Fusarium disease cycle...... 9 Figure 1.5 Plant thionin tridimensional model...... 12 Figure 1.6 Plant defensin tridimensional structure...... 14 Figure 1.7 Plant LTP tridimensional structure...... 16 Figure 1.8 Plant hevein tridimensional structure...... 18 Figure 1.9 Plant snakin tridimensional structure...... 20 Figure 1.10 Classification of cyclic peptides...... 22 Figure 1.11 Topology of the cystine knot motif...... 24 Figure 1.12 Plant cysteine knot tridimensional structure...... 24 Figure 1.13 Polyamine metabolism...... 35 Figure 1.14 Biosynthesis of higher molecular weight polyamines from putrescine...... 36 Figure 2.1 Bioinformatics Workflow...... 49 Figure 2.2 Relative normalized expression of a cyclolinopeptide gene of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 64 Figure 2.3 Relative normalized expression of a LTP gene (Lus10026418) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 65 Figure 2.4 Relative normalized expression of Hevein gene (Lus10028377) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 66 Figure 2.5 Relative normalized expression of Hevein genes (Lus0000453) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 67 Figure 2.6 Relative normalized expression of Hevein genes (Lus10006552) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 68 Figure 2.7 Relative normalized expression of a Snakin/GASA genes (Lus10017212) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 69 Figure 2.8 Relative normalized expression of a Snakin/GASA genes (Lus10042203) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare...... 70
xiii Figure 3.1 Flax shoot length after 14 days of growth differed depending on fungal inoculum...... 82 Figure 3.2 Total biomass of flax plants after 14 days of growth as affected by fungal inoculum...... 83 Figure 3.3 Total root length after 14 days of growth as affected by fungal inoculum. .... 84 Figure 3.4 Root necrosis after 14 days of growth as affected by fungal inoculum...... 85 Figure 3.5 Branching intensity in flax root systems as affected by fungal inoculum after 14 days of growth...... 86 Figure 3.6 Disease symptoms 9 days post infection (A) and 14 days post infection (B)...... 87 Figure 4.1 Hierarchical clustering and heatmap of expression ratios (log2, treatment/control) for 2,348 DEGs...... 106
Figure 4.2 PCA of average log2 expression ratios (treatment/control) for 2,348 DEGs...... 107 Figure 4.3 Scatter plots of average expression ratios (log2, treatment/control) for all 32,807 flax genes measured by RNA-Seq experiments...... 109 Figure 4.4 Venn diagram comparing the number of significant transcripts with significantly increased abundance (numbers in blue) and transcripts with significantly decreased abundance (numbers in red) of flax roots inoculated with R. irregulare (R) or F. oxysporum (F), at 9 and 14 d post infection...... 111 Figure 4.5 Venn diagram shows contrasts between genes with significant transcripts with significantlyincreased abundance (numbers in blue) and transcripts with significantly decreased abundance (numbers in red) of flax roots inoculated with R. irregulare (Rir) or F. oxysporum (Fos)...... 116 Figure 4.6 Venn diagram shows comparisons between genes with significant transcripts with increased abundance (numbers in blue) and transcripts with significant decreased abundance (numbers in red) of flax roots inoculated with the combined inoculum (R. irregulare and F. oxysporum) compared to inoculation with R. irregulare (Rir) or F. oxysporum (Fos) alone...... 121 Figure 4.7 Schematic representation of opposite modifications in the flax transcriptome upon infection with F. oxysporum or symbiosis with R. irregulare...... 126
xiv Blue Figure 4.8 Schematic representation of modifications in flax transcriptome upon co-infection with Rhizoglomus irregulare and Fusarium oxysporum...... 135 Figure 5.1 Growth of fungi in the presence of varying concentrations of potential inhibitors...... 145
xv List of abbreviations
1KP – The One Thousand Plants database 2OG – 2-oxoglutarate ADC – Arginine decarboxylase AIH – Agmatine iminohydrolase ALA – α-linolenic acid AM – Arbuscular mycorrhizal AMF – Arbuscular mycorrhizal fungi AMPs – Antimicrobial peptides ANOVAs – Post-hoc univariate analyses of variance avr – Avirulence genes BLAST – Basic Local Alignment Search Tool BSD – Broad spectrum-defense C – Cysteine CaLB – Calcium-dependent lipid-binding CCK – The cyclic cystine knot motif CD-HIT – Cluster Database at High Identity with Tolerance CDC – Crop development center CDD – Conserved Domain Database cDNA – Complementary DNA ChtBD1- Hevein or type 1 chitin binding domain CLP – Cyclolinopeptide COBALT – Constraint-based alignment tool CYP – Cytochrome family protein DAMPs – Damage-associated molecular patterns DAP – Diamonopropane DAS – Trichothecene 4, 15-diacetoxyscirpenol dcSAM – Decarboxylated S-adenosyl methionine DEG – Differentially expressed gene DNA – Deoxyribonucleic acid
xvi DNB – DNA nanoballs DON – Deoxynivalenol dpi – Days post inoculation dscDNA – Double-strand cDNA
EC50 – Half maximal effective concentration ET – Ethylene ETI – Effector-triggered immunity ETS – Effector triggered susceptibility FC – Fold change FDR – False discovery rate FHB – Fusarium Head Blight FPKM – Fragments per kilobase of transcript per million mapped reads G – Glycine GAPDH – Glyceraldehyde 3-phosphate dehydrogenase GASA – Gibberellin-regulated gene family GO – Gene Ontology HMM – Hidden Markov Model HR – Hypersensitive response HRGPs – Hydroxyproline-rich glycoproteins HSD – Honestly significant difference IDT – Integrated DNA Technologies ISR – Induced systemic resistance JA - Jasmonate K – Lysine LC – Liquid chromatography LCOs – Lipochitooligosaccharides LTP – Lipid transfer proteins MAMPs – Microbe-associated patterns MANOVA – Multivariate analysis of variance MAPK – Mitogen-activated protein kinases MIR – Mycorrhiza-induced resistance
xvii MLP – Major-latex proteins mRNA – Messenger RNA MS – Mass spectrometry MTI – MAMP-triggered immunity Myc – Mycorrhizal N – Nitrogen NADPH – Nicotinamide adenine dinucleotide phosphate NB-LRR – Nucleotide-binding site leucine-rich repeat NCPAH – N-carbamoyl putrescine amidohydrolase NRT – Nitrate transporter ns-LTP – Non-specific’ lipid transfer protein ODC – Ornithine decarboxylase PA – Polyamine PAMP – Plant AMP PAMPs – Pathogen-associated molecular patterns PCA – Principal Component Analysis PCR – Polymerase chain reaction PDA – Potato dextrose agar Pfam – The protein families database PGRC – Plant Gene Resources of Canada PR – Pathogenesis related protein family PRR – Pattern recognition receptors PSD – Pathogen-specific defense psiBLAST – Position-Specific Iterated BLAST PTI – PAMP-triggered immunity PUFAs – Polyunsaturated fatty acids Put – Putrescine qPCR – Quantitative PCR qRT-PCR – Quantitative real time PCR R – Arginine RALF – Rapid Alkalinization Factors
xviii RNA – Ribonucleic acid RNA-Seq – RNA sequencing RO – Reverse osmosis ROS – Reactive oxygen species rpsBLAST – Reverse PSI-BLAST SA – Salicylic acid SAM – S-adenosyl methionine SAMDC – SAM decarboxylase SAR – Systemic acquired resistance SMPS – Spermine synthase SNV – Single Nucleotide Variants Spd – Triamine spermidine SPDS – Spermidine synthase Spm – Tetraamine spermine SQL – Structured Query Language tSPM – Thermospermine UBI – Ubiquitin WRKY – WRKY transcription factor XR – Rapid ion flux
xix Acknowledgements I owe endless thanks to my supervisor, Dr. Deyholos, who was very generous with his time and knowledge and supported me in each step to complete this thesis. I feel very blessed to have a mentor who cared so much about me and my work. My PhD journey was very enjoyable because of your encouragement. You let me do my research on my own pace, with freedom to develop my ideas and always providing me with advice and discussion. I am very proud to have a supervisor that is an inspiration to me. I have grown so much as a person and a researcher in the past five years, and I have not enough words to thank you for everything. Thank you to my committee members Dr. Soheil Mahmoud, Dr. Richard Plunkett, Dr. Nasser Yalpani and Dr. Louise Nelson (former member) for your support and your valuable suggestions for my research. I have learned so much from you when I was preparing for my comprehensive exam and during our meetings. Your interesting questions made me reflect about my work. Thank you also for all of our chats, and for showing interest in my personal life. I would like to thank Dr. Miranda Hart and Dr. Soheil Mahmoud, and all the members of their labs for sharing the space and equipment, as well as knowledge and experiences. Special thanks to Dinesh Adhikary, who first taught me about everything in the lab. He always had encouraging words and was ready to help me. Erica Packard helped me a lot with my experiments, and kept me motivated. It was a true honor to be your mentor during your whole year of volunteer work in our lab. Thanks to Vasilis Kokkoris and Eric Vukicevich, who helped me with the plant-fungal assay. It was so great to work with you; you made our harvest times so fun! Thank you to all the students who helped at some point, including Tirhas Gebretsadikan, Becky Love and Joel Murga. Many thanks to all my friends and fellow graduate students, that helped with my experiments, my research questions and made the experience at UBC memorable. In particular, thanks to Ayelign Mengesha, Lukman Sarker, Tirhas Gebretsadikan, Dinesh Adhikary, Upama KC, and Geetkamal Hans. You kept motivated and supported me in the good and the difficult moments. Thank you to the Biology department staff, especially Barb Lucent and Tanja Vogel. Barb is the most competent administrative assistant that I have ever met. She is
xx also an amazing person, and we certainly could not do it without her. Tanja helped with my science outreach and lab supplies, and it was great to work with her. Also thank you to the international advisors, especially Danai Bélanger. Thank you to the Centre for Writing and Scholarly Communication, especially Amanda Brobbel, Lori Walter, Jessica Lowry, and Ellen Campbell, who helped me with my thesis writing reviews. Thank you to Dr. Khalid Rashid, Dr. Martin Reaney and Dr. Louise Nelson, and their students, specially Melissa Larrabee, for supplying the fungal isolates. I would also like to thank Dr. Martin Reaney for supplying the CLP mixture used in Chapter 5. I am very grateful for the contributions of my funding agencies: Science Without Borders (CAPES) – Brazil, Universidade Federal Rural do Semi-arido – Brazil, and The University of British Columbia. I have very special thanks to people who supported me in my journey through motherhood, that happened to start during my PhD. There is an African proverb that says it takes a village to raise a child. I would like to thank to my village: Tara Thompson and family, Barb Zeitner and family, Ruth Patten and Bethany Zeitner. Your incomparable friendship and support meant everything to me. Special thanks to all of the amazing staff of UBC daycare, which my son Ben attended for the past two years. Thanks to my friend Sara, who made the diagrams for Chapter 4. Many thanks to my in-laws who supported my family during our stay in Canada, coming very often to visit us, and bringing a piece of home with them. Endless thanks to my parents, who always believed in me and supported me, emotionally, financially, and with grandparents-sitters duties. Thank you to my sister that has been a great support to me, and, as a microbiologist, advised me with the experimental design of chapter 5. Finally, I would like to thank my husband and my kids. Thank you to my little ones, that gave me so much purpose of living, and made me want to be a better and successful person. Your happy presence in my life made the bad days do not look so bad, and made me feel already successful, because I have created something as extraordinary as you are. Thank you to my husband, who gave me the motivation to take this PhD in the first place, and that continuously supported me during my journey. Last, but not least, I am very thankful to God, and my church family (Calvary Chapel Kelowna). Because love and faith sustained me until this day.
xxi Dedication
I dedicate this thesis to my children, Benjamin and Elena, whose smiles were the fuel to my perseverance and hard work.
xxii Chapter 1 : Introduction Plants diseases cause an estimated 16% yield loss worldwide, despite current protection practices (Oerke & Dehne, 2004). Controlling crop diseases is important not only to reduce economic losses, but also to ensure food security (Savary, Ficke, Aubertot, & Hollier, 2012). Climate change may increase the impact of crop diseases and further reduce yields (Newbery, Qi, & Fitt, 2016). Therefore, it is necessary to develop high-yielding crops that will resist biotic and abiotic challenges, and ensure food security for the rising population. To develop more disease-resistant crop varieties, it is necessary to understand the mechanisms that plants use for defense against pathogens.
This review summarizes plant-specific responses to pathogens, with an emphasis on two major classes of low molecular-weight defense molecules: antimicrobial peptides (AMPs) and polyamines (PAs). To provide further context to these molecular defense responses, this review also considers plant responses to mutualistic (i.e. beneficial) fungi as compared to parasitic (i.e. pathogenic) fungal symbionts.
1.1 Natural and agricultural systems for plant defense Pathogens, predators, temperature, weather and soil conditions threaten plant survival and impact crop productivity. Pathogens alter or inhibit normal cell activities, resulting in plant disease and ultimately death. Plants have developed multiple physical and chemical defense mechanisms in response to unfavorable environmental conditions and the organisms that cause diseases.
Plants have constitutive defenses that are in place even before pathogens or predators are encountered. These include both structural barriers and chemical defenses. Physical barriers on the plant surface, including waxes, cuticles, and the cell walls and their reinforcements must be overcome by the pathogen in order for disease to occur (Taiz & Zeiger, 2006). Pathogens usually penetrate plant cells through natural openings, like the stomata, or through wounds. Some secondary metabolites (e.g. terpenes, phenolic compounds and alkaloids) are produced by plants, and some of these may
1 also protect plants against pathogens and pests. Some antimicrobial peptides (AMPs) are constitutively produced by plants as well, such as kalata B1 and kalata B2 from Oldenlandia affinis (Plan et al., 2010).
1.1.1 Plant molecular responses to symbiosis Recognition of a potential enemy by a plant may induce additional responses beyond the constitutive defenses described above. Host plants recognize a pathogen through two phases of an innate immune system described as the “zigzag model” (Jones & Dangl, 2006). In the first phase, pathogen-associated molecular patterns (PAMPs, e.g. chitin, flagellin) are detected by the host’s pattern recognition receptors (PRRs), which are receptor-like kinases (Ma & Ma, 2016). Pathogens can also be detected as damage-associated molecular patterns (DAMPs), which are molecules derived from plant damage caused by the pathogens (e.g. enzymatic degradation of plant cell wall) (Zamioudis & Pieterse, 2011). Detection of PAMPs or DAMPs triggers a response against a broad spectrum of microbes, including non-pathogens (Ma & Ma, 2016). This PAMP-triggered immunity (PTI) restricts pathogen growth and spread, mainly by inducing pH alkalization, callose deposition, and the activation of defense genes (Fawke, Doumane, & Schornack, 2015). If PTI fails to arrest colonization, the pathogens are able to release various types of effector molecules. Effectors include direct or indirect products of pathogen avr (avirulence) genes, such as inhibitors of host PRRs (Jones & Dangl, 2006). Pathogen effectors interfere with PTI and enable pathogen growth, resulting in effector-triggered susceptibility (ETS) (Jones & Dangl, 2006). Resistant plants will then activate a second layer of defense, which depends on recognition of effectors by NB-LRR (nucleotide-binding site leucine-rich repeat) proteins, encoded by some host R genes. This response is known as effector-triggered immunity (ETI), which is an accelerated and stronger defense than PTI (Fawke et al., 2015; Halterman et al., 2016; Jones & Dangl, 2006).
The main aspect of ETI is the HR (hypersensitive response), which is a localized host cell death in the region immediately surrounding an infection site. This causes a restriction of the available nutrients for the pathogen, therefore inhibiting pathogen
2 growth and spread (Morel & Dangl, 1997). Pathogens may change their repertoire of effector molecules in order to supress ETI; in turn, plant R genes may evolve to recognize the new effectors, so ETI can be triggered again (Jones & Dangl, 2006).
The mechanisms of PTI and ETI are summarized in Figure 1.1. Chief among these are rapid ion flux, termed XR, and oxidative burst (Morel & Dangl, 1997). XR is characterized by a rapid influx of Ca++ to the intracellular space, and an efflux of Cl- and K+ driven by H+-ATPases, promoting alkalinisation of the cytoplasm and depolarization of the cell membrane, with consequent signal transduction (Morel & Dangl, 1997). The main enzymes involved in oxidative burst are NADPH oxidases, which produce ROS
(reactive oxygen species) (Singh, 2013). H2O2 is an important ROS, involved in signal transduction (through the production of lipid oxidases) and membrane lipid oxidation, which also triggers intracellular responses and could be involved in cell death (Morel & Dangl, 1997). The ROS described above are produced by various enzymes, including peroxidases, superoxide dismutases, catalases, glutathione peroxidases (Morel & Dangl, 1997). Both ion fluxes and oxidative burst commonly trigger signal transduction pathways (e.g. kinase/phosphate cascades) and consequently activate transcription factors (e.g. WRKY), culminating in HR and cell death (Singh, 2013).
Plants produce many defense proteins in response to pathogens. These include pathogenesis-related proteins (PRs), AMPs, and enzymes such as beta-glucanases and chitinases, which degrade the fungal cell wall (Singh, 2013). Defense responses are also associated with the production of phytoalexins, which are secondary metabolites such as terpenoids, sesquiterpenoids, indoles, flavonoids, isoflavonoids, polyphenols and quinones (Koche, 2018). The main hormones produced by plants in response to pathogen attack are SA (salicylic acid), JA (jasmonate) and ET (ethylene). SA is associated with protection against pathogens that feed from the living tissues (i.e. biothophs). JA/ET are associated with protection against pathogens that feed on dead tissues (i.e. necrotrophs) and JA alone is associated with resistance to herbivores (Ma & Ma, 2016).
3 anion efflux and proton influx Recognition of pathogen signals
H2O2 Ion fluxes (XR) Oxidative burst Ca++ Intracellular signaling
(e.g. AMPs and PRs) Lipid peroxidation
Antimicrobial proteins Transcriptional activation Biotrophic SA
Necrotrophic JA/ET Hormones Defense Cell death
Herbivores JA (e.g. endonucleases, proteases) Phytoalexins ROIROS protectantprotectant mechanismsmechanisms
(e.g. Terpenoids, (e.g. superoxide dismutase, catalase, sesquiterpenoids, glutathione peroxidase, glutathione S- indols, flavonoids, transferase and polyubiquitin) isoflavonoids, Polyphenols, quinones)
Figure 1.1 Simplified diagram of plant defense responses pathway. PAMPs or effectors are recognized by the plant. Ion fluxes (XR) (Ca++ influx is highlighted) and an oxidative burst (where H2O2 is an important signaling molecule) trigger intracellular signaling, which in turn activates defense responses (production of antimicrobial proteins, hormones, and phytoalexins) and ROS (reactive oxygen species), which are toxic to the pathogens. HR response/cell death might be activated in non-host interactions. Redrawn and adapted from Morel & Dangl, (1997). Used with permission from NATURE PUBLISHING GROUP.
Plants initially respond to mutualistic fungi by engaging the immune system. This occurs because of the similarities in colonization processes, which will be further discussed later in this chapter. PTI is triggered after recognition of the fungus, and at later stages beneficial fungi (e.g. mycorrhizae) are able to evade plant defense systems and effectively colonize host roots. This association will promote plant growth and protection against pathogens (Zamioudis & Pieterse, 2011). It is suggested that after inoculation, arbuscular mycorrhizal fungi (AMF) secreted molecules (effectors) in either the apoplast or cytoplast will direct the plant to suppress the defense system (Zamioudis & Pieterse, 2011). There is a vast gap of knowledge on effectors of AMF symbiosis with plants. Nevertheless, an effector protein from Rhizoglomus irregulare, the secreted protein SP7, has been identified, which interacts with a pathogenesis-related transcription
4 factor (ERF19) in the plant nucleus, leading to reduced defense responses and higher levels of mycorrhizal colonization in Medicago truncatula (Figure 1.2) (Kloppholz et al., 2011).
Figure 1.2 Establishment and persistence of AMF in plant roots. Initially, PAMPs are perceived by PAMP receptors in the plant, triggering a signal transduction pathway that will lead to the activation of a transcription factor ERF19 in the nucleus of in Medicago truncatula and consequently expression of defense genes. Subsequently, effectors produced by AMF (e.g. SP7) will short-circuit the plant defense system by interacting with ERF19. Adapted and redrawn Kloppholz et al., (2011). Used with permission from Elsevier.
1.1.2 Host colonization by fungi Host colonization occurs in various ways, depending on the fungus. Filamentous fungi usually colonize the host using highly specialized infection structures. The steps of filamentous pathogen colonization are: adhesion to plant surface, spore germination and hyphae differentiation, penetration, and growth, invasion and reproduction (Perez- Nadales et al., 2014).
5 Some fungi establish long-term relationships with their hosts, which may involve formation of specialized structures. Some pathogens develop a haustorium, which is a specialized projection to increase absorption of nutrients from the host. Haustoria are analogous to the arbuscules formed by AMF (Fawke et al., 2015; Jacott et al., 2017), which facilitate nutrient exchange with the host. AMF usually obtain nitrogen and carbohydrates from the plant host, and in return enhance the plant’s uptake of water and minerals (principally phosphate) (Jacott et al., 2017; Jiménez-Bremont et al., 2014). Both haustoria and arbuscules are formed by invaginations of the plant protoplast (Fawke et al., 2015). There are many similarities between pathogen infection and AMF colonization, possibly leading to trade-offs between mutualistic associations and resistance to disease (Jacott et al., 2017).
Following spore germination, the colonization of the root by AMF begins with chemical communication between the two, prior to physical contact. Strigolactones are released by plants leading to increased mitochondrial density and lipid catabolism for the AMF (Besserer et al., 2006) while AMF produce sulphated and non-sulphated simple lipochitooligosaccharides (LCOs) to stimulate root growth and branching and to ultimately promote the symbiosis (Maillet et al., 2011). A penetration apparatus (appressorium) is then formed upon contact and initiates the internal colonization. AMF colonize the inner cortical cells of the roots and produce specialized structures called arbuscules (nutrient exchange centers) and vesicles (propagules and storage units) inside the cortex of plant cells, while at the same time an extensive mycelial network (extraradical mycelium) is spread beyond the root (Figure 1.3) (Smith & Read, 2008).
6 A B Arbuscules Vesicle
C Extraradical D mycelium Spore
Figure 1.3 Root sections of flax CDC Bethune plants after a month of colonization by Rhizoglomus irregulare. Red arrows indicate the B) AMF vesicles and arbuscules inside flax roots; C) Extraradical mycelium; D) Germinated spore. Pictures kindly provided by Vasilis Kokkoris, University of British Columbia.
1.1.3 Fusarium oxysporum Fusarium oxysporum is a filamentous fungus and a major plant pathogen with a wide host range (Demers et al., 2015). F. oxysporum f. sp. lini causes Fusarium wilt in flax. Fusarium wilt has been one of the most widespread diseases and major limiting factor for flax growth in North America, including western Canada (Rashid, 2003). Severe epidemics of F. oxysporum are rare, however, when they occur they may cause 90- 100% losses of flax yields (Rashid, 2003).
F. oxysporum infection begins with spore germination in the soil, in response to plant exudates. This is followed by differentiation of an infection hypha (Perez-Nadales et al.,
7 2014). No specialized structures, like appressoria or penetration pegs, are needed for the invasion of the roots, which happens instead through wounds or natural openings (Perez-Nadales et al., 2014). Subsequently, the fungus reaches the xylem vessels and colonization occurs, blocking the vascular system and impairing water transport. This results in wilting, chlorosis, necrosis, browning of the vascular system and eventually plant death (Galindo-González & Deyholos, 2016; Perez-Nadales et al., 2014). Spore production (macro- and microconidia, and chlamydospores) occurs on dead plant tissues and in the soil (Perez-Nadales et al., 2014). F. oxysporum also produces mycotoxins that contribute to its virulence. The fungus persists in the soil, as mycelia and spores, for several years (5-10), causing recurrent plant diseases (Rashid, 2003). Dispersal through fields occurs as a result of soil runoff and wind dispersion of contaminants (fungi mycelia and spores) found in the soil, infected seeds and plant debris (Rashid, 2003). The fusarium disease cycle is shown in Figure 1.4.
Currently, control of Fusarium wilt is accomplished with crop rotation, fungicides and resistant cultivars, where the last is the most important control measure (Rashid, 2003). However, there is still a range of flax varieties susceptible to particular isolates (Kroes, et al., 1999). Therefore, more studies on this important pathogen are needed to prevent the emergence of new varieties of flax resistant to F. oxysporum, and spread of the disease.
8
Figure 1.4 Fusarium disease cycle. Source (Perez-Nadales et al., 2014). Used with permission from Elsevier.
1.1.4 Agricultural protective measures Agricultural measures for crop protection include the use of chemicals, resistant varieties (through traditional breeding or genetic engineering), biological control, and modification of cultured practices (e.g. reducing tillage, removing weeds, and expanding crop rotation). The most traditional method to develop plant varieties resistant to pathogens is by plant breed selection, obtaining plants with desirable characteristics, such as pathogen resistance. Genetically engineering of plants it is an alternative to crop breeding and may be achieved using methods as CRISPR or RNAi. These methods may be used to silence pathogen genes directly, especially in the case of virus coat proteins, replicases and helicases. Expression of virus genes, such as the coat
9 protein, can also increase resistance of transgenic plants. Similarly RNAi has being used to target crucial pathogen or predator (e.g. insects, nematodes, fungi, parasitic weeds, and oomycetes) genes and its known as host induced gene silencing (HIGS) (Kloppholz et al., 2011).
In 2017, Innateâ branded potatoes, which are protected against the late blight pathogen (Phytophthra infestans), were first planted in Canada (ISAAA, 2017). This transgenic potato contains R genes from wild relatives of cultivated potato that confer resistance to the pathogen (Halterman et al., 2016).
Special attention in this review will be given to AMPs and polyamines (PAs) as novel targets for rational drug design and crop breeding. Recognizing the importance of these topics for the current study, I will discuss them in depth in the following sections of this chapter.
1.1.5 Antimicrobial peptides Antimicrobial peptides (AMPs) usually range from 10 to 50 amino acids in length and from 2 to 9 kDa in mass (Nawrot et al., 2014) . Only peptides with less than 100 amino acid residues are considered to be AMPs (Broekaert et al., 1997). Plant AMPs are encoded by small genes, may be inducible or constitutively expressed, and have been found in many plant tissues including roots, seeds, flowers, leaves and stems (Nawrot et al., 2014). AMPs share some general attributes: small size, hydrophobicity, amphipathic character, positive charge, conserved cysteine residues, disulfide bonds (Broekaert et al., 1997; Nawrot et al., 2014). All these features contribute to their broad- spectrum antimicrobial activity (Nawrot et al., 2014). Diverse types of AMPs also share similar mechanisms of action, presumably killing pathogens by their interaction with the outer membrane structures (Padovan et al., 2010a).
Plant AMPs are grouped into six main families: defensins, thionins, lipid transfer proteins (LTP), hevein-like proteins, snakins and cyclotides based mainly on homology
10 of amino acid sequences, the number of cysteine residues, and shared structural scaffolds (Broekaert et al., 1997; Nawrot et al., 2014; Padovan et al., 2010b). The main sequence patterns for each AMP class, showing the number and distribution of cysteine residues, are shown in Table 1.1.
Table 1.1 Sequence pattern for each AMP class
AMP class Sequence Pattern Reference Thionin (8-Cys type) X{2}CCX{7}CX{3}CX{8}CX{3}CX{1}CX{8}CX{6} (Lay & Anderson, 2005) Defensin X{3}CX{10}CX{5}CX{3}CX{9}CX{8}CX{1}CX{3}C (Lay & Anderson, 2005) LTP X{3}CX{9}CX{12}CCX{18}CX{1}CX{23}CX{15}CX{4} (Lay & Anderson, 2005) Hevein X{3}CX{4}CX{4}CC{5}CX{6}CX{2} (Lay & Anderson, 2005) Snakin CX{3}CX{3}CX{7,11}CX{3}CX{2}CCX{2}CX{1,3}CX{11}CX{1,2}CX{11,14}KCP (Porto & Franco, 2013) Cyclotide X{1}CX{3}CX{4}CX{4}CX{1}CX{4}CX{6} (Lay & Anderson, 2005)
1.1.5.1 Thionins Thionins have low molecular weight (approximately 5 kDa) and they are positively charged at a neutral pH (Nawrot et al., 2014). They are composed of approximately 45- 47 amino acids and are rich in arginine (R), lysine (K) and cysteine (C) residues (Broekaert et al., 1997; Padovan et al., 2010a). They can have six or eight cysteine residues, but their sequences are otherwise relatively divergent, with conserved residues restricted to the six cysteines at positions 3,4,16,27,33 and 41, an aromatic residue at position 13, and the arginine at position 10 (Broekaert et al., 1997).
Thionins present three or four conserved disulfide bonds between the cysteine residues, and their structural signature is an L-shape (Broekaert et al., 1997). The long arm of the L-shape comprises two antiparallel alpha helices (helix turn helix motif) and the short arm is formed by two short antiparallel beta strands (beta sheet), with a tyrosine (Tyr) 13 in the groove between the helices (Figure 1.5) (Broekaert et al., 1997; Nawrot et al.,
11 2014).
Figure 1.5 Plant thionin tridimensional model. Model created with the program SWISS-MODEL (Waterhouse et al., 2018) for the gene Sobic.001G312350 of Sorghum bicolor. Copyright information: CC BY-SA 4.0 Creative Commons Attribution-ShareAlike 4.0 International License.
There are five classes of thionins (Nawrot et al., 2014). These classes include: Type I (purothionins) and Type II thionins (α- hordothionin and β-hordothionin), which have four disulfide bonds and are basic; Type III thionins, which have 3 disulfide bonds and are basic (i.e.: viscotoxins, phoratoxins, ligatoxin A); Type IV thionins (crambins), which have three disulfide bonds and no charge at neutral pH; Type V thionins, which are a truncated form of thionins (Nawrot et al., 2014). Thionins have a broad spectrum of activity and are toxic against bacteria (Gram positive and negative), fungi in vitro (e.g.: Alternaria brassicicola, Fusarium culmorum) and yeast (Broekaert et al., 1997). Table 1.2 summarizes the main characteristics of thionins.
12 Table 1.2 Thionin features
Molecular weight 5 kDa
Number of amino acids 45-47
Amino acid conservation Highly divergent
Composition R+K+C
Number of Cysteine residues 6 or 8
Motif/ signature L shape
Number of disulfide bonds 3 or 4
Position of the cystine residues 3,4,16,27,33 and 41
3D structure 2 antiparallel alpha helices and 2 antiparallel beta strands with a tyr 13 in between the groove Groups Alpha and beta thionin
Classes Type I, II, III, IV and V thionin
Charge Positive at neutral pH (except crambins)
Activity Anti-bacteria/fungi/yeast
1.1.5.2 Defensins Plant defensins are small (about 5 kDa), cationic, cysteine and glycine-rich peptides with a length of approximately 45-54 amino acids (Nawrot et al., 2014). They have eight cysteine residues and form 3-4 disulfide bridges among the following cysteine residue pairs: Cys1-Cys8, Cys2-Cys5, Cys3-Cys6, and Cys4-Cys7 (Lay & Anderson, 2005). In addition to these eight cysteines (C), their conserved residues include two glycines (G) at positions 13 and 14, an aromatic residue at position 11, and a glutamic acid (E) at position 29 (Broekaert et al., 1997). Aside from these conserved residues, defensin
13 amino acids sequences are quite diverse (Nawrot et al., 2014). However, their three dimensional structure is well-conserved and comprises a prominent alpha helix and a triple stranded anti-parallel beta sheet, which is stabilized by four disulfide bridges (Figure 1.6) (Nawrot et al., 2014; Yount & Yeaman, 2004). These bridges form the CSα/β motif (cysteine-stabilized α-helix β-sheet motif) (Lacerda et al., 2014). Defensins also have two other conserved motifs: the α-core (the loop connecting the first β-strand to the α-helix) and the γ-core motif (the hairpin loop that links the antiparallel β-sheets 2 and 3 within a conserved GXC motif, which is required for their antifungal activity) (Lacerda et al., 2014).
Figure 1.6 Plant defensin tridimensional structure. Model created with the program SWISS-MODEL (Waterhouse et al., 2018) for the gene Lus10029546.g from Linum usitatissimum. Copyright information: CC BY-SA 4.0 Creative Commons Attribution- ShareAlike 4.0 International License.
Plant defensins contain a gamma-thionin domain. They were firstly termed gamma- thionins since they are very similar in size (45 to 54 amino acids), molecular weight (5 kDa), and disulfide arrangement (Padovan et al., 2010b). Furthermore, like some thionins, defensins have eight cysteine residues. However, defensins and thionins differ in their secondary structure. While thionins have two anti-parallel alpha helices, defensins have just one. Additionally, while thionins contain an anti-parallel double- stranded beta sheet, plant defensins comprise a triple-stranded anti-parallel beta sheet (Broekaert et al., 1997). Defensins are broadly distributed among plant families and
14 carry out functions such as antimicrobial proteins, proteinases, and insect amylase inhibitors. Table 1.3 summarizes the main characteristics of plant defensins.
Table 1.3 Defensin features
Molecular weight 5 kDa
Number of amino acids 45-54
Amino acid conservation Highly divergent (<35%)
Composition C+G
Number of Cysteine residues 8
Position of cysteine bridges Cys1-Cys8, Cys2-Cys5, Cys3-Cys6, and Cys4-Cys7
Motif/ signature y-core (GXC)
Number of disulfide bonds 4
Position of the cystine residues 8 C + 2 G (positions 13 and 14) + aromatic residue (position 11) +E (position 29) 3D structure Triple stranded β-sheet and an α helix in parallel
Charge Positive
Activity Anti-bacteria/fungi, proteinase, insect amylase inhibitor
1.1.5.3 Lipid Transfer Proteins Lipid transfer proteins or LTPs are one of the largest classes of AMPs among plants and other organisms (Pestana-Calsa & Calsa-Jr, 2011). They are longer in length (90- 93 amino acids) and larger in molecular weight (7-9 kDa) than many other AMPs (Broekaert et al., 1997; Nawrot et al., 2014; Yeats & Rose, 2008).
LTPs share a conserved three-dimensional structure consisting of a hydrophobic cavity
15 enclosed by four alpha helices, linked by flexible loops, which are formed by four disulfide bonds (Figure 1.7) (Yeats & Rose, 2008). Regarding the amino acid sequence, LTPs are considered highly divergent -- about 30% of the residues seem to be conserved (eight cysteines, as well as 12 positions which are occupied by hydrophobic or aromatic residues) (Broekaert et al., 1997).
Figure 1.7 Plant LTP tridimensional structure. Model created with the program SWISS-MODEL (Waterhouse et al., 2018) for the gene Lus10015279.g from Linum usitatissimum. Copyright information: CC BY-SA 4.0 Creative Commons Attribution- ShareAlike 4.0 International License.
LTPs are divided into two families (1 and 2) according to their molecular weight (Family 1 is 9 kDa and Family 2 is 7 kDa), and also according to their three dimensional structure (Padovan et al., 2010a; Yeats & Rose, 2008).
The LTPs have several biological properties that play a role in plant adaptation to diverse environmental conditions. They are involved in the plant antimicrobial defense (against bacteria and fungi) and molecular signaling, among others. LTPs are also important in industrial applications and human health, where they have been identified as major human allergens (Yeats & Rose, 2008).
16 The LTPs were named lipid transfer proteins because some members of this protein family enhance the inter-membrane exchange and/or transfer of lipids between natural or artificial membranes in vitro, although with low specificity (Padovan, Scocchi, et al., 2010). For this reason, they are also called ‘non-specific’ lipid transfer proteins or ns- LTPs (Padovan et al., 2010a). Despite their name, it is unlikely that LTPs play a role in intracellular lipid trafficking in vivo, based on a number of features of these peptides (e.g., their extracellular localization) (Broekaert et al., 1997; Padovan et al., 2010a; Yeats & Rose, 2008). Table 1.4 summarizes the LTP main characteristics.
Table 1.4 LTP features
Molecular weight 7-9 kDa
Number of amino acids 90-93
Amino acid conservation Highly divergent (30%)
Number of Cysteine residues 8
Motif/ signature Hydrophobic cavity and compact fold
Number of disulfide bonds 4
3D structure Bundle of 4 alpha helices linked by flexible loops Families Family 1 and 2
Charge Positive
Activity Anti-bacteria/fungi
1.1.5.4 Heveins Heveins are small proteins (43 amino acids in length and a molecular weight of 4.7 kDa) that have only one domain: the chitin-binding domain (Broekaert et al., 1997). These proteins are rich in cysteine (C) and glycine (G) amino acids and the cysteine residues
17 are highly conserved (Nawrot et al., 2014). Heveins may contain six, eight or ten cysteine-conserved residues and form three, four or five disulfide bonds, respectively (Broekaert et al., 1997). The number 8 cysteine-conserved residue with four disulfide bonds is the most frequent type of hevein, whereas the number 6 cysteine-conserved residues with 3 disulfide bonds is less frequent, but has been reported in species including Amaranthus caudatus and Ginkgo biloba (Nawrot et al., 2014). The three- dimensional structure of heveins consists of a triple-stranded beta-sheet and a short single turn alpha-helix connecting the second to the third p-strand (Figure 1.8).
Figure 1.8 Plant hevein tridimensional structure. Model created with the program SWISS-MODEL (A. Waterhouse et al., 2018) for the gene Lus10006552.g from Linum usitatissimum. Copyright information: CC BY-SA 4.0 Creative Commons Attribution-ShareAlike 4.0 International License.
The chitin-binding domain contains 20-40 amino acids and is rich in cysteine (C) and glycine (G) conserved residues. This domain confers the ability of hevein-type proteins to bind the chitin of fungi and inhibit its growth (Nawrot et al., 2014). Some heveins also have antibacterial properties (Broekaert et al., 1997; Nawrot et al., 2014). Other proteins such as class I chitinases may also contain a chitin-binding hevein-like domain (Yan et al., 2015). Table 1.5 summarizes hevein features.
18 Table 1.5 Hevein features
Molecular weight 4.7 kDa
Number of amino acids 43
Amino acid conservation High cysteine residues conservation
Composition C+G
Number of Cysteine residues 6 or 8 or 10
Motif/ signature Chitin binding: 20-40 aa with G+C residues conserved Number of disulfide bonds 3 or 4 or 5
3D structure Triple-stranded β-sheet and a short single turn α-helix connecting the second to the third p-strand Activity Anti-bacteria/fungi
1.1.5.5 Snakins Snakins are basic AMPs that have diverse functions including defense (antifungal and antibacterial properties), hormonal crosstalk, and development (Yeung et al., 2016). They contain about 60 amino acids. Snakins have been described with a molecular weight of 6.9 kDa (Nawrot et al., 2014). They have 12 cysteine residues and form six disulfide bonds (Nawrot et al., 2014). They are are highly divergent, with only about 38% sequence similarity, and many have been classified as the GASA/snakin family (Nawrot et al., 2014; Padovan et al., 2010a; Porto & Franco, 2013). A few snakins have been described so far and there is little information about their structures and mechanisms of action (Shi & Chan, 2014). Porto & Franco (2013) published an in silico prediction of the structure of the mature Snakin-1. They used the pattern presented in Table 1.1 to retrieve snakin sequences from the database Uniprot using the tool PHI BLAST (Porto & Franco, 2013). The structure predicted by Porto & Franco (2013) was composed of two long alpha helices (residues 2SSFCDSKCKLRCSKA16 and
19 20 32 43 45 DRCLKYCGICCEE ) and one short 310 -helix (residues NKH ). The structure is stabilized by six disulfide bonds.
In 2016, the first structure of a GASA/snakin protein (snakin-1) was determined using racemic protein crystallography (Yeung et al., 2016). The structure is very similar to Porto and Franco’s (2013) prediction. The snakin-1 structure is comprised of a helix– turn–helix domain, a short α -helix and a 310-helix, two rigidly held loops, that may have some functional importance (Figure 1.9). This peptide has 12 cysteine residues and the structure is held together by six disulfide bonds, as previously predicted. Table 1.6 summarizes snakin features:
Figure 1.9 Plant snakin tridimensional structure. Model created with the program SWISS-MODEL (A. Waterhouse et al., 2018) for the gene Lus10042203.g from Linum usitatissimum. Copyright information: CC BY-SA 4.0 Creative Commons Attribution-ShareAlike 4.0 International License.
20 Table 1.6 Snakin features
Molecular weight ~6.9 kDa
Number of amino acids 60
Amino acid conservation Divergent (38%)
Composition Rich in C
Number of Cysteine residues 12
Number of disulfide bonds 6
3D structure Helix–turn–helix domain, a short α -
helix and a 310-helix, two rigidly held loops Charge Positive
Activity Anti-bacteria/fungi
1.1.5.6 Cyclic peptides Plant cyclic peptides are defined as cyclic compounds with between 2 and 37 proteinogenic or non-proteinogenic amino acids (mainly L-amino acids) joined principally by peptide bonds (Tan & Zhou, 2006). Tan & Zhou (2006) classified these peptides in two classes: heterocyclopeptides and homocyclopeptides. Cyclic peptides containing only peptide bonds are classified as homocyclopeptides, and when other types of bonds are present, they are classified as heterocyclopeptides. These classes are further divided into five subclasses based on the number of rings in the structure: heteromonocyclopeptides, heterodicyclopeptides, homomonocyclopeptides, homodicyclopeptides, and homopolycyclopeptides. For instance, the subclass homopolycyclopeptides is composed of peptides that contain only peptide bonds and one ring. Cyclic peptides are also classified into types, based on their species of origin: Type I: Cyclopeptide Alkaloids; Type II: Depsicyclopeptides; Type III: Solanaceae-Type Cyclopeptides; Type IV: Urticaceae-Type Cyclopeptides Type V: Compositae-Type
21 Cyclopeptides Type VI: Caryophyllaceae-Type Cyclopeptides; Type VII: Rubiaceae- Type Cyclopeptides; Type VIII: Cyclotides (Figure 1.10). Sequences of cyclic peptides are available at CyBase (www.cybase.org.au), Uniprot (http://www.uniprot.org), and PhytAMP (www.phytamp.pfba-lab-tun.org), among others.
Figure 1.10 Classification of cyclic peptides. Redrawn from (Tan & Zhou, 2006).
In 2013, Arnison and colleagues suggested changing the name of Type VI homomonocyclopeptides to orbitides. Both cyclotides and orbitides are head-to-tail cyclized peptides and are ribosomally synthesized and post-translationally modified peptides (Arnison et al., 2013). These peptides differ in size (orbitides with ~9 amino acids and cyclotides with ~30 amino acids), composition (orbitides are not rich in cysteine residues, as cyclotides are) and structure (orbitides do not form disulfide bonds, as cyclotides do), but there is a striking similarity on their gene organization (Tan & Zhou, 2006). Cyclotides and orbitides will each be discussed here in further detail.
1.1.5.6.1 Cyclotides Cyclotides (Type VIII; homopolycyclopeptides) are the largest family of cyclic proteins (Gruber, 2010). Cyclotides comprise approximately 30 amino acids (ranging from 28 to 37 amino acids, and 2.5 to 4 kDa in the mature peptide) with high sequence similarity
22 and structural identity (Koehbach et al., 2013; Nawrot et al., 2014). The cyclotide protein family is estimated to include up to 50 000 members (Ireland, Clark, Daly, & Craik, 2010a; Weidmann & Craik, 2016), but only 390 accessions have been reported to date (http://www.cybase.org.au) (Accessed on April 21, 2019; Last updated: Sunday 21 April 2019).
The first cyclotide was discovered in the mid- 1970s in the plant Oldenlandia afinis as an uteronic agent that accelerates childbirth (Gran, 1973). Cyclotides have since been reported in species among the Rubiaceae, Violaceae, Cucurbitaceae, Fabaceae, Poaceae (cyclotide-like gene), Solanaceae and Apocynaceae families (Craik et al., 1999; Gould & Camarero, 2017; Simonsen et al., 2005), in a large range of tissues including leaves, roots, stems, flowers and seeds in some cases (Arnison et al., 2013; Gould & Camarero, 2017). Cyclotides have been reported to have many different biological activities, including: anti-HIV, immunosuppressant, cytotoxic, antitumor, anthielmintic, molluscicidal, antimicrobial, and insecticidal properties (Arnison et al., 2013; Craik et al., 1999; Ireland et al., 2010b).
The defining structural characteristic of cyclotides is their unique topology: the cyclic cystine knot motif (CCK), which is a combination of two structural elements: the cystine knot and the cyclic backbone (Craik et al., 1999; Nawrot et al., 2014). The CCK motif is composed of 6 cysteine residues cross-linked by disulfide bonds with the following residues: CysI-CysIV, CysII-CysV and CysIII-CysVI (Craik et al., 1999). Two disulfide bonds form the embedded ring (CysI-CysIV and CysII-CysV), and the third (CysIII- CysVI) connects the backbone segments (Craik et al., 1999). The disulfide bonds are the only post-translational modification that has been found so far in cyclotides (Arnison et al., 2013). The CCK topology is also associated with a triple stranded beta sheet backbone with 6 loops (Figures 1.11 and 1.12). Loop 1 and loop 4 are the most conserved part of the sequence (in both size and residue type) and correspond to the backbone segment of the embedded ring of the CCK motif (Craik et al., 1999; Nawrot et al., 2014).
23
Figure 1.11 Topology of the cystine knot motif. A beta sheet type structure (represented by green arrows) is associated with the cystine knot topology. (Adapted from: Craik et al., 2010). Used with permission from Elsevier.
Figure 1.12 Plant cysteine knot tridimensional structure. Model created with the program SWISS-MODEL (A. Waterhouse et al., 2018) for the gene Sobic.008G124100.1.p from Sorghum bicolor. Copyright information: CC BY-SA 4.0 Creative Commons Attribution-ShareAlike 4.0 International License.
The CCK (cysteine knot motif) makes cyclotides resistant to enzyme degradation. Outside of the conserved disulfide bonds, cyclotides are tolerant to sequence variations. Their versatile scaffolds make these peptides interesting for peptide-based drug design (Gould & Camarero, 2017). They are also proposed to have potential use in agriculture as fungicides because they present broad spectrum activity (Gould & Camarero, 2017;
24 Weidmann & Craik, 2016). Some cyclotides were detected in large amounts in plants (up to 0.1% fresh plant weight) (Weidmann & Craik, 2016).
Cyclotides have three subfamilies. The main subfamilies are the bracelet cyclotides (subfamily 1), which are positively charged, and moebius cyclotides (subfamily 2), which are neutral or slightly negatively charged (Craik et al., 1999). Representative members of the bracelet family include Circulin A and Cicloviolacin O1. Kalata B1 and Kalata B2 are representatives of the moebius family (Craik et al., 1999; Simonsen et al., 2005). The main difference between the moebius and bracelet families is a cis X-Pro peptide bond in loop 5 of the moebius family, which induces a twist in the peptide bond angle (Prabhavathi & Rajam, 2007; Wuddineh et al., 2018). The third, smaller subfamily is known as the trypsin inhibitor subfamily, which has high sequence homology to some members of the knottin family of proteins and does not show homology to the other subfamilies (Arnison et al., 2013; Pinto, 2013).
The main features of cyclotides are synthesized in Table 1.7
25 Table 1.7 Cyclotide features
Molecular weight 2.5-4 kDa
Number of amino acids 28-37
Amino acid conservation High sequence similarity and structural identity
Number of Cysteine residues 6
Motif/ signature Cyclic cysteine knot motif
Number of disulfide bonds 3
Position of the cysteine bridges CysI-CysIV, CysII-CysV, CysIII-CysVI
Subfamilies Moebius, Bracelet, Trypsin inhibtor
Charge Subfamily1 (bracelet): positive, 2 (moebius): negative or neutral Activity Anti-HIV, imunossupressor, antimicrobial, and insecticidal 3D structure Triple-stranded β-sheet and head to tail cyclised backbone (cysteine-knot topology (CCK)
1.1.5.6.2 Cyclolinopeptides (Orbitides) Cyclolinopeptides (CLPs; orbitides; Type VI, homomonocyclopeptides) are head-to-tail (N-to-C) cyclized peptides from plants that do not have disulfide bonds. Flax is one of the plants most rich in orbitides, and flax orbitides may also be called linusorbs. Orbitides are comprised of 2 to 12 amino acids, where the peptides with 5 to 9 L-amino acid residues are most frequent (Condie et al., 2011; Tan & Zhou, 2006). With a molecular weight of approximately 1 kDa (Jadhav, 2013). These peptides present a rigid structure and low amino acid complexity (Arnison et al., 2013). The least common amino acid residue is cysteine (unlike cyclotides). Acidic residues (aspartic acid, glutamic acid), amides (asparagine, glutamine), basic residues (lysine, arginine) and histidine are also rare (Arnison et al., 2013). Methionine is usually rare in orbitides in
26 general, but well represented in orbitides from Linum usitatissimum (cyclolinopeptides) (Arnison et al., 2013). The amino acids glycine and arginine are found in the majority of peptides, while serine, threonine, and tyrosine are found in 25, 31, and 54 of the currently described peptides, respectively (Arnison et al., 2013).
The plant families that have been identified as containing orbitides so far are: Annonaceae, Caryophyllaceae, Euphorbiaceae, Lamiaceae, Linaceae, Phytolaccaceae, Rutaceae, Schizandraceae, and Verbenaceae (Arnison et al., 2013). Currently the Cybase database has 110 orbitide (http://www.cybase.org.au) entries (Accessed on April 21, 2019; Last updated: Sunday 21 April 2019).
In 1959, Kaufmann and Tobschirbel isolated the first orbitide from higher plants, Cyclolinopeptide A (CLA, 295) from Linum usitatissimum. CLA has strong immunosuppressant activity (Pinto, 2013). It has also been reported to have cytotoxic, antiplatelet, antimalarial, immunomodulating, and T-cell proliferation inhibiting activities; and CLA can induce apoptosis in nematodes and a cancer cell line (Arnison et al., 2013; Jadhav, 2013). Orbitides have great potential for agricultural applications due to their diversity, stability and functionality, but their natural biological role in planta is still unknown (Arnison et al., 2013).
Flax is especially rich in cyclolinopeptides, which have been detected so far in the seeds and roots (Okinyo-Owiti et al., 2014). So far, eleven cyclolinopeptides (CLA-CLK) have been isolated from flax oil (Jadhav, 2013). The CLA-CLI sequences are available at the Cybase database (http://www.cybase.org.au). Several orbitides are encoded as a single polypeptide. For example, cyclolinopeptides C and E are encoded by a single gene and cyclolinopeptides D, F and G are encoded by a second gene (Gui et al., 2012). The concentrations of CLPs produced by a given gene are strongly correlated with each other (Gui et al., 2012).
One cyclolinopeptide (cyclolinopeptide E) is associated with a bitter off-taste of flax, developed after storage at room temperature (Bruhl et al., 2007). Recently,
27 cyclolinopeptide Single Nucleotide Variants (SNV) were identified in an Ethyl Methane- Sulfonate (EMS) population of mutant flax using the technique Ion Torrent sequencing (Galindo-González et al., 2015). The cyclolinopeptide E was one of the genes of interest used in this study to identify the SNVs in the flax mutant population (Galindo-González et al., 2015). The identification of these mutations represents a valuable resource for the development of new germplasm and infer gene function, which can lead to crop improvement (Galindo-González et al., 2015).
1.1.5.7 AMPs distribution among databases The number of sequences of AMPs described so far is still small, especially for plants (Table 1.8) (Waghu, et al., 2015a). Plant AMPs represent only 16% of the AMP sequences from all organisms available at Uniprot, where LTPs are the most abundant AMP class (Pestana-Calsa et al., 2010). As described above, although the cyclotide family may include up to 50,000 members (Ireland et al., 2010a), only 390 and 450 entries have been reported so far in the Cybase (http://www.cybase.org.au) and Uniprot databases respectively (Pestana-Calsa et al., 2010). These data show that further research is necessary to identify novel AMP sequences and to elucidate AMP evolution and mechanisms of action. Computational searches can be very helpful for identifying AMP genes from the data available in the current databases.
28 Table 1.8 Number of AMPs sequences deposited on databases
Databases Website link Plant All organisms Last access/ AMPs AMPs Reference PhytAMP 271 nr 2012-01-24 http://phytamp.hammamilab.org/m ain.php CAMP R3 * 10247 Waghu et al., 2015 http://www.camp3.bicnirrh.res.in/ APD3 321 2619 Wang et al., 2015 http://aps.unmc.edu/AP/ dbamp 1506 12 389 2019-01-08 http://140.138.77.240/~dbamp/ind ex.php dbaasp 274 12658 04-21-19/ Pirtskhalava https://dbaasp.org/home et al.,2016 nr = not represented * = difficult to access the data
1.1.5.8 AMPs mode of action AMPs have a broad spectrum of action and can kill bacteria, fungi and insects. In this review we will focus on AMPs with antifungal activity. Although the proposed mechanism of action of AMPs is similar against these diverse groups of organisms, some variations arise due to the differences in the cell wall composition of bacteria compared to fungi. To disrupt cell membranes or target intracellular components, AMPs need to penetrate cell walls (Bahar & Ren, 2013), and this is partially dependent on the concentration of available AMPs (Malanovic & Lohner, 2016).
When AMPs reach the cell membranes, they disrupt the physical integrity of the membranes (Bahar & Ren, 2013). Two AMP characteristics are essential for this action: small size (so that AMPs can easily cross cell walls and membranes), and amphipathicity (Bahar & Ren, 2013) so that the positive charge allows the AMPs to interact with the negative cell membrane surfaces (hydrophilic head groups of the phospholipids) and the hydrophobic region interacts with the hydrophobic core of the
29 lipid bilayer and helps AMPs become inserted deeper into the membrane (Mahlapuu, Håkansson, Ringstad, & Björn, 2016). Three main modes of action for membrane active peptides are highlighted in Table 1.9 (Bahar & Ren, 2013). In addition to their function in membrane disruption, AMPs may also reach intracellular targets on the cytoplasm, resulting in the inhibition of synthesis of RNA, DNA, and essential proteins (Bahar & Ren, 2013).
Table 1.9 Models of antimicrobial activity of membrane-active peptides
Model of antimicrobial activity Mechanism of action Carpet like AMP molecules coat small areas of the membrane with hydrophobic side facing inward. The pore formation take place leaving pores behind in the membrane. Toroidal pore This model is similar to the Barrel-stave model, although AMPs are continually in contact with phospholipid head groups of the membrane. Barrel-stave AMPs insert themselves perpendicularly into the membrane bilayer
According to Bahar & Ren, (2013).
1.1.5.9 AMP applications Due to their role in plant defense and their distinct chemical and physical properties, AMPs are suitable candidates for engineering enhanced plant defenses against pathogens, as well as scaffolds for drug design in the pharmaceutical industry. AMPs have also been reported to have anti-cancer, anti-inflammatory, and immunosuppressive activity (Nawrot et al., 2014). Moreover, their mode of action is distinct from conventional antibiotics, making them potential alternatives to treat resistant pathogens in medical situations (Bahar & Ren, 2013). The AMP mode of action is difficult for bacteria to develop resistance to (Mahlapuu et al., 2016).
Several transgenic plants with increased resistance to pathogens were developed using antimicrobial peptides from other plant species (Table 1.10) resulting in varying degrees of protection (Jung & Kang, 2013). Defensin, hevein and a thionins have most
30 commonly been used to engineer plants. Plants expressing defensin have increased tolerance to many fungi and oomycetes (e.g. Alternaria sp., Botrytis sp., Fusarium sp., Phytophthora sp.). Tobacco plants expressing a hevein are resistant to Phytophthora parasitica and expressing a barley hordothionin are resistant to Clavibacter michiganensis and Pseudomonas syringae pv. tabaci (Table 1.10) (Jung & Kang, 2013). Transgenic plants expressing AMPs from other organisms including Cecropin from insects and a human beta defensin conferred resistance to tobacco/rice and A. thaliana against Pseudomonas/Xanthomonas and Botrytis cinerea, respectively (Table 1.10) (Jung & Kang, 2013).
31 Table 1.10 Transgenic plants developed using plant AMPs
AMP/ SIGNAL AMP CLASS SOURCE FOR TRANSGENIC PATHOGENS TESTED* SEQUENCE AMP PLANT RsAFP2 Defensin Radish Wheat/rice Fusarium graminearum, Rhizoctonia cerealis, Magnaporthe oryzae, Rhizoctonia solani, Alternaria longipes Dm-AMP1 Defensin Dahlia merckii Rice/papaya Magnaporthe oryzae, Rhizoctonia solani, Phytophthora palmivora MsDef1 Defensin Alfalfa Tomato Fusarium oxysporum
NmDef02 Defensin Nicotiana Tobacco/potato Phytophthora parasitica, megalosiphon Peronospora hyoscyami, Phytophthora infestans, Alternaria solani WjAMP-1 Defensin Wasabi Melon Fusarium oxysporum, Alternaria solani cdef1 Defensin Chili Tomato Fusarium sp., Phytophthora infestans BjD Defensin Mustard Peanut Phaeoisariopsis personata, Cercospora arachidicola alfAFP Defensin Alfalfa Potato Verticillium dahliae
wasabi Defensin Wasabi Rice Magnaporthe grisea defensin Pn-AMP Hevein Pharbitis nil tobacco Phytophthora parasitica
hordothionin Thionin Barley tobacco C. michiganensis and Pseudomonas syringae pv. tabaci
Note: * the transgenic plants have increased resistance to the pathogens tested, unless otherwise specified. Adapted and redrawn form (Jung & Kang, 2013). Used under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
Some AMPs have been approved as medicine or are undergoing clinical trials as reviewed by (Mahlapuu et al., 2016). Most of the drugs in development are for treatment
32 of complicated polymicrobial skin infections (topical uses) but there are some for systemic administration in the treatment of life-threatening infections, especially in immunocompromised patients, therefore the study of AMPs may also contribute to human health (Mahlapuu et al., 2016). However, there is a discrepancy between the number of promising AMP-based therapeutics described in the literature and the actual number of AMP-based drugs in clinical trials. This is explained by the poor correlation between in vitro and in vivo efficacy of AMPs, and their low oral bioavailability, high production cost, difficulty of synthesis (due to the presence of several disulfide bridges), toxicity, and short half-life following intravenous injection (Mahlapuu et al., 2016).
Despite the challenges of producing AMPs in a large scale for clinical and commercial development and for the agricultural industry, they are very important targets worth it of the efforts made by the scientific community to overcome those obstacles.
1.1.6 Polyamines Polyamines (PAs) include the diamine putrescine (Put), triamine spermidine (Spd), tetraamine spermine (Spm) and its structural isomer thermospermine (tSPM). These nitrogen-containing compounds of low molecular weight are polycations at physiological pH, and occur naturally in bacteria, fungi, insects, plants and animals (van der Weerden & Anderson, 2013). Polyamines play many critical roles in processes ranging from cell division, gene regulation, and in stress responses (Nambeesan et al., 2012). They accumulate in actively growing tissues, and in response to both biotic (Jiménez- Bremont et al., 2014) and abiotic (Alcázar et al., 2010) stresses.
PA homeostasis requires a precise and coordinated regulation of PA biosynthesis, catabolism, conjugation to hydroxycinnamic acids and transport to other tissues/ organs (Nambeesan et al., 2012). A schematic of some of those processes is shown in Figure 1.13.
PAs can be synthesized directly (Ornithine decarboxylase pathway) or indirectly (Arginine decarboxylase pathway). Both pathways produce Put , which is the precursor
33 of all higher order PAs (Spd, Spm/tSPM). Spd and Spm are formed by the subsequent addition of an aminopropyl moiety to Put and Spd by SPDS (spermidine synthase) and SMPS (spermine synthase), respectively (Figure 1.14). PAs are present in a free soluble form or in a conjugated insoluble form. Free PAs can suppress pathogens by regulating defense gene expression or by producing signaling molecules as products of their catabolism. Free PAs can have a direct inhibitory effect on the growth of fungi in vitro, although at concentrations that may not be relevant to the natural context (Wojtasik, Kulma, Namysł, Preisner, & Szopa, 2015). This leads to the assumption that PA conjugates, especially as found in the cell wall, can inhibit fungal growth. Also, high concentrations of PAs elicit HR response and death of the infected plant tissue (Wojtasik et al., 2015).
The oxidation of polyamines leads to the formation of aminoaldehydes, like 4- aminobutanal, DAP (1,3-diamonopropane) and hydrogen peroxide (H2O2), and occurs at the apoplast and peroxisomes. H2O2 is an important signaling molecule that plays a role in PA-induced regulation of many biological processes in plants in response to environmental stimuli (Wuddineh et al., 2018). H2O2 may promote plant resistance to diseases through HR, or through HR-independent mechanisms, like regulation of the expression of important genes involved in defense, strengthening the cell wall, MAPK activation and may even directly affect pathogen growth (Figure 1.13) (Jiménez- Bremont et al., 2014).
34
Figure 1.13 Polyamine metabolism. 1) The presence of microorganisms is recognized by receptors and the MAPK signaling pathway is activated inducing the expression of defense genes and PA biosynthesis, 2) PA biosynthesis can occur directly by synthesis of putrescine from ornithine by ODC (ornithine decarboxylase) or 3) indirectly, from arginine by ADC (arginine decarboxylase), AIH (agmatine iminohydrolase) and NCPAH (N-carbamoyl putrescine amidohydrolase) 4) Polyamines synthesis is mediated by SPDS (Spermidine synthase) and SPMS (Spermine synthase), 5) Free polyamines can regulate the expression of defense genes, and serv e as subs tracts for the production of H2O2, DAP (1,3 diaminopropane) and aminoaldehydes (aminobutanal), by the action of the enzymes PAO (polyamine oxidase) and DAO (diamine oxidase) in the 6) peroxisomes, 7) and in the apoplast,
8) H2O2 stimulates defense gene expression, 9) and can also directly affect pathogen growth or 10) indirectly triggering HR (hypersensitive response), strengthening of the cell wall, and stomatal closure, 11) Conjugated forms of polyamine can be formed by the addition of caffeoyl, cinammoyl, and feruloyl-CoA to putrescine or spermidine, 12) PA conjugates can directly inhibit pathogen growth or can strengthen the cell wall. This work is a derivative from Jiménez-Bremont et al., (2014), used under an Attribution 3.0 Unported (CC BY 3.0) license and the work of Wojtasik et al., (2015), used under an Attribution 4.0 International (CC BY 4.0) license.
35
Polyamine biosynthesis
H2N NH2 Putrescine
H2N S + Ado SPDS dcSAM SAM H S + Ado H2N N NH2 Spermidine
H2N S + Ado SPMS Ethylene biosynthesis H S N + Ado H2N N NH2 H aminopropyl Spermine
Figure 1.14 Biosynthesis of higher molecular weight polyamines from putrescine. Spermidine and spermine are synthesized from sequential addition of aminopropyl groups to the linear skeleton on putrescine and spermidine by SPD (spermidine synthase) and SPMS (spermine synthase) respectively. Aminopropyl moieties are donated from dcSAM (decarboxylated S-adenosyl methionine) which is synthesized from SAM (S-adenosyl methionine) by SAMDC (SAM decarboxylases). Ado is 5’ adenosyl. SAM is also substrate for ethylene biosynthesis in plants. This work is a derivative from Bitrián et al., (2012) used under an Attribution 4.0 International (CC BY 4.0) and Wikipedia contributors (2019, May 11), used under an Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.
Several studies have been performed to investigate the effects of the overexpression of PA genes on normal plant physiology as well as defense. By introducing an arginine decarboxylase (ADC) gene under the control of the CaMV promoter (cauliflower mosaic virus) 35S, researchers obtained transgenic eggplants with elevated PA concentrations and enhanced tolerance to multiple abiotic stresses including salt, water deficit, temperature and metal, and as well to biotic stress, including Fusarium wilt (Prabhavathi & Rajam, 2007). Transgenic tomato plants overexpressing yeast spermidine synthase (ySpdSyn) showed a 2.1 fold increase in Spd concentration in leaves when compared with the wild type in normal conditions (Nambeesan et al., 2012). Unexpectedly, these
36 plants had increased susceptibility to the necrotrophic fungus Botrytis cinerea and wild- type levels of resistance to Alternaria solani, the bacteria Pseudomonas syringae and the tobacco hornworm (Manduca sexta) (Nambeesan et al., 2012) . This increased susceptibility to B. cinerea was associated with the down-regulation of genes related to ethylene biosynthesis and signaling. This was demonstrated by supplementing the plants with ethylene precursors and PA biosynthesis inhibitors, which restored normal resistance to Botrytis. These data are indicative of a negative relationship between Spd and ethylene. Sobolev et al., (2014) also found a correlation between the genes that control ethylene production and PA genes. In this study, they observed that transgenic tomato plants with reduced ethylene production accumulated higher levels of PAs.
Studies with the application of free PAs on culture media or directly on the surface of plants can also be very informative about PA utility as antifungals. Synthesized spermidine analogs showed antifungal activity against several phytopathogens in liquid culture (Mackintosh et al., 1997). Likewise, a study with F. oxysporum showed that free polyamines inhibit the growth of pathogenic and non-pathogenic strains in vitro (Wojtasik et al., 2015). In this experiment, fungi were transferred to PDA plates supplemented with PAs (Put, Spd, Spm) at three concentrations (3,6 and 10 mM) and the growth was measured and compared to control after 2 days of incubation using densitometric analysis. Although their findings show that free PAs may directly inhibit fungal growth, the concentrations were much higher than their natural occurrence in flax seedlings and a minimal inhibitory concentration for polyamines was not determined.
PAs may also interact with AMPs. A very interesting study with fungi revealed a role of a PA transporter (Agp2p) in modulating responses to cationic antifungal peptides, including a defensin (NaD1) from Nicotiana alata (Bleackley et al., 2014). By screening a Saccharomyces cerevisae deletion collection, it was found that Agp2p acts as a regulator of NaD1’s potency, where the cells impaired of this polyamine transporter where more resistant to this particular defensin and other antifungal cationic molecules tested. It was proposed by these authors that the lack of the transporter led to an accumulation of polyamines at the surface of the cells, and due to their positive
37 charges, other cationic peptides were repelled from the surface of the cells and unable to bind to its specific receptors and consequently the antifungal molecules were not taken up by the cells (Bleackley et al., 2014).
Polyamines not only protect plants from diseases, but also seem to promote beneficial associations with plants, including those involving nitrogen-fixing bacteria and mycorrhizal fungi (Jiménez-Bremont et al., 2014). PAs stimulate colonization of roots by mutualistic fungi, like AMF, consequently improving the host’s absorption of nutrients (Jiménez-Bremont et al., 2014; Soraya et al., 2018; Q.-S. Wu & Ying-Ning, 2009). Additionally, PA application optimized the root system architecture of mycorrhizal citrus seedlings, promoting an increase in plant growth (Wu et al., 2012). Currently there is not much information about the early molecular events involved in the changes in PA content during the establishment of a mycorrhiza-forming fungi association with plants (Jiménez-Bremont et al., 2014).
1.2 Flax Flax (Linum usitatissimum L) is an important crop and also could be a good model to understand the molecular mechanisms underlying endosymbiosis in plants. Many resources have been developed to aid in the study of flax, and unlike Arabidopsis, it can be infected by both Fusarium and AMF. Therefore, we chose flax to be the focus of this study.
Flax is a member of the Linaceae family. Cultivars selected to extract the fiber from the stem are known as fiber flax, and cultivars grown to extract the oil from the seed are known as linseed (Government of Canada, 2012) . There are considerable differences (morphological, anatomical, physiological and agronomic performance) between flax and linseed, although they are the same species. Fiber flax is typically distinguished by traits such as taller growth, and fewer branches. Fiber is extracted from the stem, whereas linseed has a shorter stature and more branches and produce larger seeds that contain approximately 40% oil (Diederichsen & Ulrich, 2009; Islam, 2018).
38 Fiber flax is grown largely in the cool-temperate regions of Canada, China and Russia, whereas linseed is grown in a larger area (Hall et al., 2016). L. usitatissimum is cultivated in several countries of Europe and Asia and in some countries of North America (Canada, USA, Mexico), South America (Argentina, Brazil, Chile, Ecuador, Peru, Uruguay), Africa (Egypt, Eritrea, Ethiopia, Kenya, Morocco, Tunisia) and Oceania (Australia, New Zealand) (Government of Canada, 2012). There are approximately 3500 accessions of cultivated flax in Canada, which are maintained by Plant Gene Resources of Canada (PGRC) (Government of Canada, 2012). Canada is the biggest producer of linseed (Government of Canada, 2012). Canada produced 816.2 thousand tonnes of flax seed during 2014/2015, whereas in 2017 the estimate was 507 thousand tonnes, which is a representative decline in the production from the previous years, in spite of the 11% increase in seeded area. This is due to changes in the environmental conditions (Flax Council Of Canada, 2015; “Flax Market Snapshot,” 2015). The value of annual exports is in the range of CAD$150-180 million (M) (Islam, 2018).
Flax has been used by humans for thousands of years. Flax fibers have been used in the textile and paper industry, and flax oils may be used for many purposes (including industrial applications), but there is a growing trend toward the use of linseed oil and seed products in nutrition due to their health benefits. Linseed consumption may reduce the risk of cardiovascular diseases, cancer, and diabetes. Linseed also possesses anti- inflammatory, and antioxidant activity (Goyal et al., 2014; Hall et al., 2016). The main nutritional component of linseed responsible for these health benefits is assumed to be α-linolenic acid (ALA), which is also a precursor to other important polyunsaturated fatty acids (PUFAs). Flax oils contain up to 64% ALA and several studies have been done to understand and increase ALA and PUFA synthesis in flax aiming to improve the use of flax fiber and oil in industry (Hall et al., 2016).
Flax growth may be limited by biotic and abiotic stresses. The main pathogens affecting flax are: Fusarium oxysporum f.sp. lini., which causes fusarium wilt; Melampsora lini, which causes rust; Septoria linicola, which causes pasmo; Oidium lini, which causes powdery mildew; Rhizoctonia solani, which causes seedling blight and root rot; and
39 Sclerotinia sclerotiorum, which causes sclerotinia stem rot and others. Minor diseases are also caused by Alternaria linicola, which causes seedling and stem blight; Colletotrichum lini, which causes anthracnose of leaves and seedling blight, Phoma exigua, which causes root rot and Selenophoma linicola, which causes dieback (Government of Canada, 2012). These diseases are usually managed by agricultural practices and crop protection products, although some diseases like pasmo and powdery mildew are more challenging, even causing local epidemics. It is important to continue to develop resistant varieties to maintain the control over these diseases (Rashid, 2003).
Several types of genomic resources are available for flax. A whole genome assembly was published Wang et al., (2012), and has subsequently be updated (You et al., 2018). Several transcriptomic studies with flax have been published including: the transcriptome of flax in response to pathogens (Dmitriev et al., 2017a; Galindo- González & Deyholos, 2016), stresses (Dash et al., 2017; Wu et al., 2018, 2019), fibers (Gorshkov et al., 2017; Zhang & Deyholos, 2016) and others. These and other resources may contribute to the identification of target genes for genetic manipulation of flax in order to produce plants with increased yields, shorter time to maturation, resistance to diseases, and fiber and seed oil quality (Hall et al., 2016).
1.3 Research objectives and outline The main objective of this research is to study the molecular responses of flax to symbiotic fungi, with emphasis on antimicrobial peptides and polyamines as defense compounds. We expect that this information will support the development of flax varieties with improved resistance to F. oxysporum.
In Chapter 2, I combined bioinformatics and real-time PCR to identify antimicrobial peptides (AMPs) from the database 1KP. I hypothesized that the tools psiBLAST and HMMER would be useful to accurately retrieve AMP sequences from a large database, and that the non-model diverse species from that database would be rich in AMPs. This was confirmed with the identification of many novel AMP gene candidates from diverse
40 taxa ranging from red algae to core Eudicots. I also hypothesized that I would be able to identify some AMPs in the flax transcriptome that show a strong response to inoculation with pathogenic fungi while showing little change upon inoculation with a mutualistic fungus. The results were supported my hypotheses, as I identified many novel candidate AMP genes, in organisms ranging taxonomically from red algae to the core Eudicots. Also, I identified several AMP candidates from flax and tested their expression using real time PCR, and found that hevein and CLPs were particularly responsive. CLPs were later used for antifungal assays.
In Chapter 3, we designed an in vitro experiment to compare the effects of two beneficial symbionts, Clonostachys rosea and Rhizoglomus irregulare, on early stages of disease progression caused by Fusarium oxysporum in flax, and compared the effects of each of these fungi on flax growth. I hypothesized that both C. rosea and R. irregulare would be good bio-protection agents and good models to study the mechanisms underlying symbiosis. Our results aligned with our hypothesis and demonstrated that both C. rosea and R. irregulare inhibited the disease phenotype, with a significative decrease in growth and biomass caused by F. oxysporum. R. irregulare showed better protective effects than C. rosea, even pre-colonization, which was an unexpected outcome. Noting the importance of R. irregulare as an endomycorrhizal found in numerous associations with plants in nature, and F. oxysporum, as one of the major causes of flax diseases, the treatments with R. irregulare and F. oxysporum were chosen for further assays in Chapter 4.
In Chapter 4, I studied how R. irregulare affected flax in the presence or absence of the pathogenic fungi F. oxysporum at the transcriptomic level. I hypothesized that the gene expression information would give us a better picture of the mechanisms by which R. irregulare was protecting flax for the negative effects of F. oxysporum reflected on plant growth. I also hypothesized that flax plants would respond with changes in gene expression to R. irregulare to facilitate AMF colonization in both single inoculation treatment and co-inoculation treatments with F. oxysporum. I hypothesized that the defense responses triggered by F. oxysporum would be stronger than the responses
41 triggered by R. irregulare. I also hypothesized that multiple mechanisms are linked to AMF bio-protective effects against F. oxysporum in flax, such as genes directly related to symbiosis and as well defense genes.
In Chapter 5, I compared the activity of cyclolinopeptides and polyamines against several phytopathogens in vitro. Although the biological role of cyclolinopeptides in planta it is not yet known, I hypothesized that their activities are related to defense in plants, due to their hydrophobic character, similar to AMPs. My results generally support my hypothesis, because CLPs inhibited fungal growth in vitro at concentrations comparable to spermidine, which has an established role in plant defense. Moreover, the effective range of both the CLPs and spermidine were what might be expected to be naturally present in plants.
In Chapter 6, I present my concluding remarks, in which I try to explain how the proteins and pathways revealed by this work can contribute to a better understanding of plant defense mechanisms, in contrast to plant’s response to beneficial fungi. I also highlight the possible applications of our findings and give my suggestions for further studies.
42 Chapter 2 : In silico prospecting for plant antimicrobial peptides (AMPs) in the transcriptomes of over 1000 species
Antimicrobial peptides (AMPs) are small, ribosomally synthesized proteins, found in nearly all forms of life. In plants, AMPs have important roles in plant defense. However, only a small fraction of plant AMPs have been described to date. The rapid expansion of DNA sequence databases provides an opportunity to mine these data for candidate AMPs. The One Thousand Plants (1KP) database (Matasci et al., 2014) contains transcriptome assemblies from 1,179 species of algae and land plants. We searched these transcriptomes for candidate AMPs using two methods: psiBLAST (Position Specifc Interactive BLAST) (Altschul et al., 1997), and HMMER (a Hidden Markov Model-basded search tool) (Eddy, 1998). Candidate genes were further evaluated using resources such as the Conserved Domain Database (CDD); (Marchler-Bauer et al., 2014). Pathogen-inducibility of selected candidate AMP genes was tested by treating Linum usitatissimum (flax) with either a pathogenic fungus (Fusarium oxysporum) or a non-pathogenic, mycorrhizal fungus (Rhizoglomus irregulare), and measuring transcript expression by qRT-PCR. Approximately 70% of flax AMPs studied were differentially expressed upon pathogen inoculation. Moreover, most of the flax AMPs we studied were induced by interaction with the pathogen and not induced by interaction with the beneficial fungus. The main protein up-regulated following Fusarium treatment was hevein, its transcripts of gene Lus10006552.g increased 29-fold at 14 days after inoculation in roots. Another important peptide induced by the pathogenic fungus was a flax cyclolinopeptide (or orbitide). This unique class of peptides with antifungal potential induced 4.7-fold in roots at 14 days. These and other new identified AMPs may represent new targets for plant genetic engineering to reduce agricultural losses due to pathogens.
2.1 Background Antimicrobial peptides (AMPs) are small cationic peptides with a broad range of reported functions including antibacterial, antifungal, antiviral, anticancer, immunomodulatory, and anti-inflammatory activities. Hence, they are promising
43 candidates for crop improvement, and for the development of new crop protection products (Hammami et al., 2009). Their size and stability also make them potentially useful in pharmaceutical and industrial applications (Bhadra et al., 2018; Torrent et al., 2012).
AMPs are classified into six main families, based on the number and position of Cys residues and other structural features (Broekaert et al., 1997; Nawrot et al., 2014; Padovan, Segat, et al., 2010). These six families are: defensins, thionins, lipid transfer proteins (LTPs), heveins, snakins and cylcotides (Table 1.1).
Thousands of AMP sequences have been described in various curated databases. These include databases that specialize in AMPs from particular taxa (e.g. eukaryotes, fungi, plants, and bacteria) as well as databases of specific types of AMPs (e.g. defensins, cyclotides, and knottins) (Table 2.1). These curated databases typically embed useful metadata such as details about AMP classification, microbiological activity, and physicochemical parameters, signature patterns and the in silico tools to search, identify and characterize AMPs (Pestana-Calsa & Calsa-Jr, 2011). On the other hand, curated databases are not frequently updated, and can become rapidly outdated, given the fast pace at which new DNA sequences are being made available. For example, PhyAMP, which is the only AMP database specific for plants, launched in 2009 and was last updated in 2012, now containing 273 accessions (Hammami et al., 2009). The general AMP database, APD, has 321 AMPs accessions from plants and contains only AMPs with empirically demonstrated antimicrobial activity.
44 Table 2.1 Antimicrobial peptide databases
Database name Taxa or AMP Number of URL groups AMP represented accessions APD General 2,619 http://aps.unmc.edu/AP/main.php
CAMPr3 Eukaryotes 10,247 http://www.camp3.bicnirrh.res.in/exLinks.php Peptaboils Fungi 317 http://peptaibol.cryst.bbk.ac.uk/home.shtml PhytAMP Plants 271 http://phytamp.pfba-lab-tun.org/main.php BACTIBASE Bacteria 177 http://bactibase.hammamilab.org/main.php Defensins Genera; 363 http://defensins.bii.a-star.edu.sg/ Knowledgebase Cybase General 778 http://www.cybase.org.au Knottin database General 3320 http://www.dsimb.inserm.fr/KNOTTIN/
dbamp General 12,389 http://140.138.77.240/~dbamp/index.php dbaasp All organisms 12,658 https://dbaasp.org/home
Predicting AMPs is challenging due to their small size and poorly conserved sequences (Torrent et al., 2012). The low sequence conservation means that sequence vs. sequence alignment methods, like BLAST (Altschul et al., 1997), are not sufficient to identify AMPs in DNA sequence databases (Torrent et al., 2012). However, each class of AMPs is defined by a signature pattern (i.e. profile) of amino acids (Lay & Anderson, 2005) which makes them suitable for searches based on sequence vs. profile alignments, e.g. using profile-HMMs (Hidden Markov Models) (Eddy, 1998). For example, using HMMs, AMPs were accurately predicted to create the CAMPR3 database (Waghu et al., 2015b). Predictions can be validated experimentally, for example by measuring the minimum inhibitory concentration (MIC) of an AMP required to arrest growth of bacteria or fungi (Torrent et al., 2012). qRT-PCR assays of differential gene expression in response to pathogen challenge can also be used to provide experimental evidence to support in silico prediction of AMPs (Pestana-Calsa et al., 2010). Databases such as 1KP (Matasci et al., 2014) and UniProtKB/Swiss-Prot (The UniProt Consortium, 2018) each contain a broad representation of sequences, but these
45 databases were created for distinct purposes. The 1KP database contains at least one transcriptome from each of 1,179 plant species, for a total of 24,268,827 sequence entries. In contrast, the UniProtKB/Swiss-Prot database contains only manually reviewed, non-redundant protein sequences, and currently includes 36,397 entries from plants (Viridiplantae). Thus, there is a trade-off between the quantity and quality of information in different sequence databases, and both types of database should be considered when prospecting for new examples of particular proteins, such as AMPs. Given that the number of AMPs identified from plants thus far is relatively small, we were motivated to develop a pipeline to annotate AMPs in novel DNA sequence and we applied this computational method to the 1KP and Uniprot databases. The candidate AMPs we identified may represent new targets for plant genetic engineering and breeding, thereby have potential utility for reducing agricultural losses due to pathogens.
2.2 Material and Methods All the supplementary files are available at: (https://osf.io/9u45x/?view_only=f5866cf2c5054427a33f52936469701b).
2.2.1 Computational methods
2.2.1.1 Seed sequences A total of 253 seed sequences (i.e. sequences that are representative of the target protein classes) were obtained from the PhytAMP database (http://phytamp.hammamilab.org). The seed sequences were distributed among the following AMP classes: 55 defensins, 76 cyclotides, 43 thionins, 45 lipid transfer proteins (LTP), 20 snakins and 14 heveins. The seed sequences were used for searches against the Uniprot (https://www.uniprot.org) and 1KP (https://sites.google.com/a/ualberta.ca/1KP/) databases using psiBLAST (Altschul et al., 1997) and ‘hmmsearch’ packages from HMMER (Eddy, 1998).
46 2.2.1.2 Local databases Two local databases were created using makeblastdb, with the -parse_seqids option: (i) all reviewed plant proteins from Uniprot (http://www.uniprot.org) (a total of 36,397 sequences); and (ii) all protein sequences from 1KP (https://sites.google.com/a/ualberta.ca/1KP/) (a total of 24,268,827 sequences).
2.2.1.3 AMPs prediction using psiBLAST Putative AMPs from Uniprot and 1KP databases were identified using the program psiBLAST (Position specifc interactive BLAST) (Altschul et al., 1997) with three iterations and exported to an xml file. Only the matches with HSP (High-scoring Segment Pair) greater than 10 and hit length lower than 140 aminoacids were exported to a FASTA file, using the custom script blast_retrive_pzo_140aa.rb (Supplementary S1). We also used this same script without the restriction of amino acid length to compare the accuracy. Subsequently, the script repeat_verify.rb (Supplementary S1) was used to filter the repeated IDs. This file was used as input for the program blastdbcmd (Altschul et al., 1997) to retrieve the complete sequences from the created databases.
2.2.1.4 AMPs prediction using HMMER Candidate AMP sequences were identified using the program HMMER (Eddy, 1998). Seed sequences from the PhytAMP database were used to generate AMPs class- based signatures. Multiple sequence alignaments for each AMP class were created using T-COFFE expresso (Notredame, Higgins, & Heringa, 2000). These were used as input to build HMM models using the program ‘hmmbuild’ of HMMER 3.1b2 package (Eddy, 1998). The generated HMMs (Supplementary S2) were queried against the protein database of Uniprot and 1KP using the ‘hmmsearch’ tool of HMMER 3.1b2 package (Eddy, 1998). We performed these analysis with the default parameters and according to the recommendations of the developer. The results were parsed with the script parse_hmmsearch.pl (Supplementary S1). Unique IDs obtained using
47 ‘hmmsearch’ were used to retrieve the complete sequences with the program Blastdbcmd (Altschul et al., 1997).
2.2.1.5 Conserved domains search Conserved domains were identified using the sequences obtained with the program psiBLAST and ‘hmmscan’. Sequences obtained with psiBLAST were used for a search against the preformatted database of CDD (The Conserved Domain Database), subdirectory “little endian” using the program rpsBLAST. The results were parsed with the script parse_rpsblast_cdd.pl (Supplementary S1).
Sequences obtained using ‘hmmsearch’ were used for a search against a preformatted Pfam database using the program ‘hmmscan’ from the HMMER 3.1b2 package (Eddy, 1998).
2.2.1.6 Pattern recognition The sequences obtained with the psiBLAST program were used for pattern recognition analysis. Alignments for each AMP group were obtained using the tool COBALT (http://www.st-va.ncbi.nlm.nih.gov/tools/cobalt/re_cobalt.cgi) (Papadopoulos & Agarwala, 2007). The results were analyzed using Jalview program (Waterhouse et al., 2009). A logo of these aligned sequences was obtained using the tool WebLogo (http://weblogo.berkeley.edu) (Crooks et al., 2004).
2.2.1.7 Clustering the sequences Sequences obtained within each method (psiBLAST and HMMER) were combined in a single file according to the AMP class and according the database, resulting in single file with all AMPs for determined class for Uniprot or 1KP databases. These sequences were clustered using the program CD-HIT, (Fu et al., 2012) in order to avoid repeated sequences. The parameters used were word size 5 and the threshold was 1.0, which means 100% identity. Further analysis of the data was performed using MySQL.
48 The bioinformatics workflow is presented in Figure 2.1.
253 seed sequences From PhytAMP
Alignment of the seed sequences (T-COFFE expresso)
Seach for AMPs at oneKP and Building of an AMP profile for Uniprot with psiblast 3 each class iterations (hmmbuild)
Predicted sequences: alignments with HSP positive greater than 10 and hit length size Seach for AMPs at oneKP and smaller than 140 (script) Uniprot (hmmsearch)
Remove sequences with Retrieve the full fasta repeated identifiers (script) sequences for AMPs predicted (blastdbcmd) Retrieve the full fasta sequences for AMPs Remove sequences with Alignment of the predicted (blastdbcmd) repeated identifiers (script) predicted Conserved Domain search sequences (CDD) (COBALT)
Group all the AMP sequences Consensus from each method (psiblast sequence and HMMER) according to (WEBLOGO) the AMP class
Eliminate the repeated sequences (CD-HIT)
Figure 2.1 Bioinformatics Workflow.
2.2.2 Differential expression of AMPs in response to pathogens
2.2.2.1 Biotic stress assay Seeds of flax (linseed type, CDC Bethune) were grown according to Galindo and Deyholos (2016), with some modifications, as follows. Seeds were surface disinfected with ethanol 70% for 1 minute, immersed in 9.6% bleach for 5 minutes, and rinsed six times, for 1 minute at a time, in autoclaved, deionized water. Seeds were subsequently dried on sterile filter paper in a laminar flow hood. The seeds were then sown in
49 autoclaved borosilicate tubes (25 x 200 mm) that had been filled with 3 g of autoclaved vermiculite, and 10 ml of a 0.1X solution of sterilized Murashige & Skoog basal medium (Sigma–Aldrich, St. Louis, MO, USA). All manipulations occurred under axenic conditions in a laminar flow hood.
Immediately after sowing, seeds were inoculated with 1 mL of 1x105 spore suspension of the Fusarium oxysporum f. sp. lini isolate (#81) (generously provided by Khalid Rashid, Agriculture and Agri-Food Canada, Morden, MB, Canada) or 396 spores of Rhizoglomus irregulare (AGTIV Specialty Crops powder) added to the vermiculite, or else a mock treatment of autoclaved inoculum. Samples were collected from roots and aerial tissues of the plants and were frozen in liquid nitrogen and stored at -80oC.
2.2.2.2 RNA extraction Plant tissues frozen in liquid nitrogen were lysed using the Tissuelyser II instrument (Qiagen, Valencia, CA, USA). RNA was extracted using the EZ-RNA kit Kit (Omega Bio- tek, Inc., GA, USA) according to the manufacturer's instructions. RNA was quantified using the ND-1000 Spectrophotometer (NanoDrop Technologies, Inc. Wilmington, DE, USA). Integrity of RNA was analyzed by 1% agarose gel electrophoresis. The cDNA was synthesized from 1 μg of the total RNA with the qScript cDNA Synthesis Kit (Qiagen, Beverly Inc, MA, USA).
2.2.2.3 Primer design To design qRT-PCR primers, the sequence of the predicted AMP was aligned to the flax transcriptome in Phytozome database (https://phytozome.jgi.doe.gov/pz/portal.html) using tblastn, and primers designed using the PrimerQuest (https://www.idtdna.com/Primerquest/) tool from IDT (Integrated DNA Technologies). The genes chosen and the sequences of the primers used in this experiment are described in Table 2.2. The primer annealing temperature was 54oC.
50 Table 2.2 Primers designed for the real time PCR assay
Primer name 1KP ID Forward Reverse
UBI - CCAAGATCCAGGACAAGGAA GAACCAGGTGGAGAGTCGAT
GAPDH - GACCATCAAACAAGGACTGGA TGCTGCTGGGAATGATGTT
Hevein_Lus10028377_9 lcl|MJAV_98665 TATGCTCAAGCACCGTAAC GGACCTGTTGTCAAATCCA
Hevein _Lus10000453_7 lcl|MJAV_96090 GTCCAACAGGTTATGGTACAG GTGCAACGGTGTCAGAAT
Hevein _Lus10006552_B11 lcl|OGSY_94086 GCAATAGCCAGTGCTCAA TGGTCCCACAGTAACCATA
Snakin_Lus10017212_A11 lcl|POZS_9649 TGGCTTCCCTTATCCTCTC ACAATCGATGGTCGGAGTA
Snakin_Lus10042203_3 lcl|OGSY_27591 CTATGGACCTGGAAGTCTGA GCAGCATTTGTTGCAGAAG
Snakin_Lus10018016_2 lcl|OGSY_90427 AAGACTGTGGAGGGAGATG TGGCCGTAAGTACCAGAA
LTP_Lus10026418_F9 lcl|OGSY_94401 CCTCCGTGTTCCTCTTCT GTCAGGTAAGACACGCAAG
LTP_Lus10010572_1 lcl|MJAV_3211 TGTCTCTGCCAGCTTCT GGGTGGAGTCTGAACATTAC
LTP_Lus10015279_10 lcl|MJAV_97231 CAGCTACCAAGTTGCAGAT CCACCTTTCCACAACTCAC
CLP2-D9 Cyclolinopeptide GATATTCGGCAAGGAAGGAC GCATATCATCGCTGCTCTC
2.2.2.4 Real time PCR assay Quantitative real-time PCR reactions (qRT-PCR) were performed using the Perfecta Mix kit (QIAGEN, Beverly Inc, MA, USA) following the manufacturer's recommendations. Ubiquitin and GAPDH genes were used as reference genes for root and shoot tissues, as described in Galindo-González and Deyholos (2016). Prior to performing qRT-PCR, the primers of the reference and target genes were optimized by gradient PCR and the amplicons were visualized on 1% agarose gel. Using cDNA (1:6 dilution, 1.0 μl) as template, melting curve analysis was conducted to determine the best annealing temperature (54oC). Three independent biological replicates and three experimental replicates were used for each treatment. The qRT- PCR reactions were performed on the CFX96 Real-Time System (BioRad). Normalized gene expression values were obtained from the CFX Manager Software (BioRad). The fold-change ratio of the treatments (fungus inoculated flax plants) in relation to the controls (uninoculated flax plants) were calculated using the "R" statistical software environment (R Development Core Team, 2013), and displayed as boxplots with the log of the relative normalized expressions for each tissue, treatment, and timepoint. A univariate post hoc variance analysis (ANOVA) and Tukey's honestly
51 significant difference (HSD) test (Tukey, 1949) were used to evaluate respectively the significant treatments and the transcripts that differed in abundance between the treatments, where the treatments are defined as the combination of inocula (mock inoculated control; Rhizoglomus irregulare inoculated; or Fusarium oxysporum inoculated); duration of treatment (9 d or 14 d after sowing and inoculation); and tissue origin (above ground or below ground tissues).
2.3 Results All the supplementary files are available at: (https://osf.io/9u45x/?view_only=f5866cf2c5054427a33f52936469701b).
2.3.1 AMP prediction and conserved domain analysis 2.3.1.1 psiBLAST In the first stage of our attempt to expand the number of annotated plant AMP sequences, we identified 4,484 candidate AMPs from the UniProtKB/Swiss-Prot and 1KP databases, by using psiBLAST to search for patterns that matched 253 previously described plant AMPs from PhytAMP. The 4,484 candidate AMPs each had unique identifiers in their source databases, but they were not necessarily all unique sequences. As expected, many more AMPs were found in the 1KP database than UniProtKB/Swiss-Prot, owing to the 1KP database’s much larger size (Table 2.3).
Given that the sequences in UniProtKB/Swiss-Prot have already been manually annotated, in a preliminary test of our initial search strategy, we compared the candidate AMP sequences we obtained from UniProtKB/Swiss-Prot using psiBLAST to the sequences retrieved from the same database using a keyword search for proteins that had been manually annotated as AMPs. We found that the results obtained by either method were very similar for each type of AMP, except for defensins, where fewer true defensins were retrieved by psiBLAST (177) as compared to the keyword search (383) (Table 2.4). Also, for the defensin sequences retrieved, only 56.49% of the sequences presented CDD domains whereas 98% of the sequences had the correct annotation after visual inspection, which means that 41.51% of the sequences were
52 false negatives, and only 2% of the sequences were false positives, according to annotation (Table 2.4, 2.6). An exception is snakins, for which a few more sequences were retrieved by psiBLAST (18 sequences) as compared to the keyword search (3 sequences), possibly because some snakins are annotated as GASA proteins (Table 2.3, 2.4). However, all the sequences retrieved had CDD domains and were annotated as snakin/GASA proteins. Overall the similarity in the results provides confidence that the strategy used could also retrieve genuine AMPs from the much larger, but unannotated, 1KP database.
To further validate our psiBLAST results, we used rpsBLAST to compare our results to the Conserved Domains Database (CDD). Of the sequences retrieved from Uniprot, 92.6% (4152 sequences) contained conserved AMP domains according to the rpsBLAST/CDD search. The percentage of hits with conserved domains for each class is shown in Table 2.5. Except for defensins, the AMPs retrieved with psiBLAST were highly correlated with the presence of expected conserved domains. We therefore consider the method psiBLAST associated with validation of the sequences using CDD search, overall, a useful tool to retrieve true AMP sequences from large databases. However, to retrieve a larger numbers of defensins, further refinements might be necessary.
Table 2.3 Comparison of the number of sequences with unique IDs retrieved with psiBLAST search against Uniprot and 1KP databases
Number of sequences retrieved with psiBLAST per 36,397 Uniprot database sequences and 24,268,827 1KP database sequences AMP Uniprot 1KP Total classes Cyclotide 116 3 119 Defensin 177 15 192 Hevein 19 215 234 LTP 135 1984 2119 Snakin 18 1636 1654 Thionin 33 133 166 Total 498 3986 4484
53 Table 2.4 Number of AMPs from Uniprot reviewed database retrieved using keyword queries
Number of reviewed
proteins Uniprot Uniprot keywords search queries Cyclotide 118 viridiplantae cyclotide AND reviewed:yes Defensin 383 viridiplantae defensin AND reviewed:yes Hevein 10 viridiplantae hevein AND reviewed:yes viridiplantae "lipid transfer protein" AND LTP 134 reviewed:yes Snakin 3 viridiplantae snakin AND reviewed:yes viridiplantae thionin NOT defensin AND Thionin 31 reviewed:yes
Table 2.5 Percentage of AMP candidates with CDD domains after rpsBLAST search
Percentage of hits with the expected conserved domain Uniprot 1KP Cyclotide 86.20% 0% Defensin 56.49% 6.6% Hevein 85.00% 62.50% LTP 96.29% 97.22% Snakin 100.00% 96.88% Thionin 87.87% 86.56%
Table 2.6 Percentage of AMPs retrieved from Uniprot using psiBLAST according with annotation
Percentage of AMPs retrieved from AMP classes Uniprot according with annotation Cyclotide 98% Defensin 96% Hevein 84.2% LTP 97.7% Snakin 100% Thionin 90.90%
54 2.3.1.2 Conserved domains search and Pattern recognition To further test whether the AMP candidates had conserved domains characteristic of each AMP class, we performed a search for conserved domains, as follows. All the sequences predicted with psiBLAST from Uniprot and 1KP were combined and grouped by AMP class. These sequences were aligned using the program COBALT (Papadopoulos & Agarwala, 2007). Aligned sequences were used as input for the program Weblogo (Crooks et al., 2004). Sequences of cyclotides and defensins retrieved from 1KP were excluded from these analyses. The following patterns for each AMP class from the sequence logo results were identified (Table 2.7).
Table 2.7 Sequence pattern of AMP classes identified with sequences retrieved from 1KP database
AMP class Sequence pattern
Cyclotide CX{3}CX{4}CX{7}CX{1}CX{4}CX{3}
Defensin CX{10}CX{5}CX{3}CX{8}GX{1}CX{8}CX{1}CX{3}C
Thionin CCX{11}CX{9}CX{5}CX{7,8}CX{6}
LTP CX{9}CX{14}CCX{19}CX{1}CX{24}CX{13}CX{4}
Hevein CX{8}CX{4}CC{5}CX{6}CX{5}CX{3}CX{4}
Snakin CX{3} CX{3} CX{8} CX{3} CX{2} CCX{2} CX{2} CX {11}CX{2} CX{11} KCP
The defensins Y core motif (Yount & Yeaman, 2004) is underlined.
The most common CDD domains for each class among the seed sequences, Uniprot sequences and 1KP sequences were: Cyclotide family (cyclotides), Gamma-thionin family (defensins), Plant thionin (thionins), Non-specific lipid-transfer protein type 1 (nsLTP1) subfamily (lipid transfer proteins), Hevein or type 1 chitin binding domain (ChtBD1) (heveins) and Gibberellin regulated protein (GASA) (snakins) (Table 2.8). A detailed list with the other domains founded associated with each class of AMPs are listed in Table 2.9.
55 Table 2.8 The most frequent domains among AMP classes
Frequency AMP Class Domain Domain name Query Uniprot 1KP Cyclotide pfam03784 Cyclotide family 72.0% 78.44% 0% Defensin pfam00304 Gamma-thionin family 100.0% 50.84% 6.6% Thionin pfam00321 Plant thionin 98.0% 86.56% 81.91% LTP cd01960 Non-specific lipid-transfer 98.0% 82.22% 85.44% protein type 1 (nsLTP1) subfamily Hevein cd00035 Hevein or type 1 chitin binding 28.5% 40.00% 19.90% domain (ChtBD1) Snakin pfam02704 Gibberellin regulated protein 100.0% 100.0% 96.88 % (GASA)
Table 2.9 List of AMP domains found among AMP classes AMP Class Domain Domain name Cyclotide pfam03784 Cyclotide family Defensin pfam00304 Gamma-thionin family pfam00537 Scorpion toxin-like domain cd00107 Knot1 smart00505 Knot1 Thionin pfam00321 Plant thionin pfam00304 Gamma-thionin LTP cd01960 Non-specific lipid-transfer protein type 1 (nsLTP1) subfamily cd01959 Non-specific lipid-transfer protein type 2 (nsLTP2) subfamily cd04660 Non-specific lipid-transfer protein (nsLTP)-like subfamily pfam14368 Probable lipid transfer cd00010 Alpha-Amylase Inhibitors (AAI), Lipid Transfer (LT) and Seed Storage (SS) Protein family pfam00234 Protease inhibitor/seed storage/LTP family Hevein cd00035 Hevein or type 1 chitin binding domain (ChtBD1) cd06921 Hevein or Type 1 chitin binding domain subfamily co-occuring with family 19 glycosyl hydrolases or with barwin domains Barwin family pfam00967 Chitin recognition protein pfam00187 Chitin binding domain smart00270 Chitinase class I pfam00182 Chitinase_glyco_hydro_19 cd00325 Snakin pfam02704 Gibberellin regulated protein (GASA)
56 2.3.1.3 HMMER In the next stage of our attempt to expand the number of annotated plant AMP sequences, we identified 21,576 candidate AMPs from the UniProtKB/Swiss-Prot and 1KP databases, by using the HMMER 3.1b2 package to search for patterns that matched 253 previously described plant AMPs from PhytAMP. The 21,576 candidate AMPs each had unique identifiers in their source databases, but they were not necessarily all unique sequences. Once again, many more AMPs were found in the 1KP database than UniProtKB/Swiss-Prot (Table 2.10). We retrieved 18 defensins from 15 different species from 1KP using the HMMER. The sequences retrieved with psiBLAST were discarded from this analysis due to the lack of defensin domains. The four putative cyclotides retrieved from 1KP did not contain the cyclotide domain, so we did not consider these peptides further in our analysis.
The number of sequences retrieved from the 1KP database with the HMMER package was almost 5.3 times higher than using psiBLAST, using our parameters. The percentage of AMP candidates with Pfam domains were very high, except for cyclotides. Aside from cyclotides, the lowest frequency of Pfam domains was 70% among Uniprot sequences for the class snakin, and the highest was 100% for Uniprot heveins (Table 2.11). These results shown that HMMER, with further validation of the sequences using Pfam, is overall, an efficient tool to retrieve valid AMP sequences from large databases, with a great increase of true positives for the AMP candidates from the 1KP database.
57 Table 2.10 Number of sequences with unique IDs retrieved from Uniprot and 1KP databases using the HMMER program
Number of sequences retrieved with HMMER AMP classes Uniprot 1KP Total Cyclotides 119 1 120 Defensins 142 18 160 Hevein 72 3822 3894 LTP 138 13902 14040 Snakins 30 3100 3130 Thionins 30 202 232 Total 531 21045 21576
Table 2.11 Number of sequences with Pfam conserved AMP domains using the program ‘hmmscan’
Frequency AMP Class Domain name Uniprot 1KP Number of Percentage Number of Percentage of sequences of conserved sequences conserved domains domains Cyclotide Cyclotide 117 98.31% 0 0.00% Defensin Gamma-thionin 126 88.73% 17 94.47% Hevein Chitin_bind_1 72 100% 3802 97.63% LTP Tryp_alpha_amyl 118 85.50% 12332 88.70% Snakin GASA 21 70.00% 3000 96.77% Thionin Thionin 27 90.00% 198 98.01%
2.3.1.4 Combined sequences To combine and then remove redundancies from the sequences retrieved with psiBLAST and HMMER for a given database, we used the program CD-HIT (Fu et al., 2012). A final total 16,870 unique sequences from 1KP database and 575 unique sequences from Uniprot was obtained (Supplementary S3). The final result is presented as Table 2.12. The final number of the most conserved domains after rpsBLAST search and ‘hmmsearch’ is represented in Tables 2.13 and 2.14.
58 Table 2.12 Combined sequences for each AMP class from Uniprot and 1KP
AMP class Combined 1KP Combined Uniprot Ciclotyde - 114 Defensins 18 184 Hevein 3174 74 LTP 11079 140 Snakin 2454 30 Thionin 146 33 Total 16870 575
Table 2.13 Frequency of the most conserved domain at Uniprot and 1KP databases after rpsBLAST search
1KP Uniprot AMP class Number of % of conserved Number of % of conserved unique domains unique domains sequences sequences Cyclotide 1 0% 114 87.50% Defensin 18 6.6% 184 52.70% Hevein 3174 80.82% 74 87.50% LTP 11079 97.57% 140 95% Snakin 2454 95.60% 30 63.33% Thionin 146 85.71% 33 87.87%
59 Table 2.14 Frequency of the most conserved domain at Uniprot and 1KP databases after ‘hmmscan’ search
1KP Uniprot AMP class Number of % of the most Number of % of the most unique conserved domains unique conserved domains sequences sequences Cyclotide 1 0.00% 114 98.72% Defensin 18 94.00% 184 70.00% Hevein 3174 97.00% 74 97.00% LTP 11079 91.00% 140 86.00% Snakin 2454 96.00% 30 53.00% Thionin 146 86.00% 33 82.00%
Where the Pfam domain considered for this analysis was the most conserved for each AMP class: most conserved domain: Cyclotide domain (Cyclotide), Gamma-thionin domain (Defensin), Chitin_bind_1 domain (Hevein), LTP_2 domain (LTP), GASA domain (Snakin) and Thionin domain (Thionin).
2.3.1.5 Distribution of AMPs by plant taxon We identified candidate AMP sequences from among the 1,179 plant species in the 1KP database and 134 plant species from the Uniprot database. We next considered the taxonomic distribution of the AMPs we found.
From the 1KP database, we retrieved a total of 16,870 candidate AMPs belonging to 1003 plant species. The range of different AMPs per species varied from 1 to 107, where the species with least number of AMPs was Undaria pinnatifida (a brown algae) and the plant with largest number of AMPs was Papaver rhoeas. The first 30 species from 1KP with the major number of AMPs can be visualized in Table 2.15. The number of AMP sequences for these plant species from the Uniprot database is shown in Figure 2.2. We found that the clades with the most significant number of AMP sequences were: the Core Eudicots (Asterids (27% of the sequences), Rosids (26% of the sequences) and others (11% of the sequences), comprising a total of 64% of the sequences), Conifers (9% of the sequences), Basal Eudicots (6% sequences), Monocots (Monocots (5% of the sequences) and Monocots/Commelinids (3% of the sequences), comprising
60 a total of 8% of the sequences), Magnoliids (3% of the sequences). The remaining 10% of the sequences are distributed to several other clades such as Mosses and Algae).
From the 417 plant families with AMP candidates from the 1KP database, the most AMP-rich transcriptomes were found in: Asteraceae (10% of the sequences), Onagraceae (9% of the sequences), Cupressaceae (8% of the sequences), Fabaceae (8% of the sequences), Papaveraceae (7% of the sequences), Lamiaceae (5% of the sequences), Podocarpaceae (5% of the sequences); Solenaceae (5% of the sequences), Boraginaceae (5% of the sequences) and Amaranthaceae (5% of the sequences); the other families presented less than 5% of the AMPs identified (Supplementary S4).
The AMP classes were distributed among species from 1KP as follows: heveins in 895 species ranging from 1 to 33 sequences per species, where the species with the most hevein sequences was Cannabis sativa; LTP in 949 species ranging from 1 to 61 sequences per species, where the species with the most hevein sequences was Glycine soja; snakins in 762 species ranging from 1 to 21 sequences per species, where Heliotropium calcicole has the most number of these predicted; thionins in 64 species ranging from 1 to 21 sequences per species, where the species with the greatest number of thionin sequences was Papaver rhoeas (Supplementary S5).
61 Table 2.15 Number of sequences for each AMP class from AMP candidates identified from 1KP for the top 30 plants with the greater total number of AMPs retrieved
Defensin Cyclotide Hevein LTP Snakin Thionin Species from 1kp database oneKP Uniprot oneKP Uniprot oneKP Uniprot oneKP Uniprot oneKP Uniprot oneKP Uniprot Total AMPs oneKP Total AMPs Uniprot Total protein uniprot Papaver rhoeas 0 0 0 0 25 0 51 0 10 0 21 0 107 0 232 Papaver setigerum 0 0 0 0 21 0 53 0 9 0 7 0 90 0 10 Glycine soja 0 35 0 0 7 2 61 461 16 110 0 22 84 595 184,082 Heliotropium calcicola 0 0 0 0 10 0 49 0 21 0 0 0 80 0 9 Cannabis sativa 0 0 0 0 33 0 36 3 4 0 6 0 79 3 672 Oenothera speciosa 0 0 0 0 5 0 60 0 14 0 0 0 79 0 15 Eschscholzia californica 0 0 0 0 21 0 42 0 15 0 0 0 78 0 184 Papaver somniferum 0 0 0 0 18 0 44 2 5 0 9 0 76 2 6,362 Eleusine coracana 0 0 0 0 14 0 54 4 5 0 1 0 74 4 409 Oenothera laciniata 0 0 0 0 8 0 53 0 8 0 3 0 72 0 25 Oenothera rosea 0 0 0 0 10 0 41 0 12 0 0 0 63 0 17 Oenothera gaura 0 0 0 0 9 0 36 0 9 0 0 0 54 0 103 Oenothera grandiflora 0 0 0 0 11 0 35 0 8 0 0 0 54 0 140 Oenothera grandis 0 0 0 0 8 0 34 0 11 0 0 0 53 0 26 Lycium sp. 0 0 0 0 11 0 27 0 10 0 0 0 48 0 10 Oenothera biennis 0 0 0 0 13 0 27 0 7 0 0 0 47 0 196 Loropetalum chinense 0 0 0 0 13 0 25 0 8 0 0 0 46 0 31 Argemone mexicana 0 0 0 0 14 0 25 0 7 0 0 0 46 0 44 Sambucus canadensis 0 0 0 0 12 0 28 0 6 0 0 0 46 0 38 Amaranthus cruentus 0 0 0 0 6 0 29 0 11 0 0 0 46 0 173 Hedera helix 0 0 0 0 10 0 31 0 5 0 0 0 46 0 203 Papaver bracteatum 0 0 0 0 8 0 24 0 4 0 10 0 46 0 40 Falcatifolium taxoides 0 0 0 0 11 0 27 0 7 0 0 0 45 0 17 Linum usitatissimum 0 0 0 0 5 0 36 12 4 0 0 0 45 12 909 Punica granatum 0 0 0 0 4 0 33 147 8 0 0 0 45 147 73,298 Cephalotaxus harringtonia 0 0 0 0 10 0 23 0 11 0 0 0 44 0 46 Apocynum androsaemifolium 0 0 0 0 12 0 26 0 5 0 0 0 43 0 30 Solanum ptychanthum 0 0 0 0 3 0 34 0 5 0 0 0 42 0 37 Aerva persica 0 0 0 0 10 0 25 0 6 0 1 0 42 0 4 The corresponding number of AMPs for each class from each plant species was identified in Uniprot.
62 2.3.1.6 Differential response of AMPs to fungi interaction To test whether a sample of candidate AMPs were differentially expressed as transcripts in response to fungal treatment (F. oxysporum, or R. irregulare), we chose 10 genes from flax to perform a qRT-PCR analysis. From the 45 putative AMPs (five heveins, 36 LTPs and four snakins) identified in our studies we chose three heveins, three LTPs, three snakins, and one cyclolinopeptide to target by qRT-PCR analysis.
Of the 10 candidate AMPs we tested (Table 2.2), seven demonstrated differential expression (fold change > 2, ANOVA < 0.05) in the above-ground biomass and/or in the below ground biomass, in response to F. oxysporum. The fold changes for all AMP classes in all treatments and tissues are represented in Figures 2.2 to 2.8. Of the 10 gene assayed, the following showed differential expression: one cyclolinopeptide (Figure 2.2), one LTP (Figure 2.3), three heveins (Figures 2.4, 2.5 and 2.6), and two snakins (Figures 2.7 and 2.8). A greater number of transcripts had significant differences in abundance in the shoots compared to the roots, however, the fold-change in the abundance of the transcripts was greater (in general) among the transcripts measured in roots. We did not detect any change in abundance of the transcripts for two LTPs (Lus10010572, Lus10015279) and one snakin (Lus10018016). Furthermore, we did not detect a significant fold change in abundance of the transcripts in any of the comparisons between Rhizoglomus (Rhi) and uninoculated control (mock) treatments.
63 5.3 6.2
4.7
Figure 2.2 Relative normalized expression of a cyclolinopeptide gene of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
64 3.2 5.0
Figure 2.3 Relative normalized expression of a LTP gene (Lus10026418) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
65 5.8 3.9 7.0 3.6
3.3 7.8 3.4
6.2
Figure 2.4 Relative normalized expression of Hevein gene (Lus10028377) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
66 4.2
3.4
3.5
5.0
Figure 2.5 Relative normalized expression of Hevein genes (Lus0000453) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
67 6.0 24.1
5.9 29.2
3.8 14.4
2.8 15.0
Figure 2.6 Relative normalized expression of Hevein genes (Lus10006552) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
68 2.9 5.0
3.7
Figure 2.7 Relative normalized expression of a Snakin/GASA genes (Lus10017212) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
69 3.0 3.3 2.7
Figure 2.8 Relative normalized expression of a Snakin/GASA genes (Lus10042203) of L. usitatissimum inoculated with Fusarium oxysporum or Rhizoglomus irregulare. Treatments are: F. oxysporum (FUS), uninoculated control (MOCK), R. irregulare (RHIZO). Numbers below each treatment indicate days post-inoculation. Error bars are the standard error of relative normalized expression values (log-fold changes) calculated by the CFX Manager Software (BioRad) program (n=3). Fold changes between treatments with significance level (ANOVA) p<0.05 are indicated in the picture. The tissues studied are above ground biomass and below ground biomass.
2.4 Discussion The 1KP is a database created from the transcriptomes of over 1,000 diverse plant species, ranging from ancestral green algae, non-vascular plants (e.g. mosses, liverworts, hornworts), and vascular plans (e.g ferns, conifers, and flowering plants). Many of these species had not been previously subjected to transcriptome sequencing (Wickett et al., 2014); this project alone increased the number of available plant genes by 100-fold in comparison with what was available in the public databases at the time of its publication. 1KP AMPs sequences have not been annotated yet, and consequently, our results represent a great increase in candidate AMP sequences (18 defensins, 3174 hevein-like proteins, 11079 lipid transfer proteins, 2454 snakins and 146 thionins).
The main plant families with AMPs described so far are Amaranthaceae,
70 Andropogoneae, Brassicaceae, Oryzeae, Santalaceae, Spermacoceae, Triticeae, Vicieae, Violaceae (Hammami et al., 2009). Here we described 418 plant families with at least 1 candidate AMP sequence, and 70 of these families presented at least 50 candidate AMP sequences (Supplementary S4). We found antimicrobial peptides among a great variety of plant groups, ranging from red algae (1 AMP sequence) to Asterids (4584 AMP sequences). Prior to our work, the majority of known plant AMP sequences belonged to the Angiosperms (Hammami et al., 2009). In our study, we also found the majority of the AMP were identified from Angiosperms (Core Eudicots: 4584 AMPs from Asterids, 4414 AMPs from Rosids and 1810 AMPs from other eudicots; 998 AMPs from Basal Eudicots, 849 AMPs from Monocots, 482 AMPs from Monocots/Commelinids, and 455 AMPs from Magnoliids). This was not unexpected, since the majority of plants sequenced on the 1KP project are angiosperms (830 plants). However, a surprisingly large number of AMPs were retrieved from conifers (1467), leptosporangiate monilophytes (ferns) (535), mosses (404) and liverworts (190) considering that only 74, 64, 41, 28 plants from these clades where sequenced. In contrast, 241 algae species were sequenced and only 125 AMP sequences were found among: green algae (114), Chromista (7), Glaucophyta (3) and red algae (1). Gymnosperms are thus a promising source of AMPs that have not been very much explored, whereas algae seems to have fewer number of AMPs. Until recently only 20 hevein-like proteins had been isolated and characterized. These belong to 10 different angiosperms; in 2016 researchers decided to investigate the occurrence and distribution of heveins in gymnosperms, revealing 11 novel heveins from Ginkgo biloba, the Ginkgotides (Wong et al., 2016); data-mining analysis revealed 48 homologs to ginkgotides in 39 different gymnosperms. We found in general 1467 AMPs from conifers, 69 from Cycadales, 11 from Ginkgoales, and 25 Gnetales. Some of the putative AMPs we have found were not described before for some plant groups. For instance, we have found 10 predicted LTPs from algae (7 from green algae and 3 from Chromista algae) and 1 from hornwort. This is the first identification of LTPs from algae or hornworts (Edstam et al., 2011; Liu et al., 2015). Hence, our data may support future studies of AMP evolution in plants.
71 The number of defensins or cyclotides we retrieved from 1KP was unexpectedly low, when compared with the other AMP classes and with the cyclotides and defensins obtained from Uniprot (Table 2.12). We are nevertheless confident in our results, since we found a high frequency of the expected CDD and Pfam domains for each AMP class in most cases, and even when we found fewer domains than expected (e.g. defensins from Uniprot) our manual inspection revealed that these were false negatives. We also found the expected number of cysteine residues described in the literature for each AMP class: cyclotides, with 6 cysteine residues ; defensins, with 8 cysteine residues (Broekaert et al., 1997; Nawrot et al., 2014; van der Weerden & Anderson, 2013) thionins, with 6 cysteine residues, may vary from 6 to 8 according to the literature (Broekaert et al., 1997; Nawrot et al., 2014), lipid transfer proteins with 8 cysteine residues (Broekaert et al., 1997; Nawrot et al., 2014), heveins, with 8 cysteine residues, may vary among 6, 8 or 10 according to the literature (Nawrot et al., 2014); and snakins, with 12 cysteine residues (Nawrot et al., 2014). These bonds are required for disulphide bond formation and structure stabilization of AMPs and are their main signature.
We were able to correlate the mRNA abundance of some of the novel AMPs we identified with exposure to fungal pathogens. Most of the flax AMPs studied (70%) were up-regulated in the presence of the pathogenic fungi (F. oxysporum) and did not change in expression or were down-regulated in the presence of the mutualistic fungi (R. irregulare). One hevein was up regulated 29-fold in flax roots in the presence of the pathogen, whereas it did not change in the presence of the mutualistic fungi in comparison with the control (non-inoculated). Cyclolinopeptide was another interesting peptide, up-regulated 4- fold in roots upon F. oxysporum inoculation. These and the other up-regulated AMP genes in this study were strongly induced by the pathogenic fungi and not induced by the AMF fungi, and are probably involved exclusively in defense responses and not in mycorrhizal associations. Therefore, special attention should be given to these genes in further searches for resistance gene candidates.
Our results indicate that our methods are very useful for automated retrieval of AMPs from large databases. The HMMER package showed a higher number of retrieved AMP
72 candidates, when compared to psiBLAST (with our parameters). Hence, we suggest that this method is more effective to prospect for AMPs. The lower number of AMP sequences retrieved with psiBLAST was possibly related to the restriction on length of the AMPs we used, a maximum of140 amino acids. We chose this parameter based in our previous tests (data not shown), where we were getting false positives. Although we retrieved fewer sequences with this constraint, this produced a higher number of true positives.
The greatest limitation of our methods was the presence of false negatives, where the AMPs domain for each class were not recognized by the domain search programs. Nevertheless, we are confident that we retrieved true AMPs and that our results may increase the number of available AMPs to help to elucidate the evolution of these sequences in green plants.
In summary, this study has identified new AMP candidates from several plant species, even in some clades that had previously not been described to contain such AMPs. The HMMER-based pipeline was the most efficient tool for identifying antimicrobial peptides from large databases.
The efficacy of the bioinformatics pipeline that we developed for prospecting AMPs with antifungal potential was partially validated by qRT-PCR with flax transcripts. Most of the peptides tested had transcript expression that increased when the plant was stressed by the pathogenic fungus (F. oxysporum) when compared to the control and to the mutualistic fungus (R. irregulare). From the genes tested using real-time PCR, one cyclolinopeptide and three heveins were most response to the pathogen, representing important targets for the rational design of antimicrobial peptides as well as plant breeding.
The cyclolinopeptides are a unique class of flax antimicrobial peptides, and its activity against Fusarium and other pathogenic fungi will be tested in follow-up experiments (Chapter 5 of this thesis).
73 Chapter 3 : The effect of non-pathogenic versus pathogenic fungi on flax growth
3.1 Background Canada is one of the world’s largest producers of linseed flax, with annual exports of CAD$150-180 million (Islam, 2018). One of the major limitations on flax growth in North America is the disease fusarium wilt, which is caused by the fungus Fusarium oxysporum (Rashid, 2003). Fusarium wilt can be controlled using resistant varieties, crop rotation, or fungicide application. However, many flax varieties are susceptible to this disease (Rashid, 2003). Moreover, there are potential negative consequences from fungicide use, including contamination of terrestrial and aquatic ecosystems, alterations of beneficial soil microbiota, inhibition of photosynthesis, and harm to human health (Muri et al., 2009; Petit et al., 2012; Wightwick et al., 2010). We were therefore motivated to search for alternative solutions to control fusarium wilt.
The use of microbes to control plant pathogens represents a shift in direction towards more sustainable agriculture, by suppressing disease and therefore reducing the amount of fungicides used (Jacott et al., 2017; Jeffries et al., 2003). For instance, both Clonostachys rosea and the arbuscular mycorrhizal (AM) fungus Rhizoglomus irregulare have been described as successful biocontrol agents for the inhibition of plant pathogens (Akköprü & Demir, 2005; Ismail, McCormick, & Hijri, 2013; Lysøe, Dees, & Brurberg, 2017; Moraga-Suazo & Sanfuentes, 2016).
The mechanisms hypothesized for the role of endophytic fungi (such as C. rosea and R. irregulare) in supressing disease include direct and indirect mechanisms, such as: 1) secretion of enzymes for degrading the structure and metabolites of the fungal cell (Chatterton & Punja, 2009; Lucini et al., 2019), 2) competition for space and nutrients in the roots (Karlsson et al., 2015; Lucini et al., 2019), 3) stimulation of the plant immune system (Moraga-Suazo & Sanfuentes, 2016; Mukherjee et al., 2013), and 4) improved plant growth and nutrition (Lucini et al., 2019). The last mechanism is evident especially in AMF, where symbiosis induces a more branched root system, consequently increasing the root interface for exchanging nutrients, and ultimately enhancing plant
74 nutrition (Lucini et al., 2019). Despite the importance of AMF in plant nutrition, a study on the effects of mycorrhization on early blight in tomato did not correlate resistance to Alternaria solani in mycorrhizal plants with an increase in nutrient supply or plant growth (Fritz et al., 2006). Therefore, improved nutritional status itself does not completely explain the bio-protective effects of AMF in plants. The mechanism for bio-protection by R. irregulare is believed to primarily involve priming the plant immune system (Conrath et al., 2015). For C. rosea, the mechanism is believed to be mycoparasitism (Karlsson et al., 2015). These mechanisms can also be deployed synergistically.
Clonostachys rosea (formerly Gliocladium roseum) is a fungus that is non-pathogenic to plants. C. rosea has diverse ecological behaviour, with the ability to act as a root and stem endophyte (lives within a plant without causing disease) (Chatterton, Jayaraman, & Punja, 2008), saprobe (feeds from decaying organic matter) (Karlsson et al., 2015) or as a mycoparasite (may parasitize and kill other fungi) (Karlsson et al., 2015). Among the fungi that C. rosea can parasitize are multiple phytopathogens, such as Pythium and Fusarium (Chatterton & Punja, 2009). For example, C. rosea strain ACM941 is a patented fungal biocontrol against Fusarium graminearum, which causes Fusarium Head Blight (FHB) in several small grain cereals; the mechanism by which this strain inhibits Fusarium growth is by secreting growth inhibitors, such as secondary metabolites (e.g. polyketide) (Demissie et al., 2018). It has been shown that C. rosea reduces the FHB index by 30-46%, the toxin deoxynivalenol (DON) levels by 22-33% and increases wheat yields by 7% when compared to the plant inoculated with Fusarium alone (Xue et al., 2014).
AM fungi are obligate plant root biotrophs that provide additional nutrients to their hosts (especially phosphorus) in exchange for photosynthetic carbon in the form of lipids (Luginbuehl et al., 2017). These improvements in plant nutrition and growth may contribute to enhanced disease resistance. AMF also enhance plant defenses by priming the plant immune system, a phenomenon called ‘mycorrhiza-induced resistance’ (MIR) (Cameron et al., 2013). MIR share similarities with the ‘induced systemic resistance’ (ISR) (Cameron et al., 2013; Nguvo & Gao, 2019), which is
75 triggered following the colonization of roots by non-pathogenic rhizobacteria, and the ‘systemic acquired resistance’ (SAR) (Cameron et al., 2013; Jacott et al., 2017), which is triggered by infection caused by phytopathogens.
Another hypothesis is that AM fungi may protect plants by directly affecting growth of pathogenic fungi. For instance, it has been observed that R. irregulare (formerly Glomus irregulare, Glomus intraradices, Rhizophagus irregulare) directly inhibited the growth of Fusarium sambucinum and the production of the trichothecene 4, 15-diacetoxyscirpenol (DAS) mycotoxin in potato roots and tubers (Ismail et al., 2013). Moreover, after colonization, AMF modifies plant root exudates, shaping the soil microbiota, favoring beneficial bacteria and fungi and supressing pathogenic fungi (Cameron et al., 2013; Filion, St-Arnaud, & Fortin, 1999; Filion, St-Arnaud, & Jabaji-Hare, 2003).
AMF form beneficial associations with Rhizobacteria, enhancing defense responses through ISR defense activation (Cameron et al., 2013). AMF protects against multiple phytopathogens such as Alternaria, Fusarium, Phytophthora, Pythium, Rhizoctonia, and Verticillium (French, 2017). For instance, R. irregulare decreases the severity of the disease Black Sigatoka on banana possibly through activation of the plant defense system (Coretta et al., 2015).
Taking this background into consideration, we aimed to study the physiological responses of flax to endophytic fungi. Therefore, we evaluated the effects of the early interaction with mutualistic fungi (R. irregulare and C. rosea) on flax infection by pathogenic fungi (F. oxysporum f. sp. lini) and also how each fungus independently affected flax growth, resulting in six in vitro treatments: a mock control; F. oxysporum; R. irregulare; C. rosea; and combinations of either R. irregulare+F. oxysporum or C. rosea+F. oxysporum. We focused on developing an assay to study the early responses of flax to these fungi, since we hypothesized that the events that results in bio-protection starts even before colonization. Plant responses were evaluated by measuring plant biomass (fresh weight), shoot and root length, root morphology, and necrosis.
76 We identified two possible biocontrol agents for fusarium wilt in flax: C. rosea and R. irregulare. The mycorrhizal fungus showed the most antagonist effects against F. oxysporum and enhanced flax growth. These effects were observed at the very early stages of interaction, i.e. pre-colonization. Further studies should be performed to better understand the mechanisms involved in AMF-mediated bio-protection.
3.2 Material and Methods
3.2.1 Fungal material Fusarium oxysporum f. sp. lini (isolate #81) was generously provided in potato dextrose agar (PDA) by Dr. Khalid Rashid (Agriculture and Agri-Food Canada, Morden, MB, Canada). Clonostachys rosea was isolated from grapevines and cultured in PDA until sporulation (E. Vukicevich, University of British Columbia). Rhizoglomus irregulare was obtained as a commercial inoculum (AGTIV® Specialty Crops powder, Premier Tech).
C. rosea and F. oxysporum were grown for two weeks on PDA medium in the dark at room temperature. The spores from the isolates were collected after flooding the plate with 0.5% Tween 20 in sterile water. A sterile inoculation loop was used to detach the spores from the surface of the medium. The spore count was performed with a haemocytometer. The spores were diluted with 0.5% Tween 20 in sterile water to the concentration of 105 spores mL−1 for further inoculation of the plants. 396 spores of R. irregulare were used per plant, according to the recommendations of the manufacturer (Premier Tech). For this, 33 mg of the inoculum was added in each test tube. For the non-mycorrhizal treatments, the same mass of autoclaved inoculum (121 oC for 90 minutes twice) was used to control for any effects of non-AMF components of the inoculum.
3.2.2 Plant material Flax CDC Bethune seeds were grown according to Galindo and Deyholos (2016), with some modifications. Flax seeds were surface disinfected with ethanol 70% for 1 minute,
77 immersed in 9.6% bleach for 5 minutes, and rinsed six times (1 minute each) in sterile distilled water. After rinsing, seeds were dried on sterile filter paper in a laminar flow hood.
3.2.3 Culture conditions Sterile seeds were grown in glass tubes (25 x 200 mm) filled with 3 g of vermiculite, 33 mg of R. irregulare inoculum (autoclaved or not) and 10 mL of MS (MS basal medium Sigma–Aldrich, St. Louis, MO, USA) media 10%, in sterile conditions. The fungal inoculations with C. rosea and F. oxysporum were performed immediately after sowing the seeds. Tubes were placed in a growth chamber at 22 oC with 16 h day/8 h night.
Six different treatments were used for our fungal assay: 1) Control: Flax seeds were germinated without fungi. We inoculated the control plants with 1 ml sterile water with 0.5% Tween 20. 2) C. rosea : Flax seeds were inoculated with 1 mL of 1x105 spore suspension of C. rosea. 3) F. oxysporum: Flax seeds inoculated with 1 mL of a 1x105 spore suspension of F. oxysporum. 4) C. rosea+F. oxysporum: Flax seeds were inoculated with 0.5 mL of 1 mL of 1x105 spore suspension of each fungus (C. rosea, F. oxysporum). 5) R. irregulare: Flax seeds were sown in vermiculite mixed with 396 spores of R. irregulare. 6) R. irregulare+F. oxysporum: To the previous treatment, 0.5 mL of 1x105 spore suspension of F. oxysporum was added.
3.2.4 Disease symptoms analysis and plant growth assessment For each treatment, approximately 12-20 plants were harvested 9 and 14 days post inoculation (dpi) for the shoot growth measurements and three plants were harvested for the roots measurements, were one plant = one biological replicate. The whole experiment was independently replicated at least two times. At harvest, we gently
78 washed the root system to remove any substrate and we measured the total fresh weight and shoot length for each plant. We then separated the roots from the shoots. Three root systems from each treatment were used for root morphological analysis and were kept in 15 mL Falcon tubes with ethanol (35% v/v) until further morphological analysis.
For the morphological analysis, we scanned the roots with a large-format scanner (Epson expression 11000XL) and processed the digital images using the program WinRHIZO (Arsenault et al., 1995). We used color analysis in which we manually defined color classes to assess the degree of root necrosis caused by F. oxysporum. The same roots were further analyzed using the same program with parameters according to (Kokkoris & Hart, 2019). The remaining roots were flash frozen in liquid nitrogen and kept in 2 mL tubes for further RNA-Seq analysis (Chapter 4).
3.2.5 Statistical analysis of growth parameters Differences among treatments in shoot length and total biomass were assessed using a multivariate analysis of variance (MANOVA) in the ‘R’ base package (R statistical computing) (R Development Core Team, 2013). Post-hoc univariate analyses of variance (ANOVAs) and Tukey’s honest significant difference (Tukey, 1949) were then used to assess which growth parameters differed among the treatments. Because only a subset of samples was processed for root traits, a separate MANOVA was used to determine if treatment had any effect on total root length, branching intensity (measured as number of tips per cm of root length), or root necrotic surface area. Post-hoc tests for treatment effects on root traits were as above. Harvest dates were analyzed separately with the goal of association of growth responses with gene expression data on each date.
3.2.6 Root colonization assessment
79 3.2.6.1 Fungal isolation To test whether the symptoms observed were caused by fungal colonization/infection, fungi (C. rosea and/or F. oxysporum) were re-isolated from the roots of the treated plants. For each treatment and control, root sections were surface sterilized in 10% sodium hypochlorite for 30 seconds and then washed with sterile distilled water. Six to seven root sections were transferred to PDA medium and grown for 7 d in the dark at room temperature. Subsequently, plates were examined for fungal growth. We verified if the colonies and spore morphology were consistent with the original inocula using microscopy.
3.2.6.2 Microscopy Flax roots inoculated with R. irregulare were stained to evaluate the extent of colonization according to Vierheilig et al (1998). Roots were washed, cleared in 10% KOH for 5 minutes at 90oC and then rinsed three times with RO (Reverse osmosis) water. Cleared roots were stained for 3.5 min in ink-vinegar (5%) Sheaffer ink at 90 oC. Roots were destained for 25 minutes in RO water with few drops of vinegar. These roots were then mounted in microscope slides to visualize the fungal structures inside the roots.
3.3 Results To test the effect of Clonostachys rosea and Rhizoglomus irregulare on pathogenicity of Fusarium oxysporum f. sp. lini, we inoculated flax seeds with each of these fungi individually and in combinations. We measured flax growth responses (fresh weight, shoot length, root length, root branching intensity) and root necrotic surface area to identify the magnitude of the infection by F. oxysporum. We harvested the plants 9 and 14 days post inoculation (dpi). We chose these timepoints based on a pilot study (data not shown) in which we measured plant growth at 9 dpi, 14 dpi, and 22 dpi, and found that by 14 dpi, F. oxysporum-inoculated plants had significantly lower biomass and shoot length than any of the other treatments; as well, at 14 dpi we noticed positive effects on flax growth of R. irregulare-inoculated plants, just before colonization events,
80 which were detectable at 22 dpi (data not shown). This experiment was replicated independently at least two times, with similar results.
3.3.1 Growth responses and disease assessment
3.3.1.1 Harvest 1 At 9 dpi there were no differences among treatments in shoot length or total biomass (n=9-23, Wilk’s λ=0.91, F=0.92, P=0.51). Similarly, there were no differences detected at nine days among treatments in root parameters including root length, branching, or necrosis (n=3, Wilk’s λ=0.15, F=1.80, P=0.08).
3.3.1.2 Harvest 2 At 14 dpi of growth, fungal inoculum affected growth in terms of shoot length and total biomass (Wilk’s λ=0.58, F=5.74, P=1.85 x 10-7). Post-hoc ANOVA and Tukey’s honest significant difference tests revealed that shoot length differed among treatments (n=12- 20, F=5.63, P=0.0001), with the R. irregulare+F. oxysporum treatment growing longer shoots than the F. oxysporum, C. rosea, or C. rosea+F. oxysporum treatments (Figure 3.1). Total biomass also differed among treatments (n=12-20, F=9.63, P=2.12 x 10-7), with inoculation with R. irregulare+F. oxysporum producing plants with greater biomass compared to those inoculated with C. rosea+F. oxysporum, uninoculated control or F. oxysporum. As expected, inoculation with F. oxysporum alone led to less biomass production than all the other treatments (Figure 3.2).
Fungal inoculum also affected the root parameters measured using WinRHIZO (n=3, Wilk’s λ=0.12, F=2.13, P=0.04). Root length was not found to differ among treatments (F=2.40, P=0.10) (Figure 3.3), nor was root necrotic surface area (F=2.33, P=0.11) (Figure 3.4). However, branching intensity was affected by treatment, with plants inoculated with Rhizoglomus producing more branched root systems than those inoculated with Fusarium (F=3.10, P=0.05) (Figure 3.5). Other than the differences on growth responses between treatments, the plants did not show disease symptoms after up to 14 days of inoculation with the fungi (Figure 3.6).
81 a ab
10 b
ab 8 b b 6 4 Shoot length (cm) at 14 days 2
C Cl ClF F R RF
Treatment
Figure 3.1 Flax shoot length after 14 days of growth differed depending on fungal inoculum. Treatments are: C, uninoculated control; Cl, Clonostachys rosea; ClF, CI Clonostachys rosea + Fusarium oxysporum; F, Fusarium oxysporum; R, Rhizoglomus irregulare; RF, Rhizoglomus irregulare + Fusarium oxysporum (n=12-20). Boxplots show the third quartile and first quartile, median (middle line) and range of data (min/max whiskers). Boxplots with different letters indicate significant differences between treatments at p < 0.05, compared using Tukey’s honest significant difference tests.
82 a
160 ab ab 140
120 b
100 b 80 c Total biomass (mg) at 14 days Total 60 40
C Cl ClF F R RF
Treatment
Figure 3.2 Total biomass of flax plants after 14 days of growth as affected by fungal inoculum. Treatments are: C, uninoculated control; Cl, Clonostachys rosea; ClF, CI Clonostachys rosea + Fusarium oxysporum; F, Fusarium oxysporum; R, Rhizoglomus irregulare; RF, Rhizoglomus irregulare + Fusarium oxysporum (n=12-20). Boxplots show the third quartile and first quartile, median (middle line) and range of data (min/max whiskers). Boxplots with different letters indicate significant difference between treatments at p < 0.05, compared using Tukey’s honest significant difference tests.
83 p=0.10 60 50 40 30 Total root length (cm) at 14 days Total 20
C Cl ClF F R RF
Treatment
Figure 3.3 Total root length after 14 days of growth as affected by fungal inoculum. Treatments are: C, uninoculated control; Cl, Clonostachys rosea; ClF, CI Clonostachys rosea + Fusarium oxysporum; F, Fusarium oxysporum; R, Rhizoglomus irregulare; RF, Rhizoglomus irregulare + Fusarium oxysporum (n=3). Boxplots show the third quartile and first quartile, median (middle line) and range of data (min/max whiskers).
84 p=0.11 5 4 3 2 1 Percent necrotic root surface area at 14 days necrotic root surface Percent 0
C Cl ClF F R RF
Treatment
Figure 3.4 Root necrosis after 14 days of growth as affected by fungal inoculum. Treatments are: C, uninoculated control; Cl, Clonostachys rosea; ClF, CI Clonostachys rosea + Fusarium oxysporum; F, Fusarium oxysporum; R, Rhizoglomus irregulare; RF, Rhizoglomus irregulare + Fusarium oxysporum (n=3).
85 a 15
ab ab ab 10
ab
b 5 Branching intensity (tips per cm) at 14 days Branching
C Cl ClF F R RF
Treatment
Figure 3.5 Branching intensity in flax root systems as affected by fungal inoculum after 14 days of growth. Treatments are: C, uninoculated control; Cl, Clonostachys rosea; ClF, CI Clonostachys rosea + Fusarium oxysporum; F, Fusarium oxysporum; R, Rhizoglomus irregulare; RF, Rhizoglomus irregulare + Fusarium oxysporum (n=3). Boxplots show the third quartile and first quartile, median (middle line) and range of data (min/max whiskers). Boxplots with different letters indicate significant difference between treatments at p < 0.05, compared using Tukey’s honest significant difference tests.
86 A B
C CL F CLF R RF C CL F CLF R RF
Figure 3.6 Disease symptoms 9 days post infection (A) and 14 days post infection (B). Treatments are: C, uninoculated control; Cl, Clonostachys rosea; F, Fusarium oxysporum; ClF, CI Clonostachys rosea + Fusarium oxysporum; R, Rhizoglomus irregulare; RF, Rhizoglomus irregulare + Fusarium oxysporum.
3.3.2 Root colonization assessment C. rosea and F. oxysporum were re-isolated from surface-sterilized roots of previously inoculated plants for the single and combined treatments at 9 and 14 dpi. The colonies and spores presented morphology consistent with the original inocula. This confirmed that the symptoms observed were a result of fungal infection. Although the AMF fungus seemed to influence flax growth, especially in the R. irregulare+F. oxysporum combination, we did not detect AMF growth inside the roots at either 9 or 14 dpi following infection, despite extensive microscopic analysis.
3.4 Discussion
Consistent with our hypothesis, we found that C. rosea and R. irregulare can protect flax against F. oxysporum at early stages of interaction. Beside the pathogen protection
87 effect, R. irregulare also significantly promoted plant growth even prior to root colonization.
We observed that R. irregulare provided a remarkable bio-protective effect against F. oxysporum in this study. This AMF not only mitigated the negative effects caused by F. oyxsporum on flax shoot length and biomass, but also improved flax biomass production compared to the other treatments (untreated, C. rosea, and F. oxysporum). The mechanisms behind AMF bio-protective effects are not completely understood. We presumed in our study that the mechanisms for these bio-protective and bio-stimulant activities are induced defense responses and enhanced plant nutrition. It has been suggested that AMF induces the activation of a SAR-like defense, known as ‘mycorrhiza-induced resistance’ (MIR) (Cameron et al., 2013; Fiorilli et al., 2018). Initial AMF infection in the root cortex is recognized by the plant immune system as microbe- associated patterns (MAMPs) from the fungus. This event activates MAMP-triggered immunity (MTI) (Cameron et al., 2013). MTI, can trigger the generation of long distance signals in the vascular system, finally inducing SAR and other defense responses (Cameron et al., 2013). It is interesting to note that the SAR-like responses induced by MIR are more correlated with the activation of cell wall and jasmonic acid-dependent responses, than with salicylic acid-dependent responses (Cameron et al., 2013); in fact, AMF can supress the activation of the salicylic acid-mediated plant defense in favor of the jasmonic acid-mediated plant defense. In our studies, as AMF had not colonized the plant yet, it would seem unlikely that SAR would have already been triggered. However, it has been suggested that MAMP-triggered immunity is locally expressed upon initial infection of the roots, and can precede colonization; therefore, MAMP immunity might have been triggered in our studies (Cameron et al., 2013). Another evidence of signaling exchange between plant and AMF pre-colonization is the synthesis of ‘Myc factors’ (mycorrhizal factors) by AMF, which stimulate mycorrhizal-responsive genes that lead to root growth and mycorrhization of plants (Kosuta et al., 2003).
Another putative mechanism for bio-protection is enhancing plant nutrition and performance, with consequently improved resistance to pathogens (Nguvo & Gao,
88 2019). For example, using physiological and transcriptomic data (Fiorilli et al., 2018), resistance of wheat to a leaf pathogen has been attributed to improved mineral nutrition of mycorrhizal plants. Our data support this hypothesis, since inoculation with R. irregulare and F. oxysporum increased flax biomass, when compared to the other treatments (Figure 3.2). However, mycorrhizal plant resistance to pathogens does not depend exclusively on improved plant nutrition (Fiorilli et al., 2018). For instance, researchers have shown that mycorrhizae reduce infection of tomato by Alternaria solani, even without an increase in plant growth or phosphate uptake; the pathogen- resistance in this case was associated with activation of plant defenses (Fritz et al., 2006). We suggest that a combination of defense stimulation and enhanced nutrition by AMF in flax plants might explain the bio-protective effects of R. irregulare against F. oxysporum in our study.
Previous research has shown that after colonization by AMF, root exudates are manipulated by the AMF to shape the rhizosphere microbiome stimulating beneficial interactions and supressing pathogens in the soil (Cameron et al., 2013; Lucini et al., 2019). However, this mechanism is not supported by evidence from our experiments, since no colonization was observed at 14 dpi. Although there is evidence in the literature that AMF extraradical mycelia and AMF modifications of plant exudates shape the soil microbiota, it seems unlikely that these events could occur pre-colonization. Further studies are necessary to investigate the role of the AMF extracellular mycelium in pre-colonization events, since it was demonstrated that extracts of AMF extraradical mycelium, from mycorrhizal plants, inhibit the germination of F. oxysporum in vitro (Filion et al., 2003). Furthermore, R. irregulare directly inhibits Fusarium growth and mycotoxin production in vitro (Ismail et al., 2013). Since no evidence of AMF colonization of the roots was found in our study, it is also very unlikely that competition for space and nutrients in the roots are happening.
Another interesting outcome of our study was that R. irregulare did not alter flax root morphology when inoculated with F. oxysporum, whereas in the absence of F. oxysporum, R. irregulare induced branching in flax roots, when compared to all the
89 other treatments. One interesting fact about this result is that branching was induced even before any evidence that AMF hyphae were interacting with the plant. The fact that mycorrhizal plants produce more branched roots is well-known from previous studies (Berta et al., 1995), however this fact is mainly associated with colonized plants, or at least with initial infection. We also hypothesize that F. oxysporum treatment is repressing the intensified root branching triggered by R. irregulare, since it has been described that some microbes in the soil can reduce or delay mycorrhization (Svenningsen et al., 2018). In summary, in our studies, root branching was associated with AMF stimuli for mycorrhization on healthy plants.
C. rosea showed protected flax against F. oxysporum as C. rosea eliminated the negative effects of F. oxysporum on flax biomass (Figure 3.2). However, the biomass of the plants co-inoculated with C. rosea and F. oxysporum did not differ from the non- inoculated control plants. Therefore, the hypothesis of bio-protection by enhancing plant growth is not supported by our data, since treatments did not improve plant nutrition in our study conditions.
We suggest that it is very likely that multiple mechanisms (including mycoparasitism; competition for space and nutrients inside the roots and stimulation of defense mechanisms) were working synergistically to promote disease suppression by C. rosea in our study. Because C. rosea and F. oxysporum where isolated from flax roots, we assumed that C. rosea was protecting flax by parasitizing F. oxysporum and that it also might be competing for space and nutrients inside the roots. Although we do not have any evidence that C. rosea was producing antifungal enzymes, our hypothesis is consistent with the dominant model of C. rosea antagonism, namely the production of secondary metabolites and mycoparasitism (Chatterton & Punja, 2009; Nygren et al., 2018). It seems that mycoparasitism is a major factor influencing C. rosea genome content, which seems to be adapted according to ecological niches (e.g. interactions with fungi of other species) (Nygren et al., 2018). For instance, researchers suggested that the main mechanism by which C. rosea antagonizes Fusarium circinatum is by parasitizing this fungi’s hyphae (Moraga-Suazo & Sanfuentes, 2016). Moreover, other
90 studies showed that the mechanisms by which C. rosea protects potato against Helminthosporium solani is mycoparasitism, competition between the fungi and triggering plant defenses (Lysøe et al., 2017). Therefore, we cannot exclude the possibility that defense mechanisms are also involved in C. rosea bio-protective effects in our study. Nor can we confirm the activation of the plant defenses since we have not assayed these samples for molecular data. Nevertheless, combined C. rosea+F. oxysporum treatment had a smaller impact on flax growth than combined R. irregulare- F. oxysporum treatment. This outcome suggests that R. irregulare is a better antagonist of F. oxysporum growth than C. rosea, at least in our in vitro culture conditions. The protective effect of mycorrhizal symbiosis (with R. irregulare) or endophytic symbiosis (with C. rosea) in flax has been poorly investigated at the molecular level. In fact, we are not aware of other studies C. rosea.
Finally, we acknowledge some limitations of studying plant-fungal interactions in vitro. For instance, its necessary to consider the quality of the plant-mycorrhizal interaction in vitro since some negative or null effects of AMF symbiosis with plants have been reported both in vitro (Kokkoris & Hart, 2019) and in vivo (Akköprü & Demir, 2005). In addition, in nature, many factors affect the colonization of plants by mutualistic fungi such as crop management (plant species, soil microbes, soil nutrients, crop rotation, tillage, and environmental conditions) (Monreal et al., 2011). In contrast, in vitro studies occur in controlled environments where few fungal interactions take place. Furthermore, plants shape the rhizosphere microbiome, possibly by root exudates (Jacoby et al., 2017). Hence, the outcome of the interactions that happens in vitro cannot be extrapolated to the field. Moreover, there is a limited information available regarding flax and AMF symbiotic association in the field (Li, 2015). A natural progression of this work is to analyse which one of these fungi has a better impact on flax growth and resistance to pathogens in the field, and use this knowledge to design ‘optimal’ microbial communities for agriculture (Jacoby et al., 2017).
91 To conclude, we successfully implemented an in vitro system to evaluate the effects of two beneficial symbionts, C. rosea and R. irregulare, on flax growth, and to test the hypothesis that these would be effective in limiting the pathogenic effects of F. oxysporum inoculation. Our results showed that both C. rosea and R. irregulare are functional bio-protective agents. Furthermore, R. irregulare had the unexpected effect of increasing flax shoot length and biomass in the presence of F. oxysporum when compared to other treatments. These results were even more remarkable since R. irregulare had not yet colonized the plant. These experiments also confirmed that R. irregulare induces lateral root branching, when compared to the pathogenic fungi. Further studies should investigate the effects of C. rosea and R. irregulare in flax diseases in the field. It is also important to analyse how C. rosea and R. irregulare would affect the soil microbiota in agricultural systems in order to preserve the natural soil communities. Several questions still remained to be answered. For instance, we were interested to know the molecular events associated with the changes observed in flax phenotype. Therefore, we carried out an RNA-Seq analysis of the most remarkable treatment (Rhizoglomus irregulare and Fusarium oxysporum), which will be further discussed in Chapter 4.
92 Chapter 4 : Comparative transcriptomics of root responses to pathogenic (Fusarium oxysporum f.sp. lini) and non-pathogenic (Rhizoglumus irregulare) fungi
4.1 Background Fusarium oxysporum f. sp. lini is a hemibiotrophic pathogen, and the cause of wilt in flax (Linum usitatissimum). In contrast, Rhizoglomus irregulare is an arbuscular mycorrhizal fungus (AMF) that generally forms mutualistic relationship with flax. Both F. oxysporum and R. irregulare colonize roots at the beginning of their interaction with the host.
AMF symbiosis has a pronounced impact on plant mineral nutrition (especially phosphorus uptake, which is poorly mobile in the soil) and also seems to be involved in activation of the plant immune system (Fiorilli et al., 2018; MacLean, Bravo, & Harrison, 2017). However, the mechanisms that create this bio-protective effect are not completely understood (Valentina Fiorilli et al., 2018). Therefore, it is important to uncover the molecular responses underlying mutualism and pathogenicity.
Efforts have been made to identify symbiosis-related plant genes. The release of the genome of Rhizoglomus irregulare was a significant step towards this objective (Tisserant et al., 2013). R. irregulare genome analysis has shown that AMF obligate biotrophy is marked by a deficiency of genes involved in the plant cell wall degradation (e.g. no genes encoding cellobiohydrolases, polysaccharide lyases, proteins with cellulose-binding motif 1, nor genes involved in lignin decomposition were identified) and a deficiency of genes related to the biosynthesis of secondary metabolite toxins (e.g. lacking polyketide synthases, modular non-ribosomal peptide synthetases, terpene cyclases, and dimethylallyl diphosphate tryptophan synthases). As well, no genes involved with sucrose metabolism (e.g. secreted invertase or sucrose transporter) were identified, corroborating the fact that the AM fungi likely rely on host plant photosynthetic bioproducts as a mains source of carbon (Tisserant et al., 2013). In contrast, through the co-evolutionary course of host and AM fungi, R. irregulare has conserved genes related to nutrient uptake and assimilation, which are fundamental for symbiosis establishment (Tisserant et al., 2013). This lack of hydrolytic cell wall enzymes,
93 nutritional deficiency and loss of metabolic pathways possibly represents an evolutionary adaptation strategy to the obligate biotrophy, stimulating the dependency of AMF and plant. These adaptations stimulate symbiosis, while avoiding the plant immune system (Tisserant et al., 2013).
Several transcriptomic studies have used RNA-Seq to explore responses to AM fungal colonization including: Helianthus annuus L. (sunflower) inoculated with R. irregulare (Vangelisti et al., 2018), Solanum lycopersicum and Lotus japonicus inoculated with R. irregulare (Sugimura & Saito, 2017), Poncirus trifoliata inoculated with Glomus versiforme (An et al., 2018), and others. The genes revealed by these studies are mainly related to known mycorrhizal processes, such as membrane transport and cell wall modifications (Vangelisti et al., 2018).
A much-debated question is what mechanisms are involved in the bio-protective effects of AMF against pathogens. The most accepted hypothesis is that AMF colonization in plants triggers a preliminary broad spectrum-defense (BSD) in the absence of the pathogen (Fiorilli et al., 2018). This phenomenon is known as priming response and leads to mycorrhiza induced resistance (MIR), which is SAR-like (Fiorilli et al., 2018; Jacott et al., 2017). The defense followed by a pathogen attack on MIR plants is stronger and specific, and it is known as pathogen-specific defense (PSD) (Fiorilli et al., 2018). Different genes are activated by BSD or PSD responses (Fiorilli et al., 2018).
One study that support the priming hypothesis investigated the effect of Xanthomonas translucens, which is a leaf pathogen, on mycorrhizal and non-mycorrhizal wheat at the transcriptomic and proteomic level (Valentina Fiorilli et al., 2018). The plants were inoculated with the pathogen 49 days following the inoculation with the AMF, and the tissues were harvest for RNA-Seq and LC-MS/MS analysis 24 h post infection. Some of the BSD products found in this study were: defense-related proteins (e.g PR proteins) and hormones (e.g. jasmonate, ethylene and abscisic acid), modified amino acids with a role in plant-microbe interactions, among other responses of the plant immune system.
94 Some remarkable PSD genes upregulated in this sutdy were two cytochrome P450 monooxygenase genes and a cinnamoyl-CoA reductase, which have been associated with protection against bacterial pathogens (Fiorilli et al., 2018). Higher concentrations of phenolic acids (Singh et al., 2004) and new isoforms of peroxidases (Garmendia et al., 2006). Superoxide dismutases (Pozo et al., 2002) have also been detected in plants inoculated with biocontrol AMF (Lioussanne, 2010). In contrast, a microarray study of the transcriptome of mycorrhizal soybean plants infected with Fusarium virguliforme found some differentially expressed genes that could not be explained by the priming defense hypothesis (Marquez et al., 2018). Likewise, other systemic changes in mycorrhizal plants in response to pathogens (such as synthesis of defense proteins and metabolites) could not be related to the priming hypothesis, since these responses were localized, or the antimicrobial compounds were not produced in enough concentration to act as a biocontrol (Singh et al., 2004). The contradicting outcomes of these studies suggest that other mechanisms may be involved in AMF pathogen-induced resistance in plants. Some other possible mechanisms that could explain the resistance to pathogens observed in mycorrhizal plants are: improved plant nutrition and direct competition or inhibition (Hage-Ahmed et al., 2013; Singh et al., 2004).
Improved nutrient uptake of plants with mycorrhizal symbionts is a well-characterized phenomenon (Jacoby et al., 2017) which leads to the production of healthier and more vigorous plants, which are consequently less prone to diseases (Singh et al., 2004). However, studies have shown that the enhanced defenses of some mycorrhizal plants to pathogens are not related to phosphorus availability in the soil, and/or to the phosphorus status on the plant tissues (Fritz et al., 2006; Singh et al., 2004). Therefore, nutrient status cannot completely explain AMF-induced resistance in plants.
There is a growing body of literature that recognizes AMF antagonism against plant pathogens. For instance, the first study that demonstrated that R. irregulare inhibits the growth and the production of Fusarium sambucinum mycotoxins was published in 2013 (Ismail et al., 2013). Additionally, several studies have shown that root exudates from mycorrhizal plants negatively affect the pathogenic fungal community in the soil. For
95 instance, the germination of Fusarium oxysporum f.sp. lycopersici spores was reduced in the presence of tomato mycorrhizal roots, whereas it was induced by non-mycorrhizal plants (Hage-Ahmed et al., 2013). Similar results were found by Lioussanne et al., (2008) who suggested that the exudates of mature mycorrhizal roots of tomato may repell P. nicotianae, therefore reducing their competence to infect the plant roots (Lioussanne et al., 2008). On the other hand, there is also evidence in the literature that some mycorrhizal roots induced the germination of phytopathogenic spores, hence, promoting disease (Singh et al., 2004).
It has also been proposed that phytopathogenic fungi and AMF compete for space and nutrients (D. P. Singh et al., 2004). However, Filion et al.,(2003) found that the bio- protective effect of R. irregulare (formerly Glomus intradices) against F. solani on bean plants could not be due only to competition for resources, since the pathogenic fungi were reduced not only on the plant, but also in the soil (Singh et al., 2004).
Thus, although these different mechanisms have been proposed to explain how AMF suppresses disease in plants, none completely fulfills the understanding of this matter. Reponses vary according to the environment, pathogen and AMF species/isolates and growing conditions (Singh et al., 2004). It is therefore suggested that these multiple mechanisms may also work synergistically, where one of the mechanisms usually stands out depending on the parts of the complex host-AMF-pathogen network (Fiorilli et al., 2018; Singh et al., 2004). On the other hand, pathogens may manipulate host genes to promote infection, such as down regulation of disease resistance proteins and major latex proteins and up-regulation of indole acetic acid, amido/amino hydrolases, expansins and glucanases, amino acid transporters and aquaporins (Galindo-González & Deyholos, 2016). In response, plants regulate the expression of defense genes. Recently, genes involved in the flax response to pathogens have been investigated by a transcriptome analysis of Fusarium-inoculated flax plants. A recent study (Dmitriev et al., 2017a) analyzed the root tips of resistant and susceptible cultivars of flax in response to pathogenic and non-pathogenic fungi at 48h post inoculation. Our own previous study (Galindo-González & Deyholos, 2016) analyzed whole plants at 8 and 18
96 dpi (days post inoculation), and identified 100 and 1000 differentially expressed genes, respectively, including genes involved in: hormone biosynthesis, flavonoid biosynthesis, lignin formation, ROS production; transcription factors and ethylene response factors.
A few other previous reports have used RNA-Seq to study responses of mycorrhizal plants to pathogens (Fiorilli et al., 2018; Li et al., 2019; L. Tian et al., 2019). However, these studies where performed with plants that had already been colonized by mycorrhizae, and none of them analyzed the interaction between R. irregulare and F. oxysporum. We therefore were motivated to help fill this knowledge gap by analyzing the transcription profiles of roots, which are the organs which first interact with the fungi studied. We focused on very early stages of infection, which are essential for the establishment of mutualism or disease, and compared a pathogenic fungus to an AMF fungi, since most of the studies have made comparisons with non-pathogenic fungi. We believe that the mutualistic symbiosis can reveal contrasting genes between mutualistic and pathogenic fungi, such as F. oxysporum and R. irregulare, and genes involved in AMF bio-protection. We used RNA-Seq to analyse of the flax root transcriptome responses to the pathogenic fungus F. oxysporum and/or mutualist R. irregulare, resulting in four treatments: a mock control; F. oxysporum; R. irregulare; or both fungi simultaneously. Here we describe the differentially abundant transcripts and hypothesize their roles in symbiosis and in AM fungi protection against pathogens.
4.2 Material and Methods
4.2.1 Experimental design Transcriptome responses were assessed for flax (L. usitatissimum CDC Bethune) roots inoculated with either F. oxysporum (isolate #81), R. irregulare, R. irregulare combined with F. oxysporum, and control mock-inoculated plants. Plants were inoculated immediately after sowing with 1 mL of 105 spores of F. oxysporum, or 396 spores of R. irregulare, or both in the combined treatment, or with 1 ml of sterile water (control).
97 Harvest was performed at 9 and 14 days post inoculation (dpi) and six to seven root samples were pooled per treatment (to improve yields and decrease variability), resulting in three pooled biological replicates for each treatment. Each pool included 6-7 CDC Bethune roots under one of the four treatments (including control) and two timepoints. Disease symptoms and growth measurements were assessed, and roots were excised and immediately frozen in liquid nitrogen. Total RNA was extracted from pooled samples . Sequencing was performed for each treatment/timepoint combination, in three replicates, for a total of 24 independent RNA sequencing reactions.
The fungal treatment methods are detailed in Chapter 3.
4.2.2 RNA extraction and cDNA synthesis The frozen tissues were lysed in liquid nitrogen using the Tissuelyser II instrument (Qiagen, Valencia, CA, USA). E.Z.N.A.® Total RNA Kit I (Omega Bio-tek, Inc.) was used to isolate the RNA for each pooled sample. The enzyme RNase-free DNase Set I (Omega Bio-tek, Inc.) was used to remove DNA contamination and the RNA quality was evaluated using a NanoDrop 1000 Spectrophotometer (Thermo Fisher). The absence of contaminating genomic DNA was verified in a 1% agarose gel. Double-stranded cDNA was synthesized from 1 μg of the total RNA with the qScript cDNA Synthesis Kit (Qiagen, Beverly Inc, MA, USA).
4.2.3 RNA sequencing The quality and quantity of RNA was evaluated using the Agilent 2100 Bioanalyzer, and approximately 5 μg of total RNA of each sample was sent to the service provider, BGI (Shenzen, China), for sequencing. Library preparation was performed using mRNA enrichment (using oligo (dT) magnetic beads), and the target RNA was obtained after purification, fragmented and reverse transcribed to double-strand cDNA (dscDNA) with a N6 random primer. End repair and A-tailing were performed on the dscDNA to ligate the sequencing adapters to the fragments. The ligation product was amplified using two specific primers and subjected to the following single-strand circularization process. The
98 PCR fragments products were heat-denatured and the single strand DNA was cyclized by splint oligo (which is a molecule reverse-complemented to one special strand of the PCR product) and the single-strand molecule was ligated using DNA ligase forming the DNA nanoballs (DNB). Single end sequencing was performed using the BGI-SEQ 500 instrument.
4.2.3.1 Bioinformatic analysis Fastq data files were mapped to the flax genome (Wang et al., 2012) using TopHat v2.1.0 (Kim et al., 2013). The mapped sequence readings were used as inputs to cufflinks (Trapnell et al., 2010), to assemble the transcriptome from the RNA-Seq data and quantify their expression. Cufflinks was run with the GTF-guide option (reference transcript annotation to guide assembly), using a file with previously annotated genes. The gtf files of the transcripts of all treatments and replicates were combined using the cuffmerge program (Trapnell et al., 2010). The cuffdiff program (Trapnell et al., 2010) was used to compare the control (uninoculated) treatment with flax plants inoculated with the individual fungal isolates or their combinationat 9 and 14 dpi. Transcript abundance was calculated as FPKM (fragments per kilobase of transcript per million mapped reads). Differential expression was calculated as the log2 fold- change ratio between the treated and control FPKMs, and ratios with FDR (q<0.05) were defined as differentially expressed genes (DEGs). Gene Ontology (GO) enrichment was calculated in AgriGO (Tian et al., 2017) using default parameters.
4.2.4 Real time PCR To validate the results from the RNA-Seq, real time qPCR (qRT-PCR) primers were designed for genes that responsed significantly (FDR<0.05, FC 2) to F. oxysporum inoculateion. The qRT-PCR reactions were then performed, including all the treatments and timepoints, using the Perfecta Mix kit (QIAGEN, Beverly Inc, MA, USA) following the manufacturer's recommendations. The ubiquitin gene was used as a reference gene, as described by Galindo-González & Deyholos., (2016). Prior to performing qRT- PCR, the amplification was optimized by gradient PCR and the amplicons of the PCRs
99 were visualized on 1% agarose gels. Using cDNA (1: 6 dilution, 1.0 μl) as template. The best temperatures for the primers are described in Table 4.1.
Table 4.1 Primers used for qRT-PCR analysis
Gene_ID Forward Reverse Temperature
- CCA AGA TCC AGG ACA AGG AA GAA CCA GGT GGA GAG TCG AT 54 oC
Lus10002741.g TGT TAT GGG TGG TGG TAG T CTT GCA AGC TCG TAA CCC 54 oC
Lus10004410.g ACT GTC CCT TCA CGG TAT AGG ATC GCT GGG AAA GTA 54 oC
Lus10005358.g ACT ACA CCC TCC CGA TAA G CCA CGA CAG CAT GAG AAA T 54 oC
Lus10005858.g CA CGG AGG ACG ATC TTT TAT TTC CGC CGC TTC ATC 54 oC
Lus10006691.g TAG TGG CCA AGT GCA AAG GGG AAG GCC TCA ACA ATA AT 54 oC
Lus10009254.g AAT CGC CGG ATT CAA CAG CGC CTT CGT CAG AAC ATT AT 54 oC
Lus10010696.g GAC GGT TGT ATG TGG GAT AG GAG CAA CGG AGC CTT ATT 54 oC
Lus10012880.g CTC CCG CTA AAC CAA TCA A CGG AGA CGT AAG CCA AAT AA 54 oC
Lus10014241.g CGG CCA CAG CTG TTT AT GAA GGT TAT GGT GCG AGT AG 54 oC
Lus10016121.g GTG ACG TGG CCA AAG ATA A CTC CCA TAG AGT AGC CAT ACA 54 oC
Lus10039210.g CTG TGT GCA AGT CGT GTA A GGA AGG CTC ATC ATC AGT AAG 54 oC
Lus10039511.g GT GTC CCA CGG AAA TAT G A CAC TTT CCA GGA AGC 54 oC
Lus10041412.g CAT GAA TCG TCC TGA TGT CC CT TCC ACT CCC TCC TAA T 54 oC
Three biological replicates (one replicate = six or seven pooled root samples) and three technical replicates for each biological replicate were used per treatment. The qRT-PCR reactions were performed on the CFX96 Real-Time System (BioRad). The CFX Manager Software (BioRad) program was used to calculate normalized expression values. To identify the significantly differentially expressed genes "R" (statistical computation R) packages were used: Univariate post hoc variance analysis (ANOVAs) was used to verify if there was significant difference between treatments; and Tukey's (1949) honestly significant difference was subsequently used to identify which treatments presented significant differential gene expression when compared with the control (mock) (e.g. F. oxysporum/mock, R. irregulare/mock, F. oxysporum/R. irregulare) at the different time points.
100 4.3 Results
4.3.1 RNA-Seq Following our observation that F. oxysporum negatively affected flax growth and that this negative effect was reversed when R. irregulare was co-inoculated with F. oxysporum (Figure 3.1, 3.2, 3.6), we decided to use RNA-Seq to investigate the molecular responses to these two fungi individually and in combination. We compared the transcriptomes of four different treatments: non-inoculated control; R. irregulare; F. oxysporum; and combined (co-inoculated with both fungi) (Table 4.2). Each treatment was sampled at two time points: 9 and 14 days post inoculation (dpi). Three independent pooled biological replicates were sequenced at each timepoint for each treatment. An average of 23.6 million reads was generated for each sample. An average of 92.8% of reads was mapped to the flax genome, indicating that the quality of the sequencing was sufficient for this analysis.
101 Table 4.2 RNA-Seq statistics
Number of Days post- Total number mapped Mapped Treatment inoculation Replicate of reads reads reads F. oxysporum 9 1 26,295,578 23,846,184 90.70% 9 2 21,207,507 20,136,471 94.90% 9 3 25,099,908 23,022,983 91.70% 14 1 28,302,664 21,718,320 76.70% 14 2 26,356,308 23,049,986 87.50% 14 3 24,013,821 20,506,614 85.40% water (mock) 9 1 26,296,784 25,614,703 97.40% 9 2 28,154,058 27,107,516 96.30% 9 3 23,256,284 21,423,289 92.10% 14 1 26,290,590 25,555,104 97.20% 14 2 27,218,277 25,835,470 94.90% 14 3 23,948,405 22,394,920 93.5%
R. irregulare 9 1 26,297,248 25,672,917 97.60% 9 2 27,085,471 25,915,966 95.70%
9 3 47,990,672 43,327,088 90.30%
14 1 26,296,505 25,548,569 97.20%
14 2 27,960,824 26,790,071 95.80% 14 3 25,439,847 24,738,422 97.20%
combined incoulum 9 1 26,840,322 25,709,225 95.80% 9 2 25,444,731 24,919,946 97.9% 9 3 25,459,037 24,889,473 97.8% 14 1 24,728,927 23,493,166 95.00% 14 2 28,124,855 26,849,645 95.50% 14 3 25,447,103 24,826,809 97.60% Total 566,160,856 525,222,951 N/A
Average 23,590,035 21,884,289 92.8%
4.3.2 Definition of differentially expressed genes We calculated the normalized, relative transcript abundance (RNA-Seq fragments per thousand bases mapped, FPKM), averaged over all replicates for each treatment and time point. Transcript abundance was expressed as a log2 ratio of the treated sample relative to the mock-treated control at each respective time point. Statistical significance
102 was inferred as a false discovery rate (FDR, or q-value). In total, transcript abundance for 2,348 flax genes was significantly different (q < 0.05) from the mock-treated control in one or more of the six treatments. In nearly all cases (94%; 2,215/2,348) in which a gene was defined as significantly different (q < 0.05), the absolute value of the log2 gene expression ratio was greater than 1, meaning that (in a linear scale), the gene had increased or decreased in abundance two or more fold. Therefore, to simplify further comparisons between treatments, and to avoid selecting an arbitrary expression ratio threshold, we decided to define all significantly different (q < 0.05) genes as differentially expressed genes (DEGs). Furthermore, any gene with a positive log2 gene expression ratio will be defined as “up-regulated”, and any gene with a negative log2 gene expression ratio will be defined as “down-regulated”, while acknowledging that we have here only measured transcript abundance, and not gene regulation per se. Tables of DEGs for each of the treatments are contained in the Supplementary data, available at: https://osf.io/9u45x/?view_only=f5866cf2c5054427a33f52936469701b.
4.3.3 Validation of RNA-Seq data by qRT-PCR We performed quantitative real-time PCR (qRT-PCR) reactions for 13 genes in all six treatments to assess the reproducibility of DEG measurements across platforms. (Table 4.3, 4.4). The genes were selected to represent a variety of expression patterns, although the majority (9/13) were differentially expressed in the F. oxysporum inoculated samples. The correlation between RNA-Seq and qRT-PCR results ranged from R= 0.8 at 9 dpi (Table 4.3) and R= 0.9 at 14 dpi (Table 4.4) for F. oxysporum treatment, to R= 0.32 and R =0.18 for R. irregulare treatments. In general, genes that were defined as DEGs in a given treatment had similar expression ratios in both RNA- Seq and qRT-PCR. The lower correlation among the R. irregulare treatments reflects the fact that fewer genes in these samples were defined as significantly different (q < 0.05) in the RNA-Seq experiment (see below). Overall, these observations provide evidence the RNA-Seq results we present here are generally valid.
103 Table 4.3 Comparison of RNA-Seq and qRT-PCR log2 expression ratios (treatment/control) for selected genes at 9 dpi
9 dpi F. oxysporum R. irregulare combined RNA- qRT- RNA- qRT- RNA- qRT- Lus id annotation Seq PCR Seq PCR Seq PCR 10002741 LTP/seed storage 2S albumin 3.0* 4.4* -2.2* -1.7 -0.4 -1.6 10004410 PR-related thaumatin superfamily 9.2* 5.1* -2.6 -0.3 4.9* -0.2 10005358 spermidine hydroxycinnamoyl trfase. 7.1* 6.0* -1.7 -0.6 2.2* -0.5 10005858 alpha/beta-Hydrolases superfamily 3.7* 2.6 -1.0 -0.4 0.4 -0.4 10006691 O-methyltransferase family 5.5* 2.8 -3.3* -0.5 1.0 -0.1 10009254 germin-like protein 10 -11.6 -1.7 1.5 -0.2 -0.1 0.2 10010696 nodulin MtN21 transporter family 0.1 -1.3 1.5* 2.4 0.1 -0.3 10012880 terpenoid cyclase superfamily 4.4* 4.7* -0.9 -1.3 0.4 -1.2 10014241 MLP-like protein 423 -2.2* -6.5* -3.0* -7.1* -3.6* -7.4* 10016121 nitrate transporter 2:1 0.1 -1.5 1.9* 2.1 1.6* 0.8 10039210 Kunitz family trypsin/protease inhibitor 10.5 8.6* 1.8 -0.4 5.3 -1.0 10039511 LTP/seed storage 2S albumin 6.3* 7.3* -0.2 -1.0 0.8 0.7 10041412 serine carboxypeptidase-like 28 -6.2 -2.1 7.4 -1.5 5.4 -1.0 R=0.8 R=0.32 R=0.51 p =0.001 p=0.29 p =0.078
Notes: Values shown are log2 scale expression ratio (treatment/control). Lus id is the numeric portion of the flax gene identifier (e.g. Lus10002741) in Phytozome. The fungal species used for each inoculation are as named, except “combined” which is the simultaneous inoculation. Data shown are the average of three replicates in RNA-Seq and three replicates in qRT-PCR. The asterisk (*) denotes statistical significance (q <0.05, for RNA-Seq) and (p <0.05) for qRT-PCR.
104 Table 4.4 Comparison of RNA-Seq and qRT-PCR log2 expression ratios (treatment/control) for selected genes at 14 dpi
14dpi F. oxysporum R. irregulare combined RNA- qRT- RNA- qRT- RNA- qRT- Lus id annotation Seq PCR Seq PCR Seq PCR 10002741 LTP/seed storage 2S albumin 2.7 3.0 -0.6 -0.5 0.8 0.9 10004410 PR-related thaumatin superfamily 11.2 8.4 -0.8 0.2 8.2 4.0 10005358 spermidine hydroxycinnamoyl trfase. 8.3 9.7 0.6 0.6 5.4 4.5 10005858 alpha/beta-Hydrolases superfamily 6.1 7.8 0.4 0.5 3.1 1.2 10006691 O-methyltransferase family 4.3 4.2 -3.7 -0.3 2.4 1.9 10009254 germin-like protein 10 -3.8 -3.9 -2.3 0.5 -0.8 -0.3 10010696 nodulin MtN21 transporter family -1.9 -0.5 0.2 0.9 1.1 0.8 10012880 terpenoid cyclase superfamily 2.8 5.6 0.1 -1.3 2.1 5.6 10014241 MLP-like protein 423 -1.9 -4.1 -4.5 -5.9 -3.2 -5.8 10016121 nitrate transporter 2:1 -2.8 -0.1 0.8 0.1 1.5 0.2 10039210 Kunitz family trypsin/protease inhibitor 8.0 7.0 -0.5 -0.8 3.8 3.7 10039511 LTP/seed storage 2S albumin 8.1 13.7 -0.5 -1.2 4.8 -1.7 10041412 serine carboxypeptidase-like 28 0.1 -2.4 -9.2 0.8 -10.7 -0.7 R=0.9 R=0.18 R=0.54 p =2.4 x 10-5 p=0.57 p =0.054
Notes: Values shown are log2 scale expression ratio (treatment/control). Lus id is the numeric portion of the flax gene identifier (e.g. Lus10002741) in Phytozome. The fungal species used for each inoculation are as named, except “combined” which is the simultaneous inoculation. Data shown are the average of three replicates in RNA-Seq and three replicates in qRT-PCR. The asterisk (*) denotes statistical significance (q <0.05, for RNA-Seq) and (p <0.05) for qRT-PCR.
4.3.4 Visualization and clustering of DEGs The 2,348 DEGs were visualized using a heat map and hierarchical clustering (Figure 4.1). The heat map showed that the transcriptome response differed more between inoculum types than between the time points sampled. Furthermore, the responses to R. irregulare and the combined inoculum are more similar to each other than either is to F. oxysporum. The heatmap also showed that the F. oxysporum 14 dpi treatment was
105 overwhelmingly the treatment that induced the highest number of responsive genes (2,117).
Figure 4.1 Hierarchical clustering and heatmap of expression ratios (log2, treatment/control) for 2,348 DEGs. Treatments are R. irregulare (Rir), F. oxysporum (Fos) or the combined inoculum (com) after 9 or 14 dpi, compared to a non-inoculated control at the same time point. Hierarchical clustering was applied to both rows and columns of the heatmap.
106 4.3.5 Principal component analysis of all treatments We conducted Principal Component Analysis (PCA) of the 2,348 DEGs expressed genes to further assess the similarity of the transcriptome responses to each treatment (Figure 4.2). The two major principal components together explained 89.9% of the total variance, and showed that the R. irregulare 9 dpi and the combined treatments were most similar, and these were much more similar to R. irregulare 14 dpi than to F. oxysporum at either 9 dpi or 14 dpi. A similar result was obtained when the expression ratios of all genes (not just those with q < 0.05) were used to conduct PCA (data not shown).
Figure 4.2 PCA of average log2 expression ratios (treatment/control) for 2,348 DEGs. Treatments are R. irregulare (Rir), and F. oxysporum (Fos), or both fungi combined (com) at 9 or 14 dpi.
107 4.3.6 Global patterns of gene expression of all treatments To further compare global patterns of plant gene expression in response to our fungal treatments, and in particular to investigate to impact of the combined treatments, we plotted the expression ratios (log2 treatment/control) for all genes, as shown in Figure 4.3. These plots show that genes that have large, positive, expression ratios are more likely than genes with large, negative, expression ratios to have conserved expression patterns in the combined inoculum as compared to the individual inocula. In other words, genes that are up-regulated by treatment with either F. oxysporum, or R. irregulare alone are more likely than down-regulated genes to show the same pattern in the combined inoculum. This is particularly evident in the comparisons involving F. oxysporum 14 dpi, and is obvious whether all genes, or only genes with q < 0.05 in both treatments, are considered.
108
Figure 4.3 Scatter plots of average expression ratios (log2, treatment/control) for all 32,807 flax genes measured by RNA-Seq experiments. Genes that were significantly differentially expressed (q<0.05) compared to control in both of the treatments in the graph are red, genes that are significantly differentially expressed in only the treatment on the y-axis are orange, and genes significantly differentially expressed in only the treatment shown on the x-axis are yellow, and genes that were not significantly different from controls in either treatment are grey. Treatments represented are R. irregulare (Rir), and F. oxysporum (Fos), or both fungi combined (com) after 9 or 14 days of treatment. Data are the average of three independent replicates.
109 4.3.7 Identification of DEGs responsive to inoculation by either F. oxysporum or R. irregulare We used Venn diagrams to compare and contrast groups of DEGs responsive to our various treatments. As shown in Figure 4.4, F. oxysporum 14 dpi was the treatment with the highest number of up-regulated (971) or down-regulated (1146) genes, as was also evident from the heatmap (Figure 4.1). F. oxysporum at 9 dpi also resulted in a large number (627) of up-regulated genes at 9 dpi, and most of these (81%; 509/627) were also up-regulated at 14 dpi. However, a relatively smaller number of genes (82) were down-regulated by F. oxysporum at 9 dpi.
R. irregulare treatment produced 50 up-regulated genes at 9 dpi, and only 9 genes up- regulated genes 14 dpi. This is far fewer than the number of genes up-regulated by F. oxysporum at either time point. The number of genes down-regulated (73) by R. irregulare 14 dpi was also comparatively low, but the number of genes down-regulated (94) by R. irregulare at 9 dpi was actually higher than in F. oxysporum at 9 dpi. The most overlap between different treatments was found among the genes up-regulated at 9 dpi, where we observed that 44% (22/50) of the genes that were up-regulated in 9 dpi R. irregulare treated plants were also up-regulated in 9 dpi F. oxysporum treated plants. There was otherwise very little overlap between the genes that responded to F. oxysporum and the genes that responded to R. irregulare.
110
Figure 4.4 Venn diagram comparing the number of significant transcripts with significantly increased abundance (numbers in blue) and transcripts with significantly decreased abundance (numbers in red) of flax roots inoculated with R. irregulare (R) or F. oxysporum (F), at 9 and 14 d post infection. We considered significant differential expression between samples and non-inoculated control when FDR < 0.05, and fold change >0 (transcripts with increased abundance) or <0 (transcripts with decreased abundance).
4.3.8 GO Functional Enrichment – Functions of F. oxysporum DEGs We used Gene Ontology (GO) enrichment analysis to infer the function of some of the DEGs that were responsive to our treatments (Table 4.5; Supplementary data S6). This analysis identifies molecular functions, biochemical processes, or cellular compartments that are represented more frequently by DEGs in a given treatment than would be expected by chance. Over 246 significantly enriched GO terms were identified by this process. Table 4.5 shows only the most informative of these 246 terms.
Given the large number of DEGs in F. oxysporum (especially up-regulated genes at 9 and 14 dpi, and down-regulated genes at 14 dpi), it was not surprising that these treatments had the largest number of enriched GO categories (Table 4.5). The categories of jasmonic acid (JA) metabolic processes (GO:0009694), and systemic
111 acquired resistance (GO:0009627) were enriched uniquely among up-regulated genes at 9 dpi, and many of these were also in the response to fungus category (GO:0009627). These categories are comprised of several genes for JA biosynthesis, and SA-responsive pathogenesis-related genes. Three other categories were enriched uniquely among up-regulated DEGs at both 9 dpi and 14 dpi: (GO:0030246) carbohydrate binding; (GO: 0016614) oxidoreductase, acting on CH-OH; and (GO:0051119) sugar transmembrane transporter. These categories include primarily lectin-like kinases, sucrose and polyol transporters, and enzymes for the synthesis of various secondary metabolites.
One category that was uniquely enriched in down-regulated F. oxysporum 14 dpi DEGs is apoplast (GO:0048046), which includes RALF peptide hormones (which cell division), and multiple xyloglucan endotransglucosylase/hydrolases, which are likely involved in cell wall loosening. The remaining categories discussed here include both up-regulated and down-regulated DEGs: (GO:0005975) carbohydrate metabolic processes, (GO:0034641) cellular N compound metabolic processes, (GO:0009755) hormone- mediated signaling pathways; (GO:0008610) lipid biosynthetic process, (GO:0004601) peroxidase activity, (GO:0008194) UDP-glycosyltransferase activity, (GO:0009753) response to JA stimulus. Interestingly, none of the aforementioned F. oxysporum- enriched GO categories were enriched in any of the other treatments. However, the following categories were enriched in both F. oxysporum up- and down-regulated DEGs, and in up-regulated DEGs from the combined the combined treatment: (GO:0006629) lipid metabolic processes; (GO:0032787) monocarboxylic acid metabolic processes; (GO:0009699) phenylpropanoid biosynthetic processes; (GO:0019438) aromatic compound biosynthetic processes; (GO:0006575) cellular amino acid derivative metabolic processes; (GO:0009698) phenylpropanoid metabolic processes; (GO:0022857) transmembrane transporter activity. These categories consist largely of genes for metabolism of various classes of secondary metabolites.
Finally, the GO categories immune response (GO:0006955) and defense response (GO:0006952) were particularly interesting because they were enriched in up-regulated
112 DEGs of F. oxysporum (9 dpi and 14 dpi) and combined treatment, but were down- regulated by R. irregulare at (9 dpi and 14 dpi). These categories include many TIR- NBS-LRR receptor kinases, transcription factors (e.g. WRKY 70), pathogenesis-related proteins (PR1, PR4), and other effectors including chitinases and salicylic-acid responsive genes.
113 Table 4.5 Gene Ontology (GO term) enrichment analysis for DEGs F. oxysporum R, irregulare combination 14 9 dpi 14 dpi 9 dpi dpi 9 dpi 14dpi GO Term description dn up dn up dn up dn up dn up dn up 0009694 P jasmonic acid metabolic proc 6
0009627 P systemic acquired resistance 5
0009620 P response to fungus 14 13
0030246 F carbohydrate binding 8 10
0016614 F oxidoreductase, acting on CH-OH 8 14
0051119 F sugar transmembrane transporter 9 9
0048046 C apoplast 14
0005975 P carbohydrate met proc 21 37
0034641 P cellular N compound met proc 14 22
0009755 P hormone-mediated sig path 11 18
0008610 P lipid biosynthetic process 14 24
0004601 F peroxidase activity 7 16 11
0008194 F UDP-glycosyltransferase activity 9 16 14
0009753 P response to JA stimulus 15 13 15
0006629 P lipid met proc 22 41 7
0032787 P monocarboxylic acid met proc 17 25 18 5
0009699 P phenylpropanoid biosyn proc 10 20 11 6
0019438 P aromatic compound biosyn proc 12 24 13 7
0006575 P cellular aa derivative met proc 14 26 15 8
0009698 P phenylpropanoid met proc 10 21 13 6
0022857 F transmembr transporter activity 28 43 37 11 10
0006955 P immune response 18 22 10 6 8
0006952 P defense response 40 46 10 6 6 12
0005618 C cell wall 6 18 45 24 5 8
GO term enrichment among DEGs, calculated GSEA analysis in AgriGO. The GO terms shown are from each of three GO domains: C (cellular component), F (molecular function), or P (biological process). Up-regulated DEGs (up) are shown in blue; and Down-regulated DEGs (dn) are shown in red. Values in colored boxes show the number of genes assigned to each GO term. Values are shown only for GO terms for which enrichment was statistically significant (FDR < 0.05). Abbreviations: process (proc), metabolism (met), signaling (sig), pathway (path). This table shows only selected GO terms; the full table can be found in the Supplementary data S6.
114 4.3.9 Functions of R. irregulare responsive DEGs Although no GO terms were uniquely enriched in DEGs from R. irregulare treatment, there were some recognizable functional group among up-regulated and down- regulated genes in either of the two time-points following inoculation with R. irregulare (Supplementary data S7, S8). DEGs down-regulated following R. irregulare treatment include heavy metal transporters for either copper or zinc; transcription factors (WRKY40, WRKY70, bHLH); 10 different genes encoding major-latex proteins (MLPs); various pathogenesis related (PR) proteins; and multiple chitinases and beta-1,3- glucanases (Table 4.7).
Fewer DEGs were up-regulated than down-regulated by R. irregulare, but among these up-regulated genes were many transporters: nodulin MtN21 /EamA-like transporter family protein (Lus10010696); two transmembrane amino acid transporters family protein (Lus10023971, Lus10025113); high-affinity K+ transporter 1 (Lus10030872); nitrate transporter 2:1(Lus10016121); and phosphate transporter 1;3 (Lus10030506). Cell wall and polysaccharide-modifying proteins also among the up-regulated DEG, including extensin family protein (Lus10008213), two hydroxyproline-rich glycoprotein family proteins (Lus10039881, Lus10033360), one plant invertase/pectin methylesterase inhibitor superfamily (Lus10027202) and a pectin lyase-like superfamily protein (Lus10022310) (Supplementary S7).
4.3.10 DEGs with opposite responses to F. oxysporum and R. irregulare Because we hypothesized that some genes of interest might respond differently to a pathogen (F. oxysporum) than a mutualist (R. irregulare), we used a second set of Venn diagrams to represent sets of genes that were up-regulated by one fungus, and down- regulated by the other (Figure 4.5). Considering all time points, 34% (20/58) of the genes up-regulated by R. irregulare were oppositely down-regulated by F. oxysporum (and most of these we up-regulated at 9 dpi in R. irregulare and down-regulated at 14 dpi by F. oxysporum, Table 4.6). Conversely, of the 114 genes down-regulated by R. irregulare at any time point 48% (55/114) were oppositely up-regulated by F. oxysporum (Table 4.7).
115
Figure 4.5 Venn diagram shows contrasts between genes with significant transcripts with significantlyincreased abundance (numbers in blue) and transcripts with significantly decreased abundance (numbers in red) of flax roots inoculated with R. irregulare (Rir) or F. oxysporum (Fos). We considered significant differential expression between samples and non-inoculated control when FDR < 0.05, and fold change >0 (transcripts with increased abundance) or <0 (transcripts with decreased abundance
116 Table 4.6 Comparison of genes up-regulated by R. irregulare that were oppositely down-regulated by F. oxysporum
F. oxysporum R. irregulare combined Lus id annotation 9 d 14 d 9 d 14 d 9 d 14 d 10039259 Ca-dependent lipid-binding family 0.13 -1.50 1.30 0.14 0.88 0.29 10031145 cytochrome P450 79B2 0.62 -4.46 1.41 0.59 1.39 0.01 10009152 Gln-amidotransferase-like superfamily -0.20 -1.70 1.06 -0.07 0.39 -1.35 10033360 hydroxyproline-rich glycoprotein 0.60 -1.59 1.39 0.25 1.10 0.79 10039881 hydroxyproline-rich glycoprotein -0.63 -4.48 1.29 -0.02 1.03 0.23 10030872 K+ transporter, high affinitiy 0.08 -1.94 1.30 -0.03 0.89 0.27 10022066 LTP/seed storage 2S albumin superfamily 0.43 -1.94 1.15 0.40 1.28 0.68 - 10016121 NO3 transporter 0.12 -2.76 1.89 0.75 1.61 1.46 10010696 nodulin MtN21 transporter family 0.10 -1.91 1.47 0.19 0.13 1.11 10018628 O-methyltransferase family -0.29 -1.86 1.30 0.17 0.44 0.69 10025256 peroxidase superfamily 0.16 -2.71 1.40 0.54 1.57 0.58 10006534 peroxidase superfamily -2.21 -2.65 -0.20 1.07 0.92 2.01 10001228 peroxidase superfamily 0.66 -2.46 1.14 0.28 1.35 -0.07 10008213 pollen Ole e1 allergen, extensin family 0.43 -2.83 1.05 0.92 1.22 0.24 10024122 polyphenol oxidase 0.59 -1.63 1.05 0.59 1.09 0.29 10011335 senescence-associated gene 29 0.34 -2.44 1.18 -0.14 0.37 -0.55 10038848 unknown function 0.22 -2.35 1.06 0.64 1.07 0.69 10003550 unknown function 0.58 -1.68 1.20 0.19 0.74 0.58 10010793 unknown function (DUF607) -0.72 -1.37 1.14 0.32 0.23 0.16 10018645 unknown function (PELPK like) 0.24 -2.47 1.16 0.10 0.98 0.50
DEGs that were up-regulated by R. irregulare but down-regulated by F. oxysporum included several genes with putative functions in nutrient transport: nodulin MtN21 /EamA-like transporter family protein (Lus10010696), amino acid transporters (Lus10023971, Lus10025113), a high-affinity K+ transporter (Lus10030872), and a nitrate transporter 2:1(Lus10016121). The nitrate transporter and the nodulin were among the genes also validated by qRT-PCR (Table 4.3, 4.4). Several DEGs with potential roles in cell-wall remodelling were also up-regulated in R. irregulare but down- regulated by F. oxysporum, namely two hydroxyproline-rich glycoprotein family proteins (Lus10039881 and Lus10033360), and a pollen Ole e 1 allergen and extensin family
117 protein (Lus10008213), as were three different peroxidases (Lus10025256, Lus10006534, and Lus10001228).
Table 4.7 Comparison of genes down-regulated by R. irregulare at any time point that were oppositely up-regulated by F. oxysporum. F. oxysporum R. irregulare combined Lus id annotation 9 d 14 d 9 d 14 d 9 d 14 d 10022191 2-oxoglutarate (2OG), Fe(II)-dependent oxygenase 4.48 6.61 -2.08 0.52 0.07 3.62 10004364 alpha/beta-hydrolases superfamily 0.27 1.22 -1.97 -1.83 -0.95 -0.36 10038524 alpha/beta-hydrolases superfamily 1.57 3.65 -1.43 -0.30 0.85 0.08 10019801 beta-1,3-glucanase 1 7.77 7.85 -4.39 -2.99 2.66 5.62 10018696 C2H2 and C2HC zinc fingers superfamily 0.91 1.45 -1.47 -0.86 -0.09 -0.35 10020686 Calmodulin-binding protein -0.24 1.12 -1.80 -2.13 -0.30 -1.25 10003231 chitinase, homolog of carrot EP3-3 6.42 7.25 -3.15 -1.86 1.31 4.10 10010866 chitinase, homolog of carrot EP3-3 3.49 5.23 -1.88 0.42 -0.48 2.41 10024366 chitinase, homolog of carrot EP3-3 5.95 6.48 -3.12 -1.38 0.73 3.02 10028275 chitinase, homolog of carrot EP3-3 1.74 2.43 -1.10 -1.07 0.08 0.03 10016464 copper transporter 1 0.91 0.92 -0.84 -1.13 -0.47 -0.37 10014284 cytochrome BC1 synthesis 1.08 2.25 -1.11 -1.42 0.33 0.13 10030189 cytochrome P450 71B10 2.36 2.88 -1.27 0.39 -0.79 0.88 10013150 cytochrome P450 82C 1.34 1.28 -0.84 -0.80 -0.29 0.06 10038200 cytochrome P450 82C4 3.21 5.92 -1.69 1.23 0.12 3.99 10011658 cytochrome P450 82C4 2.53 4.52 -1.71 0.67 -1.19 1.54 10025901 cytochrome P450 82C4 3.20 4.74 -1.78 0.63 -0.39 2.19 10025902 cytochrome P450 82C4 5.30 7.67 -2.23 0.88 0.92 4.93 10010742 extensin-like 2.14 3.70 -3.78 -4.30 -5.01 -3.91 10030362 glutathione S-transferase TAU 24 0.31 2.55 -1.72 0.49 -0.87 1.41 10033420 integrase-type DNA-binding superfamily -0.81 1.67 -4.61 -1.72 -0.63 -0.39 10002741 LTP/seed storage 2S albumin superfamily 3.04 2.74 -2.17 -0.62 -0.36 0.83 10016323 LTP/seed storage 2S albumin superfamily 2.24 2.24 -1.45 -0.67 -0.40 0.54 10022642 LYS/HIS transporter 7 1.63 2.31 -1.42 -0.60 -0.74 1.32 10032976 malectin/receptor-like protein kinase 1.32 1.45 -1.16 -0.27 -0.26 -0.01 10005605 matrixin family 3.46 3.18 -3.78 -0.85 0.17 0.98 10011820 myb domain protein 15 1.11 2.07 -1.07 -1.19 -0.06 -0.44 10024795 N-terminal nucleophile aminohydrolase 2.73 4.58 -1.17 0.46 0.20 1.18 10038332 NAC domain containing protein 42 2.78 4.27 -2.28 1.24 -1.14 2.07
118 F. oxysporum R. irregulare combined Lus id annotation 9 d 14 d 9 d 14 d 9 d 14 d 10029369 NAD(P)-binding Rossmann-fold superfamily 3.85 5.70 -2.19 0.50 0.20 2.52 10020783 NDR1/HIN1-like 25 0.83 1.87 -1.38 -0.17 0.35 0.15 10041460 NIM1-interacting 2 2.50 2.21 -2.39 -2.52 -1.74 0.31 10006691 O-methyltransferase family protein 5.47 4.32 -3.35 -3.73 1.03 2.44 10040215 ortholog of sugar beet HS1 PRO-1 2 0.66 1.31 -1.05 -0.95 -0.06 -0.31 10006302 osmotin 34 PR5 5.80 4.94 -3.43 -2.17 1.17 2.59 10017170 osmotin 34 PR5 5.34 5.27 -3.75 -0.68 0.86 2.63 10035775 P-loop containing NTP hydrolases superfamily 1.22 1.30 -2.10 -1.55 -1.08 -0.21 10032178 pathogenesis-related Bet v 1 5.99 8.69 -3.55 0.42 1.21 5.45 10015339 pathogenesis-related Bet v 1 4.46 5.87 -2.27 0.56 0.25 3.03 10020493 pathogenesis-related PR1 9.17 8.97 -3.61 -0.51 4.03 6.01 10012479 pathogenesis-related PR1 7.93 9.54 -4.04 -0.17 2.51 6.29 10012684 peroxidase superfamily 3.32 3.88 -2.60 -0.25 0.39 1.81 10000327 polynucl. transferase, RNAse H-like super fam. 0.08 1.57 -2.46 -1.67 0.42 -0.86 10035078 polynucl. transferase, RNAse H-like super fam. -0.22 1.12 -2.19 -1.58 0.44 -0.86 10003116 RmlC-like cupins superfamily 1.02 1.72 -1.01 0.84 -0.63 -0.62 10032848 salt tolerance zinc finger -0.27 1.47 -2.53 -1.39 0.03 -0.72 10038985 sigma factor binding protein 1 1.16 0.87 -2.99 -2.63 -1.62 -1.03 10027279 sigma factor binding protein 1 1.73 0.74 -2.37 -2.73 -1.21 -1.00 10008742 UDP-glycosyltransferase 74 F1 1.17 3.26 -2.00 0.91 -0.37 0.48 10034389 unknown function 2.52 3.13 -1.68 -1.17 -0.89 0.14 10032881 unknown function (DUF 1645) 0.48 1.43 -1.07 -0.67 0.17 -0.39 10037249 unknown function (DUF 4228) -0.10 1.25 -1.13 -0.25 0.17 0.24 10024074 WRKY DNA-binding protein 40 1.17 2.08 -1.19 -1.54 0.78 -0.56 10034244 WRKY DNA-binding protein 70 1.72 2.05 -1.75 -1.75 -0.49 0.07 10012870 WRKY DNA-binding protein 70 0.93 1.24 -1.14 -1.61 -0.54 -0.37 Table shows expression ratio (log2) for treatment:control. Genes shown have FDR < 0.05 in at least one of the treatments.
Many DEGs were up-regulated by F. oxysporum but down-regulated by R. irregulare This included many well-known defense-related genes including: transcription factors WRKY 40 (Lus10024074), WRKY 70 (Lus10034244; and Lus10012870); chitinases (Lus10003231, Lus10010866, Lus10024366, Lus10028275); a NDR1/HIN1-like gene
119 (Lus10020783); pathogenesis-response (PR) proteins of the PR1 family (Lus10020493 and Lus10012479), the PR2/ beta-1,3-glucanase family (Lus10019801), the PR5/osmotin family (Lus10006302 and Lus10017170), and the Bet v I/ PR10 family (Lus10032178 and Lus10015339) (Supplementary S9, S_10).
Several DEGs putatively involved in systemic acquired resistance (SAR) and salicylic acid (SA) were also up-regulated by F. oxysporum but down-regulated by R. irregulare: calmodulin-binding protein (Lus10020686); UDP-glycosyltransferase 74 F1 (Lus10008742); sigma factor binding protein 1 (Lus10038985, Lus10027279); and NIM1-interacting 2 (Lus10041460) (Supplementary S9, S_10).
4.3.11 Simultaneous inoculation with a combination of F. oxysporum and R. irregulare Treatments with the combined inoculum (R. irregulare and F. oxysporum) showed 95 genes increased in transcript abundance at 9 dpi, and 170 genes increased in transcript abundance at 14 dpi (Figure 4.6). At either time point, a much smaller number of genes increased in response to the combined inoculum compared to the 627 or 971 genes that increased at 9 and 14 dpi respectively, in response to F. oxysporum alone. The response to the combined inoculum was therefore not simply the sum of responses to the individual inocula. However, most of the 170 genes that did increase following the combined 14 dpi treatment were also induced by F. oxysporum alone at either 9 dpi (136/170) or 14 dpi (145/170). Thus, most of the transcripts induced in the combined inoculum at 14 dpi were also induced by F. oxysporum alone (at both 9 dpi and 14 dpi), although these common transcripts are only a small fraction (145/ 971) of the total transcriptomic responses to F. oxysporum alone at 14 dpi. In contrast, when the genes commonly increased in R. irregulare and the combined inoculum at 9 dpi are compared, only 20% (19/95; 9 dpi) or 5% (9/170; 14 dpi) were commonly induced by both types of treatment.
120
Figure 4.6 Venn diagram shows comparisons between genes with significant transcripts with increased abundance (numbers in blue) and transcripts with significant decreased abundance (numbers in red) of flax roots inoculated with the combined inoculum (R. irregulare and F. oxysporum) compared to inoculation with R. irregulare (Rir) or F. oxysporum (Fos) alone. We considered significant differential expression between samples and non-inoculated control when FDR < 0.05, and fold change >0 (transcripts with increased abundance) or <0 (transcripts with decreased abundance.
121 Many of the DEGs with the highest expression (log2fold change > 4) in the combined treatment at 14 dpi were also strongly expressed in the F. oxysporum solo treatment, including: two PR1 genes (Lus10004410 and Lus10012479); a 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase protein (Lus10004808); a spermidine hydroxycinnamoyl transferase (Lus10005358); a Cytochrome P450 71B 7 (Lus10034230), one peroxidase superfamily protein (Lus10020826), one O- methyltransferase family protein (Lus10007035) and one homolog of carrot EP3-3 chitinase (Lus10003231). Likewise, geranylgeranyl pyrophosphate synthase 1 (Lus10023259) had increased abundance comparable to the F. oxysporum solo treatment at 14 dpi, with log2 fold change increased by 1.4 in the combined treatment at 14 dpi.
The presence of R. irregulare in the combined inoculum resulted in the suppression (or non-activation) of some genes that were up-regulated by exposure to F. oxysporum alone. This most prominent of these were: Kunitz family trypsin and protease inhibitor protein (Lus10007888, Lus10007889, Lus10022302, Lus10026357, Lus10039209, and Lus10039210), Plant invertase/pectin methylesterase inhibitor superfamily Lus10038917, Lus10027202 and Lus10031133), Eukaryotic aspartyl protease family protein (Lus10036343 and Lus10010278), osmotin 34 (Lus10006302 and Lus10017170), ethylene responsive element binding factor 1 (Lus10004368), Major facilitator superfamily protein (Lus10002450, Lus10010534, Lus10018915, and Lus10028616), UDP-Glycosyltransferase superfamily protein (Lus10019833, Lus10014401, and Lus10022221), FAD-binding Berberine family protein (Lus10023375), and BTB/POZ domain with WD40/YVTN repeat-like protein (Lus10024643) (Supplementary S_11,S_12).
Other genes were completely repressed by R. irregulare in the combined treatment in comparison with the F. oxysporum treatment alone, including: transcription factors (Lus10024074, Lus10034244 and Lus10012870); other signaling proteins such as NDR1/HIN1-like 25 (Lus10020783) and MAP kinases (Lus10025986, Lus10040127, Lus10040128, and Lus10001081); calmodulin-binding protein (Lus10020686,
122 Lus10008742), genes related to JA pathway (Lus10027648 and Lus10039911), alpha/beta-Hydrolases superfamily protein (Lus10004364 and Lus10038524); 2- oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein (Lus10022191); Bifunctional inhibitor/lipid-transfer protein/seed storage 2S albumin superfamily protein (Lus10016323, and Lus10002741); Extensin family protein (Lus10010742) (Supplementary S_11,S_12).
Decreases in transcript abundance were observed for 43 genes at 9 dpi, and only 27 genes at 14 dpi following the combined inoculum treatment. Of these, 19% (8/43) and 26% (7/27) also decreased in abundance in the F. oxysporum solo inoculation at 9 dpi or 14 dpi, respectively. However, of the transcripts that decreased in abundance following the combined inoculation, 40% (17/43; 9 dpi) and 70% (20/27; 14 dpi) also decreased in abundance following the inoculation by R. irregulare alone.
Another interesting comparison that might help to elucidate the bio-protective effects of R. irregulare were the genes up-regulated at 9 dpi in both the combined treatment and the R. irregulare treatment. Most of these genes are related to AM symbiosis and were not differentially expressed following F. oxysporum treatments. For instance, nitrate transporter 2:1 (Lus10016121), and putative cell wall modification-related genes (Lus10033360, Lus10039881, Lus10008213). Finally, two peroxidases (Lus10025256 and Lus10001228 ), a cytochrome P450,79 B 2 (Lus10031145) and a LTP/seed storage 2S albumin superfamily (Lus10022066) that were not induced by F. oxysporum alone were induced in both flax- R. irregulare and the combined treatment.
4.4. Discussion
4.4.1. Mycorrhization and Pathogenesis We studied the interaction of flax plants with a pathogenic fungus F. oxysporum and a mutualistic fungi R. irregulare to identify the molecular processes involved in the early stages of these processes. Successful association between plants and beneficial fungi, as well as successful plant defense mechanism activation against pathogenic fungi,
123 begins with partner recognition during the early stages of the interaction (Bedini et al., 2018; García-Garrido & Ocampo, 2002). The majority of information on gene expression during plant-fungal interactions derives mostly from examination of the late stages of such interactions, especially for mycorrhizal fungi. For AMF, most of our knowledge about the bio-protective effects of these fungi against pathogenic fungi is based on analysis of the well-established symbiosis with plants prior to infection (Akköprü & Demir, 2005; Fiorilli et al., 2011; Fiorilli et al., 2018). But gene expression during the early stages of colonization/infection is of extreme importance to identify the main mechanisms involved in these processes and even for development of efficient strategies to help us control fungal disease (Rauyaree et al., 2001). Likewise, studying the roots, which is where the plant-fungi interaction begins, is fundamental.
Our study revealed two major mechanisms that are important for symbiosis: regulation of defense responses, and regulation of genes directly involved in AMF symbiosis development (Figure 4.7). Among the mechanisms involved in the regulation of defense responses are: direct regulation of proteins (e.g. PRs: Lus10032178, Lus10015339, Lus10020493 and Lus10012479; and chitinases: Lus10003231, Lus10010866, Lus10024366, and Lus10028275; Table 4.7) and secondary metabolites involved in plant defenses (e.g. several CYPs: Lus10031145; Table 4.6 and Lus10014284, Lus10030189, Lus10013150, Lus10038200, Lus10011658, Lus10025901, and Lus10025902; Table 4.7), and hormone regulation (e.g. enriched GO categories: jasmonic acid (JA) metabolic processes (GO:0009694), and systemic acquired resistance (GO:0009627); Table 4.5). Among the mechanisms involved in AMF symbioses that also influences plant defense are nutrient uptake (Lus10010696, Lus10023971, Lus10025113, Lus10030872, and Lus10016121; Table 4.6) and cell wall modifications (Lus10039881, Lus10033360 and Lus10008213, Table 4.6). Our data is supported by one of the few studies of the early stages of AMF symbiosis in sunflower, were the main GO categories enriched after 4 dpi were “response to stress”, “cell wall” and “transport activity” (Vangelisti et al., 2018). It important to bear in mind, that although Vangelisti et al., (2018) analyzed the very early stages of colonization, a few hyphae entry points and arbuscules were found in the plant roots, whereas in our
124 studies we did not find any evidence of a physical interactionbetween flax and AMF. Overall, the results from our study suggests that AMF seems to prioritize the activation of genes directly involved in mutualism or mycorrhization rather than defense (Table 4.6), and the opposite behavior was observed with the pathogenic fungus (Table 4.7). In fact it has been proposed that AMF does not induce major defense mechanisms in plants (Smith & Read, 2008). Our results suggest that R. irregulare manipulates defense responses in flax by directly affecting gene expression. For instance, several transcripts decreased in abundance in the combined treatment in comparison to the F. oxysporum solo treatment; these same transcripts also decreased in abundance following the inoculation by R. irregulare solo treatment (Figure 4.6). We observed that part of the genes induced to promote symbiosis by R. irregulare solo treatment continued to be induced in the combined treatment (Table 4.6). In contrast, most of the genes involved in the defense response induced by F. oxysporum solo treatment had decreased transcript abundance in the combined treatment (Table 4.7). Thus, it seems that direct regulation of the defense genes in response to the pathogen is also being coordinated by R. irregulare. It has been reported that there is an extensive transcriptional reprogramming during AMF colonization (Smith & Read, 2008). As well, a study showed that although mycorrhizal plants deployed some defense responses in sunflower, there was also a restriction of defense genes at 16 dpi, suggesting that AMF supresses defense mechanisms in plant roots to favour colonization (Vangelisti et al., 2018).
Although both of the fungi we tested stimulated plant responses, in terms of the number of DEGs identified, F. oxysporum stimulated a much stronger response than R. irregulare (Figure 4.1). This could be due to faster growth and colonization by this pathogenic fungus, or it could reflect the higher urgency of the plant efforts to combat this pathogenic stress, compared to a more passive response to a mutualist. The genes involved in these processes will be discussed in the subsequent sections.
125 Up-regulated F. oxysporum x Up-regulated R. irregulare x down-regulated R. irregulare down-regulated F. oxysporum
Defense Calcium signaling Ethylene and Salicylic acid biosynthesis Nutrient transport Carbohydrate binding Lipid metabolism Oxidoreductase Cell wall and polysaccharide- Sugar transmembrane transporter modifying proteins*
Figure 4.7 Schematic representation of opposite modifications in the flax transcriptome upon infection with F. oxysporum or symbiosis with R. irregulare. Boxes shown genes that were exclusively up-regulated by F. oxysporum alone or R. irregulare alone, when these two treatments were compared. * indicates that other genes involved with cell wall modifications, not represented here, might be up-regulated by F. oxysporum ã Sara Gagnon. This work is a derivative from (Valentina Fiorilli et al., 2018), used under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
4.4.1.1 Mutualism Fewer genes were up-regulated by R. irregulare at 14 dpi than at 9 dpi (Figure 4.5), and fewer DEGs were identified in comparison with other studies of AMF treatments (Fiorilli et al., 2018; Handa et al., 2015; Vangelisti et al., 2018). This is likely because the time points we studied are very early in the establishment of the symbiosis, compared to
126 other studies. We will here discuss some of the genes involved in the pre-colonization events leading to establishment of mutualism.
4.4.1.1.1 Calcium signaling Calcium signaling is required for AMF symbiosis and it is the first change that AMF perception, through Myc factors, elicits in plants in pre-symbiotic events (Bonfante & Genre, 2010). The rapid and transient calcium spiking triggers a signaling pathway known as the common symbiosis pathway (Parniske, 2008). In our studies a Ca- dependent lipid-binding family (Lus10039259, C2 domains) was activated at 9 dpi by inoculation with R. irregulare in single and combined treatments (Table 4.6). The C2 domains are transcriptional regulators of calcium signaling identified in Arabidopsis thaliana and are recognized as repressors of the abiotic stress response (de Silva et al., 2011). Other transcriptome studies involving AMF symbiosis have found the up- regulation of genes encoding calcium-depending protein kinases (Vangelisti et al., 2018). Calcium signaling is also important for pathogen defense. For instance, different transcripts involving calcium signaling (e.g. Lus10027261, Lus10028657, Lus10034463, Lus10026291, Calcium-binding EF-hand family protein; Lus10006523, Calcium- dependent lipid-binding (CaLB domain) family protein; Supplementary S9) were up- regulated only by F. oxysporum solo treatment at 9 and 14 dpi. We also observed an up-regulation of MAPK (Lus10025986, Lus10001081.g; Supplementary S9) by F. oxysporum solo treatment at 9 and 14 dpi in flax. Similar results were found by (Galindo-González & Deyholos, 2016) when analysing flax transcriptome responses to F. oxysporum and were associated to the signal transduction cascade, activated in response to pathogen perception, that would culminate with the further activation of several defense responses, like biosynthesis of hormones, PRs, polyamines, isoprenoids, ROS and others (Galindo-González & Deyholos, 2016), that will be discussed in the following sections.
127 4.4.1.1.2 Lateral root development At 14 dpi flax developed a more branched root system when inoculated with R. irregulare alone, than compared with F. oxysporum alone treatment (Figure 3.5). Among the very few genes that were up-regulated at 14 dpi by R. irregulare alone, a gene related to lateral root growth (Lus10036032, FC = 1.05; Supplementary S8) was exclusively regulated by this treatment and timepoint. As well, a nitrate transporter (Lus10016121.g; Table 4.6) was up-regulated in our study by AMF in the single and combined treatment at 9 and 14 dpi. Some nitrate transporters (e.g. NRT1) are considered pivotal to auxin transport and subsequently in lateral root formation (Gutjahr & Paszkowski, 2013), and it was also up-regulated in sunflower by AMF in a transcriptome study (Vangelisti et al., 2018). Lateral root proliferation is a marked characteristic of AMF symbiosis and it is regulated by AMF signaling molecules and plant nutrient status (Paszkowski & Gutjahr, 2013).
4.4.1.1.3 Nutrient transporters Nutrient transporter genes were up-regulated by R. irregulare treatment in flax (but not in F. oxysporum treatments) (Lus10023971, Lus10025113, Lus10030872, Lus10016121, Lus10039881, Lus10033360 and Lus10008213; Table 4.6). These results confirm the importance of transporter gene modulation during the symbiotic interaction. Nitrate, phosphate and ammonium transporters have been identified in AMF symbiosis in previous studies (Benedito et al., 2010; Fiorilli et al., 2018; Guether et al., 2009). Besides, these transporters have crucial functions in plant nutrition, metabolism and signaling, therefore affecting the growth and development of the plants, as well responses to the environment (Benedito et al., 2010). Likewise, a nodulin transporter (Lus10010696; Table 4.6) was induced by R. irregulare in both single and combined treatments, while repressed by F. oxysporum. AMF and rhizobia share genes of the common symbiosis signaling pathway, and nodulin transporters have been identified as components of AMF early symbiosis, even prior to physical interaction between plant and fungi (Kosuta et al., 2003).
128 Only one nutrient transporter, the aforementioned nitrate transporter (Lus10016121; Table 4.6), was up-regulated in the combined treatment (Table 4.6). Besides its role in nutrition and lateral root development, N is an important key signal for pathogenesis, which might be induced due to nitrogen-limiting conditions in vitro (Snoeijers et al., 2000). A study revealed that nitrogen source is one of the factors that control the expression of virulence genes in F. oxysporum (López-Berges et al., 2010). For example, ammonium repress the expression of F. oxysporum genes that control vegetative growth and root adhesion, therefore reducing infection (López-Berges et al., 2010). Studies have shown that high N reduces the severity of infections caused by facultative parasites, including the genus Fusarium (Dordas, 2008). From our results, it can be speculated that by enhancing N uptake in flax, R. irregulare is reducing F. oxysporum virulence, and consequently less defense genes are being activated, in comparison with the strong response induced by F. oxysporum alone. The augmented transport, and consequently availability, of nitrogen in the combined treatment may have contributed to produce plants bigger and with higher biomass than flax inoculated with F. oxysporum alone (Figure 3.1, 3.2). Hence, a balance between plant nutrition and other cultural practices could reduce the incidence of disease and lead to a more sustainable agriculture by reducing the use of fungicides (Dordas, 2008).
In summary, AMF presence led to differential up-regulation of specific genes involved in nutrient uptake in the solo treatment compared to the combined.
4.4.1.1.4 Cell wall modifications Cell wall modification-related genes were differentially expressed in all treatments. The most interesting observation was that many cell wall modification-related genes that were up-regulated in response to R. irregulare or the combined treatment were down- regulated by F. oxysporum treatment at 14 dpi, namely one extensin (Lus10008213, pollen Ole e1 allergen, extensin family) and two hydroxyproline-rich glycoproteins (HRGPs) (Lus10039881, Lus10033360) (Table 4.6). Other studies have found the up- regulation of genes involved in cell wall metabolism by mycorrhizal fungi (Guether et al., 2009). Previous studies have shown that HRGPs were over-expressed in sunflower
129 roots colonized by AMF (Y. Wu et al., 2017). Furthermore, HRGPs have been found to restrict the spread of fungi in plants in several studies (Basavaraju et al., 2009; Ribeiro et al., 2006; Y. Wu et al., 2017). HRGPs strengthen cell wall structures by the formation of cell wall cross-links, and acts as a physical barrier trapping pathogens in their negatively charged surfaces (Basavaraju et al., 2009). Cell wall cross-links are also activated in responses to peroxidases (Basavaraju et al., 2009), which were also up- regulated in our studies (Lus10025256, Lus10006534, Lus10001228; Table 4.6), following a similar pattern (up-regulation by R. irregulare single and combined treatments, and down-regulation by F. oxysporum single treatment). These results suggests that AMF deployed small local responses in flax roots, e.g. cell wall modifications, that were stalling/inhibiting disease progression in plants (Basavaraju et al., 2009).
4.4.1.2 Defense responses The presence of R. irregulare in the combined treatment limited the flax transcriptomic responses compared to the F. oxysporum solo treatment. For instance, genes with a well-established role in defense were enriched in up-regulated DEGs of F. oxysporum (9 dpi and 14 dpi) and combined treatment, but were down-regulated by R. irregulare at (9 dpi and 14 dpi) (GO categories: immune response (GO:0006955) and defense response (GO:0006952); Table 4.5, 4.7).
It has been shown that when AM symbiosis triggers defense responses, these are deployed for a short time, and later repressed (Smith & Read, 2008). Moreover, the extent to which these responses are activated is much less than the defenses deployed upon the pathogen attack (Smith & Read, 2008). These studies support the idea that AMF can modulate defense responses in plant. However, in our studies we observed a great suppression of defense responses in the early stages of AMF symbiosis. This observation may be explained by the fact that we are studying the events preceding colonization and many levels of defence-related mRNAs have been found to be increased in cells containing arbuscules (Smith & Read, 2008). Another possibility, as discussed before, is that the changes in flax transcriptome induced by AMF inhibit or
130 delay F. oxysporum growth and spread, reflecting the reduction of the number of DEG in comparison with F. oxysporum treatment. It is also possible that AMF directly supresses many defense responses to favour colonization (Guenoune et al., 2001; Vangelisti et al., 2018). In fact, it seems that both mechanisms are taking place simultaneously in our study.
A question arises from these results: if R. irregulare attenuated expression of most defense genes in the combined treatment, why did the plant in the combined treatment not manifest disease (Figure 3.6)? We speculate that besides the responses triggered by AMF beneficial relationship (such as nutrient uptake, hormone regulation) the defense genes that were induced in the combined treatment were sufficient to suppress the disease. For instance, PR genes (Lus10004410 and Lus10012479), spermidine hydroxycinnamoyl transferase (Lus10005358) (involved in PA biosynthesis) and one chitinase (Lus10003231) were strongly induced in the combined treatment (log2fold change > 4) and F. oxysporum solo treatment (Table 4.7). Our observations are consistent with the idea that AMF do not activate major defense responses in plants, rather AMF induce slight changes in plants defense gene expression that are later supressed but are sufficient to promote immunity (Smith & Read, 2008).
It is interesting to note that among the defense genes supressed by R. irregulare in the combined treatment, a few were up-regulated in flax in response to F. oxysporum only at 18 dpi in previous studies (Galindo-González & Deyholos, 2016): for instance, several Kunitz, aspartyl proteases, and ethylene responsive factors. This result suggests that these genes may be related to disease progress in flax in response to F. oxysporum.
4.4.1.2.1 Hormone signaling Both types of fungi altered the biosynthesis of hormones in plants, however, mostly in contrasting ways. For instance, JA biosynthesis, and SA-responsive pathogenesis- related genes were mainly increased in the flax transcriptome by F. oxysporum solo treatment (Enriched GO categories: jasmonic acid (JA) metabolic processes
131 (GO:0009694), and systemic acquired resistance (GO:0009627); Table 4.5) whereas SA-responsive genes mainly down-regulated by R. irregulare alone treatment (e.g. ethylene response factors: Lus1004255, and ethylene response binding factor, Lus10040165.g; Supplementary S9, S_11). However, JA biosynthesis genes were up- regulated in both treatments. Hormone influence on plant-fungi interactions vary according to the type of fungus, location of interaction and stage of colonization. For instance, hormone expression during AMF symbiosis is highly diverse and often contradictory (Bucher et al., 2014). It has been suggested that ethylene and SA limit AMF entry in plants, and therefore negatively affect AMF colonization (Mukherjee & Ané, 2011). Moreover, studies with mutant plants have shown that JA deficiency reduces mycorrhization (Bucher et al., 2014). Our results suggest that AMF negatively regulates ET (e.g. Lus10011829, Lus10004368, Supplementary S_11) and genes related to SA (e.g. Lus10041460, and Lus10020686, Table 4.7) and positively regulates JA (Lus10027648, Lus10039911.g; Supplementary S7) to favour colonization. Although progress has been made in characterizing the molecular mechanisms associated with AMF pre-symbiotic signaling, it is still not clear if the role of the hormones is essential for AMF symbiosis (Bucher et al., 2014). More studies are necessary to elucidate many gaps in the knowledge of AMF pre-symbiotic signaling. In contrast, F. oxysporum induces the expression of genes related to ET, SA and JA, which are involved in many signaling defense pathways and have been associated with defense to F. oxysporum in flax transcriptomic studies (Galindo-González & Deyholos, 2016).
4.4.1.2.2 Secondary metabolism An important gene possibly involved in defense responses that was up-regulated in the R. irregulare solo treatment and combined treatment, and that was down-regulated by F. oxysporum was cytochrome 79 B2 (CYP79B2) (Lus10031145; Table 4.6). In Arabidopsis, CYP79B2 is involved in the biosynthesis of precursors of indole-3-acetic acid (IAA), and indole glucosinolates and camalexin (Bak et al., 2011). Conversely, other members of the cytochrome P450 (CYP) family were up-regulated by F. oxysporum solo treatment at 9 and 14 dpi (Lus10014284, cytochrome BC1 synthesis;
132 Lus10030189, cytochrome P450 71B10; Lus10013150, cytochrome P450 82C; and Lus10038200, Lus10011658, Lus10025901, Lus10025902, cytochrome P450 82C4; Table 4.7). The CYP transcripts up-regulated by F. oxysporum in the single treatment were mostly down-regulated by R. irregulare single treatment and had mixed patterns in the combined treatment in our studies (Table 4.7). The up-regulation of several CYP genes in response to F. oxysporum in flax was observed in previous studies, supporting our findings (Galindo-González & Deyholos, 2016).
Other genes likely involved with secondary metabolite production include an O- methyltransferase (OMT, Lus10006691; Table 4.7), and geranylgeranyl pyrophosphate synthase (GGPPS, Lus10023259, Lus10017624; Supplementary S_12). Both of these were up-regulated by F. oxysporum in the single and combined treatments. OMT genes might be involved in the methylation of secondary metabolites that play important roles in the synthesis of antimicrobial compounds (phytoalexins) and lignin biosynthesis (Lam, Ibrahim, Behdad, & Dayanandan, 2007). GGPP is a key isoprenoid (Coman et., 2014). A member of 2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase protein (2- ODDs) was up-regulated by F. oxysporum in single and combined treatments at 14 dpi. These gene products are considered one of the most versatile types of oxidative enzymes, participating in wide range of reactions of metabolism, for example hormones and secondary metabolites involved in plant defenses (Farrow & Facchini, 2014). These results are supported by the work of Galindo-González & Deyholos (2016), who found that several of these processes/genes where enriched/up-regulated in flax (whole plant) in response to F. oxysporum.
4.4.2. Bio-protective effects of R. irregulare in flax roots We believe that the bioprotective effect observed (Figure 3.1) is due to multiple mechanisms. For instance, despite the slight changes in genes related to nutrient uptake, where only one nitrate transporter was up-regulated in the combined treatment (Table 4.6), flax showed increased shoot length (Figure 3.1) and biomass compared to the other treatments (Figure 3.2). These data are supported by the concept that AMF symbiosis promotes positive growth responses due to increase in the availability of
133 growing-limiting nutrients (Smith & Read, 2008). These changes also occur to the R. irregulare alone treatment and are supported by the work of Fiorilli et al., (2018). Our work also showed that co-inoculation with R. irregulare and F. oxysporum provoked changes in phytohormone-related genes, which may also influence flax growth and defense responses. AMF preferentially triggered genes involved in the JA pathway (Lus10027648, Lus10039911; Supplementary S7), probably leading to MIR (Cameron et al., 2013), whereas F. oxysporum preferentially triggered SA (e.g. Lus10041460, and Lus10020686, Table 4.7) and JA pathway (Lus10027648, Lus10039911.g; Supplementary S7), leading to SAR-like resistance. However, genes involved in the JA pathway were not strongly induced in the combined treatment (FC > 0 <1; Supplementary S7). Modifications in the cell wall possibly interfered with pathogenic colonization and spread, as suggested by the literature (Basavaraju et al., 2009; Ribeiro et al., 2006; Y. Wu et al., 2017). Even though there was a general repression of defense genes, to counter the pathogen attack, flax plants up-regulated some defense genes, such as PR (Lus10015339, Lus10020493, and Lus10012479), spermidine (Lus10005358; Table 4.3, 4.4), chitinase (Lus10003231, Lus10010866, and Lus10024366) and peroxidase (Lus10012684 ) (Table 4.7). These data suggest that the activation of the host immune system was triggered mainly in response to the pathogenic fungi, but possibly modulated by AMF. It has been shown that the effects of AMF on defense genes, such as chitinases and PRs proteins (which were up-regulated in our studies in the F. oxysporum single and combined treatments) enhance local resistance to pathogen penetration in plants, and as well have long-distance effects, respectively (Ismail & Hijri, 2012).
In summary, high throughput RNA-Seq data revealed that AMF mitigates the reductions in shoot length and weight in flax phenotype caused by the pathogen, meaning that AMF induces bio-protective effects in flax. Several of the genes reprogrammed have been described in mycorrhizal plants, however, our results showed remarkable effects at a pre-symbiotic stage. Defense responses were mainly supressed by R. irregulare in the single and combined treatment, suggesting that AMF affects defense genes directly and indirectly.
134 Figure 4.8 shows a schematic overview of flax responses to the combined treatment.
AMF Fusarium
Cell wall modifications
Hormone regulation Attenuated Nutrient defense uptake responses
Blue Figure 4.8 Schematic representation of modifications in flax transcriptome upon co-infection with Rhizoglomus irregulare and Fusarium oxysporum. Blue boxes represent the genes up-regulated by the treatment with the combined inoculum that were also up-regulated by R. irregulare (AMF) alone, whereas red boxes represents genes down-regulated in the combined treatment in comparison with the F. oxysporum treatment alone, that were also down-regulated by the AMF. The picture represents a balance between symbiotic genes regulated in flax in response to the AMF and the pathogenic fungi. ã Sara Gagnon. This work is a derivative from (Fiorilli et al., 2018). Used under a Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/).
Finally, in our study it was clear that the bio-protective effects of R. irregulare were associated with a balance between enhancing plant nutritional status, and morphological and physiological changes in response to AMF symbiosis development and as well, regulation of defense responses. The changes promoted by AMF on the flax transcriptome where not sufficient to completely inhibit the growth of the pathogen, since F. oxysporum was re-isolated from flax roots in the combined treatment. Therefore, the activation of plant defenses against F. oxysporum, even though they
135 were reduced, were also essential for the positive effects on flax growth observed in the combined treatment in comparison to flax phenotype when inoculated with F. oxysporum alone (Figure 3.1, 3.2, 3.6). Overall, we hypothesize that the effects observed on flax DEGs triggered by R. irregulare, directly and indirectly, limited F. oxysporum growth, and consequently, restricted the transcriptional response to F. oxysporum in the flax-combined treatment. This idea is supported by the results of a study where Solanum tuberosum was co-inoculated with a mutualistic fungus, Clonostachys rosea, and the pathogenic fungus Helminthosporium solani (Lysøe et al., 2017). In this study C. rosea greatly reduced the number of genes activated when compared with the inoculation with H. solani alone; these results were in agreement with the reduction of the fungus H. solani in the plant, verified the quantification of fungal DNA (Lysøe et al., 2017). Although we were not able to compare the amount of F. oxysporum that colonized the roots in the single and combined treatment, the healthy phenotype observed in the combined treatment despite the presence of F. oxysporum, suggests that the progress of disease was at least delayed. To develop a full picture of the effects of R. irregulare on F. oxysporum growth and spread in flax, additional studies will be needed to measure the amount of these fungi in flax roots.
The genes presented in this work could be tested for single and co-expression in engineered plants, with the aim of phytopathogen protection. Plant crop engineering often focuses on the manipulation of obvious genes involved in disease resistance, using strategies such as stacking R genes, over-expression of PR proteins and other antimicrobial peptides, augment of plant immune receptors, among others. We suggest that further studies should consider combining different strategies for enhancing plants tolerance to pathogens, and the genes that we found in this study would be interesting targets. For instance, tomato plants transformed with a binary vector co-expressed a snakin and a extensin-like protein, and presented enhanced tolerance to Clavibacter michiganensis (Balaji & Smart, 2012). A delay in symptom development and a reduction of canker lesions were observed relative to non-transgenic plants (Balaji & Smart, 2012).
136 To conclude, flax RNA-Seq changes upon inoculation with pathogenic or mutualist fungi was performed with success, generating novel data with statistical significance. We believe that the main mechanisms underlying mycorrhization before colonization are the nutrient uptake and cell wall modifications, followed by hormone regulation and secondary metabolite production. In contrast, upon a pathogen attack, flax plants respond mainly by activating defense genes, which despite an attempt at protection, also reflected in stalling flax growth in our studies. Finally, when co-inoculated, both fungi interfered with plant responses, where a combination of mechanisms related to AMF symbiosis development and defense mechanisms were maintained from each alone. R. irregulare seems to interfere with the F. oxysporum infection, where a great reduction in the number of genes in the solo F. oxysporum treatment was observed. Moreover, there are more similarities between the genes with increased transcript abundance between the combined treatment and F. oxysporum alone, and genes with decreased abundance between the combined treatment and R. irregulare alone. These data suggest a great regulation of flax defense responses in the combined treatment by R. irregulare.
These data will aid in understanding the early relationships of mutualism and pathogenicity in plants, as well as in the selection of target genes for genetic improvement.
137 Chapter 5 : Orbitides and free polyamines have similar fungicidal activity against three common pathogens of flax in vitro.
5.1 Background The fungi Fusarium oxysporum f.sp. lini and Septoria linicola are causes of the commercially significant diseases of flax (Linum usitatissimum), respectively, fusarium wilt and pasmo. Members of a third fungal genus, Alternaria spp., have also been found in fiber and linseed varieties of flax (Králová et al. 2006), and are a source of post- harvest spoilage and mycotoxins in a wide range of crops.
Orbitides, also known as cyclolinopeptides (CLPs), are N-to-C cyclized amino acids that do not form intramolecular disulfide bridges, and are produced by ribosomal synthesis (Arnison et al., 2013). Seeds of flax (i.e. Linum usitatissimum; linseed) are one of the richest sources of CLPs, producing at least 25 species of these molecules (Burnett et al., 2016). Orbitides are also found in other plant lineages, including the Annonaceae, Caryophyllaceae, Euphorbiaceae, Lamiaceae, Phytolaccaceae, Rutaceae, Schizandraceae, and Verbenaceae (Arnison et al., 2013). Several nutraceutical benefits have been proposed for CLPs, involving antiplatelet, antimalarial, antioxidant and immunomodulatory activities (Sharav et al., 2014; Tan & Zhou, 2006). Furthermore, the high thermostability of CLPs may be beneficial in some industrial applications, for example to withstand postharvest heat treatments applied to control insects and microbes (Zun et al, 2017; Lurie & Pedreschi, 2014).
Despite the range of health and industrial applications under development, the natural biological function of CLPs in plants is unknown, although a role for these peptides in plant defense has been proposed (Arnison et al., 2013). In support of such a role, we recently observed that transcripts encoding the precursors of CLP #5 ([1—8-NaC]- linusorb A2) and CLP #14 ([1—8-NaC]-linusorb A1) (Burnett et al., 2016) increased up to 4.7-fold in abundance upon exposure of flax roots to F. oxysporum (Figure 2.3). Other researchers have reported that flax CLPs inhibit spore formation by Aspergillus flavus (IC50 240 μg/mL) and Paecilomyces varioti bainier (IC50 340 μg/mL); this activity
138 was largely retained when CLPs were autoclaved, highlighting their thermal stability (Liu Zun et al, 2017).
Polyamines (PAs) are linear, aliphatic amines, including spermidine (a triamine) and spermine (a tetraamine). Spermidine and spermine are synthesized by the addition of an aminopropyl moiety to putrescine by spermidine and spermine synthase, respectively (Wojtasik et al. 2015). In turn, spermidine and spermine are catabolized to release ammonia (NH3) and hydrogen peroxide (H2O2). The release of hydrogen peroxide (H2O2) likely contributes to a role of PAs in plant defense (Takahashi and Kakehi 2010; Wojtasik et al. 2015), for example by facilitating cell wall lignification and cell death (Walters 2003). Strong evidence associates conjugated and bound PAs, as well as stimulation of PA oxidation, with tolerance to biotic and abiotic stress. It is speculated that these forms are sufficient to prevent the spread of pathogen infections (Gill & Tuteja, 2010; Wojtasik et al., 2015). PAs likely contribute to plant defense through other mechanisms as well. Conversely, PAs may in some circumstances provide nutrients to promote fungal growth (Wojtasik et al. 2015). Nevertheless, exogenous application of spermines to plants induce defenses against several pathogens, mainly through the activation of the plant immune system (Seifi & Shelp, 2019). We have previously shown that at 9 and/or 14 days post inoculation (dpi) of flax seedlings with F. oxysporum, flax roots showed an great increase in abundance of PA- related transcripts, such as: spermidine hydroxycinnamoyl transferase (Lus10005358; 7.10-fold up at 9 dpi, and 8.25-fold up by 14 dpi ; Supplementary S9) and PAO (Lus10005021; 1.50-fold up at 9 dpi and 1.67-fold up at 14 dpi; Supplementary S9). Other researchers reported an up to 2.6-fold increase in transcript abundance of PA biosynthetic genes, including spermine synthase and spermidine synthase, following 48 h of exposure of flax to either pathogenic or non-pathogenic strains of F. oxysporum. The same study also showed that exposure of flax to F. oxysporum for just 48 h increased the abundance of total spermidine and putrescine, and also increased conjugated forms of spermine. Finally, the authors showed that presence of PAs in solid agar medium restricted Fusarium growth in a dose-dependent manner: statistically significant reductions in the radius of hyphal growth were observed with a minimum of
139 10 mM (880 µg/mL) putrescine, 6 mM spermidine (871.5 µg/mL), and 3 mM (607.02
µg/mL) spermine. To our knowledge, the median effective concentration (EC50) of these compounds against F. oxysporum, S. linicola and Alternaria has not been reported.
To test whether CLPs could inhibit fungal growth in vitro, we performed a microdilution assay to test CLP activity against three plant pathogenic fungi of agricultural importance (F. oxysporum, S. linicola and Alternaria sp.). We compared the efficacy of CLPs to carbendazim, a broad-spectrum commercial fungicide, and to two PAs, spermidine and spermine. We chose these PAs to represent low-molecular weight, endogenous compounds that have previously been shown to inhibit fungal growth. The results presented here showed that PAs and CLPs possess antifungal activities against several fungi.
5.2 Material and Methods
5.2.1 Fungal sources and culture
Fusarium oxysporum f.sp. lini isolates 13 and 81 were generously provided on potato dextrose agar (PDA) by Khalid Rashid (Agriculture and Agri-Food Canada, Morden, MB, Canada) and Martin Reaney (University of Saskatchewan), respectively; Dr. Reaney also provided Septoria linicola isolate (14SL43) in the same conditions; Alternaria sp. isolate (BH 503) was provided by Louise Nelson (University of British Columbia).
Cultures were grown on PDA at room temperature in the dark until growth covered the plates (approximately 14 days). Spores were isolated by flooding the plate with sterile water with 1% Tween 20 and gently rubbing with a sterilized loop. The solution was filtered using a sterile cheesecloth to isolate the spores and remove the mycelia. Spore concentration was quantified with the aid of a hemocytometer to the required final concentration of 10-5 spores in each well of the microdilution assay.
140 5.2.2 Test compound sources and dilutions
Mixed cyclolinopeptides (CLPs) extracted from L. usitatissimum were kindly provided as a lyophilized powder by Martin Reaney (Prairie Tide Diversified Inc.; CLMIX, Lot M201304-001). The composition of this powder is listed in Table 5.1, and the composition-weighted mean molecular mass of the mixture is 1051.6 g/mol. Other test compounds were purchased: carbendazim (Sigma 378674, 97% pure, 191.19 g/mol, CAS 10605-21-7); spermidine (VWR CAAAA19096-06, 99% pure, 145.25 g/mol, CAS 124-20-9); spermine (VWR CAAAAL19562-06, 97% pure, 202.35 g/mol, CAS 71-44-3). In-vitro inhibitory assays for CLPs and carbendazim followed the standard broth dilution method (EUCAST, European Committee for Antimicrobial Susceptibility Testing (“EUCAST: Susceptibility testing of molds,” 2017), with a few modifications. Briefly, CLPs and carbendazim were dissolved in DMSO (Sigma) at 200X the desired final concentration. A two-fold dilution series was prepared in DMSO, with 10 different concentrations. Subsequently, another dilution was performed by taking 100 µL of each tube with 200X concentration of the substance to be tested and transferring this to 9.9 mL of malt extract 2% medium (VWR, 97063-426) (1:100 dilution). This reduced the concentration of the solvent in the culture tubes to 1% and the concentration of the tested compounds to 2X. For experiments on the effect of PAs, the highest concentration was prepared at 5X the final concentration in sterile water. A two-fold dilution series were prepared from that in water, with 10 different concentrations.
Table 5.1 Composition of CLP mixture. For calculation of molecular mass, the sulfoximine forms of the CLPs were used, where applicable.
orbitide proportion CAS # g/mol name of total CLA 22.22% 33302-55-5 1040.34 CLB 20.63% 222527-65-3 1074.38 CLD 7.94% 222527-66-4 1064.34 CLE 20.63% 222527-67-5 977.26 CLF 7.94% 351417-15-6 1084.35 CLG 20.63% 351417-16-8 1098.38
141
Microdilution assays were performed in sterile 96-well polystyrene plates (Greiner Bio- One). The CLP concentration ranged from 0-512 μg/mL and the concentration of carbendazim ranged from 0-50 μg/mL. For the CLPs and carbendazim assays, each plate was prepared by adding 100 μL of the 2X final concentration of the compound in 2% malt extract into the wells of the columns 1 to 10. Wells from the columns 11 and 12 had only the media without the tested compounds. For example, for the CLPs, we dispensed to column 1 the medium containing 512 μg/mL of CLPs in each well, 256 μg/mL to column 2, 128 μg/mL to column 3 and so forth.
The concentrations for the PA assay were chosen based on a study previously published (Wojtasik et al., 2015). The highest concentration for the 2X dilution was 10 mM for each PA, which corresponds to 1452 μg/mL of spermidine and 2023 μg/mL of spermine. Each plate was prepared by adding 20 μL of the 5X final concentration of the compound in water into the wells of the columns 1 to 10. Wells from the columns 11 and 12 had only 20 μL of water, without the compounds.
5.2.3 Fungal growth inhibition assay
For CLPs and carbendazim, to each well of the plate (except for the column 12, where it was the negative control), we added 100 μL of the spores at the concentration of 2 x 10-5, to achieve the final concentration of 1 x 10-5, as recommended by EUCAST. To the column 12 was added 100 μL of the water used to prepare the spore dilution. The in- vitro inhibitory assays of PAs were performed according to (Zeitler et al. 2013). An 80 μL aliquot of fungal spores at 1 x 10-5 resuspended in 2% malt extract media was added per well resulting in final fungi concentration of 1 x 10-5 spores and a final compound concentration of 0-10 mM of each polyamine, which corresponds to: spermidine (0 – 1452 μg/mL), and spermine (0 – 2023 μg/mL). To column 12 we added 100 μL of the water used to prepare the spore dilution.
142 An initial absorbance reading at l= 530 nm was recorded for all the plates using the Varioskan LUX Multimode Microplate Reader. After incubation for 2 days at room temperature in the dark (and 3 days for S. linicola), fungal growth was determined by measuring OD530nm with using the Varioskan LUX Multimode Microplate Reader and also by visual inspection.
The median effective concentration (EC50) was calculated as the concentration at the midpoint between the baseline growth rate and the dose point at which maximum inhibition occurred. EC50 values were calculated using the drc package in R (Ritz, Baty, Streibig, & Gerhard, 2015). We performed at least two independent replicates for each assay.
5.3 Results
5.3.1 Measurement of EC50 in a fungal inhibition assay.
To test whether a mixture of CLPs could inhibit growth of plant pathogenic fungi in vitro, we conducted a microplate assay in which wells of a 96-well plate were inoculated with fungal spores in malt extract broth, with a two-fold dilution series of mixed CLPs extracted from flax seeds, as well as controls that lacked CLPs. Fungal growth of the fungus was measured by absorbance at 530 nm. In parallel to the CLPs, we also tested three compounds as positive controls: a commercial fungicide (carbendazim) and two polyamines (spermidine and spermine). We applied this method to three phytopathogenic fungi (F. oxysporum, S. linicola, and Alternaria sp.). Two different isolates of F. oxysporum were tested. The results of this growth inhibition assay are shown in Figure 5.1. We observed that CLPs and both PAS caused dose-dependent decreases in growth of all of the fungi tested. Carbendazim also decreased growth of F. oxysporum and S. linicola, but did not have any detectable negative effect on Alternaria sp. growth.
143 To quantify the potency of each inhibitory compound, we used the drc package of the R statistical computing environment to fit a dose-response curve, from which an EC50 value and standard errors were calculated (Table 5.2, 5.3). The EC50 value is the concentration at which fungal growth was reduced to 50% of its maximum, based on the fitted curve. The EC50 ranged from 111 to 340 µg/mL for CLPs, 21 to 272 µg/mL for spermidine, 109 to 778 for spermine, and were lower than 0.6 µg/mL for carbendazim (Table 5.2).
No EC50 could be calculated for S. linicola with carbendazim, because the EC50 in this case was lower than the minimum concentration tested. Conversely, no EC50 could be calculated for F. oxysporum #81 with spermine, presumably because full inhibition of growth was not achieved at the highest concentration tested, and thus an appropriate curve could not be fit.
For S. linicola and both isolates of F. oxysporum, carbendazim was the most effective antifungal agent, usually by two to three orders of magnitude, when its EC50 value was compared to CLP or either of the PAs (Table 5.2, 5.3). Conversely, spermine was consistently the least potent growth inhibitor tested with these particular fungal species.
CLPs and spermidine were roughly comparable in EC50 values, and CLPs were either slightly more or less potent than spermidine, depending whether concentration was considered as mass per volume (Table 5.2), or as molarity (Table 5.3).
When Alternaria sp. was tested, we found that carbendazim had no detectable inhibitory effect. However, Alternaria sp. was more sensitive to the three other test compounds (CLPs, PAs) than were F. oxysporum or S. linicola. Spermidine, in particular, appeared surprisingly effective against Alternaria sp., with an EC50 of 21.14 μg/mL.
144
carbendazim CLP spermidine spermine
F. oxysporum #13
F. oxysporum #81
S. linicola absorbance (units) absorbance
Alternaria sp.
0 1 16 256 4096 0 1 16 256 4096 0 1 16 256 4096 0 1 16 256 4096
concentration (µg/mL)
Figure 5.1 Growth of fungi in the presence of varying concentrations of potential inhibitors.
Growth is represented as A530 absorbance. Data shown for each fungus and concentration represent minimum of 16 technical replicates in two independent experiments. Box-and-whisker plots show the median (black bar) surrounded by a box spanning the interquartile range (IQR, 25th to 75th percentile) of all observations. Whiskers extend to the largest and smallest observations that are not more than 1.5 time the IQR from the median. Where a vertical dashed line is present, it represents the EC50. The x-axis is in log-scale, and thus the “0” concentration of inhibitor is represented on a discontinuity.
145 Table 5.2 Summary of EC50 from fungal growth inhibition assays (mass/volume basis)
carbendazim CLP spermidine spermine (μg/mL) (μg/mL) (μg/mL) (μg/mL)
EC50 se EC50 se EC50 se EC50 se
F. oxysporum #13 0.580 0.018 269 39.0 171 11.7 778 127 F. oxysporum #81 0.540 0.064 230 14.5 164 42.7 -- 0 S. linicola < 0.5 -- 340 899 272 117 406 42.0 Alternaria sp. -- -- 111 126 21.0 0.900 109 15.0
Table 5.3 Summary of EC50 from fungal growth inhibition assays (molar basis)
carbendazim CLP spermidine spermine (μM) (μM) (μM) (μM)
EC50 se EC50 se EC50 se EC50 se
F. oxysporum #13 3.03 0.09 255.8 37.09 1177 80.55 3845 627.6 F. oxysporum #81 2.82 0.33 218.7 13.79 1129 294.0 -- 0 S. linicola -- -- 323.3 854.9 1872 805.5 2006 207.6 Alternaria sp. -- -- 105.6 119.8 144.6 6.2 539 74.13
5.4 Discussion
The natural biological roles of orbitides (i.e. cyclolinopeptides, CLPs) are not well established. Because of the hydrophobic composition of CLPs, it has been suggested that they may have a membrane-active role, possibly resembling antimicrobial peptides (Arnison et al., 2013; Arouri, Kiessling, Tamm, Dathe, & Blume, 2011). However, CLPs differ from AMPs, because the CLPs identified so far are not cationic. We were therefore motivated to test whether CLPs isolated from L. usitatissimum could inhibit growth of three common pathogens of flax: F. oxysporum, S. linicola and Alternaria sp. For comparison, we also assayed a commercial fungicide (carbendazim), and two polyamines (PAs) that have previously been shown to inhibit growth of some of these
146 fungi in other types of assays (Amini & Sidovich, 2010; Wojtasik et al., 2015). We found that the median effective concentration (EC50) for CLPs in our growth inhibition assays ranged from 111 to 340 µg/mL (Table 5.2). These results are consistent with the EC50 ranges reported by Zun et al, (2017) for CLPs with two other fungi. In our study, for each fungus assayed, EC50 values were slightly higher for the CLPs than for spermidine (21 to 272 µg/mL), and lower or similar for CLPs than spermine (109 to 778 µg/mL). In contrast, EC50 values for carbendazim for F. oxysporum and S. linicola (<0.58 µg/mL) were much lower than CLPs or PAs. Thus, we conclude that both CLPs and PAs have measurable fungicidal activity, but that their potency against F. oxysporum and S. linicola is weak when compared to a commercial fungicide.
Alternaria spp., are known to be resistant to benzimidazole fungicides such as carbendazim, and this was consistent with our observations (Eckert & Ogawa, 1985).
However, spermidine was surprisingly more effective against Alternaria sp. (EC50 21 µg/mL) than either of the other fungi we tested. These results raise an interesting question: Is the efficacy of spermidine on Alternaria sp. related the necrotrophic lifestyle of the fungus? It has been suggested that there are differences on PA biosynthesis induced by either biotrophic or necrotrophic pathogens in plants (Pal & Janda, 2017); however, the role of PA metabolism in the interaction of plants with pathogens with different lifestyles is still not clear. Further work is required to establish the role of free spermidines on the growth inhibition of necrotrophic pathogens, such as Alternaria spp.
Although for both F. oxysporum and S. linicola, all of the natural compounds we tested were orders of magnitude weaker as fungicides than carbendazim, the effective ranges we observed for some of these natural compounds are near to what might be present in planta, under some conditions. For example, the concentration of free spermidine in flax seedlings exposed to F. oxysporum is 10 µg/g of fresh weight, with the inclusion of conjugated and bound forms of spermidine bringing this to 65 µg/g of fresh weight
(Wojtasik et al., 2015). The EC50 we observed for spermidine ranged from (21 to 272 µg/mL), and suppression of fungal growth is evident far below the median (Figure 5.1). Therefore, we cannot yet exclude the possibility that spermidine has some antifungal
147 activity in the phyllosphere, rhizosphere or within the plant itself. Furthermore, our results show that free spermidine can inhibit fungal growth, even in the absence of conjugation to other plant-produced molecules or induction of plant signaling cascades. Thus spermidine may have a direct role in modulating fungal growth, in addition to its many other roles in defense and other processes, both within fungi and within plants (Jiménez-Bremont et al., 2014; Pal & Janda, 2017). On the other hand, spermine was less potent that spermidine in our assays, and is moreover less abundant in flax seedlings than spermidine. Thus, the reported ability of spermine to promote resistance to disease may be due to the modulation of the plant immune system rather than direct fungicidal activity (Seifi & Shelp, 2019; Takahashi, 2016).
Flax is one of the richest natural sources of CLPs, and the concentration of CLPs in flax seeds can be as high as 300 µg/g of fresh weight (Arnison et al., 2013; Gui et al., 2012). CLP concentrations are likely much lower in roots, but it is possible that CLP abundance could be increased in response to a pathogen in vivo. It also remains possible that CLPs could act more effectively as fungicides in planta, as part of multicomponent defenses. This is consistent with the ideas of Fisher et al., (2018), who suggested that PLP (PawL-derived Peptides), orbitides from Asteroideae spp., might have antibacterial effects in vivo working as multicomponent antimicrobials. This hypothesis was suggested since plants produce more than one PLP and those did not present antimicrobial effects in vitro (Fisher et al., 2018; Garneau, Martin, & Vederas, 2002). Further studies on the current topic are therefore recommended, in order to elucidate if particular CLPs work synergistically in vivo.
To conclude, F. oxysporum, S. linicola, and Alternaria sp. were each affected by at least one of the compounds studied (CLPs, polyamines, and carbendazim), where spermidines were the most effective natural occurring compound against phytopathogens, especially Alternaria sp, on a mass/volume basis, and CLPs were most effective on a molar basis. Conversely, carbendazim application did not inhibit the growth of Alternaria sp., whereas this fungicide greatly affected the growth of F. oxysporum and S. linicola in vitro. Although CLPs and spermidine were much less
148 potent than carbendazim, both of CLPs and spermidine showed fungicidal activity within a concentration range that might be biologically relevant. Moreover, these results show that CLPs and spermidine can have direct effect on fungal growth, independent of any other contributions from the plant host. The findings reported here shed new light on the participation of CLPs in the protection of plants against pathogens in plants, and as well the inhibitory effects of free spermidines on fungi, especially those with a necrotrophic lifestyle. These results open new opportunities for studies to test the effects of exogenous applications of CLPs and/or spermidines on fungal infection in vivo and may suggest targets for future genetic manipulation of flax in order to obtain new lines resistant to diseases. However, the complexity of the ecosystems in the agricultural field also requires several tests in order to determine the effects of a putative fungicide in the plant resistance and yields, as well the effects on the environment, including the impact on the soil microbiota and the beneficial interactions with the existent fungi (Stachowicz, 2001).
149 Chapter 6 Concluding remarks My objective was to better understand how plants defend themselves against fungal pathogens, by discovering new defensive proteins and pathways, and by contrasting the response to pathogens with the plant’s response to beneficial fungi.
In my first approach (Chapter 2), I focused on antimicrobial peptides (AMPs) as an example of defensive proteins. I found that an HMMER-based pipeline was an efficient tool for identifying novel AMPs, and I applied this to existing DNA sequence databases. This approach delivered sequences of 16,870 novel, putative AMPs from over 1,000 different plant species. Because PhytAMP, the curated plant AMP database, contains only 271 AMP sequences, my results are a successful first step to expanding knowledge about these proteins; however, significant further work is required to isolate these proteins and experimentally test their antimicrobial activity. Techniques that would support this analysis are: in vitro and in vivo assays with synthetic peptides/ or heterologously expressed peptides to quantify inhibition of fungal growth, including expression of AMPs in transgenic plants. The latter method could be an effective solution to the limitation of producing and purifying AMPs in vitro (such as cost, proper peptide folding and high hydrophobicity of the peptides, with challenge purification). Similar approaches have been used to successfully develop AMP transgenic plants (Jung & Kang, 2013). It is unfortunate that my thesis did not include testing the activities of the AMPs identified using growth inhibitory assays in vitro. The peptides that showed correlation with defense to pathogens in our qRT-PCR assay where highly hydrophobic, and therefore extremally difficult to synthesize and purify in vitro, besides being too expensive.
Although I did not test the antimicrobial activity of any putative AMPs directly (except CLPs, Chapter 5), I was able to test whether increased transcript expression of candidate AMPs was correlated with exposure to the pathogenic fungus F. oxysporum. I included orbitides (i.e. cyclolinopepetides (CLPs)) in this analysis, since flax is a rich source of CLPs, and CLPs have been proposed to have antimicrobial/antifungal activity. Using qRT-PCR, I confirmed the correlation of 70% of the tested AMPs and one CLP
150 with flax treatment with F. oxysporum. None of these transcripts were induced by the mutualistic fungus, R. irregulare. This means that the candidate AMPs I identified are strongly correlated with disease responses, and do not seem to be involved in pre- colonization responses to the mutualist. However, future studies should focus on AMP expression in tissues colonized by R. irregulare as well, since research has shown the involvement of AMPs in some steps of mycorrhizal symbiosis. For instance, a defensin from Medicago trunculata has been found to be transcribed only in arbuscule-containing cells and possibly mediates the cell restructuring processes involved with the plant cell transitioning into post-symbiotic stages (Uhe et al., 2018). Moreover, it has been suggested that a combination of early mycorrhizal inoculation with a synthetic antimicrobial peptide (BP100), derived from cecropin, is likely to reduce the effects of phytoplasma in plants (Rufo et al., 2017).
My continuing interest in studying plant defense mechanisms motivated me to create an in vitro system to investigate the plant response in the early interaction of flax with symbiotic fungi using the mycorrhizal fungus R. irregulare, the endophytic mycoparasitic C. rosea, and the pathogenic fungus F. oxysporum (Chapter 3). We compared the effects of each fungus individually as well as in combined treatments at 9 and 14 dpi. Our results showed that F. oxysporum-inoculated plants had significantly lower biomass and shoot length than any of the other treatments. Moreover, both C. rosea and R. irregulare greatly diminished the negative effects of F. oxysporum on flax growth and biomass. Co-inoculation with the mutualistic and pathogenic fungi resulted in flax growth were comparable to the control plants. Furthermore, the combined inoculation with R. irregulare + F. oxysporum resulted in increased plant growth, whereas co-inoculation with C. rosea + F. oxysporum did not. Additionally, single inoculation of flax with R. irregulare induced more branched roots than those inoculated with F. oxysporum alone.
These interesting results encouraged me to increase our understanding of the mechanisms by which R. irregulare enhances flax defenses even before colonization of the roots take place. I hypothesized that the key mechanisms for the bio-protective effects of R. irregulare against F. oxysporum in flax were: 1) activation of the expression
151 of genes involved with mutualism, such as nutrient uptake and cell wall modifications; and 2) regulation of the plant immune system. I hypothesize that the genes expressed in these treatments are key factors for mutualism and bio-protection. I used RNA-Seq to contrast the pre-colonization transcriptomic responses of flax roots following inoculation with R. irregulare and F. oxysporum, separately and in combination (Chapter 4). To our knowledge, this is the first transcriptome-wide study with flax comparing how a mutualistic fungus (R. irregulare) and a pathogenic fungus (F. oxysporum) affect gene expression. As well, this is the first study to investigate the bio-protective effects of R. irregulare against F. oxysporum in flax roots. Our results were in agreement with our hypothesis, where we found that genes involved in mycorrhizal symbiosis were activated as early as 9 dpi, and R. irregulare greatly modulated the flax defenses to F. oxysporum. We did not see major activation of defense genes, as expected; however, the defense responses were mostly inhibited at the pre-symbiotic stages in both the single and combined treatments by R. irregulare. These data suggest that R. irregulare suppresses flax defenses to favor colonization at pre-symbiotic stages.
Our RNA-Seq results still left us with two unresolved questions: 1) is R. irregulare simply slowing or reducing the progress of F. oxysporum (e.g. some competition independent of the plant)?. Or more interestingly, is R. irregulare affecting the plant directly in some way that limits the transcriptional response? These questions should mark avenues for new research. I hypothesize that AMF affect flax transcriptional response to F. oxysporum directly (by repressing defense genes), while activating genes important to AMF symbiosis development and indirectly, as the products of AMF symbiosis enhance plants nutrition and health, promotes cell wall modifications that inhibits pathogen growth and spread, and activated some defenses different from those activated by the treatment with F. oxysporum alone treatment. The main defense related transcripts activated by R. irregulare in the combined treatment were: PR proteins, spermidine, chitinase and peroxidase. These genes were not activated in the F. oxysporum solo treatment, suggesting that they are key genes for bio-protection. I recommend that a further experiment should be conducted to quantify the amount of both fungi inside the root in the combined treatment, in comparison with inoculation with
152 single fungi. This objective could be achieved using techniques such as digital droplet PCR (ddPCR) and it has been used already to successfully quantify R. irregulare (Kokkoris et al., 2019). With this approach we could determine if the amount of the pathogenic fungus was decreased in the presence of the AM fungus, and therefore, result in the lower differential expression of genes observed in the combined treatment. Also, it would be interesting to compare the root exudates found in the soil among the different treatments, to define whether R. irregulare is suppressing F. oxysporum in the soil.
I confirmed the expression of flax defense genes in response to F. oxysporum is broader than to R. irregulare. My observations are in agreement with the classic model of gene activation upon pathogen attack: with the induced expression of several defense genes and hormone regulation (such as ethylene and salicylic acid biosynthesis, and consequently SAR-like responses) as well genes involved in the metabolism of carbohydrates that might have been manipulated by the pathogen to favor colonization. These defense responses were observed as early as 9 dpi, where no physiological differences on the growth measurements were observed yet, and were greatly intensified at 14 dpi. I also confirmed the up-regulation of several genes involved in AMF symbiosis development, such as calcium signaling, lateral root development, nutrient transport, cell wall modification, hormone regulation and secondary metabolism. Several of these genes were not activated when AMF was co-inoculated with the pathogen, suggesting that F. oxysporum suppresses R. irregulare transcriptomic responses in flax roots in vivo. AMF in general inhibited plant defense, even in the alone treatment.
For the validation of the genes identified by RNA-Seq, I investigated the expression of some genes using qRT-PCR. The data showed a high correlation of the DEG genes with each method, especially for the flax- F. oxysporum treatment. These results increased the confidence in our data. We also performed qRT-PCR with AMP genes previously identified with our bioinformatics pipeline in Chapter 2. It is interesting to note that the approach outlined in Chapter 2 was more effective than RNA-Seq
153 (Chapter 4) at identifying pathogen-responsive AMPs. This demonstrates the importance of using multiple, alternative methods to identify AMPs from large data sets.
Finally, I tested the activity of CLPs against a few phytopathogenic fungi of agricultural importance and compared their potency to polyamines and carbendazim (Chapter 5). I hypothesized that if the role of CLPs in nature is related to plant defense, the activity of these peptides should be comparable to natural occurring known defense compounds, such as polyamines. Several genes related to polyamine biosynthesis were induced in response to F. oxysporum in our studies, therefore I was interested in evaluating the activity of these compounds in vitro. Our results confirmed our hypothesis, where we found that spermidines were the most effective naturally occurring compound I tested for antifungal activity, especially against Alternaria sp. on a mass/volume basis, and CLPs on a molar basis. Although the active concentration range seems high in comparison with the commercial antifungal carbendazim (except in the case of Alternaria sp), the range was reasonably close to concentrations of CLPs and spermidines that might be naturally present. Studies have shown that Alternaria sp. is extremely resistant to conventional antifungal agents (Larrabee, 2019), and therefore it is worthwhile to test the activities of exogenous applications of CLPs and/or spermidines on this and other fungal pathogens in vivo, in future studies. These peptides may also constitute targets for genetic engineering for disease resistance in plants.
Despite the limitations, the findings of this study greatly benefit scientific society, and consequently society in general, providing several new targets for disease control in plants, with some of these compounds already tested for antifungal activity in this study. Current antifungal agents pose risks to the environment and human health, and lead us to seek environmentaly friendly alternatives, such as plant natural compounds and antagonistic fungi. I showed two mutualistic fungi that could be used for bio-protection against F. oxysporum in flax, and the importance of early inoculation for bio-protection. I also shed new light on the mechanisms underlying mutualism and pathogenicity. These findings supply a fundamental background to understanding how the initial mechanisms of symbiosis differ between mutualistic fungi, such as the R. irregulare AMF, and
154 pathogenic fungi, such as F. oxysporum. In addition, these results will help to identify how R. irregulare exerts such bio-protective effects on flax under F. oxysporum attack, even prior to colonization.
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